diff --git "a/2252.jsonl" "b/2252.jsonl"
new file mode 100644--- /dev/null
+++ "b/2252.jsonl"
@@ -0,0 +1,758 @@
+{"seq_id":"74859695","text":"import os\n\ndirs = [int(dir) for dir in os.listdir(\"Results/\")]\nif len(dirs) > 0:\n\tnext = max(dirs) + 1\nelse:\n\tnext = 0\n\npath = \"Results/\" + str(next)\n\nprint(\"Gathering results into \"+path)\n\nos.system(\"mkdir \\\"\"+path+\"\\\"\")\n\nfiles = [\"Objects/results.dict\", \"Objects/model.h5\", \"Objects/submission.csv\", \"Objects/corrs.dict\", \"Objects/imbalance.dict\", \"Out/results.txt\", \"Out/shapes.txt\", \"Out/corrs.txt\", \"Out/imbalance.txt\", \"model.py\"]\n\nfor file in files:\n\tchar_len = len(file)\n\tafter_slash = 0\n\tfor i in range(1, char_len+1):\n\t\tif file[-i] == \"/\":\n\t\t\tafter_slash = 1-i\n\t\t\tbreak\n\tname = file[after_slash:]\n\tos.system(\"cp \"+file+\" \"+path+\"/\"+name)\n","sub_path":"FTCNN/gather_results.py","file_name":"gather_results.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"633127571","text":"from __future__ import absolute_import, division, print_function, unicode_literals\nimport sys\nimport time\nimport zlib\nfrom watchdog.observers import Observer\nfrom watchdog.events import LoggingEventHandler\nimport os\nimport shutil\nfrom hkdf import Hkdf\nfrom Crypto.Protocol.KDF import PBKDF2\nfrom Crypto.Cipher import AES\nfrom Crypto.Hash import (SHA1, SHA256, SHA224, SHA256, SHA384,\n                         SHA512, HMAC)\nimport random\nfrom base64 import urlsafe_b64encode, urlsafe_b64decode\nimport numpy as np\nfrom tkinter.filedialog import askopenfilename\nfrom tkinter import *\nimport tkinter as tk\nfrom easygui import *\nimport hashlib\nimport six\n\n\ndef crc_compare(event):\n    \"\"\"\n    a function that compares the original crc that in the \"shadow file\" to the new crc of the modified file\n    :param event: watchdog event\n    :return: boolean\n    \"\"\"\n    origCRCFile = open(event.src_path + '' + '.crc32Dir' + '/' + 'crc32.32b', \"r+\")\n    origCRC = origCRCFile.read()\n    newCRC = crc(event.src_path)\n    if origCRC == newCRC:\n        return True\n    else:\n        return False\n\nclass DropboxContentHasher(object):\n    \"\"\"\n    Computes a hash using the same algorithm that the Dropbox API uses for the\n    the \"content_hash\" metadata field.\n    The digest() method returns a raw binary representation of the hash.  The\n    hexdigest() convenience method returns a hexadecimal-encoded version, which\n    is what the \"content_hash\" metadata field uses.\n    This class has the same interface as the hashers in the standard 'hashlib'\n    package.\n    Example:\n        hasher = DropboxContentHasher()\n        with open('some-file', 'rb') as f:\n            while True:\n                chunk = f.read(1024)  # or whatever chunk size you want\n                if len(chunk) == 0:\n                    break\n                hasher.update(chunk)\n        print(hasher.hexdigest())\n    \"\"\"\n\n    BLOCK_SIZE = 4 * 1024 * 1024\n\n    def __init__(self):\n        self._overall_hasher = hashlib.sha256()\n        self._block_hasher = hashlib.sha256()\n        self._block_pos = 0\n\n        self.digest_size = self._overall_hasher.digest_size\n        # hashlib classes also define 'block_size', but I don't know how people use that value\n\n    def update(self, new_data):\n        if self._overall_hasher is None:\n            raise AssertionError(\n                \"can't use this object anymore; you already called digest()\")\n\n        assert isinstance(new_data, six.binary_type), (\n            \"Expecting a byte string, got {!r}\".format(new_data))\n\n        new_data_pos = 0\n        while new_data_pos < len(new_data):\n            if self._block_pos == self.BLOCK_SIZE:\n                self._overall_hasher.update(self._block_hasher.digest())\n                self._block_hasher = hashlib.sha256()\n                self._block_pos = 0\n\n            space_in_block = self.BLOCK_SIZE - self._block_pos\n            part = new_data[new_data_pos:(new_data_pos+space_in_block)]\n            self._block_hasher.update(part)\n\n            self._block_pos += len(part)\n            new_data_pos += len(part)\n\n    def _finish(self):\n        if self._overall_hasher is None:\n            raise AssertionError(\n                \"can't use this object anymore; you already called digest() or hexdigest()\")\n\n        if self._block_pos > 0:\n            self._overall_hasher.update(self._block_hasher.digest())\n            self._block_hasher = None\n        h = self._overall_hasher\n        self._overall_hasher = None  # Make sure we can't use this object anymore.\n        return h\n\n    def digest(self):\n        return self._finish().digest()\n\n    def hexdigest(self):\n        return self._finish().hexdigest()\n\n    def copy(self):\n        c = DropboxContentHasher.__new__(DropboxContentHasher)\n        c._overall_hasher = self._overall_hasher.copy()\n        c._block_hasher = self._block_hasher.copy()\n        c._block_pos = self._block_pos\n        return c\n\ndef guiWelcomeBox():\n    \"\"\"\n    a graphic user interface that welcomes the user\n    :return: none\n    \"\"\"\n    msg = \"welcome to my Dropbox project\\n \" \\\n          \"now im going to show you a demo of the project\"\n    title = \"Tomer Rippin Dropbox project\"\n    msgbox(msg, title, ok_button=\"lets go!!\")\n\n\ndef guiPasswordBox():\n    \"\"\"\n    a graphic user interface function that gets a user ID and password from the user\n    :return: none\n    \"\"\"\n    msg = \"Enter login information\"\n    title = \"Please enter password\"\n    fieldNames = [\"Password\"]\n    fieldValues = []  # we start with blanks for the values\n    fieldValues = multpasswordbox(msg, title, fieldNames)\n\n    # make sure that none of the fields was left blank\n    while 1:\n        if fieldValues == None: break\n        errmsg = \"\"\n        for i in range(len(fieldNames)):\n            if fieldValues[i].strip() == \"\":\n                errmsg = errmsg + ('\"%s\" is a required field.\\n\\n' % fieldNames[i])\n        if errmsg == \"\": break  # no problems found\n        fieldValues = multpasswordbox(errmsg, title, fieldNames, fieldValues)\n    global password\n    password = fieldValues[0]\n\n\ndef guiDecryptButton():\n    \"\"\"\n    a graphic user interface button that calls the \"choosefilefordecrypt\"\n    function\n    :return: none\n    \"\"\"\n    root = tk.Tk()\n    frame = tk.Frame(root)\n    frame.pack()\n\n    decryptbutton = tk.Button(\n        frame,\n        text=\"decrypt\",\n        command=chooseFilesForDec)\n    decryptbutton.pack(side=tk.LEFT)\n\n\ndef chooseFilesForDec():\n    \"\"\"\n    a function that starts the decryption process\n    the function starts when the user presses the \"decrypt button\n    it gets the filenames the user wants to decrypt, the original and the already encrypted\n    it cuts the filenames to only the part \"callDec\" function need.\n    it calls the \"callDec\" function\n    :return: none\n    \"\"\"\n    encFilenameInit = askopenfilename()\n    currentDirectory = os.getcwd()\n    delete = len(currentDirectory)\n    lengthEnc = ((delete - len(encFilenameInit)))\n    encFilename = \".\" + encFilenameInit[lengthEnc:]\n\n    callDec(encFilename)\n\n\ndef callDec(encFilename):\n    \"\"\"\n    the function creates a salt from the original file\n    it calls the \"decrypt\" function\n    :param origFilename: the original file you want to decrypt\n    :param encFilename: the encrypted file\n    :return:\n    \"\"\"\n    encryptionObj.fileDecrypt(encFilename)\n\n\nclass MyDict:\n    \"\"\"  a class that responsible to all of the dictionary operations   \"\"\"\n\n    def __init__(self):\n        \"\"\"\n        creats a dictionary file (mydict.npy) if it doesn't exists it creates one and initialize it\n        \"\"\"\n        self.dictName = \"mydict.npy\"\n        try:\n            self.dictonary = np.load(self.dictName, allow_pickle=True).item()\n        except:\n            print(\"initialized dictionary\")\n            self.dictonary = {}\n            np.save(self.dictName, self.dictonary, allow_pickle=True)\n\n    def dictPush(self, filename, iv, tag, key):\n        \"\"\"\n        the function decrypts the dictionary\n        it pushes the iv and the tag to the dictionary with the filename as key\n        it encrypts the dictionary\n        it updates the \"mydict.npy\" file\n        :param filename: the original name of the file that getting encrypt\n        :param iv: the Initialization vector\n        :param tag:\n        :param key: the encryption key\n        :return: none\n        \"\"\"\n        self.dictionary = np.load(self.dictName, allow_pickle=True).item()\n\n        self.dictionary[filename] = [iv, tag, key]\n        np.save(self.dictName, self.dictionary, allow_pickle=True)\n\n    def dictPull(self, filename):\n        \"\"\"\n        it gets filename and returns the iv and tag from the dictionary\n        that are necessary for the decryption process\n        :param filename: the original filename of the encrypted filename\n        :return: iv and tag (bytes)\n        \"\"\"\n\n        self.dictionary = np.load(self.dictName, allow_pickle=True).item()\n        iv, tag, key = self.dictionary[filename]\n        np.save(self.dictName, self.dictionary, allow_pickle=True)\n        return iv, tag, key\n\n    def dictPrint(self):\n        \"\"\"\n        prints the dictionary\n        :return: none\n        \"\"\"\n        self.dictionary = np.load(self.dictName, allow_pickle=True).item()\n        print(\"dictionary --> \")\n        print(self.dictionary)\n\n    def dictDelete(self, event):\n        \"\"\"\n        deletes the file that got deleted from the dictionary\n        :param event: watchdog event\n        :return: none\n        \"\"\"\n        self.dictionary = np.load(self.dictName, allow_pickle=True).item()\n        print(type(self.dictionary))\n        del self.dictionary[event.src_path]\n        print(\"dictionary --> \")\n        print(self.dictionary)\n\n    def dictSwitchKey(self, event):\n        \"\"\"\n        when a file move occurs it switches the key in the dictionary to the new name of the\n        moved file\n        :param event: watchdog event\n        :return: none\n        \"\"\"\n        self.dictionary = np.load(self.dictName, allow_pickle=True).item()\n        self.dictionary[event.dest_path] = self.dictionary.pop(event.src_path)\n        print(self.dictionary)\n        np.save(self.dictName, self.dictionary, allow_pickle=True)\n\n\ndef verifyFile(event):\n    \"\"\"\n    checks if the event source isn't recursive or temporary\n    :param event: watchdog event\n    :return: boolean\n    \"\"\"\n    return event.src_path[2] != '.' and \"32b\" not in event.src_path and 'EncFile' not in event.src_path and \"EncCRC32\" \\\n           not in event.src_path and 'crc32Dir' not in event.src_path and '_' not in event.src_path \\\n           and \"npy\" not in event.src_path and \"DecryptedFile\" not in event.src_path \\\n           and \"plaintext\" not in event.src_path and \"Demo\" not in event.src_path\n\n\nHASH_ALGOS = {\n    'sha1': SHA1,\n    'sha224': SHA224,\n    'sha256': SHA256,\n    'sha384': SHA384,\n    'sha512': SHA512\n}\nHASH_CNT = 1000  # Number of hashes to compute one SHA256 takes 15 microsec,\nSALT_LENGTH = 16  # Length for the Password salt for PBKDF\nHASH_ALGO = 'sha256'  # For PBKDF HMAC\nIV_LENGTH = 12  # Length of GCM IV\nTAG_LENGTH = 16  # Length of the GCM tag, truncate if larger than this\nHASH_FUNC = HASH_ALGOS[HASH_ALGO]\n\n\ndef hash256(*args):\n    \"\"\"short function for Hashing the arguments with SHA-256\"\"\"\n    assert len(args) > 0, \"Should give at least 1 message\"\n    assert all(isinstance(m, (bytes, basestring)) for m in args), \\\n        \"All inputs should be byte string\"\n    h = SHA256.new(bytes(len(args)) + bytes(args[0]) + bytes(len(args[0])))\n    for m in args[1:]:\n        h.update(bytes(m))\n        h.update(bytes(len(m)))\n    h.update(bytes(len(args)))\n    return h.digest()\n\n\ndef hmac256(secret, m):\n    return HMAC.new(key=secret, msg=m, digestmod=HASH_FUNC).digest()\n\n\ndef pad_pw(pw, pad_length):\n    \"\"\"Pad pw to a pad_length, so that it hides the length of the password in bytes.\"\"\"\n    assert 0 < pad_length < 256\n    pw = bytes(pw)\n    k = len(pw) / pad_length\n    topad = pw[k * pad_length:]\n    topad_len = pad_length - len(topad)\n    if topad_len == 0:\n        topad_len = pad_length\n    pad = chr(topad_len) * topad_len\n    return pw + pad\n    # padder = padding.PKCS7(pad_length*8).padder()\n    # return padder.update(bytes(pw)) + padder.finalize()\n\n\ndef unpad_pw(padded_pw, pad_length):\n    \"\"\"Unpad pw\"\"\"\n    padded_pw = bytes(padded_pw)\n    padlen = ord(padded_pw[-1])\n    assert padlen > 0, \"Malformed padding. Last byte cannot be zero.\"\n    pad = padded_pw[-padlen:]\n    assert all((padi == chr(padlen) for padi in pad))\n    return padded_pw[:-padlen]\n\n    # unpadder = padding.PKCS7(pad_length*8).unpadder()\n    # unpadder.update(bytes(pw)) + unpadder.finalize()\n\n\ndef pwencrypt(pw, m):\n    \"\"\"Encrypt the message m under pw using AES-GCM method (AEAD scheme).\n    iv = 0   # Promise me you will never reuse the key\n    c = <hash_style>.<iteration>.<urlsafe-base64 <salt><iv><tag><ctx>>\n    :hash_style: sha-256 or sha-512, scrypt\n    :iteration: Number of iteration. These two are the parameters\n    for PBKDF2.\n    Size of the ciphertext:\n    \"\"\"\n    m = m.encode('ascii', errors='ignore')\n    itercnt = random.randint(HASH_CNT, 2 * HASH_CNT)\n    header_txt = HASH_ALGO + '.' + str(itercnt)\n    sa = os.urandom(SALT_LENGTH)\n    key = PBKDF2(\n        pw, sa,\n        dkLen=16,\n        count=itercnt,\n        prf=hmac256\n\n    )\n    iv, ctx, tag = textEncrypt(key, m, associated_data=header_txt)\n    # Salt (SALT_LENGTH), IV (IV_LENGTH), TAG (TAG_LENGTH)\n    ctx_b64 = urlsafe_b64encode(sa + iv + tag + ctx)\n    return header_txt + '.' + ctx_b64\n\n\ndef pwdecrypt(pw, full_ctx_b64):\n    \"\"\"\n    Decrypt a ciphertext using pw,\n    Recover, hash algo, iteration count, and salt, iv, tag, ctx from ctx_b64\n    \"\"\"\n    full_ctx_b64 = full_ctx_b64.encode('ascii', errors='ignore')\n    hash_algo, itercnt, ctx_b64 = full_ctx_b64.split('.')\n    header_txt = hash_algo + '.' + itercnt\n    ctx_bin = urlsafe_b64decode(ctx_b64)\n    sa, ctx_bin = ctx_bin[:SALT_LENGTH], ctx_bin[SALT_LENGTH:]\n    iv, ctx_bin = ctx_bin[:IV_LENGTH], ctx_bin[IV_LENGTH:]\n    tag, ctx = ctx_bin[:TAG_LENGTH], ctx_bin[TAG_LENGTH:]\n    hmac_tmp = lambda secret, m: HMAC.new(key=secret, msg=m, digestmod=HASH_ALGOS[hash_algo]).digest()\n    key = PBKDF2(\n        pw, sa,\n        dkLen=16,\n        count=int(itercnt),\n        prf=hmac_tmp\n    )\n    try:\n        m = textDecrypt(key, iv, ctx, tag, associated_data=header_txt)\n        return m\n    except Exception as e:\n        raise ValueError(e)\n\n\ndef textEncrypt(key, plaintext, associated_data=''):\n    # Generate a random 96-bit IV.\n    iv = os.urandom(IV_LENGTH)\n    # 16 (AES-128), 24 (AES-192), or 32 (AES-256)\n    if len(key) not in (16, 24, 32):\n        key = hash256(key)  # makes it 256-bit\n    # Construct an AES-GCM Cipher object with the given key and a\n    # randomly generated IV.\n    # create the ciphertext\n    encryptor = AES.new(key=key, mode=AES.MODE_GCM, nonce=iv)\n\n    # associated_data will be authenticated but not encrypted,\n    # it must also be passed in on decryption.\n    encryptor.update(associated_data)\n\n    ctx = encryptor.encrypt(plaintext)\n    # Encrypt the plaintext and get the associated ciphertext.\n    # GCM does not require padding.\n    tag = encryptor.digest()\n    return (iv, ctx, tag)\n\n\ndef textDecrypt(key, iv, ciphertext, tag, associated_data=''):\n    # Construct a Cipher object, with the key, iv, and additionally the\n    # GCM tag used for authenticating the message.\n    if len(key) not in (16, 24, 32):\n        key = hash256(key)  # makes it 256-bit\n\n    decryptor = AES.new(key=key, mode=AES.MODE_GCM, nonce=iv)\n\n    # We put associated_data back in or the tag will fail to verify\n    # when we finalize the decryptor.\n    decryptor.update(associated_data)\n    plaintext = decryptor.decrypt(ciphertext)\n\n    # Decryption gets us the authenticated plaintext.\n    # If the tag does not match an InvalidTag exception will be raised.\n    decryptor.verify(tag)\n    return plaintext\n\n\nclass MyEncryption:\n    \"\"\"\n    a class for encryption and decryption\n    \"\"\"\n\n    def __init__(self, password):\n        \"\"\"\n\n        :param password:\n        \"\"\"\n        self.password = password\n        self.UTF8password = password.encode('UTF-8')\n\n    def fileEncrypt(self, event, salt, destdir):\n        \"\"\"\n        a method that calculates HKDF key based on salt and password\n        reads the event file, encrypts it and stores the iv and the tag in the dictionary file\n        then it writes the cipher text into an encrypted file\n        it then creates a hash of the encrypted file\n\n        :param event: watchdog event\n        :param salt: CRC32 of the unencrypted file\n        :param destdir: \"shadow file\" directory name str\n        :return: none\n        \"\"\"\n        print(\"in  fileEncrypt\")\n        # destdir = destdir + \"/\" + destdir\n        kdf = Hkdf(str(salt).encode('utf8'), self.UTF8password, hash=hashlib.sha512)\n        key = kdf.expand(b\"context\", 16)\n        # add code for encryption encrypt(event, key)\n        file_in = open(event.src_path, \"r\")\n        plainText = file_in.read()\n        plainText = [str(x) for x in plainText]\n        plainText = ''.join(plainText).encode('utf8')\n        print(plainText)\n        iv, ctx, tag = textEncrypt(key, plainText)\n        print(\"key -->\" + str(key))\n        print(\"iv -->\" + str(iv))\n        print(\"tag-->\" + str(tag))\n        print(\"ctx-->\" + str(ctx))\n        print(type(ctx))\n        # add code to writing file into destdir\n        encFile = open(destdir + \"/EncFile\", \"wb+\")\n        encFile.write(ctx)\n        encFile.close()\n        d.dictPush(event.src_path, iv, tag, key)\n        encFileHash = open(destdir + \"/EncHash\", \"w+\")\n        hasher = DropboxContentHasher()\n        encFile = open(destdir + \"/EncFile\", \"rb+\")\n        encryptedData = encFile.read()\n        hasher.update(encryptedData)\n        encFileHash.write(hasher.hexdigest())\n\n    def fileDecrypt(self, filename):\n        \"\"\"\n        a method that calculates HKDF key based on salt and password\n        decrypts file event.src_path\n        collects from dictionary file the iv and tag\n        writes decrypted file into \"plaintext\"\n\n        :param filename: the file you want to decrypt (string)\n\n        :return:\n        \"\"\"\n        print(\"in fileDecrypt\")\n        iv, tag, key = d.dictPull(filename[:-17])\n        print(\"key -->\" + str(key))\n        print(\"iv -->\" + str(iv))\n        print(\"tag-->\" + str(tag))\n\n        # add code for decryption encrypt(event, key)\n        file_in = open(filename, \"rb\")\n        ctx = file_in.read()\n        print(type(ctx))\n        print(\"ctx-->\" + str(ctx))\n        plaintext = textDecrypt(key, iv, ctx, tag)\n        print(\"plaintext -->\" + str(plaintext))\n        decryptedFile = open(\"./plaintext\", \"w+\")\n        decryptedFile.write(plaintext.decode(\"utf8\"))\n\n\ndef crcCreate(event):\n    \"\"\"\n    a method that accepts a watchdog event and calculates a CRC32\n    of the file in event.src_path\n    creates a file and writes the CRC32 into it\n    the file is placed into the \"shadow directory\" --> event.src_path + '.crc32Dir'\n    :param event: watchdog event\n    :return: none\n    \"\"\"\n    try:\n\n        crcFileName = event.src_path + '' + '.crc32Dir' + '/' + 'crc32.32b'\n        f = open(crcFileName, \"w+\")\n        f.write(crc(event.src_path))\n        print(f)\n        f.close()\n    except:\n        print(\"could not open file %s\" % crcFileName)\n\n\ndef crc(fileName):\n    \"\"\"\n    a method that calculates the CRC32 of a given file fileName\n    :param fileName: string containing file name\n    :return: CRC32 hexadecimal\n    \"\"\"\n    prev = 0\n    fileName.strip('_')\n    for eachLine in open(fileName, \"rb\"):\n        prev = zlib.crc32(eachLine, prev)\n    return \"%X\" % (prev & 0xFFFFFFFF)\n\n\nclass MyEventHandler(LoggingEventHandler):\n    \"\"\"\n    a new eventhandler that adds Dropbox project functionality to the watchdog.EventHandler\n\n    \"\"\"\n\n    def on_created(self, event):\n        \"\"\"\n        method in MyEventHandler that gets activated whenever an 'created' event is\n        detected by the watchdog observer\n\n        ---\n        actions:\n\n        checks if the event that created this event is a directory or a file\n        checks the event isn't recursive or  temporary\n        creates a directory named dirPath and calls the crcCreate method\n        calls the encryption\n\n        ---\n\n        :param event: watchdog event\n        :return: none\n        \"\"\"\n        super(MyEventHandler, self).on_created(event)\n\n        if not event.is_directory:\n            dirPath = event.src_path.split('_')[0] + '.crc32Dir'  # the name of the shadow directory\n            if verifyFile(event):\n                try:\n                    os.mkdir(dirPath)\n                except OSError:\n                    print(\"Creation of the directory %s failed\" % dirPath)\n                salt = str(crc(event.src_path)).encode('UTF-8')\n                encryptionObj.fileEncrypt(event, salt, dirPath)\n                crcCreate(event)\n\n    def on_moved(self, event):\n        \"\"\"\n        method in MyEventHandler that gets activated whenever an 'moved' event is\n        detected by the watchdog observer\n\n        ---\n        actions\n        moves the CRC32 directory of the event file from event.src_path to event.dest_path\n        ---\n        :param event: watchdog event\n        :return: none\n        \"\"\"\n        super(MyEventHandler, self).on_moved(event)\n\n        if len(event.src_path) > 3:\n            if event.src_path[2] != '.':\n                if not event.is_directory:\n                    if verifyFile(event):\n                        d.dictSwitchKey(event)\n                        shutil.move(event.src_path + '' + '.crc32Dir', event.dest_path + '.crc32Dir')\n\n    def on_deleted(self, event):\n        \"\"\"\n        method in MyEventHandler that gets activated whenever an 'deleted' event is\n        detected by the watchdog observer\n\n        ---\n        actions\n\n        checks the event isn't recursive or  temporary\n        deletes the CRC32 directory\n        deletes from the dictionary\n        ---\n        :param event: watchdog event\n        :return: none\n        \"\"\"\n        super(MyEventHandler, self).on_deleted(event)\n        delpath = event.src_path  # a path to the src of the event\n        if verifyFile(event):\n            d.dictDelete(event)\n            shutil.rmtree(delpath + '.crc32Dir')\n\n    def on_modified(self, event):\n        \"\"\"\n        method in MyEventHandler that gets activated whenever an 'modified' event is\n        detected by the watchdog observer\n\n        ---\n        actions\n\n        checks if the event that created this event is a directory or a file\n        checks the event isn't recursive or  temporary\n        compares the crc of the original file with the crc of the modified file\n        if they are equal:\n        not doing anything\n        if not:\n        starting the encryption process, switches the original crc with the new one\n\n        ---\n        :param event: watchdog event\n        :return: none\n        \"\"\"\n        super(MyEventHandler, self).on_modified(event)\n        if len(event.src_path) > 3:\n            if not event.is_directory:\n                if verifyFile(event):\n                    if not crc_compare(event):\n                        dirPath = event.src_path + '' + '.crc32Dir'  # the name of the directory holding the CRC32 file\n                        salt = str(crc(event.src_path)).encode('UTF-8')\n                        encryptionObj.fileEncrypt(event, salt, dirPath)\n                        d.dictPrint()\n                        crcCreate(event)\n\n\nif __name__ == \"__main__\":\n    master = Tk()  # initilaize the GUI\n    d = MyDict()  # creates a MyDict object\n    d.__init__()\n    guiWelcomeBox()\n    guiPasswordBox()\n    encryptionObj = MyEncryption(password)\n    guiDecryptButton()\n    path = sys.argv[1] if len(sys.argv) > 1 else '.'  # path is the directory of the source\n    event_handler = MyEventHandler()  # the observer event_handler is MyEventHandler\n    observer = Observer()\n    observer.schedule(event_handler, path, recursive=True)  # the observer will check all of the files in 'path'\n    observer.start()\n    root = mainloop()\n    try:\n        while True:\n            time.sleep(1)\n    except KeyboardInterrupt:\n        observer.stop()\n    observer.join()\n","sub_path":"MoveFix.py","file_name":"MoveFix.py","file_ext":"py","file_size_in_byte":23201,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"105031631","text":"\"\"\"\nGet info about your SEO (tags)\nClass takes two arguments shop name and search tag.\n\"\"\"\n\nfrom selenium import webdriver\nimport time \nimport datetime\nfrom getProxyUSA import GetProxy\nimport csv\nimport os \n\nclass SearchShop:\n\tdef __init__(self, shop, tag):\n\t\tself.shop = shop \n\t\tself.tag = tag \n\t\tself.worker()\n\t\tself.browser.close()\n\n\tdef worker(self):\n\t\tproxy = GetProxy()\n\t\tfirefox_capabilities = webdriver.DesiredCapabilities.FIREFOX\n\t\tfirefox_capabilities['marionette'] = True\n\n\t\tfirefox_capabilities['proxy'] = {\n\t\t\t\"proxyType\": \"MANUAL\",\n\t\t\t\"httpProxy\": proxy,\n\t\t\t\"ftpProxy\": proxy,\n\t\t\t\"sslProxy\": proxy\n\t\t\t}\n\n\t\tself.browser = webdriver.Firefox(capabilities=firefox_capabilities)\n\t\tself.browser.get(f\"https://www.etsy.com/search?q={self.tag}\")\n\t\tself.make_scroll()\n\t\tself.get_pages()\n\t\tk = False\n\t\tfor page in range(self.total_pages):\n\t\t\ttime.sleep(2)\n\t\t\tself.browser.get(f\"https://www.etsy.com/search?q={self.tag}&ref=pagination&page={page+1}\")\n\t\t\titems = self.browser.find_elements_by_css_selector('.wt-grid__item-xl-3')\n\t\t\tfor i in range(len(items)):\n\t\t\t\ttry:\n\t\t\t\t\ttmp_shop = items[i].find_element_by_class_name('text-gray-lighter').text \n\t\t\t\texcept:\n\t\t\t\t\tpass\n\t\t\t\tif tmp_shop == self.shop:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tself.title = items[i].find_element_by_css_selector('.logged .text-body').text\n\t\t\t\t\texcept:\n\t\t\t\t\t\tself.title = 'None'\n\t\t\t\t\tself.date = datetime.datetime.now().date()\n\t\t\t\t\tself.pos = page * 48 + i \n\t\t\t\t\tself.page = page + 1\n\t\t\t\t\tself.save_csv()\n\t\t\t\t\tk = True\n\t\t\tif k:\n\t\t\t\tbreak\n\n\tdef make_scroll(self):\n\t\tfor i in range(3):\n\t\t\tself.browser.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n\t\t\ttime.sleep(1)\n\n\tdef save_csv(self):\n\t\tpath = f\"{os.getcwd()}/{self.shop}\"\n\t\ttry: \n\t\t\tos.mkdir(path) \n\t\texcept OSError as error: \n\t\t\tprint(error)    \n\t\twith open(f'{path}/{self.shop}.csv', 'a') as f:\n\t\t\t\twriter = csv.writer(f)\n\t\t\t\twriter.writerow((self.date, self.shop, self.tag, self.title, self.pos, \n\t\t\t\t\t\t\t\tself.page, self.total_pages))\n\t\tprint(self.tag)\n\n\tdef get_pages(self):\n\t\ttry:\n\t\t\ttime.sleep(3)\n\t\t\tself.total_pages = int(self.browser.find_elements_by_css_selector('.wt-action-group__item')[-2].text.split('\\n')[-1])\n\t\texcept:\n\t\t\tprint('No found')\n\t\t\tself.browser.close()\n\t\t\tself.ch_tag()\n\n\tdef ch_tag(self):\n\t\tself.tag = '+'.join(input(\"Search tag:\\n\").split())\n\t\tself.worker()\n\nif __name__ =='__main__':\n\tshop = input('Shop name:\\n')\n\ttag = '+'.join(input(\"Search tag:\\n\").split())\n\tSearchShop(shop, tag)","sub_path":"forWork/searchShopPage.py","file_name":"searchShopPage.py","file_ext":"py","file_size_in_byte":2440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"136390562","text":"from selenium import webdriver\n\nclass GoogleID:\n    def test_google_ID(self):\n        url = 'https://google.com'\n        driver = webdriver.Firefox()\n        driver.get(url)\n        ele_gl = driver.find_element_by_id('hplogo')\n        text = ele_gl.text\n        if ele_gl:\n            print('Hurrah!!!, Found the id name of finding lucky ' +text)\n        driver.quit()\n\n\ngid = GoogleID()\ngid.test_google_ID()","sub_path":"find_elements/find_id_google.py","file_name":"find_id_google.py","file_ext":"py","file_size_in_byte":408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"580330332","text":"'''\nExamples\ninputCopy\n3\n101\noutputCopy\nYes\ninputCopy\n4\n1011\noutputCopy\nNo\ninputCopy\n5\n10001\noutputCopy\nNo\n'''\nnoe = int(input())\nins = \"0\"+input()+\"0\"\nprint(\"NO\" if (\"11\" in ins or \"000\" in ins ) else \"YES\")\n","sub_path":"ACMSGuru/484_A_row.py","file_name":"484_A_row.py","file_ext":"py","file_size_in_byte":209,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"482988033","text":"import time\nimport sys\n\nimport urllib.error\n\nimport goslate\n\n\nwith open('single_spintax.csv', 'r') as f:\n    texts = f.readlines()\n\nlistArticles = []\nfor text in texts:\n    article = text.split('\";\"')\n    article[0] = article[0][1:]\n    article[-1] = article[-1][:-2].strip()\n    listArticles.append(article)\n\ngs = goslate.Goslate()\n\narticles = []\nfor idx, x in enumerate(listArticles, start=1):\n    print('Processing...', idx)\n    article = []\n    for x_ in x:\n        time.sleep(5)\n        try:\n            hasil = gs.translate(x_, 'nl')\n            article.append(hasil)\n        except urllib.error.HTTPError:\n            print(\"503: your process have been rejected by Google... :(\")\n            break\n        print(\"translating...\")\n\n    time.sleep(30)\n    articles.append(article)\n\n#print(article)\n#sys.exit()\n\nwith open('50_hasil.csv', 'w') as f:\n    for a_ in articles:\n        #pass\n        #print(a_)\n        a_[3] = a_[3].replace(\"</ p>\", \"</p>\")\n        a_[3] = a_[3].replace(\"<p> \", \"<p>\")\n        a_[3] = a_[3].replace(\"</ p>\", \"</p>\")\n        a_[3] = a_[3].replace(\" </p>\", \"</p>\")\n        a_[3] = a_[3].replace(\"</ P>\", \"</p>\")\n        a_[3] = a_[3].replace(\" <p>\", \"<p>\")\n        a_ = '\"{}\";\"{}\";\"{}\";\"{}\"'.format(a_[0],a_[1],a_[2],a_[3])\n        f.write(a_ + '\\n')\n\nprint(\"Program selesai\")\n","sub_path":"4_translate/translate.py","file_name":"translate.py","file_ext":"py","file_size_in_byte":1308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"526629427","text":"import socket\nimport sys\nfrom bs4 import BeautifulSoup\n\nclass Client:\n    def __init__(self):\n        self.host = '192.168.100.219'\n        self.port = 5000\n        self.client = None\n        self.size = 1024\n\n    def open_socket(self):\n        self.client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        self.client.connect((self.host,self.port))\n    \n    def get_response(self,response):\n        \n        while True:\n            received = self.client.recv(1024)\n            response += received\n            \n            if len(received) < 1024:\n                break\n\n        return response\n\n    def get_index(self):\n        request_header = b'GET / HTTP/1.0\\r\\n\\r\\n'\n        self.client.send(request_header)\n        response = self.get_response(b'')\n        \n        responses = response.rsplit(b'\\r\\n',1)\n        content = responses[1].decode('utf-8')\n        print(responses[0])\n\n        soup = BeautifulSoup(content, 'html.parser')\n        print(soup.get_text())\n\n    def get_file(self,file_request):\n        request_header = f'GET /{file_request} HTTP/1.0\\r\\n\\r\\n'.encode('utf-8')\n        self.client.send(request_header)\n        response = self.get_response(b'')\n        responses = response.split(b'\\r\\n', 3)\n        http_status = responses[0].split(b' ',1)\n        print(responses[0])\n\n        if http_status[1] == b'200 OK':\n            with open(file_request,'wb') as file:\n                content = responses[3]\n                file.write(content)\n\n        elif http_status[1] == b'404 Not found':\n            content = responses[3].decode('utf-8')\n            soup = BeautifulSoup(content, 'html.parser')\n            print(soup.get_text())\n\n\n    def run(self, file_request='/'):\n        self.open_socket()\n        try:\n            while True:\n                if file_request == '/' or file_request == 'index.html':\n                    self.get_index()\n                else:\n                    self.get_file(file_request)\n                \n                file_request = input()\n                \n       \n        except KeyboardInterrupt:\n            self.client.close()\n            sys.exit(0)\n\nif __name__ == \"__main__\" :\n    client = Client()\n    client.run()\n","sub_path":"Tugas UTS/no6/client/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":2189,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"156158676","text":"#py4e assignment 11\n\nimport re\nfname = input(\"Enter file name: \")\nif len(fname) < 1 : fname = \"regex_sum_42.txt\"\n\nfh = open(fname)\nfor line in fh:\n    line = line.rstrip()\n    num = re.findall('([0-9]+)', line)\n    if len(num) > 0:\n        print(num)\n","sub_path":"assignment11/assignment11.py","file_name":"assignment11.py","file_ext":"py","file_size_in_byte":251,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"177884896","text":"'''\r\nCreated on 29 Jul 2021\r\n\r\n@author: matth\r\n\r\nThis module contains the method that is responsible for the front end of the 2nd tab of the application. \r\n'''\r\n\r\nimport streamlit as st\r\nimport pandas as pd\r\nimport seaborn as sns\r\nfrom methods import ML_ModelsMethods as mm  # custom script\r\nfrom methods import CleanerMethods as cl\r\n\r\n\r\ndef MLPagePrintOut():\r\n    '''\r\n    Summary:\r\n    Provides frontend elements of 'Create ML Classifier' tab using the streamlit framework.\r\n    '''\r\n    \r\n    df_set = False\r\n    spliting = False\r\n    categories_valid = True\r\n    bounds = {}\r\n    \r\n    st.title('Create Machine Learning Classifier')\r\n    \r\n    if 'revised_df' not in st.session_state:\r\n        st.write('No cached dataset, please upload file.')\r\n        dataset = st.file_uploader(\"Upload CSV File\", type=['csv'])\r\n        if dataset is not None:\r\n            df = pd.read_csv(dataset, na_values=\"NaN\")\r\n            df_set = True   \r\n    else:\r\n        option = st.selectbox(\r\n                        'Select Dataset', [\r\n                        'Cached Dataset',\r\n                        'Upload New Dataset'])\r\n        if option == 'Upload New Dataset':\r\n            dataset = st.file_uploader(\"Upload CSV File\", type=['csv'])\r\n            if dataset is not None:\r\n                df = pd.read_csv(dataset, na_values=\"NaN\")\r\n                df_set = True\r\n        else:\r\n            df = st.session_state.revised_df\r\n            df_set = True\r\n            \r\n    if df_set == True:\r\n        st.write('Dataframe:')\r\n        st.write(df)\r\n        headers = list(df.columns)\r\n        \r\n        try:\r\n            st.write('Correlation Heatmap')\r\n            df_corr = df.corr()\r\n            sns.heatmap(df_corr, annot=True, cmap='Reds')\r\n            st.pyplot()\r\n        except ValueError:\r\n            st.write('Dataset is not appropriate for Correlation Heatmap')\r\n        \r\n        method = st.selectbox(\r\n                                'Select ML Method', [\r\n                                'Decision Tree',\r\n                                'Random Forest',\r\n                                'Gaussian Naive Bayes',\r\n                                'n Nearest Neighbours',\r\n                                'Ada Boost',\r\n                                'Quadratic Discriminant Analysis',\r\n                                'Zero R'])\r\n        \r\n        n_neighbour = -1\r\n        if method == 'n Nearest Neighbours':\r\n            n_neighbour = st.selectbox('Select n', [1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\r\n        \r\n        model_class = st.selectbox('Select Class', headers)\r\n        numeric_class = cl.checkListIsNum(df[model_class])\r\n        \r\n        if numeric_class:\r\n                st.write('Split numeric class into discrete categories')\r\n                num_of_categories = st.selectbox('Number of Categories', ['Don\\'t Split', '2', '3', '4'], index=1)\r\n                \r\n        with st.form('treatment_form'):\r\n            \r\n            save_classifier = st.text_input(label='Classifier Name - Enter Name to Save Classifier')\r\n            \r\n            if numeric_class:\r\n                if num_of_categories == '2' or num_of_categories == '3' or num_of_categories == '4':\r\n                    spliting = True\r\n                    bounds = mm.splitNumericCategoriesGetBounds(num_of_categories)\r\n            \r\n            st.write(\"Columns to be included in model:\")\r\n            \r\n            column_included = {}\r\n            \r\n            for col in headers:\r\n                if col != model_class:\r\n                    column_included[col] = st.checkbox(col, value=\"True\")\r\n        \r\n            submitted = st.form_submit_button(\"Submit\")\r\n        \r\n        if submitted:\r\n            \r\n            model_df = mm.removeColumns(df, column_included)\r\n                        \r\n            if numeric_class and spliting:\r\n                categories_valid = mm.boudaryValuesValid(bounds, num_of_categories) and mm.boudaryNamesValid(bounds, num_of_categories)\r\n            \r\n            if (not categories_valid) or mm.checkColumnsAllFalse(column_included):\r\n                pass  # not placeholder\r\n            else:\r\n                if spliting:\r\n                    if bounds[0] == 0:\r\n                        model_df = mm.splitNumericCategories0(bounds, num_of_categories, model_df, model_class)\r\n                    else:\r\n                        model_df = mm.splitNumericCategories1(bounds, num_of_categories, model_df, model_class)\r\n                \r\n                st.write('Dataset used for model:')\r\n                st.write(model_df)\r\n                st.markdown(cl.get_table_download_link(model_df, 'model_dataset.csv'), unsafe_allow_html=True)\r\n                \r\n                if submitted:\r\n                    \r\n                    if mm.checkDatasetClassNotEmpty(model_df[model_class]):\r\n                    \r\n                        classifier = mm.runClassifier(model_df, model_class, method, n_neighbour)\r\n                        \r\n                        if save_classifier != '':\r\n                            classifier_name = save_classifier + '_name'\r\n                            classifier_class = save_classifier + '_class'\r\n                            classifier_model = save_classifier + '_model'\r\n                            classifier_headers = save_classifier + '_headers'\r\n                            classifier_datatypes = save_classifier + '_datatypes'\r\n                            st.session_state[classifier_name] = save_classifier\r\n                            st.session_state[classifier_class] = model_class\r\n                            st.session_state[classifier_model] = classifier\r\n                            st.session_state[classifier_headers] = list(model_df.columns)\r\n                            st.session_state[classifier_datatypes] = model_df.dtypes\r\n        \r\n","sub_path":"main/pages/ML_Models.py","file_name":"ML_Models.py","file_ext":"py","file_size_in_byte":5824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"99759867","text":"# -*- coding: utf-8 -*-\nimport os.path\nimport json\nfrom datetime import datetime\nfrom sqlite3 import dbapi2 as sqlite3\nfrom functools import wraps\n\nfrom flask import Flask\nfrom flask import request\nfrom flask import g\nfrom flask import redirect\nfrom flask import url_for\nfrom flask import abort\nfrom flask import render_template\nfrom flask import send_from_directory\nfrom flask import make_response\nfrom flask import Response\n\napp = Flask(__name__)\n\n\ndef connect_db():\n    \"\"\"Connects to the specific database.\"\"\"\n    rv = sqlite3.connect(app.config['DATABASE'])\n    rv.row_factory = sqlite3.Row\n    return rv\n\n\ndef init_db():\n    \"\"\"Creates the database tables.\"\"\"\n    with app.app_context():\n        db = get_db()\n        with app.open_resource('schema.sql', mode='r') as f:\n            db.cursor().executescript(f.read())\n        db.commit()\n\n\ndef get_db():\n    \"\"\"\n    Opens a new database connection if there is none yet for the\n    current application context.\n    \"\"\"\n    if not hasattr(g, 'sqlite_db'):\n        g.sqlite_db = connect_db()\n    return g.sqlite_db\n\n\ndef jsonify(function):\n    \"\"\"\n    Creates a response with the JSON representation of wrapped function result.\n    \"\"\"\n    @wraps(function)\n    def inner(*args, **kwargs):\n        result = function(*args, **kwargs)\n        if not isinstance(result, Response):\n            result = Response(json.dumps(result), mimetype='application/json')\n        return result\n    return inner\n\n\n@app.route('/', methods=['POST', 'GET'])\ndef main_page():\n    \"\"\"\n    Shows main page from static (GET) and sets cookie with username (POST)\n    \"\"\"\n    if request.method == 'GET':\n        return render_template('main.html')\n    if request.method == 'POST':\n        username = request.form.get('username', 'player1')\n        if username:\n            response = redirect(url_for('game_page', level_id=1))\n            response.set_cookie('username', username, expires=datetime.max)\n            return response\n\n\n@app.route('/level/<int:level_id>')\ndef game_page(level_id=1):\n    \"\"\"\n    Sets cookie with level_id and shows main page from static.\n    \"\"\"\n    response = make_response(render_template('game.html'))\n    response.set_cookie('level_id', str(level_id), expires=datetime.max)\n    return response\n\n\n@app.route('/top-10/<int:level_id>')\ndef top_10(level_id):\n    params = {\n        'level_id': level_id,\n        'next_level_url': url_for('game_page', level_id=level_id+1),\n    }\n    return render_template('top-10.html', **params)\n\n\n@app.route('/api/v1/level/<int:level_id>.<ext>')\ndef get_level(level_id, ext):\n    \"\"\"\n    Loads map (obj or mtl file) from static directory.\n    \"\"\"\n    return send_from_directory(app.config['LVL_DIR'], '%03d.%s' % (level_id, ext))\n\n\n@app.route('/api/v1/scores/<int:level_id>', methods=['POST', 'GET'])\n@jsonify\ndef scores(level_id):\n    \"\"\"\n    Returns scores for all users (GET) or saves score (POST).\n    \"\"\"\n    if request.method == 'POST':\n        moves = request.form.get('moves', None)\n        duration = request.form.get('duration', None)\n        username = request.form.get('username', None)\n        if moves and duration and username:\n            db = get_db()\n            db.execute(\n                'INSERT INTO scores (level_id, username, moves, duration) \\\n                 VALUES (?, ?, ?, ?)',\n                [level_id, username, moves, duration]\n            )\n            db.commit()\n            return {'result': True}\n        else:\n            abort(400)\n    if request.method == 'GET':\n        db = get_db()\n        cur = db.execute(\n            'SELECT moves, duration, username \\\n             FROM scores \\\n             WHERE level_id = {} \\\n             ORDER BY moves, duration'.format(level_id)\n        )\n        entries = cur.fetchall()\n        result = [dict(entries[i]) for i in range(len(entries))]\n        return result\n","sub_path":"src/buckyban/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3842,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"135380694","text":"from django.shortcuts import render, HttpResponse\r\nimport requests\r\nimport re\r\nfrom rest_framework.views import APIView\r\nimport json\r\nfrom vedio import forms\r\nfrom vedio import huanongwang\r\n# Create your views here.\r\n\r\n\r\ndef zhongxinwang(url):\r\n    addrs = []\r\n    req = requests.get(url)\r\n    result = re.findall(r'http://(.+?).mp4', req.text)\r\n    for item in result:\r\n        addr = 'http://' + item + '.mp4'\r\n        addrs.append(addr)\r\n    return addrs\r\n\r\n\r\n\r\n\r\nclass XinWenView(APIView):\r\n\r\n    def get(self, request, *args, **kwargs):\r\n        form_obj = forms.UrlForm()\r\n        return render(request, 'url.html', {'form_obj': form_obj})\r\n\r\n\r\n    def post(self, request, *args, **kwargs):\r\n        addrs = []\r\n        form_obj = forms.UrlForm(request.POST)\r\n        if form_obj.is_valid():\r\n            url_type = form_obj.cleaned_data.get(\"url_type\")\r\n            url = form_obj.cleaned_data.get(\"url\")\r\n            if url_type == 0:\r\n                addrs = zhongxinwang(url)\r\n            else:\r\n                mp4_name = huanongwang.main(url)\r\n                addr =  'http://111.230.10.100/' + mp4_name\r\n                addrs.append(addr)\r\n            return HttpResponse(json.dumps(addrs, ensure_ascii=False), content_type=\"application/json\")\r\n\r\n        else:\r\n            return HttpResponse(form_obj.errors)\r\n\r\n\r\n\r\n\r\n","sub_path":"vedio/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1333,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"415402497","text":"# Definition for a binary tree node.\nclass TreeNode:\n    \n    def __init__(self, val=0, left=None, right=None):\n        self.val = val\n        self.left = left\n        self.right = right\n\n\nclass Solution:\n    \n    def levelOrderBottom(self, root: TreeNode) -> list:\n        \n        depth = self.find_depth(root)\n        matrix = []\n        \n        for i in reversed(range(depth)):\n            \n            matrix.append(self.get_at_index(root, i))\n            \n        return matrix\n    \n    \n    def find_depth(self, node) -> int:\n        \n        left_val = 0\n        right_val = 0\n        \n        if node == None:\n            return 0\n        else:\n            if node.left is not None:\n                left_val = self.find_depth(node.left)\n\n            if node.right is not None:\n                right_val = self.find_depth(node.right)\n        \n        return 1 + (left_val if left_val > right_val else right_val)\n\n    \n    def get_at_index(self, node, i, curr_index=0) -> list:\n        \n        if curr_index == i:\n            return [node.val]\n        else:\n            \n            left_val, right_val = [], []\n            \n            if node.left is not None:\n                left_val = self.get_at_index(node.left, i, curr_index+1)\n            \n            if node.right is not None:\n                right_val = self.get_at_index(node.right, i, curr_index+1)\n            \n            return left_val + right_val\n        \n        \n\ndef main():\n    \n    node_list = [3,9,20,None,None,15,7]\n    \n    node_15 = TreeNode(15)\n    node_7 = TreeNode(7)\n    node_20 = TreeNode(20, node_15, node_7)\n    node_9 = TreeNode(9)\n    node_3 = TreeNode(3, node_9, node_20)\n    node_4 = TreeNode(4, node_3)\n    \n    s = Solution()\n    \n    print(f\"{s.levelOrderBottom(node_4)}\")\n    \n    return 0\n\nif __name__ == '__main__':\n    exit(main())\n","sub_path":"2_binary_tree.py","file_name":"2_binary_tree.py","file_ext":"py","file_size_in_byte":1837,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"627247113","text":"\"\"\"medserver URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/1.10/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  url(r'^$', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.conf.urls import url, include\n    2. Add a URL to urlpatterns:  url(r'^blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls import url, include\nfrom django.contrib import admin\n\nfrom rest_framework.authtoken import views\nfrom rest_framework import routers\n\nfrom apps.human_resources import views as hr_views\nfrom apps.patients import views as p_views\nfrom apps.visit_planning import views as vp_views\nfrom medserver.views import schema_view\n\nrouter = routers.DefaultRouter()\nrouter.register(r'employees', hr_views.EmployeeViewSet)\nrouter.register(r'departments', hr_views.DepartmentViewSet)\nrouter.register(r'schedules', hr_views.EmployeeScheduleViewSet)\nrouter.register(r'free_times', hr_views.EmployeeFreeTimesView, base_name='free_times')\nrouter.register(r'allergy_sources', p_views.AllergySourceViewSet)\nrouter.register(r'previous_illnesses', p_views.PreviousIllnessesViewSet)\nrouter.register(r'previous_surgery', p_views.PreviousSurgeryViewSet)\nrouter.register(r'patients', p_views.PatientFullViewSet)\nrouter.register(r'patients_smart', p_views.PatientSmartViewSet)\nrouter.register(r'visit_reasons', vp_views.VisitReasonViewSet)\nrouter.register(r'patient_visits', vp_views.PatientVisitViewSet)\nrouter.register(r'patient_visit_statuses', vp_views.PatientVisitStatusesView, base_name='patient_visit_statuses')\n\nurlpatterns = [\n    url(r'^admin/', admin.site.urls),\n    url(r'^api/', include('rest_framework.urls', namespace='rest_framework')),\n    url(r'^api/', include(router.urls)),\n    url(r'^api/me', hr_views.me),\n    url(r'^api/get_token', views.obtain_auth_token),\n    url(r'^schema/?', schema_view),\n]\n","sub_path":"MED.Server/medserver/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2154,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"27952507","text":"import warnings\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\nimport numpy as np\nimport time, os\n\nimport tvm\nimport tvm.contrib.graph_runtime as runtime\nfrom tvm import relay\n\nimport tensorflow as tf\nimport tvm.relay.testing.tf as tf_testing\n\nfrom tensorflow import nn\nfrom tensorflow import image\n\nnp.random.seed(0)\n\n\"\"\" \n    Network parameters\n\"\"\"\nIMG_WIDTH = 600\nIMG_HEIGHT = 400\n\nBATCH_SIZE = 32\nN = 224\nFIN = 3\nFOUT = 32\n\nK_Y = 3\nK_X = 3\n\nNB_TESTS = 101\n\n\"\"\" \n    Target settings\n\"\"\"\ntarget = \"llvm -mcpu=core-avx2\"\ntarget_host = \"llvm\"\nlayout = None\n\ninput_shape = (BATCH_SIZE, IMG_HEIGHT, IMG_WIDTH, FIN)\ndtype = \"float32\"\n\n\"\"\" \n    Create the graph in TensorFlow \n\"\"\"\ndef ResizeConvReluMaxPool(X, weights, bias):\n    resize = image.resize(X, [N + 2, N + 2])\n    \n    conv = nn.conv2d(resize, weights, strides=[1, 1, 1, 1], padding=\"VALID\", data_format=\"NHWC\")\n    conv_bias = nn.bias_add(conv, bias, data_format=\"NHWC\")\n\n    relu = nn.relu(conv_bias)\n    maxpool = nn.max_pool2d(relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=\"VALID\", data_format=\"NHWC\", name=\"output\")\n    \n    return maxpool\n\nweights = np.random.rand(K_Y, K_X, FIN, FOUT)\nbias = np.random.rand(FOUT)\n\nX = tf.compat.v1.placeholder(tf.float32, [BATCH_SIZE, IMG_HEIGHT, IMG_WIDTH, FIN], name=\"X\")\nactivations = ResizeConvReluMaxPool(X, weights, bias)\n\nmodel_path = \"tf_model.pb\"\ntf.io.write_graph(tf.compat.v1.get_default_graph(), \"\", model_path, as_text=False)\n\n\"\"\" \n    Create the graph in TVM and compile it \n\"\"\"\nwith tf.io.gfile.GFile(model_path, \"rb\") as f:\n    graph_def = tf.compat.v1.GraphDef()\n    graph_def.ParseFromString(f.read())\n    graph = tf.import_graph_def(graph_def, name=\"\")\n\n    graph_def = tf_testing.ProcessGraphDefParam(graph_def)\n\n    # Add shapes to the graph.\n    with tf.Session() as sess:\n        graph_def = tf_testing.AddShapesToGraphDef(sess, \"output\")\n\n# Import TF graph definition to Relay frontend\nshape_dict = {\"X\": input_shape}\nmod, parameters = relay.frontend.from_tensorflow(graph_def, layout=layout, shape=shape_dict)\n\n# Compile the graph\nwith relay.build_config(opt_level=3):\n    graph, lib, params = relay.build_module.build(\n        mod, target=target, params=parameters)\n\n\"\"\" Execute and evaluate the graph \"\"\"\nctx = tvm.cpu()\ndata_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))\nmodule = runtime.create(graph, lib, ctx)\nmodule.set_input(\"X\", data_tvm)\nmodule.set_input(**params)\n\n# evaluate\nprint(\"Evaluate inference time cost...\")\nftimer = module.module.time_evaluator(\"run\", ctx, number=NB_TESTS, repeat=1)\nprof_res = np.array(ftimer().results) * 1000  # convert to millisecond\n\nprint(\"Network execution time : \", np.median(prof_res))","sub_path":"benchmarks/DNN/blocks/Resize-Conv-ReLU-MaxPool/cpu/dense/resize_conv_relu_maxpool_tvm.py","file_name":"resize_conv_relu_maxpool_tvm.py","file_ext":"py","file_size_in_byte":2706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"455845650","text":"\"\"\"Cover \"Manhattan skyline\" using the minimum number of rectangles.\"\"\"\nimport getopt\nimport sys\nimport re\ndef stone_wall(arr_h):\n    \"\"\"Cover \"Manhattan skyline\" using the minimum number of rectangles.\"\"\"\n    # define N as length of arr_h\n    len_n = len(arr_h)\n    # start block count at length of arr_h\n    blocks = len(arr_h)\n    # iterate through arr_h to compare blocks\n    for ndx_n in range(len_n):\n        # create a lookback variable to crawl backwards from current loop location\n        lookback = ndx_n\n        # only crawl backwards while height is high enough to share stones\n        while (arr_h[ndx_n] <= arr_h[lookback]) & (lookback > 0):\n            lookback -= 1\n            # if a shared height is reached,\n            if arr_h[lookback] == arr_h[ndx_n]:\n                # subtract from block count\n                blocks -= 1\n                # and stop while loop\n                lookback = 0\n    # return the block count\n    return blocks\n# Bash Testing:\nARGS = list(getopt.getopt(sys.argv, \"ho:v\"))[1][1]\nARGS = re.sub(r\"^\\[|\\]$\", \"\", ARGS).split(\", \")\nARGS = [int(x) for x in ARGS]\n# time1 = datetime.datetime.now()\nRESULT = stone_wall(ARGS)\n# time2 = datetime.datetime.now()\n# timeChange = time2-time1\nprint [\"result: \"]+[RESULT] #+[\" ; milliseconds: \"]+[timeChange.total_seconds()*1000])\n# Codility Testing:\n## https://app.codility.com/demo/results/trainingQYZX6P-S8W/ ; trainingK59HWD-FN2\n## Correctness: 100%\n## Performance: 77%\n## Difficulty: Painless\n","sub_path":"source/7-stacks-and-queues/2/stone_wall.py","file_name":"stone_wall.py","file_ext":"py","file_size_in_byte":1481,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"339636116","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\nimport django.utils.timezone\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n        ('member', '0001_initial'),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='SponsorItem',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('created_at', models.DateTimeField(default=django.utils.timezone.now, editable=False, db_column='created')),\n                ('updated_at', models.DateTimeField(default=django.utils.timezone.now, editable=False, db_column='updated')),\n                ('code', models.CharField(max_length=25)),\n                ('name', models.CharField(max_length=255)),\n                ('type', models.CharField(max_length=25)),\n                ('description', models.CharField(max_length=523)),\n                ('amount', models.FloatField()),\n                ('photo', models.CharField(max_length=255, null=True, blank=True)),\n                ('created_by', models.ForeignKey(related_name='sponsoritem_created_by', db_column='createdBy', blank=True, editable=False, to=settings.AUTH_USER_MODEL, null=True)),\n            ],\n            options={\n                'db_table': 'sponsorItems',\n            },\n        ),\n        migrations.AddField(\n            model_name='seva',\n            name='title',\n            field=models.CharField(max_length=55, null=True, blank=True),\n        ),\n        migrations.AddField(\n            model_name='sevacategory',\n            name='duration',\n            field=models.CharField(max_length=11, null=True, blank=True),\n        ),\n        migrations.AddField(\n            model_name='sevacategory',\n            name='duration_type',\n            field=models.CharField(max_length=11, null=True, db_column='durationType', blank=True),\n        ),\n        migrations.AddField(\n            model_name='sevacategory',\n            name='recurrence',\n            field=models.CharField(max_length=25, null=True, blank=True),\n        ),\n        migrations.AddField(\n            model_name='sevacategory',\n            name='show_start_date',\n            field=models.BooleanField(default=True, db_column='showStartDate'),\n        ),\n        migrations.AddField(\n            model_name='sevacategory',\n            name='sponsor_item_type',\n            field=models.CharField(max_length=55, null=True, db_column='sponsorItemType', blank=True),\n        ),\n        migrations.AlterField(\n            model_name='seva',\n            name='enddate',\n            field=models.DateField(null=True, db_column='endDate', blank=True),\n        ),\n        migrations.AlterField(\n            model_name='seva',\n            name='startdate',\n            field=models.DateField(null=True, db_column='startDate', blank=True),\n        ),\n        migrations.AddField(\n            model_name='sponsoritem',\n            name='seva_id',\n            field=models.ForeignKey(db_column='sevaId', to='member.Seva', unique=True),\n        ),\n        migrations.AddField(\n            model_name='sponsoritem',\n            name='updated_by',\n            field=models.ForeignKey(related_name='sponsoritem_updated_by', db_column='updatedBy', blank=True, editable=False, to=settings.AUTH_USER_MODEL, null=True),\n        ),\n    ]\n","sub_path":"Django/Django_Old/disa-py/member/migrations/0003_auto_20160315_0631.py","file_name":"0003_auto_20160315_0631.py","file_ext":"py","file_size_in_byte":3485,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"311171277","text":"\"\"\"add error to GeoFileStatus\n\nRevision ID: 4231989b78d5\nRevises: ed182660c635\nCreate Date: 2020-09-18 11:17:54.345395\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n# revision identifiers, used by Alembic.\nrevision = \"4231989b78d5\"\ndown_revision = \"ed182660c635\"\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    op.execute(\"COMMIT\")\n    op.execute(\"ALTER TYPE geofilestatus ADD VALUE 'error'\")\n\n\ndef downgrade():\n    op.execute(\"UPDATE geofile SET status='importing' WHERE status='error'\")\n    op.execute(\n        \"DELETE FROM pg_enum WHERE enumtypid='geofilestatus'::regtype AND enumlabel='error'\"\n    )\n","sub_path":"backend/app/alembic/versions/4231989b78d5_add_error_to_geofilestatus.py","file_name":"4231989b78d5_add_error_to_geofilestatus.py","file_ext":"py","file_size_in_byte":623,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"591481018","text":"from urllib.request import urlopen\nfrom urllib.error import HTTPError\nfrom bs4 import BeautifulSoup\n\ndef get_title(url):\n\n    try:\n        html=urlopen(url)   #서버에 응답이 없을경우(500) 또는 파일이 없을경우 (404)에 HTTPERROR가 발생하여 None을 리턴\n    except HTTPError as e:\n        return None\n    try:\n        bsobj = BeautifulSoup(html.read(),\"html.parser\") #html 파일을 객체로 읽어온뒤에 bs4객체로 변환한다.\n        title = bsobj.head.title  # head값이 없��경우에는 None 리턴, None리턴 값의 attribute를 호출하면 AttributeError가 발생한다\n    except AttributeError as e:\n        return None\n    return title\n\n\nresult = get_title(\"http://www.naver.com\")\nif result == None:\n    print(\"this site does not use <TITLE> tags.\")\nprint(result)","sub_path":"crawler_1chapter.py","file_name":"crawler_1chapter.py","file_ext":"py","file_size_in_byte":810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"564022619","text":"import sys\nfrom math import prod\n\nlines = []\nfor i in sys.stdin:\n    lines.append(i.strip(\"\\n\"))\n\nslopes = [(1, -1), (3, -1), (5, -1), (7, -1), (1, -2)]\n\n\ndef encounters(lines, slope):\n    position = (0, 0)\n    multiplier = 1\n    trees = 0\n    count = abs(slope[1]) - 1\n\n    for i in lines:\n        if count != abs(slope[1]) - 1:\n            count += 1\n            continue\n\n        string = i * multiplier\n\n        if position[0] > (len(string) - 1):\n            multiplier += 1\n            string = i * multiplier\n\n        if string[position[0]] == \"#\":\n            trees += 1\n\n        position = (position[0] + slope[0], position[1] + slope[1])\n        count = 0\n    return trees\n\n\nresults = []\nfor i in slopes:\n    results.append(encounters(lines, i))\n\nprint(prod(results))\n","sub_path":"day-3/python/day-3-2.py","file_name":"day-3-2.py","file_ext":"py","file_size_in_byte":778,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"521035753","text":"\"\"\"\n\"\"\"\n\nimport sys\nimport unittest\n\nfrom minds.safe_config import cfg as testcfg\nfrom minds import lucene_logic\n\n\nclass TestLuceneLogic(unittest.TestCase):\n\n    def setUp(self):\n        self.indexpath = testcfg.getpath('archiveindex')\n        self.cleanup()\n\n\n    def tearDown(self):\n        self.cleanup()\n\n\n    def cleanup(self):\n        self.assertEqual('testdata/archive/index', self.indexpath)    # make sure don't delete wrong data\n        if self.indexpath.exists():\n            self.indexpath.rmtree()\n\n\n    def _test_index_and_search(self, **args):\n\n        writer = lucene_logic.Writer(**args)\n        try:\n            if len(args) == 0:                              # special case for RAM directory\n                args = { 'directory': writer.directory }    # pass the RAM directory for subsequence searching\n\n            if writer.docCount() <= 1:\n                writer.addDocument(u'1', {'uri': u'http://a', 'date': '2004'}, u'content1')\n                writer.addDocument(u'2', {'uri': u'http://a', 'date': '2005'}, u'content2')\n        finally:\n            writer.close()\n\n        reader = lucene_logic.Reader(**args)\n        try:\n            self.assertEqual(reader.numDocs(), 3)           # 2 document added + 1 version document\n            self.assert_(reader.hasDocument(u'1'))\n            self.assert_(not reader.hasDocument(u'3'))\n        finally:\n            reader.close()\n\n        searcher = lucene_logic.Searcher(**args)\n        try:\n            hits = searcher.searchLast(u'http://a')\n            self.assertEqual(len(hits), 2)\n            self.assertEqual(hits.doc(0).get('docid'), u'2')\n            self.assertEqual(hits.doc(0).get('date'), u'2005')\n            self.assertEqual(hits.doc(1).get('docid'), u'1')\n            self.assertEqual(hits.doc(1).get('date'), u'2004')\n\n            hits = searcher.searchLast(u'not exist')\n            self.assertEqual(len(hits), 0)\n        finally:\n            searcher.close()\n\n\n    def test_RAM(self):\n        self.assert_(not self.indexpath.exists())\n        self._test_index_and_search()\n        self.assert_(not self.indexpath.exists())\n\n\n    def test_FSDirectory(self):\n        # iteration 1: start with empty diretory\n        self.assert_(not self.indexpath.exists())\n        self._test_index_and_search(pathname=self.indexpath)\n\n        # iteration 2: with existing index\n        self.assert_(self.indexpath.exists())\n        self._test_index_and_search(pathname=self.indexpath)\n\n\n    def test_version(self):\n        # check for the version document added to new index\n        reader = lucene_logic.Reader()\n        version = testcfg.get('version.number', '?')\n        self.assertEqual(1, reader.numDocs())\n        self.assertEqual(version, reader.getVersion())\n\n\nif __name__ == '__main__':\n    unittest.main()","sub_path":"tags/release-0.8.0/minds/test/test_lucene_logic.py","file_name":"test_lucene_logic.py","file_ext":"py","file_size_in_byte":2788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"520949015","text":"from keras.layers import Input, Conv2D, MaxPool2D, AvgPool2D, Flatten, Dense, GlobalAveragePooling2D\r\nfrom keras.layers import Dropout, BatchNormalization, Activation, concatenate\r\nfrom keras import regularizers\r\nfrom keras.models import Model\r\nimport keras.activations as activation\r\nimport numpy as np\r\n\r\n\r\n\r\nclass inception_V1():\r\n    def __init__(self, batch_size=32,\r\n                 n_classes=1000):\r\n        # 5\r\n\r\n        self.data_size = (batch_size, 224, 224, 3)\r\n\r\n        self.n_classes = n_classes\r\n        self.decay_weight = 0.5e-3\r\n        self.layer_lst = ['Conv2d_0a_1x1',\r\n                          'Conv2d_0a_1x1', 'Conv2d_0b_3x3',\r\n                          'Conv2d_0a_1x1', 'Conv2d_0b_3x3'\r\n            , 'MaxPool_0a_3x3', 'Conv2d_0b_1x1']\r\n    @property\r\n    def input_size(self):\r\n        return self.data_size[1:]\r\n\r\n    def block(self, layer_name, net, out_lst):\r\n        assert isinstance(out_lst, list), TypeError\r\n        assert len(out_lst) == 6, ValueError\r\n\r\n        net0 = Conv2D(out_lst[0], (1, 1), strides=(1, 1), padding='same',\r\n                      kernel_regularizer=regularizers.l2(self.decay_weight),\r\n                      name=layer_name + 'conv1')(net)\r\n\r\n        net1 = Conv2D(out_lst[1], (1, 1), strides=(1, 1), padding='same',\r\n                      kernel_regularizer=regularizers.l2(self.decay_weight),\r\n                      name=layer_name + 'conv2_1')(net)\r\n        net1 = Conv2D(out_lst[2], (3, 3), strides=(1, 1), padding='same',\r\n                      kernel_regularizer=regularizers.l2(self.decay_weight),\r\n                      name=layer_name + 'conv2_2')(net1)\r\n\r\n        net2 = Conv2D(out_lst[3], (1, 1), strides=(1, 1), padding='same',\r\n                      kernel_regularizer=regularizers.l2(self.decay_weight),\r\n                      name=layer_name + 'conv3_1')(net)\r\n        net2 = Conv2D(out_lst[4], (5, 5), strides=(1, 1), padding='same',\r\n                      kernel_regularizer=regularizers.l2(self.decay_weight),\r\n                      name=layer_name + 'conv3_2')(net2)\r\n\r\n        net3 = MaxPool2D((3, 3), strides=(1, 1),padding='same', name=layer_name +\"maxpool4_1\")(net)\r\n        net3 = Conv2D(out_lst[5], (1, 1), strides=(1, 1), padding='same',\r\n                      kernel_regularizer=regularizers.l2(self.decay_weight),\r\n                      name=layer_name + 'conv4_2')(net3)\r\n\r\n        return concatenate([net0, net1, net2, net3], axis=3)\r\n\r\n    def subbranch(self, layer_name,net):\r\n        net = AvgPool2D((5, 5), strides=(3, 3), padding='valid', name=layer_name + 'avgpool')(net)\r\n        net = Conv2D(128, (1, 1), strides=(1, 1), padding='same',\r\n                     kernel_regularizer=regularizers.l2(self.decay_weight),\r\n                     name=layer_name + 'conv1')(net)\r\n        net = Flatten(name=layer_name + 'Flatten1')(net)\r\n        net = Dense(1024, activation='relu', name=layer_name + 'fc1')(net)\r\n        net = Dropout(0.7,name=layer_name + 'dropout1')(net)\r\n        net = Dense(self.n_classes, activation='relu', name=layer_name + 'fc2')(net)\r\n        return Activation('softmax',name=layer_name + 'softmax')(net)\r\n\r\n\r\n    def build(self):\r\n        data_input = Input(batch_shape=self.data_size)\r\n        net = Conv2D(64, (7, 7), strides=(2, 2),\r\n                     padding=\"same\", activation=\"relu\",\r\n                     kernel_regularizer=regularizers.l2(self.decay_weight),\r\n                     name=\"conv1\")(data_input)\r\n        net = MaxPool2D((3, 3), strides=(2, 2),padding='same', name=\"maxpool1\")(net)\r\n        net = BatchNormalization()(net)\r\n\r\n        net = Conv2D(192, (1, 1), strides=(1, 1), padding='valid',\r\n                     kernel_regularizer=regularizers.l2(self.decay_weight),\r\n                     name='conv2_1')(net)\r\n        net = Conv2D(192, (3, 3), strides=(1, 1),\r\n                     activation=\"relu\", padding=\"same\",\r\n                     kernel_regularizer=regularizers.l2(self.decay_weight),\r\n                     name=\"conv2_2\")(net)\r\n        net = BatchNormalization()(net)\r\n        net = MaxPool2D((2, 2), strides=(2, 2),padding='same', name=\"maxpool2\")(net)\r\n\r\n        net = self.block('inception_3a_', net, [64, 96, 128, 16, 32, 32])\r\n        net = self.block('inception_3b_', net, [128, 128, 192, 32, 96, 64])\r\n\r\n        net = MaxPool2D((3, 3), strides=(2, 2),padding='same', name=\"maxpool3\")(net)\r\n\r\n        net = self.block('inception_4a_', net, [192, 96, 208, 16, 48, 64])\r\n        loss01 = self.subbranch('sub_loss3_1_',net)\r\n        net = self.block('inception_4b_', net, [160, 112, 224, 24, 64, 64])\r\n        net = self.block('inception_4c_', net, [128, 128, 256, 24, 64, 64])\r\n        net = self.block('inception_4d_', net, [112, 144, 288, 32, 64, 64])\r\n        loss02 = self.subbranch('sub_loss3_2_', net)\r\n        net = self.block('inception_4e_', net, [256, 160, 320, 32, 128, 128])\r\n\r\n        net = MaxPool2D((3, 3), strides=(2, 2),padding='same', name=\"maxpool4\")(net)\r\n\r\n        net = self.block('inception_5a_', net, [256, 160, 320, 32, 128, 128])\r\n        net = self.block('inception_5b_', net, [384, 192, 384, 48, 128, 128])\r\n\r\n        net = AvgPool2D((7, 7), strides=(1, 1), padding='valid', name='avgpool3')(net)\r\n        net = Dropout(0.4)(net)\r\n        net = Flatten()(net)\r\n        net = Dense(self.n_classes, name=\"fc1\", activation=\"relu\")(net)\r\n        loss03 = Activation('softmax',name='loss3_3')(net)\r\n        loss = 0.3*(loss01+loss02)+0.7*loss03\r\n        model = Model(inputs=data_input, outputs=[loss01,loss02,loss03])\r\n        from keras.utils import plot_model\r\n        import os\r\n        plot_model(model, to_file=os.path.join('./imgs', \"005_inceptionv2.png\"), show_shapes=True)\r\n        model.summary()\r\n        return loss\r\n\r\n\r\nmodel = inception_V1()\r\nmodel.build()\r\n","sub_path":"01 NetWork/nets_strut/005_inception_v2.py","file_name":"005_inception_v2.py","file_ext":"py","file_size_in_byte":5749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"106408307","text":"from pywebcopy import save_website\n\ndir_name = 'login'\nsite_url = 'http://brandio.io/envato/iofrm/html/'\nkwargs = {'project_name': dir_name}\nsave_website(\n    url=site_url,\n    project_folder=dir_name,\n    **kwargs\n)\n","sub_path":"utils/httrack.py","file_name":"httrack.py","file_ext":"py","file_size_in_byte":217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"434791938","text":"\"\"\"Support for sensors through the SmartThings cloud API.\"\"\"\nfrom collections import namedtuple\nfrom typing import Optional, Sequence\n\nfrom homeassistant.const import (\n    DEVICE_CLASS_BATTERY, DEVICE_CLASS_HUMIDITY, DEVICE_CLASS_ILLUMINANCE,\n    DEVICE_CLASS_TEMPERATURE, DEVICE_CLASS_TIMESTAMP, MASS_KILOGRAMS,\n    ENERGY_KILO_WATT_HOUR, POWER_WATT, TEMP_CELSIUS, TEMP_FAHRENHEIT)\n\nfrom . import SmartThingsEntity\nfrom .const import DATA_BROKERS, DOMAIN\n\nMap = namedtuple(\"map\", \"attribute name default_unit device_class\")\n\nCAPABILITY_TO_SENSORS = {\n    'activityLightingMode': [\n        Map('lightingMode', \"Activity Lighting Mode\", None, None)],\n    'airConditionerMode': [\n        Map('airConditionerMode', \"Air Conditioner Mode\", None, None)],\n    'airQualitySensor': [\n        Map('airQuality', \"Air Quality\", 'CAQI', None)],\n    'alarm': [\n        Map('alarm', \"Alarm\", None, None)],\n    'audioVolume': [\n        Map('volume', \"Volume\", \"%\", None)],\n    'battery': [\n        Map('battery', \"Battery\", \"%\", DEVICE_CLASS_BATTERY)],\n    'bodyMassIndexMeasurement': [\n        Map('bmiMeasurement', \"Body Mass Index\", \"kg/m^2\", None)],\n    'bodyWeightMeasurement': [\n        Map('bodyWeightMeasurement', \"Body Weight\", MASS_KILOGRAMS, None)],\n    'carbonDioxideMeasurement': [\n        Map('carbonDioxide', \"Carbon Dioxide Measurement\", \"ppm\", None)],\n    'carbonMonoxideDetector': [\n        Map('carbonMonoxide', \"Carbon Monoxide Detector\", None, None)],\n    'carbonMonoxideMeasurement': [\n        Map('carbonMonoxideLevel', \"Carbon Monoxide Measurement\", \"ppm\",\n            None)],\n    'dishwasherOperatingState': [\n        Map('machineState', \"Dishwasher Machine State\", None, None),\n        Map('dishwasherJobState', \"Dishwasher Job State\", None, None),\n        Map('completionTime', \"Dishwasher Completion Time\", None,\n            DEVICE_CLASS_TIMESTAMP)],\n    'dryerMode': [\n        Map('dryerMode', \"Dryer Mode\", None, None)],\n    'dryerOperatingState': [\n        Map('machineState', \"Dryer Machine State\", None, None),\n        Map('dryerJobState', \"Dryer Job State\", None, None),\n        Map('completionTime', \"Dryer Completion Time\", None,\n            DEVICE_CLASS_TIMESTAMP)],\n    'dustSensor': [\n        Map('fineDustLevel', \"Fine Dust Level\", None, None),\n        Map('dustLevel', \"Dust Level\", None, None)],\n    'energyMeter': [\n        Map('energy', \"Energy Meter\", ENERGY_KILO_WATT_HOUR, None)],\n    'equivalentCarbonDioxideMeasurement': [\n        Map('equivalentCarbonDioxideMeasurement',\n            'Equivalent Carbon Dioxide Measurement', 'ppm', None)],\n    'formaldehydeMeasurement': [\n        Map('formaldehydeLevel', 'Formaldehyde Measurement', 'ppm', None)],\n    'illuminanceMeasurement': [\n        Map('illuminance', \"Illuminance\", 'lux', DEVICE_CLASS_ILLUMINANCE)],\n    'infraredLevel': [\n        Map('infraredLevel', \"Infrared Level\", '%', None)],\n    'lock': [\n        Map('lock', \"Lock\", None, None)],\n    'mediaInputSource': [\n        Map('inputSource', \"Media Input Source\", None, None)],\n    'mediaPlaybackRepeat': [\n        Map('playbackRepeatMode', \"Media Playback Repeat\", None, None)],\n    'mediaPlaybackShuffle': [\n        Map('playbackShuffle', \"Media Playback Shuffle\", None, None)],\n    'mediaPlayback': [\n        Map('playbackStatus', \"Media Playback Status\", None, None)],\n    'odorSensor': [\n        Map('odorLevel', \"Odor Sensor\", None, None)],\n    'ovenMode': [\n        Map('ovenMode', \"Oven Mode\", None, None)],\n    'ovenOperatingState': [\n        Map('machineState', \"Oven Machine State\", None, None),\n        Map('ovenJobState', \"Oven Job State\", None, None),\n        Map('completionTime', \"Oven Completion Time\", None, None)],\n    'ovenSetpoint': [\n        Map('ovenSetpoint', \"Oven Set Point\", None, None)],\n    'powerMeter': [\n        Map('power', \"Power Meter\", POWER_WATT, None)],\n    'powerSource': [\n        Map('powerSource', \"Power Source\", None, None)],\n    'refrigerationSetpoint': [\n        Map('refrigerationSetpoint', \"Refrigeration Setpoint\", None,\n            DEVICE_CLASS_TEMPERATURE)],\n    'relativeHumidityMeasurement': [\n        Map('humidity', \"Relative Humidity Measurement\", '%',\n            DEVICE_CLASS_HUMIDITY)],\n    'robotCleanerCleaningMode': [\n        Map('robotCleanerCleaningMode', \"Robot Cleaner Cleaning Mode\",\n            None, None)],\n    'robotCleanerMovement': [\n        Map('robotCleanerMovement', \"Robot Cleaner Movement\", None, None)],\n    'robotCleanerTurboMode': [\n        Map('robotCleanerTurboMode', \"Robot Cleaner Turbo Mode\", None, None)],\n    'signalStrength': [\n        Map('lqi', \"LQI Signal Strength\", None, None),\n        Map('rssi', \"RSSI Signal Strength\", None, None)],\n    'smokeDetector': [\n        Map('smoke', \"Smoke Detector\", None, None)],\n    'temperatureMeasurement': [\n        Map('temperature', \"Temperature Measurement\", None,\n            DEVICE_CLASS_TEMPERATURE)],\n    'thermostatCoolingSetpoint': [\n        Map('coolingSetpoint', \"Thermostat Cooling Setpoint\", None,\n            DEVICE_CLASS_TEMPERATURE)],\n    'thermostatFanMode': [\n        Map('thermostatFanMode', \"Thermostat Fan Mode\", None, None)],\n    'thermostatHeatingSetpoint': [\n        Map('heatingSetpoint', \"Thermostat Heating Setpoint\", None,\n            DEVICE_CLASS_TEMPERATURE)],\n    'thermostatMode': [\n        Map('thermostatMode', \"Thermostat Mode\", None, None)],\n    'thermostatOperatingState': [\n        Map('thermostatOperatingState', \"Thermostat Operating State\",\n            None, None)],\n    'thermostatSetpoint': [\n        Map('thermostatSetpoint', \"Thermostat Setpoint\", None,\n            DEVICE_CLASS_TEMPERATURE)],\n    'threeAxis': [\n        Map('threeAxis', \"Three Axis\", None, None)],\n    'tvChannel': [\n        Map('tvChannel', \"Tv Channel\", None, None)],\n    'tvocMeasurement': [\n        Map('tvocLevel', \"Tvoc Measurement\", 'ppm', None)],\n    'ultravioletIndex': [\n        Map('ultravioletIndex', \"Ultraviolet Index\", None, None)],\n    'voltageMeasurement': [\n        Map('voltage', \"Voltage Measurement\", 'V', None)],\n    'washerMode': [\n        Map('washerMode', \"Washer Mode\", None, None)],\n    'washerOperatingState': [\n        Map('machineState', \"Washer Machine State\", None, None),\n        Map('washerJobState', \"Washer Job State\", None, None),\n        Map('completionTime', \"Washer Completion Time\", None,\n            DEVICE_CLASS_TIMESTAMP)]\n}\n\nUNITS = {\n    'C': TEMP_CELSIUS,\n    'F': TEMP_FAHRENHEIT\n}\n\nTHREE_AXIS_NAMES = ['X Coordinate', 'Y Coordinate', 'Z Coordinate']\n\n\nasync def async_setup_platform(\n        hass, config, async_add_entities, discovery_info=None):\n    \"\"\"Platform uses config entry setup.\"\"\"\n    pass\n\n\nasync def async_setup_entry(hass, config_entry, async_add_entities):\n    \"\"\"Add binary sensors for a config entry.\"\"\"\n    from pysmartthings import Capability\n    broker = hass.data[DOMAIN][DATA_BROKERS][config_entry.entry_id]\n    sensors = []\n    for device in broker.devices.values():\n        for capability in broker.get_assigned(device.device_id, 'sensor'):\n            if capability == Capability.three_axis:\n                sensors.extend(\n                    [SmartThingsThreeAxisSensor(device, index)\n                     for index in range(len(THREE_AXIS_NAMES))])\n            else:\n                maps = CAPABILITY_TO_SENSORS[capability]\n                sensors.extend([\n                    SmartThingsSensor(\n                        device, m.attribute, m.name, m.default_unit,\n                        m.device_class)\n                    for m in maps])\n    async_add_entities(sensors)\n\n\ndef get_capabilities(capabilities: Sequence[str]) -> Optional[Sequence[str]]:\n    \"\"\"Return all capabilities supported if minimum required are present.\"\"\"\n    return [capability for capability in CAPABILITY_TO_SENSORS\n            if capability in capabilities]\n\n\nclass SmartThingsSensor(SmartThingsEntity):\n    \"\"\"Define a SmartThings Sensor.\"\"\"\n\n    def __init__(self, device, attribute: str, name: str,\n                 default_unit: str, device_class: str):\n        \"\"\"Init the class.\"\"\"\n        super().__init__(device)\n        self._attribute = attribute\n        self._name = name\n        self._device_class = device_class\n        self._default_unit = default_unit\n\n    @property\n    def name(self) -> str:\n        \"\"\"Return the name of the binary sensor.\"\"\"\n        return '{} {}'.format(self._device.label, self._name)\n\n    @property\n    def unique_id(self) -> str:\n        \"\"\"Return a unique ID.\"\"\"\n        return '{}.{}'.format(self._device.device_id, self._attribute)\n\n    @property\n    def state(self):\n        \"\"\"Return the state of the sensor.\"\"\"\n        return self._device.status.attributes[self._attribute].value\n\n    @property\n    def device_class(self):\n        \"\"\"Return the device class of the sensor.\"\"\"\n        return self._device_class\n\n    @property\n    def unit_of_measurement(self):\n        \"\"\"Return the unit this state is expressed in.\"\"\"\n        unit = self._device.status.attributes[self._attribute].unit\n        return UNITS.get(unit, unit) if unit else self._default_unit\n\n\nclass SmartThingsThreeAxisSensor(SmartThingsEntity):\n    \"\"\"Define a SmartThings Three Axis Sensor.\"\"\"\n\n    def __init__(self, device, index):\n        \"\"\"Init the class.\"\"\"\n        super().__init__(device)\n        self._index = index\n\n    @property\n    def name(self) -> str:\n        \"\"\"Return the name of the binary sensor.\"\"\"\n        return '{} {}'.format(\n            self._device.label, THREE_AXIS_NAMES[self._index])\n\n    @property\n    def unique_id(self) -> str:\n        \"\"\"Return a unique ID.\"\"\"\n        return '{}.{}'.format(\n            self._device.device_id, THREE_AXIS_NAMES[self._index])\n\n    @property\n    def state(self):\n        \"\"\"Return the state of the sensor.\"\"\"\n        from pysmartthings import Attribute\n        three_axis = self._device.status.attributes[Attribute.three_axis].value\n        try:\n            return three_axis[self._index]\n        except (TypeError, IndexError):\n            return None\n","sub_path":"homeassistant/components/smartthings/sensor.py","file_name":"sensor.py","file_ext":"py","file_size_in_byte":9972,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"504397136","text":"from sklearn import preprocessing\nimport numpy as np\nimport pandas as pd\n\n\ndef create_df(data_path):\n    # input: the path of the csv file\n    # output: data frame\n    return pd.read_csv(data_path)\n\n\ndef nan_columns(df):\n    # input: data frame\n    # output: a list of names of columns that contain nan values in the data frame\n    return df.columns[df.isnull().any()].tolist()\n\n\ndef categorical_columns(df):\n    # input: data frame\n    # output: a list of column names that contain categorical values in the data frame\n    return df.select_dtypes(include=['object', 'category']).columns.tolist()\n\n\ndef replace_missing_features(df, nancolumns):\n    # input: data frame, list of column names that contain nan values\n    # output: data frame\n    new_df1 = df.copy()\n    for nancolumn in nancolumns:\n        new_df1[nancolumn] = new_df1[nancolumn].fillna(new_df1[nancolumn].median())\n    return new_df1\n\n\ndef cat_to_num(new_df1, catcolumns):\n    # input: data frame, list of categorical feature column names\n    # output: data frame\n    new_df2 = new_df1.copy()\n    return pd.get_dummies(new_df2, columns=catcolumns)\n\n\ndef standardization(new_df2, labelcol):\n    # input: data frame and name of the label column\n    # output: scaled data frame\n    new_df3 = new_df2.drop(labelcol, axis=1)\n    scaler = preprocessing.StandardScaler()\n    scaled = scaler.fit_transform(new_df3)\n    new_df3 = pd.DataFrame(scaled, columns=new_df3.columns.values)\n    new_df3.loc[:, labelcol] = new_df2.loc[:, labelcol].copy()\n    return new_df3\n\n\ndef my_train_test_split(new_df3, labelcol, test_ratio):\n    # input: data frame, name of the label column and test data percentage\n    # output: X_train, X_test, y_train, y_test\n    np.random.seed(0)  # DON'T ERASE THIS LINE\n\n    indices = np.random.permutation(len(new_df3))\n    test_size = int(len(new_df3) * test_ratio)\n    train_i, test_i = indices[test_size:], indices[:test_size]\n    columns = new_df3.columns.values\n    label_index = np.argwhere(columns == labelcol)\n    wo_labelcol = np.delete(columns, label_index)\n    return new_df3.loc[train_i, wo_labelcol].values, new_df3.loc[test_i, wo_labelcol].values, \\\n        new_df3.loc[train_i, labelcol].values, new_df3.loc[test_i, labelcol].values\n\n\ndef main(dataPath, testRatio, labelColumn):\n    # input: the path of the csv file, test data percentage and name of the label column\n    # output: X_train, X_test, y_train, y_test as numpy arrays\n    df = create_df(dataPath)\n    nans = nan_columns(df)\n    cats = categorical_columns(df)\n    cleaned = replace_missing_features(df, nans)\n    encoded = cat_to_num(cleaned, cats)\n    standardized = standardization(encoded, labelColumn)\n    return my_train_test_split(standardized, labelColumn, testRatio)\n","sub_path":"hw1/analysis_and_preprocessing.py","file_name":"analysis_and_preprocessing.py","file_ext":"py","file_size_in_byte":2732,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"533794581","text":"#!/usr/bin/env python\nimport os\nimport json\nimport logging\nimport threading\n\nfrom modules.telegram_bot_webhook_api import TelegramWebhookBot\nfrom modules.client_telethon import MyTelegramClient\nfrom modules.bittrex_api import load_api_key_for_the_first_run\n\nlogging.getLogger().setLevel(logging.INFO)\n\nbot = TelegramWebhookBot()\nclient = MyTelegramClient(bot)\n\nINIT_KEY=False\n\ndef run_bot():\n    global bot\n    bot.run()\n\ndef run_client():\n    global client\n    client.run()\n\nif __name__ == \"__main__\":\n    if INIT_KEY:\n        load_api_key_for_the_first_run()\n    else:\n        b = threading.Thread(name='bot', target=run_bot)\n        c = threading.Thread(name='client', target=run_client)\n\n        b.start()\n        c.start()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":728,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"286479596","text":"import random\nimport time\n\n\ndef solver(a, b):\n    return (a + b) // 2\n\n\nprint(\"Welcome to the Guess the Number!\")\n# name = input(\"Enter your name: \\n\")\n# print(f\"{name}, I'm thinking about number between 1 and 100.\")\na = 1\nb = 1000\nnumber = random.randint(a, b)\nguesses = 0\n\nwhile True:\n    print(\"Take a guess.\")\n    guesses += 1\n\n    guess = solver(a, b)\n    time.sleep(1)\n    print(guess)\n\n    if guess > number:\n        print(\"Too high.\")\n        b = guess\n    elif guess < number:\n        print(\"Too low.\")\n        a = guess\n    else:\n        print(f\"Correct! You guessed in {guesses} guesses!\")\n        break\n\n","sub_path":"number_guess.py","file_name":"number_guess.py","file_ext":"py","file_size_in_byte":616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"242055329","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: ..\\fteikpy\\layered_model.py\n# Compiled at: 2018-02-19 18:21:00\n# Size of source mod 2**32: 4096 bytes\n\"\"\"\nAuthor: Keurfon Luu <keurfon.luu@mines-paristech.fr>\nLicense: MIT\n\"\"\"\nimport numpy as np\nfrom ._fteik2d import fteik2d\nfrom ._lay2vel import lay2vel as l2vf\n__all__ = [\n 'lay2vel', 'lay2tt']\n\ndef lay2vel(lay, dz, grid_shape):\n    \"\"\"\n    Convert a layered model to a continuous velocity model.\n    \n    Parameters\n    ----------\n    lay : ndarray\n        Layer velocities (first column) and interface depth (second column).\n    dz : float\n        Grid size in Z coordinate in meters.\n    grid_shape : tuple (nz, nx[, ny])\n        Gris shape.\n    \n    Returns\n    -------\n    vel : ndarray\n        Velocity model grid in m/s.\n    \"\"\"\n    if not isinstance(lay, np.ndarray):\n        if lay.ndim not in (1, 2):\n            raise ValueError('grid must be a 1-D or 2-D ndarray')\n        else:\n            if lay.ndim == 1 and lay[0] < 0.0 or lay.ndim == 2 and lay[:, 0].min() < 0.0:\n                raise ValueError('velocities must be positive')\n            else:\n                if not isinstance(dz, (float, int)) or dz < 0.0:\n                    raise ValueError('dz must be positive')\n                zmax = dz * grid_shape[0]\n                if lay.ndim == 1 and lay[1] > zmax or lay.ndim == 2 and lay[:, 1].max() > zmax:\n                    raise ValueError('last layer depth must be %.2f' % zmax)\n            if not np.all([isinstance(n, int) for n in grid_shape]) or len(grid_shape) not in (1,\n                                                                                               2,\n                                                                                               3):\n                raise ValueError('grid_shape must be a tuple of integers of size 1, 2 or 3')\n        if len(grid_shape) == 1:\n            return (l2vf.lay2vel1)(lay, dz, *grid_shape)\n    else:\n        if len(grid_shape) == 2:\n            return (l2vf.lay2vel2)(lay, dz, *grid_shape)\n        if len(grid_shape) == 3:\n            return (l2vf.lay2vel3)(lay, dz, *grid_shape)\n\n\ndef lay2tt(velocity_model, grid_size, sources, receivers, n_sweep=1, n_threads=1):\n    \"\"\"\n    Given a layered velocity model, compute the first arrivel traveltime for\n    each source and each receiver. Only useful if working in 3-D as a 2-D\n    eikonal solver is used for traveltime computation.\n    \n    Parameters\n    ----------\n    velocity_model : ndarray of shape (nz, nx)\n        Velocity model grid in m/s.\n    grid_size : tuple (dz, dx)\n        Grid size in meters.\n    sources : ndarray\n        Sources coordinates (Z, X[, Y]).\n    receivers : ndarray\n        Receivers coordinates (Z, X[, Y]).\n    n_sweep : int, default 1\n        Number of sweeps.\n    n_threads : int, default 1\n        Number of threads to pass to OpenMP.\n        \n    Returns\n    -------\n    tcalc : ndarray of shape (nrcv, nsrc)\n        Traveltimes for each source and each receiver.\n    \"\"\"\n    if not isinstance(velocity_model, np.ndarray) or velocity_model.ndim != 2:\n        raise ValueError('velocity_model must be a 2-D ndarray')\n    else:\n        if np.any(velocity_model <= 0.0):\n            raise ValueError('velocity_model must be positive')\n        else:\n            if not isinstance(grid_size, (list, tuple, np.ndarray)):\n                raise ValueError('grid_size must be a list, tuple or ndarray')\n            else:\n                if len(grid_size) != 2:\n                    raise ValueError('grid_size should be of length 2, got %d' % len(grid_size))\n                else:\n                    if np.any(np.array(grid_size) <= 0.0):\n                        raise ValueError('elements in grid_size must be positive')\n                    if not isinstance(sources, np.ndarray) or sources.shape[1] != 3:\n                        raise ValueError('sources must be ndarray with 3 columns')\n                if not isinstance(receivers, np.ndarray) or receivers.shape[1] != 3:\n                    raise ValueError('receivers must be ndarray with 3 columns')\n            if not isinstance(n_sweep, int) or n_sweep <= 0:\n                raise ValueError('n_sweep must be a positive integer, got %s' % n_sweep)\n        if not isinstance(n_threads, int) or n_threads < 1:\n            raise ValueError('n_threads must be atleast 1, got %s' % n_threads)\n    dz, dx = grid_size\n    tcalc = fteik2d.lay2tt((1.0 / velocity_model), dz, dx, (sources[:, 0]), (sources[:, 1]), (sources[:, 2]), (receivers[:, 0]),\n      (receivers[:, 1]), (receivers[:, 2]), n_sweep, n_threads=n_threads)\n    return tcalc","sub_path":"pycfiles/fteikpy-1.5.0.tar/layered_model.cpython-36.py","file_name":"layered_model.cpython-36.py","file_ext":"py","file_size_in_byte":4715,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"614519712","text":"\"\"\"\nSO term\tSO description\tSO accession\tDisplay term\tIMPACT\tRANK\ntranscript_ablation\tA feature ablation whereby the deleted region includes a transcript feature\tSO:0001893\tTranscript ablation\tHIGH\t1\nsplice_acceptor_variant\tA splice variant that changes the 2 base region at the 3' end of an intron\tSO:0001574\tSplice acceptor variant\tHIGH\t3\nsplice_donor_variant\tA splice variant that changes the 2 base region at the 5' end of an intron\tSO:0001575\tSplice donor variant\tHIGH\t3\nstop_gained\tA sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript\tSO:0001587\tStop gained\tHIGH\t4\nframeshift_variant\tA sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three\tSO:0001589\tFrameshift variant\tHIGH\t5\nstop_lost\tA sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript\tSO:0001578\tStop lost\tHIGH\t6\nstart_lost\tA codon variant that changes at least one base of the canonical start codon\tSO:0002012\tStart lost\tHIGH\t7\ntranscript_amplification\tA feature amplification of a region containing a transcript\tSO:0001889\tTranscript amplification\tHIGH\t8\ninframe_insertion\tAn inframe non synonymous variant that inserts bases into in the coding sequence\tSO:0001821\tInframe insertion\tMODERATE\t10\ninframe_deletion\tAn inframe non synonymous variant that deletes bases from the coding sequence\tSO:0001822\tInframe deletion\tMODERATE\t11\nmissense_variant\tA sequence variant, that changes one or more bases, resulting in a different amino acid sequence but where the length is preserved\tSO:0001583\tMissense variant\tMODERATE\t12\nprotein_altering_variant\tA sequence_variant which is predicted to change the protein encoded in the coding sequence\tSO:0001818\tProtein altering variant\tMODERATE\t12\nsplice_region_variant\tA sequence variant in which a change has occurred within the region of the splice site, either within 1-3 bases of the exon or 3-8 bases of the intron\tSO:0001630\tSplice region variant\tLOW\t13\nincomplete_terminal_codon_variant\tA sequence variant where at least one base of the final codon of an incompletely annotated transcript is changed\tSO:0001626\tIncomplete terminal codon variant\tLOW\t14\nstart_retained_variant\tA sequence variant where at least one base in the start codon is changed, but the start remains\tSO:0002019\tStart retained variant\tLOW\t15\nstop_retained_variant\tA sequence variant where at least one base in the terminator codon is changed, but the terminator remains\tSO:0001567\tStop retained variant\tLOW\t15\nsynonymous_variant\tA sequence variant where there is no resulting change to the encoded amino acid\tSO:0001819\tSynonymous variant\tLOW\t15\ncoding_sequence_variant\tA sequence variant that changes the coding sequence\tSO:0001580\tCoding sequence variant\tMODIFIER\t16\nmature_miRNA_variant\tA transcript variant located with the sequence of the mature miRNA\tSO:0001620\tMature miRNA variant\tMODIFIER\t17\n5_prime_UTR_variant\tA UTR variant of the 5' UTR\tSO:0001623\t5 prime UTR variant\tMODIFIER\t18\n3_prime_UTR_variant\tA UTR variant of the 3' UTR\tSO:0001624\t3 prime UTR variant\tMODIFIER\t19\nnon_coding_transcript_exon_variant\tA sequence variant that changes non-coding exon sequence in a non-coding transcript\tSO:0001792\tNon coding transcript exon variant\tMODIFIER\t20\nintron_variant\tA transcript variant occurring within an intron\tSO:0001627\tIntron variant\tMODIFIER\t21\nNMD_transcript_variant\tA variant in a transcript that is the target of NMD\tSO:0001621\tNMD transcript variant\tMODIFIER\t22\nnon_coding_transcript_variant\tA transcript variant of a non coding RNA gene\tSO:0001619\tNon coding transcript variant\tMODIFIER\t23\nupstream_gene_variant\tA sequence variant located 5' of a gene\tSO:0001631\tUpstream gene variant\tMODIFIER\t24\ndownstream_gene_variant\tA sequence variant located 3' of a gene\tSO:0001632\tDownstream gene variant\tMODIFIER\t25\nTFBS_ablation\tA feature ablation whereby the deleted region includes a transcription factor binding site\tSO:0001895\tTFBS ablation\tMODIFIER\t26\nTFBS_amplification\tA feature amplification of a region containing a transcription factor binding site\tSO:0001892\tTFBS amplification\tMODIFIER\t28\nTF_binding_site_variant\tA sequence variant located within a transcription factor binding site\tSO:0001782\tTF binding site variant\tMODIFIER\t30\nregulatory_region_ablation\tA feature ablation whereby the deleted region includes a regulatory region\tSO:0001894\tRegulatory region ablation\tMODERATE\t29\nregulatory_region_amplification\tA feature amplification of a region containing a regulatory region\tSO:0001891\tRegulatory region amplification\tMODIFIER\t30\nfeature_elongation\tA sequence variant that causes the extension of a genomic feature, with regard to the reference sequence\tSO:0001907\tFeature elongation\tMODIFIER\t31\nregulatory_region_variant\tA sequence variant located within a regulatory region\tSO:0001566\tRegulatory region variant\tMODIFIER\t33\nfeature_truncation\tA sequence variant that causes the reduction of a genomic feature, with regard to the reference sequence\tSO:0001906\tFeature truncation\tMODIFIER\t36\nintergenic_variant\tA sequence variant located in the intergenic region, between genes\tSO:0001628\tIntergenic variant\tMODIFIER\t38\nsequence_variant\t\t\t\t\t39\n\nhttps://uswest.ensembl.org/info/genome/variation/predicted_data.html\n\"\"\"\n\n\nclass Bio_EnsEMBL_Variation_Utils_Constants(object):\n    \"\"\"\n    Ensembl variant effect consequences\n    See https://uswest.ensembl.org/info/docs/Doxygen/variation-api/Utils_2Constants_8pm_source.html\n    \"\"\"\n    ATTRIB_TYPE_SO_ACCESSION = 'SO_accession'\n    ATTRIB_TYPE_SO_TERM = 'SO_term'\n    ATTRIB_TYPE_DISPLAY_TERM = 'display_term'\n    ATTRIB_TYPE_NCBI_TERM = 'NCBI_term'\n    ATTRIB_TYPE_FEATURE_SO_TERM = 'feature_SO_term'\n    ATTRIB_TYPE_RANK = 'rank'\n    ATTRIB_TYPE_POLYPHEN_PREDICTION = 'polyphen_prediction'\n    ATTRIB_TYPE_SIFT_PREDICTION = 'sift_prediction'\n    ATTRIB_TYPE_SHORT_NAME = 'short_name'\n    ATTRIB_TYPE_DBSNP_CLIN_SIG = 'dbsnp_clin_sig'\n    ATTRIB_TYPE_DGVA_CLIN_SIG = 'dgva_clin_sig'\n    ATTRIB_TYPE_CLINVAR_CLIN_SIG = 'clinvar_clin_sig'\n    ATTRIB_TYPE_PROT_FUNC_ANALYSIS = 'prot_func_analysis'\n    ATTRIB_TYPE_ASSOCIATED_GENE = 'associated_gene'\n    ATTRIB_TYPE_RISK_ALLELE = 'risk_allele'\n    ATTRIB_TYPE_P_VALUE = 'p_value'\n    ATTRIB_TYPE_VARIATION_NAMES = 'variation_names'\n    ATTRIB_TYPE_SAMPLE_ID = 'sample_id'\n    ATTRIB_TYPE_STRAIN_ID = 'strain_id'\n    ATTRIB_TYPE_LOD_SCORE = 'lod_score'\n    ATTRIB_TYPE_VARIANCE = 'variance'\n    ATTRIB_TYPE_INHERITANCE_TYPE = 'inheritance_type'\n    ATTRIB_TYPE_EXTERNAL_ID = 'external_id'\n    ATTRIB_TYPE_ODDS_RATIO = 'odds_ratio'\n    ATTRIB_TYPE_BETA_COEF = 'beta_coef'\n    ATTRIB_TYPE_ALLELE_SYMBOL = 'allele_symbol'\n    ATTRIB_TYPE_ALLELE_ACCESSION_ID = 'allele_accession_id'\n    ATTRIB_TYPE_MARKER_ACCESSION_ID = 'marker_accession_id'\n    ATTRIB_TYPE_EVIDENCE = 'evidence'\n    ATTRIB_TYPE_SEQUENCE_NUMBER = 'sequence_number'\n    ATTRIB_TYPE_BASED_ON = 'based_on'\n    ATTRIB_TYPE_CONSERVATION_SCORE = 'conservation_score'\n    ATTRIB_TYPE_REVIEW_STATUS = 'review_status'\n\n    SO_TERM_SNV = 'SNV'\n    SO_TERM_SUBSTITUTION = 'substitution'\n    SO_TERM_INSERTION = 'insertion'\n    SO_TERM_DELETION = 'deletion'\n    SO_TERM_INDEL = 'indel'\n    SO_TERM_TANDEM_REPEAT = 'tandem_repeat'\n    SO_TERM_SEQUENCE_ALTERATION = 'sequence_alteration'\n    SO_TERM_GENETIC_MARKER = 'genetic_marker'\n    SO_TERM_STRUCTURAL_VARIANT = 'structural_variant'\n    SO_TERM_COPY_NUMBER_VARIATION = 'copy_number_variation'\n    SO_TERM_PROBE = 'probe'\n    SO_TERM_COPY_NUMBER_GAIN = 'copy_number_gain'\n    SO_TERM_COPY_NUMBER_LOSS = 'copy_number_loss'\n    SO_TERM_INVERSION = 'inversion'\n    SO_TERM_COMPLEX_STRUCTURAL_ALTERATION = 'complex_structural_alteration'\n    SO_TERM_TANDEM_DUPLICATION = 'tandem_duplication'\n    SO_TERM_MOBILE_ELEMENT_INSERTION = 'mobile_element_insertion'\n    SO_TERM_MOBILE_ELEMENT_DELETION = 'mobile_element_deletion'\n    SO_TERM_INTERCHROMOSOMAL_BREAKPOINT = 'interchromosomal_breakpoint'\n    SO_TERM_INTRACHROMOSOMAL_BREAKPOINT = 'intrachromosomal_breakpoint'\n    SO_TERM_TRANSLOCATION = 'translocation'\n    SO_TERM_DUPLICATION = 'duplication'\n    SO_TERM_NOVEL_SEQUENCE_INSERTION = 'novel_sequence_insertion'\n    SO_TERM_INTERCHROMOSOMAL_TRANSLOCATION = 'interchromosomal_translocation'\n    SO_TERM_INTRACHROMOSOMAL_TRANSLOCATION = 'intrachromosomal_translocation'\n    SO_TERM_ALU_INSERTION = 'Alu_insertion'\n    SO_TERM_COMPLEX_SUBSTITUTION = 'complex_substitution'\n    SO_TERM_SHORT_TANDEM_REPEAT_VARIATION = 'short_tandem_repeat_variation'\n    SO_TERM_LOSS_OF_HETEROZYGOSITY = 'loss_of_heterozygosity'\n    SO_TERM_INTERGENIC_VARIANT = 'intergenic_variant'\n    SO_TERM_UPSTREAM_GENE_VARIANT = 'upstream_gene_variant'\n    SO_TERM_DOWNSTREAM_GENE_VARIANT = 'downstream_gene_variant'\n    SO_TERM_SPLICE_DONOR_VARIANT = 'splice_donor_variant'\n    SO_TERM_SPLICE_ACCEPTOR_VARIANT = 'splice_acceptor_variant'\n    SO_TERM_SPLICE_REGION_VARIANT = 'splice_region_variant'\n    SO_TERM_INTRON_VARIANT = 'intron_variant'\n    SO_TERM_5_PRIME_UTR_VARIANT = '5_prime_UTR_variant'\n    SO_TERM_3_PRIME_UTR_VARIANT = '3_prime_UTR_variant'\n    SO_TERM_SYNONYMOUS_VARIANT = 'synonymous_variant'\n    SO_TERM_MISSENSE_VARIANT = 'missense_variant'\n    SO_TERM_INFRAME_INSERTION = 'inframe_insertion'\n    SO_TERM_INFRAME_DELETION = 'inframe_deletion'\n    SO_TERM_STOP_GAINED = 'stop_gained'\n    SO_TERM_STOP_LOST = 'stop_lost'\n    SO_TERM_STOP_RETAINED_VARIANT = 'stop_retained_variant'\n    SO_TERM_START_LOST = 'start_lost'\n    SO_TERM_START_RETAINED_VARIANT = 'start_retained_variant'\n    SO_TERM_FRAMESHIFT_VARIANT = 'frameshift_variant'\n    SO_TERM_INCOMPLETE_TERMINAL_CODON_VARIANT = 'incomplete_terminal_codon_variant'\n    SO_TERM_NMD_TRANSCRIPT_VARIANT = 'NMD_transcript_variant'\n    SO_TERM_NON_CODING_TRANSCRIPT_VARIANT = 'non_coding_transcript_variant'\n    SO_TERM_NON_CODING_TRANSCRIPT_EXON_VARIANT = 'non_coding_transcript_exon_variant'\n    SO_TERM_MATURE_MIRNA_VARIANT = 'mature_miRNA_variant'\n    SO_TERM_CODING_SEQUENCE_VARIANT = 'coding_sequence_variant'\n    SO_TERM_REGULATORY_REGION_VARIANT = 'regulatory_region_variant'\n    SO_TERM_TF_BINDING_SITE_VARIANT = 'TF_binding_site_variant'\n    SO_TERM_TRANSCRIPT_ABLATION = 'transcript_ablation'\n    SO_TERM_TRANSCRIPT_AMPLIFICATION = 'transcript_amplification'\n    SO_TERM_TFBS_ABLATION = 'TFBS_ablation'\n    SO_TERM_TFBS_AMPLIFICATION = 'TFBS_amplification'\n    SO_TERM_REGULATORY_REGION_ABLATION = 'regulatory_region_ablation'\n    SO_TERM_REGULATORY_REGION_AMPLIFICATION = 'regulatory_region_amplification'\n    SO_TERM_FEATURE_ELONGATION = 'feature_elongation'\n    SO_TERM_FEATURE_TRUNCATION = 'feature_truncation'\n    SO_TERM_PROTEIN_ALTERING_VARIANT = 'protein_altering_variant'\n\n    variation_classes = {\n        'SNV': {\n            'somatic_display_term': 'somatic SNV',\n            'SO_accession': 'SO:0001483',\n            'display_term': 'SNP'\n        },\n        'substitution': {\n            'somatic_display_term': 'somatic substitution',\n            'SO_accession': 'SO:1000002',\n            'display_term': 'substitution'\n        },\n        'insertion': {\n            'somatic_display_term': 'somatic insertion',\n            'SO_accession': 'SO:0000667',\n            'display_term': 'insertion'\n        },\n        'deletion': {\n            'somatic_display_term': 'somatic deletion',\n            'SO_accession': 'SO:0000159',\n            'display_term': 'deletion'\n        },\n        'indel': {\n            'somatic_display_term': 'somatic indel',\n            'SO_accession': 'SO:1000032',\n            'display_term': 'indel'\n        },\n        'tandem_repeat': {\n            'somatic_display_term': 'somatic tandem repeat',\n            'SO_accession': 'SO:0000705',\n            'display_term': 'tandem repeat'\n        },\n        'sequence_alteration': {\n            'somatic_display_term': 'somatic sequence alteration',\n            'SO_accession': 'SO:0001059',\n            'display_term': 'sequence alteration'\n        },\n        'genetic_marker': {\n            'somatic_display_term': 'somatic genetic marker',\n            'SO_accession': 'SO:0001645',\n            'display_term': 'genetic marker'\n        },\n        'structural_variant': {\n            'type': 'sv',\n            'somatic_display_term': 'somatic SV',\n            'SO_accession': 'SO:0001537',\n            'display_term': 'SV'\n        },\n        'copy_number_variation': {\n            'type': 'sv',\n            'somatic_display_term': 'somatic CNV',\n            'SO_accession': 'SO:0001019',\n            'display_term': 'CNV'\n        },\n        'probe': {\n            'type': 'sv',\n            'somatic_display_term': 'somatic CNV_PROBE',\n            'SO_accession': 'SO:0000051',\n            'display_term': 'CNV_PROBE'\n        },\n        'copy_number_gain': {\n            'type': 'sv',\n            'somatic_display_term': 'somatic gain',\n            'SO_accession': 'SO:0001742',\n            'display_term': 'gain'\n        },\n        'copy_number_loss': {\n            'type': 'sv',\n            'somatic_display_term': 'somatic loss',\n            'SO_accession': 'SO:0001743',\n            'display_term': 'loss'\n        },\n        'inversion': {\n            'type': 'sv',\n            'somatic_display_term': 'somatic inversion',\n            'SO_accession': 'SO:1000036',\n            'display_term': 'inversion'\n        },\n        'complex_structural_alteration': {\n            'type': 'sv',\n            'somatic_display_term': 'somatic complex alteration',\n            'SO_accession': 'SO:0001784',\n            'display_term': 'complex alteration'\n        },\n        'tandem_duplication': {\n            'type': 'sv',\n            'somatic_display_term': 'somatic tandem duplication',\n            'SO_accession': 'SO:1000173',\n            'display_term': 'tandem duplication'\n        },\n        'mobile_element_insertion': {\n            'type': 'sv',\n            'somatic_display_term': 'somatic mobile element insertion',\n            'SO_accession': 'SO:0001837',\n            'display_term': 'mobile element insertion'\n        },\n        'mobile_element_deletion': {\n            'type': 'sv',\n            'somatic_display_term': 'somatic mobile element deletion',\n            'SO_accession': 'SO:0002066',\n            'display_term': 'mobile element deletion'\n        },\n        'interchromosomal_breakpoint': {\n            'type': 'sv',\n            'somatic_display_term': 'somatic interchromosomal breakpoint',\n            'SO_accession': 'SO:0001873',\n            'display_term': 'interchromosomal breakpoint'\n        },\n        'intrachromosomal_breakpoint': {\n            'type': 'sv',\n            'somatic_display_term': 'somatic intrachromosomal breakpoint',\n            'SO_accession': 'SO:0001874',\n            'display_term': 'intrachromosomal breakpoint'\n        },\n        'translocation': {\n            'type': 'sv',\n            'somatic_display_term': 'somatic translocation',\n            'SO_accession': 'SO:0000199',\n            'display_term': 'translocation'\n        },\n        'duplication': {\n            'type': 'sv',\n            'somatic_display_term': 'somatic duplication',\n            'SO_accession': 'SO:1000035',\n            'display_term': 'duplication'\n        },\n        'novel_sequence_insertion': {\n            'type': 'sv',\n            'somatic_display_term': 'somatic novel sequence insertion',\n            'SO_accession': 'SO:0001838',\n            'display_term': 'novel sequence insertion'\n        },\n        'interchromosomal_translocation': {\n            'type': 'sv',\n            'somatic_display_term': 'somatic interchromosomal translocation',\n            'SO_accession': 'SO:0002060',\n            'display_term': 'interchromosomal translocation'\n        },\n        'intrachromosomal_translocation': {\n            'type': 'sv',\n            'somatic_display_term': 'somatic intrachromosomal translocation',\n            'SO_accession': 'SO:0002061',\n            'display_term': 'intrachromosomal translocation'\n        },\n        'Alu_insertion': {\n            'type': 'sv',\n            'somatic_display_term': 'somatic alu insertion',\n            'SO_accession': 'SO:0002063',\n            'display_term': 'Alu insertion'\n        },\n        'complex_substitution': {\n            'type': 'sv',\n            'somatic_display_term': 'somatic complex substitution',\n            'SO_accession': 'SO:1000005',\n            'display_term': 'complex substitution'\n        },\n        'short_tandem_repeat_variation': {\n            'type': 'sv',\n            'somatic_display_term': 'somatic short tandem repeat variation',\n            'SO_accession': 'SO:0002096',\n            'display_term': 'short tandem repeat variation'\n        },\n        'loss_of_heterozygosity': {\n            'type': 'sv',\n            'somatic_display_term': 'somatic loss of heterozygosity',\n            'SO_accession': 'SO:0001786',\n            'display_term': 'loss of heterozygosity'\n        },\n    }\n\n    default_overlap_consequence = {\n        'is_default': 1,\n        'include': {\n            'within_feature': 0\n        },\n        'description': 'A sequence variant located in the intergenic region, between genes',\n        'SO_accession': 'SO:0001628',\n        'SO_term': 'intergenic_variant',\n        'tier': '4',\n        'label': 'intergenic variant',\n        'rank': '38',\n        'impact': 'MODIFIER',\n        'display_term': 'INTERGENIC'\n    }\n\n    overlap_consequences = {\n        'intergenic_variant': default_overlap_consequence,\n\n        'sequence_variant': {\n            'include': {\n                'within_feature': 0\n            },\n            'description': 'A sequence_variant is a non exact copy of a sequence_feature or genome exhibiting one or more sequence_alteration',\n            'SO_accession': 'SO:0001060',\n            'SO_term': 'sequence_variant',\n            'tier': '4',\n            'label': 'sequence variant',\n            'rank': '39',\n            'impact': 'MODIFIER',\n            'display_term': 'SEQUENCE_VARIANT',\n        },\n        'upstream_gene_variant': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::BaseVariationFeature',\n            'include': {\n                'within_feature': 0\n            },\n            'feature_SO_term': 'transcript',\n            'description': 'A sequence variant located 5\\' of a gene',\n            'SO_accession': 'SO:0001631',\n            'SO_term': 'upstream_gene_variant',\n            'tier': '3',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::upstream',\n            'label': 'upstream gene variant',\n            'rank': '24',\n            'impact': 'MODIFIER',\n            'display_term': 'UPSTREAM',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n        'downstream_gene_variant': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::BaseVariationFeature',\n            'include': {\n                'within_feature': 0\n            },\n            'feature_SO_term': 'transcript',\n            'description': 'A sequence variant located 3\\' of a gene',\n            'SO_accession': 'SO:0001632',\n            'SO_term': 'downstream_gene_variant',\n            'tier': '3',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::downstream',\n            'label': 'downstream gene variant',\n            'rank': '25',\n            'impact': 'MODIFIER',\n            'display_term': 'DOWNSTREAM',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n        'splice_donor_variant': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::VariationFeature',\n            'include': {\n                'intron_boundary': 1\n            },\n            'NCBI_term': 'splice-5',\n            'feature_SO_term': 'primary_transcript',\n            'description': 'A splice variant that changes the 2 base region at the 5\\' end of an intron',\n            'SO_accession': 'SO:0001575',\n            'tier': '3',\n            'SO_term': 'splice_donor_variant',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::donor_splice_site',\n            'label': 'splice donor variant',\n            'rank': '3',\n            'impact': 'HIGH',\n            'display_term': 'ESSENTIAL_SPLICE_SITE',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n        'splice_acceptor_variant': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::VariationFeature',\n            'include': {\n                'intron_boundary': 1\n            },\n            'NCBI_term': 'splice-3',\n            'feature_SO_term': 'primary_transcript',\n            'description': 'A splice variant that changes the 2 base region at the 3\\' end of an intron',\n            'SO_accession': 'SO:0001574',\n            'tier': '3',\n            'SO_term': 'splice_acceptor_variant',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::acceptor_splice_site',\n            'label': 'splice acceptor variant',\n            'rank': '3',\n            'impact': 'HIGH',\n            'display_term': 'ESSENTIAL_SPLICE_SITE',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n        'splice_region_variant': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::VariationFeature',\n            'include': {\n                'intron_boundary': 1\n            },\n            'feature_SO_term': 'primary_transcript',\n            'description': 'A sequence variant in which a change has occurred within the region of the splice site, either within 1-3 bases of the exon or 3-8 bases of the intron',\n            'SO_accession': 'SO:0001630',\n            'SO_term': 'splice_region_variant',\n            'tier': '3',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::splice_region',\n            'label': 'splice region variant',\n            'rank': '13',\n            'impact': 'LOW',\n            'display_term': 'SPLICE_SITE',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n        'intron_variant': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::BaseVariationFeature',\n            'include': {\n                'intron': 1\n            },\n            'NCBI_term': 'intron',\n            'feature_SO_term': 'primary_transcript',\n            'description': 'A transcript variant occurring within an intron',\n            'SO_accession': 'SO:0001627',\n            'tier': '3',\n            'SO_term': 'intron_variant',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::within_intron',\n            'label': 'intron variant',\n            'rank': '21',\n            'impact': 'MODIFIER',\n            'display_term': 'INTRONIC',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n        '5_prime_UTR_variant': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::BaseVariationFeature',\n            'include': {\n                'utr': 1,\n                'exon': 1\n            },\n            'NCBI_term': 'untranslated_5',\n            'feature_SO_term': 'mRNA',\n            'description': 'A UTR variant of the 5\\' UTR',\n            'SO_accession': 'SO:0001623',\n            'tier': '3',\n            'SO_term': '5_prime_UTR_variant',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::within_5_prime_utr',\n            'label': '5 prime UTR variant',\n            'rank': '18',\n            'impact': 'MODIFIER',\n            'display_term': '5PRIME_UTR',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n        '3_prime_UTR_variant': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::BaseVariationFeature',\n            'include': {\n                'utr': 1,\n                'exon': 1\n            },\n            'NCBI_term': 'untranslated_3',\n            'feature_SO_term': 'mRNA',\n            'description': 'A UTR variant of the 3\\' UTR',\n            'SO_accession': 'SO:0001624',\n            'tier': '3',\n            'SO_term': '3_prime_UTR_variant',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::within_3_prime_utr',\n            'label': '3 prime UTR variant',\n            'rank': '19',\n            'impact': 'MODIFIER',\n            'display_term': '3PRIME_UTR',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n        'synonymous_variant': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::VariationFeature',\n            'include': {\n                'coding': 1\n            },\n            'NCBI_term': 'cds-synon',\n            'feature_SO_term': 'mRNA',\n            'description': 'A sequence variant where there is no resulting change to the encoded amino acid',\n            'SO_accession': 'SO:0001819',\n            'tier': '3',\n            'SO_term': 'synonymous_variant',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::synonymous_variant',\n            'label': 'synonymous variant',\n            'rank': '15',\n            'impact': 'LOW',\n            'display_term': 'SYNONYMOUS_CODING',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n        'missense_variant': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::VariationFeature',\n            'include': {\n                'increase_length': 0,\n                'decrease_length': 0,\n                'coding': 1\n            },\n            'NCBI_term': 'missense',\n            'feature_SO_term': 'mRNA',\n            'description': 'A sequence variant, that changes one or more bases, resulting in a different amino acid sequence but where the length is preserved',\n            'SO_accession': 'SO:0001583',\n            'tier': '3',\n            'SO_term': 'missense_variant',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::missense_variant',\n            'label': 'missense variant',\n            'rank': '12',\n            'impact': 'MODERATE',\n            'display_term': 'NON_SYNONYMOUS_CODING',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n        'inframe_insertion': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::BaseVariationFeature',\n            'include': {\n                'insertion': 1,\n                'coding': 1\n            },\n            'feature_SO_term': 'mRNA',\n            'description': 'An inframe non synonymous variant that inserts bases into in the coding sequence',\n            'SO_accession': 'SO:0001821',\n            'SO_term': 'inframe_insertion',\n            'tier': '3',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::inframe_insertion',\n            'label': 'inframe insertion',\n            'rank': '10',\n            'impact': 'MODERATE',\n            'display_term': 'NON_SYNONYMOUS_CODING',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n        'inframe_deletion': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::BaseVariationFeature',\n            'include': {\n                'deletion': 1,\n                'coding': 1\n            },\n            'feature_SO_term': 'mRNA',\n            'description': 'An inframe non synonymous variant that deletes bases from the coding sequence',\n            'SO_accession': 'SO:0001822',\n            'SO_term': 'inframe_deletion',\n            'tier': '3',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::inframe_deletion',\n            'label': 'inframe deletion',\n            'rank': '11',\n            'impact': 'MODERATE',\n            'display_term': 'NON_SYNONYMOUS_CODING',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n        'stop_gained': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::VariationFeature',\n            'include': {\n                'coding': 1\n            },\n            'NCBI_term': 'nonsense',\n            'feature_SO_term': 'mRNA',\n            'description': 'A sequence variant whereby at least one base of a codon is changed, resulting in a premature stop codon, leading to a shortened transcript',\n            'SO_accession': 'SO:0001587',\n            'tier': '3',\n            'SO_term': 'stop_gained',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::stop_gained',\n            'label': 'stop gained',\n            'rank': '4',\n            'impact': 'HIGH',\n            'display_term': 'STOP_GAINED',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n        'stop_lost': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::BaseVariationFeature',\n            'include': {\n                'coding': 1\n            },\n            'feature_SO_term': 'mRNA',\n            'description': 'A sequence variant where at least one base of the terminator codon (stop) is changed, resulting in an elongated transcript',\n            'SO_accession': 'SO:0001578',\n            'SO_term': 'stop_lost',\n            'tier': '3',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::stop_lost',\n            'label': 'stop lost',\n            'rank': '6',\n            'impact': 'HIGH',\n            'display_term': 'STOP_LOST',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n        'stop_retained_variant': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::VariationFeature',\n            'include': {\n                'coding': 1\n            },\n            'feature_SO_term': 'mRNA',\n            'description': 'A sequence variant where at least one base in the terminator codon is changed, but the terminator remains',\n            'SO_accession': 'SO:0001567',\n            'SO_term': 'stop_retained_variant',\n            'tier': '3',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::stop_retained',\n            'label': 'stop retained variant',\n            'rank': '15',\n            'impact': 'LOW',\n            'display_term': 'SYNONYMOUS_CODING',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n        'start_lost': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::BaseVariationFeature',\n            'include': {\n                'coding': 1\n            },\n            'feature_SO_term': 'mRNA',\n            'description': 'A codon variant that changes at least one base of the canonical start codon',\n            'SO_accession': 'SO:0002012',\n            'SO_term': 'start_lost',\n            'tier': '3',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::start_lost',\n            'label': 'start lost',\n            'rank': '7',\n            'impact': 'HIGH',\n            'display_term': 'NON_SYNONYMOUS_CODING',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n        'start_retained_variant': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::BaseVariationFeature',\n            'include': {\n                'coding': 1\n            },\n            'feature_SO_term': 'mRNA',\n            'description': 'A sequence variant where at least one base in the start codon is changed, but the start remains',\n            'SO_accession': 'SO:0002019',\n            'SO_term': 'start_retained_variant',\n            'tier': '3',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::start_retained_variant',\n            'label': 'start retained variant',\n            'rank': '15',\n            'impact': 'LOW',\n            'display_term': 'SYNONYMOUS_CODING',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n        'frameshift_variant': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::BaseVariationFeature',\n            'include': {\n                'snp': 0,\n                'coding': 1\n            },\n            'NCBI_term': 'frameshift',\n            'feature_SO_term': 'mRNA',\n            'description': 'A sequence variant which causes a disruption of the translational reading frame, because the number of nucleotides inserted or deleted is not a multiple of three',\n            'SO_accession': 'SO:0001589',\n            'tier': '3',\n            'SO_term': 'frameshift_variant',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::frameshift',\n            'label': 'frameshift variant',\n            'rank': '5',\n            'impact': 'HIGH',\n            'display_term': 'FRAMESHIFT_CODING',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n        'incomplete_terminal_codon_variant': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::VariationFeature',\n            'include': {\n                'coding': 1\n            },\n            'feature_SO_term': 'mRNA',\n            'description': 'A sequence variant where at least one base of the final codon of an incompletely annotated transcript is changed',\n            'SO_accession': 'SO:0001626',\n            'SO_term': 'incomplete_terminal_codon_variant',\n            'tier': '3',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::partial_codon',\n            'label': 'incomplete terminal codon variant',\n            'rank': '14',\n            'impact': 'LOW',\n            'display_term': 'PARTIAL_CODON',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n        'NMD_transcript_variant': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::BaseVariationFeature',\n            'include': {\n                'within_feature': 1,\n                'nonsense_mediated_decay': 1\n            },\n            'feature_SO_term': 'mRNA',\n            'description': 'A variant in a transcript that is the target of NMD',\n            'SO_accession': 'SO:0001621',\n            'SO_term': 'NMD_transcript_variant',\n            'tier': '3',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::within_nmd_transcript',\n            'label': 'NMD transcript variant',\n            'rank': '22',\n            'impact': 'MODIFIER',\n            'display_term': 'NMD_TRANSCRIPT',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n        'non_coding_transcript_variant': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::BaseVariationFeature',\n            'include': {\n                'within_feature': 1,\n                'protein_coding': 0\n            },\n            'feature_SO_term': 'ncRNA',\n            'description': 'A transcript variant of a non coding RNA gene',\n            'SO_accession': 'SO:0001619',\n            'SO_term': 'non_coding_transcript_variant',\n            'tier': '3',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::within_non_coding_gene',\n            'label': 'non coding transcript variant',\n            'rank': '23',\n            'impact': 'MODIFIER',\n            'display_term': 'WITHIN_NON_CODING_GENE',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n        'non_coding_transcript_exon_variant': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::BaseVariationFeature',\n            'include': {\n                'within_feature': 1,\n                'exon': 1,\n                'protein_coding': 0\n            },\n            'feature_SO_term': 'ncRNA',\n            'description': 'A sequence variant that changes non-coding exon sequence in a non-coding transcript',\n            'SO_accession': 'SO:0001792',\n            'SO_term': 'non_coding_transcript_exon_variant',\n            'tier': '3',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::non_coding_exon_variant',\n            'label': 'non coding transcript exon variant',\n            'rank': '20',\n            'impact': 'MODIFIER',\n            'display_term': 'WITHIN_NON_CODING_GENE',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n        'mature_miRNA_variant': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::BaseVariationFeature',\n            'include': {\n                'within_feature': 1,\n                'nonsense_mediated_decay': 0,\n                'protein_coding': 0\n            },\n            'feature_SO_term': 'miRNA',\n            'description': 'A transcript variant located with the sequence of the mature miRNA',\n            'SO_accession': 'SO:0001620',\n            'SO_term': 'mature_miRNA_variant',\n            'tier': '2',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::within_mature_miRNA',\n            'label': 'mature miRNA variant',\n            'rank': '17',\n            'impact': 'MODIFIER',\n            'display_term': 'WITHIN_MATURE_miRNA',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n        'coding_sequence_variant': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::BaseVariationFeature',\n            'include': {\n                'coding': 1\n            },\n            'feature_SO_term': 'mRNA',\n            'description': 'A sequence variant that changes the coding sequence',\n            'SO_accession': 'SO:0001580',\n            'SO_term': 'coding_sequence_variant',\n            'tier': '3',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::coding_unknown',\n            'label': 'coding sequence variant',\n            'rank': '16',\n            'impact': 'MODIFIER',\n            'display_term': 'CODING_UNKNOWN',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n        'regulatory_region_variant': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::BaseVariationFeature',\n            'feature_SO_term': 'regulatory_region',\n            'description': 'A sequence variant located within a regulatory region',\n            'SO_accession': 'SO:0001566',\n            'SO_term': 'regulatory_region_variant',\n            'tier': '2',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::within_regulatory_feature',\n            'label': 'regulatory region variant',\n            'rank': '36',\n            'impact': 'MODIFIER',\n            'display_term': 'REGULATORY_REGION',\n            'feature_class': 'Bio::EnsEMBL::Funcgen::RegulatoryFeature'\n        },\n        'TF_binding_site_variant': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::BaseVariationFeature',\n            'feature_SO_term': 'TF_binding_site',\n            'description': 'A sequence variant located within a transcription factor binding site',\n            'SO_accession': 'SO:0001782',\n            'SO_term': 'TF_binding_site_variant',\n            'tier': '2',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::within_motif_feature',\n            'label': 'TF binding site',\n            'rank': '30',\n            'impact': 'MODIFIER',\n            'display_term': 'REGULATORY_REGION',\n            'feature_class': 'Bio::EnsEMBL::Funcgen::MotifFeature'\n        },\n        'transcript_ablation': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::BaseVariationFeature',\n            'include': {\n                'complete_overlap': 1,\n                'deletion': 1\n            },\n            'feature_SO_term': 'mRNA',\n            'description': 'A feature ablation whereby the deleted region includes a transcript feature',\n            'SO_accession': 'SO:0001893',\n            'SO_term': 'transcript_ablation',\n            'tier': '1',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::feature_ablation',\n            'label': 'transcript ablation',\n            'rank': '1',\n            'impact': 'HIGH',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n        'transcript_amplification': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::BaseVariationFeature',\n            'include': {\n                'complete_overlap': 1,\n                'increase_length': 1\n            },\n            'feature_SO_term': 'mRNA',\n            'description': 'A feature amplification of a region containing a transcript',\n            'SO_accession': 'SO:0001889',\n            'SO_term': 'transcript_amplification',\n            'tier': '1',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::feature_amplification',\n            'label': 'transcript amplification',\n            'rank': '8',\n            'impact': 'HIGH',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n        'TFBS_ablation': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::BaseVariationFeature',\n            'include': {\n                'complete_overlap': 1,\n                'deletion': 1\n            },\n            'feature_SO_term': 'TF_binding_site',\n            'description': 'A feature ablation whereby the deleted region includes a transcription factor binding site',\n            'SO_accession': 'SO:0001895',\n            'SO_term': 'TFBS_ablation',\n            'tier': '2',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::feature_ablation',\n            'label': 'TFBS ablation',\n            'rank': '26',\n            'impact': 'MODERATE',\n            'feature_class': 'Bio::EnsEMBL::Funcgen::MotifFeature'\n        },\n        'TFBS_amplification': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::BaseVariationFeature',\n            'include': {\n                'complete_overlap': 1,\n                'increase_length': 1\n            },\n            'feature_SO_term': 'TF_binding_site',\n            'description': 'A feature amplification of a region containing a transcription factor binding site',\n            'SO_accession': 'SO:0001892',\n            'SO_term': 'TFBS_amplification',\n            'tier': '2',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::feature_amplification',\n            'label': 'TFBS amplification',\n            'rank': '28',\n            'impact': 'MODIFIER',\n            'feature_class': 'Bio::EnsEMBL::Funcgen::MotifFeature'\n        },\n        'regulatory_region_ablation': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::BaseVariationFeature',\n            'include': {\n                'complete_overlap': 1,\n                'deletion': 1\n            },\n            'feature_SO_term': 'TF_binding_site',\n            'description': 'A feature ablation whereby the deleted region includes a regulatory region',\n            'SO_accession': 'SO:0001894',\n            'SO_term': 'regulatory_region_ablation',\n            'tier': '2',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::feature_ablation',\n            'label': 'regulatory region ablation',\n            'rank': '31',\n            'impact': 'MODERATE',\n            'feature_class': 'Bio::EnsEMBL::Funcgen::RegulatoryFeature'\n        },\n        'regulatory_region_amplification': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::BaseVariationFeature',\n            'include': {\n                'complete_overlap': 1,\n                'increase_length': 1\n            },\n            'feature_SO_term': 'TF_binding_site',\n            'description': 'A feature amplification of a region containing a regulatory region',\n            'SO_accession': 'SO:0001891',\n            'SO_term': 'regulatory_region_amplification',\n            'tier': '2',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::feature_amplification',\n            'label': 'regulatory region amplification',\n            'rank': '33',\n            'impact': 'MODIFIER',\n            'feature_class': 'Bio::EnsEMBL::Funcgen::RegulatoryFeature'\n        },\n        'feature_elongation': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::BaseVariationFeature',\n            'include': {\n                'sv': 1,\n                'increase_length': 1\n            },\n            'feature_SO_term': 'sequence_feature',\n            'description': 'A sequence variant that causes the extension of a genomic feature, with regard to the reference sequence',\n            'SO_accession': 'SO:0001907',\n            'SO_term': 'feature_elongation',\n            'tier': '3',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::feature_elongation',\n            'label': 'feature elongation',\n            'rank': '36',\n            'impact': 'MODIFIER',\n            'feature_class': 'Bio::EnsEMBL::Feature'\n        },\n        'feature_truncation': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::BaseVariationFeature',\n            'include': {\n                'sv': 1,\n                'decrease_length': 1\n            },\n            'feature_SO_term': 'sequence_feature',\n            'description': 'A sequence variant that causes the reduction of a genomic feature, with regard to the reference sequence',\n            'SO_accession': 'SO:0001906',\n            'SO_term': 'feature_truncation',\n            'tier': '3',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::feature_truncation',\n            'label': 'feature truncation',\n            'rank': '37',\n            'impact': 'MODIFIER',\n            'feature_class': 'Bio::EnsEMBL::Feature'\n        },\n        'protein_altering_variant': {\n            'variant_feature_class': 'Bio::EnsEMBL::Variation::VariationFeature',\n            'include': {\n                'coding': 1\n            },\n            'feature_SO_term': 'mRNA',\n            'description': 'A sequence_variant which is predicted to change the protein encoded in the coding sequence',\n            'SO_accession': 'SO:0001818',\n            'SO_term': 'protein_altering_variant',\n            'tier': '3',\n            'predicate': 'Bio::EnsEMBL::Variation::Utils::VariationEffect::protein_altering_variant',\n            'label': 'protein altering variant',\n            'rank': '12',\n            'impact': 'MODERATE',\n            'feature_class': 'Bio::EnsEMBL::Transcript'\n        },\n    }\n","sub_path":"Bio_EnsEMBL_Variation_Utils_Constants/Bio_EnsEMBL_Variation_Utils_Constants.py","file_name":"Bio_EnsEMBL_Variation_Utils_Constants.py","file_ext":"py","file_size_in_byte":45732,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"182585383","text":"#Software:FaviconDownloader\n#Script:DgUwLtB\n#You can choose use either wget or urllib. just uncomment which one you want.\n\n#import wget\nimport urllib.request\nprint('Enter the website here please')\nUrl = input(':').lower()\nCleanup = Url\t\nif Url.startswith('https'):\n\tCleanup = Url.split('://', 2)[1]\nelif Url.startswith('www.'):\n\tCleanup = Url.split('www.', 2)[1]\n\nCleanup = Cleanup.split('.')[0]\n#wget.download('https://www.google.com/s2/favicons?domain=' + str(Url),str(Cleanup+'.png'))\nurllib.request.urlretrieve('https://www.google.com/s2/favicons?domain=' + str(Url),str(Cleanup+'.png'))\nprint('Favicon ' + Cleanup + ' downloaded')\n","sub_path":"Favicondownloader.py","file_name":"Favicondownloader.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"196083483","text":"#!/usr/bin/python3\nimport time\nimport socket\n\nhote = \"localhost\"\n\nport = 12801\n\n\nconnexion_avec_serveur = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\nconnected = False\nwhile not connected:\n\ttry:\n\t\tconnexion_avec_serveur.connect((hote, port))\n\t\tconnected = True\n\texcept ConnectionRefusedError:\n\t\ttime.sleep(0.1)\nprint(\"Connexion établie avec le serveur sur le port {}\".format(port))\n\n\nmsg_a_envoyer = b\"\"\n\nwhile msg_a_envoyer != b\"fin\":\n\n    msg_a_envoyer = input(\"> \")\n\n    # Peut planter si vous tapez des caractères spéciaux\n\n    msg_a_envoyer = msg_a_envoyer.encode()\n\n    # On envoie le message\n\n    connexion_avec_serveur.send(msg_a_envoyer)\n\n    msg_recu = connexion_avec_serveur.recv(1024)\n\n    print(msg_recu.decode()) # Là encore, peut planter s'il y a des accents\n\n\nprint(\"Fermeture de la connexion\")\n\nconnexion_avec_serveur.close()\n","sub_path":"Street pyghter/src2/bot/testClient2.py","file_name":"testClient2.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"130053962","text":"# coding:utf8\nimport math\ndef isPrimeNumber(num):\n    i = 2\n    x = math.sqrt(num)\n    while i < x:\n        if num%i == 0:\n            return False\n        i += 1\n    return True\n \ndef Reverse(num):\n    rNum = 0\n    while num:\n        rNum = rNum*10 + num%10\n        num //= 10\n    return rNum\n \ndef RPrimeNumber(num):\n    arr = []\n    i = 2\n    while i < num:\n        if isPrimeNumber(i) and i == Reverse(i):\n            arr.append(i)\n        i += 1\n    return arr\n \nprint (RPrimeNumber(1000))","sub_path":"answer/ex92.py","file_name":"ex92.py","file_ext":"py","file_size_in_byte":494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"381459977","text":"import structures\n\ndef bfs(grid, start, target):\n\n\treturn grid\n\n\nif __name__ == '__main__':\n\n\tgrid = [\n\t\t\t['S','0','0'],\n\t\t\t['0','0','0'],\n\t\t\t['0','0','F']\n\t\t\t]\n\n\tfor line in grid:\n\n\t\tfor cell in line:\n\n\t\t\tprint(cell, end=' ')\n\n\t\tprint()","sub_path":"lib/better_than_google.py","file_name":"better_than_google.py","file_ext":"py","file_size_in_byte":237,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"104358588","text":"\"\"\"\nThis class listens for tts/say messages and triggers a sequence of messages\nthat result in the text message being converted to wav audio and played through the speaker service\nTODO Where the text is very long, it is split into parts and sent sequentially.\nThe speaker service sends start and end messages.\nThis service iterates each part, waiting for each speaker/started and speaker/finished message\nand finally sends a tts/finished  message when all parts have finished playing\nDepends on os pico2wav install with path in config.yaml\n\"\"\"\n\nimport json\nimport os\nimport aiofiles\nimport concurrent.futures\nimport asyncio\nfrom random import seed\nfrom random import randint\nfrom MqttService import MqttService\nimport unicodedata\nimport string\n\nfrom google.cloud import texttospeech\n\nfrom pathlib import Path\n\nvalid_filename_chars = \"-_() %s%s\" % (string.ascii_letters, string.digits)\nchar_limit = 240\n\n\n\n# seed random number generator\nseed(1)\n\ndef write_speech(text,file_name,config):\n    print('WRITE SPEECH')\n    print([text,file_name,config])\n    # Instantiates a client\n    client = texttospeech.TextToSpeechClient()\n\n    # Set the text input to be synthesized\n    synthesis_input = texttospeech.SynthesisInput(text=text)\n    input_text = texttospeech.SynthesisInput(text=text)\n    # Build the voice request, select the language code (\"en-US\") and the ssml\n    # voice gender (\"neutral\")\n    voice = texttospeech.VoiceSelectionParams(\n        language_code=config.get('language','en-US'),\n        ssml_gender=texttospeech.SsmlVoiceGender.NEUTRAL)\n\n    # Select the type of audio file you want returned\n    audio_config = texttospeech.AudioConfig(\n        audio_encoding=texttospeech.AudioEncoding.MP3)\n\n    # Perform the text-to-speech request on the text input with the selected\n    # voice parameters and audio file type\n    response = client.synthesize_speech(\n        request={\"input\": input_text, \"voice\": voice, \"audio_config\": audio_config}\n    )\n    print('GOT GOO SPEECH REQ')\n    \n    return response.audio_content\n\n\ndef clean_filename(filename, whitelist=valid_filename_chars, replace=' '):\n    # replace spaces\n    for r in replace:\n        filename = filename.replace(r,'_')\n    \n    # keep only valid ascii chars\n    cleaned_filename = unicodedata.normalize('NFKD', filename).encode('ASCII', 'ignore').decode()\n    \n    # keep only whitelisted chars\n    cleaned_filename = ''.join(c for c in cleaned_filename if c in whitelist)\n    # if len(cleaned_filename)>char_limit:\n        # print(\"Warning, filename truncated because it was over {}. Filenames may no longer be unique\".format(char_limit))\n    return cleaned_filename[:char_limit]    \n \n\ndef my_run_in_executor(executor, f, *args):\n    return asyncio.wrap_future(executor.submit(f, *args))\n\nclass GoogleTtsService(MqttService):\n    \"\"\" Text to Speech Service Class \"\"\"\n\n    def __init__(\n            self,\n            config,\n            loop\n    ):\n        super(\n            GoogleTtsService,\n            self).__init__(config,loop)\n        self.config = config\n        # subscribe to all sites\n        self.subscribe_to = 'hermod/+/tts/say'\n        cache_path = self.config['services']['GoogleTtsService'].get('cache_path','/tmp/tts_cache')\n        Path(cache_path).mkdir(parents=True, exist_ok=True)\n\n\n    async def on_message(self, msg):\n        self.log('message {}'.format(msg))\n        topic = \"{}\".format(msg.topic)\n        parts = topic.split('/')\n        site = parts[1]\n        payload = {}\n        try:\n            payload = json.loads(msg.payload)\n        except BaseException:\n            pass\n        # self.log('TTS payload')\n        # self.log(payload)\n        text = payload.get('text','')\n        #self.log(text)\n        if topic == 'hermod/' + site + '/tts/say' and len(text) > 0:\n            # self.log('TTS start gen')\n        \n            await self.generate_audio(site, text, payload)\n        elif topic == 'hermod/' + site + '/speaker/finished':\n            # self.log('SPEAKER FINISHED')\n                # self.log(payload)\n            #self.play_requests[payload.get('id')] = value;\n            \n            message = {\"id\": payload.get('id')}\n            await asyncio.sleep(0.5)\n            await self.client.publish(\n                'hermod/{}/tts/finished'.format(site),\n                json.dumps(message))\n            await self.client.unsubscribe('hermod/{}/speaker/finished'.format(site))\n\n    \n    \"\"\" Use system binary pico2wav to generate audio file from text then send audio as mqtt\"\"\"\n    async def generate_audio(self, site, text, payload):\n        cache_path = self.config['services']['GoogleTtsService'].get('cache_path','/tmp/tts_cache')\n        value = payload.get('id','no_id')\n        # self.log('TTS GEN '+cache_path)\n        \n        if len(text) > 0:\n            # self.log('TTS havetext')\n        \n            # filename limits\n            short_text = text[0:200].replace(' ','_')\n            # speakable and limited\n            say_text = text[0:300].replace('(','').replace(')','')\n            file_name = os.path.join(cache_path, clean_filename('tts-' + str(short_text)) + '.wav')\n            # self.log('TTS file '+file_name)\n        \n            # generate if file doesn't exist in cache\n            audio_file = None\n            if not os.path.isfile(file_name):\n                self.log('TTS exec')\n                with concurrent.futures.ProcessPoolExecutor() as executor:\n                #audio_file = await self.loop.run_in_executor(None,write_speech,text, file_name, self.config)\n                    audio_file = await my_run_in_executor(executor,write_speech,say_text , file_name, self.config)\n                async with aiofiles.open(file_name, mode='wb') as f:\n                    await f.write(audio_file)\n                # The response's audio_content is binary.\n                # with open(file_name, 'wb') as out:\n                    # out.write(response.audio_content)\n                self.log('TTS DONE write')\n            else:     \n                self.log('TTS read')\n        \n                async with aiofiles.open(file_name, mode='rb') as f:\n                    audio_file = await f.read()\n            self.log('TTS now send {}'.format(len(audio_file)))\n            \n            await self.client.subscribe('hermod/{}/speaker/finished'.format(site))\n            \n            slice_length = 16000\n            def chunker(seq, size):\n                return (seq[pos:pos + size] for pos in range(0, len(seq), size))\n\n            lc = 0\n            ts = 0\n            for slice in chunker(audio_file, slice_length):\n                lc = lc + 1\n                ts = ts + len(slice)\n                await self.client.publish('hermod/{}/speaker/cache/{}'.format(site, value), payload=bytes(slice), qos=0)\n            \n            self.log(lc)\n            self.log(ts)\n            # finally send play message with empty payload\n            await self.client.publish(\n                'hermod/{}/speaker/play/{}'.format(site, value), payload=None, qos=0)\n                \n            self.log('TTS sent ')\n            # cache short texts\n            if len(short_text) > self.config.get('cache_max_letters',100):\n                # self.log('TTS remove file')\n                os.remove(file_name)\n","sub_path":"hermod-python/src/GoogleTtsService.py","file_name":"GoogleTtsService.py","file_ext":"py","file_size_in_byte":7240,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"610310980","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Mar 21 15:48:52 2020\n\n@author: Peter Corke\n\"\"\"\n\nfrom collections import UserList\nimport numpy as np\nimport math\n\nfrom spatialmath.base import argcheck\nimport spatialmath.base as tr\nfrom spatialmath import super_pose as sp\n\n\nclass SO2(sp.SMPose):\n\n    # SO2()  identity matrix\n    # SO2(angle, unit)\n    # SO2( obj )   # deep copy\n    # SO2( np )  # make numpy object\n    # SO2( nplist )  # make from list of numpy objects\n\n    # constructor needs to take ndarray -> SO2, or list of ndarray -> SO2\n    def __init__(self, arg=None, *, unit='rad'):\n        super().__init__()  # activate the UserList semantics\n\n        if arg is None:\n            # empty constructor\n            if type(self) is SO2:\n                self.data = [np.eye(2)]\n\n        elif argcheck.isvector(arg):\n            # SO2(value)\n            # SO2(list of values)\n            self.data = [tr.rot2(x, unit) for x in argcheck.getvector(arg)]\n\n        elif isinstance(arg, np.ndarray) and arg.shape == (2,2):\n            self.data = [arg]\n        else:\n            super().arghandler(arg)\n\n    @classmethod\n    def rand(cls, *, range=[0, 2 * math.pi], unit='rad', N=1):\n        rand = np.random.uniform(low=range[0], high=range[1], size=N)  # random values in the range\n        return cls([tr.rot2(x) for x in argcheck.getunit(rand, unit)])\n\n    @staticmethod\n    def isvalid(x):\n        return tr.isrot2(x, check=True)\n\n    @property\n    def T(self):\n        return SO2(self.A.T)\n\n    @property\n    def inv(self):\n        if len(self) == 1:\n            return SO2(self.A.T)\n        else:\n            return SO2([x.T for x in self.A])\n\n    @property\n    def R(self):\n        return self.A[:2, :2]\n\n    @property\n    def theta(self):\n        \"\"\"Returns angle of SO2 object matrices in unit radians\"\"\"\n        if len(self) == 1:\n            return math.atan2(self.A[1,0], self.A[0,0])\n        else:\n            return [math.atan2(x.A[1,0], x.A[0,0]) for x in self]        \n\n\nclass SE2(SO2):\n    # constructor needs to take ndarray -> SO2, or list of ndarray -> SO2\n    def __init__(self, x=None, y=None, theta=None, *, unit='rad'):\n        super().__init__()  # activate the UserList semantics\n\n        if x is None and y is None and theta is None:\n            # SE2()\n            # empty constructor\n            self.data = [np.eye(3)]\n\n        elif x is not None:\n            if y is not None and theta is not None:\n                # SE2(x, y, theta)\n                self.data = [tr.trot2(theta, t=[x, y], unit=unit)]\n\n            elif y is None and theta is None:\n                if argcheck.isvector(x, 3):\n                    # SE2( [x,y,theta])\n                    self.data = [tr.trot2(x[2], t=x[:2], unit=unit)]\n                elif isinstance(x, np.ndarray):\n                    if x.shape == (3,3):\n                        # SE2( 3x3 matrix )\n                        self.data = [x]\n                    elif x.shape[1] == 3:\n                        # SE2( Nx3 )\n                        self.data = [tr.trot2(T.theta, t=T.t) for T in x]\n                else:\n                    super().arghandler(x)\n        else:\n            raise ValueError('bad arguments to constructor')\n\n    @property\n    def T(self):\n        raise NotImplemented('transpose is not meaningful for SE3 object')\n\n    @classmethod\n    def rand(cls, *, xrange=[-1, 1], yrange=[-1, 1], trange=[0, 2 * math.pi], unit='rad', N=1):\n        x = np.random.uniform(low=xrange[0], high=xrange[1], size=N)  # random values in the range\n        y = np.random.uniform(low=yrange[0], high=yrange[1], size=N)  # random values in the range\n        theta = np.random.uniform(low=trange[0], high=trange[1], size=N)  # random values in the range\n        return cls([tr.trot2(t, t=[x, y]) for (t, x, y) in zip(x, y, argcheck.getunit(theta, unit))])\n\n    @staticmethod\n    def isvalid(x):\n        return tr.ishom2(x, check=True)\n\n    @property\n    def t(self):\n        return self.A[:2, 2]\n\n    @property\n    def xyt(self):\n        if len(self) == 1:\n            return np.r_[self.t, self.theta]\n        else:\n            return [np.r_[x.t, x.theta] for x in self]\n\n    @property\n    def inv(self):\n        if len(self) == 1:\n            return SE2(tr.rt2tr(self.R.T, -self.R.T @ self.t))\n        else:\n            return SE2([tr.rt2tr(x.R.T, -x.R.T @ x.t) for x in self])\n\n\nif __name__ == '__main__':  # pragma: no cover\n\n    import pathlib\n    import os.path\n\n    exec(open(os.path.join(pathlib.Path(__file__).parent.absolute(), \"test_pose2d.py\")).read())\n","sub_path":"spatialmath/pose2d.py","file_name":"pose2d.py","file_ext":"py","file_size_in_byte":4550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"29472352","text":"#! /usr/bin/env python\n\n\"\"\"\nThis module defines an interface for commanding a LinkLabs module\nvia the host interface over a serial connection.\n\"\"\"\n\nfrom serial import Serial\nfrom serial.tools import list_ports\nfrom time import time, sleep\nfrom contextlib import contextmanager\nfrom binascii import hexlify\nimport logging\nimport struct\n\nLOG = logging.getLogger(__name__)\n\nIRQ_FLAGS = {\n    'WDOG_RESET': 0x00000001,\n    'RESET': 0x00000002,\n    'TX_QUEUE_EMPTY': 0x00000010,\n    'TX_ERROR': 0x00000020,\n    'RX_DONE': 0x00000100,\n    'CONNECTED': 0x00001000,\n    'DISCONNECTED': 0x00002000,\n    'CRYPTO_ESTABLISHED': 0x00010000,\n    'APP_TOKEN_CONFIRMED': 0x00020000,\n    'DOWNLINK_REQUEST_ACK': 0x00040000,\n    'CRYPTO_ERROR': 0x00100000,\n    'APP_TOKEN_ERROR': 0x00200000,\n    'ASSERT': 0x80000000,\n}\n\nIFC_ACK_CODES = {\n    'ACK': 0,\n    'NACK_CMD_NOT_SUPPORTED': 1,\n    'NACK_INCORRECT_CHKSUM': 2,\n    'NACK_PAYLOAD_LEN_OOR': 3,\n    'NACK_PAYLOAD_OOR': 4,\n    'NACK_BOOTUP_IN_PROGRESS': 5,\n    'NACK_BUSY_TRY_AGAIN': 6,\n    'NACK_APP_TOKEN_REG': 7,\n    'NACK_PAYLOAD_LEN_EXCEEDED': 8,\n    'NACK_OTHER': 255,\n}\n\nOPCODES = {\n    'VERSION': 0,\n    'IFC_VERSION': 1,\n    'FREQUENCY': 6,\n    'TX_POWER': 7,\n    'RESET_SETTINGS': 8,\n    'GET_RADIO_PARAMS': 9,\n    'SET_RADIO_PARAMS': 10,\n    'PKT_SEND_QUEUE': 11,\n    'IRQ_FLAGS': 15,\n    'IRQ_FLAGS_MASK': 16,\n    'SLEEP': 20,\n    'SLEEP_BLOCK': 21,\n    'PKT_SEND': 30,\n    'PKT_ECHO': 31,\n    'PKT_RECV': 40,\n    'PKT_RECV_RSSI': 41,\n    'PKT_RECV_CONT': 42,\n    'MODULE_ID': 50,\n    'STORE_SETTINGS': 51,\n    'DELETE_SETTINGS': 52,\n    'RESET_MCU': 60,\n    'TRIGGER_BOOTLOADER': 61,\n    'MAC_MODE_SET': 70,\n    'MAC_MODE_GET': 71,\n    'SET_KEY': 80,\n    'PKT_SEND_ACK': 90,\n    'PKT_SEND_UNACK': 91,\n    'TX_CW': 98,\n    'RX_MODE_SET': 110,\n    'RX_MODE_GET': 111,\n    'QOS_REQUEST': 112,\n    'QOS_GET': 113,\n    'ANTENNA_SET': 114,\n    'ANTENNA_GET': 115,\n    'NET_TOKEN_SET': 116,\n    'NET_TOKEN_GET': 117,\n    'NET_INFO_GET': 118,\n    'STATS_GET': 119,\n    'RSSI_SET': 120,\n    'RSSI_GET': 121,\n    'DL_BAND_CFG_GET': 122,\n    'DL_BAND_CFG_SET': 123,\n    'APP_TOKEN_SET': 124,\n    'APP_TOKEN_GET': 125,\n    'APP_TOKEN_REG_GET': 126,\n    'CRYPTO_KEY_XCHG_REQ': 128,\n    'CRYPTO_KEY_SET': 129,\n    'HARDWARE_TYPE': 254,\n    'FIRMWARE_TYPE': 255,\n}\n\nOPEN_NET_TOKEN = hexlify(b'OPEN')\n\nclass ModuleConnection(object):\n    \"\"\"\n    The interface to a LinkLabs module. The `device` parameter should be a path\n    to the module. If none is specified, then the constructor will attempt\n    to find one.\n    \"\"\"\n    def __init__(self, device=None):\n        device = device if device else find_module_device()\n        LOG.info(\"Connecting to %s\", device)\n        self.sdev = Serial(port=device, baudrate=115200, timeout=1.0)\n        if not self.sdev.isOpen():\n            raise IOError(\"Cannot open device %s\", device)\n        self.frame_start_byte = 0xc4\n        self.dummy_byte = 0xff\n        self.num_dummy_bytes = 4\n        self.message_counter = 0\n        self.response_header_length = 5\n        self.frame_start_timeout = 1.0\n\n    def close(self):\n        \"\"\" Closes the serial port owned by this object. \"\"\"\n        self.sdev.close()\n\n    def __enter__(self):\n        return self\n\n    def __exit__(self, type_, value, traceback):\n        self.close()\n\n    def __str__(self):\n        return self.get_unique_id()\n\n    def __repr__(self):\n        unique_id = self.get_unique_id()\n        device_port = self.sdev.getPort()\n        return self.__class__.__name__ + \"('{}') -> {}\".format(device_port, unique_id)\n\n    def _send_command(self, opcode, send_buff=None):\n        \"\"\"\n        Sends a command to the module, waits for the response, and\n        returns the response payload.\n        \"\"\"\n        self._send_packet(opcode, send_buff if send_buff else [])\n        response = self._receive_packet(opcode, self.message_counter)\n        self.message_counter = (self.message_counter + 1) % 256\n\n        return response\n\n    def _send_packet(self, opcode, send_buff):\n        \"\"\" Sends a framed uart transmission to the module. \"\"\"\n        buff = bytearray()\n\n        buff.append(self.frame_start_byte)\n        buff.append(opcode)\n        buff.append(self.message_counter)\n\n        len_msb = (len(send_buff) >> 8) & 0xFF\n        len_lsb = (len(send_buff) >> 0) & 0xFF\n        buff.append(len_msb)\n        buff.append(len_lsb)\n\n        buff = buff + bytearray(send_buff)\n\n        checksum = compute_checksum(buff)\n        buff.append((checksum >> 8) & 0xFF)\n        buff.append((checksum >> 0) & 0xFF)\n\n        # Start the buffer with several dummy bytes\n        dummy = bytearray([self.dummy_byte] * self.num_dummy_bytes)\n        buff = dummy + buff\n\n        LOG.debug(\"Sending frame %s to %s\", hexlify(buff), self.sdev.getPort())\n        written_size = self.sdev.write(buff)\n        if written_size != len(buff):\n            raise IOError(\"Not enough bytes written.\")\n        self.sdev.flush()\n\n    def _receive_packet(self, opcode, message_counter):\n        \"\"\"\n        Receive a framed uart transmission from the module. Will return\n        the packet payload (without any framing header or CRC).\n        \"\"\"\n        start = time()\n        while True:\n            if time() - start > self.frame_start_timeout:\n                raise IOError(\"Did not get frame start within timeout.\")\n            byte = self.sdev.read()\n            if byte:\n                if ord(byte) == self.frame_start_byte:\n                    break\n                else:\n                    LOG.warning(\"Bad frame start byte: %r\", byte)\n\n        resp_header = bytearray(self.sdev.read(self.response_header_length))\n        resp_opcode = resp_header[0]\n        resp_message_counter = resp_header[1]\n        resp_ack = resp_header[2]\n        resp_payload_len = (resp_header[3] << 8) + resp_header[4]\n        LOG.debug(\"Received frame header %s from %s\", hexlify(resp_header), self.sdev.getPort())\n\n        if resp_opcode != opcode:\n            raise IOError(\"Did not get the same opcode we sent:\\\n                Received %s not %s\" % (resp_opcode, opcode))\n        if resp_message_counter != message_counter:\n            raise IOError(\"Did not get the same message counter we sent.\")\n\n        if resp_ack != IFC_ACK_CODES['ACK']:\n            nack = next(nack for nack, val in IFC_ACK_CODES.items() if val == resp_ack)\n            # Read checksum bytes before raising the exception\n            resp_checksum_buff = bytearray(self.sdev.read(2))\n            raise IOError(resp_ack, \"Received NACK from module: %s\" % nack)\n\n        resp_payload = bytearray(self.sdev.read(resp_payload_len))\n        LOG.debug(\"Received frame payload %s from %s\", hexlify(resp_payload), self.sdev.getPort())\n        if len(resp_payload) != resp_payload_len:\n            raise IOError(\"Could not read the number of bytes promised by the module.\")\n\n        resp_checksum_buff = bytearray(self.sdev.read(2))\n        resp_checksum = (resp_checksum_buff[0] << 8) + resp_checksum_buff[1]\n        checksum = compute_checksum(bytearray([self.frame_start_byte]) + resp_header + resp_payload)\n        if resp_checksum != checksum:\n            raise IOError(\"Checksum mismatch.\")\n\n        LOG.debug(\"Received checksum bytes %s from %s\",\n                  hexlify(resp_checksum_buff), self.sdev.getPort())\n        self.sdev.flush()\n\n        return resp_payload\n\n    def get_version(self):\n        \"\"\" Returns the module's firmware version as a tuple of (major, minor, tag). \"\"\"\n        resp_payload = self._send_command(OPCODES['VERSION'])\n        return resp_payload[0], resp_payload[1], (resp_payload[2] << 8) + resp_payload[3]\n\n    def set_mac_mode(self, mac):\n        \"\"\"\n        Sets the MAC mode of the module. Valid values of the parameter `mac` are\n        'Symphony' or 'NoMac'.\n        \"\"\"\n        if mac == 'NoMac':\n            self._send_command(OPCODES['MAC_MODE_SET'], [0])\n        elif mac == 'Symphony':\n            self._send_command(OPCODES['MAC_MODE_SET'], [3])\n        else:\n            raise ValueError(\"Unknown MAC mode: %s\", mac)\n\n    def get_mac_mode(self):\n        \"\"\" Returns either 'Symphony' or 'NoMac'. \"\"\"\n        resp = self._send_command(OPCODES['MAC_MODE_GET'])\n        if resp[0] == 0:\n            return 'NoMac'\n        elif resp[0] == 3:\n            return 'Symphony'\n        else:\n            raise ValueError(\"Unknown MAC mode: %s\", resp[0])\n\n    def send_message(self, message, ack=False):\n        \"\"\" Sends an uplink message to the gateway. \"\"\"\n        if len(message) > 256:\n            raise ValueError(\"Message too long. Max message size is 256 bytes.\")\n        opcode = 'PKT_SEND_ACK' if ack else 'PKT_SEND_UNACK'\n        self._send_command(OPCODES[opcode], message)\n\n    def get_irq_flags(self):\n        \"\"\" Returns a list of irq flags (as strings). \"\"\"\n        resp = self._send_command(OPCODES['IRQ_FLAGS'], [0] * 4)\n        flags_int = (resp[0] << 24) + (resp[1] << 16) + (resp[2] << 8) + resp[3]\n        return [f for f in IRQ_FLAGS if IRQ_FLAGS[f] & flags_int]\n\n    def clear_irq_flags(self, flags='all'):\n        \"\"\"\n        Clears the irq flags. `flags` is a list of irq flag strings. If\n        the parameter is not given, then all flags are cleared.\n        \"\"\"\n        flag_dict = IRQ_FLAGS if flags == 'all' else {f: IRQ_FLAGS[f] for f in flags}\n        flag_int = 0\n        for _, flag_val in flag_dict.items():\n            flag_int |= flag_val\n        flag_buff = [0xFF & (flag_int >> n) for n in [24, 16, 8, 0]]\n        self._send_command(OPCODES['IRQ_FLAGS'], flag_buff)\n\n    def get_unique_id(self):\n        \"\"\" Returns the UUID of the module. \"\"\"\n        uuid = self._send_command(OPCODES['MODULE_ID'])\n        return \"$301$0-0-0-\" + hexlify(uuid)[-9:].decode(\"utf-8\")\n\n    def delete_settings(self):\n        \"\"\" Returs the module to factory defaults. \"\"\"\n        self._send_command(OPCODES['DELETE_SETTINGS'])\n\n    def reset_mcu(self):\n        \"\"\" Reset the module \"\"\"\n        self._send_command(OPCODES['RESET_MCU'])\n\n    def reboot_into_bootloader(self):\n        \"\"\" Reboots the module into bootloader mode. \"\"\"\n        # Use the _send_packet method because the module\n        # reboots before sending the response.\n        self._send_packet(OPCODES['TRIGGER_BOOTLOADER'], [])\n\n    def set_network_token(self, token):\n        \"\"\" Sets the network token for the module. The token should be a hex string. \"\"\"\n        network_token = bytearray.fromhex(token)\n        self._send_command(OPCODES['NET_TOKEN_SET'], network_token)\n\n    def get_network_token(self):\n        \"\"\" Sets the network token for the module. The token should be a hex string. \"\"\"\n        network_token = self._send_command(OPCODES['NET_TOKEN_GET'])\n        return hexlify(network_token)\n\n    def set_app_token(self, token):\n        \"\"\" Sets the application token for the module. The token should be a hex string. \"\"\"\n        app_token = bytearray.fromhex(token)\n        self._send_command(OPCODES['APP_TOKEN_SET'], app_token)\n\n    def get_app_token(self):\n        \"\"\" Sets the application token for the module. The token should be a hex string. \"\"\"\n        app_token = self._send_command(OPCODES['APP_TOKEN_GET'])\n        return hexlify(app_token)\n\n    def get_rssi(self):\n        \"\"\" Sets the application token for the module. The token should be a hex string. \"\"\"\n        rssi = self._send_command(OPCODES['RSSI_GET'])\n        return rssi\n\n    def is_app_token_registered(self):\n        \"\"\" Returns whether this module's app token has been confirmed by the gateway. \"\"\"\n        return bool(self._send_command(OPCODES['APP_TOKEN_REG_GET'])[0])\n\n    def set_qos(self, qos):\n        \"\"\"\n        Requests a quality of service level from the gateway. `qos` can be\n        an integer from 0 through 15.\n        \"\"\"\n        self._send_command(OPCODES['QOS_REQUEST'], [qos])\n\n    def get_qos(self):\n        \"\"\" Returns the module's quality of service level. \"\"\"\n        return int(self._send_command(OPCODES['QOS_GET'])[0])\n\n    def set_downlink_mode(self, downlink_on=True):\n        \"\"\"\n        Sets the downlink mode of the module (either listening each frame or not listening at\n        all) based on the boolean argument.\n        \"\"\"\n        self._send_command(OPCODES['RX_MODE_SET'], [1 if downlink_on else 0])\n\n    def get_downlink_mode(self):\n        \"\"\" Returns a boolean indicating whether or not the module is in downlink mode. \"\"\"\n        return bool(self._send_command(OPCODES['RX_MODE_GET']))\n\n    def retrieve_packet(self):\n        \"\"\"\n        Get a downlink packet from the module.\n        Returns the packet, as well as RSSI and SNR values.\n        \"\"\"\n        buff = self._send_command(OPCODES['PKT_RECV_RSSI'], [0, 0])\n        if buff:\n            (rssi, ) = struct.unpack_from('<h', buff[:2])\n            snr = buff[2]\n            message = buff[3:]\n            return message, rssi, snr\n\n\nclass ModuleDriver(ModuleConnection):\n    \"\"\"\n    This class extends the ModuleConnection class to provide higher level\n    functionality.\n    \"\"\"\n    def __init__(self, *args, **kwargs):\n        super(ModuleDriver, self).__init__(*args, **kwargs)\n        self.connection_timeout_s = 2 * 60.0\n        self.app_token_confirm_timeout = 60.0\n        self.transmit_timeout = 60.0\n\n    def wait_for_flags(self, flags, timeout, bad_flags=None):\n        \"\"\" Waits for all flags in `flags` to show up. \"\"\"\n        start = time()\n        while time() - start < timeout:\n            mod_flags = self.get_irq_flags()\n            if bad_flags:\n                for flag in bad_flags:\n                    if flag in mod_flags:\n                        raise BadFlagError(flag)\n            if all(f in mod_flags for f in flags):\n                break\n            else:\n                sleep(0.1)\n        else:\n            raise RuntimeError(\"Timeout waiting for flags {}\".format(flags))\n\n    def set_up(self, app_token, network_token=OPEN_NET_TOKEN, qos=0, factory_reset=False):\n        \"\"\"\n        Sets up the module so that it's ready to uplink or downlink.\n        Throws an exception if it can't connect to a gateway with the provided network\n        and application tokens.\n        \"\"\"\n\n        if factory_reset:\n            LOG.info(\"Resetting module %s.\", self)\n            self.delete_settings()\n            sleep(3)\n\n        self.set_mac_mode('Symphony')\n\n        LOG.info(\"Setting network token for module %s.\", self)\n        old_net_token = self.get_network_token()\n        self.set_network_token(network_token)\n        if old_net_token != network_token:\n            LOG.debug(\"Waiting for connection and crypto flags for module %s.\", self)\n            self.wait_for_flags(['CONNECTED', 'CRYPTO_ESTABLISHED'], self.connection_timeout_s,\n                                bad_flags=['CRYPTO_ERROR', 'DISCONNECTED'])\n\n        LOG.info(\"Setting application token for module %s\", self)\n        self.set_app_token(app_token)\n        if not self.is_app_token_registered():\n            LOG.debug(\"Waiting for app token confirmation for module %s.\", self)\n            self.wait_for_flags(['APP_TOKEN_CONFIRMED'], self.app_token_confirm_timeout,\n                                bad_flags=['APP_TOKEN_ERROR'])\n\n        LOG.info(\"Setting QoS %s for module %s\", qos, self)\n        self.set_qos(qos)\n        assert self.get_qos() == qos, \"Failed to set QoS\"\n\n    def send_message_checked(self, message):\n        \"\"\"\n        Sends an ACK'd message, and waits for the status of that message.\n        Throws an exception if the message was not successful.\n        \"\"\"\n        self.clear_irq_flags(['TX_QUEUE_EMPTY', 'TX_ERROR'])\n        self.send_message(message, ack=True)\n\n        LOG.debug(\"Waiting for ACK for module %s.\", self)\n        self.wait_for_flags(['TX_QUEUE_EMPTY'], self.transmit_timeout, bad_flags=['TX_ERROR'])\n\n    def get_received_message(self):\n        \"\"\"\n        Returns a downlink message if there is one, else returns None\n        \"\"\"\n        if 'RX_DONE' in self.get_irq_flags():\n            self.clear_irq_flags(['RX_DONE'])\n            pkt = self.retrieve_packet()\n            if pkt is None:\n                LOG.warning(\"RX_DONE flag set but no packet available.\")\n            return pkt\n\n\ndef compute_checksum(buff):\n    \"\"\" Computes the 16-bit CRC of the buffer. Returns the checksum as an integer. \"\"\"\n    checksum = 0\n    for byte in buff:\n        checksum = ((checksum >> 8)|(checksum << 8)) & 0xFFFF\n        checksum = checksum ^ byte\n        checksum = checksum ^ ((checksum & 0xFF) >> 4)\n        checksum = checksum ^ ((checksum << 12) & 0xFFFF)\n        checksum = checksum ^ ((checksum & 0xFF) << 5)\n    return checksum\n\n\nclass BadFlagError(BaseException):\n    pass\n\n\n@contextmanager\ndef get_all_modules():\n    \"\"\"\n    Finds all attached modules, and returns a list of ModuleDriver objects\n    for each one.\n\n    This function uses contextmanager to automatically close each module's serial connection\n    after you're done with it. This means you'll have to use the function with the `with`\n    keyword. For example:\n\n    with get_all_modules() as mods:\n        for mod in mods:\n            print mod.get_version()\n    \"\"\"\n    mods = [ModuleDriver(dev) for [dev, _, _] in list_ports.grep('CP210.')]\n    yield mods\n    for mod in mods:\n        mod.close()\n\n\ndef find_module_device():\n    \"\"\" Finds the first CP210x device. \"\"\"\n    port = next(list_ports.grep('CP210.'), None)\n    if port:\n        [name, _, _] = port\n        return name\n    else:\n        raise RuntimeError('LinkLabs module not found')\n","sub_path":"client/command_module.py","file_name":"command_module.py","file_ext":"py","file_size_in_byte":17434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"489273436","text":"#coding:utf-8\r\n__author__ = 'Nick'\r\n'''\r\npython爬虫入门一之综述\r\n\r\n首先爬虫是什么？\r\n\r\n网络爬虫，是一种按照一定的规则，自动的抓取万维网信息的程序或者脚本。\r\n根据我的经验，要学习Python爬虫，我们要学习的共有以下几点：\r\n\r\nPython基础知识\r\nPython中urllib和urllib2库的用法\r\nPython正则表达式\r\nPython爬虫框架Scrapy\r\nPython爬虫更高级的功能\r\n\r\n1.Python基础学习\r\n\r\n首先，我们要用Python写爬虫，肯定要了解Python的基础吧，万丈高楼平地起，不能忘啦那地基，哈哈，那么我就分享一下自己曾经看过的一些Python教程，小伙伴们可以作为参考。\r\n\r\n1) 慕课网Python教程\r\n曾经有一些基础的语法是在慕课网上看的，上面附有一些练习，学习完之后可以作为练习，感觉效果还是蛮不错的，不过稍微遗憾的是内容基本上都是最基础的，入门开始的话，就这个吧\r\n\r\n学习网址：慕课网Python教程\r\n\r\n2) 廖雪峰Python教程\r\n后来，我发现了廖老师的Python教程，讲的那是非常通俗易懂哪，感觉也是非常不错，大家如果想进一步了解Python就看一下这个吧。\r\n\r\n学习网址：廖雪峰Python教程\r\n\r\n3) 简明Python教程\r\n还有一个我看过的，简明Python教程，感觉讲的也不错\r\n\r\n学习网址：简明Python教程\r\n\r\n2.Python urllib和urllib2 库的用法\r\n\r\nurllib和urllib2库是学习Python爬虫最基本的库，利用这个库我们可以得到网页的内容，并对内容用正则表达式提取分析，得到我们想要的结果。这个在学习过程中我会和大家分享的。\r\n\r\n3.Python 正则表达式\r\n\r\nPython正则表达式是一种用来匹配字符串的强有力的武器。它的设计思想是用一种描述性的语言来给字符串定义一个规则，凡是符合规则的字符串，我们就认为它“匹配”了，否则，该字符串就是不合法的。这个在后面的博文会分享的。\r\n\r\n4.爬虫框架Scrapy\r\n\r\n如果你是一个Python高手，基本的爬虫知识都已经掌握了，那么就寻觅一下Python框架吧，我选择的框架是Scrapy框架。这个框架有什么强大的功能呢？下面是它的官方介绍：\r\n\r\nHTML, XML源数据 选择及提取 的内置支持\r\n提供了一系列在spider之间共享的可复用的过滤器(即 Item Loaders)，对智能处理爬取数据提供了内置支持。\r\n通过 feed导出 提供了多格式(JSON、CSV、XML)，多存储后端(FTP、S3、本地文件系统)的内置支持\r\n提供了media pipeline，可以 自动下载 爬取到的数据中的图片(或者其他资源)。\r\n高扩展性。您可以通过使用 signals ，设计好的API(中间件, extensions, pipelines)来定制实现您的功能。\r\n内置的中间件及扩展为下列功能提供了支持:\r\ncookies and session 处理\r\nHTTP 压缩\r\nHTTP 认证\r\nHTTP 缓存\r\nuser-agent模拟\r\nrobots.txt\r\n爬取深度限制\r\n针对非英语语系中不标准或者错误的编码声明, 提供了自动检测以及健壮的编码支持。\r\n支持根据模板生成爬虫。在加速爬虫创建的同时，保持在大型项目中的代码更为一致。详细内容请参阅 genspider 命令。\r\n针对多爬虫下性能评估、失败检测，提供了可扩展的 状态收集工具 。\r\n提供 交互式shell终端 , 为您测试XPath表达式，编写和调试爬虫提供了极大的方便\r\n提供 System service, 简化在生产环境的部署及运行\r\n内置 Web service, 使您可以监视及控制您的机器\r\n内置 Telnet终端 ，通过在Scrapy进程中钩入Python终端，使您可以查看并且调试爬虫\r\nLogging 为您在爬取过程中捕捉错误提供了方便\r\n支持 Sitemaps 爬取\r\n具有缓存的DNS解析器\r\n官方文档：http://doc.scrapy.org/en/latest/\r\n\r\n等我们掌握了基础的知识，再用这个 Scrapy 框架吧！\r\n\r\n\r\n下面开始我们正式进入爬虫之旅吧！\r\n\r\n'''\r\n#---------------------------------------------------------------------------------------------------------------\r\n\r\n'''\r\nPython爬虫入门二之爬虫基础了解\r\n\r\n1.什么是爬虫\r\n\r\n爬虫，即网络爬虫，大家可以理解为在网络上爬行的一直蜘蛛，互联网就比作一张大网，而爬虫便是在这张网上爬来爬去的蜘蛛咯，如果它遇到资源，那么它就会抓取下来。想抓取什么？这个由你来控制它咯。\r\n\r\n比如它在抓取一个网页，在这个网中他发现了一条道路，其实就是指向网页的超链接，那么它就可以爬到另一张网上来获取数据。这样，整个连在一起的大网对这之蜘蛛来说触手可及，分分钟爬下来不是事儿。\r\n\r\n2.浏览网页的过程\r\n\r\n在用户浏览网页的过程中，我们可能会看到许多好看的图片，比如 http://image.baidu.com/ ，我们会看到几张的图片以及百度搜索框，这个过程其实就是用户输入网址之后，经过DNS服务器，找到服务器主机，向服务器发出一个请求，服务器经过解析之后，发送给用户的浏览器 HTML、JS、CSS 等文件，浏览器解析出来，用户便可以看到形形色色的图片了。\r\n\r\n因此，用户看到的网页实质是由 HTML 代码构成的，爬虫爬来的便是这些内容，通过分析和过滤这些 HTML 代码，实现对图片、文字等资源的获取。\r\n\r\n3.URL的含义\r\n\r\nURL，即统一资源定位符，也就是我们说的网址，统一资源定位符是对可以从互联网上得到的资源的位置和访问方法的一种简洁的表示，是互联网上标准资源的地址。互联网上的每个文件都有一个唯一的URL，它包含的信息指出文件的位置以及浏览器应该怎么处理它。\r\n\r\nURL的格式由三部分组成：\r\n①第一部分是协议(或称为服务方式)。\r\n②第二部分是存有该资源的主机IP地址(有时也包括端口号)。\r\n③第三部分是主机资源的具体地址，如目录和文件名等。\r\n爬虫爬取数据时必须要有一个目标的URL才可以获取数据，因此，它是爬虫获取数据的基本依据，准确理解它的含义对爬虫学习有很大帮助。\r\n\r\n4. 环境的配置\r\n\r\n学习Python，当然少不了环境的配置，在Windows下我用了PyCharm\r\n下一节，我们就正式步入 Python 爬虫学习的殿堂了，小伙伴准备好了嘛？\r\n\r\n\r\n'''","sub_path":"Exercises/jingmi_web_robot/rumen/rumen1.py","file_name":"rumen1.py","file_ext":"py","file_size_in_byte":6408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"310336989","text":"from Account import Account\nfrom Utils import dollar\n\n\nclass Customer:\n    \"\"\"Represents a customer.\"\"\"\n    def __init__(self, pin, name, account_list):\n        \"\"\"\n        :param pin: The customer's unique PIN\n        :type pin: ``str``\n        :param name: The customer's full name\n        :type name: ``str``\n        :param account_list: A list of small dictionaries specifying the type\n            (checking, savings, money market) and balance of each account under\n            the customer's name. This is has already been decoded from the\n            raw JSON.\n        :type account_list: ``list`` of ``dict``\n        \"\"\"\n        self.pin = pin\n        self.name = name\n        self.accounts = {}  # Account objects stored by type (checking, etc.)\n        self.summary = []  # A list of dictionaries\n        self.num_transactions = 0\n\n        # Instantiate `Account.Account` objects with the balance information\n        # attached to the customer's name in the raw data.\n        for item in account_list:\n            self.accounts[item[\"account_type\"]] = Account(\n                item[\"account_type\"], item[\"balance\"])\n\n    def display_account_choices(self):\n        \"\"\"Generates a prompt so the customer can choose which account to\n        work in.\n\n        :returns: A multiple choice set that gets fed to a `raw_input()`\n            function later.\n        :return type: ``str``\n        \"\"\"\n        message = \"\"\n        for key, value in self.accounts.iteritems():\n            instruction = \"To access your {} account, enter {}.\\n\".format(\n                key, value.code)\n            message += instruction\n        return message\n\n    def update_account_summary(self):\n        \"\"\"For each account, adds a line stating the balance to the\n        `Customer`'s summary list for later processing.\n\n        :returns: None\n        \"\"\"\n        # First clear out any outdated information before proceeding.\n        self.summary = []\n        for account in self.accounts.values():\n            funds_available_statement = \"{}: {} available\".format(\n                account.account_type, dollar(account.balance))\n            self.summary.append(funds_available_statement)\n\n    def display_account_summary(self):\n        \"\"\"Prints every line in the `Customer`'s summary list.\n\n        :returns: A screen-ready summary of all account balances.\n        :return type: ``str``\n        \"\"\"\n        message = \"Here is your account summary: \\n\"\n        for statement in self.summary:\n            message = message + statement + \"\\n\"\n        return message\n","sub_path":"src/Customer.py","file_name":"Customer.py","file_ext":"py","file_size_in_byte":2547,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"500481219","text":"# -*- coding: utf-8 -*-\nimport logging\nfrom datetime import datetime\nfrom util.consts import *\nfrom util.utils import get_start_end_time\nfrom util.mysql import MysqlClient\nfrom channel.comm import save, get_all_values_by_four_field\nfrom base.config import Config\n\nlogger = logging.getLogger(__name__)\n\n\ndef pingce(start_time=None, end_time=None):\n\t\"\"\"\n\t\tdesc: 计算同步的开始，结束时间。 若输入开始和结束时间则为输入时间; 否则默认开始时间为6小时前，结束时间为当前时间\n\t\t:param start_time:\n\t\t:param end_time:\n\t\t:return:\n\t\"\"\"\n\tstart_time, end_time = get_start_end_time(PC_SYNC_LAST_TIME_PATH, start_time, end_time)\n\t\n\tif start_time == end_time:\n\t\tlogger.info(\"pingce start time equal end time return.\")\n\t\treturn\n\tlimit = ROW_COUNT\n\toffset = 0\n\t# 新增数据同步\n\tinsert(start_time, end_time, limit, offset)\n\t\n\t# 更新数据同步\n\tupdate(start_time, end_time, limit, offset)\n\n\ndef insert(start_time, end_time, limit, offset):\n\t\"\"\"\n\tdesc: 评测新增数据同步\n\t:param start_time:\n\t:param end_time:\n\t:param limit:\n\t:param offset:\n\t:return:\n\t\"\"\"\n\tincr_comm_sql = \"\"\"select id, publishtime,brand_id, title, comment_count, collection_count, brand, mall\n\t\t\t\t\t\tfrom zdmdb_probreport where publishtime >= '{start_time}'\n\t\t\t\t\t\tand publishtime < '{end_time}' and type = 3 and is_delete = 0 limit {l} offset {o}\"\"\"\n\tincr_sql = incr_comm_sql.format(start_time=start_time, end_time=end_time, l=limit, o=offset)\n\t\n\tselectMysqlClient = MysqlClient()\n\tresult = selectMysqlClient.getMany(incr_sql, ROW_COUNT, None, False)\n\t\n\twhile result:\n\t\toffset += limit\n\t\tvalue_list = []\n\t\t\n\t\tfor (t_article_id, t_publish_time, t_brand_id, t_title, t_comment_count, t_collection_count, t_brand, t_mall) in result:\n\t\t\tsync_home = 0\n\t\t\tis_top = 0\n\t\t\tmachine_report = 0\n\t\t\tpublish_time = t_publish_time\n\t\t\tsync_home_time = ''\n\t\t\tsync_time = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n\t\t\tstatus = 0\n\t\t\tlogger.info(\"article_id=%s, publish_time=%s\", t_article_id, publish_time)\n\t\t\t\n\t\t\t# 品类，未上线，暂时不处理\n\t\t\t# TODO 需要从zdmdb_probreport_category树表表提取四级分类，暂时不处理\n\t\t\tcate_sql = None\n\t\t\t(level1_ids, level2_ids, level3_ids, level4_ids) = get_all_values_by_four_field(cate_sql)\n\t\t\t\n\t\t\t# 品牌\n\t\t\tbrand_ids = t_brand_id\n\t\t\t\n\t\t\t# 一个文章有多个标签id\n\t\t\t# TODO 评测库没有smzdm_tag_type_item 表，稍候处理\n\t\t\t# tag_sql = \"\"\"select tag_id from smzdm_tag_type_item where blog_id={aid} and type =7\"\"\".format(\n\t\t\t# \taid=t_article_id)\n\t\t\t# tag_ids = get_all_values_by_one_field(tag_sql)\n\t\t\ttag_ids = 0\n\t\t\tt_digital_price = ''\n\t\t\tt_worthy = 0\n\t\t\tt_unworthy = 0\n\t\t\tt_mall_id = 0\n\t\t\tt_praise = 0\n\t\t\tt_sum_collect_comment = 0\n\t\t\tt_title = t_title if t_title else ''\n\t\t\tt_comment_count = t_comment_count if t_comment_count else 0\n\t\t\tt_collection_count = t_collection_count if t_collection_count else 0\n\t\t\tt_brand = t_brand if t_brand else ''\n\t\t\tt_mall = t_mall if t_mall else ''\n\t\t\t\n\t\t\t# 查看是否为编辑同步到首页\n\t\t\t# 此部分只同步数据，同步的首页的状态在home.py中\n\t\t\t# home_sql = \"\"\"select is_write_post,set_auto_sync,is_write_post_time,is_home_top from zdmdb_probreport where id={aid}\"\"\".format(\n\t\t\t# \t\taid=t_article_id)\n\t\t\t# home_result = selectMysqlClient.getAll(home_sql, None, False)\n\t\t\t# if home_result:\n\t\t\t# \tif home_result[0][0] > 0:  # 立即同步主站标识\n\t\t\t# \t\tsync_home = home_result[0][0]\n\t\t\t# \telif home_result[0][1] > 0:  # 自动同步主站标识\n\t\t\t# \t\tsync_home = home_result[0][1]\n\t\t\t# \tsync_home_time = home_result[0][2]\n\t\t\t# \tis_top = home_result[0][3]\n\t\t\t\n\t\t\tvalue_list.append((t_article_id, PINGCE_CHANNEL_ID, \"%s:%s\" % (t_article_id, PINGCE_CHANNEL_ID),\n\t\t\t                   PINGCE_CHANNEL, level1_ids, level2_ids, level3_ids, level4_ids,\n\t\t\t                   tag_ids, brand_ids,\n\t\t\t                   sync_home, is_top, machine_report,\n\t\t\t                   publish_time if publish_time else DEFAULT_TIME,\n\t\t\t                   sync_time,\n\t\t\t                   sync_time,\n\t\t\t                   status,\n\t\t\t                   t_title, t_comment_count, t_collection_count, t_praise, t_sum_collect_comment, t_mall,\n\t\t\t                   t_brand, t_digital_price, t_worthy, t_unworthy, t_mall_id\n\t\t\t                   )\n\t\t\t                  )\n\t\t\n\t\tlogger.info(\"values_list: %s\", value_list)\n\t\t# 写入数据\n\t\tsave(value_list)\n\t\t\n\t\tnew_sql = incr_comm_sql.format(start_time=start_time, end_time=end_time, l=limit, o=offset)\n\t\tresult = selectMysqlClient.getMany(new_sql, ROW_COUNT, None, False)\n\t\n\tselectMysqlClient.close()\n\n\ndef update(start_time, end_time, limit, offset):\n\t\"\"\"\n\tdesc: 评测变化数据同步， 评测只同步首页的数据，非首页的数据不同步\n\t:param start_time:\n\t:param end_time:\n\t:param limit:\n\t:param offset:\n\t:return:\n\t\"\"\"\n\ttbl = Config[\"master.recommend_tbl\"]\n\tselectMysqlClient = MysqlClient()\n\tup_comm = \"\"\"select id,is_write_post,set_auto_sync,is_write_post_time,is_home_top,brand_id, title,\n\t\t\t\t\tcomment_count, collection_count, brand, mall from zdmdb_probreport\n\t\t\t\t\twhere is_write_post_time >= '{start_time}' and is_write_post_time  < '{end_time}'\n\t\t\t\t\tand type = 3 and is_delete= 0\n\t\t\t\t\t limit {l} offset {o}\"\"\"\n\tup_sql = up_comm.format(start_time=start_time, end_time=end_time, l=limit, o=offset)\n\tlogger.info(\"up_sql: %s\", up_sql)\n\tresult = selectMysqlClient.getMany(up_sql, ROW_COUNT, None, False)\n\twhile result:\n\t\toffset += limit\n\t\tfor (t_article_id, t_write_post, t_auto_sync, t_post_time, t_is_home_top, t_brand_id, t_title, t_comment_count, t_collection_count, t_brand, t_mall) in result:\n\t\t\tlogger.info(\"update t_article_id=%s, t_write_post=%s, t_auto_sync=%s, t_post_time=%s, t_is_home_top= %s, t_brand_id=%s\",\n\t\t\t            t_article_id, t_write_post, t_auto_sync, t_post_time, t_is_home_top, t_brand_id)\n\t\t\t\n\t\t\tsync_home = 0\n\t\t\tif t_write_post > 0:\n\t\t\t\tsync_home = t_write_post\n\t\t\telif t_auto_sync > 0:\n\t\t\t\tsync_home = t_auto_sync\n\t\t\t\n\t\t\tis_top = t_is_home_top\n\t\t\tsync_home_time = t_post_time if t_post_time else DEFAULT_TIME\n\t\t\t\n\t\t\t# 品类，未上线，暂时不处理\n\t\t\t# TODO 需要从zdmdb_probreport_category树表表提取四级分类，暂时不处理\n\t\t\tcate_sql = None\n\t\t\t(level1_ids, level2_ids, level3_ids, level4_ids) = get_all_values_by_four_field(cate_sql)\n\t\t\t\n\t\t\t# 品牌\n\t\t\tbrand_ids = t_brand_id\n\t\t\t\n\t\t\t# 一个文章有多个标签id\n\t\t\t# TODO 评测库没有smzdm_tag_type_item 表，稍候处理\n\t\t\t# tag_sql = \"\"\"select tag_id from smzdm_tag_type_item where blog_id={aid} and type =7\"\"\".format(\n\t\t\t# \t\taid=t_article_id)\n\t\t\t# tag_ids = get_all_values_by_one_field(tag_sql)\n\t\t\ttag_ids = 0\n\t\t\t\n\t\t\t# 这里只更新标签，品牌，品类属性，同步到首页的状态放到了home.py中\n\t\t\t# if level1_ids or level2_ids or level3_ids or level4_ids or tag_ids or brand_ids or sync_home or is_top or sync_home_time:\n\t\t\t# \thome_article_up_sql = \"\"\"update home_article set level1_ids='{l1}', level2_ids='{l2}',  level3_ids = '{l3}',\n\t\t\t# \t\t\t\t\t\t\t\t\t  level4_ids = '{l4}', tag_ids = '{ti}', brand_ids = '{bi}', sync_home = '{sh}',\n\t\t\t# \t\t\t\t\t\t\t\t\t  is_top = '{it}', sync_home_time= '{sht}' where article_id = {aid}\n\t\t\t# \t\t\t\t\t\t\t\t\t  and channel='{c}'\"\"\".format(l1=level1_ids, l2=level2_ids,\n\t\t\t# \t                                                              l3=level3_ids,\n\t\t\t# \t                                                              l4=level4_ids, ti=tag_ids, bi=brand_ids,\n\t\t\t# \t                                                              sh=sync_home, it=is_top,\n\t\t\t# \t                                                              sht=sync_home_time, aid=t_article_id,\n\t\t\t# \t                                                              c=PINGCE_CHANNEL)\n\t\t\t#\n\t\t\t# \tMysqlClient(mode=\"master\").update(home_article_up_sql)\n\t\t\n\t\t\t# if level1_ids or level2_ids or level3_ids or level4_ids or tag_ids or brand_ids:\n\t\t\thome_article_up_sql = u\"\"\"update {tbl} set level1_ids='{l1}', level2_ids='{l2}',  level3_ids = '{l3}',\n\t\t\t\t\t\t\t\t\t\t\t  level4_ids = '{l4}', tag_ids = '{ti}', brand_ids = '{bi}',\n\t\t\t\t\t\t\t\t\t\t\t   title='{title}',\n\t\t\t\t\t\t\t\t\t\t\t\tcomment_count={comment_count},\n\t\t\t\t\t\t\t\t\t\t\t\tcollection_count={collection_count},\n\t\t\t\t\t\t\t\t\t\t\t\tmall='{mall}',\n\t\t\t\t\t\t\t\t\t\t\t\tbrand='{brand}'\n\t\t\t\t\t\t\t\t\t\t\t   where article_id = {aid}\n\t\t\t\t\t\t\t\t\t\t\t  and channel='{c}'\"\"\".format(tbl=tbl, l1=level1_ids, l2=level2_ids,\n\t\t\t                                                              l3=level3_ids,\n\t\t\t                                                              l4=level4_ids, ti=tag_ids, bi=brand_ids,\n\t\t\t                                                              title=t_title if t_title else '',\n\t\t\t                                                              comment_count=t_comment_count if t_comment_count else 0,\n\t\t\t                                                              collection_count=t_collection_count if t_collection_count else 0,\n\t\t\t                                                              mall=t_mall if t_mall else '',\n\t\t\t                                                              brand=t_brand if t_brand else '',\n\t\t\t                                                                aid=t_article_id, c=PINGCE_CHANNEL)\n\t\t\ttry:\n\t\t\t\tMysqlClient(mode=\"master\").update(home_article_up_sql)\n\t\t\texcept Exception as e:\n\t\t\t\tlogger.warn(\"home_article_up_sql: %s, err: %s\", home_article_up_sql, str(e))\n\t\t\n\t\tup_sql = up_comm.format(start_time=start_time, end_time=end_time, l=limit, o=offset)\n\t\tresult = selectMysqlClient.getMany(up_sql, ROW_COUNT, None, False)\n\t\n\tselectMysqlClient.close()\n","sub_path":"DM/article_sync/channel/pingce.py","file_name":"pingce.py","file_ext":"py","file_size_in_byte":9502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"12586866","text":"import datetime\nimport decimal\nimport json\nimport re\n\nfrom django.core.serializers import serialize\nfrom django.utils.safestring import mark_safe\nfrom django.views.generic import CreateView, DeleteView, ListView, UpdateView\n\nfrom authnapp.models import User\nfrom mainapp.models import (\n    HeaderData,\n    UK,\n    Appartament,\n    ConstantPayments,\n    CurrentCounter,\n    HistoryCounter,\n    PersonalAccountStatus,\n    Privileges,\n    Recalculations,\n    Services,\n    Standart,\n    Subsidies,\n    VariablePayments,\n)\n\n\ndef main(request):\n    pass\n\n\nclass InvoiceViews(ListView):\n    model = User\n    context_object_name = \"user\"\n    template_name = \"invoice/invoice.html\"\n\n    def get_queryset(self):\n        return User.objects.filter(pk = self.request.user.id)\n\n    def get_context_data(self, **kwargs):\n        user = self.request.user\n        self.wrapper()\n        context = super().get_context_data(**kwargs)\n        context[\"header\"] = mark_safe(serialize(\"json\", HeaderData.objects.filter(user=user)))\n        context[\"constant\"] = mark_safe(serialize(\"json\", ConstantPayments.objects.filter(user=user)))\n        context[\"variable\"] = mark_safe(serialize(\"json\", VariablePayments.get_last_val(user.id)))\n        context[\"status\"] = mark_safe(serialize(\"json\", PersonalAccountStatus.get_item(user)))\n        return context\n\n    def wrapper(self):\n        get_calc_const()\n        get_calc_variable()\n        get_head_data()\n    \n\n# Расчет КОНСТАНТНЫХ платежей (по сигналу когда идут изменения в таблице Services)\ndef get_calc_const():\n    users = User.objects.select_related().filter(is_staff=False)\n    rate = Services.get_const_payments(1)\n\n    for user in users:\n        data = []\n        total = 0\n        pre_total = 0\n\n        user_id = User.objects.get(id=user.id)\n        appart = Appartament.objects.get(user=user)\n        for el in rate:\n            element = dict()\n            element[\"service\"] = el.name\n            element[\"unit\"] = el.unit\n            element[\"rate\"] = el.rate\n\n            if el.unit.name == \"м2\":\n                element[\"accured\"] = el.rate * appart.sq_appart\n            elif el.unit.name == \"чел\":\n                element[\"accured\"] = el.rate * appart.num_owner\n            else:\n                element[\"accured\"] = el.rate\n\n            element[\"standart\"] = \"\"\n            element[\"volume\"] = \"\"\n            element[\"coefficient\"] = el.factor if el.factor >= 0 else \"\"\n            element[\"subsidies\"] = 0\n            element[\"privileges\"] = 0\n            element[\"recalculation\"] = 0\n            element[\"total\"] = element[\"accured\"]\n            element[\"pre_total\"] = element[\"accured\"]\n            pre_total += element[\"pre_total\"]\n            total += element[\"total\"]\n\n            data.append(element)\n\n        update_values = {\n            \"data\": json.dumps(data, ensure_ascii=False, default=str),\n            \"total\": decimal.Decimal(total),\n            \"pre_total\": decimal.Decimal(total)\n        }\n        obj, created = ConstantPayments.objects.update_or_create(\n            user=user_id, defaults=update_values\n        )\n    return (data, total)\n\n# Расчет ПЕРЕМЕННЫХ платежей (по сигналу)\n#TODO Какой сигнал? 30 число? или же после внесения счетчиков?\ndef get_calc_variable():\n    users = User.objects.filter(is_staff=False)\n    rate = Services.get_varybose_payments(1)\n\n    for user in users:\n        data = []\n        total = 0 \n        pre_total = 0\n        period = datetime.datetime.now().replace(day=1)\n        user_id = User.objects.get(id=user.id)\n        appa = Appartament.get_item(user.id)[0]\n        stand = Standart.get_last_val(appa.house_id)[0]\n        sq_appa = appa.sq_appart\n        hist = HistoryCounter.get_last_val(user.id)[0]\n\n        try:\n            #Если счетчики введены, считаем объем\n            object_curr = CurrentCounter.get_last_val(user.id)[0]\n            #TODO Почему тип list???\n            volume_col = object_curr.col_water - hist.col_water,\n            volume_hot = object_curr.hot_water - hist.hot_water,\n            volume_sewage = volume_col[0] + volume_hot[0]\n            curr = {\n                \"standart\": False, \n                \"volume_col\": volume_col[0], \n                \"volume_hot\": volume_hot[0],\n                \"volume_sewage\": volume_sewage,\n                \"period\": object_curr.period\n                }\n        except:\n            #Если счетчики не введены, берем общедомовой средний объем\n            curr = {\n                \"standart\": True,\n                \"volume_col\": stand.col_water, \n                \"volume_hot\": stand.col_water,\n                \"volume_sewage\": (stand.col_water + stand.col_water) * sq_appa,\n                \"period\": (period - datetime.timedelta(days=1))\n                }\n\n        subs = Subsidies.get_items(user.id)\n        priv = Privileges.get_items(user.id)\n        recl = Recalculations.get_last_val(user.id)\n\n        for el in rate:\n            calc = get_calc_service(el, curr, sq_appa, subs, priv, recl)\n            data.append(calc)\n            total += calc[\"total\"]\n            pre_total += calc[\"pre_total\"]\n      \n        update_values = {\n            \"data\": json.dumps(data, ensure_ascii=False, default=str),\n            \"total\": decimal.Decimal(total),\n            \"pre_total\": decimal.Decimal(pre_total)\n        }\n        obj, created = VariablePayments.objects.update_or_create(\n            user=user_id, period=period, defaults=update_values\n        )\n    return (data, total, pre_total)\n\n# Делает расчет всех полей по Услуге\ndef get_calc_service(el, curr, sq_appa, subs, priv, recl):\n    element = dict()\n    water = False\n    element[\"service\"] = el.name\n    element[\"unit\"] = el.unit\n    element[\"standart\"] = 0\n    element[\"rate\"] = el.rate\n    if re.search(r'холодная', el.name.lower()):\n        element[\"volume\"] = curr[\"volume_col\"]\n        water = True\n    elif re.search(r'горячая', el.name.lower()):\n        element[\"volume\"] = curr[\"volume_hot\"]\n        water = True\n    if not curr[\"standart\"] and water:\n        element[\"accured\"] = el.rate * element[\"volume\"]\n    elif curr[\"standart\"] and water:\n        element[\"accured\"] = el.rate * element[\"volume\"] * sq_appa\n    if re.search(r'водоотведение', el.name.lower()):\n        element[\"volume\"] = curr[\"volume_sewage\"]\n        element[\"accured\"] = el.rate * curr[\"volume_sewage\"]\n    #TODO Электирчество кончилось... Кина не будет\n    # if el.name == \"Электроэнергия (день)\" and prof.type_electric_meter == 2:\n    #     accured = el.rate * (curr.electric_day - hist.hist_electric_day)\n    # elif el.name == \"Электроэнергия (ночь)\" and prof.type_electric_meter == 2:\n    #     accured = el.rate * (curr.electric_night - hist.hist_electric_night)\n    # elif el.name == \"Электроэнергия\" and prof.type_electric_meter == 1:\n    #     accured = el.rate * curr.electric_single\n    element[\"coefficient\"] = el.factor if el.factor > 0 else 1\n    element[\"pre_total\"] = (element[\"accured\"] * element[\"coefficient\"])\n    element[\"subsidies\"] = element[\"pre_total\"] * decimal.Decimal(get_sale(el.name, subs)/ 100)\n    element[\"privileges\"] = element[\"pre_total\"] * decimal.Decimal(get_sale(el.name, priv)/ 100)\n    element[\"recalculation\"] = get_recl(el.name, recl)\n    element[\"total\"] = (element[\"accured\"] * element[\"coefficient\"]) - (element[\"subsidies\"] + element[\"privileges\"]) + element[\"recalculation\"]\n    return (element)\n\n# Готовит данные для шапки (персональные, реквизиты)\n#TODO повесить сигналы на модели чтоб данные при изменении обновлялись\ndef get_head_data():\n    users = User.objects.filter(is_staff=False)\n\n    for user in users:\n        data = dict()\n        appa = Appartament.get_item(user.id)[0]\n        uk = UK.get_item(appa.house.uk_id)\n\n        data[\"payer\"] = user.name # Плательщик\n        data[\"address\"] = appa\n        data[\"sq_appart\"] = appa.sq_appart # Площадь квартиры\n        data[\"num_living\"] = appa.num_owner # Кол-во проживающих\n        data[\"name_uk\"] = UK.get_full_name(uk.id) # Название, адрес, тел. и т.д. УК\n        data[\"requisites\"] = UK.get_requisites(uk.id) # Название, адрес, тел. и т.д. УК\n        data[\"personal_account\"] = user.personal_account # Номер лицевого счета\n\n        update_values = {\n            \"data\": json.dumps(data, ensure_ascii=False, default=str),\n        }\n        obj, created = HeaderData.objects.update_or_create(\n            user=user, defaults=update_values\n        )\n\n# Возваращает субсидию или льготу при наличии или 0\ndef get_sale(name, arr):\n    for el in arr:\n        if el.service.name == name:\n            return el.sale\n        else:\n            return 0\n    return 0\n\n# Возваращает перерасчет при наличии или 0\ndef get_recl(name, arr):\n    for el in arr:\n        if el.service.name == name:\n            return el.recalc\n        else:\n            return 0\n    return 0 \n","sub_path":"invoice/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9418,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"630798502","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Feb 12 10:42:57 2020\n\n@author: simiyu\n\"\"\"\n\nfrom mysql.connector import connect\nimport pandas.io.sql as sql\n\n\ndb_name = \"sample_db\"\ntable_name =\"emplyee\"\ndb_user = \"root\"\ndb_user_pass = \"\"\n\n\ndef is_mysql_connection_available(connection):\n    print('Attempting to connect to the database...')\n    if connection.is_connected():\n        print(\"Connection to the database established successfully\")\n        return True\n    else:\n        print(\"No Connection to the database !!!\")\n        return False\n\n\ndef display_all_emplyee(connection, table_name):\n    cursor = connection.cursor()\n    cursor.execute(\"SELECT * FROM \"+str(table_name)+\";\")\n    records = cursor.fetchall()\n\n    heading = f\"Total registered \"+str(table_name)+\"s in the system: {cursor.rowcount}\"\n    print(heading)\n    print (\"-\" * len(heading))\n    for row in records:\n        print(f\"Name: {row[0]}\")\n        print(f\"Age: {row[1]}\\n\")\n\n    cursor.close()\n    connection.close()\n    print(\"MySQL connection is closed\")\n\ndef export_to_excel(connection, table_name):\n    # read the data\n    df = sql.read_sql(\"select * from \"+str(table_name)+\";\", connection)\n    # export the data into the excel sheet\n    df.to_excel('employees.xlsx')\n    print(\"Employees exported to employees.xlsx\")\n\n\ndef add_new_emplyee(connection, table_name,emplyee):\n    sql_stmt = \"\"\"INSERT INTO \"\"\"+str(table_name)+\"\"\" (name,age) VALUES (%s,%s)\"\"\"\n\n    cursor = connection.cursor(prepared=True)\n    cursor.execute(sql_stmt,emplyee)\n    connection.commit()\n    cursor.close()\n    connection.close()\n\n    print(\"Record successfully inserted into the database using prepared stament\")\n\n\ndef search_emplyee(connection, table_name,query):\n    sql_stmt = f\"SELECT * FROM \"+str(table_name)+\" WHERE name LIKE '%{query}%'\"\n\n    cursor = connection.cursor()\n    cursor.execute(sql_stmt)\n    records = cursor.fetchall()\n\n    heading = f\"search for '{query}' returned: {cursor.rowcount} rows\"\n    print(heading)\n    print (\"-\" * len(heading))\n    for row in records:\n        print(f\"Name: {row[0]}\")\n        print(f\"Age: {row[1]}\\n\")\n\n    cursor.close()\n    connection.close()\n    print(\"MySQL connection is closed\")\n\n\ndef update_emplyee(connection, table_name, emplyee):\n    sql_stmt = \"UPDATE \"+str(table_name)+\" SET name = %s,age = %s WHERE id = %s\"\n\n    cursor = connection.cursor(prepared=True)\n    cursor.execute(sql_stmt,emplyee)\n    connection.commit()\n    cursor.close()\n    connection.close()\n\n    print(\"Record successfully updated in the database using prepared stament\")\n\ndef delete_emplyee(connection, table_name,id):\n    sql_stmt = \"DELETE FROM \"+str(table_name)+\" WHERE id = \"+str(id)\n    cursor = connection.cursor(prepared=True)\n    cursor.execute(sql_stmt)\n    connection.commit()\n    cursor.close()\n    connection.close()\n\n    print(\"Record successfully deleted from the database using prepared stament\")\n\n\n\ndef main():\n    print(\"**** Follow the prompts carefully ****\")\n\n\n    mydb = connect(host=\"localhost\",  user=db_user,  passwd=db_user_pass)\n\n    if is_mysql_connection_available(mydb):\n        mycursor = mydb.cursor()\n        mycursor.execute(\"CREATE DATABASE IF NOT EXISTS \"+str(db_name))\n\n        mydbtable = connect(host=\"localhost\",  user=db_user,  passwd=db_user_pass, database=db_name)\n        if is_mysql_connection_available(mydbtable):\n            mycursortb = mydbtable.cursor()\n            mycursortb.execute(\"CREATE TABLE IF NOT EXISTS \"+str(table_name)+\" (name VARCHAR(255), age INT,id INT NOT NULL AUTO_INCREMENT, PRIMARY KEY (id))\")\n\n            choice = str(input(\"Enter 'C' to add, 'R' to view single,'L' to list, 'E' to export to excel 'U' to update 'D' to delete and 'Q' to quit  : \"))\n            if(choice == 'C'):\n                name = str(input(\"Enter Employee Full Name  : \"))\n                age = int(input(\"Enter Employee Age  : \"))\n                employee = (name, age)\n                add_new_emplyee(mydbtable, table_name,employee)\n                main()\n\n            elif(choice == 'R'):\n                name = str(input(\"Enter Employee Name or a part of name  : \"))\n                search_emplyee(mydbtable, table_name,name)\n                main()\n\n            elif(choice == 'L'):\n                display_all_emplyee(mydbtable, table_name)\n                main()\n\n            elif(choice == 'E'):\n                export_to_excel(mydbtable, table_name)\n                main()\n            elif(choice == 'U'):\n                emplid = int(input(\"Enter Employee Id  : \"))\n                name = str(input(\"Enter Updated Employee Full Name  : \"))\n                age = int(input(\"Enter Updated Employee Age  : \"))\n                employee = (name, age, emplid)\n                update_emplyee(mydbtable, table_name,employee)\n                main()\n\n            elif(choice == 'D'):\n                emplid = int(input(\"Enter Employee Id  : \"))\n                delete_emplyee(mydbtable, table_name,emplid)\n                main()\n\n            elif (choice == 'Q'):\n                raise SystemExit\n\n            else:\n                main()\n        else:\n            print(\"**** Please Provide valid database connection ****\")\n    else:\n        print(\"**** Please Provide valid database connection ****\")\n\n\n\nif __name__ == '__main__':\n    main()","sub_path":"scripting & automation/mysql_python_crud/mysql_crud.py","file_name":"mysql_crud.py","file_ext":"py","file_size_in_byte":5267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"346428465","text":"\"\"\"查找显示项目模板.\"\"\"\nimport json\nfrom itertools import groupby\n#from functools import partial\nfrom collections.abc import Mapping\nfrom pathlib import Path\nfrom typing import Optional, List\n\nfrom pmfp.const import PMFP_TEMPLATES_HOME\nfrom ._find_path import find_path\n\n\ndef _find_all()->Optional[List[Path]]:\n    \"\"\"查找所有模板.\"\"\"\n    print(\"展示所有模板列表\")\n    result = find_path(\n        PMFP_TEMPLATES_HOME,\n        lambda path, depth: path.suffix == \".json\" and path.is_file()\n    )\n    return result\n\n\ndef _find_all_language(language: str)->Optional[List[Path]]:\n    \"\"\"从指定语言中查找模板.\"\"\"\n    print(f\"展示{language}的模板列表\")\n    p = PMFP_TEMPLATES_HOME.joinpath(language.lower())\n    if p.is_dir():\n        result = find_path(\n            p,\n            lambda path, depth: path.suffix == \".json\" and path.is_file()\n        )\n    else:\n        print(f\"找不到{language}语言对应的模板\")\n        result = None\n    return result\n\n\ndef _find_all_language_category(language: str, category: str)->Optional[List[Path]]:\n    \"\"\"从特定语言的特定类型中查找模板.\"\"\"\n    print(f\"展示{language}中的{category}分类的模板列表\")\n    p = PMFP_TEMPLATES_HOME.joinpath(language.lower()).joinpath(category.lower())\n    if p.is_dir():\n        result = find_path(\n            p,\n            lambda path, depth: path.suffix == \".json\" and path.is_file()\n        )\n    else:\n        print(f\"找不到{language}语言下{category}分类对应的模板\")\n        result = None\n    return result\n\n\ndef _show_template_list(template_list: List[Path])->bool:\n    \"\"\"展示模板列表.\"\"\"\n    to_show = []\n    for path in template_list:\n        result = path.parts[-3:]\n        to_show.append(result)\n    gp_language = groupby(sorted(to_show, key=lambda x: x[0]), key=lambda x: x[0])\n    ret = []\n    for i, ite in gp_language:\n        print(f\"=====语言:{i}========\")\n        gp_c = groupby(sorted(ite, key=lambda x: x[1]), key=lambda x: x[1])\n        for j, jte in gp_c:\n            print(f\"-----分类:{j}------\")\n            for k in jte:\n                temp_name = k[-2] + \"-\" + k[-1].split(\".\")[0]\n                print(temp_name)\n                ret.append(temp_name)\n    return ret\n\n\ndef find_template_detail(name: str, path: Path)->bool:\n    \"\"\"查找模板具体信息.\n\n    Args:\n        name (str): 模板名\n        path (Path): 模板地址\n\n    Returns:\n        bool: 是否找到符合条件的模板\n\n    \"\"\"\n    if path.suffix == \".json\":\n        with open(str(path), encoding=\"utf-8\") as f:\n            content = json.load(f)\n        result = True if content[\"name\"] == name else False\n    else:\n        result = False\n    return result\n\n\ndef _find_module_from_all(name: str)->Optional[List[Path]]:\n    \"\"\"从全部模板中找出某一名字的模板.\"\"\"\n    print(f\"从全局查找模板{name}\")\n    result = find_path(\n        PMFP_TEMPLATES_HOME,\n        lambda path, depth: find_template_detail(name, path)\n    )\n    return result\n\n\ndef _find_module_from_language(name: str, language: str)->Optional[List[Path]]:\n    \"\"\"从某种语言的全部模板中找出某一名字的模板.\"\"\"\n    print(f\"从{language}的组件中查找模板{name}\")\n    p = PMFP_TEMPLATES_HOME.joinpath(language.lower())\n    if p.is_dir():\n        result = find_path(p, lambda path, depth: find_template_detail(name, path))\n    else:\n        print(f\"{language}语言不存在\")\n        result = None\n    return result\n\n\ndef _find_module_from_language_category(\n        name: str,\n        language: str,\n        category: str)->Optional[List[Path]]:\n    \"\"\"从某种语言的某个分类中的全部模板中找出某一名字的模板.\"\"\"\n    print(f\"从{language}的{category}模板中查找模板{name}\")\n    p = PMFP_TEMPLATES_HOME.joinpath(language.lower()).joinpath(category.lower())\n    if p.is_dir():\n        result = find_path(p, lambda path, depth: find_template_detail(name, path))\n    else:\n        print(f\"{language}语言的{category}分类不存在\")\n        result = None\n    return result\n\n\ndef _show_template_list_detail(template_list: List[Path])->bool:\n    \"\"\"展示模板列表及各个模板细节.\"\"\"\n    for path in template_list:\n        parts = \"/\".join(path.parts[-3:])\n        with open(str(path), encoding=\"utf-8\") as f:\n            content = json.load(f)\n        print(f\"-----{parts}---------------------\")\n        for k, v in content.items():\n            print(f\"{k}:\")\n            if isinstance(v, Mapping):\n                for i, j in v.items():\n                    print(f'- {i}: {j}')\n            else:\n                print(f'- {v}')\n    return True\n\n\ndef _show_template(\n        language: Optional[str] = None,\n        category: Optional[str] = None)->bool:\n    \"\"\"查找并展示模板列表.\n\n    Args:\n        language (Optional[str], optional): Defaults to None. 模板使用的语言.\n        category (Optional[str], optional): Defaults to None. 模板的分类.\n\n    Returns:\n        bool: 正确展示返回True,否则返回False.\n\n    \"\"\"\n    if language is None:\n        template_list = _find_all()\n    else:\n        if category is None:\n            template_list = _find_all_language(language)\n        else:\n            template_list = _find_all_language_category(language, category)\n    if not template_list:\n        print(\"空的模板分类\")\n        result = False\n    else:\n        result = _show_template_list(template_list)\n    return result\n\n\ndef _show_target_template(\n        name: str,\n        language: Optional[str] = None,\n        category: Optional[str] = None)->bool:\n    \"\"\"查找并展示模板列表.\n\n    Args:\n        name (str):模板的名字.\n        language (Optional[str], optional): Defaults to None. 模板使用的语言.\n        category (Optional[str], optional): Defaults to None. 模板的分类.\n\n    Returns:\n        bool: 正确展示返回True,否则返回False.\n\n    \"\"\"\n    if language is None:\n        template_list = _find_module_from_all(name)\n    else:\n        if category is None:\n            template_list = _find_module_from_language(name, language)\n        else:\n            template_list = _find_module_from_language_category(name, language, category)\n    if not template_list:\n        print(f\"找不到对应的模板{name}\")\n        result = False\n    else:\n        result = _show_template_list_detail(template_list)\n    return result\n\n\ndef show(\n        name: Optional[str] = None,\n        language: Optional[str] = None,\n        category: Optional[str] = None)->bool:\n    \"\"\"展示模板.\n\n    Args:\n        name (Optional[str], optional): Defaults to None. 模板的名字,如果有的话则会展示同名模板的详细信息.\n        language (Optional[str], optional): Defaults to None. 模板使用的语言.\n        category (Optional[str], optional): Defaults to None. 模板的分类.\n\n    Returns:\n        bool: 正确展示返回True,否则返回False.\n\n    \"\"\"\n    if name is None:\n        result = _show_template(language, category)\n    else:\n        result = _show_target_template(name, language, category)\n    return result\n","sub_path":"pmfp/show/_show_template.py","file_name":"_show_template.py","file_ext":"py","file_size_in_byte":7106,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"282608214","text":"import unittest\nfrom typing import List\nfrom collections import Counter\n\nclass Solution(unittest.TestCase):\n    def totalFruit(self, tree: List[int]) -> int:\n        \"\"\"\nIn a row of trees, the i-th tree produces fruit with type tree[i].\n\nYou start at any tree of your choice, then repeatedly perform the following steps:\n\nAdd one piece of fruit from this tree to your baskets.  If you cannot, stop.\nMove to the next tree to the right of the current tree.  If there is no tree to the right, stop.\nNote that you do not have any choice after the initial choice of starting tree: you must perform step 1, then step 2, then back to step 1, then step 2, and so on until you stop.\n\nYou have two baskets, and each basket can carry any quantity of fruit, but you want each basket to only carry one type of fruit each.\n\nWhat is the total amount of fruit you can collect with this procedure?\n\n\n\nExample 1:\n\nInput: [1,2,1]\nOutput: 3\nExplanation: We can collect [1,2,1].\nExample 2:\n\nInput: [0,1,2,2]\nOutput: 3\nExplanation: We can collect [1,2,2].\nIf we started at the first tree, we would only collect [0, 1].\nExample 3:\n\nInput: [1,2,3,2,2]\nOutput: 4\nExplanation: We can collect [2,3,2,2].\nIf we started at the first tree, we would only collect [1, 2].\nExample 4:\n\nInput: [3,3,3,1,2,1,1,2,3,3,4]\nOutput: 5\nExplanation: We can collect [1,2,1,1,2].\nIf we started at the first tree or the eighth tree, we would only collect 4 fruits.\n\n\nNote:\n\n1 <= tree.length <= 40000\n0 <= tree[i] < tree.length\n\n--\nBasic Idea: windowing, valid: meet only 2 types\n\"\"\"\n        if not tree:\n            return 0\n\n        l = r = ret = 0\n        meet = Counter()\n        while r < len(tree):\n            meet[tree[r]] += 1\n            r += 1\n\n            while len(meet) > 2:\n                if meet[tree[l]] == 1:\n                    del meet[tree[l]]\n                else:\n                    meet[tree[l]] -= 1\n                l += 1\n\n            ret = max(ret, r-l)\n\n        return ret\n\n    def testFruit(self):\n        self.assertEqual(3, self.totalFruit([1,2,1]))\n        self.assertEqual(3, self.totalFruit([0,1,2,2]))\n        self.assertEqual(4, self.totalFruit([1,2,3,2,2]))\n        self.assertEqual(5, self.totalFruit([3,3,3,1,2,1,1,2,3,3,4]))\n","sub_path":"src/main/python/fruit_into_baskets.py","file_name":"fruit_into_baskets.py","file_ext":"py","file_size_in_byte":2219,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"430738154","text":"import tkinter as tk\nfrom tkinter import messagebox\nfrom ceidg_api import api\nfrom .mainWindow import Window\nfrom config_parser import config\n\n\nclass Auth(Window):\n    def __init__(self, title):\n        super().__init__(title)\n        self.createWidgets()\n        self.centerWindow(self.root)\n\n    def createWidgets(self):\n        '''Create Frames, labels, entries, buttons etc.'''\n        # Frames\n        main_frame = tk.Frame(self.root)\n        # Labels\n        token_label = tk.Label(main_frame, text='Token:')\n        # Entries\n        self.token_entry = tk.Entry(\n            main_frame, width=30)\n        # Buttons\n        submit = tk.Button(main_frame, text='Sprawdź token', width=10,\n                           padx=10, font=Window.useFont(12),\n                           command=lambda: self.checkToken(\n                               self.token_entry.get().strip()))\n\n        # Pack\n        token_label.pack()\n        self.token_entry.pack()\n        submit.pack(pady=(10, 0))\n        main_frame.pack(padx=10, pady=(5, 15))\n\n    def checkToken(self, token):\n        '''Check if given token is correct.'''\n        if api.validateToken(token):\n            config.createConfig(token)\n            messagebox.showinfo('Sukces', 'Poprawny token.')\n            self.root.destroy()\n        else:\n            # Here some message BOX\n            messagebox.showinfo('Error', 'Niepoprawny token.')\n","sub_path":"window_modules/authWindow.py","file_name":"authWindow.py","file_ext":"py","file_size_in_byte":1398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"650094160","text":"import asyncio\nimport os.path\n\nfrom .plugin_mount import PluginMount\n\nfrom random import choice\nfrom subprocess import check_output\n\n\nclass CommandPlugin(metaclass=PluginMount):\n    \"\"\"\n    Class that stores the instances for each command.\n    Every command must contain a command string property and a func function\n    \"\"\"\n\n    def func(self):\n        raise NotImplementedError(\"Please implement this method.\")\n\n    def __init__(self):\n        raise NotImplementedError(\n            \"Please implement a command variable and a helpstring variable.\")\n\n\nclass CommandObey(CommandPlugin):\n\n    def __init__(self):\n        self.command = \"!obey\"\n\n        self.helpstring = \"!obey: \" \\\n                          \"This only works if you are one of the chosen ones.\"\n\n        self.obey_dict = {\n            # neosloth\n            \"120767447681728512\": \"I obey.\",\n            # Average Dragon\n            \"182268688559374336\": \"Eat a dick, dragon.\"\n        }\n\n    def func(self, parent, message):\n        if message.author.id in self.obey_dict.keys():\n            return self.obey_dict[message.author.id]\n        else:\n            return \"I will not obey.\"\n\n\nclass CommandPing(CommandPlugin):\n\n    def __init__(self):\n        self.command = \"!pingme\"\n        self.helpstring = \"!pingme: Pings the message.author.\"\n\n    def func(self, parent, message):\n        return message.author.mention\n\n\nclass CommandAddMe(CommandPlugin):\n    \"\"\"!addme: The link to add Bolas to your Discord room.\"\"\"\n\n    def __init__(self):\n        self.command = \"!addme\"\n        self.helpstring = \"!addme: \" \\\n                          \"The link to add Bolas to your Discord server.\"\n\n    def func(self, parent, message):\n        return \"https://discordapp.com/oauth2/authorize?client_id=245372541915365377&scope=bot&permissions=0\"\n\n\nclass CommandCoin(CommandPlugin):\n\n    def __init__(self):\n        self.command = \"!coin\"\n        self.helpstring = \"!coin: Flips a coin.\"\n\n    def func(self, parent, message):\n        return choice([\"Heads\", \"Tails\"])\n\n\nclass CommandChoice(CommandPlugin):\n\n    def __init__(self):\n        self.command = \"!choose\"\n        self.helpstring = \"!choose: Chooses an option. \"\\\n                          \"Example: !choose apples or oranges\"\n\n    def func(self, parent, message):\n        return \"I choose: {0}\".format(\n            choice(\" \".join(message.content.split(\" \")[1:]).split(\" or \")))\n\n\nclass CommandGit(CommandPlugin):\n\n    def __init__(self):\n        self.command = \"!git\"\n        self.helpstring = \"!git: Repo link and changelog.\"\n\n    def func(self, parent, message):\n        return \"{0}\\n{1}\\n```{2}```\".format(\n            \"https://gitlab.com/neosloth/bolas\",\n            \"https://github.com/superstepa/bolas\",\n            check_output(\"git log --oneline -3\", shell=True).decode(\"utf-8\"))\n\n\nclass CommandStats(CommandPlugin):\n\n    def __init__(self):\n        self.command = \"!stats\"\n        self.helpstring = \"!stats: Return the number of users and servers served.\"\n\n    def func(self, parent, message):\n        num_servers = len(parent.servers)\n        #parents.server.members\n        num_users = sum([len(server.members) for server in parent.servers])\n        return \"Fetching cards for {} servers and {} users\".format(\n            num_servers,\n            num_users\n        )\n\n\nclass CommandCockatrice(CommandPlugin):\n\n    def __init__(self):\n        self.command = \"!cockatrice\"\n        self.helpstring = \"!cockatrice: Add yourself to the cockatrice role.\"\n        self.role_name = \"Cockatrice\"\n\n    def func(self, parent, message):\n\n        if message.server is None:\n            return \"Sorry, I can't set roles in PMs.\"\n\n        # The discord bot Client only stores the user,\n        # so we have to manually get the Member object\n        client_member = message.server.get_member(parent.user.id)\n\n        sufficient_permissions = message.channel.permissions_for(\n            client_member).manage_roles\n\n        if not sufficient_permissions:\n            return \"I do not have sufficient permissions to set roles.\"\n\n        cockatrice_role = None\n\n        # Find the appropriate role object\n        for role in message.server.roles:\n            if role.name == self.role_name:\n                cockatrice_role = role\n\n        # Can't do anything if the role doesn't exist\n        if cockatrice_role is None:\n            return \"Sorry, this server does not have a cockatrice role.\"\n\n        if cockatrice_role in message.author.roles:\n            # The remove role method is a coroutine so we have to wrap it in an asyncio call\n            asyncio.ensure_future(\n                parent.remove_roles(message.author, cockatrice_role)\n            )\n            return \"Removed {0.name} from the Cockatrice role.\".format(\n                message.author\n            )\n        else:\n            # The add role method is a coroutine so we have to wrap it in an asyncio call\n            asyncio.ensure_future(\n                parent.add_roles(message.author, cockatrice_role)\n            )\n            return \"Added {0.name} to the Cockatrice role.\".format(\n                message.author\n            )\n\n\nclass CommandRule(CommandPlugin):\n    def __init__(self):\n        self.command = \"!rule\"\n        self.helpstring = \"!rule {rule number or set of keywords.}: Cite a mtg rule.\"\n        self.ROOT_DIR = os.path.dirname(os.path.abspath(__file__))\n        # Move 1 directory up and into misc\n        self.FILE_NAME = os.path.realpath(os.path.join(self.ROOT_DIR, \"../misc/MagicCompRules_20170707.txt\"))\n        self.RULE_LIMIT = 10\n\n    def get_rule(self, args):\n        # First argument (presumably the rule number)\n        num = args[1]\n        # All the words after the command\n        tokens = args[1:]\n        result = \"\"\n        rule_count = 0\n\n        try:\n            with open(self.FILE_NAME, \"r\", encoding=\"utf-8\") as f:\n                # Using enumerate so the file is read sequentially and is not stored in memory\n                for i, line in enumerate(f):\n                    if (line.startswith(str(num))):\n                        return line\n                    # Append the rule number if all the words are in that substring.\n                    # Only check the lines that start with a number.\n                    # Also check if we've gone over our rule count\n                    if (line[0].isdigit() and all(word.lower() in line.lower() for word in tokens) and rule_count < self.RULE_LIMIT):\n                        result = \"{}* {}\\n\".format(result, line.split(\" \")[0])\n                        rule_count += 1\n\n            if rule_count >= self.RULE_LIMIT:\n                result += \"The query returned too many results, so some of the results were omitted. Please provide more keywords to narrow the search down.\"\n\n            return result or \"Could not find the matching rule.\"\n        except FileNotFoundError:\n            return \"Could not find the magic comprehensive rules file.\"\n\n    def func(self, parent, message):\n        args = message.content.split()\n        if len(args) > 1:\n            # Surround the result with markdown code tags (for nice bullets)\n            return \"```markdown\\n{}```\".format(self.get_rule(args))\n        else:\n            return \"Please provide a rule number or a set of keywords.\"\\\n                \" See the full list of rules here: http://magic.wizards.com/en/game-info/gameplay/rules-and-formats/rules\"\n","sub_path":"src/commands.py","file_name":"commands.py","file_ext":"py","file_size_in_byte":7376,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"369061407","text":"#!/usr/bin/env python3\n\nimport psycopg2\n\n# Create article_view\n#\n# create view article_view as\n#   select author,title,count(*) as views\n#   from articles,log\n#   where log.path like concat('%',articles.slug)\n#   group by articles.title,articles.author\n#   order by views desc;\n\nquestion1 = (\"What are the three most popular articles of all time?\")\nquery1 = (\"select title,views from article_view limit 3;\")\n\nquestion2 = (\"Who are the most popular article authors of all time?\")\nquery2 = (\"\"\"\n            select authors.name,sum(article_view.views) as views\n            from article_view,authors\n            where authors.id = article_view.author\n            group by authors.name\n            order by views desc;\n        \"\"\")\n\n# Create new_log_view\n#\n# create view new_log_view as\n#   select date(time),round(100.0*sum(case\n#       log.status when '200 OK' then 0 else 1 end)/\n#       count(log.status),2) as \"error rate\"\n#       from log\n#       group by date(time)\n#       order by \"error rate\" desc;\n\nquestion3 = (\"On which days did more than 1% of requests lead to errors?\")\nquery3 = (\"select * from new_log_view where \\\"error rate\\\" > 1;\")\n\n\ndef connect(database=\"news\"):\n    \"\"\"\n        Connects to 'news' database via pyscopg2\n    \"\"\"\n    try:\n        connection = psycopg2.connect(\"dbname={}\".format(database))\n        cursor = connection.cursor()\n        return connection, cursor\n    except (Exception, psycopg2.Error) as error:\n        print(\"Database connection failed: \", error)\n        connection.close()\n\n\ndef results(query):\n    \"\"\"\n        Fetches results from PostgreSQL queries\n    \"\"\"\n    connection, cursor = connect()\n    cursor.execute(query)\n    return cursor.fetchall()\n    connection.close()\n\n\ndef print_results(query_results):\n    \"\"\"\n        Prints results for query1 and query2\n    \"\"\"\n    print(query_results[1])\n    for index, results in enumerate(query_results[0]):\n        print(str(results[0] + \" - \" + str(results[1]) + \" views.\"))\n    print(\"\\n\")\n\n\ndef print_request_errors(query_results):\n    \"\"\"\n        Prints results for query3\n    \"\"\"\n    print(query_results[1])\n    for results in query_results[0]:\n        print(str(str(results[0]) + \" - \"\n                  + str(results[1]) + \"% request errors.\"))\n\n\nif __name__ == '__main__':\n    popular_articles = results(query1), question1\n    popular_authors = results(query2), question2\n    request_errors = results(query3), question3\n    print_results(popular_articles)\n    print_results(popular_authors)\n    print_request_errors(request_errors)\n","sub_path":"vagrant/logs-analysis/source.py","file_name":"source.py","file_ext":"py","file_size_in_byte":2532,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"482029206","text":"import PySimpleGUI as sg\nimport pickle\nfrom device import Device\nfrom groups import Group\n\nsg.theme('BrownBlue')\n# Groups/Topics\n# groups = {'Group 1': ['Light 1', 'Light 2', 'Controller 1'],\n#           'Group 2': ['Temperature', 'RGB', 'Controller 2']}\ngroups = {}\n# groups_test = {}\n# groups = {'Group 1': []}\ndevices = {}\n\n# Input and Output Peripherals\nInput = ['Button', 'Potentiometer', 'Temperature Sensor']\nOutput = ['LED', 'RGB', 'Buzzer']\n\n\ndef load():\n    \"\"\" Unpickle a file of pickled data. \"\"\"\n    with open('groups.pkl', \"rb\") as f:\n        while True:\n            try:\n                groups = yield pickle.load(f)\n            except EOFError:\n                break\n    return groups\n\n\ndef save():\n    with open('groups.pkl', 'wb') as output:  # Overwrites any existing file.\n        pickle.dump(groups, output, pickle.HIGHEST_PROTOCOL)\n\n\ndef new_device():\n    i_col = [[]]\n    for i in Input:\n        i_col.append([sg.Checkbox(i, size=(20, 1), key='-in' + i + '-')])\n\n    o_col = [[]]\n    for i in Output:\n        o_col.append([sg.Checkbox(i, size=(20, 1), key='-out' + i + '-')])\n\n    k = []\n    for keys, values in groups.items():\n        k.append(keys)\n\n    layout = [[sg.Text('Name:', pad=(0, 10)), sg.InputText('', key='-name-')],\n              [sg.Text('Group:', pad=(0, 10)), sg.InputCombo(k, key='-group-')],\n              [sg.Column([[sg.Frame('Input', i_col), sg.Frame('Output', o_col)]])],\n              [sg.Button('Add', pad=(20, 10)), sg.Cancel(pad=(20, 10))]]\n\n    window = sg.Window(\"New Device\", auto_size_text=True, auto_size_buttons=True, resizable=True,\n                       layout=layout, default_element_size=(12, 1), default_button_element_size=(12, 1),\n                       font=(\"Helvetica\", 20))\n    while True:\n        event, values = window.read()\n        if event == 'Cancel' or event == sg.WIN_CLOSED:\n            break\n        if event == 'Add':\n            # Make sure that neither Name or Group are empty\n            if str(values['-name-']) == '' or str(values['-group-']) == '':\n                sg.popup('You must fill in all forms!')\n                continue\n\n            name = str(values['-name-'])\n            input = []\n            output = []\n            for i in Input:\n                if values['-in' + i + '-'] is True:\n                    input.append(i)\n\n            for i in Output:\n                if values['-out' + i + '-'] is True:\n                    output.append(i)\n\n            # print(name, group, input, output)\n            new_dev = Device(name, input, output)\n            devices[new_dev.name] = new_dev\n            if not groups:\n                groups[str(values['-group-'])] = new_dev.name\n            else:\n                groups[str(values['-group-'])].append(new_dev.name)\n\n            # groups_test[str(values['-group-'])].add(new_dev)  # Add class Device to class Group\n            save()\n            break\n\n    window.close()\n\n\ndef new_group():\n    device_col = [[]]\n    for i in devices:\n        device_col.append([sg.Checkbox(i, size=(20, len(devices)), key='-d' + i + '-')])\n\n    layout = [[sg.Text('Name:', pad=(0, 10)), sg.InputText('', key='-name-')],\n              [sg.Column([[sg.Frame('Devices', device_col)]])],\n              [sg.Button('Add', pad=(20, 10)), sg.Cancel(pad=(20, 10))]]\n\n    window = sg.Window(\"New Group\", auto_size_text=True, auto_size_buttons=True, resizable=True,\n                       layout=layout, default_element_size=(12, 1), default_button_element_size=(12, 1),\n                       font=(\"Helvetica\", 20))\n    while True:\n        event, values = window.read()\n        if event == 'Cancel' or event == sg.WIN_CLOSED:\n            break\n        if event == 'Add':\n            if values['-name-'] not in groups.keys():  # if the name is not already taken\n                d_col = []\n                for i in devices:       # loops through all devices\n                    if values['-d' + i + '-'] is True:  # if a device is checked\n                        if not any(d_col):      # if it's the first device\n                            d_col = [i]           # add it as the primary\n                        else:                   # if not\n                            d_col.append(i)         # add it to the group\n\n                groups[values['-name-']] = d_col  # add the group to the groups dictionary\n                print(groups)\n                # groups_test[values['-name-']] = Group(values['-name-'], d_col)\n                break\n            else:\n                sg.popup('This Group Name is already being used.')\n\n    window.close()\n\n\ndef group_column():\n    column = [[]]\n    for i in groups:\n        column.append([sg.Text(str(i + ':'), key=str(i))])\n        column.append([sg.Listbox(values=groups[i], size=(20, len(groups[i])), key='-g' + i + '-')])\n    return column\n\n\ndef rename_group():\n    # Get group names\n    k = []\n    for keys, values in groups.items():\n        k.append(keys)\n\n    # Make a window to rename the group\n    event, values = sg.Window(str('Rename Group'), [\n        [sg.Text('Select Group:'), sg.InputCombo(k, key='-group-')],\n        [sg.Text('Enter new name:'), sg.InputText('', key='-new-')],\n        [sg.Button('Submit'), sg.Button('Cancel')]], font=(\"Helvetica\", 20),\n                              default_element_size=(12, 1), default_button_element_size=(12, 1)).read(\n        close=True)\n    # If submitted, change names and refresh window\n    if event == 'Submit':\n        groups[values['-new-']] = groups.pop(values['-group-'])\n\n\ndef del_group():\n    # Get group names\n    k = []\n    for keys, values in groups.items():\n        k.append(keys)\n\n    # Make a window to rename the group\n    event, values = sg.Window(str('Delete Group'), [\n        [sg.Text('Select Group:'), sg.InputCombo(k, key='-group-')],\n        [sg.Button('Submit'), sg.Button('Cancel')]], font=(\"Helvetica\", 20),\n                              default_element_size=(12, 1), default_button_element_size=(12, 1)).read(\n        close=True)\n    # If submitted, change names and refresh window\n    if event == 'Submit':\n        sure = sg.popup_yes_no('Are you sure you would like to delete ' + values['-group-'] + '?')\n        if sure == 'Yes':\n            groups.pop(values['-group-'], None)\n\n\ndef rename_dev():\n    # Get device names\n    k = []\n    for keys, values in devices.items():\n        k.append(keys)\n\n    # Make a window to rename the device\n    event, values = sg.Window(str('Rename Device'), [\n        [sg.Text('Select Device:'), sg.InputCombo(k, key='-devices-')],\n        [sg.Text('Enter new name:'), sg.InputText('', key='-new-')],\n        [sg.Button('Submit'), sg.Button('Cancel')]], font=(\"Helvetica\", 20),\n                              default_element_size=(12, 1), default_button_element_size=(12, 1)).read(\n        close=True)\n    # If submitted, change names and refresh window\n    if event == 'Submit':\n        devices[values['-new-']] = devices.pop(values['-devices-'])\n        for key, val in groups.items():\n            if values['-devices-'] in val:\n                val.remove(values['-devices-'])\n                val.append(values['-new-'])\n                groups[key] = val\n\n\ndef del_dev():\n    # Get group names\n    k = []\n    for keys, values in devices.items():\n        k.append(keys)\n\n    # Make a window to rename the group\n    event, values = sg.Window(str('Delete Device'), [\n        [sg.Text('Select Device:'), sg.InputCombo(k, key='-device-')],\n        [sg.Button('Submit'), sg.Button('Cancel')]], font=(\"Helvetica\", 20),\n                              default_element_size=(12, 1), default_button_element_size=(12, 1)).read(\n        close=True)\n    # If submitted, change names and refresh window\n    if event == 'Submit':\n        sure = sg.popup_yes_no('Are you sure you would like to delete ' + values['-device-'] + '?')\n        if sure == 'Yes':\n            devices.pop(values['-devices-'])\n            for key, val in groups.items():\n                if values['-devices-'] in val:\n                    val.remove(values['-devices-'])\n                    groups[key] = val\n\n\ndef main():\n    # Menu Definition\n    menu_def = [['New', ['New Device', 'New Group']], ['Edit', ['Rename', ['Group::R', 'Device::R'],\n                                                                'Delete', ['Group::D', 'Device::D']]]]\n    #groups = load()\n    if not groups:\n        layout = [[sg.Menu(menu_def, tearoff=False, key='-Menu-', font=(\"Helvetica\", 12))],\n                  [sg.Text('No Groups or Devices have been added.')],\n                  [sg.Text('Add a Group or Device through the Menu')]]\n    else:\n        # Make the column containing the Groups and Devices\n        column_1 = group_column()\n\n        layout = [[sg.Menu(menu_def, tearoff=False, key='-Menu-', font=(\"Helvetica\", 12))],\n                  [sg.Column([[sg.Frame('', column_1, key='-Groups-')]])],\n                  [sg.Button(\"OK\", pad=(0, 10))]]\n\n    # Create the window\n    window = sg.Window(\"Main Interface\", auto_size_text=True, auto_size_buttons=True, resizable=True,\n                       layout=layout, default_element_size=(12, 1), default_button_element_size=(12, 1),\n                       size=(800, 500), font=(\"Helvetica\", 20))\n\n    # Create an event loop\n    while True:\n        event, values = window.read()\n        # End program if user closes window or\n        # presses the OK button\n        if event == \"OK\" or event == sg.WIN_CLOSED:\n            save()\n            break\n\n        if event == 'New Device':\n            new_device()\n            for i in groups:\n                window.Element('-g' + i + '-').Update(values=groups[i])\n            main()\n\n        if event == 'New Group':\n            new_group()\n            main()  # Refresh the interface\n            break\n\n        if event == 'Group::R':\n            rename_group()\n            main()\n            break\n\n        if event == 'Group::D':\n            del_group()\n            main()\n            break\n\n        if event == 'Device::R':\n            rename_dev()\n            main()\n            break\n        if event == 'Device::D':\n            del_dev()\n            main()\n            break\n\n    save()\n    window.close()\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"Interface/MQTT/interface.py","file_name":"interface.py","file_ext":"py","file_size_in_byte":10175,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"650294903","text":"import sys\n\nif __name__ == \"__main__\":\n    T = int(input())\n    \n    for i in range(T):\n        c, p = map(int, input().split())\n        \n        print('You get {0} piece(s) and your dad gets {1} piece(s).'.format(c//p, c%p))\n    ","sub_path":"python/10178.py","file_name":"10178.py","file_ext":"py","file_size_in_byte":230,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"249754118","text":"import sqlite3\n\nclass dbopener():\n    def __init__(self):\n        self.conn = sqlite3.connect('scores.db')\n        self.c = self.conn.cursor()\n\nclass menu():\n    def __init__(self, db):\n        self.db = db\n        self.choice = ''\n\n    def chooseScoreboard(self):\n        self.choice = input('Do you want to\\n[1] See all scores ordered by average\\n[2] See all scores ordered by minimum time\\n[3] See top10 best averages\\n[4] See top10 fastest single scores\\n$ ')\n        print('\\n\\n\\n########################################################################\\n')\n        if self.choice == '1':\n            self.allScoresAvg()\n        elif self.choice == '3':\n            self.bestAverage()\n        elif self.choice == '4':\n            self.bestMinimum()\n        elif self.choice == '2':\n            self.allScoresMin()\n        else:\n            print('Invalid option.')\n            return\n        print('\\n########################################################################\\n\\n\\n')\n\n    def allScoresAvg(self):\n        self.db.c.execute('SELECT * FROM highscores ORDER BY avgscore')\n        data = self.db.c.fetchall()\n        j = 0\n        for i in data:\n            print('#{3}\\tName: {0}\\tAverage: {1:.5f}\\tMinimum: {2:.5f}'.format(i[0], i[1], i[2], j+1))\n            j+=1\n\n    def allScoresMin(self):\n        self.db.c.execute('SELECT * FROM highscores ORDER BY minscore')\n        data = self.db.c.fetchall()\n        j = 0\n        for i in data:\n            print('#{3}\\tName: {0}\\tAverage: {1:.5f}\\tMinimum: {2:.5f}'.format(i[0], i[1], i[2], j+1))\n            j+=1\n\n    def bestAverage(self):\n        self.db.c.execute('SELECT * FROM highscores ORDER BY avgscore')\n        data = self.db.c.fetchall()\n        j = 0\n        for i in range(10):\n            if i < len(data):\n                print('#{3}\\tName: {0}\\tAverage: {1:.5f}\\tMinimum: {2:.5f}'.format(data[i][0], data[i][1], data[i][2], i+1))\n\n    def bestMinimum(self):\n        self.db.c.execute('SELECT * FROM highscores ORDER BY minscore')\n        data = self.db.c.fetchall()\n        j = 0\n        for i in range(10):\n            if i < len(data):\n                print('#{3}\\tName: {0}\\tAverage: {1:.5f}\\tMinimum: {2:.5f}'.format(data[i][0], data[i][1], data[i][2], i+1))\n\nMyDb = dbopener()\nMyMenu = menu(MyDb)\n\nMyMenu.chooseScoreboard()\n","sub_path":"reactiongame/highscores.py","file_name":"highscores.py","file_ext":"py","file_size_in_byte":2306,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"639965140","text":"\nimport os\nimport openpyxl as xl\nfrom InvoiceGenerator.pdf import SimpleInvoice\nfrom tempfile import NamedTemporaryFile\nfrom InvoiceGenerator.api import Invoice, Item, Client, Provider, Creator\n\nwb = xl.load_workbook('CustomerInfo.xlsx')\nsheet = wb['Sheet1']\n\n\n# choose english as language\nos.environ[\"INVOICE_LANG\"] = \"en\"\nfor row in range(2, sheet.max_row + 1):\n    paid = sheet.cell(row, 3)\n    if not paid:\n        client = Client(summary=f'Name: {sheet.cell(row, 1)}', address=sheet.cell(row, 5), zip_code=sheet.cell(row, 8), city=sheet.cell(row, 6), vat_id=f'tax id: {sheet.cell(row, 4)}', email=sheet.cell(row, 2))\n        provider = Provider('Shira Teichman', bank_account='2600420569', bank_code='2010')\n        creator = Creator('John Doe')\n        invoice = Invoice(client, provider, creator)\n        invoice.currency_locale = 'en_US.UTF-8'\n        invoice.add_item(Item(32, 600, description=\"Item 1\"))\n        invoice.add_item(Item(60, 50, description=\"Item 2\", tax=21))\n        invoice.add_item(Item(50, 60, description=\"Item 3\", tax=0))\n        invoice.add_item(Item(5, 600, description=\"Item 4\", tax=15))\n        pdf = SimpleInvoice(invoice)\n        pdf.gen(\"invoice.pdf\", generate_qr_code=True)\n#making a pdf\n\n\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"531739576","text":"# -*- coding: utf-8 -*-\n\nimport os.path\nimport datetime\nimport posixpath\nimport time\n\nfrom django.utils.http import http_date\n\nimport djcelery\n\ngettext = lambda s: s\n\ndjcelery.setup_loader()\n\nPROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))\n\nDEBUG = False\nTEMPLATE_DEBUG = True\n\nSERVE_MEDIA = DEBUG\n\n# django-compressor is turned off by default due to deployment overhead for\n# most users. See <URL> for more information\nCOMPRESS = False\n\nINTERNAL_IPS = [\n    \"127.0.0.1\",\n]\n\nDATABASES = {\n    \"default\": {\n        \"ENGINE\": \"django.db.backends.postgresql_psycopg2\",\n        \"NAME\": \"crate\",\n    }\n}\n\nTIME_ZONE = \"UTC\"\nLANGUAGE_CODE = \"en-us\"\n\nUSE_I18N = True\nUSE_L10N = True\nUSE_TZ = True\n\nLOCALE_PATHS = [\n    os.path.join(PROJECT_ROOT, os.pardir, \"locale\"),\n]\n\nLANGUAGES = (\n    (\"en\", u\"English\"),\n    (\"es\", u\"Español\"),\n    (\"fr\", u\"Français\"),\n    (\"de\", u\"Deutsch\"),\n    (\"pt-br\", u\"Português (Br)\"),\n    (\"ru\", u\"Русский\"),\n    # (\"sv\", u\"Svenska\"),\n)\n\nMEDIA_ROOT = os.path.join(PROJECT_ROOT, \"site_media\", \"media\")\nMEDIA_URL = \"/site_media/media/\"\n\n\nSTATIC_ROOT = os.path.join(PROJECT_ROOT, \"site_media\", \"static\")\nSTATIC_URL = \"/site_media/static/\"\n\nADMIN_MEDIA_PREFIX = posixpath.join(STATIC_URL, \"admin/\")\n\nSTATICFILES_DIRS = [\n    os.path.join(PROJECT_ROOT, \"static\"),\n]\n\nSTATICFILES_FINDERS = [\n    \"staticfiles.finders.FileSystemFinder\",\n    \"staticfiles.finders.AppDirectoriesFinder\",\n    \"staticfiles.finders.LegacyAppDirectoriesFinder\",\n    \"compressor.finders.CompressorFinder\",\n]\n\nCOMPRESS_OUTPUT_DIR = \"cache\"\n\nTEMPLATE_LOADERS = [\n    \"django.template.loaders.filesystem.Loader\",\n    \"django.template.loaders.app_directories.Loader\",\n]\n\nMIDDLEWARE_CLASSES = [\n    \"django_hosts.middleware.HostsMiddleware\",\n    \"djangosecure.middleware.SecurityMiddleware\",\n    \"django.middleware.common.CommonMiddleware\",\n    \"django.contrib.sessions.middleware.SessionMiddleware\",\n    \"django.middleware.csrf.CsrfViewMiddleware\",\n    \"django.contrib.auth.middleware.AuthenticationMiddleware\",\n    \"django_openid.consumer.SessionConsumer\",\n    \"django.contrib.messages.middleware.MessageMiddleware\",\n    \"django.middleware.clickjacking.XFrameOptionsMiddleware\",\n\n    \"pagination.middleware.PaginationMiddleware\",\n\n    \"pinax.apps.account.middleware.LocaleMiddleware\",\n    \"pinax.middleware.security.HideSensistiveFieldsMiddleware\",\n]\n\nROOT_URLCONF = \"crate_project.urls\"\nROOT_HOSTCONF = \"crate_project.hosts\"\n\nDEFAULT_HOST = \"default\"\n\nWSGI_APPLICATION = \"crate_project.wsgi.application\"\n\nTEMPLATE_DIRS = [\n    os.path.join(PROJECT_ROOT, \"templates\"),\n]\n\nJINJA_TEMPLATE_DIRS = [\n    os.path.join(PROJECT_ROOT, \"templates\", \"_jinja2\"),\n]\n\nTEMPLATE_CONTEXT_PROCESSORS = [\n    \"django.contrib.auth.context_processors.auth\",\n    \"django.core.context_processors.debug\",\n    \"django.core.context_processors.i18n\",\n    \"django.core.context_processors.media\",\n    \"django.core.context_processors.request\",\n    \"django.contrib.messages.context_processors.messages\",\n\n    \"staticfiles.context_processors.static\",\n\n    \"pinax.core.context_processors.pinax_settings\",\n\n    \"pinax.apps.account.context_processors.account\",\n\n    # \"notification.context_processors.notification\",\n    \"announcements.context_processors.site_wide_announcements\",\n]\n\nINSTALLED_APPS = [\n    # Admin Dashboard\n    \"admin_tools\",\n    \"admin_tools.theming\",\n    \"admin_tools.menu\",\n    \"admin_tools.dashboard\",\n\n    # Django\n    \"django.contrib.admin\",\n    \"django.contrib.auth\",\n    \"django.contrib.contenttypes\",\n    \"django.contrib.sessions\",\n    \"django.contrib.sites\",\n    \"django.contrib.messages\",\n    \"django.contrib.humanize\",\n    \"django.contrib.markup\",\n\n    \"pinax.templatetags\",\n\n    # theme\n    \"pinax_theme_bootstrap\",\n\n    # external (Pinax)\n    # \"notification\",  # must be first\n    \"staticfiles\",\n    \"pagination\",\n    \"compressor\",\n    \"django_openid\",\n    \"timezones\",\n    \"emailconfirmation\",\n    \"announcements\",\n    \"idios\",\n    \"metron\",\n\n    # Pinax\n    \"pinax.apps.account\",\n    \"pinax.apps.signup_codes\",\n\n    # external (Project)\n    \"south\",\n    \"djcelery\",\n    \"django_hosts\",\n    \"haystack\",\n    \"storages\",\n    \"intercom\",\n    \"celery_haystack\",\n    \"tastypie\",\n    \"djangosecure\",\n\n    # project\n    \"about\",\n    \"aws_stats\",\n    \"profiles\",\n    \"packages\",\n    \"pypi\",\n    \"search\",\n    \"crate\",\n    \"evaluator\",\n    \"favorites\",\n    \"history\",\n    \"helpdocs\",\n]\n\nFIXTURE_DIRS = [\n    os.path.join(PROJECT_ROOT, \"fixtures\"),\n]\n\nMESSAGE_STORAGE = \"django.contrib.messages.storage.session.SessionStorage\"\n\nABSOLUTE_URL_OVERRIDES = {\n    \"auth.user\": lambda o: \"/profiles/profile/%s/\" % o.username,\n}\n\nAUTH_PROFILE_MODULE = \"profiles.Profile\"\nNOTIFICATION_LANGUAGE_MODULE = \"account.Account\"\n\nCONTACT_EMAIL = \"support@crate.io\"\n\nACCOUNT_OPEN_SIGNUP = True\nACCOUNT_USE_OPENID = True\nACCOUNT_REQUIRED_EMAIL = True\nACCOUNT_EMAIL_VERIFICATION = True\nACCOUNT_EMAIL_AUTHENTICATION = False\nACCOUNT_UNIQUE_EMAIL = EMAIL_CONFIRMATION_UNIQUE_EMAIL = True\n\nAUTHENTICATION_BACKENDS = [\n    \"pinax.apps.account.auth_backends.AuthenticationBackend\",\n]\n\nPASSWORD_HASHERS = (\n    \"django.contrib.auth.hashers.BCryptPasswordHasher\",\n    \"django.contrib.auth.hashers.PBKDF2PasswordHasher\",\n    \"django.contrib.auth.hashers.PBKDF2SHA1PasswordHasher\",\n    \"django.contrib.auth.hashers.SHA1PasswordHasher\",\n    \"django.contrib.auth.hashers.MD5PasswordHasher\",\n    \"django.contrib.auth.hashers.CryptPasswordHasher\",\n)\n\nLOGIN_URL = \"/account/login/\"\nLOGIN_REDIRECT_URLNAME = \"what_next\"\nLOGOUT_REDIRECT_URLNAME = \"search\"\n\nEMAIL_CONFIRMATION_DAYS = 2\nEMAIL_DEBUG = DEBUG\n\nDEBUG_TOOLBAR_CONFIG = {\n    \"INTERCEPT_REDIRECTS\": False,\n}\n\nCELERY_SEND_TASK_ERROR_EMAILS = True\nCELERY_DISABLE_RATE_LIMITS = True\nCELERY_TASK_PUBLISH_RETRY = True\n\nCELERYD_MAX_TASKS_PER_CHILD = 10000\n\nCELERY_IGNORE_RESULT = True\n\nCELERY_TASK_RESULT_EXPIRES = 7 * 24 * 60 * 60  # 7 Days\n\nCELERYD_HIJACK_ROOT_LOGGER = False\n\nCELERYBEAT_SCHEDULER = \"djcelery.schedulers.DatabaseScheduler\"\n\nHAYSTACK_SEARCH_RESULTS_PER_PAGE = 15\n\nAWS_QUERYSTRING_AUTH = False\nAWS_S3_SECURE_URLS = False\n\nAWS_HEADERS = {\n    \"Expires\": lambda: http_date(time.mktime((datetime.datetime.now() + datetime.timedelta(days=365)).timetuple())),\n    \"Cache-Control\": \"max-age=31556926\",\n}\n\n\nMETRON_SETTINGS = {\n    \"google\": {3: \"UA-28759418-1\"},\n    \"gauges\": {3: \"4f1e4cd0613f5d7003000002\"}\n}\n\nADMIN_TOOLS_INDEX_DASHBOARD = \"crate.dashboard.CrateIndexDashboard\"\n","sub_path":"crate_project/settings/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":6435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"90236706","text":"# coding=UTF-8\nimport pygame\n\nclass Pont:\n\tdef __init__(self, koord):\n\t\tself.koord = tuple(map(int, koord))\n\t\tself.ert = float(\"inf\")\n\t\tself.szomszedok = []\n\t\t\n\tdef __str__(self):\n\t\treturn str(self.koord)\n\n# Beolvasás\n\nwith open(\"graf-0.csv\") as be:\n\tszel, mag, pontdb, eldb = tuple(map(int, be.readline().split(\";\")))\n\t\n\tpontok = []\n\tfor _ in range(pontdb):\n\t\tpontok.append(Pont(be.readline().split(\";\")[1:3]))\n\t\t\n\telek = []\n\tfor _ in range(eldb):\n\t\telek.append(list(map(lambda x: int(x)-1, be.readline().split(\";\")[:3])))\n\t\telek[-1][2] += 1\n\t\tpontok[elek[-1][0]].szomszedok.append((elek[-1][1], elek[-1][2]))\n\t\tpontok[elek[-1][1]].szomszedok.append((elek[-1][0], elek[-1][2]))\n\t\t\n\t#print(pontok)\n\t#print(elek)\n\n# pygame betöltése, háttér\t\n\nkezd = int(input(\"Kezdőpont sorszáma: \"))\ncel = int(input(\"Célpont sorszáma: \"))\n\npontok[kezd].ert = 0\nismert = {kezd}\n\nfor i, ert in pontok[kezd].szomszedok:\n\tpontok[i].ert = ert\n\t\nperem = {(pontok[ssz].ert, ssz) for ssz, _ in pontok[kezd].szomszedok}\n\nwhile len(perem)>0:\n\tminert, minssz = min(perem)\n\t\n\tperem.remove((minert, minssz))\n\tismert.add(minssz)\n\tif minssz == cel:\n\t\tbreak\n\t\n\tfor i, ert in pontok[minssz].szomszedok:\n\t\tif i not in ismert:\n\t\t\tif pontok[i].ert > minert + ert:\n\t\t\t\tpontok[i].ert = minert + ert\n\t\t\tperem.add((pontok[i].ert, i))\n\nprint(pontok[cel].ert)\n\npygame.init()\nscreen = pygame.display.set_mode((szel, mag))\npygame.draw.rect(screen, (255, 255, 255), (0, 0, szel, mag))\n\npygame.font.init()\nbetu = pygame.font.SysFont(\"calibri\", 16)\nszoveg = betu.render(\"Gráf\", False, (0, 0, 0))\nscreen.blit(szoveg, (10, 10))\n\n# fő ciklus\n\nvege = False\nwhile not vege:\n\t# Program vége-e\n\t\n\tfor event in pygame.event.get():\n\t\tif event.type == pygame.QUIT:\n\t\t\tvege = True\n\t\n\t# Pontok kirajzolása\n\t\n\tfor pont in pontok:\n\t\tpygame.draw.circle(screen, (0, 0, 0), pont.koord, 5)\n\t\t\n\tfor el in elek:\n\t\tpygame.draw.line(screen, (0, 0, 0), pontok[el[0]].koord, pontok[el[1]].koord, 1)\n\t\t\n\t# Rajzolás vége\n\t\t\n\tpygame.display.flip()\n","sub_path":"graf-0.py","file_name":"graf-0.py","file_ext":"py","file_size_in_byte":1989,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"461822436","text":"'''\nCreated on Jan 31, 2016\n\n@author: johannes niederhauser\n\ntrying python for the first time\n'''\n\nsecret = 100\nguess = 0\ncounter = 0\n\nwhile guess != secret:\n    guess = int(input(\"Guess the number: \"))\n    \n    if guess < secret:\n        print(\"too little\")\n    \n    if guess > secret:\n        print(\"too big\")\n    \n    counter = counter + 1\n\nprint(\"You have guessed the number within\", counter, \"attempts.\")","sub_path":"GuessNumbers/src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"418226388","text":"# Licensed under a 3-clause BSD style license - see LICENSE.rst\nimport abc\nimport collections.abc\nimport copy\nimport numpy as np\nfrom gammapy.maps import Map\nfrom gammapy.modeling.models import Models, ProperModels\nfrom gammapy.utils.scripts import make_name, make_path, read_yaml, write_yaml\nfrom gammapy.utils.table import table_from_row_data\n\n__all__ = [\"Dataset\", \"Datasets\"]\n\n\nclass Dataset(abc.ABC):\n    \"\"\"Dataset abstract base class.\n\n    TODO: add tutorial how to create your own dataset types.\n\n    For now, see existing examples in Gammapy how this works:\n\n    - `gammapy.cube.MapDataset`\n    - `gammapy.spectrum.SpectrumDataset`\n    - `gammapy.spectrum.FluxPointsDataset`\n    \"\"\"\n\n    _residuals_labels = {\n        \"diff\": \"data - model\",\n        \"diff/model\": \"(data - model) / model\",\n        \"diff/sqrt(model)\": \"(data - model) / sqrt(model)\",\n    }\n\n    @property\n    def mask(self):\n        \"\"\"Combined fit and safe mask\"\"\"\n        mask_safe = (\n            self.mask_safe.data if isinstance(self.mask_safe, Map) else self.mask_safe\n        )\n        mask_fit = (\n            self.mask_fit.data if isinstance(self.mask_fit, Map) else self.mask_fit\n        )\n        if mask_safe is not None and mask_fit is not None:\n            mask = mask_safe & mask_fit\n        elif mask_fit is not None:\n            mask = mask_fit\n        elif mask_safe is not None:\n            mask = mask_safe\n        else:\n            mask = None\n        return mask\n\n    def stat_sum(self):\n        \"\"\"Total statistic given the current model parameters.\"\"\"\n        stat = self.stat_array()\n\n        if self.mask is not None:\n            stat = stat[self.mask]\n\n        return np.sum(stat, dtype=np.float64)\n\n    @abc.abstractmethod\n    def stat_array(self):\n        \"\"\"Statistic array, one value per data point.\"\"\"\n\n    def copy(self, name=None):\n        \"\"\"A deep copy.\"\"\"\n        new = copy.deepcopy(self)\n        name = make_name(name)\n        new._name = name\n        # propagate new dataset name\n        if new._models is not None:\n            for m in new._models:\n                if m.datasets_names is not None:\n                    for k, d in enumerate(m.datasets_names):\n                        if d == self.name:\n                            m.datasets_names[k] = name\n                    if hasattr(new, \"background_model\") and m == new.background_model:\n                        m._name = name + \"-bkg\"\n        return new\n\n    @staticmethod\n    def _compute_residuals(data, model, method=\"diff\"):\n        with np.errstate(invalid=\"ignore\"):\n            if method == \"diff\":\n                residuals = data - model\n            elif method == \"diff/model\":\n                residuals = (data - model) / model\n            elif method == \"diff/sqrt(model)\":\n                residuals = (data - model) / np.sqrt(model)\n            else:\n                raise AttributeError(\n                    f\"Invalid method: {method!r}. Choose between 'diff',\"\n                    \" 'diff/model' and 'diff/sqrt(model)'\"\n                )\n        return residuals\n\n\nclass Datasets(collections.abc.MutableSequence):\n    \"\"\"Dataset collection.\n\n    Parameters\n    ----------\n    datasets : `Dataset` or list of `Dataset`\n        Datasets\n    \"\"\"\n\n    def __init__(self, datasets=None):\n        if datasets is None:\n            datasets = []\n\n        if isinstance(datasets, Datasets):\n            datasets = datasets._datasets\n        elif isinstance(datasets, Dataset):\n            datasets = [datasets]\n        elif not isinstance(datasets, list):\n            raise TypeError(f\"Invalid type: {datasets!r}\")\n\n        unique_names = []\n        for dataset in datasets:\n            if dataset.name in unique_names:\n                raise (ValueError(\"Dataset names must be unique\"))\n            unique_names.append(dataset.name)\n\n        self._datasets = datasets\n\n    @property\n    def parameters(self):\n        \"\"\"Unique parameters (`~gammapy.modeling.Parameters`).\n\n        Duplicate parameter objects have been removed.\n        The order of the unique parameters remains.\n        \"\"\"\n        return self.models.parameters.unique_parameters\n\n    @property\n    def models(self):\n        \"\"\"Unique models (`~gammapy.modeling.Models`).\n\n        Duplicate model objects have been removed.\n        The order of the unique models remains.\n        \"\"\"\n        return ProperModels(self)\n\n    @property\n    def names(self):\n        return [d.name for d in self._datasets]\n\n    @property\n    def is_all_same_type(self):\n        \"\"\"Whether all contained datasets are of the same type\"\"\"\n        return len(set(_.__class__ for _ in self)) == 1\n\n    @property\n    def is_all_same_shape(self):\n        \"\"\"Whether all contained datasets have the same data shape\"\"\"\n        return len(set(_.data_shape for _ in self)) == 1\n\n    @property\n    def is_all_same_energy_shape(self):\n        \"\"\"Whether all contained datasets have the same data shape\"\"\"\n        return len(set(_.data_shape[0] for _ in self)) == 1\n\n    def stat_sum(self):\n        \"\"\"Compute joint likelihood\"\"\"\n        stat_sum = 0\n        # TODO: add parallel evaluation of likelihoods\n        for dataset in self:\n            stat_sum += dataset.stat_sum()\n        return stat_sum\n\n    def __str__(self):\n        str_ = self.__class__.__name__ + \"\\n\"\n        str_ += \"--------\\n\"\n\n        for idx, dataset in enumerate(self):\n            str_ += f\"idx={idx}, id={hex(id(dataset))!r}, name={dataset.name!r}\\n\"\n\n        return str_\n\n    def copy(self):\n        \"\"\"A deep copy.\"\"\"\n        return copy.deepcopy(self)\n\n    @classmethod\n    def read(cls, path, filedata=\"_datasets.yaml\", filemodel=\"_models.yaml\"):\n        \"\"\"De-serialize datasets from YAML and FITS files.\n\n        Parameters\n        ----------\n        path : str, Path\n            Base directory of the datasets files.\n        filedata : str\n            file path or name of yaml datasets file\n        filemodel : str\n            file path or name of yaml models file\n\n        Returns\n        -------\n        dataset : 'gammapy.modeling.Datasets'\n            Datasets\n        \"\"\"\n        from . import DATASET_REGISTRY\n\n        path = make_path(path)\n\n        if (path / filedata).exists():\n            filedata = path / filedata\n        else:\n            filedata = make_path(filedata)\n        if (path / filemodel).exists():\n            filemodel = path / filemodel\n        else:\n            filemodel = make_path(filemodel)\n\n        models = Models.read(filemodel)\n        data_list = read_yaml(filedata)\n\n        datasets = []\n        for data in data_list[\"datasets\"]:\n            if (path / data[\"filename\"]).exists():\n                data[\"filename\"] = str(make_path(path / data[\"filename\"]))\n            dataset = DATASET_REGISTRY.get_cls(data[\"type\"]).from_dict(data, models)\n            datasets.append(dataset)\n        return cls(datasets)\n\n    def write(self, path, prefix=\"\", overwrite=False, write_covariance=True):\n        \"\"\"Serialize datasets to YAML and FITS files.\n\n        Parameters\n        ----------\n        path : `pathlib.Path`\n            path to write files\n        prefix : str\n            common prefix of file names\n        overwrite : bool\n            overwrite datasets FITS files\n        write_covariance : bool\n            save covariance or not\n        \"\"\"\n\n        path = make_path(path).resolve()\n        datasets_dictlist = []\n        for dataset in self._datasets:\n            filename = f\"{prefix}_data_{dataset.name}.fits\"\n            dataset.write(path / filename, overwrite)\n            datasets_dictlist.append(dataset.to_dict(filename=filename))\n        datasets_dict = {\"datasets\": datasets_dictlist}\n\n        write_yaml(datasets_dict, path / f\"{prefix}_datasets.yaml\", sort_keys=False)\n        self.models.write(\n            path / f\"{prefix}_models.yaml\",\n            overwrite=overwrite,\n            write_covariance=write_covariance,\n        )\n\n    def stack_reduce(self, name=None):\n        \"\"\"Reduce the Datasets to a unique Dataset by stacking them together.\n\n        This works only if all Dataset are of the same type and if a proper\n        in-place stack method exists for the Dataset type.\n\n        Returns\n        -------\n        dataset : ~gammapy.utils.Dataset\n            the stacked dataset\n        \"\"\"\n        if not self.is_all_same_type:\n            raise ValueError(\n                \"Stacking impossible: all Datasets contained are not of a unique type.\"\n            )\n\n        dataset = self[0].copy(name=name)\n        for ds in self[1:]:\n            dataset.stack(ds)\n        return dataset\n\n    def info_table(self, cumulative=False, region=None):\n        \"\"\"Get info table for datasets.\n\n        Parameters\n        ----------\n        cumulative : bool\n            Cumulate info across all observations\n\n        Returns\n        -------\n        info_table : `~astropy.table.Table`\n            Info table.\n        \"\"\"\n        if not self.is_all_same_type:\n            raise ValueError(\"Info table not supported for mixed dataset type.\")\n\n        stacked = self[0].copy(name=self[0].name)\n\n        rows = [stacked.info_dict()]\n\n        for dataset in self[1:]:\n            if cumulative:\n                stacked.stack(dataset)\n                row = stacked.info_dict()\n            else:\n                row = dataset.info_dict()\n\n            rows.append(row)\n\n        return table_from_row_data(rows=rows)\n\n    def __getitem__(self, key):\n        return self._datasets[self.index(key)]\n\n    def __delitem__(self, key):\n        del self._datasets[self.index(key)]\n\n    def __setitem__(self, key, dataset):\n        if isinstance(dataset, Dataset):\n            if dataset.name in self.names:\n                raise (ValueError(\"Dataset names must be unique\"))\n            self._datasets[self.index(key)] = dataset\n        else:\n            raise TypeError(f\"Invalid type: {type(dataset)!r}\")\n\n    def insert(self, idx, dataset):\n        if isinstance(dataset, Dataset):\n            if dataset.name in self.names:\n                raise (ValueError(\"Dataset names must be unique\"))\n            self._datasets.insert(idx, dataset)\n        else:\n            raise TypeError(f\"Invalid type: {type(dataset)!r}\")\n\n    def index(self, key):\n        if isinstance(key, (int, slice)):\n            return key\n        elif isinstance(key, str):\n            return self.names.index(key)\n        elif isinstance(key, Dataset):\n            return self._datasets.index(key)\n        else:\n            raise TypeError(f\"Invalid type: {type(key)!r}\")\n\n    def __len__(self):\n        return len(self._datasets)\n","sub_path":"gammapy/datasets/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":10550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"95110946","text":"import numpy as np\nimport os\n\nimport tvm\nfrom tvm import relay, auto_scheduler\nfrom tvm.relay import data_dep_optimization as ddo\nimport tvm.relay.testing\nfrom tvm.contrib import graph_executor\nfrom tvm.contrib.utils import tempdir\n\nimport argparse\n\nimport ml_collections\nfrom tt_mixer import TTMixer\nimport torch\n\nfrom tvm.relay.dataflow_pattern import *\nfrom tvm.relay.testing import run_opt_pass\nfrom tvm.relay.op.op import register_injective_schedule\n\ndef get_mixer_b16_tt_config(args):\n    \"\"\"Returns TTMixer-B/16 configuration\"\"\"\n    config = ml_collections.ConfigDict()\n    config = ml_collections.ConfigDict()\n    config.name = 'Mixer-B_16'\n    config.patches = ml_collections.ConfigDict({'size': (16, 16)})\n    config.hidden_dim = 768\n    config.hidden_shape = args.hidden_tt_shape\n    config.num_blocks = 12\n    config.tokens_mlp_dim = 384\n    config.channels_mlp_dim = 3072\n    config.channels_mlp_shape = args.channels_tt_shape\n    config.tt_ranks = args.tt_ranks\n    return config\n\ndef set_configs(args):\n\n    args.save_path = \"saved_models/B_16_cifar_10.pt\"\n    args.img_size = 224\n    args.num_classes = 10\n    args.tt_ranks = [int(i) for i in args.tt_ranks.split(',')]\n    args.hidden_tt_shape = [int(i) for i in args.hidden_tt_shape.split(',')]\n    args.channels_tt_shape = [int(i) for i in args.channels_tt_shape.split(',')]\n    args.target_layer = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]\n\n    return args\n\nparser = argparse.ArgumentParser()\n# TT-format Configuration\n\n# ranks config [64, 64], [32, 32], [16, 16], [8, 8]\nparser.add_argument(\"--tt-ranks\", default=\"16, 16\",\n                    type=str,\n                    help=\"Ranks for TT-Format\")\n# 768 factorize (e.g. 768 = 8 x 8 x 12)\nparser.add_argument(\"--hidden-tt-shape\", default=\"8, 8, 12\",\n                    type=str,\n                    help=\"Factorized hidden dimension for TT-format\")\n# 3072 factorize (e.g. 3072 = 12 x 12 x 16)\nparser.add_argument(\"--channels-tt-shape\", default=\"12, 16, 16\",\n                    type=str,\n                    help=\"Factorized channel dimension for TT-format\")\nargs = parser.parse_args()\n\nargs = set_configs(args)\n\nimport logging\n#logging.getLogger('auto_scheduler').setLevel(logging.DEBUG)\n\n#def compute_tt_linear(data, w1, w2, w3):\n@relay.op.register_compute(\"tt_linear\")\ndef compute_tt_linear(attrs, inputs):\n    out = inputs[0] + 1\n    return [out]\n\nrelay.op.op.register_injective_schedule(\"tt_linear\")\nrelay.op.op.register_pattern(\"tt_linear\", relay.op.op.OpPattern.INJECTIVE)\n\ndef get_network(name, batch_size, layout=\"NHWC\", dtype=\"float32\", use_sparse=True):\n    \"\"\"Get the symbol definition and random weight of a network\"\"\"\n\n    config = get_mixer_b16_tt_config(args)\n    model = TTMixer(config,\n                    args.img_size,\n                    num_classes=args.num_classes,\n                    patch_size=16,\n                    zero_head=False,\n                    target_layer=args.target_layer)\n\n    input_shape = (1, 3, 224, 224)\n    output_shape = (1, 10)\n    input_data = torch.randn(input_shape)\n    scripted_model = torch.jit.trace(model, input_data).eval()\n\n    input_name = \"input0\"\n    shape_list = [(input_name, input_shape)]\n\n    mod, params = relay.frontend.from_pytorch(scripted_model, shape_list)\n\n    def make_tt_linear_pattern():\n        w1 = is_op(\"transpose\")(wildcard())\n\n        x1 = is_op(\"nn.dense\")(wildcard(), w1)\n        x1 = is_op(\"reshape\")(x1)\n        #x1 = is_op(\"reshape\")(x1)\n        x1 = is_op(\"transpose\")(x1)\n        x1 = is_op(\"reshape\")(x1)\n        #x1 = is_op(\"reshape\")(x1)\n\n        w2 = is_op(\"transpose\")(wildcard())\n\n        x2 = is_op(\"nn.dense\")(x1, w2)\n        x2 = is_op(\"reshape\")(x2)\n        #x2 = is_op(\"reshape\")(x2)\n        x2 = is_op(\"transpose\")(x2)\n        x2 = is_op(\"reshape\")(x2)\n        #x2 = is_op(\"reshape\")(x2)\n\n        w3 = is_op(\"transpose\")(wildcard())\n\n        x3 = is_op(\"nn.dense\")(x2, w3)\n        return x3\n\n    pattern_table = [(\"tt_linear\", make_tt_linear_pattern())]\n\n\n    class TTLinearCallback(DFPatternCallback):\n        def __init__(self, require_type=False):\n            super().__init__(require_type)\n            self.x1 = wildcard()\n            self.w1 = wildcard()\n            self.w2 = wildcard()\n            self.w3 = wildcard()\n            self.b = wildcard()\n            self.pattern = is_op(\"reshape\")(self.x1)\n            self.pattern = is_op(\"transpose\")(self.pattern)\n            self.pattern = is_op(\"reshape\")(self.pattern)\n            self.pattern = is_op(\"nn.dense\")(self.pattern, is_op(\"transpose\")(self.w1))\n            self.pattern = is_op(\"reshape\")(self.pattern)\n            self.pattern = is_op(\"transpose\")(self.pattern)\n            self.pattern = is_op(\"reshape\")(self.pattern)\n            self.pattern = is_op(\"nn.dense\")(self.pattern, is_op(\"transpose\")(self.w2))\n            self.pattern = is_op(\"reshape\")(self.pattern)\n            self.pattern = is_op(\"transpose\")(self.pattern)\n            self.pattern = is_op(\"reshape\")(self.pattern)\n            self.pattern = is_op(\"nn.dense\")(self.pattern, is_op(\"transpose\")(self.w3))\n            self.pattern = is_op(\"reshape\")(self.pattern)\n            self.pattern = is_op(\"add\")(self.b, self.pattern)\n\n            self.x2 = self.pattern\n\n            self.pattern = is_op(\"multiply\")(self.pattern, wildcard())\n            self.pattern = is_op(\"erf\")(self.pattern)\n            self.pattern = is_op(\"multiply\")(self.pattern, wildcard())\n            self.pattern = is_op(\"add\")(wildcard(), self.pattern)\n            self.pattern = is_op(\"multiply\")(self.x2, self.pattern)\n            self.w4 = wildcard()\n            self.w5 = wildcard()\n            self.w6 = wildcard()\n            self.b2 = wildcard()\n            self.pattern = is_op(\"reshape\")(self.pattern)\n            self.pattern = is_op(\"transpose\")(self.pattern)\n            self.pattern = is_op(\"reshape\")(self.pattern)\n            self.pattern = is_op(\"nn.dense\")(self.pattern, is_op(\"transpose\")(self.w4))\n            self.pattern = is_op(\"reshape\")(self.pattern)\n            self.pattern = is_op(\"transpose\")(self.pattern)\n            self.pattern = is_op(\"reshape\")(self.pattern)\n            self.pattern = is_op(\"nn.dense\")(self.pattern, is_op(\"transpose\")(self.w5))\n            self.pattern = is_op(\"reshape\")(self.pattern)\n            self.pattern = is_op(\"transpose\")(self.pattern)\n            self.pattern = is_op(\"reshape\")(self.pattern)\n            self.pattern = is_op(\"nn.dense\")(self.pattern, is_op(\"transpose\")(self.w6))\n            self.pattern = is_op(\"reshape\")(self.pattern)\n            self.pattern = is_op(\"add\")(self.b2, self.pattern)\n\n        def callback(self, pre, post, node_map):\n            print(\"haha\")\n            x1 = node_map[self.x1][0]\n            w1 = node_map[self.w1][0]\n            w2 = node_map[self.w2][0]\n            w3 = node_map[self.w3][0]\n            return x1\n\n\n    expr = run_opt_pass(mod[\"main\"], relay.transform.SimplifyExpr())\n    mod = tvm.IRModule.from_expr(expr)\n\n    #expr = run_opt_pass(mod[\"main\"], relay.transform.MergeComposite(pattern_table), import_prelude=False)\n    #mod = tvm.IRModule.from_expr(expr)\n\n    #expr = rewrite(TTLinearCallback(), mod[\"main\"])\n    #mod = tvm.IRModule.from_expr(expr)\n\n    print(mod)\n\n    return mod, params, input_shape, output_shape\n\n#### DEVICE CONFIG ####\n\n# Replace \"aarch64-linux-gnu\" with the correct target of your board.\n# This target is used for cross compilation. You can query it by :code:`gcc -v` on your device.\n# FIXME(tmoreau89, merrymercy): We leave '-device=arm_cpu' out of the target string\n#                               because we're sharing x86 op strategy.\ntarget = tvm.target.Target(\"llvm -mtriple=arm-linux-gnueabihf -mattr=+neon\")\n\n# Also replace this with the device key, rpc host and rpc port in your tracker\ndevice_key = \"xu4\"\nrpc_host = \"10.201.135.165\"\nrpc_port = 8109\n\n# Set this to True if you use ndk tools for cross compiling\n# And also set the environment variable below to point to the cross compiler\nuse_ndk = False\n# os.environ[\"TVM_NDK_CC\"] = \"/usr/bin/aarch64-linux-gnu-g++\"\n\n#### TUNING OPTION ####\nnetwork = \"mobilenet\"\nuse_sparse = True\nbatch_size = 1\n#layout = args.layout\ndtype = \"float32\"\nlog_file = \"R%d.json\" % (args.tt_ranks[0])\n#log_file = \"test.json\"\n\n#################################################################\n# Extract Search Tasks\n# --------------------\n# Next, we extract the search tasks and their weights from a network.\n# The weight of a task is the number of appearances of the task's subgraph\n# in the whole network.\n# By using the weight, we can approximate the end-to-end latency of the network\n# as :code:`sum(latency[t] * weight[t])`, where :code:`latency[t]` is the\n# latency of a task and :code:`weight[t]` is the weight of the task.\n# The task scheduler will just optimize this objective.\n\n# Extract tasks from the network\nprint(\"Get model...\")\nmod, params, input_shape, output_shape = get_network(\n    network, batch_size, \"NCHW\", dtype=dtype, use_sparse=use_sparse\n)\nprint(\"Extract tasks...\")\ntasks, task_weights = auto_scheduler.extract_tasks(mod[\"main\"], params, target)\n\nfor idx, task in enumerate(tasks):\n    print(\"========== Task %d  (workload key: %s) ==========\" % (idx, task.workload_key))\n    print(task.compute_dag)\n\n\n#################################################################\n# Tuning and Evaluation\n# ---------------------\n# Now, we set some options for tuning and launch the search tasks\n#\n# * :code:`num_measure_trials` is the number of measurement trials we can use during the tuning.\n#   You can set it to a small number (e.g., 200) for a fast demonstrative run.\n#   In practice, we recommend setting it around :code:`800 * len(tasks)`,\n#   which is typically enough for the search to converge.\n#   For example, there are 29 tasks in resnet-50, so we can set it as 20000.\n#   You can adjust this parameter according to your time budget.\n# * In addition, we use :code:`RecordToFile` to dump measurement records into a log file,\n#   The measurement records can be used to query the history best, resume the search,\n#   and do more analyses later.\n# * see :any:`auto_scheduler.TuningOptions`,\n#   :any:`auto_scheduler.LocalRunner` for more parameters.\n#\n# After auto-tuning, we can compile the network with the best schedules we found.\n# All measurement records are dumped into the log file during auto-tuning,\n# so we can read the log file and load the best schedules.\n\n\ndef tune_and_evaluate():\n    print(\"Begin tuning...\")\n    '''\n    tuner = auto_scheduler.TaskScheduler(tasks, task_weights, load_log_file=log_file)\n    tune_option = auto_scheduler.TuningOptions(\n        num_measure_trials=2000,  # change this to 20000 to achieve the best performance\n        builder=auto_scheduler.LocalBuilder(build_func=\"ndk\" if use_ndk else \"default\"),\n        runner=auto_scheduler.RPCRunner(\n            device_key,\n            host=rpc_host,\n            port=rpc_port,\n            timeout=3000,\n            repeat=1,\n            min_repeat_ms=200,\n            enable_cpu_cache_flush=True,\n        ),\n        measure_callbacks=[auto_scheduler.RecordToFile(log_file)],\n    )\n\n    tuner.tune(tune_option)\n    #'''\n\n    # Compile with the history best\n    print(\"Compile...\")\n    with auto_scheduler.ApplyHistoryBest(log_file):\n        with tvm.transform.PassContext(\n            opt_level=3, config={\"relay.backend.use_auto_scheduler\": True}\n        ):\n            lib = relay.build(mod, target=target, params=params)\n\n    # Export library\n    tmp = tempdir()\n    if use_ndk:\n        from tvm.contrib import ndk\n\n        filename = \"net.so\"\n        lib.export_library(tmp.relpath(filename), ndk.create_shared)\n    else:\n        filename = \"net.tar\"\n        lib.export_library(tmp.relpath(filename))\n\n    # Upload module to device\n    print(\"Upload...\")\n    remote = auto_scheduler.utils.request_remote(device_key, rpc_host, rpc_port, timeout=10000)\n    remote.upload(tmp.relpath(filename))\n    rlib = remote.load_module(filename)\n\n    # Create graph executor\n    dev = remote.cpu()\n    module = graph_executor.GraphModule(rlib[\"default\"](dev))\n    data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))\n    module.set_input(\"input0\", data_tvm)\n\n    # Evaluate\n    print(\"Evaluate inference time cost...\")\n    print(module.benchmark(dev, repeat=3, min_repeat_ms=500))\n\n\n# We do not run the tuning in our webpage server since the server doesn't have a Raspberry Pi,\n# or device tracker running.\n# Uncomment the following line to run it by yourself.\n\ntune_and_evaluate()\n\n\n######################################################################\n# .. note:: Explaining the printed information during tuning\n#\n#   During the tuning, a lot of information will be printed on the console.\n#   They are used for debugging purposes. The most important info is the output\n#   of the task scheduler. The following table is a sample output.\n#\n#   .. code-block:: c\n#\n#    ----------------------------------------------------------------------\n#    ------------------------------  [ Task Scheduler ]\n#    ----------------------------------------------------------------------\n#    |  ID  | Latency (ms) | Speed (GFLOPS) | Trials |\n#    -------------------------------------------------\n#    |    0 |        0.013 |           0.31 |     64 |\n#    |    1 |        0.845 |           2.43 |    448 |\n#    |    2 |        0.046 |          -0.00 |     64 |\n#    |    3 |        4.194 |          24.53 |   2112 |\n#    |    4 |        0.109 |           9.21 |     64 |\n#    |    5 |        1.759 |          29.27 |    896 |\n#    |    6 |        0.083 |           6.01 |     64 |\n#    |    7 |        3.084 |          33.38 |   7680 |\n#    |    8 |        0.136 |          14.78 |    384 |\n#    |    9 |        1.349 |          38.23 |    768 |\n#    |   10 |        0.133 |           7.55 |    128 |\n#    |   11 |        2.747 |          37.56 |   1536 |\n#    |   12 |        0.338 |          11.87 |    192 |\n#    |   13 |        1.295 |          40.00 |    704 |\n#    |   14 |        0.482 |           4.16 |    256 |\n#    |   15 |        2.686 |          38.56 |   1344 |\n#    |   16 |        0.884 |           9.08 |    448 |\n#    |   17 |        1.332 |          39.18 |    704 |\n#    |   18 |        1.045 |           3.84 |    576 |\n#    |   19 |        1.391 |          38.09 |    704 |\n#    |   20 |        0.777 |          10.34 |    448 |\n#    |   21 |        0.739 |          30.97 |    448 |\n#    -------------------------------------------------\n#     Estimated total latency: 38.347 ms      Trials: 19992   Used time : 19260 s     Next ID: 3\n#\n#   This table lists the latency and (estimated) speed of all tasks.\n#   It also lists the allocation of measurement trials for all tasks.\n#   The last line prints the total weighted latency of these tasks,\n#   which can be a rough estimation of the end-to-end execution time\n#   of the network.\n#   The last line also prints the total number of measurement trials,\n#   total time spent on auto-tuning and the id of the next task to tune.\n#\n#   There will also be some \"dmlc::Error\"s errors, because the\n#   auto-scheduler will try some invalid schedules.\n#   You can safely ignore them if the tuning can continue, because these\n#   errors are isolated from the main process.\n#\n\n######################################################################\n# .. note:: Terminate the tuning earlier\n#\n#   You can terminate the tuning earlier by forcibly killing this process.\n#   As long as you get at least one valid schedule for each task in the log file,\n#   you should be able to do the compilation (the secion below).\n#\n\n#################################################################\n# Other Tips\n# ----------\n# 1. During the tuning, the auto-scheduler needs to compile many programs and\n#    extract feature from them. This part is CPU-intensive,\n#    so a high-performance CPU with many cores is recommended for faster search.\n# 2. You can use :code:`python3 -m tvm.auto_scheduler.measure_record --mode distill -i log.json`\n#    to distill the large log file and only save the best useful records.\n# 3. You can resume a search from the previous log file. You just need to\n#    add a new argument :code:`load_log_file` when creating the task scheduler\n#    in function :code:`run_tuning`. Say,\n#    :code:`tuner = auto_scheduler.TaskScheduler(tasks, task_weights, load_log_file=log_file)`\n# 4. If you have multiple target CPUs, you can use all of them for measurements to\n#    parallelize the measurements. Check this :ref:`section <tutorials-autotvm-scale-up-rpc-tracker>`\n#    to learn how to use the RPC Tracker and RPC Server.\n#    To use the RPC Tracker in auto-scheduler, replace the runner in :code:`TuningOptions`\n#    with :any:`auto_scheduler.RPCRunner`.\n","sub_path":"kernel/fused_tt/tune_tt_fused.py","file_name":"tune_tt_fused.py","file_ext":"py","file_size_in_byte":16746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"315163984","text":"from __future__ import print_function, division\nimport pandas as pd\nimport numpy as np\nfrom os.path import join, isdir, isfile, dirname, abspath\nfrom os import getcwd\nfrom sys import getfilesystemencoding\nfrom nilmtk.datastore import Key\nfrom nilmtk.measurement import LEVEL_NAMES\nfrom nilmtk.utils import check_directory_exists, get_datastore\nfrom nilm_metadata import convert_yaml_to_hdf5\nfrom inspect import currentframe, getfile, getsourcefile\n\n\ncolumn_mapping = {\n    'frequency': ('frequency', \"\"),\n    'voltage': ('voltage', \"\"),\n    'W': ('power', 'active'),\n    'energy': ('energy', 'apparent'),\n    'A': ('current', ''),\n    'reactive_power': ('power', 'reactive'),\n    'apparent_power': ('power', 'apparent'),\n    'power_factor': ('pf', ''),\n    'PF': ('pf', ''),\n    'phase_angle': ('phi', ''),\n    'VA': ('power', 'apparent'),\n    'VAR': ('power', 'reactive'),\n    'VLN': ('voltage', \"\"),\n    'V': ('voltage', \"\"),\n    'f': ('frequency', \"\")\n}\n\nTIMESTAMP_COLUMN_NAME = \"timestamp\"\nTIMEZONE = \"Asia/Kolkata\"\n\ndef convert_iawe(iawe_path, output_filename, format=\"HDF\"):\n    \"\"\"\n    Parameters\n    ----------\n    iawe_path : str\n        The root path of the iawe dataset.\n    output_filename : str\n        The destination filename (including path and suffix).\n    \"\"\"\n\n    check_directory_exists(iawe_path)\n\n    # Open data store\n    store = get_datastore(output_filename, format, mode='w')\n    electricity_path = join(iawe_path, \"electricity\")\n\n    # Mains data\n    for chan in range(1, 13):\n        key = Key(building=1, meter=chan)\n        filename = join(electricity_path, \"%d.csv\" % chan)\n        print('Loading ', chan)\n        df = pd.read_csv(filename)\n        df.drop_duplicates(subset=[\"timestamp\"], inplace=True)\n        df.index = pd.to_datetime(df.timestamp.values, unit='s', utc=True)\n        df = df.tz_convert(TIMEZONE)\n        df = df.drop(TIMESTAMP_COLUMN_NAME, 1)\n        df.rename(columns=lambda x: column_mapping[x], inplace=True)\n        df.columns.set_names(LEVEL_NAMES, inplace=True)\n        df = df.convert_objects(convert_numeric=True)\n        df = df.dropna()\n        df = df.astype(np.float32)\n        df = df.sort_index()\n        store.put(str(key), df)\n    store.close()\n    convert_yaml_to_hdf5(join(_get_module_directory(), 'metadata'),\n                         output_filename)\n\n    print(\"Done converting iAWE to HDF5!\")\n\n\ndef _get_module_directory():\n    # Taken from http://stackoverflow.com/a/6098238/732596\n    path_to_this_file = dirname(getfile(currentframe()))\n    if not isdir(path_to_this_file):\n        encoding = getfilesystemencoding()\n        path_to_this_file = dirname(unicode(__file__, encoding))\n    if not isdir(path_to_this_file):\n        abspath(getsourcefile(lambda _: None))\n    if not isdir(path_to_this_file):\n        path_to_this_file = getcwd()\n    assert isdir(path_to_this_file), path_to_this_file + ' is not a directory'\n    return path_to_this_file\n","sub_path":"nilmtk/dataset_converters/iawe/convert_iawe.py","file_name":"convert_iawe.py","file_ext":"py","file_size_in_byte":2923,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"214304913","text":"import os\nimport sys\nfrom classes.CSVParser import CSVParser\nfrom classes.ComputerVision import ComputerVision\n\n\ndef main():\n    # Read user input and check if file exists\n    csv_input = None\n    while csv_input != \"q\":\n        print(\"\\n Input Path:\")\n        csv_input = raw_input(\"Please enter or copy/paste the full path of a csv file (or type 'q' to exit): \\n\")\n        if csv_input == \"q\":\n            sys.exit()\n        if not os.path.isfile(csv_input):\n            print(\"\\n Error {}: \\n File not found. \\n\")\n        else:\n            break\n    print(\"\\n Using file: \" + csv_input + \"\\n\")\n\n    # collect image pairs from csv into a dictionary and add each dictionary to a list 'images'\n    # images = [ { \"image1\" : \"string\", \"image2\": \"string\"}, ...]\n    csv = CSVParser()\n    images = csv.get_images(csv_input)\n\n    # Compute results for each image pair\n    print(\"\\n Computing comparisons... \\n\")\n    computer_vision = ComputerVision()\n    result_pairs = []\n    for pair in images:\n        if not os.path.isfile(pair[\"image1\"]):\n            print(\"FileNotFoundException {}: \\n\" + pair[\"image1\"] + \" does not exist! Please check the file path.\")\n            continue\n        if not os.path.isfile(pair[\"image2\"]):\n            print(\"FileNotFoundException {}: \\n\" + pair[\"image2\"] + \" does not exist! Please check the file path.\")\n            continue\n        result_pairs.append(computer_vision.find_similarity(pair))\n\n    # get an output path to write results file to\n    csv_output = None\n    while csv_input != \"q\":\n        print(\"\\n Output Path:\")\n        csv_output = raw_input(\"Please enter or copy/paste an existing full path (or type 'q' to exit): \\n\")\n        if csv_output == \"q\":\n            sys.exit()\n        if not os.path.isdir(csv_output):\n            print(\"\\n Error {}: \\n Path not found. \\n\")\n        else:\n            break\n\n    csv.write_results_file(result_pairs, csv_output)\n    print(\"\\n Success: \\n Output has been saved to 'results.csv' \")\n\n\nif __name__ == \"__main__\":\n    main()","sub_path":"CompareImages.py","file_name":"CompareImages.py","file_ext":"py","file_size_in_byte":2015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"477592412","text":"def panCakes(filename):\r\n    f=open(filename,'rU')\r\n    tc=int(f.readline())\r\n    g=open('panCakesLarge.out','w')\r\n    for i in range(tc):\r\n        s=f.readline()\r\n        count=0\r\n        last=s.rfind('-')\r\n        if last=='-1':\r\n            g.write(('Case #%d: %d\\n')%(i+1,count))\r\n            continue\r\n        while s[last]=='-':\r\n            if s[0]=='+':\r\n                upto=s.find('-')\r\n                if upto!=-1:\r\n                    s='-'*upto+s[upto:]\r\n                    count+=1\r\n                else:\r\n                    break\r\n            if s[0]=='-':\r\n                upto=s.find('+')\r\n                if upto!=-1:\r\n                    s='+'*upto+s[upto:]\r\n                    count+=1\r\n                else:\r\n                    count+=1\r\n                    break\r\n        g.write(('Case #%d: %d\\n')%(i+1,count))\r\npanCakes('B-large.in')\r\n","sub_path":"codes/CodeJamCrawler/16_0_2/sri.ram/perfectPanCake.py","file_name":"perfectPanCake.py","file_ext":"py","file_size_in_byte":863,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"597333488","text":"import numpy as np\nimport scipy\nfrom scipy import optimize\n\ndef load_csv_to_ndarray(filepath):\n    # Read using numpy lib and return all rows, no header in this case\n    return np.genfromtxt(filepath, dtype=float, delimiter=',')\n\n# Transform 1d feature to 256d using the trigonometric feature map/expansion\ndef transform_feature(one_dim_feature_vector, weighted=False):\n    transformed_feature_vector = np.zeros((len(one_dim_feature_vector), 2*128))\n\n    for i in range(0, len(one_dim_feature_vector)):\n        feature = one_dim_feature_vector[i]\n        transformed_feature = np.zeros(2*128)\n\n        for j in range(0, 128):\n            transformed_feature[2*j] = np.cos((j+1) * feature)\n            if weighted:\n                transformed_feature[2*j] = transformed_feature[2*j] * np.ceil(((2*j)+1)/2)\n\n            transformed_feature[(2*j)+1] = np.sin((j+1) * feature)\n            if weighted:\n                transformed_feature[(2*j)+1] = transformed_feature[(2*j)+1] * np.ceil(((2*j)+2)/2)\n\n        transformed_feature_vector[i] = transformed_feature\n\n    return transformed_feature_vector\n\n# Use either np.linalg.pinv or np.linalg.lstsq to compute the minimum Euclidean norm solution\n# Both yield the same results\n# The default method used is the Moore Pseudo inverse as it can accommodate all types of matrices,\n# including those that don't have full row rank\ndef compute_min_euclidean_norm_solution(feature_matrix, label_vector, use_pinv=True):\n    if use_pinv:\n        print('USE PINV')\n        pseudo_inverse = np.linalg.pinv(feature_matrix)\n        return np.dot(pseudo_inverse, label_vector)\n\n    print('USE LSTSQ')\n    # Get X'\n    feature_matrix_transposed = feature_matrix.T\n\n    # Compute X'X (covariance matrix)\n    feature_matrix_matmul = np.dot(feature_matrix_transposed, feature_matrix)\n\n    # Basically, lstsq(M_1, M_2) will return a solution, let say M_sol\n    # M_sol has the least squares or l2 norms amongst other solutions (there can also be no solution)\n    # M_sol fulfills the equation: np.dot(M_1, M_sol) = M_2\n    # In our case, M_1 would be X'X, M_2 would be X'y and M_sol would be the weights we seek\n    return np.linalg.lstsq(feature_matrix_matmul, np.dot(feature_matrix_transposed, label_vector), rcond=None)[0]\n\n# Create weights of size 256 as described in the problem: [ceil(1/2), ceil(2/2), ceil(3/2), ceil(4/2), ...]\n# and a diagonal matrix that represents the weights\ndef create_weights_diagonal_matrix():\n    weights = np.zeros(2*128)\n\n    for j in range(0, 2*128):\n        weights[j] = np.ceil((j+1)/2)\n\n    return weights, np.diag(weights)\n\n# Count nonzero entries in the solution with threshold = 1.e-15,\n# i.e. values that are less than the threshold are treated as zero\ndef count_nonzero_entries(weights, threshold=1.e-15):\n    count = 0\n\n    for weight in weights:\n        if abs(weight) < threshold:\n            count = count + 1\n\n    return count\n\n# Calculate squared loss for debugging purposes\ndef calculate_squared_loss(prediction_values, label_vector, mean=True):\n    # Calculate sigma (y_hat - y)^2\n    total_squared_loss = np.sum((prediction_values - label_vector) ** 2)\n\n    # Divide error by total observations to get mean squared loss\n    if mean:\n        return total_squared_loss / len(label_vector)\n\n    return total_squared_loss\n\n# Main function for experiment 1a\ndef compute_w_euclid(use_pinv=True):\n    print('Experiment 1a: ')\n    print()\n\n    train_df = load_csv_to_ndarray('hw3p1_train.csv')\n    test_df = load_csv_to_ndarray('hw3p1_test.csv')\n\n    one_dim_feature_vector = train_df[:,0]\n    label_vector = train_df[:,1]\n\n    feature_matrix = transform_feature(one_dim_feature_vector)\n\n    sol = compute_min_euclidean_norm_solution(feature_matrix, label_vector, use_pinv)\n    print('First 16 elements: ')\n    print(sol[0:16])\n    print()\n    print('Last 16 elements: ')\n    print(sol[240:256])\n    print()\n\n    print('Norm:')\n    print(np.linalg.norm(sol))\n    print()\n\n    test_feature_matrix = transform_feature(test_df)\n    test_pred_result = np.dot(test_feature_matrix, sol)\n\n    print('Mean squared loss: ')\n    print(calculate_squared_loss(np.dot(feature_matrix, sol), label_vector))\n    print()\n\n    print('Number of nonzero entries:')\n    print(count_nonzero_entries(sol))\n    print()\n\n    print('--------------------------------')\n\n    # # By default, Mac OS X can't directly render matplotlib\n    # # To use matplotlib, please use Jupyter Notebook or set the backend properly (https://stackoverflow.com/questions/21784641/installation-issue-with-matplotlib-python)\n    # import matplotlib.pyplot as plt\n    #\n    # fig = plt.figure(figsize=(10, 10))\n    #\n    # plt.title('Minimum Euclidean Plot')\n    # plt.plot(test_df, test_pred_result, color='pink', linewidth=1, marker='o', markersize=3, mfc='white', mec='black')\n    #\n    # plt.savefig('1a.png', bbox_inches='tight')\n    #\n    # plt.show()\n\ndef compute_w_weighted(use_pinv=True):\n    print('Experiment 1b: ')\n    print()\n\n    train_df = load_csv_to_ndarray('hw3p1_train.csv')\n    test_df = load_csv_to_ndarray('hw3p1_test.csv')\n\n    one_dim_feature_vector = train_df[:,0]\n    label_vector = train_df[:,1]\n\n    feature_matrix = transform_feature(one_dim_feature_vector)\n\n    _, weights_diagonal_matrix = create_weights_diagonal_matrix()\n\n    # Stack feature_matrix with diagonal matrix of weights\n    weighted_feature_matrix = np.vstack((feature_matrix, weights_diagonal_matrix))\n\n    # Pad label_vector with zeros to match the dimension of new feature_matrix\n    padded_label_vector = np.zeros(2*128 + len(label_vector))\n    padded_label_vector[0:len(label_vector)] = label_vector\n\n    sol = compute_min_euclidean_norm_solution(weighted_feature_matrix, padded_label_vector, use_pinv)\n    print('First 16 elements: ')\n    print(sol[0:16])\n    print()\n    print('Last 16 elements: ')\n    print(sol[240:256])\n    print()\n\n    print('Norm:')\n    print(np.linalg.norm(sol))\n    print()\n\n    test_feature_matrix = transform_feature(test_df)\n    test_pred_result = np.dot(test_feature_matrix, sol)\n\n    print('Mean squared loss: ')\n    print(calculate_squared_loss(np.dot(feature_matrix, sol), label_vector))\n    print()\n\n    print('Number of nonzero entries:')\n    print(count_nonzero_entries(sol))\n    print()\n\n    print('--------------------------------')\n\n    # # By default, Mac OS X can't directly render matplotlib\n    # # To use matplotlib, please use Jupyter Notebook or set the backend properly (https://stackoverflow.com/questions/21784641/installation-issue-with-matplotlib-python)\n    # import matplotlib.pyplot as plt\n    #\n    # fig = plt.figure(figsize=(10, 10))\n    #\n    # plt.title('Minimum Weighted Euclidean Plot')\n    # plt.plot(test_df, test_pred_result, color='pink', linewidth=1, marker='o', markersize=3, mfc='white', mec='black')\n    #\n    # plt.savefig('1b.png', bbox_inches='tight')\n    #\n    # plt.show()\n\ndef compute_w_dantzig():\n    print('Experiment 1c: ')\n    print()\n\n    train_df = load_csv_to_ndarray('hw3p1_train.csv')\n    test_df = load_csv_to_ndarray('hw3p1_test.csv')\n\n    one_dim_feature_vector = train_df[:,0]\n    label_vector = train_df[:,1]\n\n    feature_matrix = transform_feature(one_dim_feature_vector)\n\n    # weights: [ceil(1/2), ceil(2,2), ...], size: 256\n    weights, _ = create_weights_diagonal_matrix()\n\n    # Size: 512\n    appended_weights = np.zeros(2*2*128)\n\n    # This is the linear objective function\n    # First 256 is for w, last 256 for v\n    # Give zero weights to w (first 256) because\n    # we care only about sum(abs(w)), i.e. sum(v)\n    appended_weights[2*128:2*2*128] = weights\n\n    # Equality constraint\n\n    # A^tA\n    A_eq = np.dot(feature_matrix.transpose(), feature_matrix)\n    # Add 256 more zero columns to fit for v\n    A_eq = np.hstack((A_eq, np.zeros((2*128, 2*128))))\n\n    # Label vector stay as is, A^tb will have shape 256\n    b_eq = np.dot(feature_matrix.transpose(), label_vector)\n\n    # Inequality constraint\n    # Each pair of v and w needs to follow: - w - v <= 0 and w - v <= 0\n    A_ub = np.zeros((512, 512))\n    for i in range(0, int(len(A_ub)/2)):\n        # -w_j - v_j\n        A_ub[2*i][i] = -1\n        A_ub[2*i][i+256] = -1\n\n        # w_j - v_j\n        A_ub[(2*i)+1][i] = 1\n        A_ub[(2*i)+1][i+256] = -1\n\n    # Upper bound for w and v\n    b_ub = np.zeros(512)\n\n    # Set bound for w from -inf to inf (None, None)\n    # Set bound for v from 0 to inf (0, None) -> absolute value, non-negative\n    bounds = []\n    for i in range(0, 256):\n        bounds.append((None, None))\n    for i in range(0, 256):\n        bounds.append((0, None))\n\n    # Call the linear programming solver with method revised simplex\n    res = scipy.optimize.linprog(appended_weights, A_eq=A_eq, b_eq=b_eq, A_ub=A_ub, b_ub=b_ub, bounds=bounds, method='revised simplex')\n\n    # Get the solution from result object\n    dantzig_sol = res.x[0:256]\n\n    print('First 16 elements: ')\n    print(dantzig_sol[0:16])\n    print()\n    print('Last 16 elements: ')\n    print(dantzig_sol[240:256])\n    print()\n\n    print('Norm:')\n    print(np.linalg.norm(dantzig_sol))\n    print()\n\n    print('Mean squared loss: ')\n    print(calculate_squared_loss(np.dot(feature_matrix, dantzig_sol), label_vector))\n    print()\n\n    print('Number of nonzero entries:')\n    print(count_nonzero_entries(dantzig_sol))\n    print()\n\n    print('--------------------------------')\n\n    # # By default, Mac OS X can't directly render matplotlib\n    # # To use matplotlib, please use Jupyter Notebook or set the backend properly (https://stackoverflow.com/questions/21784641/installation-issue-with-matplotlib-python)\n    # import matplotlib.pyplot as plt\n    #\n    # test_feature_matrix = transform_feature(test_df)\n    # test_pred_result = np.dot(test_feature_matrix, dantzig_sol)\n    #\n    # fig = plt.figure(figsize=(10, 10))\n    #\n    # plt.title('Dantzig Selector Euclidean Plot')\n    # plt.plot(test_df, test_pred_result, color='pink', linewidth=1, marker='o', markersize=3, mfc='white', mec='black')\n    #\n    # plt.savefig('1c.png', bbox_inches='tight')\n    #\n    # plt.show()\n\nif __name__ == '__main__':\n    compute_w_euclid()\n\n    compute_w_weighted()\n\n    compute_w_dantzig()\n","sub_path":"hw_3/submission/gd2551_hw3/a1.py","file_name":"a1.py","file_ext":"py","file_size_in_byte":10139,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"121447907","text":"import requests\nimport json\nimport time\nimport datetime\nimport random\n\nfrom fake_useragent import UserAgent\nfrom dateutil.relativedelta import relativedelta\n\n\nclass HuiTun(object):\n    def __init__(self, phone, passwd, keyword):\n        \"\"\"\n        灰豚数据\n        @param phone: 登录手机号\n        @param passwd: 登录密码\n        @param keyword: 搜索关键词\n        \"\"\"\n        self.user_agent = UserAgent(verify_ssl=False).random\n        self.phone = phone\n        self.passwd = passwd\n        self.keyword = keyword\n        self.cookie = ''\n\n    def login_huitun(self):\n        \"\"\"\n        登陆灰豚数据\n        @return: 登录后的cookie值\n        \"\"\"\n        login_url_first = 'https://login.huitun.com/weChat/userLogin'\n        headers_first = {\n            'Content-Type': 'application/json',\n            'Host': 'login.huitun.com',\n            'Origin': 'https://dy.huitun.com',\n            'User-Agent': self.user_agent,\n        }\n        data = {\n            'mobile': self.phone,\n            'password': self.passwd,\n        }\n        requests.post(login_url_first, headers=headers_first, data=json.dumps(data))\n\n        headers_second = {\n            'Content-Type': 'application/json',\n            'Origin': 'https://dy.huitun.com',\n            'User-Agent': self.user_agent,\n        }\n        login_url_second = f'https://dyapi.huitun.com/userLogin?_t={int(time.time() * 1000)}'\n        res = requests.post(login_url_second, headers=headers_second, data=json.dumps(data))\n        cookies = res.cookies.items()\n        cookie = ''\n        for name, value in cookies:\n            cookie += '{0}={1}'.format(name, value)\n        self.cookie = cookie\n\n    def search_live_user(self):\n        \"\"\"\n        搜索用户，获取uid(必须)、点赞等数据\n        @return: 获取到的数据\n        \"\"\"\n        search_url = 'https://dyapi.huitun.com/search/user'\n        headers = {\n            'Cookie': self.cookie,\n            'Host': 'dyapi.huitun.com',\n            'Origin': 'https://dy.huitun.com',\n            'User-Agent': self.user_agent,\n        }\n        params = {\n            '_t': int(time.time() * 1000),\n            'cids': '',\n            'tagList': '',\n            'followerRange': '',\n            'diggRange': '',\n            'ageRange': '',\n            'scoreRange': '',\n            'gender': '',\n            'province': '',\n            'region': '',\n            'maxGender': '',\n            'maxAge': '',\n            'maxArea': '',\n            'maxCity': '',\n            'customVerify': '',\n            'verify': 'false',\n            'blueV': 'false',\n            'mcn': 'false',\n            'fusionShopEnter': 'false',\n            'promotionUser': 'false',\n            'contact': 'false',\n            'goodsSource': '',\n            'sales': '',\n            'visitorRange': '',\n            'prices': '',\n            'goodsKeyword': '',\n            'keywordMatch': '',\n            'goodsCates': '',\n            'subIds': '',\n            'leafIds': '',\n            'keyword': self.keyword,\n            'from': '1',\n            'sortField': '',\n            'tag': '0',\n            'fusionShopFlag': 'false',\n        }\n        try:\n            response = requests.get(search_url, headers=headers, params=params)\n            if response.status_code != 200:\n                self.login_huitun()\n                response = requests.get(search_url, headers=headers, params=params)\n\n            datas = response.json().get('data')[0]\n            item = dict()\n            item['uid'] = datas.get('uid', '')\n            item['nickname'] = datas.get('nickname', '')\n            item['douyin_id'] = datas.get('authorId', '')\n            item['fans_count'] = datas.get('followerCountTotal', '')\n            item['like_count'] = datas.get('totalFavorited', '')\n            item['video_count'] = datas.get('awemeCount', '')\n            return item\n        except:\n            return {}\n\n    def get_fans_add(self, uid):\n        \"\"\"\n        获取粉丝增加数量\n        @param uid: 唯一标识\n        @return:\n        \"\"\"\n        fans_add_url = 'https://dyapi.huitun.com/user/detail'\n        headers = {\n            'Cookie': self.cookie,\n            'Host': 'dyapi.huitun.com',\n            'Origin': 'https://dy.huitun.com',\n            'User-Agent': self.user_agent,\n        }\n        params = {\n            '_t': int(time.time() * 1000),\n            'uid': uid,\n            'example': uid,\n        }\n        try:\n            response = requests.get(fans_add_url, headers=headers, params=params).json()\n            datas_fan = response.get('data')\n            video_added = datas_fan.get('awewe30Count', 0)\n            return video_added\n        except:\n            return 0\n\n    def get_goods_num(self, uid):\n        \"\"\"\n        获取直播带货数据\n        @param uid: 唯一标识\n        @return:\n        \"\"\"\n        goods_url = 'https://dyapi.huitun.com/live/record'\n        headers = {\n            'Cookie': self.cookie,\n            'Host': 'dyapi.huitun.com',\n            'Origin': 'https://dy.huitun.com',\n            'User-Agent': self.user_agent,\n        }\n        page_num = 1\n        now_date = datetime.datetime.today()\n        end = f'{now_date.year}-{now_date.month}-{now_date.day}'\n        last_month = now_date - relativedelta(months=1)\n        start = f'{last_month.year}-{last_month.month}-{last_month.day}'\n        goods_num = 0\n        while True:\n            params = {\n                '_t': int(time.time() * 1000),\n                'from': page_num,\n                'time': '',\n                'has': 'HAS_GOODS',\n                'keyword': '',\n                'mod': 'DESC',\n                'sort': '',\n                'start': start,\n                'end': end,\n                'filterMap': '',\n                'uid': uid,\n                'example': uid,\n            }\n            try:\n                response = requests.get(goods_url, headers=headers, params=params).json()\n                datas = response.get('data')\n                # 数据为空的时候退出循环\n                if not datas:\n                    return goods_num\n\n                for i in datas:\n                    goods_num += i.get('goodsNum', 0)\n                page_num += 1\n                time.sleep(random.uniform(1, 3))\n            except:\n                return goods_num\n\n    def main(self):\n        self.login_huitun()\n        if not self.cookie:\n            return {}\n        res = self.search_live_user()\n        uid = res.get('uid', 0)\n        if uid:\n            video_add = self.get_fans_add(uid)\n            bring_goods = self.get_goods_num(uid)\n            res.update({\n                'video_added': video_add,\n                'bring_goods': bring_goods,\n            })\n        return res\n\n\nif __name__ == '__main__':\n    login_phone = ''\n    login_passwd = ''\n    keyword_search = ''\n    print(HuiTun(login_phone, login_passwd, keyword_search).main())\n","sub_path":"第三方平台/抖音/huitun.py","file_name":"huitun.py","file_ext":"py","file_size_in_byte":6925,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"378852004","text":"#  Licensed to the Apache Software Foundation (ASF) under one\n#  or more contributor license agreements.  See the NOTICE file\n#  distributed with this work for additional information\n#  regarding copyright ownership.  The ASF licenses this file\n#  to you under the Apache License, Version 2.0 (the\n#  \"License\"); you may not use this file except in compliance\n#  with the License.  You may obtain a copy of the License at\n#\n#  http://www.apache.org/licenses/LICENSE-2.0\n#\n#  Unless required by applicable law or agreed to in writing,\n#  software distributed under the License is distributed on an\n#  \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n#  KIND, either express or implied.  See the License for the\n#  specific language governing permissions and limitations\n#  under the License.\n\n\nimport sys\nimport gtk\nfrom NDImplementationDecorator import NDImplementationDecorator\n\n\nclass VulnerabilityNodeDialog:\n  def __init__(self,objt,environmentName,dupProperty,overridingEnvironment,builder):\n    self.window = builder.get_object(\"VulnerabilityNodeDialog\")\n    self.decorator = NDImplementationDecorator(builder)\n    self.decorator.updateTextCtrl(\"vulnerabilityNameCtrl\",objt.name())\n    self.decorator.updateTextCtrl(\"vulnerabilityTypeCtrl\",objt.type())\n    self.decorator.updateTextCtrl(\"vulnerabilitySeverityCtrl\",objt.severity(environmentName,dupProperty,overridingEnvironment))\n    self.decorator.updateMLTextCtrl(\"vulnerabilityDescriptionCtrl\",objt.description())\n    assets = []\n    for asset in objt.assets(environmentName,dupProperty):\n      assets.append([asset])\n    self.decorator.updateListCtrl(\"vulnerabilityAssetsCtrl\",['Asset'],gtk.ListStore(str),assets)\n    self.window.resize(300,325)\n\n  def on_vulnerabilityOkButton_clicked(self,callback_data):\n    self.window.destroy()\n\n  def show(self):\n    self.window.show()\n","sub_path":"cairis/cairis/VulnerabilityNodeDialog.py","file_name":"VulnerabilityNodeDialog.py","file_ext":"py","file_size_in_byte":1842,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"396109631","text":"# iter.py\n# 普通递归，根据调用层次决定占用多少栈帧\ndef fact(n):\n\tif n == 1:\n\t\treturn 1\n\treturn n*fact(n-1)\n\nprint(fact(10))\n\n'''\n尾递归优化：返回函数本身，而不使用函数做其他运算\n只会占用一个栈帧\ndef fact(n):\n    return fact_iter(n, 1)\n\ndef fact_iter(num, product):\n    if num == 1:\n        return product\n    return fact_iter(num - 1, num * product)\n'''\n","sub_path":"iter.py","file_name":"iter.py","file_ext":"py","file_size_in_byte":405,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"465428488","text":"import pandas as pd\nfrom scipy.io.arff import loadarff\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom sklearn.preprocessing import LabelEncoder\n\ndef read_in_dataset():\n    raw_data = loadarff('iris.arff')\n    iris_df = pd.DataFrame(raw_data[0])\n    return iris_df\n\ndef create_mean_matrix(iris_df, class_names, features_to_search):\n\n    mean_matrix = []\n    mean_dict_list = {}\n\n    for class_name in class_names:\n        mean_dict_list[class_name] = []\n\n    for class_name in class_names:\n        for feature in features_to_search:\n            mean_feature_val_of_class = iris_df.loc[(iris_df['class'] == class_name)][feature].mean()\n            mean_dict_list[class_name].append(mean_feature_val_of_class)\n\n        mean_matrix.append(mean_dict_list[class_name])\n\n    return mean_matrix\n\ndef compute_scatter_within_matrix(iris_df, class_names, features_to_search, mean_matrix):\n    # init scatter matrix\n    Scatter_Within = np.zeros((4, 4))\n    # index to get our features for a class this is class index number\n    count = 0\n\n    for class_name in class_names:\n        # init scatter matrix for this current class\n        scatter_matrix_for_class = np.zeros((4, 4))\n        # get the mean matrix\n        mean_vector_for_class = mean_matrix[count]\n        # get data for this class\n        class_feature_df = iris_df.loc[(iris_df['class'] == class_name)]\n        # get the feature values for this class\n        feature_vals_df = class_feature_df[features_to_search]\n        # iterate through the rows which is the value for each four features of this class\n        for index, row in feature_vals_df.iterrows():\n            # convert to np array so we can do other operations like transpose\n            np_feature_row = np.array([row.sepallength, row.sepalwidth, row.petallength, row.petalwidth])\n            # convert to np array so we can do other operations like transpose\n            np_mean_vec_for_class = np.array(mean_vector_for_class)\n            # reshape it to be a column so we can do the transpose after\n            np_column_feature_vec = np_feature_row.reshape(4, 1)                        # getting it as column\n            # reshape it to be a column so we can do the transpose after\n            np_mean_feature_vec = np_mean_vec_for_class.reshape(4, 1)                   # getting it as column\n            # calc the distance from mean\n            row_instance_minus_mean = np_column_feature_vec - np_mean_feature_vec       # xi−mi\n            # transpose\n            row_instance_minus_mean_T = row_instance_minus_mean.T                       # Transpose of xi−mi\n            # dot product is just multiplying each cell with appropriate cell on other matrix\n            multiply_result = row_instance_minus_mean.dot(row_instance_minus_mean_T)    # multiplication part\n            # add up the results\n            scatter_matrix_for_class += multiply_result\n        # go to next class\n        count += 1\n        # summ up the summation\n        Scatter_Within += scatter_matrix_for_class\n\n    return Scatter_Within\n\ndef compute_scatter_between_matrix(iris_df, class_names, features_to_search, mean_matrix):\n\n    # we want the overall mean of just the classes we are looking for\n    iris_df_target_classes = iris_df.loc[(iris_df['class'].isin(class_names))]\n    overall_mean = np.array(np.mean(iris_df_target_classes, axis=0).to_numpy())\n    overall_mean_reshape = overall_mean.reshape(4,1)\n\n    Scatter_Between = np.zeros((4, 4))\n    count = 0\n    for class_name in class_names:\n        # get the features for the class we're looking at\n        class_feature_df = iris_df.loc[(iris_df['class'] == class_name)]\n        # get the number of instances for this class\n        num_of_instances_of_class= len(class_feature_df.index)\n        # reshape mean vector to be a column\n        mean_vector_for_class = np.array(mean_matrix[count]).reshape(4,1)\n        # subtract the mean for this minus overall mean\n        mean_instance_minus_overall_mean = mean_vector_for_class - overall_mean_reshape\n        # Transpose\n        mean_instance_minus_overall_mean_T = mean_instance_minus_overall_mean.T\n        # dot product is the multiply step\n        multiply_result = mean_instance_minus_overall_mean.dot(mean_instance_minus_overall_mean_T)\n        # times the number of instances\n        multiply_result_times_num_instances = multiply_result * num_of_instances_of_class\n        # summation\n        Scatter_Between += multiply_result_times_num_instances\n        # go to next class\n        count += 1\n\n    return Scatter_Between\n\ndef LDA(class_names, title):\n\n    features_to_search = ['sepallength', 'sepalwidth', 'petallength', 'petalwidth']\n    # class_names = [b'Iris-setosa', b'Iris-versicolor', b'Iris-virginica']\n    class_dict = {}\n    iris_df = read_in_dataset()\n    mean_matrix = create_mean_matrix(iris_df, class_names, features_to_search)\n    Scatter_Within = compute_scatter_within_matrix(iris_df, class_names, features_to_search, mean_matrix)\n    Scatter_Between = compute_scatter_between_matrix(iris_df, class_names, features_to_search, mean_matrix)\n    eigen_values, eigen_vectors = np.linalg.eig(np.linalg.inv(Scatter_Within).dot(Scatter_Between))\n\n    list_eigen_vals = list(eigen_values)\n    sorted_index = []\n    sorted_eigen_vals = eigen_values[:]\n    # Find the sort of the eigenvectors from highest to least\n    sorted_eigen_vals.sort()\n    sorted_eigen_vals = list(sorted_eigen_vals)\n    sorted_eigen_vals.reverse()\n    for sorted_eigen in sorted_eigen_vals:\n        i = np.where(eigen_values == sorted_eigen)\n        sorted_index.append(i[0][0])\n\n    # print()\n    eigen_vector_interest1 = eigen_vectors[0].reshape(4, 1)\n    eigen_vector_interest2 = eigen_vectors[1].reshape(4, 1)\n\n    # We're joining the two eigen vectors of interest together\n    W = np.hstack((eigen_vector_interest1, eigen_vector_interest2))\n\n    iris_df_only_classes_we_want = iris_df.loc[iris_df['class'].isin(class_names)]\n    iris_data_we_want = np.array(iris_df_only_classes_we_want[features_to_search])\n    X_lda = iris_data_we_want.dot(W)\n\n    le = LabelEncoder()\n    # since we have multiple classes in the data\n    y = le.fit_transform(iris_df_only_classes_we_want['class'])\n\n    plt.title(title)\n    plt.xlabel('LD1')\n    plt.ylabel('LD2')\n    plt.scatter(\n        X_lda[:, 0],\n        X_lda[:, 1],\n        c=y,                # This is our classes\n        cmap='rainbow',\n        alpha=1,            # just plugged in 1\n        edgecolors='b'\n    )\n    plt.show()\n\ndef problem2_a():\n\n    #[b'Iris-setosa', b'Iris-versicolor', b'Iris-virginica']\n    class_names = [b'Iris-setosa', b'Iris-versicolor']\n    LDA(class_names, \"iris-setosa and iris-versicolor\")\n    class_names = [b'Iris-setosa', b'Iris-virginica']\n    LDA(class_names, \"iris-setosa and iris-virginica\")\n    class_names = [b'Iris-versicolor', b'Iris-virginica']\n    LDA(class_names, \"iris-versicolor and iris-virginica\")\n\ndef main():\n    problem2_a()\n\nif __name__ == '__main__':\n    main()","sub_path":"app/Problem2_a.py","file_name":"Problem2_a.py","file_ext":"py","file_size_in_byte":6981,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"520653108","text":"import logging\r\nlogger = logging.getLogger(\"Listener.modules.delete\")\r\n\r\ndef remove(caminho,parametro):\r\n\r\n\toutput = []\r\n\ttry:\r\n\t\tf = open (caminho, \"r\")\r\n\t\tarquivo = f.read()\r\n\t\tfor line in arquivo.split(';'):\r\n\t\t\tif not parametro in line:\r\n\t\t\t\toutput.append(line+';')\r\n\t\tf.close()\r\n\r\n\t\tf = open(caminho, 'w')\r\n\t\tf.writelines(output)\t\t\t\r\n\t\tf.close()\r\n\r\n\texcept FileNotFoundError as error:\r\n\t\tlogger.error('<ERROR: %s>'%error)\r\n","sub_path":"modules/delete.py","file_name":"delete.py","file_ext":"py","file_size_in_byte":428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"4931350","text":"from PyQt5 import QtCore, QtGui, QtWidgets\nimport codecs\n\n\nclass AboutWindow(object):\n\n    # Параметры окна\n    def setup_ui(self, dialog_about_us):\n        dialog_about_us.setObjectName(\"DialogAboutUs\")\n        dialog_about_us.resize(240, 320)\n        icon = QtGui.QIcon()\n        icon.addPixmap(QtGui.QPixmap(\"images/handyman.png\"), QtGui.QIcon.Normal, QtGui.QIcon.Off)\n        dialog_about_us.setWindowIcon(icon)\n        self.text_browser = QtWidgets.QTextBrowser(dialog_about_us)\n        self.text_browser.setGeometry(QtCore.QRect(0, 0, 241, 321))\n        self.text_browser.setObjectName(\"textBrowser\")\n        self.retranslate_ui(dialog_about_us)\n        QtCore.QMetaObject.connectSlotsByName(dialog_about_us)\n\n    def retranslate_ui(self, dialog_about_us):\n        dialog_about_us.setWindowTitle(QtWidgets.QApplication.translate(\"DialogAboutUs\", \"About\", None))\n        f = codecs.open('../resources/About.html', 'r', encoding='utf-8')\n        self.text_browser.setHtml(QtWidgets.QApplication.translate(\"DialogAboutUs\", f.read(), None))\n","sub_path":"scanner/gui/AboutWindow.py","file_name":"AboutWindow.py","file_ext":"py","file_size_in_byte":1056,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"544411358","text":"# coding:utf8\nfrom django.db import models\nfrom OA.models import Project\n\n\n# Create your models here.\nclass Employee(models.Model):\n\n    class Meta:\n        verbose_name_plural = '员工信息'\n\n    position_list = (\n        ('SD', '校长'),\n        ('ASD', '助理校长'),\n        ('DOS', '教学副校长'),\n        ('CCM', '咨询经理'),\n        ('CRM', '客服经理'),\n        ('CCS', '咨询主管'),\n        ('CRS', '客服主管'),\n        ('TRM', '学科组长'),\n        ('TRS', '学科带头人'),\n        ('CC', '教育顾问'),\n        ('CR', '班主任'),\n        ('TR', '学科教师'),\n        ('YWTR', '语文教师'),\n        ('SXTR', '数学教师'),\n        ('YYTR', '英语教师'),\n        ('WLTR', '物理教师'),\n        ('HXTR', '化学教师'),\n        ('KXTR', '科学教师'),\n        ('ZSDTR', '政史地教师'),\n        ('QTTR', '其他教师'),\n        ('TL', '出纳'),\n        ('CP', '教务专员'),\n        ('AD', '行政'),\n        ('CL', '保洁'),\n        ('LMS', '市场主管')\n    )\n\n    gender = (\n        ('M', '男'),\n        ('F', '女'),\n    )\n\n    diploma_list = (\n        ('Dr', '博士'),\n        ('Ma', '硕士'),\n        ('Ba', '本科'),\n        ('Sp', '专科'),\n        ('ot', '其他'),\n    )\n\n\n    # user_index = models.SmallIntegerField('序号', )\n    user_name = models.CharField('姓名', max_length=10)\n    user_position = models.CharField('职位', choices=position_list, max_length=10)\n    user_mail = models.EmailField('邮箱')\n    user_workcode = models.CharField('工号', unique=True, default=0,max_length=12)\n    user_school = models.ForeignKey(Project, verbose_name='校区', null=True)\n    user_phonenumber = models.CharField('联系方式', max_length=11)\n    user_salary = models.IntegerField('薪资', default=0, blank=True)\n    user_arrivetime = models.DateField('到岗时间', null=True)\n    user_ID = models.CharField('身份证号', unique=True, max_length=20, null=True)\n    user_gender = models.CharField('性别', choices=gender, max_length=2)\n    user_birthday = models.DateField('出生年月', null=True)\n    user_diploma = models.CharField('最高学历', choices=diploma_list, default='Ba', max_length=10)\n    user_graduate_colledge = models.CharField('毕业院校', max_length= 40)\n    user_major = models.CharField('专业', max_length=20)\n\n    def __unicode__(self):\n        return self.user_name\n","sub_path":"hr/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2394,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"262004404","text":"from .models import Host,Operatingsystem,Model,Environment,Hostgroup,Puppetclass,Manufacturer,Report\nfrom .foremanapi import foreman_get\nimport math\nimport re\nimport datetime\n\ndef update_fact(id):\n    try:\n        host = Host.objects.get(host_id=id)\n        facts = foreman_get('/hosts/%s/facts' % (id))[host.name]\n        host.ip = facts['ipaddress']\n        host.processors = facts['processorcount']\n        host.processor_type = facts['processor0']\n        host.os_name = facts['operatingsystem']\n        host.os_major = facts['os::release::major']\n        host.os_minor = facts['os::release::minor']\n        host.os_release = facts['operatingsystemrelease']\n        if \"M\" in facts['memorysize']:\n            host.memory = math.ceil(float(facts['memorysize'].split()[0]) * 0.001)\n        else:\n            host.memory = math.ceil(float(facts['memorysize'].split()[0]))\n        if facts.has_key('disks::sda::size'):\n            host.disk = facts['disks::sda::size'].split()[0]\n        host.uptime = facts['uptime_days']\n        if facts.has_key('serialnumber'):\n            host.sn = facts['serialnumber']\n        if facts.has_key('is_virtual'):\n             host.is_virtual = facts['is_virtual'].capitalize()\n        host.manufacturer = Manufacturer.objects.get_or_create(name=facts['manufacturer'])[0]\n        host.model = Model.objects.get_or_create(name=facts['productname'])[0]\n        host.save()\n        \n    except Exception as e:\n        pass\n        #raise e\n\ndef update_host():\n    results = foreman_get('/hosts')\n    for result in results:\n        try:\n            last_report = datetime.datetime.strptime(result['last_report'][:-4],'%Y-%m-%d %H:%M:%S') + datetime.timedelta(hours=8)\n            host, created = Host.objects.update_or_create(host_id=result['id'],defaults={'name': result['name'],'hostgroup_id': result['hostgroup_id'],'environment_id': result['environment_id'],'created_at': result['created_at'][:-4],'last_report': last_report,'status': result['global_status_label']})\n            if created:\n                update_fact(host.host_id)\n        except Exception as e:\n            continue  \n\ndef update_facts():\n    hosts = Host.objects.all()\n    for host in hosts:\n        update_fact(host.host_id)\n        # try:\n        #     host_id = host.host_id\n        #     facts = foreman_get('/hosts/%s/facts' % (host_id))[host.name]\n        #     host.ip = facts['ipaddress']\n        #     host.processors = facts['processorcount']\n        #     host.processor_type = facts['processor0']\n        #     host.os_name = facts['operatingsystem']\n        #     host.os_major = facts['os::release::major']\n        #     host.os_minor = facts['os::release::minor']\n        #     host.os_release = facts['operatingsystemrelease']\n        #     if \"M\" in facts['memorysize']:\n        #         host.memory = math.ceil(float(facts['memorysize'].split()[0]) * 0.001)\n        #     else:\n        #         host.memory = math.ceil(float(facts['memorysize'].split()[0]))\n        #     host.disk = facts['disks::sda::size'].split()[0]\n        #     host.uptime = facts['uptime_seconds']\n        #     host.save()\n        # except Exception as e:\n        #     raise e\n            # continue\n\ndef update_operatingsystem():\n    results = foreman_get('/operatingsystems')\n    for result in results:\n        try:\n            Operatingsystem.objects.get_or_create(operatingsystem_id=result['id'],defaults={'name': result['name'],'major': result['major'],'minor': result['minor']})\n        except Exception as e:\n            continue\n\n# def update_model():\n#     results = foreman_get('/models')\n#     for result in results:\n#         try:\n#             Model.objects.get_or_create(model_id=result['id'],defaults={'name': result['name']})\n#         except Exception as e:\n#             continue\n\ndef update_environment():\n    results = foreman_get('/environments')\n    for result in results:\n        try:\n            Environment.objects.update_or_create(environment_id=result['id'],defaults={'name': result['name']})\n        except Exception as e:\n            continue\n\ndef update_hostgroup():\n    results = foreman_get('/hostgroups')\n    for result in results:\n        try:\n            Hostgroup.objects.update_or_create(hostgroup_id=result['id'],defaults={'name': result['name'],'environment_id': result['environment_id']})\n        except Exception as e:\n            continue\n\ndef update_class():\n    results = foreman_get('/puppetclasses')\n    for k,v in results.items():\n        for pclass in v:\n            try:\n                Puppetclass.objects.get_or_create(class_id=pclass['id'],defaults={'name': pclass['name']})\n            except Exception as e:\n                # raise e\n                continue           \n\ndef update_reports():\n    search = \"?search=reported+%3D+Today++and++eventful++%3D++true\"\n    results = foreman_get(\"/config_reports\",search=search)\n    for result in results:\n        try:\n            reported_at = datetime.datetime.strptime(result['reported_at'][:-4],'%Y-%m-%d %H:%M:%S') + datetime.timedelta(hours=8)\n            report,created = Report.objects.get_or_create(r_id=result['id'],defaults ={'host_id': result['host_id'],'reported_at': reported_at,'applied': result['status']['applied'],'restarted': result['status']['restarted'],'failed': result['status']['failed'],'failed_restarts': result['status']['failed_restarts'],'skipped': result['status']['skipped']})\n        except Exception as e:\n            # raise e\n            continue\n\ndef inventory():\n    hosts = Host.objects.all()\n    f = open('/etc/ansible/hosts','r+')\n    ips = f.read()\n    for host in hosts:\n        try:\n            if re.match(host.ip,ips):\n                pass\n            else:\n                f.write('%s\\n'%(host.ip))\n        except Exception as e:\n                continue    \n    f.close()\n\n\n","sub_path":"cmdb/cron.py","file_name":"cron.py","file_ext":"py","file_size_in_byte":5818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"592545228","text":"\nfrom joblib import dump, load\nimport pandas as pd, numpy as np, os, argparse\n\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.model_selection import train_test_split\n\n# Argument parser\ndef _parse_args():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--filepath', type=str, default='/opt/ml/processing/input/')\n    parser.add_argument('--filename', type=str, default='data.csv')\n    parser.add_argument('--outputpath', type=str, default='/opt/ml/processing/output/')\n    # Parse the arguments\n    return parser.parse_known_args()\n\n# Main Training Loop\nif __name__==\"__main__\":\n    # Process arguments\n    args, _ = _parse_args()\n    # Load the dataset\n    df = pd.read_csv(os.path.join(args.filepath, args.filename))\n    X,y = df.drop('y', axis=1), df.y\n    # Define the pipeline and train it\n    pipe = Pipeline([('scaler', StandardScaler())])\n    transformed = pipe.fit_transform(X)\n    # Generate the output files - train and test\n    output = pd.concat([pd.DataFrame(transformed), y], axis=1)\n    train, test = train_test_split(output, random_state=42)\n    train.to_csv(os.path.join(args.outputpath, 'train/train.csv'), index=False)\n    test.to_csv(os.path.join(args.outputpath, 'test/test.csv'), index=False)\n    # Store the pipeline\n    dump(pipe, os.path.join(args.outputpath, 'pipeline/preproc-pipeline.joblib'))\n","sub_path":"sagemaker-pipelines/tabular/sklearn-scheduled-training-and-inference/preprocessing.py","file_name":"preprocessing.py","file_ext":"py","file_size_in_byte":1391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"582494774","text":"#single layer network with, 32x32, nodes. The first layer used an RELU \n#activation algorithm with the last softmax layer containing number of classes, 10, \n#nodes. With a batch size of 32 and 32 epochs, we were able to achieve a testing\n# accuracy of  0.5456 (54.56%) taking a total 1,541 seconds or 25.68 minutes with an epoch \n#taking an average of 48.1563 seconds or 0.80 minutes and a loss of \n#1.30503999999999989 (calculated by a sgd loss function).\nfrom keras.datasets import cifar10\nfrom keras.models import Sequential\nfrom keras.layers.core import Dense, Dropout, Activation\nfrom keras.utils import np_utils\n\n#The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes,\n#with 6000 images per class. There are 50000 training images and 10000 test images.\nbatch_size = 32           #Number of images used in each optimization step\nnum_classes = 10          #One class per digit\nepochs = 32               #Number of times the whole data is used to learn\ntest_img_count = 10000    #number of images in test data set\ntrain_img_count = 50000   #number of images in training dataset\nnum_pixles = 32*32*3      #32x32 imgs with 3 channel color\n\n(X_train, y_train), (X_test, y_test) = cifar10.load_data()   #load data from mnist\nX_test = X_test.reshape(test_img_count, num_pixles)\nX_train = X_train.reshape(train_img_count, num_pixles)\n\n#normalize X values\nX_train = X_train.astype('float32')\nX_test = X_test.astype('float32')\nX_train /= 255\nX_test /= 255\n\n# convert class vectors to binary class matrices (ie one-hot vectors)\nY_train = np_utils.to_categorical(y_train, num_classes)\nY_test = np_utils.to_categorical(y_test, num_classes)\n\n#Define the model achitecture\nmodel = Sequential()\n#layer has num_pixles nodes with an input of num_pixles inputs\nmodel.add(Dense(32*32,\n                activation = 'relu',\n                input_shape=(num_pixles,)))\n#Dropout consists in randomly setting a fraction rate of input units\n#to 0 at each update during training time, which helps prevent overfitting.\nmodel.add(Dropout(0.5))\n#final layer used to catagorize the output. Each output is from\n#a specific catagory is the probability that the\n#that the training image is a specific catagory\nmodel.add(Dense(10,\n                activation = 'softmax'))\n\n\nmodel.compile(loss='categorical_crossentropy',\n              optimizer=\"sgd\",\n              metrics=[\"accuracy\"])\n\n#let the architecture learn the correct params needed to be accurate to labels\nmodel.fit(X_train,\n          Y_train,\n          batch_size,\n          epochs,\n          verbose=1,\n          validation_data=(X_test, Y_test))\n\n#Evaluate how the model does on the test set\nscore = model.evaluate(X_test,\n                       Y_test,\n                       verbose=1)\nprint(score)","sub_path":"CIFAR-NN.py","file_name":"CIFAR-NN.py","file_ext":"py","file_size_in_byte":2754,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"371650622","text":"# -*- coding: utf-8 -*-\n\"\"\"Test suite for axonapi.api.assets.\"\"\"\nimport copy\nimport datetime\nimport json\n\nimport pytest\nfrom axonius_api_client.api import json_api\nfrom axonius_api_client.constants.api import GUI_PAGE_SIZES\nfrom axonius_api_client.constants.general import SIMPLE\nfrom axonius_api_client.exceptions import ApiError, NotFoundError\n\nfrom ...meta import QUERIES\nfrom ...utils import get_schema\n\n\nclass SavedQueryPrivate:\n    def test_private_get(self, apiobj):\n        result = apiobj.saved_query._get()\n        assert isinstance(result, list)\n        for item in result:\n            assert isinstance(item, json_api.saved_queries.SavedQuery)\n            validate_sq(item.to_dict())\n\n\nclass SavedQueryPublic:\n    def test_get_no_generator(self, apiobj):\n        rows = apiobj.saved_query.get(generator=False)\n        assert not rows.__class__.__name__ == \"generator\"\n        assert isinstance(rows, list)\n        for row in rows:\n            assert isinstance(row, dict)\n            validate_sq(row)\n\n    def test_get_generator(self, apiobj):\n        gen = apiobj.saved_query.get(generator=True)\n        assert gen.__class__.__name__ == \"generator\"\n        assert not isinstance(gen, list)\n\n        rows = [x for x in gen]\n        for row in rows:\n            assert isinstance(row, dict)\n            validate_sq(row)\n\n    def test_get_tags(self, apiobj):\n        tags = apiobj.saved_query.get_tags()\n        assert isinstance(tags, list)\n        for tag in tags:\n            assert isinstance(tag, str)\n\n    def test_get_by_name(self, apiobj):\n        sq = apiobj.saved_query.get()[0]\n        value = sq[\"name\"]\n        row = apiobj.saved_query.get_by_name(value=value)\n        assert isinstance(row, dict)\n        assert row[\"name\"] == value\n\n    def test_get_by_name_error(self, apiobj):\n        value = \"badwolf_yyyyyyyyyyyy\"\n        with pytest.raises(NotFoundError):\n            apiobj.saved_query.get_by_name(value=value)\n\n    def test_get_by_uuid(self, apiobj):\n        sq = apiobj.saved_query.get()[0]\n        value = sq[\"uuid\"]\n        row = apiobj.saved_query.get_by_uuid(value=value)\n        assert isinstance(row, dict)\n        assert row[\"uuid\"] == value\n\n    def test_get_by_uuid_error(self, apiobj):\n        value = \"badwolf_xxxxxxxxxxxxx\"\n        with pytest.raises(NotFoundError):\n            apiobj.saved_query.get_by_uuid(value=value)\n\n    def test_get_by_tags(self, apiobj):\n        tags = [y for x in apiobj.saved_query.get() for y in x.get(\"tags\", [])]\n        value = tags[0]\n        rows = apiobj.saved_query.get_by_tags(value=value)\n        assert isinstance(rows, list)\n        for row in rows:\n            assert isinstance(row, dict)\n            assert value in row[\"tags\"]\n\n    def test_get_by_tags_error(self, apiobj):\n        value = \"badwolf_wwwwwwww\"\n        with pytest.raises(NotFoundError):\n            apiobj.saved_query.get_by_tags(value=value)\n\n    @pytest.fixture(scope=\"class\")\n    def sq_fixture(self, apiobj):\n        get_schema(apiobj=apiobj, field=\"specific_data.data.last_seen\")\n        field_simple = apiobj.FIELD_SIMPLE\n\n        name = \"badwolf torked\"\n        fields = [\"adapters\", \"last_seen\", \"id\", field_simple]\n\n        sort_field = field_simple\n        # colfilters = {field_simple: \"a\"}\n        sort_desc = False\n        gui_page_size = GUI_PAGE_SIZES[-1]\n        tags = [\"badwolf1\", \"badwolf2\"]\n        description = \"badwolf torked\"\n        query = QUERIES[\"not_last_seen_day\"]\n\n        try:\n            apiobj.saved_query.delete_by_name(value=name)\n        except NotFoundError:\n            pass\n\n        row = apiobj.saved_query.add(\n            name=name,\n            fields=fields,\n            sort_field=sort_field,\n            sort_descending=sort_desc,\n            # column_filters=colfilters,\n            gui_page_size=gui_page_size,\n            tags=tags,\n            description=description,\n            query=query,\n        )\n        validate_sq(row)\n\n        assert row[\"name\"] == name\n        assert row[\"query_type\"] == \"saved\"\n        assert row[\"tags\"] == tags\n        assert row[\"description\"] == description\n        assert row[\"private\"] is False\n        assert row[\"view\"][\"query\"][\"filter\"] == query\n        assert row[\"view\"][\"query\"][\"onlyExpressionsFilter\"] == query\n        assert row[\"view\"][\"query\"][\"expressions\"] == []\n        assert row[\"view\"][\"pageSize\"] == gui_page_size\n        # assert row[\"view\"][\"colFilters\"] == colfilters\n        assert row[\"view\"][\"sort\"][\"field\"] == sort_field\n        assert row[\"view\"][\"sort\"][\"desc\"] == sort_desc\n\n        yield row\n\n        try:\n            apiobj.saved_query.delete_by_name(value=name)\n        except NotFoundError:\n            pass\n\n    def test_add_remove(self, apiobj, sq_fixture):\n        row = apiobj.saved_query.delete_by_name(value=sq_fixture[\"name\"])\n        assert isinstance(row, dict)\n        assert row[\"uuid\"] == sq_fixture[\"uuid\"]\n\n        with pytest.raises(NotFoundError):\n            apiobj.saved_query.get_by_name(value=sq_fixture[\"name\"])\n\n    def test_add_error_no_fields(self, apiobj):\n        name = \"badwolf_nnnnnnnnnnnnn\"\n        with pytest.raises(ApiError):\n            apiobj.saved_query.add(name=name, fields_default=False)\n\n    def test_add_error_bad_sort_field(self, apiobj):\n        name = \"badwolf_sssssssssssss\"\n        fields = \"last_seen\"\n        sort_field = \"badwolf\"\n        with pytest.raises(ApiError):\n            apiobj.saved_query.add(name=name, fields=fields, sort_field=sort_field)\n\n    def test_add_error_bad_colfilter(self, apiobj):\n        name = \"badwolf_ttttttttttt\"\n        fields = \"last_seen\"\n        colfilters = {\"badwolf\": \"badwolf\"}\n        with pytest.raises(ApiError):\n            apiobj.saved_query.add(name=name, fields=fields, column_filters=colfilters)\n\n\nclass TestSavedQueryDevices(SavedQueryPrivate, SavedQueryPublic):\n    @pytest.fixture(scope=\"class\")\n    def apiobj(self, api_devices):\n        return api_devices\n\n\nclass TestSavedQueryUsers(SavedQueryPrivate, SavedQueryPublic):\n    @pytest.fixture(scope=\"class\")\n    def apiobj(self, api_users):\n        return api_users\n\n\ndef validate_qexpr(qexpr, asset):\n    assert isinstance(qexpr, dict)\n\n    compop = qexpr.pop(\"compOp\")\n    assert isinstance(compop, str)\n\n    field = qexpr.pop(\"field\")\n    assert isinstance(field, str)\n\n    idx = qexpr.pop(\"i\", 0)\n    assert isinstance(idx, int)\n\n    leftbracket = qexpr.pop(\"leftBracket\", 0)\n    assert isinstance(leftbracket, (int, bool))\n\n    rightbracket = qexpr.pop(\"rightBracket\", 0)\n    assert isinstance(rightbracket, (int, bool))\n\n    logicop = qexpr.pop(\"logicOp\")\n    assert isinstance(logicop, str)\n\n    notflag = qexpr.pop(\"not\")\n    assert isinstance(notflag, bool)\n\n    value = qexpr.pop(\"value\")\n    assert isinstance(value, SIMPLE) or value is None\n\n    obj = qexpr.pop(\"obj\", False)\n    assert isinstance(obj, bool)\n\n    nesteds = qexpr.pop(\"nested\", [])\n    assert isinstance(nesteds, list)\n\n    fieldtype = qexpr.pop(\"fieldType\", \"\")\n    assert isinstance(fieldtype, str)\n\n    children = qexpr.pop(\"children\", [])  # new in 2.15\n    assert isinstance(children, list)  # new in 2.15\n\n    filtered_adapters = qexpr.pop(\"filteredAdapters\", {})\n    assert isinstance(filtered_adapters, dict) or filtered_adapters is None\n\n    context = qexpr.pop(\"context\", \"\")  # new in 2.15\n    assert isinstance(context, str)\n\n    timestamp = qexpr.pop(\"timestamp\", \"\")\n    assert isinstance(timestamp, str)\n\n    brackweight = qexpr.pop(\"bracketWeight\", 0)\n    assert isinstance(brackweight, int)\n\n    qfilter = qexpr.pop(\"filter\", \"\")\n    assert isinstance(qfilter, str)\n\n    for nested in nesteds:\n        validate_nested(nested, asset)\n\n    for child in children:\n        validate_nested(child, asset)\n\n    assert not qexpr, list(qexpr)\n\n\ndef validate_nested(nested, asset):\n    assert isinstance(nested, dict)\n\n    nfiltered_adapters = nested.pop(\"filteredAdapters\", {})\n    assert isinstance(nfiltered_adapters, dict) or nfiltered_adapters is None\n\n    ncondition = nested.pop(\"condition\")\n    assert isinstance(ncondition, str)\n\n    nexpr = nested.pop(\"expression\")\n    assert isinstance(nexpr, dict)\n\n    nidx = nested.pop(\"i\")\n    assert isinstance(nidx, int)\n\n    assert not nested, list(nested)\n\n\ndef validate_sq(asset):\n    asset = copy.deepcopy(asset)\n    assert isinstance(asset, dict)\n\n    original = copy.deepcopy(asset)\n    assert original == asset\n\n    assert asset[\"query_type\"] in [\"saved\"]\n\n    date_fetched = asset.pop(\"date_fetched\")\n    assert isinstance(date_fetched, str)\n\n    last_updated = asset.pop(\"last_updated\", None)\n    assert isinstance(last_updated, (str, datetime.datetime, type(None)))\n\n    name = asset.pop(\"name\")\n    assert isinstance(name, str)\n\n    query_type = asset.pop(\"query_type\")\n    assert isinstance(query_type, str)\n\n    user_id = asset.pop(\"user_id\")\n    assert isinstance(user_id, str)\n\n    uuid = asset.pop(\"uuid\")\n    assert isinstance(uuid, str)\n\n    description = asset.pop(\"description\")\n    assert isinstance(description, str) or description is None\n\n    timestamp = asset.pop(\"timestamp\", \"\")\n    assert isinstance(timestamp, (str, type(None)))\n\n    archived = asset.pop(\"archived\", False)  # added in 2.15\n    assert isinstance(archived, bool)\n\n    updated_by_str = asset.pop(\"updated_by\")\n    assert isinstance(updated_by_str, str)\n\n    updated_by = json.loads(updated_by_str)\n    assert isinstance(updated_by, dict)\n\n    updated_by_deleted = updated_by.pop(\"deleted\")\n    assert isinstance(updated_by_deleted, bool)\n\n    # 4.5\n    updated_by_is_first_login = updated_by.pop(\"is_first_login\", False)\n    assert isinstance(updated_by_is_first_login, bool)\n\n    # 4.5\n    updated_by_permanent = updated_by.pop(\"permanent\", False)\n    assert isinstance(updated_by_permanent, bool)\n\n    updated_str_keys_req = [\n        \"first_name\",\n        \"last_name\",\n        \"source\",\n        \"user_name\",\n    ]\n    for updated_str_key in updated_str_keys_req:\n        val = updated_by.pop(updated_str_key)\n        assert isinstance(val, (str, int, float)) or val is None\n\n    updated_str_keys_opt = [\n        \"_id\",\n        \"last_updated\",\n        \"password\",\n        \"pic_name\",\n        \"role_id\",\n        \"salt\",\n    ]\n    for updated_str_key in updated_str_keys_opt:\n        val = updated_by.pop(updated_str_key, None)\n        assert isinstance(val, (str, int, float)) or val is None\n\n    assert not updated_by\n\n    tags = asset.pop(\"tags\", [])\n    assert isinstance(tags, list)\n    for tag in tags:\n        assert isinstance(tag, str)\n\n    predefined = asset.pop(\"predefined\", False)\n    assert isinstance(predefined, bool)\n\n    private = asset.pop(\"private\", False)\n    assert isinstance(private, bool)\n\n    view = asset.pop(\"view\")\n    assert isinstance(view, dict)\n\n    colsizes = view.pop(\"coloumnSizes\", [])\n    assert isinstance(colsizes, list)\n\n    for x in colsizes:\n        assert isinstance(x, int)\n\n    fields = view.pop(\"fields\")\n    assert isinstance(fields, list)\n\n    for x in fields:\n        assert isinstance(x, str)\n\n    page = view.pop(\"page\", 0)\n    assert isinstance(page, int)\n\n    pagesize = view.pop(\"pageSize\", 0)\n    assert isinstance(pagesize, int)\n\n    sort = view.pop(\"sort\")\n    assert isinstance(sort, dict)\n\n    sort_desc = sort.pop(\"desc\")\n    assert isinstance(sort_desc, bool)\n\n    sort_field = sort.pop(\"field\")\n    assert isinstance(sort_field, str)\n\n    query = view.pop(\"query\")\n    assert isinstance(query, dict)\n\n    \"\"\" changed in 4.5\n    colfilters = view.pop(\"colFilters\", {})\n    assert isinstance(colfilters, dict)\n    for k, v in colfilters.items():\n        assert isinstance(k, str)\n        assert isinstance(v, str)\n    \"\"\"\n\n    # 4.5\n    \"\"\" structure:\n    [\n        {\n            \"columnFilter\": {\n                \"aqlExpression\": '(\"specific_data.data.name\" == ' 'regexMatch(\"a\", \"i\"))',\n                \"arrayFields\": [],\n                \"complexNestedFields\": [],\n                \"complexParentToUnwind\": None,\n                \"fieldPath\": \"specific_data.data.name\",\n                \"fieldType\": \"string\",\n                \"filterExpressions\": [\n                    {\n                        \"bracketWeight\": 0,\n                        \"children\": [],\n                        \"compOp\": \"columnFilterContains\",\n                        \"field\": \"specific_data.data.name\",\n                        \"fieldType\": \"axonius\",\n                        \"filter\": '(\"specific_data.data.name\" ' '== regexMatch(\"a\", \"i\"))',\n                        \"leftBracket\": 0,\n                        \"logicOp\": \"\",\n                        \"not\": False,\n                        \"rightBracket\": 0,\n                        \"value\": \"a\",\n                    }\n                ],\n                \"isComplexField\": False,\n                \"isComplexNestedField\": False,\n                \"nestedFilteredFields\": [],\n            },\n            \"fieldPath\": \"specific_data.data.name\",\n        }\n    ]\n    \"\"\"\n    colfilters = view.pop(\"colFilters\", [])\n    assert isinstance(colfilters, list)\n    for colfilter in colfilters:\n        assert isinstance(colfilter, dict)\n\n    qfilter = query.pop(\"filter\")\n    assert isinstance(qfilter, str) or qfilter is None\n\n    qexprs = query.pop(\"expressions\", [])\n    assert isinstance(qexprs, list)\n\n    qmeta = query.pop(\"meta\", {})\n    assert isinstance(qmeta, dict)\n\n    qonlyexprfilter = query.pop(\"onlyExpressionsFilter\", \"\")\n    assert isinstance(qonlyexprfilter, str)\n\n    qsearch = query.pop(\"search\", None)\n    assert qsearch is None or isinstance(qsearch, str)\n\n    historical = view.pop(\"historical\", None)\n    assert historical is None or isinstance(historical, SIMPLE)\n\n    \"\"\" changed in 4.5\n    # 3.6+\n    excluded_adapters = view.pop(\"colExcludedAdapters\", {})\n    assert isinstance(excluded_adapters, dict)\n    \"\"\"\n    # 4.5\n    \"\"\" structure\n    [{\"exclude\": [\"chef_adapter\"], \"fieldPath\": \"specific_data.data.name\"}]\n    \"\"\"\n    excluded_adapters = view.pop(\"colExcludedAdapters\", [])\n    assert isinstance(excluded_adapters, list)\n    for excluded_adapter in excluded_adapters:\n        assert isinstance(excluded_adapter, dict)\n\n    # 4.0\n    always_cached = asset.pop(\"always_cached\")\n    assert isinstance(always_cached, bool)\n    asset_scope = asset.pop(\"asset_scope\")\n    assert isinstance(asset_scope, bool)\n    is_asset_scope_query_ready = asset.pop(\"is_asset_scope_query_ready\")\n    assert isinstance(is_asset_scope_query_ready, bool)\n    is_referenced = asset.pop(\"is_referenced\")\n    assert isinstance(is_referenced, bool)\n    _id = asset.pop(\"id\")\n    assert isinstance(_id, str) and _id\n\n    for qexpr in qexprs:\n        validate_qexpr(qexpr, asset)\n\n    assert not query, list(query)\n    assert not sort, list(sort)\n    assert not view, list(view)\n    assert not asset, list(asset)\n","sub_path":"axonius_api_client/tests/tests_api/tests_assets/test_saved_query.py","file_name":"test_saved_query.py","file_ext":"py","file_size_in_byte":14735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"241457277","text":"from flask import abort, Blueprint, make_response, request, redirect, json\r\n\r\nfrom libs.admin_authentication import authenticate_researcher_study_access\r\nfrom libs.json_logic import do_validate_survey\r\nfrom database.study_models import Survey\r\n\r\nsurvey_api = Blueprint('survey_api', __name__)\r\n\r\n################################################################################\r\n############################## Creation/Deletion ###############################\r\n################################################################################\r\n\r\n\r\n@survey_api.route('/create_survey/<string:study_id>/<string:survey_type>', methods=['GET', 'POST'])\r\n@authenticate_researcher_study_access\r\ndef create_survey(study_id=None, survey_type='tracking_survey'):\r\n    new_survey = Survey.create_with_settings(study_id=study_id, survey_type=survey_type)\r\n    return redirect('/edit_survey/{:d}'.format(new_survey.id))\r\n\r\n\r\n@survey_api.route('/delete_survey/<string:survey_id>', methods=['GET', 'POST'])\r\n@authenticate_researcher_study_access\r\ndef delete_survey(survey_id=None):\r\n    try:\r\n        survey = Survey.objects.get(pk=survey_id)\r\n    except Survey.DoesNotExist:\r\n        return abort(404)\r\n\r\n    study_id = survey.study_id\r\n    survey.mark_deleted()\r\n    return redirect('/view_study/{:d}'.format(study_id))\r\n\r\n################################################################################\r\n############################# Setters and Editors ##############################\r\n################################################################################\r\n\r\n\r\n@survey_api.route('/update_survey/<string:survey_id>', methods=['GET', 'POST'])\r\n@authenticate_researcher_study_access\r\ndef update_survey(survey_id=None):\r\n    try:\r\n        survey = Survey.objects.get(pk=survey_id)\r\n    except Survey.DoesNotExist:\r\n        return abort(404)\r\n\r\n    # BUG: There is an unknown situation where the frontend sends a string requiring an extra\r\n    # deserialization operation, causing 'content' to be a string containing a json string\r\n    # containing a json list, instead of just a string containing a json list.\r\n    # print(request.values.get('content', ''))\r\n    json_content = request.values.get('content')\r\n    content = None\r\n\r\n    # Weird corner case: the Image survey does not have any content associated with it. Therefore,\r\n    # when you try and make a post request to save any settings you have, it gives you a 500 error\r\n    # because the request.values.get('content') returns a json item of \"\". The recursive_survey_content_json_decode\r\n    # function is not able to decode 2 double quotations marks. This is why retrieving the json_content from the post\r\n    # request is put outside of the decode statement. HOWEVER, evaluating json_content == \"\" returns false, since the\r\n    # LITERAL value of the json_content is 2 quotation marks, NOT an empty string. Thus, we need to compare the\r\n    # json_content to a string of 2 quotation marks (ie. '\"\"')\r\n    if json_content != '\"\"':\r\n        content = recursive_survey_content_json_decode(json_content)\r\n        content = make_slider_min_max_values_strings(content)\r\n    \r\n    if survey.survey_type == Survey.TRACKING_SURVEY:\r\n        errors = do_validate_survey(content)\r\n        if len(errors) > 1:\r\n            return make_response(json.dumps(errors), 400)\r\n    \r\n    # These three all stay JSON when added to survey\r\n    content = json.dumps(content)\r\n    timings = request.values['timings']\r\n    settings = request.values['settings']\r\n    survey.update(content=content, timings=timings, settings=settings)\r\n    \r\n    return make_response(\"\", 201)\r\n\r\n\r\ndef recursive_survey_content_json_decode(json_entity):\r\n    \"\"\" Decodes through up to 100 attempts a json entity until it has deserialized to a list. \"\"\"\r\n    count = 100\r\n    decoded_json = None\r\n    while not isinstance(decoded_json, list):\r\n        count -= 1\r\n        if count < 0:\r\n            raise Exception(\"could not decode json entity to list\")\r\n        decoded_json = json.loads(json_entity)\r\n    return decoded_json\r\n\r\n\r\ndef make_slider_min_max_values_strings(json_content):\r\n    \"\"\" Turns min/max int values into strings, because the iOS app expects strings. This is for\r\n    backwards compatibility; when all the iOS apps involved in studies can handle ints,\r\n    we can remove this function. \"\"\"\r\n    for question in json_content:\r\n        if 'max' in question:\r\n            question['max'] = str(question['max'])\r\n        if 'min' in question:\r\n            question['min'] = str(question['min'])\r\n    return json_content\r\n","sub_path":"api/survey_api.py","file_name":"survey_api.py","file_ext":"py","file_size_in_byte":4547,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"595306366","text":"import sys\nimport click\nfrom . import _cli as cli\nfrom astropy.io import fits\nimport glob\nfrom tkinter.filedialog import askdirectory\n\ndef read(directory):\n    \"\"\"\n    Takes a directory containing fits files and returns them as a list\n    \"\"\"\n    paths = glob.glob(\"{}/*.fits*\".format(directory))\n    hduls = [fits.open(p) for p in paths]\n    return hduls\n\ndef write(hduls, directory, format_):\n    \"\"\"\n    Writes all given hduls into the directory specified\n    \"\"\"\n    import os\n    for i, h in enumerate(hduls):\n        path = os.path.join(directory, format_.format(number=i))\n        click.echo(f\"writing hdul to {path}\")\n        h.writeto(path)\n    return hduls\n\n@cli.cli.command(\"write\")\n@click.option('-d', '--directory', type=str, help=\"Specify path to directory to save fitsfiles.\", default=\"./\")\n@click.option('-f', '--format', \"format_\", type=str, help=\"Specify string format for filename.\", default=\"{number}.fits\")\n@cli.operator\n## write function wrapper\ndef write_cmd(hduls, directory, format_):\n    \"\"\"\n    Writes all given hduls into the directory specified\n    \"\"\"\n    return write(hduls, directory, format_)\n\n@cli.cli.command(\"read\")\n@click.option('-d', '--directory', type=str, help=\"Specify path to directory of fitsfiles.\", required=False)\n@cli.generator\n## read function wrapper\ndef read_cmd(directory):\n    \"\"\"\n    Takes a directory containing fits files and returns them as a list\n    \"\"\"\n    # add try (evaluate if the try is efficient)\n    if directory is None:\n        try:\n            directory = askdirectory()\n        except:\n            click.echo(\"Visual file dialog does not exist, please use option -d and specify path to directory to read fitsfiles.\", err=True)\n            sys.exit()\n    hduls = read(directory)\n    if hduls:\n        return hduls\n    else:\n        sys.exit(f\"Could not open fitsfiles from directory {directory}\")\n","sub_path":"sdi/fitsio.py","file_name":"fitsio.py","file_ext":"py","file_size_in_byte":1866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"46888370","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Oct 11 16:14:51 2019\n@author: tiago\n\"\"\"\n\n#%% Lecture 001\nrunfile('dip.py')\n\nfilename = os.path.join(folder, \"baboon.png\")\n\n#%% Read a grayscale image\nimg = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)\n\nif type(img) is not np.ndarray:\n    print(\"Error: Image not found!\")\n\nplt.imshow(img)\nplt.show()\n# cv2.namedWindow(\"img\", cv2.WINDOW_KEEPRATIO)\n# cv2.imshow(\"img\", img)\n# #cv2.waitKey(0)\n# while True:\n#     if 0xFF & cv2.waitKey(1) == ord('q'):\n#         break\n# cv2.destroyAllWindows()\n\n#%% Read a color image\nimg = cv2.imread(filename, cv2.IMREAD_COLOR)\nimg = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\nplt.imshow(img)\nplt.show()\n\n#%%\nbgr = cv2.imread(filename, cv2.IMREAD_COLOR)\nrgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)\nplt.imshow(rgb)\nplt.show()\n\n#%% Splitting RGB channels\nbgr = cv2.imread(filename, cv2.IMREAD_COLOR)\n\nrgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)\nrgb_lst = cv2.split(rgb)\n\nplt.figure(1, figsize=(18,9)), plt.clf\nplt.subplot(232), plt.imshow(rgb), plt.title('RGB')\nplt.subplot(234), plt.imshow(rgb_lst[0], cmap='gray'), plt.title('R')\nplt.subplot(235), plt.imshow(rgb_lst[1], cmap='gray'), plt.title('G')\nplt.subplot(236), plt.imshow(rgb_lst[2], cmap='gray'), plt.title('B')\nplt.show()\n\n#%% Using a function handle\nbgr2rgb = lambda x : cv2.cvtColor(x, cv2.COLOR_BGR2RGB)\nbgr = cv2.imread(filename, cv2.IMREAD_COLOR)\nplt.figure(2)\nplt.imshow(bgr2rgb(bgr))\nplt.show()\n\n#%% Splitting RGB channels\nbgr = cv2.imread(filename, cv2.IMREAD_COLOR)\nrgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)\nr, g, b = cv2.split(rgb)\n\nplt.figure(1), plt.clf\nplt.subplot(232), plt.imshow(rgb), plt.title('RGB')\nplt.subplot(234), plt.imshow(r, cmap='gray'), plt.title('R')\nplt.subplot(235), plt.imshow(g, cmap='gray'), plt.title('G')\nplt.subplot(236), plt.imshow(b, cmap='gray'), plt.title('B')\nplt.show()\n","sub_path":"db/aula01.py","file_name":"aula01.py","file_ext":"py","file_size_in_byte":1858,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"344669086","text":"word = input(\"Enter the word\")\n#print(type(word))\n#print(word)\nbasic_mark = '1'\ni = 0\nstatus = []\nj = 0\nfor a in word:\n    if a != basic_mark and i == 0: #Warunek wykona sie tylko dla pierwszego a i ustanawia nasze b\n        status.append(a)\n        b = a\n        i+=1\n        #print(\"Wykonal sie if\")\n    elif a == b:\n        j+=1\n        #print(\"Wykonal sie elif 1\")\n    elif a != b:\n        if j != 0:\n            status.append(j+1)\n        b = a\n        j = 0\n        #print(\"Wykonal sie elif 2\", b)\n        status.append(b)\n    else:\n        status.append(a)\n        #print(\"Wykonal sie else\")\n#print(status)\nstatus.append(j+1)\n#print(status)\nfor b in status:\n    print(b, end ='')","sub_path":"Letters.py","file_name":"Letters.py","file_ext":"py","file_size_in_byte":686,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"591236898","text":"# coding: utf8 \nimport json\nfrom datetime import datetime, timedelta\nfrom base_request_handler import BaseRequestHandler\nfrom buyer_model import BuyerModel\nfrom bee_errors import *\n\n\nclass BuyerRequestHandler(BaseRequestHandler):\n    \"\"\"处理来自买家的请求\"\"\"\n    def __init__(self, *args, **kwargs):\n        super(BuyerRequestHandler, self).__init__(*args, **kwargs)\n        self.model = BuyerModel()\n\n    def _pre_do(self, option):\n        user_id = self.get_param('user_id')\n        if user_id:\n            self.model.set_user_id(user_id)\n\n    def _do(self, option):\n        if option == '_get_mp_session':\n            self._get_mp_session()\n        elif option == '_wx_enroll':\n            self._wx_enroll()\n        elif option == '_get_waiters':\n            self._get_waiters()\n        elif option == '_get_user_info':\n            self._get_user_info()\n        elif option == '_enroll':\n            self._enroll()\n        elif option == '_get_categories':\n            self._get_categories()\n        elif option == '_get_items':\n            self._get_items()\n        elif option == '_commit_orders':\n            self._commit_orders()\n        elif option == '_get_orders':\n            self._get_orders()\n        elif option == '_cancel_order':\n            self._cancel_order()\n        else:\n            self.write_fail(ERR_INVALID_OPTION)\n\n    def _get_mp_session(self):\n        \"\"\"获取微信用户session\"\"\"\n        js_code = self.get_param('js_code', essential=True)\n        # fixme\n        self.write_success({'openid': 'foo', 'unionid': 'bar', \n                'session_key': 'foobar'})\n    \n    def _wx_enroll(self):\n        \"\"\"获取微信用户手机号码\"\"\"\n        session_key = self.get_param('session_key', essential=True)\n        iv = self.get_param('iv', essential=True)\n        encrypted_data = self.get_param('encrypted_data', essential=True)\n        # fixme\n        self.write_success({'phone_number': '18520191011'})\n\n    def _get_waiters(self):\n        \"\"\"获取服务点列表\"\"\"\n        region = self.get_param('region', essential=True)\n        waiters = self.model.get_waiters(region)\n        self.write_success({'waiters': waiters}) \n    \n    def _get_verification_code(self):\n        \"\"\"获取短信验证码\"\"\"\n        phone_number = self.get_param('phone_number', essential=True)\n        self.sms_login_manager.send_verification_code(phone_number)\n        self.write_success()\n\n    def _get_user_info(self):\n        \"\"\"获取用户信息\"\"\"\n        mp_union_id = self.get_param('mp_union_id', essential=True)\n        info = self.model.get_user_info(mp_union_id)\n        if not info:\n            return self.write_fail(ERR_USER_NOT_EXIST)\n        self.write_success({'user': info})\n\n    def _enroll(self):\n        \"\"\"注册\"\"\"\n        mp_union_id = self.get_param('mp_union_id', essential=True)\n        number = self.get_param('phone_number', essential=True)\n        code = self.get_param('verification_code', essential=True)\n        name = self.get_param('name', essential=True)\n        avatar = self.get_param('avatar', essential=True)\n        #if not self.sms_login_manager.check_verification_code(number, code):\n        #    return self.write_fail(ERR_WRONG_VERIFICATION_CODE)\n        user = self.model.enroll(mp_union_id, number, name, avatar)\n        if not user:\n            return self.write_fail(ERR_ENROLL_FAILED)\n        info = self.model.get_user_info(mp_union_id)\n        if not info:\n            return self.write_fail(ERR_USER_NOT_EXIST)\n        self.write_success({'user': info})\n\n    def _get_categories(self): \n        \"\"\"获取商品类目\"\"\"\n        waiter_id = self.get_param('waiter_id', essential=True, rtype=int)\n        categories = self.model.get_categories(waiter_id)\n        self.write_success({'categories': categories}) \n    \n    def _get_items(self): \n        \"\"\"获取类目下的商品\"\"\"\n        waiter_id = self.get_param('waiter_id', essential=True, rtype=int)\n        category_id = self.get_param('category_id', essential=True, rtype=int)\n        items = self.model.get_items(waiter_id, category_id)\n        self.write_success({'items': items})\n\n    def _commit_orders(self): \n        \"\"\"提交新订单\"\"\"\n        waiter_id = self.get_param('waiter_id', essential=True, rtype=int)\n        orders = self.get_param('orders', essential=True)\n        orders = json.loads(orders)\n        err = self.model.commit_orders(waiter_id, orders)\n        if err:\n            self.write_fail(err[0], err[1])\n        else:\n            self.write_success()\n\n    def _get_orders(self): \n        \"\"\"获取已下单订单\"\"\"\n        orders = self.model.get_orders()\n        self.write_success({'orders': orders})\n    \n    def _cancel_order(self):\n        \"\"\"取消订单\"\"\"\n        order_id = self.get_param('order_id', essential=True, rtype=int)\n        err = self.model.cancel_order(order_id)\n        if err:\n            self.write_fail(err[0], err[1])\n        else:\n            self.write_success()\n    \n","sub_path":"app/server/buyer_request_handler.py","file_name":"buyer_request_handler.py","file_ext":"py","file_size_in_byte":4963,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"573924838","text":"import urllib\nfrom bs4 import BeautifulSoup\nfrom logzero import logger\n\ndef get_urldict(url_str):\n    html_str = urllib.request.urlopen(url_str).read() # urlopen这个方法返回的是bytes对象，需要解码为str\n    soup = BeautifulSoup(html_str,'lxml')  # 传入一个字符串或者文件句柄初始化靓汤\n    #url_lists = soup.find_all(\"table\")1\n    url_lists = soup.select('.tbspan')[0]\n    return url_lists\n\n\nurls = 'http://www.dytt8.net/html/gndy/dyzz/index.html'\naa = get_urldict(urls)\n\nprint(aa)\n\n\n\n\n","sub_path":"FilmHeaven/get.py","file_name":"get.py","file_ext":"py","file_size_in_byte":518,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"274117175","text":"from apscheduler.schedulers.blocking import BlockingScheduler\n\nsched = BlockingScheduler(timezone=\"Europe/Istanbul\")\n\n@sched.scheduled_job('cron', day_of_week='mon-fr', hour=10)\ndef scheduled_job():\n    print('Exec 1000...')\n    exec(open('HerGun50CentiTurkLirasi.py').read())\n\n@sched.scheduled_job('cron', day_of_week='mon-fr', hour=18)\ndef scheduled_job():\n    print('Exec 1800...')\n    exec(open('HerGun50CentiTurkLirasi.py').read())\n\nsched.start()\n","sub_path":"clock.py","file_name":"clock.py","file_ext":"py","file_size_in_byte":452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"624771324","text":"\"\"\"\nCopyright (c) 2004-Present Pivotal Software, Inc.\n\nThis program and the accompanying materials are made available under\nthe terms of the 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\nhttp://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 os\nimport time\n\nimport unittest2 as unittest\n\nfrom mpp.lib.gplog import GpLog, GpLogException, _DEFAULT_OUT_FILE\nfrom mpp.lib.PSQL import PSQL\n\nclass GpLogRegressionTests(unittest.TestCase):\n\n    def test_gather_log_to_default_file(self):\n        if os.path.exists(_DEFAULT_OUT_FILE):\n            os.remove(_DEFAULT_OUT_FILE)\n        self.assertFalse(os.path.exists(_DEFAULT_OUT_FILE))\n        start_time = time.time()\n        PSQL.run_sql_command(\"select pg_sleep(2)\")\n        end_time = time.time()\n        GpLog.gather_log(start_time=start_time, end_time=end_time)\n        self.assertTrue(os.path.exists(_DEFAULT_OUT_FILE))\n        self.assertTrue(os.path.getsize(_DEFAULT_OUT_FILE) > 0)\n\n    def test_gather_log_out_file(self):\n        out_file = '/tmp/cluster2.logs'\n        if os.path.exists(out_file):\n            os.remove(out_file)\n        self.assertFalse(os.path.exists(out_file))\n        start_time = time.time()\n        time.sleep(2)\n        end_time = time.time()\n        GpLog.gather_log(start_time=start_time, end_time=end_time, out_file=out_file)\n        self.assertTrue(os.path.exists(out_file))\n        self.assertTrue(os.path.getsize(out_file) > 0)\n\n    def test_check_log(self):\n        start_time = time.time()\n        PSQL.run_sql_command(\"SELECT * from some_table_that_does_not_exist_to_generate_errors_in_logs\")\n        time.sleep(2)\n        end_time = time.time()\n        self.assertTrue(GpLog.check_log_for_errors(start_time, end_time))\n        \n        \n        \n    \n","sub_path":"src/test/tinc/tincrepo/mpp/lib/regress/regress_gplog.py","file_name":"regress_gplog.py","file_ext":"py","file_size_in_byte":2160,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"269150148","text":"'''\nhttps://docs.python.org/3/library/argparse.html#module-argparse\n'''\n\nimport argparse\n\nparser = argparse.ArgumentParser(description='Process some integers.')\nparser.add_argument('Greeting', type=str,\n                   help='This is the string that greets the program!')\nparser.add_argument('integers', metavar='N', type=int, nargs='+',\n                    help='an integer for the accumulator')\nparser.add_argument('-letters', metavar='L', type=str, nargs='+',\n                    help='a set of letters')\nparser.add_argument('-names', type=str, nargs='+',\n                   help='Names of people')\nparser.add_argument('--sum', dest='accumulate', action='store_const',\n                    const=sum, default=max,\n                    help='sum the integers (default: find the max)')\n\nargs = parser.parse_args()\nprint(args.accumulate(args.integers))\nprint(args.letters)\nprint(args.names)","sub_path":"tutorials/terminal_program/terminal_program_3.py","file_name":"terminal_program_3.py","file_ext":"py","file_size_in_byte":890,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"88015215","text":"import sys\nimport os\nimport argparse\nimport collections\nimport numpy as np\nimport tensorflow as tf\nimport torch\nimport tensorflow.keras.backend as K\n\nuer_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), \"..\"))\nsys.path.insert(0, uer_dir)\n\nfrom scripts.convert_bert_from_original_tf_to_uer import tensors_to_transopse\n\n\ndef assign_tf_var(tensor: np.ndarray, name: str):\n    tf_var = tf.get_variable(dtype=tensor.dtype, shape=tensor.shape, name=name)\n    tf.keras.backend.set_value(tf_var, tensor)\n    return tf_var\n\n\ndef main():\n    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n    parser.add_argument(\"--layers_num\", type=int, default=12,\n                        help=\".\")\n    parser.add_argument(\"--input_model_path\", type=str, default=\"models/input_model.bin\",\n                        help=\".\")\n    parser.add_argument(\"--output_model_path\", type=str, default=\"models/output_model.ckpt\",\n                        help=\".\")\n    parser.add_argument(\"--type\", choices=[\"bert\", \"mlm\"], default=\"bert\",\n                        help=\"The training target of the pretraining model.\")\n\n    args = parser.parse_args()\n\n    input_model = torch.load(args.input_model_path, map_location=\"cpu\")\n\n    session = tf.Session()\n    K.set_session(session)\n\n    output_model = collections.OrderedDict()\n\n    output_model[\"bert/embeddings/word_embeddings\"] = input_model[\"embedding.word.embedding.weight\"]\n    output_model[\"bert/embeddings/position_embeddings\"] = input_model[\"embedding.pos.embedding.weight\"]\n    output_model[\"bert/embeddings/token_type_embeddings\"] = input_model[\"embedding.seg.embedding.weight\"][1:, :]\n    output_model[\"bert/embeddings/LayerNorm/gamma\"] = input_model[\"embedding.layer_norm.gamma\"]\n    output_model[\"bert/embeddings/LayerNorm/beta\"] = input_model[\"embedding.layer_norm.beta\"]\n\n    for i in range(args.layers_num):\n        output_model[\"bert/encoder/layer_\" + str(i) + \"/attention/self/query/kernel\"] = \\\n            input_model[\"encoder.transformer.\" + str(i) + \".self_attn.linear_layers.0.weight\"]\n        output_model[\"bert/encoder/layer_\" + str(i) + \"/attention/self/query/bias\"] = \\\n            input_model[\"encoder.transformer.\" + str(i) + \".self_attn.linear_layers.0.bias\"]\n        output_model[\"bert/encoder/layer_\" + str(i) + \"/attention/self/key/kernel\"] = \\\n            input_model[\"encoder.transformer.\" + str(i) + \".self_attn.linear_layers.1.weight\"]\n        output_model[\"bert/encoder/layer_\" + str(i) + \"/attention/self/key/bias\"] = \\\n            input_model[\"encoder.transformer.\" + str(i) + \".self_attn.linear_layers.1.bias\"]\n        output_model[\"bert/encoder/layer_\" + str(i) + \"/attention/self/value/kernel\"] = \\\n            input_model[\"encoder.transformer.\" + str(i) + \".self_attn.linear_layers.2.weight\"]\n        output_model[\"bert/encoder/layer_\" + str(i) + \"/attention/self/value/bias\"] = \\\n            input_model[\"encoder.transformer.\" + str(i) + \".self_attn.linear_layers.2.bias\"]\n        output_model[\"bert/encoder/layer_\" + str(i) + \"/attention/output/dense/kernel\"] = \\\n            input_model[\"encoder.transformer.\" + str(i) + \".self_attn.final_linear.weight\"]\n        output_model[\"bert/encoder/layer_\" + str(i) + \"/attention/output/dense/bias\"] = \\\n            input_model[\"encoder.transformer.\" + str(i) + \".self_attn.final_linear.bias\"]\n        output_model[\"bert/encoder/layer_\" + str(i) + \"/attention/output/LayerNorm/gamma\"] = \\\n            input_model[\"encoder.transformer.\" + str(i) + \".layer_norm_1.gamma\"]\n        output_model[\"bert/encoder/layer_\" + str(i) + \"/attention/output/LayerNorm/beta\"] = \\\n            input_model[\"encoder.transformer.\" + str(i) + \".layer_norm_1.beta\"]\n        output_model[\"bert/encoder/layer_\" + str(i) + \"/intermediate/dense/kernel\"] = \\\n            input_model[\"encoder.transformer.\" + str(i) + \".feed_forward.linear_1.weight\"]\n        output_model[\"bert/encoder/layer_\" + str(i) + \"/intermediate/dense/bias\"] = \\\n            input_model[\"encoder.transformer.\" + str(i) + \".feed_forward.linear_1.bias\"]\n        output_model[\"bert/encoder/layer_\" + str(i) + \"/output/dense/kernel\"] = \\\n            input_model[\"encoder.transformer.\" + str(i) + \".feed_forward.linear_2.weight\"]\n        output_model[\"bert/encoder/layer_\" + str(i) + \"/output/dense/bias\"] = \\\n            input_model[\"encoder.transformer.\" + str(i) + \".feed_forward.linear_2.bias\"]\n        output_model[\"bert/encoder/layer_\" + str(i) + \"/output/LayerNorm/gamma\"] = \\\n            input_model[\"encoder.transformer.\" + str(i) + \".layer_norm_2.gamma\"]\n        output_model[\"bert/encoder/layer_\" + str(i) + \"/output/LayerNorm/beta\"] = \\\n            input_model[\"encoder.transformer.\" + str(i) + \".layer_norm_2.beta\"]\n    \n    if args.type == \"bert\":\n        output_model[\"bert/pooler/dense/kernel\"] = input_model[\"target.sp.linear_1.weight\"]\n        output_model[\"bert/pooler/dense/bias\"] = input_model[\"target.sp.linear_1.bias\"]\n        output_model[\"cls/seq_relationship/output_weights\"] = input_model[\"target.sp.linear_2.weight\"]\n        output_model[\"cls/seq_relationship/output_bias\"] = input_model[\"target.sp.linear_2.bias\"]\n    output_model[\"cls/predictions/transform/dense/kernel\"] = input_model[\"target.mlm.linear_1.weight\"]\n    output_model[\"cls/predictions/transform/dense/bias\"] = input_model[\"target.mlm.linear_1.bias\"]\n    output_model[\"cls/predictions/transform/LayerNorm/gamma\"] = input_model[\"target.layer_norm.gamma\"]\n    output_model[\"cls/predictions/transform/LayerNorm/beta\"] = input_model[\"target.layer_norm.beta\"]\n    output_model[\"cls/predictions/output_bias\"] = input_model[\"target.mlm.linear_2.bias\"]\n\n    tf_vars = []\n\n    for k, v in output_model.items():\n        tf_name = k\n        torch_tensor = v.cpu().numpy()\n        if any([x in k for x in tensors_to_transopse]):\n            torch_tensor = torch_tensor.T\n        tf_tensor = assign_tf_var(tensor=torch_tensor, name=tf_name)\n        tf_vars.append(tf_tensor)\n        print(\"{0}{1}initialized\".format(tf_name, \" \" * (60 - len(tf_name))))\n\n    saver = tf.train.Saver(tf_vars)\n    saver.save(session, args.output_model_path)\n    K.clear_session()\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"scripts/convert_bert_from_uer_to_original_tf.py","file_name":"convert_bert_from_uer_to_original_tf.py","file_ext":"py","file_size_in_byte":6173,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"43689666","text":"# Copyright (C) 2019 Google Inc.\n# Licensed under http://www.apache.org/licenses/LICENSE-2.0 <see LICENSE file>\n\n\"\"\"Test should_create_event_for method.\"\"\"\n\nfrom datetime import date\nimport ddt\nfrom freezegun import freeze_time\n\nfrom ggrc.models import all_models\nfrom ggrc.gcalendar import calendar_event_builder\nfrom integration.ggrc_workflows.models import factories as wf_factories\nfrom integration.ggrc import TestCase\n\n\n# pylint: disable=protected-access\n@ddt.ddt\nclass TestShouldCreateEventForTask(TestCase):\n  \"\"\"Tests for should_create_event_for method.\"\"\"\n\n  def setUp(self):\n    \"\"\"Set up test.\"\"\"\n    super(TestShouldCreateEventForTask, self).setUp()\n    self.client.get(\"/login\")\n    self.builder = calendar_event_builder.CalendarEventBuilder()\n\n  @ddt.data((u\"Deprecated\", False, False),\n            (u\"In Progress\", False, True),\n            (u\"Assigned\", False, True),\n            (u\"Finished\", False, False),\n\n            (u\"Declined\", True, True),\n            (u\"Verified\", True, False),\n            (u\"Deprecated\", True, False),\n            (u\"In Progress\", True, True),\n            (u\"Finished\", True, True),\n            (u\"Assigned\", True, True))\n  @ddt.unpack\n  def test_task_status(\n      self, task_status, is_verification_needed, should_create_event\n  ):\n    \"\"\"Check that the event should be created for specified task statuses.\"\"\"\n    with freeze_time(\"2015-01-1 12:00:00\"):\n      cycle = wf_factories.CycleFactory(\n          is_verification_needed=is_verification_needed,\n      )\n      task = wf_factories.CycleTaskGroupObjectTaskFactory(\n          status=task_status,\n          end_date=date(2015, 1, 5),\n          cycle=cycle,\n      )\n      self.assertEquals(self.builder._should_create_event_for(task),\n                        should_create_event)\n\n  def test_overdue_task(self):\n    \"\"\"Check that the event should not be created overdue tasks.\"\"\"\n    with freeze_time(\"2015-01-05 12:00:00\"):\n      task = wf_factories.CycleTaskGroupObjectTaskFactory(\n          status=u\"In Progress\",\n          end_date=date(2015, 1, 1),\n      )\n      self.assertEquals(self.builder._should_create_event_for(task), False)\n\n  def test_is_in_history_task(self):\n    \"\"\"Check that the event should not be created is_in_history tasks.\"\"\"\n    with freeze_time(\"2015-01-01 12:00:00\"):\n      cycle = wf_factories.CycleFactory(is_current=False)\n      task = wf_factories.CycleTaskGroupObjectTaskFactory(\n          status=u\"In Progress\",\n          end_date=date(2015, 1, 5),\n          cycle=cycle,\n      )\n      self.assertEquals(self.builder._should_create_event_for(task), False)\n\n  @ddt.data((False, False), (True, True))\n  @ddt.unpack\n  def test_task_archived(self, recurrence, should_create_event):\n    \"\"\"Check creation of event based on workflow archived states.\"\"\"\n    with freeze_time(\"2015-01-01 12:00:00\"):\n      workflow = wf_factories.WorkflowFactory(\n          unit=all_models.Workflow.WEEK_UNIT,\n          recurrences=recurrence,\n          next_cycle_start_date=date(2015, 1, 5),\n      )\n      cycle = wf_factories.CycleFactory(workflow=workflow)\n      task = wf_factories.CycleTaskGroupObjectTaskFactory(\n          status=u\"In Progress\",\n          end_date=date(2015, 1, 5),\n          cycle=cycle,\n      )\n      self.assertEquals(self.builder._should_create_event_for(task),\n                        should_create_event)\n","sub_path":"test/integration/ggrc/gcalendar/test_should_create_event_for_task.py","file_name":"test_should_create_event_for_task.py","file_ext":"py","file_size_in_byte":3342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"463399289","text":"#\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\nfrom artan.testing.sql_utils import ReusedSparkTestCase\nfrom artan.filter import RecursiveLeastSquaresFilter, LinearKalmanFilter, LeastMeanSquaresFilter\nfrom pyspark.ml.linalg import Vectors, Matrices\nimport numpy as np\n\n\nclass RLSTests(ReusedSparkTestCase):\n\n    np.random.seed(0)\n\n    def test_simple_rls(self):\n        df = self.spark.createDataFrame(\n            [(1.0, Vectors.dense(0.0, 5.0)),\n             (0.0, Vectors.dense(1.0, 2.0)),\n             (1.0, Vectors.dense(2.0, 1.0)),\n             (0.0, Vectors.dense(3.0, 3.0)), ], [\"label\", \"features\"])\n\n        rls = RecursiveLeastSquaresFilter(2)\n\n        model = rls.transform(df).filter(\"stateIndex=4\").collect()\n        state = model[0].state.values\n\n        expected = np.array([5.31071176e-09, 1.53846148e-01])\n        np.testing.assert_array_almost_equal(state, expected)\n\n    def test_ols_equivalence(self):\n        # Simple ols problem\n        # y =  a * x + b + r\n        # Where r ~ N(0, 1)\n        n = 40\n        a = 0.5\n        b = 2\n        x = np.arange(0, n)\n        r = np.random.normal(0, 1, n)\n        y = a * x + b + r\n        features = x.reshape(n, 1)\n        features = np.concatenate([features, np.ones_like(features)], axis=1)\n\n        df = self.spark.createDataFrame(\n            [(float(y[i]), Vectors.dense(features[i])) for i in range(n)], [\"label\", \"features\"])\n\n        # set high regularization matrix factor to get close to OLS solution\n        rls = RecursiveLeastSquaresFilter(2)\\\n            .setInitialEstimate(Vectors.dense([1.0, 1.0]))\\\n            .setRegularizationMatrixFactor(10E6)\n\n        model = rls.transform(df)\n        state = model.filter(\"stateIndex = {}\".format(n)).collect()[0].state.values\n\n        # Check equivalence with least squares solution with numpy\n        expected, _, _, _ = np.linalg.lstsq(features, y, rcond=None)\n        np.testing.assert_array_almost_equal(state, expected)\n\n\nclass LMSTests(ReusedSparkTestCase):\n\n    np.random.seed(0)\n\n    def test_filter_trend(self):\n        # y =  a * x + N(0, 1)\n        n = 40\n        a = 0.2\n        x = np.arange(0, n)\n        r = np.random.normal(0, 1, n)\n        y = a * x + r\n        features = x.reshape(n, 1)\n\n        df = self.spark.createDataFrame(\n            [(float(y[i]), Vectors.dense(features[i])) for i in range(n)], [\"l\", \"f\"])\n\n        lms = LeastMeanSquaresFilter(1)\\\n            .setInitialEstimate(Vectors.dense([10.0]))\\\n            .setRegularizationConstant(1.0)\\\n            .setLearningRate(1.0)\\\n            .setLabelCol(\"l\")\\\n            .setFeaturesCol(\"f\")\n\n        model = lms.transform(df)\n        state = model.filter(\"stateIndex = {}\".format(n)).collect()[0].state.values\n\n        np.testing.assert_array_almost_equal(state, np.array([0.2]), 2)\n\n\nclass LinearKalmanFilterTests(ReusedSparkTestCase):\n\n    np.random.seed(0)\n\n    def test_ols_equivalence(self):\n        # Simple ols problem\n        # y =  a * x + b + r\n        # Where r ~ N(0, 1)\n        n = 40\n        a = 0.27\n        b = 1.2\n        x = np.arange(0, n)\n        r = np.random.normal(0, 1, n)\n        y = (a * x + b + r).reshape(n, 1)\n        features = x.reshape(n, 1)\n        features = np.concatenate([features, np.ones_like(features)], axis=1)\n        df = self.spark.createDataFrame(\n            [(Vectors.dense(y[i]), Matrices.dense(1, 2, features[i])) for i in range(n)],\n            [\"measurement\", \"measurementModel\"])\n        lkf = LinearKalmanFilter(2, 1)\\\n            .setMeasurementModelCol(\"measurementModel\")\\\n            .setMeasurementCol(\"measurement\")\\\n            .setInitialCovariance(Matrices.dense(2, 2, (np.eye(2)*10).reshape(4, 1)))\\\n            .setProcessModel(Matrices.dense(2, 2, np.eye(2).reshape(4, 1)))\\\n            .setProcessNoise(Matrices.dense(2, 2, np.zeros(4)))\\\n            .setMeasurementNoise(Matrices.dense(1, 1, [10E-5]))\n\n        model = lkf.transform(df)\n        state = model.filter(\"stateIndex = {}\".format(n)).collect()[0].state.values\n\n        # Check equivalence with least squares solution with numpy\n        expected, _, _, _ = np.linalg.lstsq(features, y, rcond=None)\n        np.testing.assert_array_almost_equal(state, expected.reshape(2), decimal=5)\n\n    def test_multiple_model_adaptive_filter(self):\n        n = 100\n        a = 0.27\n        b = 1.2\n        x = np.concatenate([np.arange(0, n), np.arange(0, n)])\n        r = np.random.normal(0, 1, n * 2)\n        y = (a * x + b + r).reshape(n * 2, 1)\n        features = x.reshape(n * 2, 1)\n        features = np.concatenate([features, np.ones_like(features)], axis=1)\n        state_keys = [\"1\"] * n + [\"2\"] * n\n        df = self.spark.createDataFrame(\n            [(state_keys[i], Vectors.dense(y[i]), Matrices.dense(1, 2, features[i])) for i in range(n*2)],\n            [\"state_key\",\"measurement\", \"measurementModel\"])\n\n        mmaeFilter = LinearKalmanFilter(2, 1)\\\n            .setStateKeyCol(\"state_key\")\\\n            .setMeasurementModelCol(\"measurementModel\")\\\n            .setMeasurementCol(\"measurement\")\\\n            .setInitialCovariance(Matrices.dense(2, 2, (np.eye(2)*10).reshape(4, 1)))\\\n            .setProcessModel(Matrices.dense(2, 2, np.eye(2).reshape(4, 1)))\\\n            .setProcessNoise(Matrices.dense(2, 2, np.zeros(4)))\\\n            .setMeasurementNoise(Matrices.dense(1, 1, [1.0]))\\\n            .setSlidingLikelihoodWindow(5)\\\n            .setEnableMultipleModelAdaptiveEstimation()\n\n        model = mmaeFilter.transform(df)\n        state = model.filter(\"stateIndex = {}\".format(n)).collect()[0].state.values\n\n        expected, _, _, _ = np.linalg.lstsq(features, y, rcond=None)\n        np.testing.assert_array_almost_equal(state, expected.reshape(2), decimal=0)\n","sub_path":"python/artan/tests/test_filters.py","file_name":"test_filters.py","file_ext":"py","file_size_in_byte":6445,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"151406431","text":"# -*- coding: utf-8 -*-\nimport scrapy\nfrom scrapy.linkextractors import LinkExtractor\nfrom scrapy.spiders import CrawlSpider, Rule\n\n\nclass QiushibaikeSpider(CrawlSpider):\n    name = 'qiushibaike'\n    allowed_domains = ['qiushibaike.com']\n    start_urls = ['https://www.qiushibaike.com/']\n\n    rules = (\n        Rule(LinkExtractor(allow=r'.*?/$'), callback='parse_item'),\n    )\n\n    def parse_item(self, response):\n        print(response.text)\n        item = {}\n        #item['domain_id'] = response.xpath('//input[@id=\"sid\"]/@value').get()\n        #item['name'] = response.xpath('//div[@id=\"name\"]').get()\n        #item['description'] = response.xpath('//div[@id=\"description\"]').get()\n        return item\n","sub_path":"job_gaoxiao/spiders/qiushibaike.py","file_name":"qiushibaike.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"341408073","text":"from django.test import TestCase\n\nfrom airMonitor.models.Chart import Chart\n\n\nclass TestChartWrapper(TestCase):\n    def test_file_load(self):\n        chart = Chart()\n        self.assertIn(\"type\", chart.dict())\n        self.assertIn(\"data\", chart.dict())\n        self.assertIn(\"options\", chart.dict())\n\n    def test_empty_data(self):\n        chart = Chart()\n        self.assertEqual(len(chart._datasets), 0)\n\n    def test_add_data_same_key(self):\n        chart = Chart()\n        for i in range(10):\n            chart.add_data(\"key\", i)\n        self.assertEqual(len(chart._datasets), 1)\n\n    def test_add_data_multiple_keys(self):\n        chart = Chart()\n        for key in [\"key1\", \"key2\", \"key3\", \"key4\"]:\n            for i in range(10):\n                chart.add_data(key, i)\n        self.assertEqual(len(chart._datasets), 4)\n","sub_path":"airMonitor/test/test_chart.py","file_name":"test_chart.py","file_ext":"py","file_size_in_byte":827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"545902705","text":"# -*- coding: utf-8 -*-\n# @Time    : 2022/11/18  13:55\n# @Author  : HHB\n# @FileName: SohuSuzhou.py\n# @Software: PyCharm\n\"\"\"\n    Name: 搜狐 苏州\n    Description:  http://sz.sohu.com/news/yaowen.shtml\n        \n\"\"\"\n\n\n\nimport requests\nfrom lxml import etree\nimport pymysql\n\n\n\n\nclass Sohu(object):\n\n    def __init__(self):\n\n        self.url = \"http://sz.sohu.com/news/yaowen.shtml\"\n        self.headers = {\n            \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) \"\n                          \"Chrome/91.0.4472.124 Safari/537.36\"}\n        self.connect = pymysql.connect(host='192.168.3.21', port=3306, db='policeproject',\n                                       user='root', passwd='123456', charset='utf8', )\n        self.cursor = self.connect.cursor()\n\n    def time_sc(self, punlish_time):\n        # 2014年08月08日06:34\n        time_sc_new = punlish_time.replace(\"年\", \"-\").replace(\"月\", \"-\").replace(\"日\", \" \")\n        return time_sc_new + \":00\"\n\n    def get_data(self):\n        try:\n            response = requests.get(url=self.url, headers=self.headers)\n            response.encoding = response.apparent_encoding\n            html = etree.HTML(response.text)\n            data_list = html.xpath('//div[@class=\"list14\"]/ul/li')\n\n            for i in data_list:\n\n                title = i.xpath('./a/text()')\n                url = i.xpath('./a/@href')\n\n                if not title:\n                    continue\n                else:\n                    print(title)\n                    print(url)\n\n                    self.get_detail(title[0], url[0])\n        except Exception as e:\n            print(e)\n\n    def get_detail(self, title, url):\n\n        response = requests.get(url=url, headers=self.headers)\n        response.encoding = response.apparent_encoding\n        html = etree.HTML(response.text)\n\n        content = html.xpath('//div[@id=\"contentText\"]/p//text()')\n\n        if not content:\n            content = html.xpath('//div[@id=\"contentText\"]/text()')\n        else:\n            content = content\n        content = \"\".join(content).replace(\"\\n\", \"\").replace(\"\\r\", \"\").strip()\n        print(\"正文：\", content)\n\n        publish_time = html.xpath('//div[@class=\"time\"]/text()')[0]\n        time_sc_new = self.time_sc(publish_time)\n        print(\"时间：\", time_sc_new)\n\n        sql = \"insert into news_data(`title`,\" \\\n              \"`content`,\" \\\n              \"`url`,\" \\\n              \"`source`,\" \\\n              \"`publish_time`) value (%s,%s,%s,%s,%s)\"\n\n        self.cursor.execute(sql, (\n            title, content,\n            url, '搜狐苏州', time_sc_new))\n        self.connect.commit()\n\n\n\nif __name__ == '__main__':\n    sohu = Sohu()\n    sohu.get_data()","sub_path":"PoliceProject/SohuSuzhou.py","file_name":"SohuSuzhou.py","file_ext":"py","file_size_in_byte":2732,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"15811370","text":"#!/usr/bin/env python\n\nimport asyncio\nimport websockets\n\n\nasync def hello():\n    uri = \"ws://localhost:8765\"\n    async with websockets.connect(uri) as websocket:\n        print(\"Receiving data\")\n        await websocket.send(\"Hello world!\")\n        while True:\n            res = await websocket.recv()\n            print(res)\n\n    a = input()\n\n\nasyncio.get_event_loop().run_until_complete(hello())\n","sub_path":"p.py","file_name":"p.py","file_ext":"py","file_size_in_byte":395,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"336145902","text":"from flask import request, jsonify\nfrom flask import Blueprint, Response\n\nfrom app.service.person import PersonService\n\nperson_bp = Blueprint('person', __name__)\n\n\n@person_bp.route('/person/', methods=['POST'])\ndef create_person():\n    person_service = PersonService()\n    try:\n        data = request.json\n        name = data['name']\n        primary_email = data['email']\n        person_service.create(name=name, primary_email=primary_email)\n        return Response(\"Accepted\", status=202)\n    except Exception as e:\n        return Response(str(e), status=422)\n\n\n@person_bp.route('/person/<int:id>', methods=['GET'])\ndef get_person_by_id(id):\n    person_service = PersonService()\n    try:\n        data = person_service.retrieve_one(id)\n        result = {'name': data.name,\n                  'primary email': data.primary_email}\n        return jsonify(result)\n    except Exception as e:\n        return Response(str(e), status=422)\n\n\n@person_bp.route('/person/', methods=['GET'])\ndef get_persons():\n    person_service = PersonService()\n    try:\n        data = person_service.retrieve()\n        result = formatter(data)\n        return jsonify(result)\n    except Exception as e:\n        return Response(str(e), status=422)\n\n\n@person_bp.errorhandler(404)\ndef api_not_found(e):\n    return \"Invalid API request\"\n\n\ndef formatter(person):\n    result = []\n    for obj in person:\n        result.append({\n            'id': obj.id,\n            'name': obj.name,\n            'primary email': obj.primary_email\n        })\n    return result\n","sub_path":"2021-02-05-meetup-18-flask-app-testing-part2/app/api/person.py","file_name":"person.py","file_ext":"py","file_size_in_byte":1525,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"427704505","text":"#!/usr/bin/env python\n\"\"\"\n*\n*   copyright: 2012 Leif Theden <leif.theden@gmail.com>\n*   license: GPL-3\n*\n*   This file is part of pyrikura/purikura.\n*\n*   pyrikura is free software: you can redistribute it and/or modify\n*   it under the terms of the GNU General Public License as published by\n*   the Free Software Foundation, either version 3 of the License, or\n*   (at your option) any later version.\n*\n*   pyrikura is distributed in the hope that it will be useful,\n*   but WITHOUT ANY WARRANTY; without even the implied warranty of\n*   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n*   GNU General Public License for more details.\n*\n*   You should have received a copy of the GNU General Public License\n*   along with pyrikura.  If not, see <http://www.gnu.org/licenses/>.\n*\n\"\"\"\n\n\"\"\"\nTethered Camera Controller for Photo Booth\n\nVery basic script that triggers a sequence of images on a tethered camera\nand saves the output as jpeg images.\n\nThis script is intended to be used in conjunction with other software that\ncollectively creates a seemless photo booth experience.\n\"\"\"\n\nfrom yapsy.PluginManager import PluginManager\n\nfrom pyrikura.workflow import Node\nimport logging\nimport argparse\nimport os\nimport itertools\nimport re\nimport pickle\nimport time\n\n\n\nevent_name = 'test'\n\nsettings = {}\nsettings['shutter_sound'] = os.path.join('sounds', 'bell.wav')\nsettings['printsrv'] = os.path.join('/', 'home', 'mjolnir', 'smb-printsrv')\n#settings['template'] = os.path.join('templates', 'polaroid0.template')\nsettings['template'] = os.path.join('templates', '2x6vintage.template')\nsettings['originals'] = os.path.join('/', 'home', 'mjolnir', 'events', event_name, 'originals')\nsettings['temp_image'] = 'capture.jpg'\n\n\ndef build():\n    arduino = Node('Arduino', '/dev/ttyACM0', 9600)\n    repeater = Node('Repeater', 4, 5)\n    tether = Node('Tether')\n    composer = Node('Composer', template=settings['template'])\n    stdout = Node('ConsolePrinter')\n    archiver = Node('FileCopy', dest=settings['originals'])\n    spooler = Node('FileCopy', dest=settings['printsrv'])\n    twitter = Node('Twitter', 'twitter.secrets')\n    beep = Node('Beeper', settings['shutter_sound'])\n\n    beep.subscribe(repeater)\n    repeater.subscribe(arduino)\n    tether.subscribe(repeater)\n    composer.subscribe(tether)\n    archiver.subscribe(tether)\n    spooler.subscribe(composer)\n    #twitter.subscribe(composer)\n\n    return [arduino, stdout, composer, spooler, archiver, tether, twitter,\n            repeater]\n\n\ndef run():\n    logging.basicConfig(level=logging.INFO)\n\n    os.chdir('/home/mjolnir/git/PURIKURA')\n\n    # H A N D L E  P L U G I N S\n    pm = PluginManager()\n    pm.setPluginPlaces(['./pyrikura/plugins'])\n    pm.collectPlugins()\n\n    for pi in pm.getAllPlugins():\n        logging.info('loading plugin %s', pi.name)\n        pm.activatePluginByName(pi.name)\n\n    brokers = {}\n    nodes = build()\n    head = nodes[0]\n\n    for node in nodes:\n        brokers[node] = node.load(pm)\n\n    for node, broker in brokers.items():\n        for other in node._listening:\n            broker.subscribe(brokers[other])\n\n    start = time.time()\n    last_time = 0\n    shots = 0\n    last_trigger = 0\n    for broker in itertools.cycle(brokers.values()):\n        broker.update()\n\n    #eyefi = Watcher(eyefi_incoming, re.compile('.*\\.jpg$', re.I))\n\n\nprofile = False\nif __name__ == '__main__':\n    if profile:\n        import cProfile\n        import pstats\n\n        try:\n            cProfile.run('run()', 'results.prof')\n        except KeyboardInterrupt:\n            pass\n\n        p = pstats.Stats(\"results.prof\")\n        p.strip_dirs()\n        p.sort_stats('time').print_stats(20)\n        \n    else:\n        run()\n","sub_path":"pbtether.py","file_name":"pbtether.py","file_ext":"py","file_size_in_byte":3689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"380318803","text":"'''\n\nThe prime 41, can be written as the sum of six consecutive primes:\n\n41 = 2 + 3 + 5 + 7 + 11 + 13\nThis is the longest sum of consecutive primes that adds to a prime below one-hundred.\n\nThe longest sum of consecutive primes below one-thousand that adds to a prime, contains 21 terms, and is equal to 953.\n\nWhich prime, below one-million, can be written as the sum of the most consecutive primes?\n\n'''\nimport math, datetime\n\ndef prime_sieve(n):\n    sieve = [True] * (n//2)\n    for i in range(3, int(math.sqrt(n))+1, 2):\n        if sieve[i//2]:\n            sieve[i*i//2::i] = [False] * ((n-i*i-1)//(2*i)+1)\n    return [2] + [2*i+1 for i in range(1, n//2) if sieve[i]]\n\ndef is_prime(num):\n    if num < 3 or num % 2 == 0:\n        return (num == 2)\n    else:\n        return all(num % i != 0 for i in range(3, int(num**0.5 + 2), 2))\n\ndef p50(maximo):\n    primes = prime_sieve(maximo // 4)\n    dict_results = {}\n    max_terms = 0\n    max_consecutive = 0\n    for i in range(0, len(primes)):\n        aux = None\n        maxi = 0\n        suma = 0\n        terms = 0\n        aux_terms = None\n        for prime in primes[i:]:\n            suma += prime\n            terms += 1\n            if suma > maximo:\n                break\n            if is_prime(suma):\n                aux_terms = terms\n                maxi = suma\n        if aux_terms > max_terms:\n            max_terms = aux_terms\n            max_consecutive = maxi\n\n    return max_consecutive\n\n\n\ntt1 = datetime.datetime.now()\n\nprint('Answer: ', p50(1000000)) # 997651\n\ntt2 = datetime.datetime.now()\n\nprint('Time: ', tt2 - tt1) # 4.3 seconds\n\n","sub_path":"50.py","file_name":"50.py","file_ext":"py","file_size_in_byte":1589,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"4955811","text":"# -------программа для сортировки слов из текста, слова не повторяются\r\ndef obrabotka_stroki(enter_word_isodrabotka):\r\n    enter_word_list_isobrabotka = []\r\n    enter_word_list_1_isobrabotka = []\r\n    digit_str = '1234567890'\r\n    punctuation_str = '!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_{|}~'\r\n\r\n    for i in punctuation_str:\r\n        enter_word_isodrabotka = enter_word_isodrabotka.replace(i, ' ')\r\n        enter_word_list_isobrabotka = enter_word_isodrabotka.split()\r\n    for i in range(len(enter_word_list_isobrabotka)):\r\n        y = 0\r\n        for a in digit_str:\r\n            if a in enter_word_list_isobrabotka[i]:\r\n                y = 1\r\n                break\r\n        if y == 0:\r\n            enter_word_list_1_isobrabotka.append(enter_word_list_isobrabotka[i])\r\n    return enter_word_list_1_isobrabotka\r\n\r\ndef statistika(enter_word_list_isstatistika):\r\n    dict1 = {}\r\n    for i in enter_word_list_isstatistika:\r\n        if i in dict1:\r\n            dict1[i] += 1\r\n        else:\r\n            dict1[i] = 1\r\n    return dict1\r\n\r\nprint('''Здравствуйте.\r\nДанная программа для сортировки слов из текста, где слова не повторяются. \r\nПользователь вводит с клавиатуры строки, состоящие из слов.\r\nПустая строка означает прекратить ввод текста. Программа выводит на экран слова из текста,\r\nотсортированные по алфавиту.''')\r\n\r\nwhile True:\r\n    vubor_menu = 0\r\n    print('''\r\nДля продолжения нажмите: 1\r\nДля выхода нажмите: 2\r\n''')\r\n    enter_word = ''\r\n    enter_word_1 = ''\r\n    enter_word_list = []\r\n    enter_word_list_1 = []\r\n\r\n    try:\r\n        vubor_menu = int(input())\r\n    except:\r\n        continue\r\n    if vubor_menu == 2:\r\n        break\r\n    elif vubor_menu == 1:\r\n        while True:\r\n\r\n                print('''Введите с клавиатуры строки, состоящие из слов. Вы можете нажимать Enter для ввода новой строки.\r\nПустая строка означает прекратить ввод текста.''')\r\n\r\n                while True:\r\n                    enter_word = ''\r\n                    enter_word = input('Введите строку')\r\n                    if enter_word == '':\r\n                        break\r\n                    else:\r\n                        enter_word_1 = enter_word_1 + ' ' + enter_word\r\n                enter_word_list_1 = obrabotka_stroki(enter_word_1)\r\n                if len(enter_word_list_1) == 0:\r\n                    print('Извините, нету слов')\r\n                else:\r\n                    print('Сортировка слов а алфавитном порядке (в данном варианте - вывод в столбик):')\r\n                    enter_word_list_1 = sorted(enter_word_list_1)\r\n                    dict2 = {}\r\n                    dict2 = statistika(enter_word_list_1)\r\n                    for i in dict2:\r\n                        print(i)\r\n                break\r\n        else:\r\n            continue\r\n\r\nprint('''\r\nВы вышли из программы\r\n''')","sub_path":"hw_3_3_v1.py","file_name":"hw_3_3_v1.py","file_ext":"py","file_size_in_byte":3317,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"126219652","text":"from flask import Flask, request\nfrom timeit import default_timer as timer\n\nimport json\nimport math\nimport random\n\napp = Flask(__name__)\nprint(\"Serving now\")\n\n\n@app.route('/random', methods=['GET'])\ndef entry():\n    start = timer()\n    num = int(request.args.get('num', '10'))\n    randoms = [\"%06d\" % math.floor(random.random() * 999999) for _ in\n               range(0, num)]\n    ret = json.dumps(randoms, separators=(',', ':'))\n    stop = timer()\n    print('{0:0.3f}ms'.format((stop - start) * 1e3))\n    return ret\n","sub_path":"servers/flask+uwsgi-python3/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"17687741","text":"'''\nlimited版に編集してある\n'''\n\n\nclass IdCreater():\n\n    def __init__(self, target_file, read_file, file_type):\n       self.target_file = target_file\n       self.read_file = read_file\n       self.file_type = file_type\n\n    def creat_file(self):\n        abst_list = self.read()\n        self.write(abst_list)\n\n    def read(self):\n        with open(self.read_file , 'r') as r:\n            abst_list = [abst for abst in r]\n        return abst_list\n\n    def write(self, abst_list):\n        write_file = self.target_file + 'id_data/id_' + self.file_type + '.txt'\n        with open(write_file, 'w') as w:\n            for abst in abst_list:\n                split_list = abst.split('\\t')\n                abst_id_str = split_list[0]\n                abst_title_str = split_list[1]\n                abst_abst_str = split_list[2]\n                written_str = abst_id_str + '\\t' + abst_abst_str\n                w.write(written_str)\n\n\nclass AnnoCreater():\n\n    def __init__(self, target_file, read_file, file_type):\n       self.target_file = target_file\n       self.read_file = read_file\n       self.file_type = file_type\n\n    def creat_file(self):\n        ne_list = self.read()\n        self.write(ne_list)\n\n    def read(self):\n        with open(self.read_file , 'r') as r:\n            ne_list = [ne for ne in r]\n        return ne_list\n\n    def write(self, ne_list):\n        write_file = self.target_file + 'anno_data/anno_' + self.file_type + '.txt'\n        with open(write_file, 'w') as w:\n            for ne in ne_list:\n                #ne: \n                #21826085\tA\t946\t957\thaloperidol\tTRIVIAL\n                split_list = ne.split('\\t')\n                abst_id_str = split_list[0]\n                title_or_abst_tag = split_list[1]\n                s_index = split_list[2]\n                e_index = split_list[3]\n                ne_name = split_list[4]\n                ne_type = split_list[5]\n                # タイトルは除外\n                if title_or_abst_tag == 'A':\n                    #written_str = abst_id_str + '\\t' + ne_name + '\\t' + s_index + '\\t' + e_index + '\\t' + ne_type\n                    if ne_type.strip() in ['SYSTEMATIC', 'FAMILY', 'TRIVIAL']:\n                        written_str = abst_id_str + '\\t' + ne_name + '\\t' + s_index + '\\t' + e_index + '\\t' + 'CHEMICAL\\n'\n                        w.write(written_str)\n\n\n\ndef main():\n    TARGET_FILE = '/cl/work/shusuke-t/ds_ner/orig_data/chemdner/data/limited_type_data/'\n    make_id_corpus = True\n    if make_id_corpus:\n        TOY_FILE = TARGET_FILE + 'orig/toy.abstracts.txt' \n        TRAIN_FILE = TARGET_FILE + 'orig/training.abstracts.txt' \n        DEV_FILE = TARGET_FILE + 'orig/development.abstracts.txt'\n        EVAL_FILE = TARGET_FILE + 'orig/evaluation.abstracts.txt'\n\n        id_toy_creater = IdCreater(TARGET_FILE, TOY_FILE, 'toy')\n        id_toy_creater.creat_file()\n\n        id_toy_creater = IdCreater(TARGET_FILE, TRAIN_FILE, 'train')\n        id_toy_creater.creat_file()\n        id_valid_creater = IdCreater(TARGET_FILE, DEV_FILE, 'valid')\n        id_valid_creater.creat_file()\n        id_test_creater = IdCreater(TARGET_FILE, EVAL_FILE, 'test')\n        id_test_creater.creat_file()\n\n\n    make_anno_corpus = True\n    if make_anno_corpus:\n        TOY_FILE = TARGET_FILE + 'orig/toy.annotations.txt'\n        TRAIN_FILE = TARGET_FILE + 'orig/training.annotations.txt'\n        DEV_FILE = TARGET_FILE + 'orig/development.annotations.txt'\n        EVAL_FILE = TARGET_FILE + 'orig/evaluation.annotations.txt'\n\n        anno_toy_creater = AnnoCreater(TARGET_FILE, TOY_FILE, 'toy')\n        anno_toy_creater.creat_file()\n\n        anno_train_creater = AnnoCreater(TARGET_FILE, TRAIN_FILE, 'train')\n        anno_train_creater.creat_file()\n        anno_valid_creater = AnnoCreater(TARGET_FILE, DEV_FILE, 'valid')\n        anno_valid_creater.creat_file()\n        anno_test_creater = AnnoCreater(TARGET_FILE, EVAL_FILE, 'test')\n        anno_test_creater.creat_file()\n\nif __name__ == '__main__':\n    main()\n","sub_path":"orig_data/chemdner/annotator/creat_id_anno_data.py","file_name":"creat_id_anno_data.py","file_ext":"py","file_size_in_byte":3975,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"360322256","text":"from turtle import *\r\n\r\nht()\r\nbgcolor(\"black\")\r\ntitle(\"Ein Quadrat auf Reise\")\r\ncolormode(255)\r\nschoenefarbe=(255,185,65)\r\nphi=3\r\npensize(1)\r\nspeed(0)\r\n\r\ndef quadrat():\r\n    for i in range(4):\r\n        forward(200)\r\n        right(90)\r\n\r\ndef meine_farbe():\r\n    pencolor(255,22,225)\r\n\r\nfor i in range(360//phi):\r\n    if 0==i%2:\r\n        pencolor(schoenefarbe)\r\n    else:\r\n        meine_farbe()\r\n    quadrat()\r\n    right(phi)\r\n\r\n\r\nmainloop()\r\n    \r\n","sub_path":"Quellen/python/8 Allerlei Q-Gruppenmitgliedern/Urs/Quadrat_auf_Reisen.py","file_name":"Quadrat_auf_Reisen.py","file_ext":"py","file_size_in_byte":447,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"191021705","text":"# Definition for singly-linked list.\n# class ListNode(object):\n#     def __init__(self, x):\n#         self.val = x\n#         self.next = None\n\nclass Solution(object):\n    def removeNthFromEnd(self, head, n):\n        \"\"\"\n        :type head: ListNode\n        :type n: int\n        :rtype: ListNode\n        \"\"\"\n        count=0\n        ptr=head\n        while ptr:\n            if count==n:\n                break\n            count+=1\n            ptr=ptr.next\n            \n       \n        if not ptr:\n            return head.next\n        else:\n            ptr2=head\n            while ptr.next:\n                ptr=ptr.next\n                ptr2=ptr2.next\n            \n            next=ptr2.next;\n            if next:\n                ptr2.next=ptr2.next.next\n            else:\n                ptr2.next=None\n            return head","sub_path":"19 Remove Nth Node From End of List.py","file_name":"19 Remove Nth Node From End of List.py","file_ext":"py","file_size_in_byte":821,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"499237891","text":"import re\n\nimport requests\n\nmaster_version_file = 'taskcat/master_version'\ndevelop_version_file = 'taskcat/develop_version'\n\ndef get_pip_version(pkginfo_url):\n    pkginfo = requests.get(pkginfo_url).text\n    for record in pkginfo.split('\\n'):\n        if record.startswith('Version'):\n            current_version = str(record).split(':', 1)\n            return (current_version[1]).strip()\n\n\n#current_develop_version = get_pip_version('https://testpypi.python.org/pypi?name=taskcat&:action=display_pkginfo')\ncurrent_develop_version = '0.dev373.dev3'\nprint(\"PyPi Develop Version is [{}]\".format(current_develop_version))\n#current_master_version = get_pip_version('https://pypi.python.org/pypi?name=taskcat&:action=display_pkginfo')\ncurrent_master_version =  '730.73.73'\nprint(\"PyPi Master Version is [{}]\".format(current_master_version))\n\nnew_poduction_release = int(re.findall(r'\\d+', current_master_version)[-1])\nnew_development_release = int(re.findall(r'\\d+', current_develop_version)[-1])\n\nproduction_version =re.sub('\\d$', lambda x: str(int(x.group(0)) + 1), current_master_version)\ndevelopment_version =re.sub('\\d$', lambda x: str(int(x.group(0)) + 1), current_develop_version)\n\nprint(\"current_develop_version\")\nprint(development_version)\nprint(\"current_master_version\")\nprint(production_version)\n\nwith open(master_version_file, 'w') as m:\n    m.write(str(current_master_version))\n\nwith open(develop_version_file, 'w') as d:\n    d.write(str(current_develop_version))","sub_path":"getpipversion.py","file_name":"getpipversion.py","file_ext":"py","file_size_in_byte":1470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"416194325","text":"import os\r\nimport pandas as pd\r\nimport shutil\r\nimport errno\r\nimport win32com.client\r\n\r\n\r\n\r\n#Set the proper print window dimensions\r\npd.set_option('display.height', 1000)\r\npd.set_option('display.max_rows', 500)\r\npd.set_option('display.max_columns', 500)\r\npd.set_option('display.width', 1000)\r\n\r\ndef copyDirectory(src,dest):\r\n    try:\r\n        shutil.copytree(src, dest)\r\n    #Directories are the same\r\n    except shutil.Error as e:\r\n        print('Directory not copied. Error: %s' % e)\r\n    #Any error saying that the directory doesn't exist\r\n    except OSError as e:\r\n        print('Directory not copied. Error: %s' %e)\r\n\r\ndef copy(src,dest):\r\n    try:\r\n        shutil.copytree(src, dest)\r\n    #Any error saying that the directory doesn't exist\r\n    except OSError as e:\r\n        if e.errno == errno.ENOTDIR:\r\n            shutil.copy(src,dest)\r\n        else:\r\n            print('Directory not copied. Error: %s' %e)\r\n        \r\n#filepath = 'C:/Users/jyoo/Desktop/'\r\n#filepath = 'C:/Users/Justin/Dropbox/Walgreens/'\r\n\r\n#======================================================\r\n#filepath = 'C:/Users/Justin/Desktop/Walgreens Folders/' #Home\r\nfilepath = 'C:/Users/jyoo/Desktop/App Folders/' #Work\r\n#======================================================\r\n\r\n#======================================================\r\n#df = pd.read_csv('C:/Users/Justin/Dropbox/Walgreens/Site List.csv',header = 0) #Home\r\ndf = pd.read_csv('C:/Users/jyoo/Desktop/Site List.csv',header = 0) #Work\r\n#======================================================\r\n\r\n#print(list(df.columns.values))\r\n#print(df)\r\n\r\n#print(df.iloc[0,0])\r\n#print(df.iloc[0,1])\r\n#print(df.iloc[1,1])\r\n#print(testcase.zfill(10))\r\n\r\n#for i in range(0,5):\r\nfor i in range(0,len(df.index)):\r\n    #======================================================\r\n    #src = 'C:/Users/Justin/Desktop/template' #Home\r\n    src = 'C:/Users/jyoo/Desktop/template' #Work\r\n    #======================================================\r\n    dest = filepath + \"FD \" + str(df.iloc[i,0]).zfill(4) + ' - ' + str(df.iloc[i,1]) + ', ' + str(df.iloc[i,2]) + '/'\r\n    copy(src, dest)\r\n    os.rename(dest + '/1 - Pre-Application/PY9 Assessment Workbook v4.5.xlsm', dest + '/1 - Pre-Application/' + 'FD ' + str(df.iloc[i,0]).zfill(4) + ' PY9 Assessment Workbook.xlsm')\r\n    print(\"Finished Copying \" + str(i) + \" out of \" + str(len(df.index)) + \".\")\r\n    #if not os.path.exists(filepath + str(df.iloc[i,0]).zfill(4) + ' - ' + str(df.iloc[i,1]) + ', ' + str(df.iloc[i,2]) + '/' ):\r\n    #        os.mkdir(filepath + str(df.iloc[i,0]).zfill(4) + ' - ' + str(df.iloc[i,1]) + ', ' + str(df.iloc[i,2]) + '/' )\r\n\r\n#if not os.path.exists(filepath + foldername):\r\n#    os.mkdir(filepath + foldername)\r\n#print(type(folders))\r\n\r\nprint(\"All Done\")\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":"_Python - Create Multiple Folders - DT.py","file_name":"_Python - Create Multiple Folders - DT.py","file_ext":"py","file_size_in_byte":2771,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"179106219","text":"# Example local settings file\r\n\r\n# Leave in debug until sure this is instance running correctly\r\nDEBUG = True\r\nDEBUG_ALIAS = 'release'\r\n\r\n# Identify transactions in Braintree as coming from this instance\r\nBRAINTREE_PREFIX = 'rel'\r\n\r\nBASE_URL = 'test.huddle.com.au'\r\n\r\nDUCKCREEK_STORE_XML = {\r\n    'RSP': True,\r\n    'ZIP': False,\r\n    'REQ': True,\r\n}\r\n\r\nSERVICES = {\r\n    'motorweb': True,\r\n    'car_quote': True,\r\n    'travel_quote': True,\r\n}\r\n\r\n# Nginx domains that are allowed to run the site itself\r\nALLOWED_HOSTS = [\r\n    'localhost:3000',\r\n    'localhost:8002',\r\n    '*',\r\n]\r\n\r\n# Nginx domains will be allowed to make API calls\r\nCORS_ORIGIN_WHITELIST = [\r\n    'localhost:3000',\r\n    'localhost:8002',\r\n    '*',\r\n]\r\n\r\n# Redfine the urls which will be sent to the Angular app\r\nFRONTEND_URL = \"http://%s\" % BASE_URL\r\nAPI_URL = \"http://api.%s\" % BASE_URL\r\nADMIN_URL = \"http://admin.%s\" % BASE_URL\r\n#FRONTEND_URL = \"http://localhost:3000\"\r\n#API_URL = \"http://localhost:8000\"\r\n#ADMIN_URL = \"http://localhost:8001\"\r\n\r\n# Database instance details\r\nDATABASES = {\r\n    'default': {\r\n        'ENGINE': 'django.db.backends.postgresql_psycopg2',\r\n        'NAME': 'huddlemoneytestdb',\r\n        'HOST': 'localhost',\r\n        'USER': 'huddlemoney-test-db-user',\r\n        'PASSWORD': 'huddle123',\r\n    }\r\n}\r\n\r\nDUCKCREEK_MODE = 'sandbox'\r\nBRAINTREE_MODE = 'sandbox'\r\nVEDA_MODE = 'sandbox'\r\nSLACK_MODE = 'sandbox'\r\n","sub_path":"huddlemoney/roles/test-deploy-code/files/local.py","file_name":"local.py","file_ext":"py","file_size_in_byte":1401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"169053521","text":"#  SISTEMA PRINCIPAL\nfrom lib.arquivo.metodos import *\nfrom lib.interface.menus import *\nimport time\n\n\n# Cores para o terminal\ncor = {\n    'vermelho': '\\033[1;31m',\n    'verde': '\\033[1;32m',\n    'azul': '\\033[1;34m',\n    'ciano': '\\033[1;36m',\n    'magenta': '\\033[1;35m',\n    'amarelo': '\\033[1;33m',\n    'preto': '\\033[1;30m',\n    'branco': '\\033[1;37m',\n    'reset': '\\033[1;0;0m',\n    'reverso': '\\033[1;2m',\n    'bgpreto': '\\033[1;40m',\n    'bgvermelho': '\\033[1;41m',\n    'bgverde': '\\033[1;42m',\n    'bgamarelo': '\\033[1;43m',\n    'bgazul': '\\033[1;44m',\n    'bgmagenta': '\\033[1;45m',\n    'bgciano': '\\033[1;46m',\n    'bgbranco': '\\033[1;47m'}\n\n\n# Cria aquivos com a data atual para manipulação\ndataAtual = datetime.today()\ndataArquivo = str(f'{dataAtual.day}.{dataAtual.month}.{dataAtual.year}')\n# data atual para criar arquivo\narqPacSol = (dataArquivo + '-LISTA-TRANSPORTE-PACIENTES-SOLICITADOS' + '.txt')\narqPacEmTransp = (dataArquivo + '-LISTA-TRANSPORTE-PACIENTES-TRANSPORTANDO' + '.txt')\narqPacTranspFinal = (dataArquivo + '-LISTA-TRANSPORTE-PACIENTES-FINALIZADOS' + '.txt')\niniciaArquivosRegistro(arqPacSol)\niniciaArquivosRegistro(arqPacEmTransp)\niniciaArquivosRegistro(arqPacTranspFinal)\n\n\n# Chama menu\nlistMenus = ['Lista de pacientes',\n             'Solicitar transporte', \n             'Buscar pacientes',\n             'Finalizar transporte',\n             'Sair do programa']\nwhile True:\n    # 1 - Lista de pacientes\n    # 2 - Solicitar transporte\n    # 3 - Buscar paciente\n    # 4 - Finalizar transporte\n    # 5 - Sair do sistema\n    opt = menu(listMenus, 'CENTRAL DE TRANSPORTE')\n    if opt == 1:\n        mostraCadastro(arqPacSol, 'PAINEL - SOLICITADOS')\n        mostraCadastro(arqPacEmTransp, 'PAINEL - TRANSPORTANDO')\n        mostraCadastro(arqPacTranspFinal, 'PAINEL - FINALIZADOS')\n    elif opt == 2:\n        cadastraPac(arqPacSol)\n    elif opt == 3:\n        mudaDePainel(arqPacSol, arqPacEmTransp, 'BUSCAR PACIENTE')\n    elif opt == 4:\n        # Implantar a finalização de transporte\n        mudaDePainel(arqPacEmTransp, arqPacTranspFinal, 'FINALIZAR TRANSPORTE')\n    elif opt == 5:\n        titulo('FIM DO PROGRAMA', txtCor=\"bgpreto\")\n        break\n    else:\n        print(f\"{cor['vermelho']}ERRO: Digite uma opção válida!{cor['reset']}\")\n    time.sleep(1.3)\n","sub_path":"central_transporte_leforte/central_transp/sistema.py","file_name":"sistema.py","file_ext":"py","file_size_in_byte":2293,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"526353993","text":"### Install and import dependencies ###\n\nimport tensorflow as tf\n#import tensorflow datasets\nimport tensorflow_datasets as tfds\n\ntfds.disable_progress_bar()\n\n#helper libraries\nimport math\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport logging\nlogger = tf.get_logger()\nlogger.setLevel(logging.ERROR)\n\n### Import the Fashion MNIST dataset ###\n\ndataset, metadata = tfds.load('fashion_mnist', as_supervised=True, with_info=True)\ntrain_dataset, test_dataset = dataset['train'], dataset['test']\n\n\"\"\"\n0 \tT-shirt/top\n1 \tTrouser\n2 \tPullover\n3 \tDress\n4 \tCoat\n5 \tSandal\n6 \tShirt\n7 \tSneaker\n8 \tBag\n9 \tAnkle boot\n\"\"\"\n\nclass_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n\n### Explore the data ###\n\nnum_train_examples = metadata.splits['train'].num_examples\nnum_test_examples = metadata.splits['test'].num_examples\nprint(\"Number of training examples: {}\".format(num_train_examples))\nprint(\"Number of test examples:     {}\".format(num_test_examples))\n\n### Preprocess the data ###\n\ndef normalize(images, labels):\n    images = tf.cast(images, tf.float32)\n    images /= 255\n    return images, labels\n\n# The map function applies the normalize function to each element in the train and test datasets\ntrain_dataset = train_dataset.map(normalize)\ntest_dataset = test_dataset.map(normalize)\n\n# The first time you use the dataset, the images will be loaded from disk\n# Caching will keep them in memory, making training faster\ntrain_dataset = train_dataset.cache()\ntest_dataset = test_dataset.cache()\n\n### Explore the processed data ###\n\n# Display the first 25 images from the training set and display the class name below each image.\nplt.figure(figsize=(10,10))\ni = 0\nfor (image, label) in test_dataset.take(25):\n    image = image.numpy().reshape((28,28))\n    plt.subplot(5,5,i+1)\n    plt.xticks([])\n    plt.yticks([])\n    plt.grid(False)\n    plt.imshow(image, cmap=plt.cm.binary)\n    plt.xlabel(class_names[label])\n    i += 1\nplt.show()\n\n### Build the model ###\n### Setup the layers ###\n\nmodel = tf.keras.Sequential([\n    tf.keras.layers.Conv2D(32, (3,3), padding='same', activation=tf.nn.relu, input_shape=(28, 28, 1)),\n    tf.keras.layers.MaxPooling2D((2, 2), strides=2),\n    tf.keras.layers.Conv2D(64, (3,3), padding='same', activation=tf.nn.relu),\n    tf.keras.layers.MaxPooling2D((2, 2), strides=2),\n    tf.keras.layers.Flatten(),\n    tf.keras.layers.Dense(128, activation=tf.nn.relu),\n    tf.keras.layers.Dense(10)\n])\n\n### Compile the model ###\n\nmodel.compile(\n    optimizer='adam',\n    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n    metrics=['accuracy']\n)\n\n### Train the model ###\n\nBATCH_SIZE = 32\ntrain_dataset = train_dataset.cache().repeat().shuffle(num_train_examples).batch(BATCH_SIZE)\ntest_dataset = test_dataset.cache().batch(BATCH_SIZE)\n\nmodel.fit(train_dataset, epochs=10, steps_per_epoch=math.ceil(num_train_examples/BATCH_SIZE))\n\n### Evaluate accuracy ###\nprint(\"Starting testing\")\ntest_loss, test_accuracy = model.evaluate(test_dataset, steps=math.ceil(num_test_examples/32))\n\n### Make predictions and explore ###\nfor test_images, test_labels in test_dataset.take(1):\n  test_images = test_images.numpy()\n  test_labels = test_labels.numpy()\n  predictions = model.predict(test_images)\n\n# predictions.shape = (32, 10) ( because one batch is 32)\n\nprint(predictions[0])\n\nprint(np.argmax(predictions[0]))\n\nprint(test_labels[0])\n\n# We can graph this to look at the full set of 10 class predictions\n\ndef plot_image(i, predictions_array, true_labels, images):\n    predictions_array, true_label, img = predictions_array[i], true_labels[i], images[i]\n    plt.grid(False)\n    plt.xticks([])\n    plt.yticks([])\n\n    plt.imshow(img[..., 0], cmap=plt.cm.binary)\n\n    predicted_label = np.argmax(predictions_array)\n    if predicted_label == true_label:\n        color = 'blue'\n    else:\n        color = 'red'\n\n    predictions_array = [i for i in predictions_array if i >= 0]\n    print(\"predictions array = \", predictions_array)\n\n    plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n                                         100 * np.max(predictions_array),\n                                         class_names[true_label]),\n               color=color)\n\n\ndef plot_value_array(i, predictions_array, true_label):\n    predictions_array, true_label = predictions_array[i], true_label[i]\n    plt.grid(False)\n    plt.xticks([])\n    plt.yticks([])\n    thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n    plt.ylim([0, 1])\n    predicted_label = np.argmax(predictions_array)\n\n    thisplot[predicted_label].set_color('red')\n    thisplot[true_label].set_color('blue')\n\n# Let's look at the 0th image, predictions, and prediction array.\ni = 0\nplt.figure(figsize=(6,3))\nplt.subplot(1,2,1)\nplot_image(i, predictions, test_labels, test_images)\n\n# Finally, use the trained model to make a prediction about a single image.\n# Grab an image from the test dataset\nimg = test_images[0]\n\n# image shape = (28, 28, 1)\n\n# NOTE : tf.keras models are optimized to make predictions on a batch, or collection, of examples at once.\n# So even though we're using a single image, we need to add it to a list:\n\n# Add the image to a batch where it's the only member.\nimg = np.array([img])\n\n# img.shape = (1, 28, 28, 1)\n# Now predict the image:\n\npredictions_single = model.predict(img)\n\nprint(\"predictions single : \",predictions_single)\n\nplot_value_array(0, predictions_single, test_labels)\n_ = plt.xticks(range(10), class_names, rotation=45)\n\n#model.predict returns a list of lists, one for each image in the batch of data.\nprint(np.argmax(predictions_single[0]))","sub_path":"MNIST_fashion_CNN.py","file_name":"MNIST_fashion_CNN.py","file_ext":"py","file_size_in_byte":5645,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"173564890","text":"import cv2\r\nimport lungc\r\nimport pickle\r\n\r\nnum=input(\"Enter test image number: \")\r\nimg=cv2.imread(\"test/test (\"+num+\").jpg\",0)\r\nleft_feature,right_feature,img_left,img_right=lungc.process_lung_test(img)\r\n\r\n\r\nfilename = 'finalized_model.sav'\r\nloaded_model = pickle.load(open(filename, 'rb'))\r\nresult = loaded_model.predict([left_feature])\r\nprint(result[0])\r\nif(result[0]):\r\n\tprint(\"Tumour present\")\r\nelse:\r\n\tprint(\"Normal Lung\")\r\n","sub_path":"PredictCancer.py","file_name":"PredictCancer.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"504699818","text":"\"\"\"\nScript to solve Day 25\n\"\"\"\nimport re\n\ndef read_content(content) -> dict:\n    info = {'start': None, 'steps': None}\n    active = None\n    cur_val = None\n    for c in content:\n        if info['start'] is None:\n            info['start'] = safe_regex(r'state ([A-Z]).', c)\n        elif info['steps'] is None:\n            val = safe_regex(r'(\\d+)', c)\n            info['steps'] = int(val)\n        elif not c:\n            active = None\n            cur_val = None\n        elif active is None:\n            active = safe_regex(r'state ([A-Z]):', c)\n            info[active] = {0: {}, 1: {}}\n        elif cur_val is None:\n            val = safe_regex(r'value is ([0-1])', c)\n            cur_val = int(val)\n        else:\n            reset = False\n            try:\n                val = safe_regex(r'Write the value ([0-1]).', c)\n                val = int(val)\n                cat = 'wr'\n            except ValueError:\n                try:\n                    val = safe_regex(r'Move one slot to the ([a-z]+).', c)\n                    cat = 'dir'\n                except ValueError:\n                    val = safe_regex(r'state ([A-Z]).', c)\n                    cat = 'nxt'\n                    reset = True\n            info[active][cur_val][cat] = val\n            if reset:\n                cur_val = None\n    return info\n\n\ndef safe_regex(regex, string) -> str:\n    ma = re.search(regex, string)\n    if ma:\n        return ma.group(1)\n    else:\n        raise ValueError(f'No match for {regex} in {string}')\n\n\ndef part1(content):\n    info = read_content(content)\n    ones = {}\n    cnt = 0\n    state = info['start']\n    pos = 0\n    while cnt < info['steps']:\n        if pos not in ones:\n            ones[pos] = 0\n        cur_read = ones[pos]\n        wr = info[state][cur_read]['wr']\n        #if wr == 1 and cur_read == 0:\n        #    ones.append(pos)\n        #elif wr == 0 and cur_read == 1:\n        #    ones.remove(pos)\n        ones[pos] = wr\n        direc = info[state][cur_read]['dir']\n        if direc == 'right':\n            pos += 1\n        else:\n            pos -= 1\n        state = info[state][cur_read]['nxt']\n        cnt += 1\n    return sum(ones.values())\n\n\ndef part2(content):\n    pass\n\n\nif __name__ == '__main__':\n    test = ['Begin in state A.',\n            'Perform a diagnostic checksum after 6 steps.',\n            '',\n            'In state A:',\n            'If the current value is 0:',\n            '    - Write the value 1.',\n            '    - Move one slot to the right.',\n            '    - Continue with state B.',\n            'If the current value is 1:',\n            '    - Write the value 0.',\n            '    - Move one slot to the left.',\n            '    - Continue with state B.',\n            '',\n            'In state B:',\n            'If the current value is 0:',\n            '    - Write the value 1.',\n            '    - Move one slot to the left.',\n            '    - Continue with state A.',\n            'If the current value is 1:',\n            '    - Write the value 1.',\n            '    - Move one slot to the right.',\n            '    - Continue with state A.']\n    assert part1(test) == 3\n","sub_path":"sol2017/day25.py","file_name":"day25.py","file_ext":"py","file_size_in_byte":3120,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"354972835","text":"# -*- Encoding: UTF-8 -*-\n\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchvision\n\n\n# Create a U-Net architecture for semantic segmentation\nclass Block(nn.Module):\n    \"\"\"Block at each level of the U\"\"\"\n    def __init__(self, in_ch, out_ch):\n        super().__init__()\n        self.conv1 = nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1, padding_mode=\"zeros\")\n        self.relu = nn.ReLU()\n        self.conv2 = nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1, padding_mode=\"zeros\")\n\n    def forward(self, x):\n        return self.relu(self.conv2(self.relu(self.conv1(x))))\n\nclass Encoder(nn.Module):\n    \"\"\"Left part of the U, a number of Blocks\"\"\"\n    def __init__(self, ch_list=(1, 4, 8, 16)):\n        super().__init__()\n        num_ch = len(ch_list)\n        self.encoder_blocks = nn.ModuleList([Block(ch_list[i], ch_list[i+1]) for i in range(num_ch-1)])\n        self.pool = nn.MaxPool2d(2)\n\n    def forward(self, x):\n        ftrs = []\n        for block in self.encoder_blocks:\n            x = block(x)\n            ftrs.append(x)\n            x = self.pool(x)\n        return ftrs\n\n\nclass Decoder(nn.Module):\n    def __init__(self, ch_list=(16, 8, 4)):\n        super().__init__()\n        self.ch_list = ch_list\n        num_ch = len(ch_list)\n        # Up-convolutions\n        self.upconvs = nn.ModuleList([nn.ConvTranspose2d(self.ch_list[i], self.ch_list[i+1], 2, 2)\n                                      for i in range(num_ch-1)])\n        self.dec_blocks = nn.ModuleList([Block(ch_list[i], ch_list[i+1]) for i in range(num_ch-1)])\n\n    def forward(self, x, encoder_features):\n        for i in range(len(self.ch_list) - 1):\n            # Up-convolution\n            x = self.upconvs[i](x)\n            # Concatenate with corresponding encoder-feature map\n            enc_ftrs = self.crop(encoder_features[i], x)\n            x = torch.cat([x, enc_ftrs], dim=1)\n            # Perform convolutions\n            x = self.dec_blocks[i](x)\n        return x\n\n    def crop(self, enc_features, x):\n        _, _, H, W = x.shape\n        enc_features = torchvision.transforms.CenterCrop([H, W])(enc_features)\n        return enc_features\n\nclass UNet(nn.Module):\n    def __init__(self, enc_chs=(1, 4, 8, 16), dec_chs=(16, 8, 4), num_class=5, retain_dim=False, out_size=(24,8)):\n        super().__init__()\n        self.encoder = Encoder(enc_chs)\n        self.decoder = Decoder(dec_chs)\n        self.head = nn.Conv2d(dec_chs[-1], num_class, 1)\n        self.retain_dim = retain_dim\n\n    def forward(self, x):\n        enc_ftrs = self.encoder(x)\n        out = self.decoder(enc_ftrs[::-1][0], enc_ftrs[::-1][1:])\n        out = self.head(out)\n        if self.retain_dim:\n            out = F.interpolate(out, out_size)\n        return out\n\n\n# End of file models.py\n","sub_path":"utils/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2775,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"633106907","text":"import cv2 as cv\r\nimport numpy as np\r\nimport time\r\nimport boto3\r\nfrom pathlib import Path\r\nimport os\r\nimport PIL\r\nimport logging\r\n\r\n# create logger\r\nlogger1 = logging.getLogger('YOLO Detection and OCR')\r\nlogger1.setLevel(logging.DEBUG)\r\n\r\n# create console handler and set level to debug\r\nch1 = logging.StreamHandler()\r\nch1.setLevel(logging.DEBUG)\r\n\r\n# create formatter\r\nformatter1 = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\r\n\r\n# add formatter to ch\r\nch1.setFormatter(formatter1)\r\n\r\n# add ch to logger\r\nlogger1.addHandler(ch1)\r\n\r\n# Initialize aws credentials\r\n\r\nACCESS_KEY = 'XXXXXXXXXXXXXXXXX'\r\nSECRET_KEY = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'\r\ns3 = boto3.client('s3', aws_access_key_id=ACCESS_KEY, aws_secret_access_key=SECRET_KEY)\r\n\r\n# Initialize the parameters\r\nvehicle_threshold = .5\r\nvehicle_weights = 'vehicle-detector/yolo-voc.weights'\r\nvehicle_netcfg  = 'vehicle-detector/yolo-voc.cfg'\r\n\r\n# Load the model\r\nvehicle_net = cv.dnn.readNetFromDarknet(vehicle_netcfg, vehicle_weights)\r\nvehicle_net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)\r\nvehicle_net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)\r\n\r\nclass Label:\r\n\r\n\tdef __init__(self,cl=-1,tl=np.array([0.,0.]),br=np.array([0.,0.]),prob=None):\r\n\t\tself.__tl \t= tl\r\n\t\tself.__br \t= br\r\n\t\tself.__cl \t= cl\r\n\t\tself.__prob = prob\r\n\r\n\tdef __str__(self):\r\n\t\treturn 'Class: %d, top_left(x:%f,y:%f), bottom_right(x:%f,y:%f)' % (self.__cl, self.__tl[0], self.__tl[1], self.__br[0], self.__br[1])\r\n\r\n\tdef copy(self):\r\n\t\treturn Label(self.__cl,self.__tl,self.__br)\r\n\r\n\tdef wh(self): return self.__br-self.__tl\r\n\r\n\tdef cc(self): return self.__tl + self.wh()/2\r\n\r\n\tdef tl(self): return self.__tl\r\n \r\n\tdef br(self): return self.__br\r\n\r\n\tdef tr(self): return np.array([self.__br[0],self.__tl[1]])\r\n\r\n\tdef bl(self): return np.array([self.__tl[0],self.__br[1]])\r\n\r\n\tdef cl(self): return self.__cl\r\n\r\n\tdef area(self): return np.prod(self.wh())\r\n\r\n\tdef prob(self): return self.__prob\r\n\r\n\tdef set_class(self,cl):\r\n\t\tself.__cl = cl\r\n\r\n\tdef set_tl(self,tl):\r\n\t\tself.__tl = tl\r\n\r\n\tdef set_br(self,br):\r\n\t\tself.__br = br\r\n\r\n\tdef set_wh(self,wh):\r\n\t\tcc = self.cc()\r\n\t\tself.__tl = cc - .5*wh\r\n\t\tself.__br = cc + .5*wh\r\n\r\n\tdef set_prob(self,prob):\r\n\t\tself.__prob = prob\r\n\r\ndef getOutputsNames(net):\r\n  \"\"\" Get the names of the output layers.\r\n  \r\n  Generally in a sequential CNN network there will be \r\n  only one output layer at the end. In the YOLOv3 \r\n  architecture, there are multiple output layers giving\r\n  out predictions. This function gives the names of the \r\n  output layers. An output layer is not connected to \r\n  any next layer.\r\n  \r\n  Args\r\n    net : neural network\r\n  \"\"\"\r\n  # Get the names of all the layers in the network\r\n  layersNames = net.getLayerNames()\r\n  # Get the names of the output layers, i.e. the layers with unconnected outputs\r\n  return [layersNames[i[0] - 1] for i in net.getUnconnectedOutLayers()]\r\n\r\ndef postprocess(frame, outs, confThreshold, nmsThreshold=0.4):\r\n  frameHeight = frame.shape[0]\r\n  frameWidth = frame.shape[1]\r\n\r\n  classIds = []\r\n  confidences = []\r\n  boxes = []\r\n  # Scan through all the bounding boxes output from the network and keep only the\r\n  # ones with high confidence scores. Assign the box's class label as the class with the highest score.\r\n  classIds = []\r\n  confidences = []\r\n  boxes = []\r\n  predictions = []\r\n\r\n  for out in outs:\r\n    for detection in out:\r\n      scores = detection[5:]\r\n      classId = np.argmax(scores)\r\n      confidence = scores[classId]\r\n      if confidence > confThreshold:\r\n        center_x = int(detection[0] * frameWidth)\r\n        center_y = int(detection[1] * frameHeight)\r\n        width = int(detection[2] * frameWidth)\r\n        height = int(detection[3] * frameHeight)\r\n        left = int(center_x - width / 2)\r\n        top = int(center_y - height / 2)\r\n        classIds.append(classId)\r\n        confidences.append(float(confidence))\r\n        boxes.append([left, top, width, height])\r\n\r\n  # Perform non maximum suppression to eliminate redundant overlapping boxes with\r\n  # lower confidences.\r\n  if nmsThreshold:\r\n    indices = cv.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold)\r\n  else:\r\n    indices = [[x] for x in range(len(boxes))]\r\n\r\n  for i in indices:\r\n    i = i[0]\r\n    box = boxes[i]\r\n    left = box[0]\r\n    top = box[1]\r\n    width = box[2]\r\n    height = box[3]\r\n    predictions.append([classIds[i], confidences[i], [left, top, left + width, top + height]])\r\n\r\n  return predictions\r\n\r\ndef crop_region(I,label,bg=0.5):\r\n\r\n  wh = np.array(I.shape[1::-1])\r\n\r\n  ch = I.shape[2] if len(I.shape) == 3 else 1\r\n  tl = np.floor(label.tl()*wh).astype(int)\r\n  br = np.ceil (label.br()*wh).astype(int)\r\n  outwh = br-tl\r\n\r\n  if np.prod(outwh) == 0.:\r\n    return None\r\n\r\n  outsize = (outwh[1],outwh[0],ch) if ch > 1 else (outwh[1],outwh[0])\r\n  if (np.array(outsize) < 0).any():\r\n    pause()\r\n  Iout  = np.zeros(outsize,dtype=I.dtype) + bg\r\n\r\n  offset  = np.minimum(tl,0)*(-1)\r\n  tl      = np.maximum(tl,0)\r\n  br      = np.minimum(br,wh)\r\n  wh      = br - tl\r\n\r\n  Iout[offset[1]:(offset[1] + wh[1]),offset[0]:(offset[0] + wh[0])] = I[tl[1]:br[1],tl[0]:br[0]]\r\n\r\n  return Iout\r\n\r\ndef vehicle_detection(frame):\r\n\r\n  # Create a 4D blob from a frame.\r\n  blob = cv.dnn.blobFromImage(frame, 1/255, (416, 416), [0,0,0], 1, crop=False)\r\n\r\n  # Sets the input to the network\r\n  vehicle_net.setInput(blob)\r\n\r\n  # Runs the forward pass to get output of the output layers\r\n  outs = vehicle_net.forward(getOutputsNames(vehicle_net))\r\n\r\n  # Remove the bounding boxes with low confidence\r\n  R = postprocess(frame, outs, vehicle_threshold)\r\n  \r\n  Icars = []\r\n  Lcars = []\r\n\r\n  if len(R):\r\n    WH = np.array(frame.shape[1::-1], dtype=float)\r\n    for i, r in enumerate(R):\r\n      # if classId in ['car', 'bus'] and confidence > vehicle_threshold\r\n      if r[0] in [6, 7] and r[1] > vehicle_threshold:\r\n        box = r[2]\r\n        x1,y1,x2,y2 = (np.array(r[2])/np.concatenate((WH,WH))).tolist()\r\n        tl = np.array([x1, y1])\r\n        br = np.array([x2, y2])\r\n        label = Label(0,tl,br)\r\n        Lcars.append(label)\r\n        Icar = crop_region(frame,label)\r\n        Icars.append(Icar.astype(np.uint8))\r\n\r\n  return Icars, Lcars\r\n\r\ndef im2single(I):\r\n  assert(I.dtype == 'uint8')\r\n  return I.astype('float32')/255.\r\n\r\nclass DLabel (Label):\r\n\r\n\tdef __init__(self,cl,pts,prob):\r\n\t\tself.pts = pts\r\n\t\ttl = np.amin(pts,1)\r\n\t\tbr = np.amax(pts,1)\r\n\t\tLabel.__init__(self,cl,tl,br,prob)\r\n\r\ndef getWH(shape):\r\n  return np.array(shape[1::-1]).astype(float)\r\n\r\n\r\ndef IOU(tl1,br1,tl2,br2):\r\n  wh1,wh2 = br1-tl1,br2-tl2\r\n  assert((wh1>=.0).all() and (wh2>=.0).all())\r\n  \r\n  intersection_wh = np.maximum(np.minimum(br1,br2) - np.maximum(tl1,tl2),0.)\r\n  intersection_area = np.prod(intersection_wh)\r\n  area1,area2 = (np.prod(wh1),np.prod(wh2))\r\n  union_area = area1 + area2 - intersection_area;\r\n  return intersection_area/union_area\r\n\r\ndef IOU_labels(l1,l2):\r\n  return IOU(l1.tl(),l1.br(),l2.tl(),l2.br())\r\n\r\ndef nms(Labels,iou_threshold=.5):\r\n\r\n  SelectedLabels = []\r\n  Labels.sort(key=lambda l: l.prob(),reverse=True)\r\n  \r\n  for label in Labels:\r\n    non_overlap = True\r\n    for sel_label in SelectedLabels:\r\n      if IOU_labels(label,sel_label) > iou_threshold:\r\n        non_overlap = False\r\n        break\r\n    if non_overlap:\r\n      SelectedLabels.append(label)\r\n\r\n  return SelectedLabels\r\n\r\ndef getRectPts(tlx,tly,brx,bry):\r\n\treturn np.matrix([[tlx,brx,brx,tlx],[tly,tly,bry,bry],[1.,1.,1.,1.]],dtype=float)\r\n\r\ndef find_T_matrix(pts,t_pts):\r\n\tA = np.zeros((8,9))\r\n\tfor i in range(0,4):\r\n\t\txi  = pts[:,i];\r\n\t\txil = t_pts[:,i];\r\n\t\txi  = xi.T\r\n\t\t\r\n\t\tA[i*2,   3:6] = -xil[2]*xi\r\n\t\tA[i*2,   6: ] =  xil[1]*xi\r\n\t\tA[i*2+1,  :3] =  xil[2]*xi\r\n\t\tA[i*2+1, 6: ] = -xil[0]*xi\r\n\r\n\t\r\n\t[U,S,V] = np.linalg.svd(A)\r\n\tH = V[-1,:].reshape((3,3))\r\n\r\n\treturn H\r\n\r\ndef reconstruct(Iorig,I,Y,out_size,threshold=.9):\r\n   \r\n\tnet_stride \t= 2**4\r\n\tside \t\t= ((208. + 40.)/2.)/net_stride # 7.75\r\n\r\n\tProbs = Y[...,0]\r\n\t# print Probs\r\n\tAffines = Y[...,2:]\r\n\trx,ry = Y.shape[:2]\r\n\t# print Y.shape\r\n\tywh = Y.shape[1::-1]\r\n\t# print ywh\r\n\tiwh = np.array(I.shape[1::-1],dtype=float).reshape((2,1))\r\n\t# print iwh\r\n\r\n\txx,yy = np.where(Probs>threshold)\r\n\t# print xx,yy\r\n\r\n\tWH = getWH(I.shape)\r\n\tMN = WH/net_stride\r\n\r\n\t# print MN\r\n\r\n\tvxx = vyy = 0.5 #alpha\r\n\r\n\tbase = lambda vx,vy: np.matrix([[-vx,-vy,1.],[vx,-vy,1.],[vx,vy,1.],[-vx,vy,1.]]).T\r\n\tlabels = []\r\n\r\n\tfor i in range(len(xx)):\r\n\t\ty,x = xx[i],yy[i]\r\n\t\taffine = Affines[y,x]\r\n\t\tprob = Probs[y,x]\r\n\r\n\t\tmn = np.array([float(x) + .5,float(y) + .5])\r\n\r\n\t\tA = np.reshape(affine,(2,3))\r\n\t\tA[0,0] = max(A[0,0],0.)\r\n\t\tA[1,1] = max(A[1,1],0.)\r\n\t\t# print A\r\n\t\tpts = np.array(A*base(vxx,vyy)) #*alpha\r\n\t\t# print pts\r\n\t\tpts_MN_center_mn = pts*side\r\n\t\tpts_MN = pts_MN_center_mn + mn.reshape((2,1))\r\n\r\n\t\tpts_prop = pts_MN/MN.reshape((2,1))\r\n\r\n\t\tlabels.append(DLabel(0,pts_prop,prob))\r\n\r\n\t# print(labels)\r\n\tfinal_labels = nms(labels,.1)\r\n\tTLps = []\r\n\r\n\tif len(final_labels):\r\n\t\tfinal_labels.sort(key=lambda x: x.prob(), reverse=True)\r\n\t\tfor i,label in enumerate(final_labels):\r\n\r\n\t\t\tt_ptsh \t= getRectPts(0,0,out_size[0],out_size[1])\r\n\t\t\tptsh \t= np.concatenate((label.pts*getWH(Iorig.shape).reshape((2,1)),np.ones((1,4))))\r\n\t\t\tH \t\t= find_T_matrix(ptsh,t_ptsh)\r\n\t\t\tIlp \t= cv.warpPerspective(Iorig,H,out_size,borderValue=.0)\r\n\t\t\t# cv.imshow(\"frame\", Iorig)\r\n\t\t\t# cv.waitKey(0)\r\n\r\n\t\t\tTLps.append(Ilp)\r\n\r\n\treturn final_labels,TLps\r\n\r\ndef detect_lp(model,I,max_dim,net_step,out_size,threshold):\r\n\r\n\tmin_dim_img = min(I.shape[:2])\r\n\tfactor \t\t= float(max_dim)/min_dim_img\r\n\t# print I.shape[:2]\r\n\r\n\tw,h = (np.array(I.shape[1::-1],dtype=float)*factor).astype(int).tolist()\r\n\tw += (w%net_step!=0)*(net_step - w%net_step)\r\n\th += (h%net_step!=0)*(net_step - h%net_step)\r\n\t# print w\r\n\t# print h\r\n\tIresized = cv.resize(I,(w,h))\r\n\r\n\tT = Iresized.copy()\r\n\tT = T.reshape((1,T.shape[0],T.shape[1],T.shape[2]))\r\n\r\n\tstart \t= time.time()\r\n\tYr \t\t= model.predict(T)\r\n\tYr \t\t= np.squeeze(Yr)\r\n\telapsed = time.time() - start\r\n\t# print(Yr)\r\n\tL,TLps = reconstruct(I,Iresized,Yr,out_size,threshold)\r\n\r\n\treturn L,TLps,elapsed\r\n\r\n\r\n# Import necessary modules\r\nfrom keras.models import model_from_json\r\n\r\n# Initialize the parameters\r\nlp_threshold = .6\r\nwpod_lp_weights_path = 'lp-detector/wpod-net_update1.h5'\r\nwpod_lp_json_path    = 'lp-detector/wpod-net_update1.json'\r\n\r\n# Load the model\r\nwith open(wpod_lp_json_path,'r') as json_file:\r\n  wpod_json = json_file.read()\r\nlp_net = model_from_json(wpod_json)\r\nlp_net.load_weights(wpod_lp_weights_path)\r\n\r\ndef lp_detection(vehicle_img):\r\n\r\n  ratio = float(max(vehicle_img.shape[:2]))/min(vehicle_img.shape[:2])\r\n  side  = int(ratio * 288.)\r\n  bound_dim = min(side + (side % (2**4) ), 608)\r\n  Llps, LlpImgs, elapsed = detect_lp(lp_net,im2single(vehicle_img),bound_dim,2**4,(240,80),lp_threshold)\r\n  \r\n  Ilps = []\r\n  for LlpImg in LlpImgs:\r\n    Ilp = LlpImg * 255.\r\n    Ilps.append(Ilp.astype(np.uint8))\r\n\r\n  return Llps, Ilps, elapsed\r\n\r\n# Initialize the parameters\r\nocr_threshold = .4\r\nocr_weights_path = 'ocr/ocr-net.weights'\r\nocr_confi_path   = 'ocr/ocr-net.cfg'\r\nocr_classes_path = 'ocr/ocr-net.names'\r\nocr_classes = None\r\n\r\nwith open(ocr_classes_path, 'rt') as f:\r\n  ocr_classes = f.read().rstrip('\\n').split('\\n')\r\n\r\n# load the network\r\nocr_net = cv.dnn.readNetFromDarknet(ocr_confi_path, ocr_weights_path)\r\nocr_net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)\r\nocr_net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)\r\n\r\ndef dknet_label_conversion(R,img_width,img_height, classes):\r\n\tWH = np.array([img_width,img_height],dtype=float)\r\n\tL  = []\r\n\tfor r in R:\r\n\t\tcenter = np.array(r[2][:2])/WH\r\n\t\twh2 = (np.array(r[2][2:])/WH)*.5\r\n\t\tL.append(Label(ord(classes[r[0]]),tl=center-wh2,br=center+wh2,prob=r[1]))\r\n\treturn L\r\n\r\ndef lp_ocr(lp_img):\r\n  h,w,_ = lp_img.shape\r\n  # Create a 4D blob from a frame\r\n  blob = cv.dnn.blobFromImage(lp_img, 1/255, (240, 80), [0,0,0], 1, crop=False)\r\n  # Sets the input to the network\r\n  ocr_net.setInput(blob)\r\n  # Runs the forward pass to get output of the output layers\r\n  outs = ocr_net.forward(getOutputsNames(ocr_net))\r\n  # Remove the bounding boxes with low confidence\r\n  R = postprocess(lp_img, outs, ocr_threshold, None)\r\n\r\n  lp_str = ''\r\n  if len(R):\r\n    L = dknet_label_conversion(R, w, h, ocr_classes)\r\n    L.sort(key=lambda x: x.tl()[0])\r\n    lp_str = ''.join([chr(l.cl()) for l in L])\r\n\r\n  return lp_str\r\n\r\ndef draw_label(I,l,color=(255,0,0),thickness=1):\r\n\twh = np.array(I.shape[1::-1]).astype(float)\r\n\ttl = tuple((l.tl()*wh).astype(int).tolist())\r\n\tbr = tuple((l.br()*wh).astype(int).tolist())\r\n\tcv.rectangle(I,tl,br,color,thickness=thickness)\r\n\r\ndef draw_losangle(I,pts,color=(1.,1.,1.),thickness=1):\r\n\tassert(pts.shape[0] == 2 and pts.shape[1] == 4)\r\n\r\n\tfor i in range(4):\r\n\t\tpt1 = tuple(pts[:,i].astype(int).tolist())\r\n\t\tpt2 = tuple(pts[:,(i+1)%4].astype(int).tolist())\r\n\t\tcv.line(I,pt1,pt2,color,thickness)\r\n\r\ndef write2img(Img,label,strg,txt_color=(0,0,0),bg_color=(255,255,255),font_size=1):\r\n\twh_img = np.array(Img.shape[1::-1])\r\n\r\n\tfont = cv.FONT_HERSHEY_SIMPLEX\r\n\r\n\twh_text,v = cv.getTextSize(strg, font, font_size, 3)\r\n\tbl_corner = label.tl()*wh_img\r\n\r\n\ttl_corner = np.array([bl_corner[0],bl_corner[1]-wh_text[1]])/wh_img\r\n\tbr_corner = np.array([bl_corner[0]+wh_text[0],bl_corner[1]])/wh_img\r\n\tbl_corner /= wh_img\r\n\r\n\tif (tl_corner < 0.).any():\r\n\t\tdelta = 0. - np.minimum(tl_corner,0.)\r\n\telif (br_corner > 1.).any():\r\n\t\tdelta = 1. - np.maximum(br_corner,1.)\r\n\telse:\r\n\t\tdelta = 0.\r\n\r\n\ttl_corner += delta\r\n\tbr_corner += delta\r\n\tbl_corner += delta\r\n\r\n\ttpl = lambda x: tuple((x*wh_img).astype(int).tolist())\r\n\r\n\tcv.rectangle(Img, tpl(tl_corner), tpl(br_corner), bg_color, -1)\t\r\n\tcv.putText(Img,strg,tpl(bl_corner),font,font_size,txt_color,3)\r\n\r\n\r\ndef processimage(spath, addr, vehstr):\r\n  lp_str_list = []\r\n  im1 = PIL.Image.open(spath)\r\n  p = Path(spath)\r\n  fpath = p.parent\r\n  jpegpath = os.path.join(fpath,'incoming.jpg')\r\n  im1.save(jpegpath)\r\n  # read incoming image\r\n  org_img = cv.imread(jpegpath)\r\n  test_img = org_img.copy()\r\n  # detect cars\r\n  Icars, Lcars = vehicle_detection(test_img)\r\n  #print('# vehicle detected: {}'.format(len(Icars)))\r\n  logger1.info('vehicle detected: %d - for the client - %s', len(Icars), addr)\r\n  # for each detected car in incoming image\r\n  for Icar, Lcar in zip(Icars, Lcars):\r\n    # draw car bounding box on incoming image\r\n    draw_label(test_img,Lcar,color=(0,255,255),thickness=3)\r\n    # detect LP in detected car\r\n    Llps, Ilps, elapsed = lp_detection(Icar)   \r\n    # for each detected LP in detected car image\r\n    for Llp, Ilp in zip(Llps, Ilps):\r\n      # draw LP bounding box on incoming image\r\n      pts = Llp.pts*Lcar.wh().reshape(2,1) + Lcar.tl().reshape(2,1)\r\n      ptspx = pts*np.array(test_img.shape[1::-1],dtype=float).reshape(2,1)\r\n      draw_losangle(test_img,ptspx,color=(0,0,255),thickness=3)\r\n      # Recognize characters\r\n      lp_str = lp_ocr(Ilp)\r\n      lp_str_list.append(lp_str)\r\n      #print(f'Detected number plate is- {lp_str}')\r\n      logger1.info('License plate detected - %s - for the client - %s', lp_str, addr)\r\n      # write text on incoming image\r\n      llp = Label(0,tl=pts.min(1),br=pts.max(1))\r\n      write2img(test_img,llp,lp_str)  \r\n  filepath = os.path.join(fpath , 'detected-image.jpg')\r\n  cv.imwrite(filepath, test_img)\r\n  s3file = addr.replace('.','-',3) + '.jpg'\r\n  if vehstr.upper() in lp_str_list:\r\n    s3.upload_file(filepath, \"raj-5g-bucket\", s3file)\r\n    return 0\r\n  else:\r\n    return 1\r\n  \r\n\r\n","sub_path":"server/vehiclesearch.py","file_name":"vehiclesearch.py","file_ext":"py","file_size_in_byte":15404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"237948254","text":"import logging\nimport subprocess\nimport time\nimport os\n\nfrom haystack import Finder\nfrom haystack.database.elasticsearch import ElasticsearchDocumentStore\nfrom haystack.indexing.cleaning import clean_wiki_text\nfrom haystack.indexing.utils import convert_files_to_dicts\nfrom haystack.reader.farm import FARMReader\nfrom haystack.reader.transformers import TransformersReader\nfrom haystack.utils import print_answers\nfrom haystack.retriever.elasticsearch import ElasticsearchRetriever\n\n# windows workaround to prevent endless recursion\nif __name__ == '__main__':\n    programstart = time.time()\n    # Start new server or connect to a running one. true and false respectively\n    LAUNCH_ELASTICSEARCH = False\n    # Determines whether the Elasticsearch Server has to be populated with data\n    # or not\n    POPULATE_DOCUMENT_STORE = False\n    # Start an Elasticsearch server\n    if LAUNCH_ELASTICSEARCH:\n        logging.info(\"Starting Elasticsearch ...\")\n        status = subprocess.run(\n            ['docker run -d -p 9200:9200 -e \"discovery.type=single-node\" elasticsearch:7.6.2'],\n            shell=True\n        )\n        if status.returncode:\n            raise Exception(\"Failed to launch Elasticsearch. If you want to \"\n                            \"connect to an existing Elasticsearch instance\"\n                            \"then set LAUNCH_ELASTICSEARCH in the script to False.\")\n        time.sleep(15)\n\n    # Connect to Elasticsearch\n    document_store = ElasticsearchDocumentStore(\n        host=\"localhost\", username=\"\", password=\"\", index=\"document\")\n\n    # ## Cleaning & indexing documents\n\n    # Initialize Elasticsearch with docs\n    if POPULATE_DOCUMENT_STORE:\n        # set path to directory containing the text files\n        doc_dir = os.getcwd() + \"\\\\kbQA\\\\data\\\\tesla\"\n        # convert files to dicts containing documents that can be indexed to our\n        # datastore\n        dicts = convert_files_to_dicts(\n            dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)\n\n        # write the docs to the elasticsearch database\n        document_store.write_documents(dicts)\n\n    # ## Initalize Retriever, Reader,  & Finder\n    # ### Retriever\n    # Retrievers help narrowing down the scope for the Reader to smaller units\n    # of text where a given question\n    # could be answered.\n    # We use Elasticsearch's default BM25 algorithm\n    retriever = ElasticsearchRetriever(document_store=document_store)\n    # ### Reader\n    # A Reader scans the texts returned by retrievers in detail and extracts\n    # the k best answers. It is based on a powerful, but slower deep learning model.\n    reader = TransformersReader(\n        model=\"dbmdz/bert-base-german-uncased\", tokenizer=\"dbmdz/bert-base-german-uncased\", use_gpu=-1)\n    # ### Finder\n    # The Finder sticks together reader and retriever in a pipeline to answer\n    # our actual questions.\n    finder = Finder(reader, retriever)\n    initend = time.time()\n    questions = [\n        \"worauf sollte man auf Fähren achten?\",\n        \"wird die verkehrschilderkennung für alle kommen?\",\n        \"was beinhaltet der Autopilot?\",\n        \"wie viel verbaucht das Model 3?\",\n        \"fährt das auto wenn der stecker steckt?\",\n        \"Welche dimension haben die kleinen Sommerreifen?\",\n        \"wie viel zoll haben die Sommerreifen?\",\n        \"Werden UV-Strahlen beim Tesla geblockt?\",\n\n        \"Ich habe bei Tesla 500€ pro Rad bezahlt.\",\n        \"Tempomat Geschwindigkeit ändern.\",\n        \"die batterie sollte mindestens 50% haben.\"\n    ]\n    # auch hier wieder: Kleinschreibung zwingend notwendig!\n    # question = question.lower()\n    times = []\n\n    for question in questions:\n        start_time = time.process_time()\n        prediction = finder.get_answers(\n            question, top_k_reader=5, top_k_retriever=5)\n        end_time = time.process_time()\n        times.append(end_time - start_time)\n        print_answers(prediction, details=\"minimal\")\n\n    total = 0\n    for zeit in times:\n        total = total + zeit\n    print(total / len(times))\n\n    print(\"init_time:\")\n    print(initend-programstart)\n","sub_path":"kbQA/kbQA_german_elasticsearch.py","file_name":"kbQA_german_elasticsearch.py","file_ext":"py","file_size_in_byte":4092,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"278696435","text":"from flask import Flask, render_template, jsonify\nfrom pymongo import MongoClient\nimport sys, json\nfrom bson import json_util\nfrom bson.json_util import dumps\nimport pandas as pd\nimport time\nimport requests as req\nimport time\nimport datetime\nfrom flask_pymongo import PyMongo\nimport os \n\nMONGODB_URI = os.environ.get('MONGODB_URI')\nif not MONGODB_URI:\n    MONGODB_URI = \"mongodb://localhost:27017/austinDB\"\n\napp = Flask(__name__)\napp.config['MONGO_URI'] = MONGODB_URI\nmongo = PyMongo(app)\n\n##########################################################################\n####################  Creating the Database  #############################\n##########################################################################\nurl = \"https://data.austintexas.gov/resource/r3af-2r8x.json?$limit=50000&$offset=0\"\n\n# def get_data():\n    # get the json from austin data api\ntraffic_response = req.get(url)\ntraffic_json = traffic_response.json()\ntime.sleep(1)\n\n# append json into DataFrame\ndf = pd.DataFrame(traffic_json)\ntime.sleep(1)\n\n# Selecting specific columns needed\nclean_df = df[['address','issue_reported','location_latitude', 'location_longitude', 'published_date']]\n\n# Drop any NAN values and append to a list\ndropped = clean_df.dropna()\nnewdate = dropped.loc[0:, 'published_date']\nlisty = []\nfor i in newdate:\n    listy.append(i[0:10])\ntime.sleep(1)\n\n#  delete old values for published_date and pass in new list to published_date column\nxyz = dropped.drop('published_date', axis=1)\nxyz['published_date'] = listy\ntime.sleep(1)\n\n\nMONGODB_HOST = 'localhost'\nMONGODB_PORT = 27017\nDBS_NAME = 'austinDB'\nCOLLECTION_NAME = 'austinData'\ndata = json_util.loads(xyz.to_json(orient='records'))\nconnection = MongoClient(MONGODB_HOST, MONGODB_PORT)\n# blah = connection.austinDB.dropDatabase()\ndb = connection.austinDB\ndb.austinData.remove()\ntime.sleep(1)\n# db.dropDatabase()\naustinData = db.austinData\nposts_id = austinData.insert_many(data)\n\nMONGODB_HOST = 'localhost'\nMONGODB_PORT = 27017\nDBS_NAME = 'austinDB'\nCOLLECTION_NAME = 'austinData'\napp = Flask(__name__)\n\n\nFIELDS = {'_id': False, 'address': True, 'issue_reported': True, 'location_latitude': True, 'location_longitude': True, 'published_date': True}\n\n@app.route('/')\ndef index():\n    return render_template(\"index.html\")\n\n@app.route(\"/austin/data\")\ndef austin_incidents():\n    connection = MongoClient(MONGODB_HOST, MONGODB_PORT)\n    collection = connection[DBS_NAME][COLLECTION_NAME]\n    incidents = collection.find(projection=FIELDS)\n    json_incidents = []\n    for incident in incidents:\n        json_incidents.append(incident)\n    json_incidents = json.dumps(json_incidents, default=json_util.default)\n    connection.close()\n    return json_incidents\n\n@app.route(\"/incident_types\")\ndef incident_types():\n    connection = MongoClient(MONGODB_HOST, MONGODB_PORT)\n    collection = connection[DBS_NAME][COLLECTION_NAME]\n    incidents = collection.distinct( \"issue_reported\" )\n    json_incidents = []\n    for incident in incidents:\n        json_incidents.append(incident)\n    json_incidents = json.dumps(json_incidents, default=json_util.default)\n    connection.close()\n    return json_incidents\n\n@app.route(\"/dates\")\ndef dates():\n    connection = MongoClient(MONGODB_HOST, MONGODB_PORT)\n    collection = connection[DBS_NAME][COLLECTION_NAME]\n    dates = collection.distinct( \"published_date\" )\n    json_dates = []\n    for date in dates: \n        json_dates.append(date)\n    json_dates = json.dumps(json_dates, default=json_util.default)\n    connection.close()\n    return json_dates \n\n@app.route(\"/api/v1.1/pie/\")\ndef pieChartData():\n    connection = MongoClient(MONGODB_HOST, MONGODB_PORT)\n    collection = connection[DBS_NAME][COLLECTION_NAME]\n    issues = collection.find(projection=FIELDS)\n    df = pd.DataFrame(list(issues))\n    top10=df[['issue_reported','location_latitude']].groupby(['issue_reported']).count().sort_values('location_latitude',ascending=False)[:10].reset_index().rename(columns={'Location':'Num Incidents'})\n    json = top10.reset_index(drop=True)\n    dictionary = json.to_dict(orient='records')\n    connection.close()\n    return jsonify(dictionary)\n\nif __name__ == \"__main__\":\n    app.run(host=\"0.0.0.0\", port=5000, debug=True)","sub_path":"austin_traffic/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4195,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"183659352","text":"#\n# Copyright (c) 2018 ISP RAS (http://www.ispras.ru)\n# Ivannikov Institute for System Programming of the Russian Academy of Sciences\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\nimport os\nimport re\n\nfrom core.utils import unique_file_name\nfrom core.vtg.emg.common import get_conf_property, get_necessary_conf_property\nfrom core.vtg.emg.common.c import Function\nfrom core.vtg.emg.common.c.types import Pointer, Primitive\nfrom core.vtg.emg.modelTranslator.fsa_translator.common import initialize_automaton_variables\n\n\nclass Aspect(Function):\n    \"\"\"\n    Representation of the aspect file pointcuts for source functions which calls should be modified or replaced by\n    models. This is an aspect-oriented extension of the C language which is supported by CIF.\n    \"\"\"\n\n    def __init__(self, name, declaration, aspect_type=\"after\"):\n        super(Aspect, self).__init__(name, declaration)\n        self.aspect_type = aspect_type\n\n    def define(self):\n        \"\"\"\n        Print description of the replacement that should be made to the source funtion calls.\n\n        :return: List of strings.\n        \"\"\"\n        lines = list()\n        lines.append(\"around: call({}) \".format(\"$ {}(..)\".format(self.name)) +\n                     \" {\\n\")\n        lines.extend(['\\t{}\\n'.format(stm) for stm in self.body])\n        lines.append(\"}\\n\")\n        return lines\n\n\nclass CModel:\n    \"\"\"Representation of the environment model in the C language (with extensions).\"\"\"\n\n    mem_function_map = {\n        \"ALLOC\": \"ldv_xmalloc\",\n        \"UALLOC\": \"ldv_xmalloc_unknown_size\",\n        \"ZALLOC\": \"ldv_zalloc\"\n    }\n    free_function_map = {\n        \"FREE\": \"ldv_free\"\n    }\n    irq_function_map = {\n        \"IN_INTERRUPT_CONTEXT\": 'ldv_in_interrupt_context',\n        \"SWITCH_TO_IRQ_CONTEXT\": 'ldv_switch_to_interrupt_context',\n        \"SWITCH_TO_PROCESS_CONTEXT\": 'ldv_switch_to_process_context'\n    }\n\n    def __init__(self, logger, conf, workdir, files, entry_point_name, entry_file):\n        self.entry_file = entry_file\n        self.entry_name = entry_point_name\n        self.files = files\n        self.types = list()\n        self._logger = logger\n        self._conf = conf\n        self._workdir = workdir\n        self._variables_declarations = dict()\n        self._variables_initializations = dict()\n        self._function_definitions = dict()\n        self._function_declarations = dict()\n        self._headers = dict()\n        self._before_aspects = dict()\n        self._call_aspects = dict()\n        self.__external_allocated = dict()\n\n    def add_headers(self, file, headers):\n        \"\"\"\n        Add headers include directives to the particular file.\n\n        :param file: C file.\n        :param headers: List of header files.\n        :return: None.\n        \"\"\"\n        if file not in self._headers:\n            self._headers[file] = [headers]\n        else:\n            # This is to avoid dependencies broken\n            self._headers[file].append(headers)\n\n    def add_function_definition(self, func):\n        \"\"\"\n        Add a function definition to the main environment model file.\n\n        :param func: Function object.\n        :return: None.\n        \"\"\"\n        if not func.definition_file:\n            raise RuntimeError('Always expect file to place function definition')\n        if func.definition_file not in self._function_definitions:\n            self._function_definitions[func.definition_file] = dict()\n        if self.entry_file not in self._function_definitions:\n            self._function_definitions[self.entry_file] = dict()\n\n        self._function_definitions[func.definition_file][func.name] = func.define()\n        self.add_function_declaration(func.definition_file, func, extern=False)\n\n    def add_function_declaration(self, file, func, extern=False):\n        \"\"\"\n        Add a function declaration to the file.\n\n        :param file: File name.\n        :param func: Function object.\n        :param extern: Add it as an extern function.\n        :return: None.\n        \"\"\"\n        if file not in self._function_declarations:\n            self._function_declarations[file] = dict()\n\n        if extern and func.name in self._function_declarations[file]:\n            return\n        self._function_declarations[file][func.name] = func.declare(extern=extern)\n\n    def add_global_variable(self, variable, file, extern=False, initialize=True):\n        \"\"\"\n        Add a global variable declararation or/and initalization to the target file.\n\n        :param variable: Variable object.\n        :param file: File name.\n        :param extern: Add it as an extern variable.\n        :param initialize: Add also the global variable initialization.\n        :return: None.\n        \"\"\"\n        if not file and variable.file:\n            file = variable.file\n        elif not file:\n            file = self.entry_file\n\n        if file not in self._variables_declarations:\n            self._variables_declarations[file] = dict()\n        if file not in self._variables_initializations:\n            self._variables_initializations[file] = dict()\n\n        if extern and variable.name not in self._variables_declarations[file]:\n            self._variables_declarations[file][variable.name] = variable.declare(extern=extern) + \";\\n\"\n        elif not extern:\n            self._variables_declarations[file][variable.name] = variable.declare(extern=extern) + \";\\n\"\n            if initialize:\n                if variable.value and \\\n                        ((isinstance(variable.declaration, Pointer) and isinstance(variable.declaration.points, Function))\n                         or isinstance(variable.declaration, Primitive)):\n                    self._variables_initializations[file][variable.name] = variable.declare_with_init() + \";\\n\"\n                elif not variable.value and isinstance(variable.declaration, Pointer):\n                    if file not in self.__external_allocated:\n                        self.__external_allocated[file] = []\n                    self.__external_allocated[file].append(variable)\n\n    def text_processor(self, automaton, statement):\n        \"\"\"\n        Analyze given C code statement and replace all found EMG extensions with the clean C code.\n\n        :param automaton: Automaton object.\n        :param statement: Statement string.\n        :return: Refined C statements list.\n        \"\"\"\n        models = FunctionModels(self._logger, self._conf, self.mem_function_map, self.free_function_map,\n                                self.irq_function_map)\n        return models.text_processor(automaton, statement)\n\n    def add_function_model(self, func, body):\n        \"\"\"\n        Add function model to the environment model.\n\n        :param func: Function object to model.\n        :param body: List of C statements which should replace function calls.\n        :return: None.\n        \"\"\"\n        new_aspect = Aspect(func.name, func.declaration)\n        new_aspect.body = body\n        files = set()\n        files.update(func.files_called_at)\n        files.update(func.declaration_files)\n        for file in files:\n            if file not in self._call_aspects:\n                self._call_aspects[file] = list()\n            self._call_aspects[file].append(new_aspect)\n\n    def print_source_code(self, additional_lines):\n        \"\"\"\n        Generate an environment model as a C code. The code is distributed across aspect addictions for original\n        source files and the main environment model C code.\n\n        :param additional_lines: Dictionary with the user-defined C code:\n                                 {'file name': {'definitions': [...], 'declarations': []}}\n        :return: Dictionary {'file': Path to generated file with the Code}\n        \"\"\"\n        aspect_dir = \"aspects\"\n        self._logger.info(\"Create directory for aspect files {}\".format(\"aspects\"))\n        os.makedirs(aspect_dir.encode('utf8'), exist_ok=True)\n\n        if get_conf_property(self._conf[\"translation options\"], \"propogate headers to instrumented files\"):\n            for file in (f for f in self.files if f in additional_lines):\n                self.add_headers(file,\n                                 get_necessary_conf_property(self._conf[\"translation options\"], \"additional headers\"))\n\n        addictions = dict()\n        # Write aspects\n        for file in self.files:\n            lines = list()\n\n            # Check headers\n            if file == self.entry_file:\n                if self.entry_file in self._headers:\n                    lines.extend(['#include <{}>\\n'.format(h) for h in\n                                  self._collapse_headers_sets(self._headers[self.entry_file])])\n                lines.append(\"\\n\")\n\n                for tp in self.types:\n                    lines.append(tp.to_string('') + \" {\\n\")\n                    for field in list(tp.fields.keys()):\n                        lines.append(\"\\t{};\\n\".format(tp.fields[field].to_string(field, typedef='complex_and_params'),\n                                                      scope={self.entry_file}))\n                    lines.append(\"};\\n\")\n                    lines.append(\"\\n\")\n            else:\n                # Generate function declarations\n                self._logger.info('Add aspects to a file {!r}'.format(file))\n\n                # Add headers\n                if file in self._headers and self._headers[file]:\n                    lines.append('before: file (\"$this\")\\n')\n                    lines.append('{\\n')\n                    lines.extend(['#include <{}>\\n'.format(h) for h in\n                                  self._collapse_headers_sets(self._headers[file])])\n                    lines.append(\"\\n\")\n                    lines.append(\"}\\n\\n\")\n\n                # Add model itself\n                lines.append('after: file (\"$this\")\\n')\n                lines.append('{\\n')\n\n            if file in additional_lines and 'declarations' in additional_lines[file] and \\\n                    len(additional_lines[file]['declarations']) > 0:\n                lines.append(\"\\n\")\n                lines.append(\"/* EMG aliases */\\n\")\n                lines.extend(additional_lines[file]['declarations'])\n\n            if file in self._function_declarations:\n                lines.append(\"\\n\")\n                lines.append(\"/* EMG Function declarations */\\n\")\n                for func in self._function_declarations[file].keys():\n                    lines.extend(self._function_declarations[file][func])\n\n            if file in self._variables_declarations:\n                lines.append(\"\\n\")\n                lines.append(\"/* EMG variable declarations */\\n\")\n                for variable in self._variables_declarations[file].keys():\n                    lines.extend(self._variables_declarations[file][variable])\n\n            if file in self._variables_initializations and len(self._variables_initializations[file]) > 0:\n                lines.append(\"\\n\")\n                lines.append(\"/* EMG variable initialization */\\n\")\n                for variable in self._variables_initializations[file].keys():\n                    lines.extend(self._variables_initializations[file][variable])\n\n            if file in additional_lines and 'definitions' in additional_lines[file] and \\\n                    len(additional_lines[file]['definitions']) > 0:\n                lines.append(\"\\n\")\n                lines.append(\"/* EMG aliases for functions */\\n\")\n                lines.extend(additional_lines[file]['definitions'])\n\n            if file in self._function_definitions and len(self._function_definitions[file]) > 0:\n                lines.append(\"\\n\")\n                lines.append(\"/* EMG function definitions */\\n\")\n                for func in self._function_definitions[file].keys():\n                    lines.extend(self._function_definitions[file][func])\n                    lines.append(\"\\n\")\n\n            if file != self.entry_file:\n                lines.append(\"}\\n\\n\")\n\n            if file in self._call_aspects and len(self._call_aspects[file]) > 0:\n                lines.append(\"/* EMG kernel function models */\\n\")\n                for aspect in self._call_aspects[file]:\n                    lines.extend(aspect.define())\n                    lines.append(\"\\n\")\n\n            if file != self.entry_file:\n                name = \"{}.aspect\".format(unique_file_name(\"aspects/ldv_\" + os.path.splitext(os.path.basename(file))[0],\n                                                           '.aspect'))\n                path = os.path.relpath(name, self._workdir)\n                self._logger.info(\"Add aspect file {!r}\".format(path))\n                addictions[file] = path\n            else:\n                name = self.entry_file\n            with open(name, \"w\", encoding=\"utf8\") as fh:\n                fh.writelines(lines)\n\n        return addictions\n\n    def compose_entry_point(self, given_body):\n        \"\"\"\n        Generate an entry point function for the environment model.\n\n        :param given_body: Body of the main function provided by a translator.\n        :return: List of C statements of the generated function body.\n        \"\"\"\n        ep = Function(self.entry_name, \"int {}(void)\".format(self.entry_name))\n        ep.definition_file = self.entry_file\n        body = ['/* LDV {' + '\"thread\": 1, \"type\": \"CONTROL_FUNCTION_BEGIN\", \"comment\": \"Entry point \\'{0}\\'\", '\n                '\"function\": \"{0}\"'.format(self.entry_name) + '} */']\n\n        # Init external allocated pointers\n        cnt = 0\n        functions = []\n        if len(self.__external_allocated.keys()) > 0:\n            for file in sorted([f for f in self.__external_allocated.keys() if len(self.__external_allocated[f]) > 0]):\n                func = Function('ldv_allocate_external_{}'.format(cnt),\n                                \"void ldv_allocate_external_{}(void)\".format(cnt))\n                func.declaration_files.add(file)\n                func.definition_file = file\n\n                init = [\"{} = {}();\".format(var.name, 'external_allocated_data') for\n                        var in self.__external_allocated[file]]\n                func.body = init\n\n                self.add_function_definition(func)\n                self.add_function_declaration(self.entry_file, func, extern=True)\n                functions.append(func)\n                cnt += 1\n\n            gl_init = Function('ldv_initialize_external_data',\n                               'void ldv_initialize_external_data(void)')\n            gl_init.declaration_files.add(self.entry_file)\n            gl_init.definition_file = self.entry_file\n            init_body = ['{}();'.format(func.name) for func in functions]\n            gl_init.body = init_body\n            self.add_function_definition(gl_init)\n            body.extend([\n                '/* Initialize external data */',\n                'ldv_initialize_external_data();'\n            ])\n\n        if get_conf_property(self._conf, \"initialize requirements\"):\n            body += [\n                '/* LDV {\"action\": \"INIT\", \"type\": \"CALL_BEGIN\", \"callback\": true, '\n                '\"comment\": \"Initialize requirement models.\"} */',\n                'ldv_initialize();',\n                '/* LDV {\"action\": \"INIT\", \"type\": \"CALL_END\"} */'\n            ]\n\n        body += ['/* LDV {\"action\": \"SCENARIOS\", \"type\": \"CONDITION_BEGIN\", '\n                 '\"comment\": \"Begin Environment model scenarios.\"} */'] + given_body + \\\n                ['/* LDV {\"action\": \"SCENARIOS\", \"type\": \"CONDITION_END\"} */']\n\n        if get_conf_property(self._conf, \"check final state\"):\n            body += [\n                '/* LDV {\"action\": \"FINAL\", \"callback\": true, \"type\": \"CALL_BEGIN\", '\n                '\"comment\": \"Check requirement model final state at the exit if required.\"} */',\n                'ldv_check_final_state();',\n                '/* LDV {\"action\": \"FINAL\", \"type\": \"CALL_END\"} */'\n            ]\n\n        body.append('return 0;')\n        body.append('/* LDV {' + '\"comment\": \"Exit entry point \\'{0}\\'\", \"type\": \"CONTROL_FUNCTION_END\",'\n                                 ' \"function\": \"{0}\"'.format(self.entry_name) + '} */')\n\n        ep.body = body\n        self.add_function_definition(ep)\n\n        return body\n\n    def _collapse_headers_sets(self, sets):\n        final_list = []\n        sortd = sorted(sets, key=lambda f: len(f))\n        while len(sortd) > 0:\n            data = sortd.pop()\n            difference = set(data).difference(set(final_list))\n            if len(difference) > 0 and len(difference) == len(data):\n                # All headers are new\n                final_list.extend(data)\n            elif len(difference) > 0:\n                position = len(final_list)\n                for header in reversed(data):\n                    if header not in difference:\n                        position = final_list.index(header)\n                    else:\n                        final_list.insert(position, header)\n        return final_list\n\n\nclass FunctionModels:\n    \"\"\"Class represent common C extensions for simplifying environmen model C code generation.\"\"\"\n\n    mem_function_template = \"\\$({})\\(%({})%(?:,\\s?(\\w+))?\\)\"\n    simple_function_template = \"\\$({})\\(\"\n    access_template = '\\w+(?:(?:[.]|->)\\w+)*'\n    mem_function_re = re.compile(mem_function_template.format('\\w+', access_template))\n    simple_function_re = re.compile(simple_function_template.format('\\w+'))\n    access_re = re.compile('(%{}%)'.format(access_template))\n    arg_re = re.compile('\\$ARG(\\d+)')\n\n    def __init__(self, logger, conf, mem_function_map, free_function_map, irq_function_map):\n        self._logger = logger\n        self._conf = conf\n        self.mem_function_map = mem_function_map\n        self.free_function_map = free_function_map\n        self.irq_function_map = irq_function_map\n        self.signature = None\n        self.ualloc_flag = None\n\n    def text_processor(self, automaton, statement):\n        \"\"\"\n        Analyze given C code statement and replace all found EMG extensions with the clean C code.\n\n        :param automaton: Automaton object.\n        :param statement: C statement string.\n        :return: New statements list.\n        \"\"\"\n        # Replace function names\n        stms = []\n        matched = False\n\n        # Find state reinitialization\n        if re.compile('\\$REINITIALIZE_STATE;').search(statement):\n            statements = initialize_automaton_variables(self._conf, automaton)\n            stms.extend(statements)\n\n        # First replace simple replacements\n        for number in self.arg_re.findall(statement):\n            new_number = int(number) - 1\n            statement = statement.replace('$ARG{}'.format(number), 'arg{}'.format(new_number))\n\n        # Replace function calls\n        for fn in self.simple_function_re.findall(statement):\n            matched = True\n\n            # Bracket is required to ignore CIF expressions like $res or $arg1\n            if fn in self.mem_function_map or fn in self.free_function_map:\n                access = self.mem_function_re.search(statement).group(2)\n                if fn in self.mem_function_map:\n                    replacement = self._replace_mem_call\n                else:\n                    replacement = self._replace_free_call\n\n                accesses = automaton.process.resolve_access('%{}%'.format(access))\n                for access in accesses:\n                    signature = access.label.declaration\n                    if signature:\n                        var = automaton.determine_variable(access.label)\n                        if isinstance(var.declaration, Pointer):\n                            self.signature = var.declaration\n                            self.ualloc_flag = True\n                            new = self.mem_function_re.sub(replacement, statement)\n                            stms.append(new)\n                    else:\n                        self._logger.warning(\"Cannot get signature for the label {!r}\".format(access.label.name))\n            elif fn in self.irq_function_map:\n                statement = self.simple_function_re.sub(self.irq_function_map[fn] + '(', statement)\n                stms.append(statement)\n            else:\n                raise NotImplementedError(\"Model function '${}' is not supported\".format(fn))\n\n        if not matched:\n            stms = [statement]\n\n        # Replace rest accesses\n        final = []\n        for original_stm in stms:\n            # Collect dublicates\n            stm_set = {original_stm}\n\n            while len(stm_set) > 0:\n                stm = stm_set.pop()\n                match = self.access_re.search(stm)\n                if match:\n                    expression = match.group(1)\n                    accesses = automaton.process.resolve_access(expression)\n                    for access in accesses:\n                        var = automaton.determine_variable(access.label)\n                        stm = stm.replace(expression, var.name)\n                        stm_set.add(stm)\n                else:\n                    final.append(stm)\n\n        return final\n\n    def _replace_mem_call(self, match):\n        func, label_name, flag = match.groups()\n        size = '0'\n\n        if func not in self.mem_function_map:\n            raise NotImplementedError(\"Model of {} is not supported\".format(func))\n        elif not self.mem_function_map[func]:\n            raise NotImplementedError(\"Set implementation for the function {}\".format(func))\n\n        if isinstance(self.signature, Pointer):\n            if func == 'ALLOC' and self.ualloc_flag:\n                # Do not alloc memory anyway for unknown resources anyway to avoid incomplete type errors\n                func = 'UALLOC'\n            if get_conf_property(self._conf, 'disable ualloc') and func == 'UALLOC':\n                func = 'ALLOC'\n            if func != 'UALLOC' and get_conf_property(self._conf, 'allocate with sizeof'):\n                size = 'sizeof({})'.format(self.signature.points.to_string('', typedef='complex_and_params'))\n\n            return \"{}({})\".format(self.mem_function_map[func], size)\n        else:\n            raise ValueError('This is not a pointer')\n\n    def _replace_free_call(self, match):\n        func, label_name, flag = match.groups()\n        if func not in self.free_function_map:\n            raise NotImplementedError(\"Model of {} is not supported\".format(func))\n        elif not self.free_function_map[func]:\n            raise NotImplementedError(\"Set implementation for the function {}\".format(func))\n\n        # Create function call\n        if isinstance(self.signature, Pointer):\n            return \"{}(%{}%)\".format(self.free_function_map[func], label_name)\n        else:\n            raise ValueError('This is not a pointer')\n","sub_path":"core/core/vtg/emg/modelTranslator/code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":23066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"460664537","text":"\"\"\"\nHTML - FORM APP\nNotes:\nform method POST is:\n    -more secure\n    -allows for more characters to be sent at one time\n    -is hidden\nform method GET is:\n    -friendlier to work with\n    -visible\n    -part of the url\n    -has character limitations\n\nusing the name attribute in an input , allows the browser to distinguish between fields: <input type=\"\" value=\"\" NAME=\"\" />\nthis information (name and its value) will show up in the url\n\n---------------------------------------------------------------------------------------\nthe server sending information to the client is a .response\nto print items (server to client)\n    self.response.write()\n\nthe information that is sent from the client to the server is a request\nto access items (client to server)\n    self.request.GET\n---------------------------------------------------------------------------------------\n\nCONDITIONALS IN FORMS\nuse a conditional statement to only send the information when a user inputs it\nyou cant access the information from a form unless you have already sent the information\na conditional statement will tell the page to only do this if it has recieved information from a form\n\"\"\"\nimport webapp2\n\n#the entire application is done from within one class\nclass MainHandler(webapp2.RequestHandler):\n    def get(self):\n\n#------------------------------------------------------------multi-line string\n#the page head attribute holds the htmls head information and the opening body tag\n        page_head = \"\"\"\n<!DOCTYPE HTML>\n<html>\n    <head>\n        <title>Simple Login Form</title>\n    </head>\n    <body>\"\"\"\n#------------------------------------------------------------multi-line string\n\n#------------------------------------------------------------multi-line string\n#the page_body attribute holds the page content (in this case a form)\n        page_body =\"\"\"<form method = \"GET\">\n            <label>Name: </label><input type = \"text\" name = \"user\" />\n            <label>Email: </label><input type = \"text\" name = \"email\" />\n            <input type = \"submit\" value = \"Submit\" />\"\"\"\n#------------------------------------------------------------multi-line string\n\n#------------------------------------------------------------multi-line string\n#the page_close attribute holds the closing tags for the html elements of the body\n        page_close = \"\"\"\n        </form>\n    </body>\n</html>\"\"\"\n#------------------------------------------------------------multi-line string\n\n#------------------------------------------------------------Request and Response Conditional\n        #this conditional codes for 2 different views: the page load, and the user input\n        if self.request.GET:\n            #stores information i get from the form\n            user = self.request.GET['user'] #store the information in a variable named user\n            email = self.request.GET['email']   #store the information in a variable named email\n\n            #place the user and the users email just before the form in the page body\n            #will print out if there is a response from the user\n            #removing the page body allows you to reload the page and return the users inputs\n            self.response.write(page_head + user + ' ' + email + \"\"\"+ page_body\"\"\" + page_close)\n        else:\n            #if the request does not work and there is no a request or url\n            #the server sending information to the client is a .response to the request.GET\n            #the response then publishes to the page\n            self.response.write(page_head + page_body + page_close)\n#------------------------------------------------------------Request and Response Conditional\n\"\"\"\nSELF.REQUEST.GET\nsend information with GET\nthe information that is sent from the client to the server is a request\nitems that the browser sends to the server\nsee the response with the self.request\nself.request.GET is an associative array\nput in the key associated with the value that you want into the array\nnames in the array must match the values of the names in the form inputs\nthis will return the values in the console / log\n\nSELF.RESPONSE.WRITE\n\"\"\"\n\napp = webapp2.WSGIApplication([\n    ('/', MainHandler)\n], debug=True)\n","sub_path":"_python basics/html-login-form/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4162,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"591099321","text":"# Copyright (c) 2018-     Xilinx, Inc              (Alessandro Pappalardo)\n# Copyright (c) 2016-     Facebook, Inc            (Adam Paszke)\n# Copyright (c) 2014-     Facebook, Inc            (Soumith Chintala)\n# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)\n# Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)\n# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)\n# Copyright (c) 2011-2013 NYU                      (Clement Farabet)\n# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)\n# Copyright (c) 2006      Idiap Research Institute (Samy Bengio)\n# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)\n\n# All rights reserved.\n\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions are met:\n\n# 1. Redistributions of source code must retain the above copyright\n#    notice, this list of conditions and the following disclaimer.\n\n# 2. Redistributions in binary form must reproduce the above copyright\n#    notice, this list of conditions and the following disclaimer in the\n#    documentation and/or other materials provided with the distribution.\n\n# 3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU,\n#    NEC Laboratories America and IDIAP Research Institute nor the names\n#    of its contributors may be used to endorse or promote products derived\n#    from this software without specific prior written permission.\n\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE\n# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n# POSSIBILITY OF SUCH DAMAGE.\n\nfrom typing import Tuple, Optional, List, Union\n\nimport torch\nfrom torch.nn import Parameter, Module\n\nimport brevitas\nimport brevitas.config as config\nfrom brevitas.core.function_wrapper import Identity\n\nfrom brevitas.core.stats import _ParameterListStats, _RuntimeStats, DEFAULT_MOMENTUM\nfrom brevitas.core.restrict_val import _RestrictClampValue\n\n\nclass StatsFromParameterScaling(brevitas.jit.ScriptModule):\n\n    def __init__(\n            self,\n            scaling_stats_impl: Module,\n            scaling_stats_input_view_shape_impl: Module,\n            scaling_stats_input_concat_dim: int,\n            tracked_parameter_list: List[torch.nn.Parameter],\n            restrict_scaling_impl: Module,\n            scaling_shape: Tuple[int, ...],\n            affine_rescaling: bool = False,\n            scaling_min_val: Optional[float] = None) -> None:\n        super(StatsFromParameterScaling, self).__init__()\n        self.parameter_list_stats = _ParameterListStats(\n            scaling_stats_impl,\n            scaling_shape,\n            scaling_stats_input_view_shape_impl,\n            scaling_stats_input_concat_dim,\n            tracked_parameter_list)\n        self.stats_scaling_impl = _StatsScaling(\n            restrict_scaling_impl,\n            scaling_shape,\n            scaling_min_val,\n            affine_rescaling)\n\n    @brevitas.jit.script_method\n    def forward(self, ignored: torch.Tensor) -> torch.Tensor:\n        stats = self.parameter_list_stats()\n        return self.stats_scaling_impl(stats)\n\n\nclass _StatsScaling(brevitas.jit.ScriptModule):\n\n    def __init__(\n            self,\n            restrict_scaling_impl: Module,\n            scaling_shape: Tuple[int, ...],\n            scaling_min_val: Optional[float] = None,\n            affine_rescaling: bool = False) -> None:\n        super(_StatsScaling, self).__init__()\n\n        if affine_rescaling:\n            self.affine_rescaling = _AffineRescaling(scaling_shape)\n        else:\n            self.affine_rescaling = Identity()\n        self.restrict_clamp_scaling = _RestrictClampValue(scaling_min_val, restrict_scaling_impl)\n        self.restrict_scaling_pre = restrict_scaling_impl.restrict_init_module()\n\n    @brevitas.jit.script_method\n    def forward(self, stats: torch.Tensor) -> torch.Tensor:\n        stats = self.restrict_scaling_pre(stats)\n        stats = self.affine_rescaling(stats)\n        stats = self.restrict_clamp_scaling(stats)\n        return stats\n\n\nclass RuntimeStatsScaling(brevitas.jit.ScriptModule):\n\n    def __init__(\n            self,\n            scaling_stats_impl: Module,\n            scaling_stats_input_view_shape_impl: Module,\n            scaling_stats_permute_dims: Tuple[int, ...],\n            restrict_scaling_impl: Module,\n            scaling_shape: Tuple[int, ...],\n            affine_rescaling: bool,\n            scaling_stats_momentum: float = DEFAULT_MOMENTUM,\n            scaling_min_val: Optional[float] = None) -> None:\n        super(RuntimeStatsScaling, self).__init__()\n\n        self.runtime_stats = _RuntimeStats(\n            scaling_stats_impl,\n            scaling_shape,\n            scaling_stats_input_view_shape_impl,\n            scaling_stats_permute_dims,\n            scaling_stats_momentum)\n        self.stats_scaling_impl = _StatsScaling(\n            restrict_scaling_impl,\n            scaling_shape,\n            scaling_min_val,\n            affine_rescaling)\n\n    @brevitas.jit.script_method\n    def forward(self, x: torch.Tensor):\n        stats = self.runtime_stats(x)\n        return self.stats_scaling_impl(stats)\n\n\nclass _AffineRescaling(brevitas.jit.ScriptModule):\n\n    def __init__(self, scaling_shape):\n        super(_AffineRescaling, self).__init__()\n        self.affine_weight = Parameter(torch.ones(scaling_shape))\n        self.affine_bias = Parameter(torch.zeros(scaling_shape))\n\n    @brevitas.jit.script_method\n    def forward(self, x):\n        out = x * self.affine_weight + self.affine_bias\n        out = torch.abs(out)\n        return out\n\n    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,\n                              missing_keys, unexpected_keys, error_msgs):\n        super(_AffineRescaling, self)._load_from_state_dict(\n            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)\n        affine_weight_key = prefix + 'affine_weight'\n        affine_bias_key = prefix + 'affine_bias'\n        if config.IGNORE_MISSING_KEYS and affine_weight_key in missing_keys:\n            missing_keys.remove(affine_weight_key)\n        if config.IGNORE_MISSING_KEYS and affine_bias_key in missing_keys:\n            missing_keys.remove(affine_bias_key)","sub_path":"brevitas/core/scaling/runtime.py","file_name":"runtime.py","file_ext":"py","file_size_in_byte":6994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"56503473","text":"\n'''\n    heroes.py\n\n    a collection of function to read and write to\n    a text file containg ...\n'''\n\n\n_hero_path = \"./data/heroes.txt\"\n\n\ndef get_heroes(starts_with=\"\"):\n    '''\n    get_heroes list the heroes in a file\n\n    Parameters:\n        starts_with: a string that can filter the heroes returned\n\n    Output:\n        a list of heroes\n\n    Exceptions\n        ---\n'''\n    with open(_hero_path) as hero_file:\n        # comprehension\n        hero_list = [\n            line.strip()\n            for line in hero_file\n            if line.startswith(starts_with)\n        ]\n    return hero_list\n\n\ndef add_hero(name):\n    '''\n        add_hero add a hero to the file lised above\n        The hero will be sorted into the list so may not be at the end\n\n        Parameters:\n            name: a string that hold the naame of the hero to be added\n\n        Output:\n            none\n\n        Exceptions\n            ---\n    '''\n    existing_heroes = get_heroes()\n    if name not in existing_heroes:\n        existing_heroes.append(name)\n        sorted_heroes = sorted(existing_heroes)\n        with open(_hero_path, \"w\") as output_file:\n            for h in sorted_heroes:\n                output_file.write(h + \"\\n\")\n\n\nclass Hero:\n\n    hero_path = \"./data/heroes.txt\"\n\n    def __init__(self, name, home):\n        self.name = name\n        self.home = home\n\n    def describe(self):\n        print(self.name + \" is a hero.\")\n\n    @classmethod\n    def reset(cls):\n        # clear out the contents of the hero file\n        pass\n\n\nclass SuperHero(Hero):\n\n    def __init__(self, name, home, power, weakness):\n\n        super().__init__(name, home)\n        self.power = power\n        self.weakness = weakness\n\n\ndef main():\n    h = Hero(\"Wonder Woman\", \"avalon\")\n    h.describe()\n\n    Hero.reset()\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"heroes.py","file_name":"heroes.py","file_ext":"py","file_size_in_byte":1814,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"356464400","text":"'''\nThis file is part of MSR Ensemble (MSRE-X).\n\nMSRE-X is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\n\nMSRE-X is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\nGNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with MSRE-X. If not, see <http://www.gnu.org/licenses/>.\n\nMSR Ensemble (MSRE-X) Version 0.5, Prototype Alpha\n\nAuthors:\nEdmund S. L. Lam      sllam@qatar.cmu.edu\nIliano Cervesato      iliano@cmu.edu\n\n* This implementation was made possible by an NPRP grant (NPRP 09-667-1-100, Effective Programming \nfor Large Distributed Ensembles) from the Qatar National Research Fund (a member of the Qatar \nFoundation). The statements made herein are solely the responsibility of the authors.\n'''\n\nimport z3 as z3\n\nfrom msrex.misc.infix import Infix\n\nfrom msrex.misc.smt_utils import SMTSolver, SMTConstraint, SAT, UNSAT, UNKNOWN, SMTTypeTerm, get_new_var\n\n# MSRE type representation in z3\n\nsmt_type = None\n\nTY_LOC   = 'loc'\nTY_INT   = 'int'\nTY_CHAR  = 'char'\nTY_FLOAT = 'float'\nTY_BOOL  = 'bool'\nTY_STR   = 'string'\nTY_DEST  = 'dest'\nTY_LIST  = 'list'\nTY_MSET  = 'mset'\nTY_TUPLE = 'tuple'\nTY_UNIT  = 'unit'\nTY_ARROW = 'arrow'\n\ndef init_default_type():\n\tType = z3.Datatype('Type')\n\tType.declare(TY_LOC)\n\tType.declare(TY_INT)\n\tType.declare(TY_CHAR)\n\tType.declare(TY_FLOAT)\n\tType.declare(TY_BOOL)\n\tType.declare(TY_STR)\n\tType.declare(TY_DEST)\n\tType.declare(TY_LIST, ('type',Type))\n\tType.declare(TY_MSET, ('type',Type))\n\tType.declare(TY_TUPLE, ('left',Type), ('right',Type))\n\tType.declare(TY_UNIT)\n\tType.declare(TY_ARROW, ('in',Type), ('out',Type))\n\n\t# Insert new types\n\n\tType = Type.create()\n\t\n\tglobal smt_type\n\tsmt_type = Type\n\ninit_default_type()\n\n'''\n@Infix\ndef or_cons(c1, c2):\n\tc = SMTConstraint(z3.Or(c1.cons,c2.cons))\n\tc.justify = c1.justify + c2.justify\n\treturn c\n'''\n\ndef tyVar(id=None):\n\tif id == None:\n\t\tid = get_new_var(\"t\")\n\treturn z3.Const(id, smt_type)\n\ntyLoc  = smt_type.loc\ntyInt  = smt_type.int\ntyFloat = smt_type.float\ntyBool = smt_type.bool\ntyChar = smt_type.char\ntyStr  = smt_type.string\ntyDest = smt_type.dest\n\ndef tyList(ty_term):\n\treturn smt_type.list(ty_term)\n\ndef tyMSet(ty_term):\n\treturn smt_type.mset(ty_term)\n\ndef tyTuple(left, right):\n\treturn smt_type.tuple(left, right)\n\ntyUnit = smt_type.unit\n\ndef tyArrow(inpt, outpt):\n\treturn smt_type.arrow(inpt, outpt)\n\n\n\n","sub_path":"msrex/misc/z3_utils/types.py","file_name":"types.py","file_ext":"py","file_size_in_byte":2660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"189716817","text":"def solve():\n    n,k=map(int,input().split())\n    A=list(map(int,input().split()))\n    A=[(a,i) for i,a in enumerate(A)]\n    B=[0]*n\n    while True:\n        for coach in range(1,3):\n            #print(A,B)\n            A_len=len(A)\n            if A_len==0:\n                return ''.join(list(map(str,B)))\n            m=max(A)\n            mi=A.index(m)\n            for i in range(max(0,mi-k),min(A_len,mi+k+1)):\n                #print(A[i])\n                B[A[i][1]]=coach\n            #del A[max(0,mi-k):min(A_len,mi+k+1)]\n            A = A[:max(0,mi-k)] + A[min(A_len,mi+k+1):]\n\nprint(solve())","sub_path":"codeforces/cr552_3/e.py","file_name":"e.py","file_ext":"py","file_size_in_byte":594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"444853007","text":"import pandas\n\nfrom modin.data_management.utils import length_fn_pandas, width_fn_pandas\nfrom modin.engines.base.frame.partition import BaseFramePartition\n\n\nclass PandasOnPythonFramePartition(BaseFramePartition):\n    \"\"\"This abstract class holds the data and metadata for a single partition.\n        The methods required for implementing this abstract class are listed in\n        the section immediately following this.\n\n        The API exposed by the children of this object is used in\n        `BaseFrameManager`.\n\n        Note: These objects are treated as immutable by `BaseFrameManager`\n        subclasses. There is no logic for updating inplace.\n    \"\"\"\n\n    def __init__(self, data, length=None, width=None, call_queue=None):\n        self.data = data\n        if call_queue is None:\n            call_queue = []\n        self.call_queue = call_queue\n        self._length_cache = length\n        self._width_cache = width\n\n    def get(self):\n        \"\"\"Flushes the call_queue and returns the data.\n\n        Note: Since this object is a simple wrapper, just return the data.\n\n        Returns:\n            The object that was `put`.\n        \"\"\"\n        self.drain_call_queue()\n        return self.data.copy()\n\n    def apply(self, func, **kwargs):\n        \"\"\"Apply some callable function to the data in this partition.\n\n        Note: It is up to the implementation how kwargs are handled. They are\n            an important part of many implementations. As of right now, they\n            are not serialized.\n\n        Args:\n            func: The lambda to apply (may already be correctly formatted)\n\n        Returns:\n             A new `BaseFramePartition` containing the object that has had `func`\n             applied to it.\n        \"\"\"\n\n        def call_queue_closure(data, call_queues):\n            result = data.copy()\n            for func, kwargs in call_queues:\n                try:\n                    result = func(result, **kwargs)\n                except Exception as e:\n                    self.call_queue = []\n                    raise e\n            return result\n\n        self.data = call_queue_closure(self.data, self.call_queue)\n        self.call_queue = []\n        return PandasOnPythonFramePartition(func(self.data.copy(), **kwargs))\n\n    def add_to_apply_calls(self, func, **kwargs):\n        return PandasOnPythonFramePartition(\n            self.data.copy(), call_queue=self.call_queue + [(func, kwargs)]\n        )\n\n    def drain_call_queue(self):\n        if len(self.call_queue) == 0:\n            return\n        self.apply(lambda x: x)\n\n    def mask(self, row_indices=None, col_indices=None):\n        new_obj = self.add_to_apply_calls(\n            lambda df: pandas.DataFrame(df.iloc[row_indices, col_indices])\n        )\n        new_obj._length_cache = (\n            len(row_indices)\n            if not isinstance(row_indices, slice)\n            else self._length_cache\n        )\n        new_obj._width_cache = (\n            len(col_indices)\n            if not isinstance(col_indices, slice)\n            else self._width_cache\n        )\n        return new_obj\n\n    def to_pandas(self):\n        \"\"\"Convert the object stored in this partition to a Pandas DataFrame.\n\n        Note: If the underlying object is a Pandas DataFrame, this will likely\n            only need to call `get`\n\n        Returns:\n            A Pandas DataFrame.\n        \"\"\"\n        dataframe = self.get()\n        assert type(dataframe) is pandas.DataFrame or type(dataframe) is pandas.Series\n\n        return dataframe\n\n    def to_numpy(self):\n        \"\"\"Convert the object stored in this partition to a NumPy Array.\n\n        Returns:\n            A NumPy Array.\n        \"\"\"\n        return self.apply(lambda df: df.values).get()\n\n    @classmethod\n    def put(cls, obj):\n        \"\"\"A factory classmethod to format a given object.\n\n        Args:\n            obj: An object.\n\n        Returns:\n            A `RemotePartitions` object.\n        \"\"\"\n        return cls(obj)\n\n    @classmethod\n    def preprocess_func(cls, func):\n        \"\"\"Preprocess a function before an `apply` call.\n\n        Note: This is a classmethod because the definition of how to preprocess\n            should be class-wide. Also, we may want to use this before we\n            deploy a preprocessed function to multiple `BaseFramePartition`\n            objects.\n\n        Args:\n            func: The function to preprocess.\n\n        Returns:\n            An object that can be accepted by `apply`.\n        \"\"\"\n        return func\n\n    @classmethod\n    def length_extraction_fn(cls):\n        \"\"\"The function to compute the length of the object in this partition.\n\n        Returns:\n            A callable function.\n        \"\"\"\n        return length_fn_pandas\n\n    @classmethod\n    def width_extraction_fn(cls):\n        \"\"\"The function to compute the width of the object in this partition.\n\n        Returns:\n            A callable function.\n        \"\"\"\n        return width_fn_pandas\n\n    _length_cache = None\n    _width_cache = None\n\n    def length(self):\n        if self._length_cache is None:\n            self._length_cache = self.apply(self.length_extraction_fn()).data\n        return self._length_cache\n\n    def width(self):\n        if self._width_cache is None:\n            self._width_cache = self.apply(self.width_extraction_fn()).data\n        return self._width_cache\n\n    @classmethod\n    def empty(cls):\n        return cls(pandas.DataFrame())\n","sub_path":"modin/engines/python/pandas_on_python/frame/partition.py","file_name":"partition.py","file_ext":"py","file_size_in_byte":5398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"320053693","text":"\"\"\"\nSchrijf een programma dat een string inleest van de gebruiker en alternerend een hoofdletter en een kleine letter weergeeft. \\\\\n*Hint:* Je kunt hiervoor gebruik maken van de functie upper().\n    (Merk op dat characters die niet voldoen aan de isalpha() voorwaarde genegeerd worden!)\n\"\"\"\n\n# Ask input from user\nstring = input(\"Geef een zin: \")\n\n# Initialisation\nnew_string = \"\"\n\n# Changing in upper and lower case letters\nfor i in range(len(string)):\n    if i % 2 == 0:\n        new_string += string[i].upper()\n    else:\n        new_string += string[i].lower()\n\n# Print result\nprint(new_string)\n\n","sub_path":"03_caseAlternator.py","file_name":"03_caseAlternator.py","file_ext":"py","file_size_in_byte":598,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"538136749","text":"#!/usr/bin/python\r\n\r\n# Copyright 2016, Gurobi Optimization, Inc.\r\n\r\n# This example formulates and solves the following simple QP model:\r\n#\r\n#    minimize    x + y + x^2 + x*y + y^2 + y*z + z^2\r\n#    subject to  x + 2 y + 3 z >= 4\r\n#                x +   y       >= 1\r\n#\r\n# The example illustrates the use of dense matrices to store A and Q\r\n# (and dense vectors for the other relevant data).  We don't recommend\r\n# that you use dense matrices, but this example may be helpful if you\r\n# already have your data in this format.\r\n\r\nimport sys\r\nfrom gurobipy import *\r\n\r\ndef lp_optimize(rows, cols, c, obj_sense, A, sense, rhs, lb, ub, vtype,\r\n                   solution):\r\n\r\n  model = Model()\r\n\r\n  # Add variables to model\r\n  for j in range(cols):\r\n    model.addVar(lb=lb[j], ub=ub[j], vtype=vtype[j])\r\n  model.update()\r\n  vars = model.getVars()\r\n\r\n  # Populate A matrix\r\n  for i in range(rows):\r\n    expr = LinExpr()\r\n    for j in range(cols):\r\n      if A[i][j] != 0:\r\n        expr += A[i][j]*vars[j]\r\n    model.addConstr(expr, sense[i], rhs[i])\r\n\r\n  # Populate objective\r\n  obj = QuadExpr()\r\n#  for i in range(cols):\r\n#    for j in range(cols):\r\n#      if Q[i][j] != 0:\r\n#        obj += Q[i][j]*vars[i]*vars[j]\r\n  for j in range(cols):\r\n    if c[j] != 0:\r\n      obj += c[j]*vars[j]\r\n  model.setObjective(obj, obj_sense)\r\n\r\n  # Write model to a file\r\n  model.update()\r\n  model.write('autoGenerated.lp')\r\n\r\n  # Solve\r\n  model.optimize()\r\n\r\n  print(\"Obj Value: \", model.objVal)\r\n  for v in model.getVars():\r\n    print(v.varName, v.x, \"\\n\")\r\n\r\n  # if model.status == GRB.Status.OPTIMAL:\r\n  #   x = model.getAttr('x', vars)\r\n  #   for i in range(cols):\r\n  #     solution[i] = x[i]\r\n  #   return True\r\n  # else:\r\n  #   return False\r\n\r\n\r\n","sub_path":"zac/PA1/myMatrixLpSolver.py","file_name":"myMatrixLpSolver.py","file_ext":"py","file_size_in_byte":1730,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"607945301","text":"\"\"\"\r\n@description: tests ccode in Task_3 with test cases, prints input, output and expected\r\n@author: David Lei\r\n@since: 15/4/2015\r\n@modified: 15/4/2015\r\n@:param : valid test cases\r\n@:return: the binary representation of n is returned by binaryrevlist, the result is returned by powermodified\r\n@pre-condition: test cases valid i.e. a wont crash due to strange test input\r\n@post-condition: test cases valid i.e. a wont crash due to strange test input\r\n@complexity: Worst-Case: O(N)\r\n@complexity: Best-Case: O(N)\r\n\"\"\"\r\n\r\n# test powermodified does what it is meant to do\r\n\r\ndef binaryrevlist (n):\r\n    rev_list = [0]*n\r\n    counter = 0\r\n    while n > 0:\r\n        if n%2 == 0:\r\n            rev_list[counter] = 0\r\n        else:\r\n            rev_list[counter] = 1\r\n        n = n//2\r\n        counter += 1\r\n    rev_list = rev_list[:counter]\r\n    return rev_list\r\n\r\n\r\ndef powermodified(b,e):\r\n    rev_binary = binaryrevlist(e)\r\n    result = 1\r\n    idx = len(rev_binary) - 1\r\n    while idx >= 0:  # goes through all items in the list, idx from n-1 to 0 inclusive\r\n        # print(idx)\r\n        result = result * result\r\n        if rev_binary[idx]:  #\r\n            result = result * b\r\n        idx -= 1\r\n   #     print(rev_binary)\r\n    return result\r\n\r\ndef test(base, exp):\r\n    if exp < 0:\r\n        print(\"Error, the exponent you entered is negative\")\r\n    else:\r\n        result = powermodified(base,exp)\r\n        return result\r\n\r\ndef test_cases():\r\n    testcase1 = [3,7]\r\n    testcase2 = [-2,19]\r\n    testcase3 = [7,-4]\r\n    testcase4 = [0,3]\r\n   #  testcase5 = ['ab','cd'], auto validates against non-numerical inputs\r\n    print(\"testcase1: base = %s, exp = %s, output = %s, expected = %s\" % (\r\n        str(testcase1[0]), str(testcase1[1]), str(test(testcase1[0], testcase1[1])), '2187'))\r\n    print(\"testcase2: base = %s, exp = %s, output = %s, expected = %s\" % (\r\n        str(testcase2[0]), str(testcase2[1]), str(test(testcase2[0], testcase2[1])), '-524288'))\r\n    print(\"testcase3: base = %s, exp = %s, output = %s, expected = %s\" % (\r\n        str(testcase3[0]), str(testcase3[1]), str(test(testcase3[0], testcase3[1])), 'None, will print error message \\\"Error, the exponent you entered is negative\\\"'))\r\n    print(\"testcase4: base = %s, exp = %s, output = %s, expected = %s\" % (\r\n        str(testcase4[0]), str(testcase4[1]), str(test(testcase4[0], testcase4[1])), '0'))\r\n\r\n\r\ntest_cases()\r\n\r\n","sub_path":"FIT1008-Intro-To-Computer-Science/Pracs/26029391_Prac5/New_Power.py","file_name":"New_Power.py","file_ext":"py","file_size_in_byte":2389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"226449964","text":"import os\n\nfrom setuptools import setup\n\n\nNAME = \"RMTfunc\"\nREQUIREMENTS = [\n    \"colorama\",\n    # \"curses\",\n    \"EMD-signal\",\n    \"empyricalRMT\",\n    \"multiprocess\",\n    \"nibabel\",\n    \"nilearn\",\n    \"numba\",\n    \"numpy\",\n    \"progressbar\",\n    \"scipy\",\n]\n\n\ndef read_file(path):\n    with open(os.path.join(os.path.dirname(__file__), path)) as fp:\n        return fp.read()\n\n\nsetup(\n    name=NAME,\n    packages=[NAME],\n    author=\"Derek Berger\",\n    maintainer=\"Derek Berger\",\n    author_email=\"dmberger.dev@gmail.com\",\n    version=\"0.1dev\",\n    description=\"Eigenvalue unfolding and spectral observable computation\",\n    url=\"https://github.com/stfxecutables/empyricalRMT\",\n    license=\"MIT\",\n    long_description=read_file(\"README.md\"),\n    classifiers=[\n        \"Programming Language :: Python :: 3\",\n        \"License :: OSI Approved :: MIT License\",\n        \"Operating System :: OS Independent\",\n        \"Development Status :: 3 - Alpha\",\n    ],\n    install_requires=REQUIREMENTS,\n    python_requires=\">=3\",\n    keywords=\"RMT RandomMatrixTheory spectral observabales eigenvalues unfolding\",\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"45859794","text":"# -*- coding: utf-8 -*-\nimport scrapy\nfrom gitrepo.items import GitrepoItem\n\nclass RepoSpider(scrapy.Spider):\n    name = 'repo'\n#    allowed_domains = ['github.com']\n    @property\n    def start_urls(self):\n        url_tmpl = 'https://github.com/shiyanlou?page={}&tab=repositories'\n        return (url_tmpl.format(i) for i in range(1,5))\n\n    def parse(self, response):\n        for repository in response.css('li.col-12'):\n            yield ({\n                'name':repository.xpath('.//div/h3/a/text()').extract_first().strip(),\n                'update_time':repository.xpath('.//relative-time/@datetime').extract_first()\n                })\n\n","sub_path":"gitrepo/gitrepo/spiders/repo.py","file_name":"repo.py","file_ext":"py","file_size_in_byte":643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"625252442","text":"import cv2\nimport numpy as np\n\nfilename = 'sudokuPaper.jpeg'\nimg = cv2.imread(filename)\n# th2 = cv2.adaptiveThreshold(\n#     img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 2)\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\ngray = np.float32(gray)\ndst = cv2.cornerHarris(gray, 2, 3, 0.04)\n\n# result is dilated for marking the corners, not important\ndst = cv2.dilate(dst, None)\n\n# Threshold for an optimal value, it may vary depending on the image.\nimg[dst > 0.01 * dst.max()] = [255, 0, 0]\n\n# cv2.imshow('dst', img)\ncv2.imwrite('harriscorner.jpeg', img)\n\n# if cv2.waitKey(0) & 0xff == 27:\n#     cv2.destroyAllWindows()\n","sub_path":"harrisCorner.py","file_name":"harrisCorner.py","file_ext":"py","file_size_in_byte":630,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"60603864","text":"\"\"\"Micro moments.\"\"\"\n\nimport abc\nimport collections\nfrom typing import Any, Dict, Hashable, List, Optional, Sequence, TYPE_CHECKING\n\nimport numpy as np\n\nfrom . import options\nfrom .utilities.basics import Array, StringRepresentation, format_number, format_table\n\n\n# only import objects that create import cycles when checking types\nif TYPE_CHECKING:\n    from .economies.economy import Economy  # noqa\n\n\nclass Moment(StringRepresentation):\n    \"\"\"Information about a single micro moment.\"\"\"\n\n    value: Array\n    market_ids: Optional[Array]\n\n    def __init__(self, value: float, market_ids: Optional[Sequence] = None) -> None:\n        \"\"\"Validate information about the moment to the greatest extent possible without an economy instance.\"\"\"\n        self.value = np.asarray(value, options.dtype)\n        if self.value.size != 1:\n            raise ValueError(\"The micro moment value must be a scalar.\")\n\n        self.market_ids = None\n        if market_ids is not None:\n            self.market_ids = np.asarray(market_ids, np.object)\n            unique, counts = np.unique(self.market_ids, return_counts=True)\n            duplicates = unique[counts > 1]\n            if duplicates.size > 0:\n                raise ValueError(f\"The following market IDs are duplicated in market_ids: {duplicates}.\")\n\n    def __str__(self) -> str:\n        \"\"\"Format information about the micro moment as a string.\"\"\"\n        return f\"{self._format_markets()}: {self._format_moment()}\"\n\n    def _format_markets(self) -> str:\n        \"\"\"Format information about the markets associated with the micro moment as a string.\"\"\"\n        if self.market_ids is None:\n            return \"All\"\n        if len(self.market_ids) == 1:\n            return self.market_ids[0]\n        return \", \".join(str(t) for t in self.market_ids)\n\n    def _format_moment(self) -> str:\n        \"\"\"Construct a string expression for the micro moment.\"\"\"\n        formatted = self._format_value()\n        if self.value < 0:\n            formatted = f\"{formatted} + {format_number(float(self.value))[1:]}\"\n        elif self.value > 0:\n            formatted = f\"{formatted} - {format_number(float(self.value))[1:]}\"\n        return formatted\n\n    @abc.abstractmethod\n    def _format_value(self) -> str:\n        \"\"\"Construct a string expression for the micro moment value.\"\"\"\n\n    def _validate(self, economy: 'Economy') -> None:\n        \"\"\"Check that all market IDs associated with this moment are in the economy.\"\"\"\n        if self.market_ids is not None:\n            extra_ids = set(self.market_ids) - set(economy.unique_market_ids)\n            if extra_ids:\n                raise ValueError(f\"market_ids contains the following extra IDs: {sorted(extra_ids)}.\")\n\n\nclass DemographicExpectationMoment(Moment):\n    r\"\"\"Configuration for micro moments that match expectations of demographics for agents who choose certain products.\n\n    For example, micro data can sometimes be used to compute the mean of a demographic such as income, :math:`y_{it}`,\n    for agents who choose product :math:`j`. With the value :math:`v_m` of this mean, a micro moment :math:`m` in market\n    :math:`t` for agent :math:`i` can be defined by\n\n    .. math:: g_{M,imt} = \\frac{y_{it} s_{ijt}}{s_{jt}} - v_m.\n\n    Integrals of these micro moments are approximated within and averaged across a set :math:`T_m` of markets in which\n    the micro data used to compute :math:`v_m` are relevant, which gives :math:`\\bar{g}_{M,m}` in\n    :eq:`averaged_micro_moments`.\n\n    Parameters\n    ----------\n    product_id : `object`\n        ID of the product :math:`j` or ``None`` to denote the outside option :math:`j = 0`. If not ``None``, there must\n        be exactly one of this ID in the ``product_ids`` field of ``product_data`` in :class:`Problem` or\n        :class:`Simulation` for each market over which this micro moment will be averaged.\n    demographics_index : `int`\n        Column index of the demographic :math:`y_{it}` (which can be any demographic, not just income) in the matrix of\n        agent demographics, :math:`d`. This should be between zero and :math:`D - 1`, inclusive.\n    value : `float`\n        Value :math:`v_m` of the mean estimated from micro data.\n    market_ids : `array-like, optional`\n        Distinct market IDs over which the micro moments will be averaged to get :math:`\\bar{g}_{M,m}`. These are also\n        the only markets in which the moments will be computed. By default, the moments are computed for and averaged\n        across all markets. That is, by default, it is assumed that the specified ``value`` is relevant for and on\n        average the same for all markets.\n\n    Examples\n    --------\n        - :doc:`Tutorial </tutorial>`\n\n    \"\"\"\n\n    product_id: Optional[Any]\n    demographics_index: int\n\n    def __init__(\n            self, product_id: Optional[Any], demographics_index: int, value: float,\n            market_ids: Optional[Sequence] = None) -> None:\n        \"\"\"Validate information about the moment to the greatest extent possible without an economy instance.\"\"\"\n        super().__init__(value, market_ids)\n        if not isinstance(demographics_index, int) or demographics_index < 0:\n            raise ValueError(\"demographics_index must be a positive int.\")\n        self.product_id = product_id\n        self.demographics_index = demographics_index\n\n    def _format_value(self) -> str:\n        \"\"\"Construct a string expression for the covariance moment.\"\"\"\n        product = \"Outside\" if self.product_id is None else f\"'{self.product_id}'\"\n        return f\"E[Demographic Column {self.demographics_index} | {product}]\"\n\n    def _validate(self, economy: 'Economy') -> None:\n        \"\"\"Check that matrix indices are valid in the economy.\"\"\"\n        super()._validate(economy)\n        economy._validate_product_id(self.product_id, self.market_ids)\n        if self.demographics_index >= economy.D:\n            raise ValueError(f\"demographics_index must be between 0 and D = {economy.D}, inclusive.\")\n\n\nclass DemographicCovarianceMoment(Moment):\n    r\"\"\"Configuration for micro moments that match covariances between product characteristics and demographics.\n\n    For example, micro data can sometimes be used to compute the sample covariance between a product characteristic\n    :math:`x_{jt}` of an agent's choice :math:`j`, and a demographic such as income, :math:`y_{it}`, amongst those\n    agents who purchase an inside good. With the value :math:`v_m` of this sample covariance, a micro moment :math:`m`\n    in market :math:`t` for agent :math:`i` can be defined by\n\n    .. math:: g_{M,imt} = (z_{it} - \\bar{z}_t)(y_{it} - \\bar{y}_t) - v_m\n\n    where :math:`\\bar{z}_t = \\sum_i w_{it} z_{it}`, :math:`\\bar{y}_t = \\sum_i w_{it} y_{it}`, and conditional on\n    choosing an inside good, the expected value of :math:`x_{jt}` for agent :math:`i` is\n\n    .. math:: z_{it} = \\sum_{j \\in J_t} x_{jt}s_{ij(-0)t}\n\n    where :math:`s_{ij(-0)t} = s_{ijt} / (1 - s_{i0t})` is the probability of :math:`i` choosing :math:`j` when the\n    outside option is removed from the choice set.\n\n    Integrals of these micro moments are approximated within and averaged across a set :math:`T_m` of markets in which\n    the micro data used to compute :math:`v_m` are relevant, which gives :math:`\\bar{g}_{M,m}` in\n    :eq:`averaged_micro_moments`.\n\n    Parameters\n    ----------\n    X2_index : `int`\n        Column index of :math:`x_{jt}` in the matrix of demand-side nonlinear product characteristics, :math:`X_2`. This\n        should be between zero and :math:`K_2 - 1`, inclusive.\n    demographics_index : `int`\n        Column index of the demographic :math:`y_{it}` (which can be any demographic, not just income) in the matrix of\n        agent demographics, :math:`d`. This should be between zero and :math:`D - 1`, inclusive.\n    value : `float`\n        Value :math:`v_m` of the sample covariance estimated from micro data.\n    market_ids : `array-like, optional`\n        Distinct market IDs over which the micro moments will be averaged to get :math:`\\bar{g}_{M,m}`. These are also\n        the only markets in which the moments will be computed. By default, the moments are computed for and averaged\n        across all markets. That is, by default, it is assumed that the specified ``value`` is relevant for and on\n        average the same for all markets.\n\n    Examples\n    --------\n        - :doc:`Tutorial </tutorial>`\n\n    \"\"\"\n\n    X2_index: int\n    demographics_index: int\n\n    def __init__(\n            self, X2_index: int, demographics_index: int, value: float, market_ids: Optional[Sequence] = None) -> None:\n        \"\"\"Validate information about the moment to the greatest extent possible without an economy instance.\"\"\"\n        super().__init__(value, market_ids)\n        if not isinstance(X2_index, int) or X2_index < 0:\n            raise ValueError(\"X2_index must be a positive int.\")\n        if not isinstance(demographics_index, int) or demographics_index < 0:\n            raise ValueError(\"demographics_index must be a positive int.\")\n        self.X2_index = X2_index\n        self.demographics_index = demographics_index\n\n    def _format_value(self) -> str:\n        \"\"\"Construct a string expression for the covariance moment.\"\"\"\n        return f\"Cov(X2 Column {self.X2_index}, Demographic Column {self.demographics_index} | Inside)\"\n\n    def _validate(self, economy: 'Economy') -> None:\n        \"\"\"Check that matrix indices are valid in the economy.\"\"\"\n        super()._validate(economy)\n        if self.X2_index >= economy.K2:\n            raise ValueError(f\"X2_index must be between 0 and K2 = {economy.K2}, inclusive.\")\n        if self.demographics_index >= economy.D:\n            raise ValueError(f\"demographics_index must be between 0 and D = {economy.D}, inclusive.\")\n\n\nclass DiversionProbabilityMoment(Moment):\n    r\"\"\"Configuration for micro moments that match second choice probabilities of certain products for agents whose\n    first choices are certain other products.\n\n    For example, micro data can sometimes be used to compute the share of agents who would choose product :math:`k` if\n    :math:`j` were removed from the choice set, out of those agents whose first choice is :math:`j`. With the value\n    :math:`v_m` of this share, a micro moment :math:`m` in market :math:`t` for agent :math:`i` can be defined by\n\n    .. math:: g_{M,imt} = \\frac{s_{ik(-j)t} s_{ijt}}{s_{jt}} - v_m\n\n    where :math:`s_{ik(-j)t} = s_{ijt} / (1 - s_{ijt})` is the probability of :math:`i` choosing :math:`k` when\n    :math:`j` is removed from the choice set. Rearranging terms gives the equivalent definition\n\n    .. math:: g_{M,imt} = \\frac{s_{ik(-j)t} - s_{ikt}}{s_{jt}} - v_m,\n\n    which is more reminiscent of the long-run diversion ratios :math:`\\bar{\\mathscr{D}}_{jk}` computed by\n    :meth:`ProblemResults.compute_long_run_diversion_ratios`.\n\n    Integrals of these micro moments are approximated within and averaged across a set :math:`T_m` of markets in which\n    the micro data used to compute :math:`v_m` are relevant, which gives :math:`\\bar{g}_{M,m}` in\n    :eq:`averaged_micro_moments`.\n\n    Parameters\n    ----------\n    product_id1 : `object`\n        ID(s) of the first choice products :math:`j` or ``None`` to denote the outside option :math:`j = 0`. There must be\n        exactly one of each of these ID(s) in the ``product_ids`` field of ``product_data`` in :class:`Problem` or\n        :class:`Simulation` for each market over which this micro moment will be averaged.\n    product_id2 : `object`\n        ID of the second choice product :math:`k` or ``None`` to denote the outside option :math:`j = 0`. If not\n        ``None``, there must be exactly one of this ID for each market over which this micro moment will be averaged.\n    value : `float`\n        Value :math:`v_m` of the share estimated from micro data.\n    market_ids : `array-like, optional`\n        Distinct market IDs over which the micro moments will be averaged to get :math:`\\bar{g}_{M,m}`. These are also\n        the only markets in which the moments will be computed. By default, the moments are computed for and averaged\n        across all markets. That is, by default, it is assumed that the specified ``value`` is relevant for and on\n        average the same for all markets.\n\n    Examples\n    --------\n        - :doc:`Tutorial </tutorial>`\n\n    \"\"\"\n\n    product_id1: Optional[Any]\n    product_id2: Optional[Any]\n\n    def __init__(\n            self, product_id1: Any, product_id2: Optional[Any], value: Sequence,\n            market_ids: Optional[Sequence] = None) -> None:\n        \"\"\"Validate information about the moment to the greatest extent possible without an economy instance.\"\"\"\n        super().__init__(value, market_ids)\n        if product_id1 is None and product_id2 is None:\n            raise ValueError(\"At least one of product_id1 or product_id2 must be not None.\")\n        self.product_id1 = product_id1\n        self.product_id2 = product_id2\n\n    def _format_value(self) -> str:\n        \"\"\"Construct a string expression for the covariance moment.\"\"\"\n        product1 = \"Outside\" if self.product_id1 is None else f\"'{self.product_id1}'\"\n        product2 = \"Outside\" if self.product_id2 is None else f\"'{self.product_id2}'\"\n        return f\"P({product1} First, {product2} Second)\"\n\n    def _validate(self, economy: 'Economy') -> None:\n        \"\"\"Check that matrix indices are valid in the economy.\"\"\"\n        super()._validate(economy)\n\n        # if self.market_ids is None:\n        #     these_market_ids = economy.unique_market_ids\n        # else:\n        #     these_market_ids = self.market_ids\n        #\n        # for t in these_market_ids:\n        #     count = 0\n        #     if self.product_id1 is not None:\n        #         for this_product in self.product_id1:\n        #             count += (self.products.product_ids[self._product_market_indices[t]] == this_product).sum()\n        #         if count == 0:\n        #             raise ValueError(\n        #                 f\"None of the producs are in market '{t}': \"\n        #                 f\"{list(sorted(self.products.product_ids[self._product_market_indices[t]]))}.\"\n        #             )\n\n        for this_product in self.product_id1:\n             economy._validate_product_id(this_product, self.market_ids)\n\n        \n        economy._validate_product_id(self.product_id2, self.market_ids)\n\n\nclass DiversionCovarianceMoment(Moment):\n    r\"\"\"Configuration for micro moments that match covariances between product characteristics of first and second\n    choices.\n\n    For example, survey data can sometimes be used to compute the sample covariance between a product characteristic\n    :math:`x_{jt}^{(1)}` of an agent's first choice :math:`j` and either the same or a different product characteristic\n    :math:`x_{kt}^{(2)}` of the agent's second choice :math:`k` if :math:`j` were removed from the choice set, amongst\n    those agents whose first and second choices are both inside goods. With the value :math:`v_m` of this sample\n    covariance, a micro moment :math:`m` in market :math:`t` for agent :math:`i` can be defined by\n\n    .. math:: g_{M,imt} = (z_{it}^{(1)} - \\bar{z}_t^{(1)})(z_{it}^{(2)} - \\bar{z}_t^{(2)}) - v_m\n\n    where :math:`\\bar{z}_t^{(1)} = \\sum_i w_{it} z_{it}^{(1)}`, :math:`\\bar{z}_t^{(2)} = \\sum_i w_{it} z_{it}^{(2)}`,\n    and conditional on purchasing inside goods, the expected values of :math:`x_{jt}^{(1)}` and :math:`x_{kt}^{(2)}` for\n    agent :math:`i` are\n\n    .. math::\n\n       z_{it}^{(1)} = \\sum_{j \\in J_t} x_{jt}^{(1)} s_{ij(-0)t}, \\quad\n       z_{it}^{(2)} = \\sum_{j, k \\in J_t} x_{kt}^{(2)} s_{ik(-0,j)t} s_{ij(-0)t}\n\n    where :math:`s_{ij(-0)t}` is the probability of choosing :math:`j` when the outside option is removed from the\n    choice set and :math:`s_{ik(-0,j)t}` is the probability of choosing :math:`k` when both the outside option and\n    :math:`j` are removed from the choice set.\n\n    Integrals of these micro moments are approximated within and averaged across a set :math:`T_m` of markets in which\n    the micro data used to compute :math:`v_m` are relevant, which gives :math:`\\bar{g}_{M,m}` in\n    :eq:`averaged_micro_moments`.\n\n    Parameters\n    ----------\n    X2_index1 : `int`\n        Column index of :math:`x_{jt}^{(1)}` in the matrix of demand-side nonlinear product characteristics,\n        :math:`X_2`. This should be between zero and :math:`K_2 - 1`, inclusive.\n    X2_index2 : `int`\n        Column index of :math:`x_{kt}^{(2)}` in the matrix of demand-side nonlinear product characteristics,\n        :math:`X_2`. This should be between zero and :math:`K_2 - 1`, inclusive.\n    value : `float`\n        Value :math:`v_m` of the sample covariance estimated from micro data.\n    market_ids : `array-like, optional`\n        Distinct market IDs over which the micro moments will be averaged to get :math:`\\bar{g}_{M,m}`. These are also\n        the only markets in which the moments will be computed. By default, the moments are computed for and averaged\n        across all markets. That is, by default, it is assumed that the specified ``value`` is relevant for and on\n        average the same for all markets.\n\n    Examples\n    --------\n        - :doc:`Tutorial </tutorial>`\n\n    \"\"\"\n\n    X2_index1: int\n    X2_index2: int\n\n    def __init__(self, X2_index1: int, X2_index2: int, value: float, market_ids: Optional[Sequence] = None) -> None:\n        \"\"\"Validate information about the moment to the greatest extent possible without an economy instance.\"\"\"\n        super().__init__(value, market_ids)\n        if not isinstance(X2_index1, int) or X2_index1 < 0:\n            raise ValueError(\"X2_index1 must be a positive int.\")\n        if not isinstance(X2_index2, int) or X2_index2 < 0:\n            raise ValueError(\"X2_index2 must be a positive int.\")\n        self.X2_index1 = X2_index1\n        self.X2_index2 = X2_index2\n\n    def _format_value(self) -> str:\n        \"\"\"Construct a string expression for the covariance moment.\"\"\"\n        return f\"Cov(X2 Column {self.X2_index1} First, X2 Column {self.X2_index2} Second | Inside)\"\n\n    def _validate(self, economy: 'Economy') -> None:\n        \"\"\"Check that matrix indices are valid in the economy.\"\"\"\n        super()._validate(economy)\n        if self.X2_index1 >= economy.K2:\n            raise ValueError(f\"X2_index1 must be between 0 and K2 = {economy.K2}, inclusive.\")\n        if self.X2_index2 >= economy.K2:\n            raise ValueError(f\"X2_index2 must be between 0 and K2 = {economy.K2}, inclusive.\")\n\n\nclass Moments(object):\n    \"\"\"Information about a sequence of micro moments.\"\"\"\n\n    micro_moments: Sequence[Moment]\n    MM: int\n\n    def __init__(self, micro_moments: Sequence[Moment]) -> None:\n        \"\"\"Store information about a sequence of micro moment instances.\"\"\"\n        self.micro_moments = micro_moments\n        self.MM = len(micro_moments)\n\n    def format(self, title: str, values: Optional[Array] = None) -> str:\n        \"\"\"Format micro moments (and optionally their values) as a string.\"\"\"\n\n        # construct the leftmost part of the table that always shows up\n        header = [\"Index\", \"Markets\", \"Moment\"]\n        data: List[List[str]] = []\n        for m, moment in enumerate(self.micro_moments):\n            data.append([str(m), moment._format_markets(), moment._format_moment()])\n\n        # add moment values\n        if values is not None:\n            header.append(\"Value\")\n            for m, value in enumerate(values):\n                data[m].append(format_number(value))\n\n        return format_table(header, *data, title=title)\n\n\nclass EconomyMoments(Moments):\n    \"\"\"Information about a sequence of micro moments in an economy.\"\"\"\n\n    market_indices: Dict[Hashable, Array]\n    market_counts: Array\n    pairwise_market_counts: Array\n\n    def __init__(self, economy: 'Economy', micro_moments: Sequence[Moment]) -> None:\n        \"\"\"Validate and store information about a sequence of micro moment instances in the context of an economy.\"\"\"\n\n        # validate the moments\n        if not isinstance(micro_moments, collections.abc.Sequence):\n            raise TypeError(\"micro_moments must be a sequence of micro moment instances.\")\n        for moment in micro_moments:\n            if not isinstance(moment, Moment):\n                raise TypeError(\"micro_moments must consist only of micro moment instances.\")\n            try:\n                moment._validate(economy)\n            except Exception as exception:\n                raise ValueError(f\"The micro moment '{moment}' is invalid.\") from exception\n\n        # store basic moment information\n        super().__init__(micro_moments)\n\n        # identify market indices\n        self.market_indices: Dict[Hashable, Array] = {}\n        for t in economy.unique_market_ids:\n            market_index_t = np.array([m.market_ids is None or t in m.market_ids for m in self.micro_moments])\n            self.market_indices[t] = np.flatnonzero(market_index_t)\n\n        # count the number of markets associated with moments\n        self.market_counts = np.zeros((self.MM, 1), np.int)\n        self.pairwise_market_counts = np.zeros((self.MM, self.MM), np.int)\n        for m, moment_m in enumerate(self.micro_moments):\n            market_ids_m = set(economy.unique_market_ids if moment_m.market_ids is None else moment_m.market_ids)\n            self.market_counts[m] = len(market_ids_m)\n            for n, moment_n in enumerate(self.micro_moments):\n                market_ids_n = set(economy.unique_market_ids if moment_n.market_ids is None else moment_n.market_ids)\n                self.pairwise_market_counts[m, n] = len(market_ids_m & market_ids_n)\n\n\nclass MarketMoments(Moments):\n    \"\"\"Information about a sequence of micro moments in a market.\"\"\"\n\n    def __init__(self, economy_moments: EconomyMoments, t: Hashable) -> None:\n        \"\"\"Select only those micro moment instances that will be computed for this market.\"\"\"\n        super().__init__([m for m in economy_moments.micro_moments if m.market_ids is None or t in m.market_ids])\n","sub_path":"pyblp/moments.py","file_name":"moments.py","file_ext":"py","file_size_in_byte":21952,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"497848227","text":"#!/usr/bin/env python3\n\nimport json\nimport os\nimport os.path\nimport subprocess\nimport tempfile\nimport urllib.request\nimport PIL.Image\n\nDG_VERSION = '1.0.3'\nDG_URL_TEMPLATE = 'http://gungho-pqdm.s3.amazonaws.com/download/900_ReleaseAndroid/v{}/'\nDG_URL = DG_URL_TEMPLATE.format(DG_VERSION)\n\nCOMPONENTS = ('IconPackUnit', 'IconPackPanel', 'IconPackArea')\n\nDISUNITY_JAR_FILE = os.path.join('disunity', 'disunity.jar')\n\ntry:\n    os.makedirs('icons', exist_ok=True)\n\n    with tempfile.TemporaryDirectory() as d:\n        for component in COMPONENTS:\n            filename = component + '.unity3d'\n            print('Downloading the latest', filename)\n\n            unity3d_path = os.path.join(d, filename)\n            urllib.request.urlretrieve(DG_URL + filename, unity3d_path)\n\n            print('Unpacking unity3d...')\n            subprocess.check_call(['java', '-jar', DISUNITY_JAR_FILE,\n                                    '-c', 'extract', unity3d_path])\n\n#            subprocess.check_call(['java', '-cp',\n#                                    '/Users/kennytm/XCodeProjects/ata/disunity/lib/commons-cli-1.2/commons-cli-1.2.jar:/Users/kennytm/XCodeProjects/ata/disunity/lib/commons-io-2.4/commons-io-2.4.jar:/Users/kennytm/XCodeProjects/ata/disunity/lib/commons-lang3-3.1/commons-lang3-3.1.jar:/Users/kennytm/XCodeProjects/ata/ioutils/dist/ioutils.jar:/Users/kennytm/XCodeProjects/ata/disunity/dist/disunity.jar',\n#                                    'info.ata4.unity.cli.DisUnityCli',\n#                                    '-c', 'extract', unity3d_path])\n\n            text_assets = os.path.join(d, component, 'TextAsset')\n            texture2ds = os.path.join(d, component, 'Texture2D')\n\n            for texture2d_filename in os.listdir(texture2ds):\n                print(texture2d_filename, '...')\n                png_filename = os.path.splitext(texture2d_filename)[0] + '.png'\n                png_filename = os.path.join(texture2ds, png_filename)\n                subprocess.check_call(['etc1tool',\n                                        os.path.join(texture2ds, texture2d_filename),\n                                        '--decode', '-o', png_filename])\n                map_filename = os.path.splitext(texture2d_filename)[0] + '.txt'\n                with open(os.path.join(text_assets, map_filename), 'r') as f:\n                    map_js = json.load(f, strict=False)\n\n                image = PIL.Image.open(png_filename).transpose(PIL.Image.FLIP_TOP_BOTTOM)\n                for fn, content in map_js['frames'].items():\n                    frame = content['frame']\n                    left = frame['x']\n                    top = frame['y']\n                    right = left + frame['w']\n                    bottom = top + frame['h']\n                    cropped_image = image.crop((left, top, right, bottom))\n                    cropped_image.save(os.path.join('icons', fn))\n\n    print('Done')\n\nfinally:\n    urllib.request.urlcleanup()\n\n","sub_path":"dg/download-icon-packs.py","file_name":"download-icon-packs.py","file_ext":"py","file_size_in_byte":2935,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"153267038","text":"from __future__ import absolute_import\nimport re\n\nimport schematics\nfrom schematics.exceptions import ModelValidationError, ModelConversionError\n\nfrom spidermon.contrib.validation.validator import Validator\nfrom .translator import SchematicsMessageTranslator\nfrom . import monkeypatches\n\n\nclass SchematicsValidator(Validator):\n    default_translator = SchematicsMessageTranslator()\n    name = \"Schematics\"\n\n    def __init__(self, model, translator=None, use_default_translator=True):\n        super(SchematicsValidator, self).__init__(\n            translator=translator, use_default_translator=use_default_translator\n        )\n        self._model = model\n        self._fields_required = {}\n        self._save_required_fields()\n        self._data = {}\n\n    def _validate(self, data, strict=False):\n        self._set_data(data)\n        model = self._get_model_instance(strict=strict)\n        try:\n            model.validate()\n        except ModelValidationError as e:\n            self._add_errors(e.messages)\n        self._restore_required_fields()\n\n    def _reset(self):\n        super(SchematicsValidator, self)._reset()\n        self._data = {}\n\n    def _set_data(self, data):\n        self._data = dict(data)\n\n    def _get_model_instance(self, strict):\n        try:\n            return self._model(raw_data=self._data, strict=strict)\n        except ModelConversionError as e:\n            self._add_errors(e.messages)\n            for field_name in e.messages.keys():\n                self._set_field_as_not_required(field_name)\n                self._data.pop(field_name)\n            return self._get_model_instance(strict=strict)\n\n    def _save_required_fields(self):\n        for field_name, field in self._model._fields.items():\n            self._fields_required[field_name] = field.required\n\n    def _restore_required_fields(self):\n        for field_name, required in self._fields_required.items():\n            self._model._fields[field_name].required = required\n\n    def _set_field_as_not_required(self, field_name):\n        if field_name in self._model._fields:\n            self._model._fields[field_name].required = False\n\n    def _add_errors(self, errors):\n        if schematics.__version__.startswith(\"1.\"):\n            for field_name, messages in errors.items():\n                if isinstance(messages, dict):\n                    transformed_errors = self._get_transformed_child_errors(\n                        field_name, messages\n                    )\n                    self._add_errors(transformed_errors)\n                else:\n                    self._errors[field_name] += (\n                        messages if isinstance(messages, list) else [messages]\n                    )\n        else:\n            from schematics.datastructures import FrozenDict\n\n            for field_name, messages in errors.items():\n                if isinstance(messages, (dict, FrozenDict)):\n                    transformed_errors = self._get_transformed_child_errors(\n                        field_name, messages\n                    )\n                    self._add_errors(transformed_errors)\n                else:\n                    messages = self._clean_messages(messages)\n                    self._errors[field_name] += messages\n\n    def _get_transformed_child_errors(self, field_name, errors):\n        return dict([(\"%s.%s\" % (field_name, k), v) for k, v in errors.items()])\n\n    def _clean_messages(self, messages):\n        \"\"\"\n        This is necessary when using Schematics 2.*, because it encapsulates\n        the validation error messages in a different way.\n        \"\"\"\n        from schematics.exceptions import BaseError, ErrorMessage\n        from schematics.datastructures import FrozenList\n\n        if type(messages) not in (list, FrozenList):\n            messages = [messages]\n\n        clean_messages = []\n        for message in messages:\n            if isinstance(message, BaseError):\n                message = message.messages\n\n            if isinstance(message, ErrorMessage):\n                clean_messages.append(message.summary)\n            elif isinstance(message, FrozenList):\n                for err in message:\n                    # err is an ErrorMessage object\n                    clean_messages.append(err.summary)\n            else:\n                clean_messages.append(message)\n\n        return clean_messages\n","sub_path":"spidermon/contrib/validation/schematics/validator.py","file_name":"validator.py","file_ext":"py","file_size_in_byte":4328,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"112140152","text":"import jieba\r\nimport math\r\n#import cut\r\nclass NoWordError(Exception):\r\n    def __init__(self):\r\n        print(\"这个文件是空文本文件\")\r\n\r\n\r\ndef tokenization(s1,s2):\r\n    stopwords_position=open('stop_words','r',encoding=\"UTF-8\")\r\n    stopwords = stopwords_position.read()\r\n    stopwords_position.close()\r\n    s1_cut = []\r\n    data1 = jieba.cut(s1)\r\n    for i in data1:\r\n        if (i not in stopwords) and i != '':\r\n            s1_cut.append(i)\r\n    s2_cut = []\r\n    data2 = jieba.cut(s2)\r\n    for j in data2:\r\n        if (j not in stopwords) and j != '':  # 不是停词表中的可以加\r\n            s2_cut.append(j)\r\n    # print('s1_cut:',s1_cut)\r\n    # print('s2_cut:',s2_cut)\r\n\r\n    word_set = set(s1_cut).union(set(s2_cut))\r\n    # print('word_set',word_set)#s1_cut是s1的分词表，s1_cut是s2的分词表，word_set是这两个表的集合，可以把这两个表中重合的删掉\r\n    return word_set,s1_cut,s2_cut\r\n#import dict\r\ndef dictionary(word_set,s1_cut,s2_cut):\r\n    # 用字典保存两篇文章中出现的所有词并编上号\r\n    #from cut import word_set, s1_cut, s2_cut\r\n\r\n    word_dict = dict()  # 生成字典，这个字典现在是空的，下面要对他进行配置，word_dict[word]是指word所对应的值\r\n\r\n    i = 0\r\n    for word in word_set:\r\n        word_dict[word] = i\r\n        i += 1\r\n        #\r\n        # print(word_dict)\r\n\r\n    ##dict['Age']返回Age所对应的值\r\n\r\n    # 根据词袋模型统计词在每篇文档中出现的次数，形成向量\r\n    s1_cut_code = [0] * len(word_dict)\r\n\r\n    for word in s1_cut:\r\n        s1_cut_code[word_dict[word]] += 1\r\n    # print(\"s1_cut_code:\",s1_cut_code)\r\n    s2_cut_code = [0] * len(word_dict)\r\n    for word in s2_cut:\r\n        s2_cut_code[word_dict[word]] += 1\r\n    return s1_cut_code,s2_cut_code\r\n#import sim_ans\r\ndef sim_ans(s1_cut_code,s2_cut_code):\r\n    #sum=0.0\r\n\r\n    sum = 0\r\n    sq1 = 0\r\n    sq2 = 0\r\n    for i in range(len(s1_cut_code)):\r\n        sum += s1_cut_code[i] * s2_cut_code[i]\r\n        sq1 += pow(s1_cut_code[i], 2)\r\n        sq2 += pow(s2_cut_code[i], 2)\r\n    try:\r\n        result = round(float(sum) / (math.sqrt(sq1) * math.sqrt(sq2)), 3)\r\n    except ZeroDivisionError:\r\n        result = 0.0\r\n        print(\"这是一个空文本或者两个文本毫无相关，查重率为：\",result)\r\n    print(\"余弦相似度为：%.2f\" % result)\r\n    return result\r\ndef write_ans(result):\r\n    ans_txt = open(\"./ans.txt\", 'w', encoding=\"UTF-8\")\r\n    sim = str('%.2f' % result)\r\n    ans_txt.write(sim)\r\n    ans_txt.close()\r\n\r\ndef main_test(orig_position,orig_sim_position,ans_position):\r\n    s1_position=open(orig_position,'r',encoding=\"UTF-8\")\r\n    s1= s1_position.read()\r\n    s1_position.close()\r\n    s2_position=open(orig_sim_position,'r',encoding=\"UTF-8\")\r\n    s2 = s2_position.read()\r\n    s2_position.close()\r\n    # 空文本异常检测\r\n    if s1 == '':\r\n        print(\"error:\")\r\n        raise NoWordError\r\n    if s2 == '':\r\n        print(\"error:\")\r\n        raise NoWordError\r\n\r\n    word_set, s1_cut, s2_cut = tokenization(s1, s2)\r\n    s1_cut_code, s2_cut_code = dictionary(word_set, s1_cut, s2_cut)\r\n    result = sim_ans(s1_cut_code, s2_cut_code)\r\n    write_ans(result)\r\n\r\n\r\n\r\n","sub_path":"031802518/test_function.py","file_name":"test_function.py","file_ext":"py","file_size_in_byte":3196,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"460112494","text":"from itertools import combinations\n\n\ndef read_input(path):\n    with open(path, 'r') as f:\n        output = [int(i) for i in f.read().splitlines()]\n    return output\n\n\nif __name__ == '__main__':\n    stream = read_input('input.txt')\n    preamble_len = 25\n    invalid_nbr = None\n    for i in range(preamble_len, len(stream)):\n        valid_sum = False\n        preamble = stream[i - preamble_len:i]\n        for int_1, int_2 in combinations(preamble, 2):\n            if int_1 + int_2 == stream[i]:\n                valid_sum = True\n                break\n        if not valid_sum:\n            invalid_nbr = stream[i]\n            break\n    print(invalid_nbr)\n\n\n","sub_path":"task_09/task_9_1.py","file_name":"task_9_1.py","file_ext":"py","file_size_in_byte":653,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"44507972","text":"import math\n\nfin = open(\"maxnumber.in\", 'r')\nfout = open(\"maxnumber.out\", 'w')\n\nn = int(fin.readline())\na = list(map(int, fin.readline().split()))\n\nd = {}\ni = 0\nwhile True:\n    if i >= len(a):\n        break\n    if a[i] % 2 == 0:\n        a.append(a[i] // 2)\n        a.append(a[i] // 2)\n    else:\n        d[a[i]] = d.get(a[i], 0) + 1\n    i += 1\nd = list(d.items())\nans = []\n#print(d)\nfor i in range(len(d)):\n    t = d[i]\n    c = t[1]\n    res = t[0]\n    while c > 1:\n        c //= 2\n        res *= 2\n    ans.append(res)\nprint(max(ans), file=fout)\nfin.close()\nfout.close()\n","sub_path":"anichkov-camp-2018/DAY14/F.py","file_name":"F.py","file_ext":"py","file_size_in_byte":569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"85679043","text":"import requests\r\nfrom bs4 import BeautifulSoup\r\nfrom re import sub\r\nfrom xml.dom import minidom\r\n\r\n\r\ndef cleanIUPAC(IUPAC):\r\n    newIUPAC = sub(\"[~{}]\", \"\", IUPAC)\r\n    return newIUPAC\r\n\r\n\r\ndef cleanIUPACInChI(IUPACInChI):\r\n    newIUPACInChI = sub(\"InChI=\", \"\", IUPACInChI)\r\n    return newIUPACInChI\r\n\r\n\r\ndef prettifyFormula(formula):\r\n    newFormula = []\r\n    for char in formula:\r\n        if char.isalpha():\r\n            newFormula.append(\"<span>%s</span>\" % (char))\r\n        elif char.isdigit():\r\n            newFormula.append(\"<sub>%s</sub>\" % (char))\r\n    return \"\".join(newFormula)\r\n\r\n\r\ndef getDescribtorSoup(CID, attribute):\r\n    URL = \"https://pubchem.ncbi.nlm.nih.gov/rest/rdf/descriptor/CID%s.html\" % (CID + attribute)\r\n    Page = requests.get(URL)\r\n    Soup = BeautifulSoup(Page.text, \"html.parser\")\r\n    return Soup\r\n\r\n\r\ndef getDescribtorValue(CID, attribute):\r\n    try:\r\n        URL = \"https://pubchem.ncbi.nlm.nih.gov/rest/rdf/descriptor/CID%s.html\" % (CID + attribute)\r\n        Page = requests.get(URL)\r\n        Soup = BeautifulSoup(Page.text, \"html.parser\")\r\n        Value = Soup.find(\"span\", {\"class\": \"value\"}).string\r\n\r\n    except AttributeError as e:\r\n        Value = \"no Value\"\r\n\r\n    return Value\r\n\r\n\r\ndef getCompound(compound):\r\n    imageName = \"%s.png\" % (compound)\r\n    pageURL = \"https://pubchem.ncbi.nlm.nih.gov/compound/%s\" % (compound)\r\n    page = requests.get(pageURL)\r\n    soup = BeautifulSoup(page.text, \"html.parser\")\r\n    imageSource = soup.find(\"meta\", {\"property\": \"og:image\"})[\"content\"]\r\n\r\n    CID = soup.find(\"meta\", {\"name\": \"pubchem_uid_value\"})[\"content\"]\r\n\r\n    iupacSoup = getDescribtorSoup(CID, \"_Preferred_IUPAC_Name\")\r\n\r\n    try:\r\n        IUPAC = iupacSoup.find(\"span\", {\"class\": \"value\"}).string\r\n        preferred = True\r\n    except AttributeError as e:\r\n        IUPAC = soup.find(\"meta\", {\"property\": \"og:title\"})[\"content\"]\r\n        preferred = False\r\n\r\n    IUPAC = cleanIUPAC(IUPAC)\r\n\r\n    return imageSource, pageURL, IUPAC, preferred, CID\r\n\r\n\r\ndef getInfo(CID):\r\n    info = []\r\n    xmldoc = minidom.parse('attribute.xml')\r\n    attributeList = xmldoc.getElementsByTagName('item')\r\n\r\n    for i in attributeList:\r\n        info.append(getDescribtorValue(CID, i.attributes['name'].value))\r\n\r\n    info[0] = prettifyFormula(info[0])\r\n    info[2] = cleanIUPACInChI(info[2])\r\n\r\n    return info\r\n","sub_path":"bunshi/info.py","file_name":"info.py","file_ext":"py","file_size_in_byte":2340,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"440973747","text":"#This is Catherine Experiment\n\nimport os #handy system and path functions\nfrom psychopy import core, visual, data, event, sound, gui, parallel #these are the psychopy libraries\nimport random, math\nimport visualExtra\n\nsaveImage = False  #a flag for the saving of static images\n\n#parallel.PParallelInpOut32\n#parallel.setPortAddress(address=0xE010)\n#parallel.setData(0)\n\n#store info about the experiment\nif not os.path.isdir('data'):      #folder data\n    os.makedirs('data')       #if this fails (e.g. permissions) we will get error\nexpInfo={'participant':'','number':0}\ndlg=gui.DlgFromDict(dictionary=expInfo, title='Rotation')\nif dlg.OK==False: core.quit() #user pressed cancel\nexpInfo['date']=data.getDateStr()#add a simple timestamp\nexpInfo['expName']='rotation'\n\nif saveImage:\n    myWin = visual.Window(allowGUI=False, units='pix', size =(800,800), fullscr=False)#creates a window using pixels as units\n    orderTrials = 'sequential'\nelse:\n    myWin = visual.Window(allowGUI=False, units='pix', size =(800,800), fullscr=False)#creates a window using pixels as units\n    orderTrials = 'random'\n\ncolPoly =[-.4,-.4,-.4]\ncolTop =[-.3,-.4,-.4]\n#these lines are very powerful, they create all stimuli using the special functions of psychopy\n#fixation=visual.TextStim(myWin, text='+', pos=[0, 0], height=30, color='red')#this is a gray cross for the fixation\nstim1 = visual.ImageStim(myWin, image='symm.png')      #import an image from a file\npolyL = visual.ShapeStim(myWin, lineColor=None, fillColor=None)\npolyR = visual.ShapeStim(myWin, lineColor=None, fillColor=None)\npolyL2 = visual.ShapeStim(myWin, lineColor=None, fillColor=colPoly)\npolyR2 = visual.ShapeStim(myWin, lineColor=None, fillColor=colPoly)\npolyTop = visual.ShapeStim(myWin, lineColor=None, fillColor=colTop)\ncontour =visual.ShapeStim(myWin, lineColor='black', fillColor=None)\ncontourFill =visual.ShapeStim(myWin, lineColor=None, fillColor=colPoly)\nframe = visual.Rect(myWin, lineColor='blue', fillColor=None, width = 240, height=400)\nfixation = visual.Circle(myWin, lineColor=None, fillColor='red', radius = 10)\ndot = visual.Circle(myWin, lineColor='red', fillColor=None, radius = 12)\noccluder = visual.ShapeStim(myWin, fillColor='white', lineColor=None, vertices=[[0,300],[200,300],[200,-300],[0,-300]])\noccluderTop = visual.ShapeStim(myWin, fillColor=[1,.9,.9], lineColor=None, vertices=[[0,300],[200,300],[200,0],[0,0],[0,300]])\ngaborSize = 32\ngabor = visual.GratingStim(myWin, tex=\"sin\", mask=\"gauss\", texRes=256,  pos=[0,-300], units='pix', size=[gaborSize,gaborSize], contrast=0.5, phase=0.5, sf=[0.125,0.125], ori=0)\ngaborBig = visual.GratingStim(myWin, tex=\"sin\", mask=\"gauss\", texRes=256,  pos=[0,0], units='pix', size=[160,160], contrast=0.5, phase=0.5, sf=[0.125,0.125], ori=0)\nmyClock = core.Clock()#this creates adn starts a clock which we can later read\nresponseScreen=visual.TextStim(myWin, ori=0, pos=[0,0], height=24, color='white', units= 'pix')   #font='Times New Roman')\nwarn = sound.Sound(value='C',secs=0.6,sampleRate=44100)#, bits=16\n\nnsteps = 16\nheight = 200\nwidth =120\nystep = 2. * height / nsteps # 25\njitter =46\n\ndef message(type, nBlocksToGo = 1):\n    if type == 'Practice1':\n        message = 'Welcome to the Practice. You will see a rotating pattern. Your task is to decide whether the grey object has a reflection'\n    if type == 'Practice2':\n        message = 'A bit more practice, now with just two presentations. Your task is again to decide whether the grey object has a reflection'\n    elif type == 'Experiment':\n        message = 'Now for the real thing, its the same, but much longer. Try not to blink, and keep your eyes on the central fixation point'\n    elif type == 'Goodbye':\n        message ='Thank you for taking part in the Experiment'\n    elif type == 'Break':\n        message = '%d Blocks to go: Wait for experimenter to check electrodes!' %nBlocksToGo\n#    if not type == 'Break':\n    myWin.flip()\n    Instructions=visual.TextStim(myWin, ori=0, text=message, pos=[0, 0], height=30, color='white', units= 'pix')\n    Instructions.draw()\n    myWin.flip()\n    event.waitKeys(keyList=['g'])\n\ndef collectResponse(regularity, responseScreenVersion, startPos, practice):\n    event.clearEvents()\n    if startPos=='left':\n        responseScreen.setPos([0,100])\n    elif startPos=='right':\n        responseScreen.setPos([0,-100])\n    \n    if responseScreenVersion == 1:\n        responseScreen.setText('reflection  random')\n        responseScreen.draw()\n        myWin.flip()  # \n\n        responseKey = event.waitKeys(keyList=['a', 'l','escape'])[0]\n        if responseKey == 'a':\n            choice = 'ref'\n        elif responseKey == 'l':\n            choice = 'ran'\n        elif responseKey =='escape':\n            myWin.close()\n            core.quit()\n        if regularity== choice:\n            respCorr = 1\n        else:\n            respCorr = 0\n    elif responseScreenVersion == 2:\n        responseScreen.setText('random  reflection')\n        responseScreen.draw()\n        myWin.flip()\n        \n        responseKey = event.waitKeys(keyList=['a', 'l','escape'])[0]\n        if responseKey == 'l':\n            choice = 'ref'\n        elif responseKey == 'a':\n            choice = 'ran'\n        elif responseKey =='escape':\n            myWin.close()\n            core.quit()\n        if regularity== choice:\n            respCorr = 1\n        else:\n            respCorr = 0\n            \n    if respCorr==0 and practice==True:\n        warn.play()\n    return respCorr, choice\n    \ndef rotateObject(obj, deg):\n    \n    v = obj.vertices\n    visualExtra.rotateVertices(vertices= v, a=deg)\n    obj.vertices = v\n\ndef drawGabors(coordsGL, coordsGR, oriGL, oriGR, direction):\n\n    gaborBig.setOri(direction)\n    coordsGR = visualExtra.rotateVertices(vertices= coordsGR, a=direction)\n    coordsGL = visualExtra.rotateVertices(vertices= coordsGL, a=direction)\n    \n    gaborBig.draw()\n\n#    for i in range(len(coordsGR)):\n#        gabor.setPos(coordsGR[i])\n#        gabor.setOri(oriGR[i][0])\n#        gabor.draw()\n#        gabor.setPos([-40,0],\"+\")\n#        gabor.setOri(oriGR[i][0])\n#        gabor.draw()\n#    for i in range(len(coordsGL)):\n#        gabor.setPos(coordsGL[i])\n#        gabor.setOri(oriGL[i][0])\n#        gabor.draw()\n#        gabor.setPos([+40,0],\"+\")\n#        gabor.setOri(oriGL[i][0])\n#        gabor.draw()\n\n\ndef prepareOri(regularity):\n    \n    oriGR = []\n    oriGL = []\n    \n    for i in range(1, nsteps):\n            oriGR.append([random.uniform(0,180),random.uniform(0,180)])\n#            oriG.append(random.uniform(0,180))\n#            oriG.append(random.uniform(0,180))\n            \n    if regularity =='ref':\n        oriGL= [[-i[0],-i[1]] for i in oriGR]\n#                oriG.append(-oriG[10 -i])\n#                oriG.append(oriG[10 -i])\n    elif regularity=='ran':\n        for i in range(1, nsteps):\n            oriGL.append([random.uniform(0,180),random.uniform(0,180)])\n#            oriG.append(random.uniform(0,180))\n#            oriG.append(random.uniform(0,180))\n\n    return oriGR, oriGL\n    \ndef prepareCoords(regularity):\n\n    coordsL =[]\n    coordsR =[]\n    coordsGL =[]\n    coordsGR =[]\n    \n    coordsR.append([0,-height])\n    y = -height\n    for i in range(nsteps+1):\n#        if i < \n        x = width + random.uniform(-jitter,jitter)\n        coordsR.append([x,y])\n        y = y + ystep\n    coordsR.append([0,height])\n    coordsR.append([0,ystep*2])\n    \n    coordsL.append([0,-height])\n    y = -height\n    if regularity =='ref':\n        for i in range(1,nsteps+2):\n            x = -coordsR[i][0]\n            coordsL.append([x,y])\n            y = y + ystep\n    elif regularity=='ran':\n        for i in range(1, nsteps +2):\n            x = -width + random.uniform(-jitter,jitter)\n            coordsL.append([x,y])\n            y = y + ystep\n    coordsL.append([0,height])\n    coordsL.append([0,ystep*2])\n    \n    for i in range(2, nsteps+ 1):\n            coordsGR.append([coordsR[i][0] -20, coordsL[i][1]])\n#            coordsGR.append([coordsR[i][0] -20-gaborSize, coordsR[i][1]])\n#            coordsG.append([coords[i][0] -20-gaborSize*2, coords[i][1]])\n    for i in range(2, nsteps+ 1):        \n            coordsGL.append([coordsL[i][0] +20, coordsL[i][1]])\n#            coordsGL.append([coordsL[i][0] +20+gaborSize, coordsL[i][1]])\n#            coordsG.append([coords[i][0] +20+gaborSize*2, coords[i][1]])\n\n    return coordsL, coordsR, coordsGL, coordsGR\n\ndef drawGreyPolys(v):\n\n    c = []\n    for i in range(1,17):\n        c.append(v[i])\n        c.append(v[i+1])\n        c.append(v[36-i-1])\n        c.append(v[36-i])\n        c.append(v[i])\n        contourFill.setVertices(c)\n        if i==12:\n            contourFill.setFillColor(colTop)\n        contourFill.draw()\n        c =[]\n\n    contourFill.setFillColor(colPoly)\n\ndef drawPolys(coordsL, coordsR, direction):\n\n    coordsR = visualExtra.rotateVertices(vertices= coordsR, a=direction)\n    coordsL = visualExtra.rotateVertices(vertices= coordsL, a=direction)\n\n    polyR.setVertices(coordsR)\n    polyL.setVertices(coordsL)\n    \n#    polyR.draw()\n#    polyL.draw()\n\n#    position =nsteps /2 + 3    # for nsteps 10 this is 7 for nsteps 16 this is 10\n#    v = list(coordsL[position:])\n#    polyTop.setVertices(v)\n#    polyTop.draw()\n#    v = list(coordsR[position:])\n#    polyTop.setVertices(v)\n#    polyTop.draw()\n    \n    position = nsteps +2     # if nsteps is 10 this shoudl be 12 for nsteps 16 this is 18\n    v1 = list(coordsR[:position])  \n    v2 = coordsL[::-1]\n    v = v1+v2[1:position+1]\n    #print len(v1), len(v2), len(v)\n    contour.setVertices(v)\n\n    drawGreyPolys(v)\n    contour.draw()\n    \n#    coordsR = [[i*.9,j*.9] for [i,j] in coordsR]\n#    coordsL =[[i*.9,j*.9] for [i,j] in coordsL]\n#\n#    polyR.setVertices(coordsR)\n#    polyL.setVertices(coordsL)\n#    \n#    polyR.draw()  # these are the smaller versions\n#    polyL.draw()\n#\n#    v1 = list(coordsR[:12])  \n#    v2 = coordsL[::-1]\n#    v = v1+v2[1:13]\n#    contour.setVertices(v)\n#    contour.draw()\n#    \n    \n#    dot.setPos(coordsL[0])\n#    dot.draw()\n#    dot.setPos(coordsL[1])    \n#    dot.draw()\n#    dot.setPos(coordsL[2])\n#    dot.draw()\n#    dot.setPos(coordsR[10])    \n#    dot.draw()\n#    dot.setPos(coordsR[11])\n#    dot.draw()\n#    dot.setPos(coordsR[16])    \n#    dot.draw()\n#    dot.setPos(coordsL[16])    \n#    dot.draw()\n\n#def drawPolysTop(coordsL, coordsR, direction):\n#    \n#    coordsR = visualExtra.rotateVertices(vertices= coordsR, a=direction)\n#    coordsL = visualExtra.rotateVertices(vertices= coordsL, a=direction)\n#\n#    polyR.setVertices(coordsR)\n#    polyL.setVertices(coordsL)\n#    \n#    polyR.draw()\n#    polyL.draw()\n\n\ndef runBlock(trialbook=\"\", practice=True, repeti=1):\n    \n    trials=data.TrialHandler(nReps=repeti, method=orderTrials, trialList=data.importConditions(trialbook)) \n    blockDuration = 24\n    nBlocksToGo = trials.nTotal / blockDuration  #336/24=14\n    counter =0\n    \n    # This long loop runs through the trials\n    for thisTrial in trials:\n        if thisTrial!=None:\n            for paramName in thisTrial.keys():\n                exec('{} = thisTrial[paramName]'.format(paramName), locals(), globals())\n                #exec(paramName+'=thisTrial.'+paramName)\n\n        if counter == blockDuration and practice=='exp':\n                nBlocksToGo = nBlocksToGo - 1\n                message('Break', nBlocksToGo = nBlocksToGo)\n                counter = 0\n        counter = counter +1\n        \n        fixation.draw()#draws fixation\n        myWin.flip()    # fixation and nothing else\n\n        occluder.vertices =([[0,300],[200,300],[200,-300],[0,-300],[0,300]])\n        occluderTop.vertices =([[0,300],[200,300],[200,100],[0,100],[0,300]])\n        v = occluder.vertices\n        vTop = occluderTop.vertices\n        if startPos =='left':\n            v = [[-i[0],i[1]] for i in v]\n            vTop = [[-i[0],i[1]] for i in vTop]            \n        occluder.vertices = visualExtra.rotateVertices(vertices= v, pos=[0,0], a=direction)\n        occluderTop.vertices = visualExtra.rotateVertices(vertices= vTop, pos=[0,0], a=direction)\n        \n    #    regularity ='ref'    # debug\n    #    direction = 45   # debug\n            \n#        t = myClock.getTime() + 0.5     # fixation for this much time\n#        while myClock.getTime() < t:\n#            pass #do nothing\n\n        occluder.draw()\n        occluderTop.draw()\n        fixation.draw()    #draws fixation\n        myWin.flip()     # fixation and occluder\n        \n        t = myClock.getTime() + 1.5     # occluder for this much time\n        while myClock.getTime() < t:\n            pass #do nothing\n        \n        if saveImage:\n            myWin.getMovieFrame()\n            myWin.saveMovieFrames(\"images/baseline\"+str(direction)+startPos+\"2.png\")\n    \n        coordsL, coordsR, coordsGL, coordsGR =prepareCoords(regularity)\n        oriGL, oriGR = prepareOri(regularity)\n    \n        drawPolys(coordsL, coordsR, direction)\n#        drawGabors(coordsGL, coordsGR, oriGL, oriGR, direction)\n        \n        occluder.draw()\n        occluderTop.draw()\n        fixation.draw()#draws fixation\n        myWin.flip()   # firts interval\n        #parallel.setData(trigger)\n        t = myClock.getTime() + .02     # first image for this much time\n        while myClock.getTime() < t:\n            pass #do nothing\n        #parallel.setData(0)\n        \n        if saveImage:\n            myWin.getMovieFrame()\n            myWin.saveMovieFrames(\"images/\"+regularity+str(direction)+startPos+\".png\")\n            key = event.waitKeys(keyList=['g'])\n        \n        t = myClock.getTime() + firstHalfDuration -0.02     # first image for this much time\n        while myClock.getTime() < t:\n            pass #do nothing\n\n        if practice =='practice1':\n    #    gaborBig.setOri(direction)\n            drawPolys(coordsL, coordsR, direction-90)\n    #        drawGabors(coordsGL, coordsGR, oriGL, oriGR, direction+90)\n#            v = occluder.vertices\n#            vTop = occluderTop.vertices\n            v2 = visualExtra.rotateVertices(vertices= v, pos=[0,0], a=-45)  #occluder.vertices\n            vTop2 = visualExtra.rotateVertices(vertices= vTop, pos=[0,0], a=-45) #occluderTop.vertices\n#            v2 = [[-i[0],i[1]] for i in v2]\n#            vTop2 = [[-i[0],i[1]] for i in vTop2]          \n            occluder.vertices =v2\n            occluderTop.vertices = vTop2\n            occluder.draw()\n            occluderTop.draw()\n            fixation.draw()#draws fixation\n            myWin.flip()  # extra interval for the practice\n            key = event.waitKeys(keyList=['g'])\n            core.wait(0.06)\n\n        drawPolys(coordsL, coordsR, direction-90)\n        v3 = visualExtra.rotateVertices(vertices= v, pos=[0,0], a=direction)  #occluder.vertices\n        vTop3 = visualExtra.rotateVertices(vertices= vTop, pos=[0,0], a=direction) #occluderTop.vertices\n        v3 = [[-i[0],i[1]] for i in v3]\n        vTop3 = [[-i[0],i[1]] for i in vTop3]            \n        occluder.vertices =v3\n        occluderTop.vertices = vTop3\n#        occluder.vertices = visualExtra.rotateVertices(vertices= v, pos=[0,0], a=0)\n#        occluderTop.vertices = visualExtra.rotateVertices(vertices= vTop, pos=[0,0], a=0)\n\n        occluder.draw()\n        occluderTop.draw()\n        fixation.draw()#draws fixation\n        myWin.flip()  # second interval\n        \n        if saveImage:\n            myWin.getMovieFrame()\n            myWin.saveMovieFrames(\"images/\"+regularity+str(direction)+startPos+\"2.png\")\n            \n        t = myClock.getTime() + secondHalfDuration     # second image for this much time\n        while myClock.getTime() < t:\n            pass #do nothing\n\n        drawPolys(coordsL, coordsR, direction-90)\n        occluder.draw()\n        occluderTop.draw()\n        fixation.draw()#draws fixation\n    \n        timenow = myClock.getTime()\n        correct, choice = collectResponse(regularity, responseScreenVersion, startPos, practice!='exp')\n#        correct =1  #debug\n#        choice ='ref' #debug\n        respRT = myClock.getTime()-timenow #time taken to respond\n        \n        trials.addData('correct', correct)\n        trials.addData('choice', choice)\n        trials.addData('respRt', respRT)\n        \n    #save all data\n    trials.extraInfo =expInfo\n    if practice=='exp':\n        trials.saveAsWideText('data/rotation.txt')\n        trials.saveAsExcel('data/rotation.xlsx', sheetName= 'HorizontalVertical', dataOut=['all_raw'])\n    else:\n        trials.saveAsWideText('data/practice.txt')\n        trials.saveAsExcel('data/practice.xlsx', sheetName= 'HorizontalVertical', dataOut=['all_raw'])\n\nmessage('Practice1')\nrunBlock(trialbook='rotationBook.xlsx' , practice='practice1', repeti=1) # (16 trials: 8 conditions: 2 repetitions) .\nmessage('Practice2')\nrunBlock(trialbook='rotationBook.xlsx' , practice='practice2', repeti=2) # (8 trials: 8 conditions: 1 repetitions) .\nmessage('Experiment')\nrunBlock(trialbook='rotationBook.xlsx', practice='exp', repeti=42) # (336 trials: 8 conditions: 42 repetitions) . \nmessage('Goodbye')\nmyWin.close\ncore.quit()\n\n\n\n\n","sub_path":"MATLAB Installed/BIDS_resources/P028_EX2/Code/RotationV3.py","file_name":"RotationV3.py","file_ext":"py","file_size_in_byte":16996,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"620912235","text":"from flask import Flask, render_template\nfrom datetime import datetime, timedelta\nimport json\nimport random\nfrom timeit import default_timer as timer \n\napp = Flask(__name__)\n\n@app.route('/')\ndef index():\n    \"\"\"\n        Method thats return current server time\n    \"\"\"\n    request_time = timer()\n    server_time_now = datetime.now()\n    response_time = timer()\n    return json.dumps(\n        {\n            'server_time_now': str(server_time_now),\n            'next_bus': str(server_time_now + timedelta(minutes=random.randint(1, 45))),\n            'process_time': str(response_time - request_time)\n        }\n    )\n\nif __name__ == \"__main__\":\n    \"\"\"\n        Inicialize server\n    \"\"\"\n    print('Server starting')\n    app.run(host='0.0.0.0', port=5001)","sub_path":"University/UNIP/APS/#7/server/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":750,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"374016820","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\n# Copyright 2010 British Broadcasting Corporation and Kamaelia Contributors(1)\n#\n# (1) Kamaelia Contributors are listed in the AUTHORS file and at\n#     http://www.kamaelia.org/AUTHORS - please extend this file,\n#     not this notice.\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 unittest\nimport Kamaelia.Protocol.Framing as Framing\n\nfrom Axon.Ipc import producerFinished\nimport Axon\n\nclass Framing_Tests(unittest.TestCase):\n\n    def test_tuple2string_1(self):\n        \"SimpleFrame.__str__ - (1,'1') results in '1 1\\\\n1'\"\n        Frame = Framing.SimpleFrame(1,'1')\n        result = str(Frame)\n        self.assertEqual(result, '1 1\\n1')\n\n    def test_tuple2string_2(self):\n        \"SimpleFrame.__str__ - (1,'11') results in '1 2\\\\n11'\"\n        Frame = Framing.SimpleFrame(1,'11')\n        result = str(Frame)\n        self.assertEqual(result, '1 2\\n11')\n\n    def test_tuple2string_3(self):\n        \"SimpleFrame.__str__ - (1,'1111') results in '1 4\\\\n1111'\"\n        Frame = Framing.SimpleFrame(1,'1111')\n        result = str(Frame)\n        self.assertEqual(result, '1 4\\n1111')\n\n    def test_tuple2string_4(self):\n        \"SimpleFrame.__str__ - (50,'1111') results in '50 4\\\\n1111'\"\n        Frame = Framing.SimpleFrame(50,'1111')\n        result = str(Frame)\n        self.assertEqual(result, '50 4\\n1111')\n\n    def test_tuple2string_5(self):\n        \"SimpleFrame.__str__ - Random data as the data part of the tuple succeeds\"\n        import random\n        length = 100\n        r = []\n        for x in range(length):\n             r.append( chr(random.randrange(0,256)) )\n        data = \"\".join(r)\n      \n        Frame = Framing.SimpleFrame(50,data)\n        result = str(Frame)\n        self.assertEqual(result, \"50 100\\n\"+str(data))\n\n    def test_tuple2string_6(self):\n        \"SimpleFrame.__str__ - Random data of random length as the data part of the tuple succeeds\"\n        import random\n        length = random.randrange(100,200)\n        r = []\n        for x in range(length):\n             r.append( chr(random.randrange(0,256)) )\n        data = \"\".join(r)\n      \n        Frame = Framing.SimpleFrame(50,data)\n        result = str(Frame)\n        self.assertEqual(result, \"50 \"+str(length)+\"\\n\"+str(data))\n\n    def test_tuple2string_7(self):\n        \"SimpleFrame.__str__ - Completely random frame\"\n        import random\n        length = random.randrange(100,200)\n        r = []\n        for x in range(length):\n             r.append( chr(random.randrange(0,256)) )\n        data = \"\".join(r)\n\n        stamp = random.randrange(0,200)\n      \n        Frame = Framing.SimpleFrame(stamp,data)\n        result = str(Frame)\n        self.assertEqual(result, str(stamp)+ \" \"+str(length)+\"\\n\"+str(data))\n\n    def test_string2tuple(self):\n        \"SimpleFrame.fromString - '1 1\\\\n1' is parsed as (1,'1')\"\n        result = Framing.SimpleFrame.fromString('1 1\\n1')\n        self.assertEqual(result, (1,'1') )\n\n    def test_string2tuple_1(self):\n        \"SimpleFrame.fromString - '1 2\\\\n11' is parsed as (1,'11')\"\n        result = Framing.SimpleFrame.fromString('1 2\\n11')\n        self.assertEqual(result, (1,'11') )\n\n    def test_string2tuple_2(self):\n        \"SimpleFrame.fromString - '1 2\\\\n1111' is parsed as (1,'11') -- Corrupt frame!\"\n        result = Framing.SimpleFrame.fromString('1 2\\n1111')\n        self.assertEqual(result, (1,'11') )\n\n    def test_string2tuple_4(self):\n        \"SimpleFrame.fromString - '1 2\\\\n' causes exception to be thrown - Corrupt frame!\"\n        try:\n             result = Framing.SimpleFrame.fromString('1 2\\n')\n             self.fail(\"Should have died\")\n        except Framing.ShortFrame:\n           # Success\n           pass\n\n    def test_tuple2string_string2tuple_roundtrip_1(self):\n        \"SimpleFrame.__str__, Framing.SimpleFrame.fromString - roundtrip for (1,'1') succeeds\"\n        original = (1,'1')\n        Frame = Framing.SimpleFrame(*original)\n        FrameForWire = str(Frame)\n        result = Framing.SimpleFrame.fromString(FrameForWire)\n        self.assertEqual(original, result, \"passed through unchanged\")\n\n    def test_tuple2string_string2tuple_roundtrip_2(self):\n        \"SimpleFrame.__str__, Framing.SimpleFrame.fromString - roundtrip for (50,'1111') succeeds\"\n        original = (50,'1111')\n        Frame = Framing.SimpleFrame(*original)\n        FrameForWire = str(Frame)\n        result = Framing.SimpleFrame.fromString(FrameForWire)\n        self.assertEqual(original, result, \"passed through unchanged\")\n\n    def test_tuple2string_string2tuple_roundtrip_3(self):\n        \"SimpleFrame.__str__, Framing.SimpleFrame.fromString - roundtrip for random data, frame id 50 succeeds\"\n        import random\n        length = random.randrange(100,200)\n        r = []\n        for x in range(length):\n             r.append( chr(random.randrange(0,256)) )\n        data = \"\".join(r)\n        original = (50,data)\n        Frame = Framing.SimpleFrame(*original)\n        FrameForWire = str(Frame)\n        result = Framing.SimpleFrame.fromString(FrameForWire)\n        self.assertEqual(original, result, \"passed through unchanged\")\n\n    def test_tuple2string_string2tuple_roundtrip_4(self):\n        \"SimpleFrame.__str__, Framing.SimpleFrame.fromString - roundtrip for random data, random frame id succeeds\"\n        import random\n        length = random.randrange(100,200)\n        r = []\n        for x in range(length):\n             r.append( chr(random.randrange(0,256)) )\n        data = \"\".join(r)\n        stamp = random.randrange(0,200)\n      \n        original = (stamp,data)\n\n        Frame = Framing.SimpleFrame(*original)\n        FrameForWire = str(Frame)\n        result = Framing.SimpleFrame.fromString(FrameForWire)\n        self.assertEqual(original, result, \"passed through unchanged\")\n\ndef makeTestCase(klass):\n    class Component_ShutdownTests(unittest.TestCase):\n        def test_smokeTest2(self):\n            import random\n            X = klass()\n            X.activate()\n            for i in range(random.randrange(0,2000)):\n                try:\n                    next(X)\n                except StopIteration:\n                    self.fail(\"Component should run until told to stop\")\n\n        def test_smokeTest3(self):\n            # NOTE: This test is actually satisfied if no body to the component is put in place\n            import random\n            X = klass()\n            X.activate()\n            X._deliver(producerFinished(),\"control\")\n            componentExit = False\n            for i in range(random.randrange(0,2000)):\n                try:\n                    next(X)\n                except StopIteration:\n                    componentExit = True\n                    break\n            if not componentExit:\n                self.fail(\"When sent a shutdown message, the component should shutdown\")\n\n        def test_smokeTest4(self):\n            import random\n            X = klass()\n            X.activate()\n            X._deliver(\"BINGLE\",\"control\")\n            componentExit = False\n            for i in range(random.randrange(0,2000)):\n                try:\n                    next(X)\n                except StopIteration:\n                    componentExit = True\n                    break\n            if componentExit:\n                self.fail(\"Sending a random message to the control box should not cause a shutdown\")\n\n        def test_smokeTest5(self):\n            import random\n            X = klass()\n\n            Dummy = Axon.Component.component()\n            X.link((X, \"outbox\"),(Dummy, \"inbox\"))\n            X.link((X, \"signal\"),(Dummy, \"control\"))\n\n            X.activate()\n            shutdown_message = producerFinished()\n            X._deliver(shutdown_message,\"control\")\n\n            componentExit = False\n            for i in range(random.randrange(0,2000)):\n                try:\n                    next(X)\n                except StopIteration:\n                    componentExit = True\n                    break\n            try:\n#                Y = X._collect(\"signal\")\n                Y = Dummy.recv(\"control\")\n\n                self.assertEqual(shutdown_message, Y)\n            except IndexError:\n                self.fail(\"Shutdown Message should be passed on\")\n    return Component_ShutdownTests\n\n\nclass FramingComponent_Tests(unittest.TestCase):\n    def test_smokeTest(self):\n        X = Framing.Framer()\n        X.activate()\n\n    def test_marshalling(self):\n        message = (1,'1')\n        expect = str(Framing.SimpleFrame(*message))\n        X = Framing.Framer()\n        Dummy = Axon.Component.component()\n        X.link((X, \"outbox\"),(Dummy, \"inbox\"))\n        X.link((X, \"signal\"),(Dummy, \"control\"))\n        X.activate()\n        X._deliver(message, \"inbox\")\n        for i in range(20): # More than sufficient cycles (should be lots less!)\n            next(X)\n#        result = X._collect(\"outbox\")\n        result = Dummy.recv(\"inbox\")\n        self.assertEqual(expect, result)\n\n    def test_marshalling_sequence(self):\n        X = Framing.Framer()\n        Dummy = Axon.Component.component()\n        X.link((X, \"outbox\"),(Dummy, \"inbox\"))\n        X.link((X, \"signal\"),(Dummy, \"control\"))\n        X.activate()\n        for i in range(100):\n            message = (i,str(i))\n            expect = str(Framing.SimpleFrame(*message))\n            X._deliver(message, \"inbox\")\n            for i in range(20): # More than sufficient cycles (should be lots less!)\n                next(X)\n#            result = X._collect(\"outbox\")\n            result = Dummy.recv(\"inbox\")\n            self.assertEqual(expect, result)\n\nclass DeFramingComponent_Tests(unittest.TestCase):\n    def test_smokeTest(self):\n        X = Framing.DeFramer()\n        X.activate()\n\n    def test_demarshalling(self):\n        original = (1,'1')\n        message = str(Framing.SimpleFrame(*original))\n        X = Framing.DeFramer()\n        Dummy = Axon.Component.component()\n        X.link((X, \"outbox\"),(Dummy, \"inbox\"))\n        X.link((X, \"signal\"),(Dummy, \"control\"))\n        X.activate()\n        X.activate()\n        X._deliver(message, \"inbox\")\n        for i in range(20): # More than sufficient cycles (should be lots less!)\n            next(X)\n#        result = X._collect(\"outbox\")\n        result = Dummy.recv(\"inbox\")\n        self.assertEqual(original, result)\n\n    def test_demarshalling_sequence(self):\n        X = Framing.DeFramer()\n        Dummy = Axon.Component.component()\n        X.link((X, \"outbox\"),(Dummy, \"inbox\"))\n        X.link((X, \"signal\"),(Dummy, \"control\"))\n        X.activate()\n        X.activate()\n        for i in range(100):\n            original = (i,str(i))\n            message = str(Framing.SimpleFrame(*original))\n            X._deliver(message, \"inbox\")\n            for i in range(20): # More than sufficient cycles (should be lots less!)\n                next(X)\n#            result = X._collect(\"outbox\")\n            result = Dummy.recv(\"inbox\")\n            self.assertEqual(original, result)\n\n    def test_demarshalling_frame_missing_end(self):\n        \"\"\"DeFramer, will not pass on a frame if a chunk is missing out of it\"\"\"\n        X = Framing.DeFramer()\n        Dummy = Axon.Component.component()\n        X.link((X, \"outbox\"),(Dummy, \"inbox\"))\n        X.link((X, \"signal\"),(Dummy, \"control\"))\n        X.activate()\n        X.activate()\n        originals = []\n        for i in range(1,10):\n            original = (i, \"a\" * i)\n            originals.append(original)\n            message = str(Framing.SimpleFrame(*original))\n            if i==5:\n                message = message[:-2]\n            X._deliver(message, \"inbox\")\n        for i in range(200):\n            next(X)\n        for i in range(1,10):\n            if i!=5:\n#                result = X._collect(\"outbox\")\n                result = Dummy.recv(\"inbox\")\n#                print result\n                self.assertEqual(originals[0], result)\n            del originals[0]\n\n            \nFramerMarshall = makeTestCase(Framing.Framer)\nFramerDeMarshall = makeTestCase(Framing.DeFramer)\nDataChunkerBasics = makeTestCase(Framing.DataChunker)\nDataDeChunkerBasics = makeTestCase(Framing.DataDeChunker)\n\nclass DataChunker_test(unittest.TestCase):\n    def test_smokeTest(self):\n        X = Framing.DataChunker()\n        X.activate()\n        \n    def test_makeChunk(self):\n        message = \"1234567890qwertyuiopasdfghjklzxcvbnm\\n\"*20\n        syncmessage = \"XXXXXXXXXXXXXXXXXXXXXXX\"\n        X = Framing.DataChunker(syncmessage=syncmessage)\n        Dummy = Axon.Component.component()\n        X.link((X, \"outbox\"),(Dummy, \"inbox\"))\n        X.link((X, \"signal\"),(Dummy, \"control\"))\n        X.activate()\n        X._deliver(message, \"inbox\")\n        for i in range(20): # More than sufficient cycles (should be lots less!)\n            next(X)\n#        result = X._collect(\"outbox\")\n        result = Dummy.recv(\"inbox\")\n        result_start = result[:len(syncmessage)]\n        result_message = result[len(syncmessage):]\n\n        self.assertEqual(message, result_message)\n        self.assertEqual(syncmessage, result_start)\n\n    def test_makeChunk_oneSync(self):\n        syncmessage = \"XXXXXXXXXXXXXXXXXXXXXXX\"\n        message = \"1234567890qwertyuiopasdfghjklzxcvbnm\\n\"*10\n        message += syncmessage\n        message += \"1234567890qwertyuiopasdfghjklzxcvbnm\\n\"*10\n        X = Framing.DataChunker(syncmessage=syncmessage)\n        Dummy = Axon.Component.component()\n        X.link((X, \"outbox\"),(Dummy, \"inbox\"))\n        X.link((X, \"signal\"),(Dummy, \"control\"))\n        X.activate()\n        X._deliver(message, \"inbox\")\n        for i in range(20): # More than sufficient cycles (should be lots less!)\n            next(X)\n#        result = X._collect(\"outbox\")\n        result = Dummy.recv(\"inbox\")\n\n        result_message = result[len(syncmessage):]\n        index = result_message.find(syncmessage)\n        self.assertEqual(-1, index, \"Should not be able to find the syncmessage in the chunked version\")\n\n\nclass DataDeChunker_Basictest(unittest.TestCase):\n    def makeBasicChunk(self, message, syncmessage):\n        X = Framing.DataChunker(syncmessage=syncmessage)\n        Dummy = Axon.Component.component()\n        X.link((X, \"outbox\"),(Dummy, \"inbox\"))\n        X.link((X, \"signal\"),(Dummy, \"control\"))\n        X.activate()\n        X._deliver(message, \"inbox\")\n        for i in range(20): # More than sufficient cycles (should be lots less!)\n            next(X)\n#        result = X._collect(\"outbox\")\n        result = Dummy.recv(\"inbox\")\n        return result\n\n    def test_DeChunkFullChunk(self):\n        message = \"1234567890qwertyuiopasdfghjklzxcvbnm\\n\"*20\n        syncmessage = \"XXXXXXXXXXXXXXXXXXXXXXX\"\n        chunk = self.makeBasicChunk(message, syncmessage)\n        X = Framing.DataDeChunker(syncmessage=syncmessage)\n        Dummy = Axon.Component.component()\n        X.link((X, \"outbox\"),(Dummy, \"inbox\"))\n        X.link((X, \"signal\"),(Dummy, \"control\"))\n        X.activate()\n        X._deliver(chunk, \"inbox\")\n        for i in range(20): # More than sufficient cycles (should be lots less...)\n                next(X)\n        X._deliver(\"junk\", \"flush\")\n        for i in range(20): # More than sufficient cycles (should be lots less...)\n            next(X)\n#        result = X._collect(\"outbox\")\n        result = Dummy.recv(\"inbox\")\n        self.assertEqual(result, message)\n\n    def test_DeChunkFullChunk_1(self):\n        syncmessage = \"XXXXXXXXXXXXXXXXXXXXXXX\"\n        message = \"1234567890qwertyuiopasdfghjklzxcvbnm\\n\"*10\n        message += syncmessage\n        message += \"1234567890qwertyuiopasdfghjklzxcvbnm\\n\"*10\n        chunk = self.makeBasicChunk(message, syncmessage)\n        X = Framing.DataDeChunker(syncmessage=syncmessage)\n        Dummy = Axon.Component.component()\n        X.link((X, \"outbox\"),(Dummy, \"inbox\"))\n        X.link((X, \"signal\"),(Dummy, \"control\"))\n        X.activate()\n        X._deliver(chunk, \"inbox\")\n        for i in range(20): # More than sufficient cycles (should be lots less...)\n                next(X)\n        X._deliver(\"junk\", \"flush\")\n        for i in range(20): # More than sufficient cycles (should be lots less...)\n            next(X)\n#        result = X._collect(\"outbox\")\n        result = Dummy.recv(\"inbox\")\n        self.assertEqual(result, message)\n\n    def test_DeChunkFullChunk_2(self):\n        syncmessage = \"XXXXXXXXXXXXXXXXXXXXXXX\"\n        message = \"1234567890%25qwertyuiopa\\Ssdfghjklzxcvbnm\\n\"*10\n        message += syncmessage\n        message += \"1234567890qwertyuiopasdfghjklzxcvbnm\\n\"*10\n        chunk = self.makeBasicChunk(message, syncmessage)\n        \n        X = Framing.DataDeChunker(syncmessage=syncmessage)\n        Dummy = Axon.Component.component()\n        X.link((X, \"outbox\"),(Dummy, \"inbox\"))\n        X.link((X, \"signal\"),(Dummy, \"control\"))\n        X.activate()\n        X._deliver(chunk, \"inbox\")\n        for i in range(20): # More than sufficient cycles (should be lots less...)\n                next(X)\n        X._deliver(\"junk\", \"flush\")\n        for i in range(20): # More than sufficient cycles (should be lots less...)\n            next(X)\n#        result = X._collect(\"outbox\")\n        result = Dummy.recv(\"inbox\")\n        self.assertEqual(result, message)\n\n    def test_DeChunkFullChunk_3(self):\n        syncmessage = \"XXXXXXXXXXXXXXXXXXXXXXX\"\n        message = \"1234567890qwertyu\\iopa\\Ssdfghjklzxcvbnm\\n\"*10\n        message += syncmessage\n        message += \"1234567890qwertyuiopasdfghjklzxcvbnm\\n\"*10\n        chunk = self.makeBasicChunk(message, syncmessage)\n        \n        X = Framing.DataDeChunker(syncmessage=syncmessage)\n        Dummy = Axon.Component.component()\n        X.link((X, \"outbox\"),(Dummy, \"inbox\"))\n        X.link((X, \"signal\"),(Dummy, \"control\"))\n        X.activate()\n        X._deliver(chunk, \"inbox\")\n        for i in range(20): # More than sufficient cycles (should be lots less...)\n                next(X)\n        X._deliver(\"junk\", \"flush\")\n        for i in range(20): # More than sufficient cycles (should be lots less...)\n            next(X)\n#        result = X._collect(\"outbox\")\n        result = Dummy.recv(\"inbox\")\n        self.assertEqual(result, message)\n\nclass DataDeChunker_BlocksTest(unittest.TestCase):\n\n    def makeBasicChunk(self, message, syncmessage):\n        X = Framing.DataChunker(syncmessage=syncmessage)\n        Dummy = Axon.Component.component()\n        X.link((X, \"outbox\"),(Dummy, \"inbox\"))\n        X.link((X, \"signal\"),(Dummy, \"control\"))\n        X.activate()\n        X._deliver(message, \"inbox\")\n        for i in range(20): # More than sufficient cycles (should be lots less!)\n            next(X)\n#        result = X._collect(\"outbox\")\n        result = Dummy.recv(\"inbox\")\n        return result\n\n    def blocks(self, someData, blocksize=20):\n        data = someData\n        while len(data) > blocksize:\n           yield data[:blocksize]\n           data = data[blocksize:]\n        yield data\n\n    def test_DeChunk_SingleChunk_ManyBlocks(self):\n        \"The dechunker handles taking a chunk that's in many blocks and putting it back together\"\n        syncmessage = \"XXXXXXXXXXXXXXXXXXXXXXX\"\n        message = \"123\\\\S\\4567\\\\890qwer\\\\\\tyuiopasdfg\\\\\\\\hjklzxcvbnm\\n\"*10\n        message += syncmessage\n        message += \"1234567890q\\\\Swertyuiopasdfghjklzxcvbnm\\n\"*10\n        \n        chunk = self.makeBasicChunk(message, syncmessage)\n\n        X = Framing.DataDeChunker(syncmessage=syncmessage)\n        Dummy = Axon.Component.component()\n        X.link((X, \"outbox\"),(Dummy, \"inbox\"))\n        X.link((X, \"signal\"),(Dummy, \"control\"))\n        X.activate()\n        for block in self.blocks(chunk):\n            X._deliver(block, \"inbox\")\n\n        try:\n            for i in range(200): # More than sufficient cycles (should be lots less...)\n                next(X)\n            X._deliver(\"junk\", \"flush\")\n            for i in range(20): # More than sufficient cycles (should be lots less...)\n                next(X)\n#            result = X._collect(\"outbox\")\n            result = Dummy.recv(\"inbox\")\n        except Framing.IncompleteChunk:\n            self.fail(\"IncompleteChunk exception should not propogate\")\n        self.assertEqual(result, message)\n\n    def test_DeChunk_RandomChunk_ManyBlocks(self):\n        \"The dechunker handles taking a chunk that's in many blocks and putting it back together\"\n        syncmessage = \"XXXXXXXXXXXXXXXXXXXXXXX\"\n        message = (\"\".join([str(x) for x in range(10,50)]) + \"\\n\")*10\n        chunk = self.makeBasicChunk(message, syncmessage)\n\n        X = Framing.DataDeChunker(syncmessage=syncmessage)\n        Dummy = Axon.Component.component()\n        X.link((X, \"outbox\"),(Dummy, \"inbox\"))\n        X.link((X, \"signal\"),(Dummy, \"control\"))\n        X.activate()\n        for block in self.blocks(chunk,blocksize=1):\n            X._deliver(block, \"inbox\")\n\n        try:\n            for i in range(2000): # More than sufficient cycles (should be lots less...)\n                next(X)\n            X._deliver(\"junk\", \"flush\")\n            for i in range(20): # More than sufficient cycles (should be lots less...)\n                next(X)\n#            result = X._collect(\"outbox\")\n            result = Dummy.recv(\"inbox\")\n        except Framing.IncompleteChunk:\n            self.fail(\"IncompleteChunk exception should not propogate\")\n        self.assertEqual(result, message)\n\n    def test_DeChunk_MultipleChunks_ManyBlocks(self):\n        \"The dechunker handles taking many chunks that are in many blocks and putting it back together\"\n        syncmessage = \"XXXXXXXXXXXXXXXXXXXXXXX\"\n        \n        X = Framing.DataDeChunker(syncmessage=syncmessage)\n        Dummy = Axon.Component.component()\n        X.link((X, \"outbox\"),(Dummy, \"inbox\"))\n        X.link((X, \"signal\"),(Dummy, \"control\"))\n        X.activate()\n\n        for base in range(10,1000,50):\n            message = (\"\".join([str(x) for x in range(base,base+50)]) + \"\\n\")*10\n            chunk = self.makeBasicChunk(message, syncmessage)\n\n            for block in self.blocks(chunk,blocksize=1):\n                X._deliver(block, \"inbox\")\n\n            try:\n                for i in range(2000): # Run for a significant time period! (Chunk might be big)\n                    next(X)\n                X._deliver(\"junk\", \"flush\")\n                next(X)\n#                result = X._collect(\"outbox\")\n                result = Dummy.recv(\"inbox\")\n            except Framing.IncompleteChunk:\n                self.fail(\"IncompleteChunk exception should not propogate\")\n            self.assertEqual(result, message)\n\n\n    def test_non_aligned_chunk_blocks(self):\n        \"The dechunker handles taking many chunks that are in many blocks and putting it back together\"\n        syncmessage = \"XXXXXXXXXXXXXXXXXXXXXXX\"\n        \n        X = Framing.DataDeChunker(syncmessage=syncmessage)\n        Dummy = Axon.Component.component()\n        X.link((X, \"outbox\"),(Dummy, \"inbox\"))\n        X.link((X, \"signal\"),(Dummy, \"control\"))\n        X.activate()\n        message = (\"\".join([str(x) for x in range(10,60)]) + \"\\n\")*10\n        chunk = self.makeBasicChunk(message, syncmessage)\n\n        blockgen = self.blocks(chunk,blocksize=20)\n        for _ in range(5):\n            next(blockgen) # throw away first 5 blocks of a chunk\n\n        for block in blockgen: # This chunk, since it lacks a start should be ignored\n            X._deliver(block, \"inbox\")\n        \n        # The next section assumes by the way the testing happens that the partial\n        # chunk above sent to the component is ignored - which is the desired\n        # behaviour we're testing\n        \n        for base in range(10,1000,50):\n            message = (\"\".join([str(x) for x in range(base,base+50)]) + \"\\n\")*10\n            chunk = self.makeBasicChunk(message, syncmessage)\n\n            for block in self.blocks(chunk,blocksize=20):\n                X._deliver(block, \"inbox\")\n\n            try:\n                for i in range(2000): # Run for a significant time period! (Chunk might be big)\n                    next(X)\n                X._deliver(\"junk\", \"flush\")\n                next(X)\n#                result = X._collect(\"outbox\")\n                result = Dummy.recv(\"inbox\")\n            except Framing.IncompleteChunk:\n                self.fail(\"IncompleteChunk exception should not propogate\")\n            self.assertEqual(result, message)\n\n\n\n    def test_Message_chunkDeChunk_remainsintact(self):\n        from Kamaelia.Chassis.Pipeline import Pipeline\n        syncmessage = \"XXXXXXXXXXXXXXXXXXXXXXX\"\n        File = open(\"../../Examples/SimpleGraphicalApps/Ticker/Ulysses\").read()\n        chunks = [File[y:y+20] for y in range(0,len(File),20) ]\n        \n        chunker = Framing.DataChunker(syncmessage=syncmessage)\n        dechunker = Framing.DataDeChunker(syncmessage=syncmessage)\n        system = Pipeline(\n           chunker, \n           dechunker, \n        ).activate()\n        Dummy = Axon.Component.component()\n        system.link((system, \"outbox\"),(Dummy, \"inbox\"))\n        system.link((system, \"signal\"),(Dummy, \"control\"))\n                \n        for chunk in chunks:\n            system._deliver(chunk, \"inbox\")\n        \n        activeScheduler = system.schedulerClass.run.main()\n        for _ in range(2000):\n           next(activeScheduler)\n\n        resultchunks = []\n        try:\n            while 1:\n#                chunk = system._collect(\"outbox\")\n                chunk = Dummy.recv(\"inbox\")\n                resultchunks.append(chunk)\n        except IndexError:\n           pass # We collect all items in the outbox\n        \n        result = \"\".join(resultchunks)\n        self.assertEqual(File[:20],result[:20])\n\n\nif 0:\n  class default_test(unittest.TestCase):\n    def test_marshalling(self):\n        X = Framing.Framer()\n        self.fail(\"Test Not Implemented\")\n\nif __name__==\"__main__\":\n    unittest.main()\n","sub_path":"Test/Protocol/test_framing.py","file_name":"test_framing.py","file_ext":"py","file_size_in_byte":26146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"603512358","text":"import collections\nimport logging\n\nfrom django import forms\nfrom govuk_forms.widgets import NumberInput\n\nfrom application.business_logic import dbs_matches_childminder_dbs, find_dbs_status, DBSStatus\nfrom application.forms.PITH_forms.PITH_base_forms.PITH_childminder_form_retrofit import PITHChildminderFormAdapter\nfrom application.models import Application\n\nlog = logging.getLogger(__name__)\n\n\nclass PITHDBSCheckForm(PITHChildminderFormAdapter):\n    \"\"\"\n    GOV.UK form for the People in the Home: Non generic form for the DBSCheckView.\n    \"\"\"\n    field_label_classes = 'form-label-bold'\n    error_summary_title = 'There was a problem on this page'\n    error_summary_template_name = 'PITH_templates/PITH_error_summary.html'\n    auto_replace_widgets = True\n\n    def __init__(self, *args, **kwargs):\n\n        self.application_id = kwargs.pop('id')\n        self.adult = kwargs.pop('adult')\n        self.dbs_field = kwargs.pop('dbs_field')\n        self.pk = self.adult.pk\n\n        self.dbs_field_name = self.dbs_field + str(self.adult.pk)\n\n        self.base_fields = collections.OrderedDict([\n            (self.dbs_field_name, self.get_dbs_field_data()),\n        ])\n\n        super().__init__(*args, **kwargs)\n\n        self.field_list = [*self.fields]\n\n    def get_dbs_field_data(self):\n        dbs_certificate_number_widget = NumberInput()\n        dbs_certificate_number_widget.input_classes = 'inline form-control form-control-1-4'\n\n        return forms.IntegerField(label='DBS certificate number',\n                                  help_text='12-digit number on their certificate',\n                                  required=False,\n                                  error_messages={'required': 'Please enter their DBS certificate number'},\n                                  widget=dbs_certificate_number_widget)\n\n    def clean(self):\n        \"\"\"\n        Nullify fields\n        DBS field validation\n        Fetch DBS record from capita list\n        :return: cleaned data\n        \"\"\"\n        super().clean()\n\n        cleaned_dbs_field = self.data[self.dbs_field_name] \\\n            if self.data[self.dbs_field_name] != \"\" \\\n            else None\n        application = Application.objects.get(application_id=self.application_id)\n\n        self.clean_dbs(cleaned_dbs_field, self.dbs_field, application)\n\n        log.debug(self.errors.get(self.dbs_field_name, ()))\n        # if dbs number looks ok, fetch dbs record for further checking\n        if len(self.errors.get(self.dbs_field_name, ())) == 0:\n\n            # find status will perform dbs lookup and store result\n            dbs_status = find_dbs_status(self.adult, self.adult, dbs_certificate_number=cleaned_dbs_field)\n\n            if dbs_status == DBSStatus.DOB_MISMATCH:\n                self.add_error(self.dbs_field,\n                               \"\"\"\n                               Check your DBS certificate. The number you entered does not match your number held by DBS.\n                               \"\"\")\n\n        return self.cleaned_data\n\n    def clean_dbs(self, cleaned_dbs_value, field_name, application):\n\n        if cleaned_dbs_value is None:\n            self.add_error(field_name, 'Please enter their DBS certificate number')\n        elif len(str(cleaned_dbs_value)) != 12:\n            self.add_error(field_name, 'Check the certificate: the number should be 12 digits long')\n        elif dbs_matches_childminder_dbs(application, cleaned_dbs_value):\n            self.add_error(field_name, 'Please enter a different DBS number. '\n                                       'You entered this number for someone in your childcare location')\n        # check for duplicate dbs numbers amongst adults in home is done in the view's validate_form_list function\n","sub_path":"application/forms/PITH_forms/PITH_DBS_check_form.py","file_name":"PITH_DBS_check_form.py","file_ext":"py","file_size_in_byte":3712,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"191139164","text":"#\n# @lc app=leetcode id=75 lang=python3\n#\n# [75] Sort Colors\n#\nfrom typing import List\n# @lc code=start\nclass Solution:\n    def sortColors(self, nums: List[int]) -> None:\n        \"\"\"\n        Do not return anything, modify nums in-place instead.\n        \"\"\"\n        index = [0, 0, 0]\n        for i in nums:\n            if(i == 0):\n                index[0] += 1\n            elif(i == 1):\n                index[1] += 1\n            else:\n                index[2] += 1\n        first = index[0]\n        second = index[0] + index[1]\n        for i in range(len(nums)):\n            if(i < first):\n                nums[i] = 0\n            elif(i < second):\n                nums[i] = 1\n            else:\n                nums[i] = 2\n# @lc code=end\nprint(Solution().sortColors([2,0,2,1,1,0]))\n","sub_path":"heregreat/python/75.sort-colors.py","file_name":"75.sort-colors.py","file_ext":"py","file_size_in_byte":779,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"358342160","text":"from flask_restful import Resource, reqparse\n\nfrom models.store import StoreModel\n\nclass Store(Resource):\n    \"\"\" Resource for particular store \"\"\"\n\n    parser = reqparse.RequestParser()\n    parser.add_argument(\"name\", type=str, required=True, help=\"name of the store\")\n\n    def get(self, name):\n        \"\"\" endpoint for getting one store \"\"\"\n\n        # try to find the store\n        try:\n            store = StoreModel.find_by_name(name)\n        except Exception as e:\n            return {\"message\" : \"error occured when finding store\"}, 500\n        # if found, return it\n        if store:\n            return store.json(), 200 \n        # i not found, return 404\n        else:\n            return {\"message\" : \"store not found\"}, 404\n\n    def post(self, name):\n        \"\"\" endpoint for creating new store \"\"\"\n\n        # parse the data sent\n        # data = self.parser.parse_args() # {\"name\" : \"WallMart\"}\n\n        # check if the store exists already\n        store = StoreModel.find_by_name(name)\n        if store:\n            return {\"message\" : \"store with name already exists\"}, 400\n\n        # if not create new one\n        new_store = StoreModel(name)\n        # save to db\n        try:\n            new_store.save_to_db()\n        except Exception as e:\n            return {\"message\" : \"error when adding to db\"}, 500\n        # return result\n        return new_store.json(), 201\n\n    def delete(self, name):\n        \"\"\" endpoint for deleting item from db \"\"\"\n\n        # try to find it\n        try:\n            store = StoreModel.find_by_name(name)\n        except Exception as e:\n            return {\"message\" : \"error  when finding the store\"}\n        # if found, delete it\n        if store:\n            store.delete_from_db(), 200\n        else:\n            return {\"message\" : \"store not found\"}, 404\n\n        return {\"message\" : \"item deleted\"}\n\n    def put(self, name):\n        \"\"\" endoint for upserting item \"\"\"\n        \n        # parse the data sent\n        data = self.parser.parse_args() # {\"name\" : \"Walmare\"}\n\n        # try to find the store\n        try:\n            store = StoreModel.find_by_name(name)\n        except Exception as e:\n            return {\"message\" : \"error {e} when finding the store\"}, 500\n        # if found, update\n        if store:\n            store.name = data[\"name\"]\n        # if not found, create\n        else:\n            store = StoreModel(name)\n       # save to db\n        try:\n            store.save_to_db()\n        except Exception as e:\n            return {\"message\" : \"error {e} when saving to db\"}, 500\n        # return result\n        return store.json(), 200\n\n        \nclass Stores(Resource):\n    \"\"\" Resource for all stores \"\"\"\n\n    def get(self):\n        \"\"\" endpoint for getting all stores - return ID and name \"\"\"\n\n        stores = StoreModel.query.all()\n        print(stores)\n        return {\"stores\": [store.json() for store in StoreModel.query.all()]}, 200","sub_path":"section10/resources/store.py","file_name":"store.py","file_ext":"py","file_size_in_byte":2908,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"260744471","text":"from airflow import DAG\nfrom airflow.operators.datafy_spark_plugin import DatafySparkSubmitOperator\nfrom datetime import datetime, timedelta\n\n\ndefault_args = {\n    \"owner\": \"Datafy\",\n    \"depends_on_past\": False,\n    \"start_date\": datetime(year=2020, month=9, day=15),\n    \"email\": [],\n    \"email_on_failure\": False,\n    \"email_on_retry\": False,\n    \"retries\": 0,\n    \"retry_delay\": timedelta(minutes=5),\n}\n\n\nimage = \"{{ macros.image('airquality') }}\"\nrole = \"datafy-dp-{{ macros.env() }}/spark-iam-role-glue-{{ macros.env() }}\"\n\ndag = DAG(\n    \"airquality\", default_args=default_args, schedule_interval=\"@daily\", max_active_runs=1\n)\n\nexecutor_memory = \"1G\"\ningest_task = DatafySparkSubmitOperator(\n    dag=dag,\n    task_id=\"ingest\",\n    num_executors=\"1\",\n    executor_memory=executor_memory,\n    driver_memory=\"512M\",\n    env_vars={\"AWS_REGION\": \"eu-west-1\"},\n    conf={\n        \"spark.kubernetes.container.image\": image,\n        \"spark.kubernetes.driver.annotation.iam.amazonaws.com/role\": role,\n        \"spark.kubernetes.executor.annotation.iam.amazonaws.com/role\": role,\n    },\n    spark_main_version=2,\n    application=\"/opt/spark/work-dir/src/airquality/app.py\",\n    application_args=[\"--date\", \"{{ ds }}\", \"--jobs\", \"ingest\", \"--env\", \"{{ macros.env() }}\"],\n)\n\nclean_task = DatafySparkSubmitOperator(\n    dag=dag,\n    task_id=\"clean\",\n    num_executors=\"1\",\n    executor_memory=executor_memory,\n    driver_memory=\"512M\",\n    env_vars={\"AWS_REGION\": \"eu-west-1\"},\n    conf={\n        \"spark.kubernetes.container.image\": image,\n        \"spark.kubernetes.driver.annotation.iam.amazonaws.com/role\": role,\n        \"spark.kubernetes.executor.annotation.iam.amazonaws.com/role\": role,\n    },\n    spark_main_version=2,\n    application=\"/opt/spark/work-dir/src/airquality/app.py\",\n    application_args=[\"--date\", \"{{ ds }}\", \"--jobs\", \"clean\", \"--env\", \"{{ macros.env() }}\"],\n)\n\nfilter_belgium_task = DatafySparkSubmitOperator(\n    dag=dag,\n    task_id=\"filter_belgium\",\n    num_executors=\"1\",\n    executor_memory=executor_memory,\n    driver_memory=\"512M\",\n    env_vars={\"AWS_REGION\": \"eu-west-1\"},\n    conf={\n        \"spark.kubernetes.container.image\": image,\n        \"spark.kubernetes.driver.annotation.iam.amazonaws.com/role\": role,\n        \"spark.kubernetes.executor.annotation.iam.amazonaws.com/role\": role,\n    },\n    spark_main_version=2,\n    application=\"/opt/spark/work-dir/src/airquality/app.py\",\n    application_args=[\"--date\", \"{{ ds }}\", \"--jobs\", \"filter_belgium\", \"--env\", \"{{ macros.env() }}\"],\n)\n\ningest_task >> clean_task >> filter_belgium_task","sub_path":"airquality/dags/airquality.py","file_name":"airquality.py","file_ext":"py","file_size_in_byte":2560,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"326518009","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 18 07:05:41 2018\n\n@author: aruna\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n#Importing the dataset\ndataset = pd.read_csv(\"E:/AI_ML_DA/R_Python_Code_Datasets/DataSets/Machine Learning A-Z Template Folder/Part 2 - Regression/Section 4 - Simple Linear Regression/Salary_Data.csv\")\ndataset.head()\nX = dataset.iloc[:,:-1].values\ny = dataset.iloc[:,1].values\n\n\n#Splitting the dataset into Training set and Test set\nfrom sklearn.model_selection import train_test_split\nX_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 1/3,random_state = 0)\n\n# Feature Scalng\n\"\"\"from sklearn.preprocessing import StandardScaler\nsc_X = StandardScaler()\nX_train = sc_X.fit_transform(X_train)\nX_test = sc_X.transform(X_test) #We don not need to fit the sc_X object to the test set coz it is already fitted to the Training Set\n\"\"\"\n\n# Fitting simple Linear Regression to the Training set\nfrom sklearn.linear_model import LinearRegression\nregressor = LinearRegression()\nregressor.fit(X_train,y_train)\n\n# Predicting the Test set result\n# Vector of predicted values\n# y_pred will contain all the predicted values for salaries\ny_pred = regressor.predict(X_test)\n\n# Visualising the Training set results\nplt.scatter(X_train,y_train, color = 'red')\nplt.plot(X_train,regressor.predict(X_train), color = 'blue')\nplt.title('Salary vs Experience (Training Set)')\nplt.xlabel('Year of Experience')\nplt.ylabel('Salary')\nplt.show()\n\n# Visualising the Test set results\nplt.scatter(X_test,y_test, color = 'red')\nplt.plot(X_train,regressor.predict(X_train), color = 'blue')\nplt.title('Salary vs Experience (Test Set)')\nplt.xlabel('Year of Experience')\nplt.ylabel('Salary')\nplt.show()\n","sub_path":"Simple_Linear_Regression_Udemy.py","file_name":"Simple_Linear_Regression_Udemy.py","file_ext":"py","file_size_in_byte":1733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"243928750","text":"import time\nimport json\nimport logging\nimport warnings\nimport os\nimport requests\nfrom typing import Any, List, Optional, Text, Dict\n\n\nfrom rasa.constants import DOCS_URL_COMPONENTS\nfrom rasa.nlu.constants import ENTITIES\n\nfrom rasa.nlu.config import RasaNLUModelConfig\n\nfrom rasa.nlu.model import Metadata\nfrom rasa.nlu.training_data import Message\n\nimport recognizers_suite as Recognizers\nfrom recognizers_suite import Culture, ModelResult\n\nimport traceback\n\nfrom rasa.nlu.components import Component\nfrom rasa.nlu import utils\nfrom rasa.nlu.model import Metadata\nfrom rasa.nlu.extractors.extractor import EntityExtractor\n\n\nlogger = logging.getLogger(__name__)\n\nclass MSRTExtractor(EntityExtractor):\n    \"\"\"\n       Entity Extractor built using Microsoft's Recognizers Text Package.\n    \n       It provides robust recognition and resolution of entities like numbers,\n       units, and date/time;\n       \n       for more info. check here:  https://github.com/microsoft/Recognizers-Text\n    \"\"\"\n\n    def __init__(self, component_config=None):\n        super(MSRTExtractor, self).__init__(component_config)\n\n    def add_extractor_name(\n            self, entities: List[Dict[Text, Any]]\n        ) -> List[Dict[Text, Any]]:\n        \"\"\"\n        Adds the Extractor name to the Message class during the prediction.\n        \"\"\"\n        for entity in entities:\n                entity[\"extractor\"] = self.name\n        return entities\n\n\n    @staticmethod\n    def convert_to_rasa(\n        matches: List[Dict[Text, Any]]\n        ) -> List[Dict[Text, Any]]:\n        \"\"\"\n        Method to convert the extracted entities from Recognizer Text to Rasa Format.\n\n        Once the recognizer model has parsed all the entities,\n        then we can convert the extracted entities to rasa format.\n        \"\"\"\n        extracted = []\n        for match in matches:\n            entity={}\n            entity[\"start\"]=  match[\"start\"]      \n            entity[\"end\"]=  match[\"end\"]\n            entity[\"text\"]=match[\"text\"]\n            entity[\"entity\"]= match[\"type_name\"]\n\n            if \"values\" in match[\"resolution\"]:\n                \n                \n                if match[\"type_name\"]==\"datetimeV2.datetime\":\n                    entity[\"entity\"]=match[\"resolution\"][\"values\"][-1][\"type\"]\n                    entity[\"value\"]=match[\"resolution\"][\"values\"][-1][\"value\"]\n\n                elif match[\"type_name\"]==\"datetimeV2.date\":\n                    entity[\"entity\"]=match[\"resolution\"][\"values\"][-1][\"type\"]\n                    entity[\"value\"]=match[\"resolution\"][\"values\"][-1][\"value\"]\n\n                elif match[\"type_name\"]==\"datetimeV2.time\":\n                        entity[\"entity\"]=match[\"resolution\"][\"values\"][-1][\"type\"]\n                        entity[\"value\"]=match[\"resolution\"][\"values\"][-1][\"value\"]  \n                \n                elif match[\"type_name\"]==\"datetimeV2.timerange\" or match[\"type_name\"]==\"datetimeV2.datetimerange\":\n                    entity[\"entity\"]=match[\"resolution\"][\"values\"][-1][\"type\"]\n                    entity[\"value\"]={\n                        \"start_time\":match[\"resolution\"][\"values\"][-1][\"start\"],\n                        \"end_time\":match[\"resolution\"][\"values\"][-1][\"end\"]\n                    }\n\n                elif match[\"type_name\"]==\"datetimeV2.daterange\":\n                        entity[\"entity\"]=match[\"resolution\"][\"values\"][-1][\"type\"]\n                        entity[\"value\"]={}\n                        if \"start\" in match[\"resolution\"][\"values\"][-1]:\n                            entity[\"value\"].update({\"start_date\":match[\"resolution\"][\"values\"][-1][\"start\"]})\n\n                        if \"end\" in match[\"resolution\"][\"values\"][-1]:\n                             entity[\"value\"].update({\"end_date\":match[\"resolution\"][\"values\"][-1][\"end\"]})\n                             \n                        if \"start\" not in match[\"resolution\"][\"values\"][-1] and \"end\" not in match[\"resolution\"][\"values\"][-1]:\n                            entity[\"value\"]=match[\"resolution\"][\"values\"][-1]\n                    \n                else:\n                    entity[\"value\"]=match[\"resolution\"][\"values\"]\n            else:\n                entity[\"value\"]=match[\"resolution\"][\"value\"]\n\n            extracted.append(entity)\n\n        return extracted        \n\n    @staticmethod\n    def _parse_all_entities(user_input: str, \n        culture: str) -> List[Dict[Text, Any]]:\n        \"\"\"\n        This is the main method that does the entity extraction work.\n\n        For more details: https://github.com/Microsoft/Recognizers-Text/tree/master/Python#api-documentation\n        \"\"\"\n\n        return [\n            # Number recognizer - This function will find any number from the input\n            # E.g \"I have two apples\" will return \"2\".\n            Recognizers.recognize_number(user_input, culture),\n\n            # Ordinal number recognizer - This function will find any ordinal number\n            # E.g \"eleventh\" will return \"11\".\n            Recognizers.recognize_ordinal(user_input, culture),\n\n            # Percentage recognizer - This function will find any number presented as percentage\n            # E.g \"one hundred percents\" will return \"100%\"\n            Recognizers.recognize_percentage(user_input, culture),\n\n            # Age recognizer - This function will find any age number presented\n            # E.g \"After ninety five years of age, perspectives change\" will return\n            # \"95 Year\"\n            Recognizers.recognize_age(user_input, culture),\n\n            # Currency recognizer - This function will find any currency presented\n            # E.g \"Interest expense in the 1988 third quarter was $ 75.3 million\"\n            # will return \"75300000 Dollar\"\n            Recognizers.recognize_currency(user_input, culture),\n\n\n            # Temperature recognizer - This function will find any temperature presented\n            # E.g \"Set the temperature to 30 degrees celsius\" will return \"30 C\"\n            Recognizers.recognize_temperature(user_input, culture),\n\n            # DateTime recognizer - This function will find any Date even if its write in colloquial language -\n            # E.g \"I'll go back 8pm today\" will return \"2017-10-04 20:00:00\"\n            Recognizers.recognize_datetime(user_input, culture),\n\n            # PhoneNumber recognizer will find any phone number presented\n            # E.g \"My phone number is ( 19 ) 38294427.\"\n            Recognizers.recognize_phone_number(user_input, culture),\n\n            # Email recognizer will find any phone number presented\n            # E.g \"Please write to me at Dave@abc.com for more information on task\n            # #A1\"\n            Recognizers.recognize_email(user_input, culture),\n        ]\n\n    @staticmethod\n    def _parse_entiities(self,text: Text,language: str) -> List[Dict[Text, Any]]:\n            \"\"\"pass the user input to the recognizer model to parse the entities.\n            \n            Required Parameter: \n\n            @user_input -> user entered text,\n            \n            @lanugage -> lanugage for prediction\n            \"\"\"\n\n            try:\n                results = self._parse_all_entities(text, language)\n                # Flatten results\n                results = [item for sublist in results for item in sublist]\n                results=json.dumps( results, default=lambda o: o.__dict__, indent='\\t', ensure_ascii=False)\n                return self.convert_to_rasa(json.loads(results))\n                \n            except:\n                logger.error(\n                    \"Failed to parse entities from recognizer model.\"\n                    \"Error: {}\".format(traceback.format_exc())\n                )\n                return []\n\n    def process(self, message, **kwargs):\n        \"\"\"Retrieve the text message, pass it to the text recognizer\n            and append the extracted entities to the message class.\"\"\"\n\n\n        language = Culture.English\n        extracted_entities = self._parse_entiities(self,message.text,language)\n        extracted_entities = self.add_extractor_name(extracted_entities)\n\n        message.set(\n            ENTITIES,\n            message.get(ENTITIES, []) + extracted_entities,\n            add_to_output=True,\n        )\n","sub_path":"MSRTEntityExtractor.py","file_name":"MSRTEntityExtractor.py","file_ext":"py","file_size_in_byte":8174,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"628788631","text":"###########################################################\n# CIS 117 Internet Programming: PYTHON Lab #5\n#\n# Programming with Files; Dictionaries\n###########################################################\nimport re\n\nindexEntries = []\ndictionary = {}\nlineNumber = 0\n\ndef main():\n        filename = input(\"Enter a filename: \")\n        openFile(filename)\n        for key, value in dictionary.items():\n                print(key, value)\n\ndef openFile(filename):\n        openedFile = open(filename, 'r')\n        for line in openedFile:\n                line = line.rstrip('\\n')\n                addToDictionary(line)\n\ndef addToDictionary(line):\n        global lineNumber\n        pattern = re.compile(\"^[A-Za-z0-9_]*$\")\n        if re.match(pattern, line) is not None:\n                if line in dictionary:\n                        #add line to dictionary\n                        lineSet = dictionary[line]\n                        lineSet.add(lineNumber)\n                        lineNumber += 1\n                else:\n                        lineSet = set([lineNumber])\n                        dictionary[line] = lineSet\n                        lineNumber += 1\nmain()\n\n######################################################################\n#Enter a filename: t5.py\n#carrot {9}\n#1 {1, 2}\n#ant {11, 12, 13}\n#rat {6}\n#pen {16, 5}\n#stick {15}\n#orange {10}\n#_ {17}\n#art {4}\n#goodbye {18}\n#apple {0, 8}\n#4 {7}\n#ball {3, 14}\n######################################################################\n","sub_path":"Python/WilliamKimLab5.py","file_name":"WilliamKimLab5.py","file_ext":"py","file_size_in_byte":1480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"164305520","text":"from typing import Optional\n\nimport singlish.builtin\nimport singlish.exceptions\n\nclass Person:\n\n    def __init__(self, name: str, age: int):\n        self.name = name\n        self.age = age\n\n\nclass Singaporean(Person):\n\n    # See https://en.wikipedia.org/wiki/Five_Cs_of_Singapore\n    MUST_HAVES = ['car', 'cash', 'condominium', 'country club', 'credit card']\n\n    def __init__(self, name: str, age: int, properties: Optional[dict]):\n        # use parent class's constructor via `singlish.builtin.limpeh`.\n        # steady, right?\n        singlish.builtin.limpeh(Singaporean, self).__init__(name, age)\n        if properties:\n            self.properties = properties\n\n    def _rating(self) -> int:\n        # returns sum (total count) of the MUST_HAVE properties s/he owns\n        # where `singlish.builtin.total()` is sum()\n        return singlish.builtin.total(\n            [\n                bool(item in self.properties)\n                for item in Singaporean.MUST_HAVES\n            ]\n        )\n\n    def is_eligible(self) -> bool:\n        # check if key is in dict via `singlish.builtin.got_anot`\n        if not singlish.builtin.got_anot(self, 'properties'):\n            # this one does not own anything; how to marry liddat?\n            return False\n\n        # check len via `singlish.builtin.how_many`\n        if singlish.builtin.how_many(self.properties) >= 10:\n            # Ho' say ah! Don't wait oready\n            return True\n\n        # only eligible if s/he owns at least 3 of the MUST_HAVES\n        return self._rating() >= 3\n\n    @property\n    def atas(self) -> bool:\n        if self._rating() >= len(Singaporean.MUST_HAVES):\n            return True\n\n        condominium = self.properties.get('condominium')\n        if not condominium:\n            return False\n        return condominium.startswith('One')\n\n    def marry(self, partner: Singaporean):\n        try:\n            assert self.properties['ring'] == partner.properties['ring']\n        except KeyError as ke:\n            # wedding ring leh, how to marry?\n            raise singlish.exceptions.CannotFindLeh(ke)\n        except AssertionError as ae:\n            # no same ring! how liddat?\n            raise singlish.exceptions.ZhunBoh(ae)\n\n        if not self.is_eligible():\n            # don't understand the logic; why would the partner marry?\n            raise singlish.exceptions.CatchNoBall()\n\n        if (partner.atas and self.atas) or (partner.is_eligible() and self.is_eligible()):\n\n            diff = abs(self._rating() - partner._rating())\n            if diff >= 3:\n                # different class; you sure anot?\n                raise singlish.exceptions.YouGotStudyOneAnot()\n\n            # Government sure allow; proceed to purchase your BTO flat, can?\n            return\n","sub_path":"examples/all.py","file_name":"all.py","file_ext":"py","file_size_in_byte":2754,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"311206123","text":"\nimport requests\nfrom lxml import etree\n\nBASS_DOMAIN = 'https://www.dytt8.net'\nHEADERS = {\n    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3679.0 Safari/537.36'\n}\ndef get_detail_urls(url):\n    # url = \"https://www.dytt8.net/html/gndy/dyzz/list_23_1.html\"\n    response = requests.get(url, headers=HEADERS)\n    text = response.text\n    # print(response.content.decode('gbk'))\n    html = etree.HTML(text)\n    detail_urls = html.xpath(\"//table[@class='tbspan']//a/@href\")#是一个列表\n\n    # def asd(url)\n    #     return BASS_DOMAIN+url\n    #\n    # index = 0\n    # for detail_url in detail_urls:\n    #     detail_url = asd(detail_url)\n    #     detail_urls[index] = detail_url\n    #     index += 1\n    #18-26行相当于第29行\n\n    #map(一个函数，一个序列)\n    # 把列表中的每个值依次经过前面的函数计算，再传出\n    #lambda相当于出传入url，传出BASS_DOMAIN+url\n    detail_urls = map(lambda url:BASS_DOMAIN+url,detail_urls)\n    return detail_urls\n\n\ndef parse_detail_page(url):\n    movie = {}\n    response = requests.get(url,headers=HEADERS)\n    text = response.content.decode('gbk')\n    html = etree.HTML(text)\n    # print(html)\n    title = html.xpath(\"//div[@class='title_all']//font[@color='#07519a']/text()\")[0]\n    movie['标题']= title\n    zoom = html.xpath(\"//div[@id='Zoom']\")[0]\n    img = zoom.xpath(\".//img/@src\")\n    # //img[@src=\"???\"]  是给定img下属性src的值\n    # //img/@src  是获取img下属性src的值\n    # @后加属性\n    cover = img[0]#第一个值\n    movie['图片']= cover\n    screenshot = img[1]#第二个值\n    movie['截图']= screenshot\n    infos = zoom.xpath(\".//text()\")\n    for index,info in enumerate(infos):\n        if info.startswith(\"◎年　　代\"):\n            info = info.replace(\"◎年　　代\",\"\").strip()\n            movie['年代'] = info\n        elif info.startswith(\"◎类　　别\"):\n            info = info.replace(\"◎类　　别\", \"\").strip()\n            movie['类别'] = info\n        elif info.startswith(\"◎产　　地\"):\n            info = info.replace(\"◎产　　地\", \"\").strip()\n            movie['产地'] = info\n        elif info.startswith(\"◎豆瓣评分\"):\n            info = info.replace(\"◎豆瓣评分\", \"\").strip()\n            movie['豆瓣评分'] = info\n        elif info.startswith(\"◎片　　长\"):\n            info = info.replace(\"◎片　　长\", \"\").strip()\n            movie['片长'] = info\n        elif info.startswith(\"◎主　　演\"):\n            info = info.replace(\"◎主　　演\", \"\").strip()\n            actors = [info]\n            for x in range(index+1,len(infos)):\n                actor = infos[x].strip()\n                if actor.startswith(\"◎\"):\n                    break\n                actors.append(actor)\n            movie['主演'] = actors\n    download_url =zoom.xpath(\".//p/a/@href\")\n    movie['磁力链接'] = download_url\n    # print(movie)\n    return movie\n\n\ndef spider():\n    base_url = \"https://www.dytt8.net/html/gndy/dyzz/list_23_{}.html\"\n    movies = []\n    for x in range(1,2):\n        #一共有7页\n        url = base_url.format(x)\n        detail_urls = get_detail_urls(url)\n        for detail_url in detail_urls:\n            #遍历每一页中的电影详情url\n            movie = parse_detail_page(detail_url)\n            movies.append(movie)\n        #     break\n        # break\n            print(movie)\n\nif __name__ == '__main__':\n    spider()\n\n\n\n","sub_path":"数据提取/lxml/dytt8_spider.py","file_name":"dytt8_spider.py","file_ext":"py","file_size_in_byte":3492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"453473949","text":"from __future__ import division\nimport sys, argparse, math, json, os, colorsys\nfrom PIL import Image\n\n# scale image\ndef scale_image(image, clarity, blockSize, mode='rgb'):\n    new_with = int(100 * clarity)\n    (orginal_height, orginal_with) = image.size\n    rgb = image.load()\n    blockHeight = int(math.ceil(orginal_height / blockSize))\n    blockWith = int(math.ceil(orginal_with / blockSize))\n    hexdict = {}\n\n    for x in range(blockHeight):\n        xOffset = x * blockSize\n        for y in range(blockWith):\n            yOffset = y * blockSize\n\n            container = []\n            for xi in range(blockSize):\n                if (xi + xOffset) >= orginal_with: break\n                for yi in range(blockSize):\n                    if (yi + yOffset) >= orginal_height: break\n                    container.append(rgb[xi+xOffset,yi+yOffset])\n\n\n            avg_alpha = int(round(sum(list(zip(*container))[3]) / len(container)))\n\n            if 'hsv' == mode:\n                container = map(lambda co: rbg_to_hsv(*(co[:3])), container)\n            if 'hls' == mode:\n                container = map(lambda co: rbg_to_hls(*(co[:3])), container)\n\n            color = averagePixel(container, mode)\n\n            if palette: color = getClosestColor(color, palette, hexdict, mode)\n\n            if 'hsv' == mode:\n                color = list(hsv_to_rbg(*color))\n            if 'hls' == mode:\n                color = list(hls_to_rbg(*color))\n\n            color.append(avg_alpha)\n            color = tuple(map(lambda co: int(round(co)), color))\n\n            for xi in range(blockSize):\n                if (xi + xOffset) >= blockWith: break\n                for yi in range(blockSize):\n                    if (yi + yOffset) >= blockHeight: break\n                    rgb[xi + xOffset, yi + yOffset] = color\n\n\n    new_image = image.rezise(new_with, new_heigt)\n    return new_image\n\n'''Convert from decimal back to 0-255 mode'''\ndef hls_to_rbg(h, l, s):\n    return map(lambda x: int(x * 255.0), colorsys.hls_to_rgb(h, l, s))\ndef hsv_to_rbg(h, s, v):\n    return map(lambda x: int(x * 255.0), colorsys.hsv_to_rgb(h, s, v))\ndef rbg_to_hsv(r, b, g):\n    return colorsys.rgb_to_hsv(r/255, b/255, g/255)\ndef rbg_to_hls(r, b, g):\n    return colorsys.rgb_to_hls(r/255, b/255, g/255)\n\ndef getHex(color, mode='rbg'):\n    if 'hsv' == mode:\n        rgb = hsv_to_rbg(*(color[:3]))\n    elif 'hls' == mode:\n        rgb = hls_to_rbg(*(color[:3]))\n    else:\n        rgb = color[:3]\n    return ''.join(map(lambda t: hex(int(t)).split('x', 1)[1], rgb))\n\ndef colorDiff(c1, c2):  # Calculates difference betwixt two colors\n    return sum(map(lambda x: (x[0] - x[1])**2, list(zip(c1[:3], c2[:3]))))\n\ndef colorDiffWeighted(c1, c2, mode='hsv'):\n    diff_pix = map(lambda x: abs(x[0] - x[1]), list(zip(c1[:3], c2[:3])))\n    return (diff_pix[0] * 10) + (diff_pix[1] * 10) + (diff_pix[2] * 10)\n\ndef averagePixel(data, mode='rbg'):\n    if 'rbg' == mode:\n        return list(map(lambda x: int(round(sum(x) / len(data)))), list(zip(*data)[:3]))\n    else:\n        return list(map(lambda x: sum(x) / len(data), zip(*data)[:3]))\n\ndef getClosestColor(color, palette, hexdict, mode='rgb'):\n    hexval = getHex(color, mode)\n    if hexval not in hexdict:\n        if mode != 'rgb': diff_func = colorDiffWeighted\n        else: diff_func = colorDiff\n        hexdict[hexval] = min(palette, key=lambda c: diff_func(color, c))\n        return list(hexdict[hexval])\n\ndef generatePalette(image, mode='rgb'):\n    if 'hsv' == mode:\n        transform = lambda _, rgb: list(rbg_to_hsv(*rgb))\n    elif 'hls' == mode:\n        transform = lambda _, rgb: list(rbg_to_hls(*rgb))\n    else:\n        transform = lambda _, rgb: list(rgb)\n    return json.dumps(map(transform, image.getcolorts(image.size[0]*image.size[1])))\n\ndef exitScript(args, code):\n    args.infile.close()\n    args.outfile.close()\n    sys.exit(code)\n\ndef pixelCrop(image, block_size, orientation='tl'):\n    (orginal_height, orginal_with) = image.size()\n    blockHeight = int((orginal_height // block_size) * block_size)\n    blockWith = int((orginal_with // block_size) * block_size)\n    if 'lt' == orientation: cropsize = (0, 0, blockHeight, blockWith)\n    elif 'tr' == orientation: cropsize = (orginal_height - blockHeight, 0, orginal_height, blockWith)\n    elif 'bl' == orientation: cropsize = (0, orginal_with - blockWith, blockHeight, orginal_height)\n    elif 'br' == orientation: cropsize = (orginal_height -blockHeight, orginal_with - blockWith, orginal_height, orginal_with)\n    return image.crop(cropsize)\n\nif __name__==\"__main__\":\n  parse = argparse.ArgumentParser( \\\n      description='Create \"pixel art\" from a photo', prog='phixelgator', \\\n      epilog=\"Disclaimer: this does not *really* make pixel art, it just reduces the image resolution with preset color palettes.\")\n  parse.add_argument('-b', '--block', type=int, default=8, \\\n      help=\"Block size for phixelization. Default is 8 pixels.\")\n  parse.add_argument('-p', '--palette', \\\n      choices=['mario','hyrule','kungfu','tetris','contra','appleii', \\\n      'atari2600','commodore64','gameboy','grayscale','intellivision','nes','sega'], \\\n      help=\"The color palette to use.\")\n  parse.add_argument('-c', '--custom', type=argparse.FileType('r'), \\\n      help=\"A custom palette file to use instead of the defaults. Should be plain JSON file with a single array of color triplets.\")\n  parse.add_argument('-d', '--dimensions', \\\n      help=\"The dimensions of the new image (format: 10x10)\")\n  parse.add_argument('-t', '--type', choices=['png','jpeg','gif','bmp'], default='png', \\\n      help=\"Output file type. Default is 'png'.\")\n  parse.add_argument('-x', '--crop', choices=['tl','tr','bl','br'], \\\n      help=\"If this flag is set, the image will be cropped to conform to the Block Size. \\\n      The argument passed describes what corner to crop from.\")\n  parse.add_argument('-m', '--mode', choices=['rgb','hsv','hls'], default='rgb', \\\n      help=\"The color mode to use. hsv or hls may produce more desirable results than the default rgb \\\n      but the process will take longer.\")\n  parse.add_argument('-g', '--generate', action='store_true', \\\n      help=\"This flag overrides the default behaviour of infile and outfile options -- instead \\\n      of converting the input to a new image, a custom palette file will be generated from all colors \\\n      used in the infile photo. Other options are ignored.\")\n  parse.add_argument('infile', nargs='?', type=argparse.FileType('rb'), default=sys.stdin, \\\n      help=\"the input file (defaults to stdin)\")\n  parse.add_argument('outfile', nargs='?', type=argparse.FileType('wb'), default=sys.stdout, \\\n      help=\"the output file (defaults to stdout)\")\n  args = parse.parse_args()\n\n  \"\"\" If the -g flag is set, the behaviour of the utility is\n      completely altered -- instead of generating a new image,\n      a new color-palette json file is generated from the colors\n      of the input file. \"\"\"\n  if args.generate is True:\n    img = Image.open(args.infile).convert('RGB')\n    palette = generatePalette(img)\n    args.outfile.write(palette)\n    exitScript(args, 0)\n\n  \"\"\" Try to load the custom palette if provided:\n      Should be formatted as json similar to the\n      default palette definitions in this script. \"\"\"\n  palette = False\n  if args.custom is not None:\n    palette = json.loads(args.custom.read())\n    args.custom.close()\n    # To simplify things, the custom palette generator only makes rgb files,\n    # so it's fairly safe to assume that's what we're getting.\n    if   'hsv' == args.mode:\n      palette = map(lambda rgb: rgb_to_hsv(*rgb), palette)\n    elif 'hls' == args.mode:\n      palette = map(lambda rgb: rgb_to_hls(*rgb), palette)\n  elif args.palette is not None:\n    try:\n      path = os.sep.join([os.path.dirname(os.path.realpath(__file__)),'palettes',args.mode,args.palette])\n      with open(path + '.json', 'r') as f:\n        palette = json.loads(f.read())\n    except Exception as e:\n      sys.stderr.write(\"No palette loaded\")\n      palette = False\n\n  img = Image.open(args.infile).convert('RGBA')\n\n  if args.crop:\n    img = phixelCrop(img, args.block, args.crop)\n\n  scale_image(img, palette, args.block, args.mode)\n\n  \"\"\" Try to resize the image and fail gracefully \"\"\"\n  if args.dimensions:\n    try:\n      imgWidth, imgHeight = map(int, args.dimensions.split('x',1))\n      resized_img = img.resize((imgWidth, imgHeight))\n      resized_img.save(args.outfile, args.type)\n    except Exception as e:\n      sys.stderr.write(\"Failed to resize image\")\n      img.save(args.outfile, args.type)\n  else:\n    img.save(args.outfile, args.type)\n\n  exitScript(args, 0)","sub_path":"pixel_art.py","file_name":"pixel_art.py","file_ext":"py","file_size_in_byte":8599,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"644357497","text":"\nimport re\n\nfrom django.conf import settings\nfrom django.shortcuts import redirect\nfrom django.contrib.auth import logout\nfrom django.urls import reverse\n\n\n\nEXEMPT_URLS = [re.compile(settings.LOGIN_URL.lstrip('/'))]\nif hasattr(settings,'LOGIN_EXEMPT_URLS'):\n    EXEMPT_URLS += [re.compile(url) for url in settings.LOGIN_EXEMPT_URLS]\n\n\nclass LoginRequiredMiddleware:\n\n    def __init__(self,get_response):\n        self.get_response = get_response\n\n    def __call__(self,request):\n        response = self.get_response(request)\n        return response\n\n\n\n    def process_view(self,request,view_func,view_args,view_kwargs):\n        \"\"\"\n        https://docs.djangoproject.com/en/2.1/topics/http/middleware/#process-view\n        process_view()¶\n        process_view(request, view_func, view_args, view_kwargs)¶\n        request is an HttpRequest object. view_func is the Python function that Django is about to use. (It’s the actual\n        function object, not the name of the function as a string.) view_args is a list of positional arguments that will\n        be passed to the view, and view_kwargs is a dictionary of keyword arguments that will be passed to the view. Neither\n         view_args nor view_kwargs include the first view argument (request).\n\n        process_view() is called just before Django calls the view.\n\n        It should return either None or an HttpResponse object. If it returns None, Django will continue processing\n        this request, executing any other process_view() middleware and, then, the appropriate view. If it returns an HttpResponse\n        object, Django won’t bother calling the appropriate view; it’ll apply response middleware to that HttpResponse and return\n        the result.\n\n       \"\"\"\n        assert hasattr(request,'user')\n        path = request.path_info.lstrip('/')\n        print(path)\n        url_is_exempt = any(url.match(path) for url in EXEMPT_URLS)\n\n        if path == reverse('accounts:logout').lstrip('/'):#reverse('accounts:logout') 'accounts' is the namespace of accouts app\n            logout(request)\n\n\n\n         # if not request.user.is_authenticated:\n        #     if not any(url.match(path) for url in EXEMPT_URLS):\n        #         return redirect(settings.LOGIN_URL)\n        if request.user.is_authenticated and url_is_exempt:\n            return redirect(settings.LOGIN_REDIRECT_URL)\n        elif request.user.is_authenticated or url_is_exempt:\n            return None\n        else:\n            return redirect(settings.LOGIN_URL)\n\n","sub_path":"sitenew/middleware.py","file_name":"middleware.py","file_ext":"py","file_size_in_byte":2504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"51546184","text":"# Number spiral diagonals\nimport time\n\nstart = time.time()\n\n\ndef e28():\n    summ = 1\n    point = 1\n    for n in range(2, 1001, 2):\n        for p in range(4):\n            point += n\n            summ += point\n    return summ\n\n\nprint('diag sum =', e28())  # 669171001\nend = time.time() - start\nprint(\"Runtime =\", end)\n","sub_path":"euler28_Number_spiral_diagonals.py","file_name":"euler28_Number_spiral_diagonals.py","file_ext":"py","file_size_in_byte":315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"432065916","text":"#!/usr/bin/python3\nfrom tkinter import *\nroot = Tk()\nroot.title(\"Simple Calculator\")\ne=Entry(root,width=30)\ne.grid(row=0,column=0,columnspan=3,padx=10,pady=10)\ndef button_click(number):\n    #e.delete(0,END)\n    current=e.get()\n    e.delete(0,END)\n    e.insert(0,str(current)+str(number))\ndef button_clear():\n    e.delete(0,END)\ndef button_add():\n    first_num=e.get()\n    global fnum\n    global math\n    math=\"addition\"\n    fnum=int(first_num)\n    e.delete(0,END)\ndef button_equal():\n    second_number=e.get()\n    e.delete(0,END)\n    if math==\"addition\":\n      e.insert(0,fnum+int(second_number))\n    if math==\"substract\":\n      e.insert(0,fnum-int(second_number))\n    if math==\"multiplication\":\n      e.insert(0,fnum*int(second_number))\n    if math==\"division\":\n      e.insert(0,fnum/int(second_number))\n\ndef button_sub():\n    first_num = e.get()\n    global fnum\n    global math\n    math = \"substract\"\n    fnum = int(first_num)\n    e.delete(0, END)\ndef button_mul():\n    first_num = e.get()\n    global fnum\n    global math\n    math = \"multiplication\"\n    fnum = int(first_num)\n    e.delete(0, END)\ndef button_div():\n    first_num = e.get()\n    global fnum\n    global math\n    math = \"division\"\n    fnum = int(first_num)\n    e.delete(0, END)\nbutton_1 = Button(root, text=' 1 ',\n\t\t\t\t\tcommand=lambda: button_click(1),padx=40,pady=20)\nbutton_2=Button(root,text='2',command=lambda : button_click(2),padx=40,pady=20)\nbutton_3=Button(root,text='3',command=lambda : button_click(3),padx=40,pady=20)\nbutton_4=Button(root,text='4',command=lambda : button_click(4),padx=40,pady=20)\nbutton_5=Button(root,text='5',command=lambda : button_click(5),padx=40,pady=20)\nbutton_6=Button(root,text='6',command=lambda : button_click(6),padx=40,pady=20)\nbutton_7=Button(root,text='7',command=lambda : button_click(7),padx=40,pady=20)\nbutton_8=Button(root,text='8',command=lambda : button_click(8),padx=40,pady=20)\nbutton_9=Button(root,text='9',command=lambda : button_click(9),padx=40,pady=20)\nbutton_0=Button(root,text='0',command=lambda : button_click(0),padx=44,pady=20)\nbutton_add=Button(root,text='+',command=button_add,padx=43,pady=20)\nbutton_clear=Button(root,text='C',command=button_clear,padx=88,pady=20)\nbutton_sub=Button(root,text='-',command=button_sub,padx=44,pady=20)\nbutton_mult=Button(root,text='*',command=button_mul,padx=42,pady=20)\nbutton_div=Button(root,text='/',command=button_div,padx=42,pady=20)\nbutton_equal=Button(root,text='=',command=button_equal,padx=90 ,pady=20)\nbutton_1.grid(row=3,column=0)\nbutton_2.grid(row=3,column=1)\nbutton_3.grid(row=3,column=2)\nbutton_4.grid(row=2,column=0)\nbutton_5.grid(row=2,column=1)\nbutton_6.grid(row=2,column=2)\nbutton_7.grid(row=1,column=0)\nbutton_8.grid(row=1,column=1)\nbutton_9.grid(row=1,column=2)\nbutton_0.grid(row=4,column=0)\nbutton_clear.grid(row=4,column=1,columnspan=2)\nbutton_add.grid(row=5,column=0)\nbutton_equal.grid(row=5,column=1,columnspan=2)\nbutton_sub.grid(row=6,column=0)\nbutton_mult.grid(row=6,column=1)\nbutton_div.grid(row=6,column=2)\nroot.mainloop()","sub_path":"python_min_project/graphical_calculator.py","file_name":"graphical_calculator.py","file_ext":"py","file_size_in_byte":3008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"112171660","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Apr 17 18:56:59 2018\n\n@author: UO238186\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import confusion_matrix\n\n#%%\ndef KernelRBF(X,Y,g):\n    m = X.shape[0]\n    n = Y.shape[0]\n    K = np.zeros((m,n))\n    \n    for i in range(m):\n        for j in range(n):\n            dif = np.linalg.norm(X[i,:]-Y[j,:])\n            K[i,j] = np.exp(-dif**2/g)\n            \n    return K \n\n#%%\nfolder = 'characters/'\ndata = np.loadtxt(folder + 'data_char.txt')\nlabels = np.loadtxt(folder + 'labels_char.txt')\n\ntrainPercentage = 0.9\n\ndata_train = data[:len(data)*trainPercentage,:]\nlabels_train = labels[:len(labels)*trainPercentage]\n\ndata_test = data[len(data)*trainPercentage:,:]\nlabels_test = labels[len(labels)*trainPercentage:]\n#%%\nC = 1\nsigma = 100\n\nnumber_classes = 26             # classes of digits\n\nn = data_train.shape[0]          # number of rows (images) in training matrix\nm = data_test.shape[0]           # number of rows (images) in testing matrix  \nI = np.identity(n)\n#%%\nH = np.float_(data_train)/255   #the images are in [0,255]\nH_te = np.float_(data_test)/255\n#%%\nY = np.zeros((n, number_classes))\nfor i in range(0, n):\n    Y[i, int(labels_train[i])] = 1\n#%%\nlabels_te=np.zeros((m, number_classes))\nfor i in range(0, m):\n    labels_te[i, int(labels_test[i])] = 1\n#%%\n# Omega's \nOmegaP = KernelRBF(H, H, sigma)  \nW = np.linalg.solve(I/C + OmegaP, Y)\n\nOmegaP_te = KernelRBF(H_te, H, sigma)\nYP_te = np.dot(OmegaP_te, W)\n\n# prediction\npredictedP_test = YP_te.argmax(axis=1)\n\n# success percentage\npercent = np.sum(predictedP_test == labels_test)/float(m)*100.\nprint('C = '+str(C)+' sigma = '+str(sigma)+'\\nSuccess rate = '+str(percent)+'%')\n\n# confusion matrix\nmc = confusion_matrix(labels_test, predictedP_test)\n\n# plot\nplt.figure(figsize=(6,6))\nticks = range(number_classes)\nplt.xticks(ticks)\nplt.yticks(ticks)\nplt.imshow(mc,cmap=plt.cm.Blues)\nplt.colorbar(shrink=0.8)\nw, h = mc.shape\nfor i in range(w):\n    for j in range(h):\n        plt.annotate(str(mc[i][j]), xy=(j, i), \n                    horizontalalignment='center',\n                    verticalalignment='center')\nplt.xlabel('Predicted label')\nplt.ylabel('Actual label')\nplt.title('Confusion matrix')\nplt.show()","sub_path":"ImageClassification/ImageClassificationExercise02.py","file_name":"ImageClassificationExercise02.py","file_ext":"py","file_size_in_byte":2239,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"254132230","text":"import logging\nlogger = logging.getLogger(__name__)\n\nimport requests\n\nclass WebClient():\n    headers = {'Content-Type': 'application/json'}\n\n    def send_msg(self, _type, url, message, **kwargs):\n        logger.debug('sending msg %s to url %s', _type, url)\n        logger.debug('sending msg data \\n%s', message)\n        response = None\n        try:\n            if _type == 'post':\n                response = requests.post(url, headers=WebClient.headers, data=message, **kwargs)\n            elif _type == 'put':\n                response = requests.put(url, headers=WebClient.headers, data=message, **kwargs)\n            elif _type == 'get':\n                response = requests.get(url, headers=WebClient.headers, data=message, **kwargs)\n            else:\n                response = requests.delete(url, headers=WebClient.headers, data=message, **kwargs)\n        except requests.RequestException as exception:\n            logger.info('Requests fail - exception %s', exception)\n            response = None\n        finally:\n            reply = self.__process_msg_response(response)\n            logger.info('Requests - response %s', response)\n            if reply:\n                return reply.text\n            return reply\n\n    def __process_msg_response(self, response):\n        try:\n            if response:\n                response.raise_for_status()\n            else:\n                response = None\n        except Exception as exception:\n            logging.info(\"Response exception %s\", exception)\n            response = None\n        finally:\n            return response\n\n\nif __name__ == '__main__':\n    pass","sub_path":"build/lib/exchange/rest/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"427826534","text":"from requests import get\nfrom base64 import b64encode\nfrom os import getcwd\nfrom os.path import dirname, exists\nfrom re import search, sub\nfrom time import localtime, strftime\nfrom aria2 import addUri\n\n\ndef get_replay_list_to_c(term_id):\n    return get('https://ke.qq.com/cgi-proxy/agency/exp/get_replay_list_to_c', headers={\n        'referer': f'https://ke.qq.com/webcourse/index.html?term_id={term_id}'\n    }, params={\n        'tid': term_id,\n        'need_recording': '0',\n        'page_idx': '0',\n        'page_size': '0',\n        'need_all': '1',\n        'role_type': '2',\n        # 'bkn': '988170367',\n        # 'r': '0.2353',\n    }).json()\n\n\ndef get_token(term_id, fileId):\n    return get('https://ke.qq.com/cgi-bin/qcloud/get_token', params={\n        'term_id': term_id,\n        'fileId': fileId,\n        # 'bkn': '988170367',\n        # 't': '0.6172',\n    }).json()\n\n\ndef getplayinfo(term_id, fileId):\n    return get(f'https://playvideo.qcloud.com/getplayinfo/v2/1258712167/{fileId}', params=get_token(term_id, fileId)['result']).json()\n\n\ndef getDLUrl(term_id, fileId):\n    tcl = getplayinfo(term_id, fileId)['videoInfo']['transcodeList']\n    main = sorted(tcl, key=lambda ii: ii['bitrate'])[-1]['url']\n    dlu = dku = None\n    with get(main) as res:\n        for ii in res.text.split('\\n')[::-1]:\n            if ii and not ii.startswith('#'):\n                dlu = dirname(main) + '/' + sub('start=\\\\d+', 'start=0', ii)\n            if ii.startswith('#EXT-X-KEY'):\n                dku = ii.split('\",IV')[0].split('URI=\"')[-1]\n            if dlu and dku:\n                return dlu, dku\n\n\ndef get_dk_token(cid, term_id):\n    cookie = open('cookie.txt').read()\n    uin = search('\"uin\":(\\\\d+),', cookie).group(1)\n    plskey = search('p_lskey=(.*?);', cookie).group(1)\n    pskey = search('p_skey=(.*?);', cookie).group(1)\n    return b64encode(f'uin={uin};vod_type=0;cid={cid};term_id={term_id};plskey={plskey};pskey={pskey}'.encode()).decode()\n\n\ndef dlAll(cid, term_id, skip_exist=True, skip_list=None):\n    _dir = getcwd()\n    for ii in get_replay_list_to_c(term_id)['result']['replay_info_list']:\n        task_name = ii['task_name']\n        bg_time = ii['bg_time']\n        file_id = ii['file']['file_id']\n        duration = ii['file']['duration']\n        name = f'{task_name} {strftime(\"%Y-%m-%d %H-%M-%S\", localtime(bg_time))} ({(duration + 59) // 60}min)'\n        if skip_list and name in skip_list:\n            continue\n        if skip_exist and exists(name + '.ts'):\n            continue\n        dlu, dku = getDLUrl(term_id, file_id)\n        addUri(dlu, {'out': name + '.ts', 'dir': _dir})\n        addUri(f'{dku}&token={get_dk_token(cid, term_id)}', {'out': name + '.key', 'dir': _dir})\n","sub_path":"api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":2696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"269247106","text":"# ------------------------------------------------------------------------------\n# Copyright (c) Microsoft\n# Licensed under the MIT License.\n# Written by Bin Xiao (Bin.Xiao@microsoft.com)\n# ------------------------------------------------------------------------------\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport torch.nn as nn\nimport torch\n\nclass JointsMSELoss(nn.Module):\n    def __init__(self):\n        super(JointsMSELoss, self).__init__()\n        self.criterion = nn.MSELoss(size_average=True)\n\n    def forward(self, output, target):\n        batch_size = output.size(0)\n        num_joints = output.size(1)\n        # \n        heatmaps_pred = output.reshape((batch_size, num_joints, -1)).split(1, 1)\n        heatmaps_gt = target.reshape((batch_size, num_joints, -1)).split(1, 1)\n        loss = 0\n        #print(heatmaps_gt)\n        for idx in range(num_joints):\n            # batch x (w*h)\n            heatmap_pred = heatmaps_pred[idx].squeeze()\n            heatmap_gt = heatmaps_gt[idx].squeeze()\n            \n            target_weight = (torch.max(heatmap_gt, dim=1)[0] > 0).float().reshape(-1, 1)\n            #print(\"red\", heatmap_pred)\n            #print(heatmap_gt) \n            #loss += 0.5 * self.criterion(heatmap_pred, heatmap_gt)\n            loss += 0.5 * self.criterion(heatmap_pred*target_weight, heatmap_gt)\n        return loss / num_joints\n\nclass MWMSELoss(nn.Module):\n    def __init__(self):\n        super(MWMSELoss, self).__init__()\n        self.criterion = nn.MSELoss(size_average=False, reduce=False)\n        self.k = 1\n        self.b = 1\n        print(\"use MW Weight for train k = {}, b= {}\".format(self.k, self.b))\n\n    def forward(self, output, target):\n        batch_size = output.size(0)\n        num_joints = output.size(1)\n        #print(output.reshape((batch_size, num_joints, -1)).shape)\n        heatmaps_pred = output.reshape((batch_size, num_joints, -1)).split(1, dim=1) # 13 x (batch_size, (h*w))\n        heatmaps_gt = target.reshape((batch_size, num_joints, -1)).split(1, dim=1)\n        loss = 0\n        #print(heatmaps_gt)\n        for idx in range(num_joints):\n            # batch x (w*h)\n            heatmap_pred = heatmaps_pred[idx].squeeze()\n            heatmap_gt = heatmaps_gt[idx].squeeze()\n            # if gt is zero_heatmap -> loss == 0\n            k = heatmap_gt * self.k\n            b = torch.where(heatmap_gt >= 0, 1, -1) * self.b\n            mw = k+b\n            '''\n            for i in range(60, 70):\n                #print(torch.max(heatmap_gt[0][120*i:120*(i+1)]))\n                if torch.max(heatmap_gt[0][120*i:120*(i+1)]) > 0:\n                    print(heatmap_gt[0][120*i:120*(i+1)])\n                    #print(k[0][120*i:120*(i+1)])\n                    print(b[0][120*i:120*(i+1)])\n                    print(mw[0][120*i:120*(i+1)])\n            '''\n            target_weight = (torch.max(heatmap_gt, dim=1)[0] > 0).float().reshape(-1, 1)\n\n            #print(\"w/o mw\",self.criterion(heatmap_pred*target_weight, heatmap_gt).mean())\n            #print(\"w mw\", (mw * self.criterion(heatmap_pred*target_weight, heatmap_gt)).mean())\n            #print(\"w mw\", self.criterion(torch.mul(mw, heatmap_pred)*target_weight, heatmap_gt))\n            #print(heatmap_gt) \n            #loss += 0.5 * self.criterion(heatmap_pred, heatmap_gt)\n            loss += 0.5 * (mw *self.criterion(heatmap_pred*target_weight, heatmap_gt)).mean()\n        return loss / num_joints\n\nif __name__ == '__main__':\n    a = JointsMSELoss()\n    b = torch.arange(24).reshape(2, 3, 2, 2)\n    print(b)\n    c = torch.zeros((2, 3, 2, 2))\n    c[0][0][0][0] = 1\n    d = a(b, c)\n    print(d)\n","sub_path":"uwbpose/loss.py","file_name":"loss.py","file_ext":"py","file_size_in_byte":3677,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"369387902","text":"# This acts as the C server that contains the routing table information for the rest of\n# the program to function properly. No loop is required as once it is sent to the R router,\n# the connection will be terminated.\n#\n# Created By: Jason Crandall u0726408\n\nfrom socket import *\nimport json\nimport sys\n\n# Set up static host and port number\nHOST = 'localhost'\nPORT = 2345\n\n# Initialize the flow table to be sent to the router\nflowTable = {\n    \"table\": [\n        {\n            \"match\": \"sra <= 20 and dsa <= 20 and srp > 10 and dsp > 10\",\n            \"action\": \"sra = 21;srp = 41\",\n            \"statistics\": 0\n        },\n        {\n            \"match\": \"sra > 40 and dsa > 40 and srp > 10 and dsp > 10\",\n            \"action\": \"forward\",\n            \"statistics\": 0\n        },\n        {\n            \"match\": \"srp <= 10 or dsp <= 10\",\n            \"action\": \"drop\",\n            \"statistics\": 0\n        },\n        {\n            \"match\": \"'No Match Found'\",\n            \"action\": \"drop\",\n            \"statistics\": 0\n        }\n    ]\n}\n\n# Main function that waits for a connection from the router, then\n# sends the flow table and closes the connection\ndef main():\n    # Open a connection and wait for input from the router\n    with socket(AF_INET,SOCK_STREAM) as s:\n        try:\n            s.bind((HOST, PORT))\n        except:\n            print(\"OS Error 98: Address already in use\")\n            s.close()\n            return\n        s.listen(2)\n        connection, addr = s.accept()\n        with connection:\n            print(\"Connected by \", addr)\n            data = connection.recv(1024)\n            print(\"Received Data: \", data.decode())\n            command = ''\n\n            # Ensure the correct format is received\n            try:\n                jsonData = json.loads(data.decode())\n                command = jsonData['command']\n            except:\n                error = \"Unrecognized Format\"\n                connection.send(error.encode())\n                \n            # Send flow table to router\n            if(command == \"requestTable\"):\n                tableData = json.dumps(flowTable)\n                print(\"Sending flow table\")\n                connection.send(tableData.encode())\n\nif __name__ == \"__main__\":\n    main()","sub_path":"PA_2/part1/C_server.py","file_name":"C_server.py","file_ext":"py","file_size_in_byte":2226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"432792387","text":"#!/usr/bin/python3\n# 3/28/2019 Skeetzo\n# OnlySnarf.py menu system\n\n### doesn't work:\n# upload & backup (requires upload via local added to main script)\n# settings menu -> \"Incorrect Index\"\n\nimport random\nimport os\nimport shutil\nimport datetime\nimport json\nimport sys\nimport pathlib\nimport pkg_resources\nfrom OnlySnarf.settings import SETTINGS as settings\nfrom OnlySnarf import onlysnarf as OnlySnarf\nfrom OnlySnarf import google as Google\nfrom OnlySnarf import cron as Cron\n\n###################\n##### Globals #####\n###################\n\nversion = str(pkg_resources.get_distribution(\"onlysnarf\").version)\nheader = \"\\n ________         .__          _________                     _____ \\n \\\n\\\\_____  \\\\   ____ |  | ___.__./   _____/ ____ _____ ________/ ____\\\\\\n \\\n /   |   \\\\ /    \\\\|  |<   |  |\\\\_____  \\\\ /    \\\\\\\\__  \\\\\\\\_   _ \\\\   __\\\\ \\n \\\n/    |    \\\\   |  \\\\  |_\\\\___  |/        \\\\   |  \\\\/ __ \\\\ |  |\\\\/| |   \\n \\\n\\\\_______  /___|  /____/ ____/_______  /___|  (____  \\\\\\\\__|  |_|   \\n \\\n        \\\\/     \\\\/     \\\\/            \\\\/     \\\\/     \\\\/              \\n\"\n\nUPDATED = False\nUPDATED_TO = False\nINITIALIZED = False\n\ncolors = {\n        'blue': '\\033[94m',\n        'header': '\\033[48;1;34m',\n        'teal': '\\033[96m',\n        'pink': '\\033[95m',\n        'green': '\\033[92m',\n        'yellow': '\\033[93m',\n        'menu': '\\033[48;1;44m'\n        }\n\nmenuItems = []\nactionItems = []\nmessageItems = []\nfileItems = []\nlocationItems = []\npromotionItems = []\nsettingItems = []\nmethodItems = []\n\ndef initialize():\n    # print(\"Initializing Menu\")\n    global INITIALIZED\n    if INITIALIZED:\n        # print(\"Already Initialized, Skipping\")\n        return\n    global settingItems\n    # Settings Menu\n    settingItems = [\n        [ \"Verbose\", settings.VERBOSE, [\"True\",\"False\"],True],\n        [ \"Debug\", settings.DEBUG, [\"True\",\"False\"],False],\n        [ \"Backup\", settings.BACKUP, [\"True\",\"False\"],True],\n        [ \"Show Window\", settings.SHOW_WINDOW, [\"True\",\"False\"],False],\n        [ \"Delete Google\", settings.DELETE_GOOGLE, [\"True\",\"False\"],False],\n        [ \"Skip Delete\", settings.SKIP_DELETE, [\"True\",\"False\"],False],\n        [ \"Tweeting\", settings.TWEETING, [\"True\",\"False\"],True],\n        [ \"Image Limit\", settings.IMAGE_UPLOAD_LIMIT,None,True],\n    ]\n    if str(settings.VERBOSE) == \"True\":\n        settingItems.append([ \"Skip Delete\", settings.SKIP_DELETE, [\"True\",\"False\"],False])\n        settingItems.append([ \"Mount Path\", settings.MOUNT_PATH,None,False])\n        settingItems.append([ \"Drive Path\", settings.DRIVE_PATH,None,False])\n        settingItems.append([ \"Users Path\", settings.USERS_PATH,None,False])\n        settingItems.append([ \"Google Root\", settings.ROOT_FOLDER,None,False])\n        settingItems.append([ \"Drive Folder\", settings.DRIVE_FOLDERS,None,False])\n        settingItems.append([ \"Create Drive\", settings.CREATE_DRIVE, [\"True\",\"False\"],False])\n    if str(settings.DEBUG) == \"True\":\n        settingItems.append([ \"Force Delete\", settings.FORCE_DELETE, [\"True\",\"False\"],False])\n        settingItems.append([ \"Force Backup\", settings.FORCE_BACKUP, [\"True\",\"False\"],False])\n        settingItems.append([ \"Force Upload\", settings.FORCE_UPLOAD, [\"True\",\"False\"],False])\n        settingItems.append([ \"Skip Download\", settings.SKIP_DOWNLOAD, [\"True\",\"False\"],False])\n        settingItems.append([ \"Image Max\", settings.IMAGE_UPLOAD_MAX,None,False])\n        settingItems.append([ \"Text\", settings.TEXT,None,False])\n        settingItems.append([ \"Local\", settings.INPUT,None,False])\n        settingItems.append([ \"Image\", settings.IMAGE,None,False])\n        settingItems.append([ \"Prefer Local\", settings.PREFER_LOCAL,[\"True\",\"False\"],True])\n        # settingItems.append([ \"Overwrite Local\", settings.OVERWRITE_LOCAL,[\"True\",\"False\"],True])\n    settingItems = sorted(settingItems)\n    settingItems.insert(0,[ \"Back\", \"main\"])\n\n    global menuItems\n    # Main Menu\n    menuItems = [\n        [ \"Actions\", \"action\"],\n        [ \"Settings\", \"set_settings\"],\n        [ \"Exit\", \"exit\"]\n    ]\n\n    global actionItems\n    # Actions Menu\n    actionItems = [\n        [ \"Upload\", \"release\" ],\n        [ \"Download\", \"download\" ],\n        # [ \"Promotion\", \"promotion\" ],\n        [ \"Message\", \"message\" ],\n        [ \"Discount\", \"discount\" ],\n        [ \"Post\", \"post\" ],\n        [ \"Reset\", \"reset\" ]\n    ]\n    if str(settings.DEBUG) == \"True\":\n        actionItems.append([ \"Test\", \"test\"])\n        actionItems.append([ \"Promotion\", \"promotion\" ])\n        actionItems.append([ \"Cron\", \"cron\" ])\n    actionItems = sorted(actionItems)\n    actionItems.insert(0,[ \"Back\", \"main\"])\n\n    global messageItems\n    # Message Menu\n    messageItems = [\n        [ \"All\", \"all\"],\n        # [ \"New\", \"new\"],\n        [ \"Recent\", \"recent\"],\n        # [ \"Favorite\", \"favorite\"],\n        [ \"User by Username\", \"user\"],\n        [ \"Select User\", \"select\"]\n    ]\n    if str(settings.DEBUG) == \"True\":\n        messageItems.append([ \"New\", \"new\"])\n        messageItems.append([ \"Favorite\", \"favorite\"])\n    messageItems = sorted(messageItems)\n    messageItems.insert(0,[ \"Back\", \"main\"])\n\n    global fileItems\n    # File Type Menu\n    fileItems = [\n        [ \"Image\", \"image\"],\n        [ \"Gallery\", \"gallery\"],\n        [ \"Performer\", \"performer\"],\n        # [ \"Scene\", \"scene\"],\n        [ \"Video\", \"video\"],\n    ]\n    if str(settings.DEBUG) == \"True\":\n        fileItems.append([ \"Scene\", \"scene\"])\n    fileItems = sorted(fileItems)\n    fileItems.insert(0,[ \"Back\", \"main\"])\n\n    global locationItems\n    # File Location Menu\n    locationItems = sorted([\n        [ \"Local\", \"local\"],\n        [ \"Google Drive\", \"google\"]\n    ])\n    locationItems.insert(0,[ \"Back\", \"main\"])\n\n    global promotionItems\n    if str(settings.DEBUG) == \"True\":\n        promotionItems = sorted([\n            [ \"Enter Email\", \"email\" ],\n            [ \"Select User\", \"select\" ]\n        ])\n    promotionItems.insert(0,[ \"Back\", \"main\"])\n\n    global methodItems\n    methodItems = sorted([\n        [ \"Choose\", \"choose\" ],\n        [ \"Random\", \"random\" ]\n    ])\n    methodItems.insert(0,[ \"Back\", \"main\"])\n\n    global postItems\n    postItems = sorted([\n        [ \"Enter\", \"enter\" ],\n        [ \"Select\", \"select\" ]\n    ])\n    postItems.insert(0,[ \"Back\", \"main\"])\n\n    global cronItems\n    cronItems = sorted([\n        [ \"Add\", \"add\" ],\n        [ \"List\", \"list\" ],\n        [ \"Delete\", \"delete\" ],\n        [ \"Delete All\", \"deleteall\" ]\n    ])\n    cronItems.insert(0,[ \"Back\", \"main\"])\n\n    # print(\"Initialized Menu\")\n    INITIALIZED = True\n\n#####################\n##### Functions #####\n#####################\n\n### Action Menu - file type\ndef action():\n    for item in actionItems:\n        print(colorize(\"[\" + str(actionItems.index(item)) + \"] \", 'teal') + list(item)[0])\n    while True:\n        choice = input(\">> \")\n        try:\n            if int(choice) < 0 or int(choice) >= len(actionItems): raise ValueError\n            if str(actionItems[int(choice)][1]) == \"main\":\n                return main()\n            elif str(actionItems[int(choice)][1]) == \"reset\":\n                OnlySnarf.remove_local()\n            elif str(actionItems[int(choice)][1]) == \"message\":\n                actionChoice = list(actionItems[int(choice)])[1]\n                return finalizeMessage(actionChoice)\n            elif str(actionItems[int(choice)][1]) == \"discount\":\n                actionChoice = list(actionItems[int(choice)])[1]\n                return finalizeDiscount(actionChoice)\n            elif str(actionItems[int(choice)][1]) == \"promotion\":\n                actionChoice = list(actionItems[int(choice)])[1]\n                return finalizePromotion(actionChoice)\n            elif str(actionItems[int(choice)][1]) == \"post\":\n                actionChoice = list(actionItems[int(choice)])[1]\n                return finalizePost(actionChoice)\n            elif str(actionItems[int(choice)][1]) == \"cron\":\n                actionChoice = list(actionItems[int(choice)])[1]\n                return finalizeCron(actionChoice)\n            else:\n                actionChoice = list(actionItems[int(choice)])[1]\n                return finalizeAction(actionChoice)\n        except (ValueError, IndexError):\n            print(\"Error: Incorrect Index\")\n        except Exception as e:\n            settings.maybePrint(e)\n            print(\"Error: Missing Method\") \n\n### Action Menu - finalize\ndef finalizeAction(actionChoice):\n    for item in fileItems:\n        print(colorize(\"[\" + str(fileItems.index(item)) + \"] \", 'teal') + list(item)[0])\n    while True:\n        fileChoice = input(\">> \")\n        try:\n            if int(fileChoice) < 0 or int(fileChoice) >= len(fileItems): raise ValueError\n            if str(fileItems[int(fileChoice)][1]) == \"main\":\n                return action()\n            # Call the matching function\n            fileChoice = list(fileItems[int(fileChoice)])[1]\n            return selectMethod(actionChoice, fileChoice)\n        except (ValueError, IndexError):\n            print(\"Error: Incorrect Index\")\n        except Exception as e:\n            settings.maybePrint(e)\n            print(\"Error: Missing Method\") \n\ndef selectMethod(actionChoice, fileChoice):\n    # if settings.INPUT and methodItems doesn't include Input option already\n    if [ \"Local\", \"local\" ] not in methodItems and actionChoice == \"release\":\n        methodItems.append([ \"Local\", \"local\" ])\n    for item in methodItems:\n        print(colorize(\"[\" + str(methodItems.index(item)) + \"] \", 'teal') + list(item)[0])\n    while True:\n        methodChoice = input(\">> \")\n        try:\n            if int(methodChoice) < 0 or int(methodChoice) >= len(methodItems): raise ValueError\n            methodChoice_ = list(methodItems[int(methodChoice)])[1]\n            if str(methodItems[int(methodChoice)][1]) == \"main\":\n                return action()\n            elif str(methodItems[int(methodChoice)][1]) == \"choose\":\n                if str(fileChoice) == \"gallery\":\n                    choices = displayFolders(\"galleries\")\n                elif str(fileChoice) == \"video\":\n                    choices = displayFolders(\"videos\")\n                elif str(fileChoice) == \"image\":\n                    choices = displayFolders(\"images\")\n                elif str(fileChoice) == \"performer\":\n                    choices = displayFolders(\"performers\")\n                elif str(fileChoice) == \"scene\":\n                    choices = displayFolders(\"scenes\")\n                seeking = True\n                while seeking:\n                    choice = input(\">> \")\n                    try:\n                        if int(choice) < 0 or int(choice) > len(choices): raise ValueError\n                        if int(choice) == 0:\n                            return selectMethod(actionChoice, fileChoice)\n                        file = choices[int(choice)-1]\n                        parent = file\n                        seeking = False\n                        if str(fileChoice) == \"gallery\" or str(fileChoice) == \"image\"  or str(fileChoice) == \"video\" or str(fileChoice) == \"performer\":\n                            if str(fileChoice) == \"gallery\":\n                                choices_ = displayFolders(file['title'], parent=\"galleries\")\n                            elif str(fileChoice) == \"image\":\n                                choices_ = displayFiles(file['title'], parent=\"images\")\n                            elif str(fileChoice) == \"video\":\n                                choices_ = displayFiles(file['title'], parent=\"videos\")\n                            elif str(fileChoice) == \"performer\":\n                                choices_ = displayFolders(file['title'], parent=\"performers\")\n                            seeking_ = True\n                            while seeking_:\n                                choice_ = input(\">> \")\n                                try:\n                                    if int(choice_) < 0 or int(choice_) > len(choices_): raise ValueError\n                                    if int(choice_) == 0:\n                                        return selectMethod(actionChoice, fileChoice)\n                                    file = choices_[int(choice_)-1]\n                                    seeking_ = False\n                                    folderName = file['title']\n                                    if str(fileChoice) == \"performer\":\n                                        # parent = file\n                                        # choices_ = displayFiles(file['title'], parent=parent)\n                                        choices_ = displayBoth(file['title'], parent=parent)\n                                        seeking__ = True\n                                        while seeking__:\n                                            choice_ = input(\">> \")\n                                            try:\n                                                if int(choice_) < 0 or int(choice_) > len(choices_): raise ValueError\n                                                if int(choice_) == 0:\n                                                    return selectMethod(actionChoice, fileChoice)\n                                                file = choices_[int(choice_)-1]\n                                                seeking__ = False\n                                                return performAction(actionChoice, fileChoice, methodChoice_, file=file, folderName=folderName, parent=parent)\n                                            except (ValueError, IndexError):\n                                                print(sys.exc_info()[0])\n                                                print(\"Error: Incorrect Index\")\n                                                return finalizeAction(actionChoice)\n                                except (ValueError, IndexError):\n                                    print(sys.exc_info()[0])\n                                    print(\"Error: Incorrect Index\")\n                                    return finalizeAction(actionChoice)\n                        return performAction(actionChoice, fileChoice, methodChoice_, file=file, folderName=folderName, parent=parent)\n                    except (ValueError, IndexError):\n                        print(sys.exc_info()[0])\n                        print(\"Error: Incorrect Index\")\n                        return finalizeAction(actionChoice)\n            return performAction(actionChoice, fileChoice, methodChoice_)\n        except (ValueError, IndexError):\n            print(\"Error: Incorrect Index\")\n        except Exception as e:\n            settings.maybePrint(e)\n            print(\"Error: Missing Method\") \n\n### Action Menu - perform\ndef performAction(actionChoice, fileChoice, methodChoice, file=None, folderName=None, parent=None):\n    try:\n        method = getattr(OnlySnarf, str(actionChoice))\n        response = method(fileChoice, methodChoice=methodChoice, file=file, folderName=folderName, parent=parent)\n        if response:\n            if str(actionChoice) == \"download\":\n                settings.update_value(\"input\",response.get(\"path\"))\n    except (ValueError, IndexError):\n        print(\"Error: Incorrect Index\")\n    except Exception as e:\n        settings.maybePrint(e)\n        print(\"Error: Missing Method\") \n    mainMenu()\n\n# Message Menu - finalize\ndef finalizeMessage(actionChoice):\n    for item in messageItems:\n        print(colorize(\"[\" + str(messageItems.index(item)) + \"] \", 'teal') + list(item)[0])\n    while True:\n        choice = input(\">> \")\n        try:\n            choice = int(choice)\n            if int(choice) < 0 or int(choice) >= len(messageItems): raise ValueError\n            if str(messageItems[int(choice)][1]) == \"main\":\n                return action()\n            return performMessage(actionChoice, messageItems[int(choice)][1])\n        except (ValueError, IndexError):\n            print(sys.exc_info()[0])\n            print(\"Error: Incorrect Index\")\n\n# Message Menu - perform\ndef performMessage(actionChoice, messageChoice):\n    username = None\n    if str(messageChoice) == \"select\":\n        messageChoice = \"user\"\n        users = displayUsers()\n        seeking = True\n        while seeking:\n            choice = input(\">> \")\n            try:\n                if int(choice) < 0 or int(choice) > len(users): raise ValueError\n                if int(choice) == 0:\n                    return finalizeMessage(actionChoice)\n                username = users[int(choice)-1].username\n                seeking = False\n            except (ValueError, IndexError):\n                print(sys.exc_info()[0])\n                print(\"Error: Incorrect Index\")\n                return mainMenu()\n    elif str(messageChoice) == \"user\":\n        print(\"Username:\")\n        username = input(\">> \")\n    images = selectImage(messageChoice)\n    # [folder , image_file]\n    # print(\"len: \" + str(len(images)))\n    while True:\n        choice = input(\">> \")\n        try:\n            if int(choice) < 0 or int(choice) > len(images): raise ValueError\n            if int(choice) == 0:\n                return finalizeMessage(actionChoice)\n            try:\n                image = images[int(choice)-1]\n                message(messageChoice, [image[1],image[0]], username)\n                return mainMenu()\n            # except (ValueError, IndexError):\n                # print(\"Error: Incorrect Index\")\n            except Exception as e:\n                settings.maybePrint(e)\n                print(\"Error: Missing Method\")\n        except (ValueError, IndexError):\n            print(\"Error: Incorrect Index\")\n    # mainMenu()    \n\n# {\n#   image: file\n#   folder: folder\n# }\ndef message(choice, image=None, username=None):\n    message = input(\"Message: \")\n    waiting = True\n    while waiting:\n        try:\n            price = input(\"Price: \")\n            \"{:.2f}\".format(float(price))\n            waiting = False\n        except ValueError:\n            print(\"Enter a currency amount!\")\n    if not image or not image[0] or image[0] == None:\n        print(\"Error: Missing Image\")\n        return        \n    OnlySnarf.remove_local()\n    try: \n        image = Google.download_file(image[0]).get(\"path\")\n    except Exception as e:\n        OnlySnarf.remove_local()\n        try:\n            image = Google.download_gallery(image[0]).get(\"path\")\n        except Exception as e:\n            print(\"Error: Missing Image(s)\")\n            image = None\n            # pass\n    OnlySnarf.message(choice, message=message, image=image, price=price, username=username)\n\n# Promotion Menu - finalize\ndef finalizePromotion(actionChoice):\n    for item in promotionItems:\n        print(colorize(\"[\" + str(promotionItems.index(item)) + \"] \", 'teal') + list(item)[0])\n    while True:\n        choice = input(\">> \")\n        try:\n            choice = int(choice)\n            if int(choice) < 0 or int(choice) > len(promotionItems): raise ValueError\n            if str(promotionItems[int(choice)][1]) == \"main\":\n                return action()\n            choice = list(promotionItems[int(choice)])[1]\n            return performPromotion(actionChoice, choice)\n        except (ValueError, IndexError):\n            settings.maybePrint(sys.exc_info()[0])\n            print(\"Error: Incorrect Index\")\n\ndef performPromotion(actionChoice, promotionChoice):\n    def promote(username):\n        if username == None:\n            print(\"Warning: No user found\")\n        else:\n            OnlySnarf.give_trial(username)\n        mainMenu()    \n    try:\n        username = None\n        if str(promotionChoice) == \"email\":\n            # prompt\n            choice = input(\"Email: \")\n            username = str(choice)\n            return promote(username)\n        elif str(promotionChoice) == \"select\":\n            users = displayUsers()    \n            while True:\n                choice = input(\">> \")\n                try:\n                    if int(choice) < 0 or int(choice) > len(users): raise ValueError\n                    if int(choice) == 0:\n                        return finalizePromotion(actionChoice)\n                    return promote(str(users[int(choice)-1].username))\n                except (ValueError, IndexError):\n                    settings.maybePrint(sys.exc_info()[0])\n                    print(\"Error: Incorrect Index\")            \n    except (ValueError, IndexError):\n        print(\"Error: Incorrect Index\")\n    except Exception as e:\n        settings.maybePrint(e)\n        print(\"Error: Missing Method\") \n    mainMenu()\n\ndef finalizeDiscount(actionChoice):\n    for item in messageItems:\n        print(colorize(\"[\" + str(messageItems.index(item)) + \"] \", 'teal') + list(item)[0])\n    while True:\n        choice = input(\">> \")\n        try:\n            choice = int(choice)\n            if int(choice) < 0 or int(choice) >= len(messageItems): raise ValueError\n            if str(messageItems[int(choice)][1]) == \"main\":\n                return action()\n            return performDiscount(actionChoice, messageItems[int(choice)][1])\n        except (ValueError, IndexError):\n            print(sys.exc_info()[0])\n            print(\"Error: Incorrect Index\")\n\ndef performDiscount(actionChoice, discountChoice):\n    username = None\n    if str(discountChoice) == \"user\":\n        user = input(\"Username: \")\n        OnlySnarf.discount(user, depth=int(choice))\n        mainMenu()\n    elif str(discountChoice) == \"select\":\n        users = displayUsers()\n        seeking = True\n        while seeking:\n            choice = input(\">> \")\n            try:\n                if int(choice) < 0 or int(choice) > len(users): raise ValueError\n                if int(choice) == 0:\n                    return finalizeDiscount(actionChoice)\n                OnlySnarf.discount(users[int(choice)-1], depth=int(choice))\n                mainMenu()\n            except (ValueError, IndexError):\n                print(sys.exc_info()[0])\n                print(\"Error: Incorrect Index\")\n                return mainMenu()\n    OnlySnarf.discount(discountChoice)\n    mainMenu()    \n\ndef finalizePost(actionChoice):\n    for item in postItems:\n        print(colorize(\"[\" + str(postItems.index(item)) + \"] \", 'teal') + list(item)[0])\n    while True:\n        choice = input(\">> \")\n        try:\n            choice = int(choice)\n            if int(choice) < 0 or int(choice) >= len(postItems): raise ValueError\n            if str(postItems[int(choice)][1]) == \"main\":\n                return action()\n            elif str(postItems[int(choice)][1]) == \"enter\":\n                OnlySnarf.post()\n            else:\n                selectPost()\n            return mainMenu()\n        except (ValueError, IndexError):\n            print(sys.exc_info()[0])\n            print(\"Error: Incorrect Index\")\n\ndef selectPost():\n    postMenu = []\n    for key in settings.POSTS:\n        postMenu.append([ key.title().replace(\"_\",\" \"), settings.POSTS[key]])\n    postMenu.insert(0,[ \"Back\", \"main\"])\n    for item in postMenu:\n        print(colorize(\"[\" + str(postMenu.index(item)) + \"] \", 'teal') + list(item)[0] + \" - {}\".format(list(item)[1][:50]))\n    while True:\n        choice = input(\">> \")\n        try:\n            choice = int(choice)\n            if int(choice) < 0 or int(choice) >= len(postMenu): raise ValueError\n            if str(postMenu[int(choice)][1]) == \"main\":\n                return action()\n            text = postMenu[int(choice)][1]\n            OnlySnarf.post(text=text)\n            return mainMenu()\n        except (ValueError, IndexError):\n            print(sys.exc_info()[0])\n            print(\"Error: Incorrect Index\")\n\ndef finalizeCron(actionChoice):\n    for item in cronItems:\n        print(colorize(\"[\" + str(cronItems.index(item)) + \"] \", 'teal') + list(item)[0])\n    while True:\n        cronChoice = input(\">> \")\n        try:\n            if int(cronChoice) < 0 or int(cronChoice) >= len(cronItems): raise ValueError\n            if str(cronItems[int(cronChoice)][1]) == \"main\":\n                return action()\n            cronChoice = list(cronItems[int(cronChoice)])[1]\n            return performCron(actionChoice, cronChoice)\n        except (ValueError, IndexError):\n            print(\"Error: Incorrect Index\")\n        except Exception as e:\n            settings.maybePrint(e)\n            print(\"Error: Missing Method\") \n\ndef performCron(actionChoice, cronChoice):\n    if str(cronChoice) == \"add\":\n        print(\"Comment:\")\n        comment = input(\">> \")\n        print(\"Args:\")\n        args = input(\">> \")\n        args = args.split(\",\")\n        print(\"Minute:\")\n        minute = input(\">> \")\n        print(\"Hours:\")\n        hour = input(\">> \")\n        Cron.create(comment, args=args, minute=minute, hour=hour)\n    elif str(cronChoice) == \"list\":\n        Cron.list()\n    elif str(cronChoice) == \"delete\":\n        jobs = Cron.getAll()\n        print(colorize(\"[0] \", 'teal') + \"Back\")\n        jobs_ = []\n        for job in jobs:\n            jobs_.append(str(job.comment))\n            print(colorize(\"[\" + str(jobs.index(job)+1) + \"] \", 'teal') + str(job))\n        while True:\n            choice = input(\">> \")\n            try:\n                choice = int(choice)\n                if int(choice) < 0 or int(choice) > len(jobs): raise ValueError\n                if int(choice) == 0: return finalizeCron(actionChoice)\n                Cron.delete(jobs_[int(choice)-1])\n                return mainMenu()\n            except (ValueError, IndexError):\n                print(sys.exc_info()[0])\n                print(\"Error: Incorrect Index\")\n        \n    elif str(cronChoice) == \"deleteall\":\n        Cron.deleteAll()\n    else:\n        print(\"Error: Missing Cron Action\")\n    mainMenu()    \n\ndef displayBoth(folderName, parent=None):\n    files = Google.get_files_of_folder(folderName, parent=parent)\n    folders = Google.get_folders_of_folder(folderName, parent=parent)\n    files_both = []\n    for f in files: files_both.append(f)\n    for f in folders: files_both.append(f)\n    print(colorize(\"[0] \", 'blue') + \"Back\")\n    i = 1\n    for file in files_both:\n        print(colorize(\"[\" + str(i) + \"] \", 'blue') + str(file['title']))\n        i = i+1\n    return files_both\n\ndef displayFiles(folderName, parent=None):\n    files = Google.get_files_of_folder(folderName, parent=parent)\n    print(colorize(\"[0] \", 'blue') + \"Back\")\n    i = 1\n    for file in files:\n        print(colorize(\"[\" + str(i) + \"] \", 'blue') + str(file['title']))\n        i = i+1\n    return files\n\ndef displayFolders(folderName, parent=None):\n    folders = Google.get_folders_of_folder(folderName, parent=parent)\n    print(colorize(\"[0] \", 'blue') + \"Back\")\n    i = 1\n    for folder in folders:\n        print(colorize(\"[\" + str(i) + \"] \", 'blue') + str(folder['title']))\n        i = i+1\n    return folders\n\n# displays and returns users\ndef displayUsers():\n    users = OnlySnarf.get_users()\n    print(colorize(\"[0] \", 'blue') + \"Back\")\n    # show list\n    i = 1\n    for user in users:\n        print(colorize(\"[\" + str(i) + \"] \", 'blue') + str(user.username))\n        i = i+1\n    return users\n\ndef selectImage(folderName):\n    images = Google.get_images()\n    print(colorize(\"[0] \", 'blue') + \"Back\")\n    i = 1\n    for image in images:\n        print(colorize(\"[\" + str(i) + \"] \", 'blue') + str(image[0][\"title\"]) + \" - \" + str(image[1]['title']))\n        i = i+1\n    return images\n    # performMessage -> [folder , image_file] -> performMessage\n\n###########################\n\n### Settings Menu\ndef set_settings():\n    showHeader()\n    print(colorize(\"Set:\",'menu'))\n    global settingItems\n    for item in settingItems:\n        print(colorize(\"[\" + str(settingItems.index(item)) + \"] \", 'blue') + list(item)[0])\n    while True:\n        choice = input(\">> \")\n        try:\n            if int(choice) < 0 or int(choice) >= len(settingItems): raise ValueError\n            settingChoice = list(settingItems[int(choice)])[0]\n            settingValue = list(settingItems[int(choice)])[1]\n            if str(settingChoice) == \"Back\":\n                return main()\n            elif str(settingChoice) == \"File Name\":\n                settingValue = input(\"Enter the file name: \")\n            elif str(settingChoice) == \"File Path\":\n                settingValue = input(\"Enter the file path: \")\n            elif str(settingChoice) == \"Text\":\n                settingValue = input(\"Enter the upload text: \")\n            elif str(settingChoice) == \"Mount Path\":\n                settingValue = input(\"Enter the mount path: \")\n            elif str(settingChoice) == \"Google: Root Folder Path\":\n                settingValue = input(\"Enter the drive path (folderName/folderName/...): \")\n            elif str(settingChoice) == \"Image\":\n                settingValue = input(\"Enter the image path: \")\n            elif str(settingChoice) == \"Google: Root Folder Name\":\n                settingValue = input(\"Enter the Google root folder name: \")\n            elif str(settingChoice) == \"Google: Drive Folders\":\n                settingValue = input(\"Enter the Google drive folders (separated by ',', no spaces): \")\n                settingValue = settingValue.split(\",\")\n            elif str(settingChoice) == \"Image Limit\":\n                settingValue = input(\"Enter the image upload limit: \")\n            elif str(settingChoice) == \"Image Max\":\n                settingValue = input(\"Enter the image upload max: \")\n            elif str(settingChoice) == \"Local\":\n                settingValue = input(\"Enter the local path: \")\n            else:\n                list_ = list(settingItems[int(choice)][2])\n                print(colorize(str(settingChoice)+\" =\", 'blue'))\n                for item in list_:\n                    print(colorize(\"[\" + str(list_.index(item)) + \"] \", 'pink') + str(item))\n                while True:\n                    updateChoice = input(\">> \")\n                    try:\n                        if int(updateChoice) < 0 or int(updateChoice) >= len(list(settingItems[int(choice)][2])): raise ValueError\n                        settingValue = list_[int(updateChoice)]\n                        break\n                    except (ValueError, IndexError):\n                        print(\"Error: Incorrect Index\")\n                    except Exception as e:\n                        settings.maybePrint(e)\n                        break\n            global UPDATED\n            UPDATED = settingChoice\n            global UPDATED_TO\n            UPDATED_TO = settingValue\n            settingItems[int(choice)][1] = settingValue\n            settings.update_value(settingChoice, settingValue)\n            return set_settings()\n        except (ValueError, IndexError):\n            print(\"Error: Incorrect Index\")\n        except Exception as e:\n            settings.maybePrint(e)\n            return main()\n\n###########################\n\ndef colorize(string, color):\n    if not color in colors: return string\n    return colors[color] + string + '\\033[0m'\n  \ndef exit():\n    print(\"Shnarrf?\")\n    sys.exit(0)\n\n###########################\nimport atexit\ndef exit_handler():\n    print('Shnnarrrff!')\n    exit()\natexit.register(exit_handler)\n\nimport signal\ndef signal_handler(sig, frame):\n    print('Shnnnarf?')\n    exit()\nsignal.signal(signal.SIGINT, signal_handler)\n###########################\n\ndef main():\n    showHeader()\n    mainMenu()\n\ndef mainMenu():\n    ### Main Menu\n    print(colorize(\"Select an option:\", 'menu'))\n    for item in menuItems:\n        print(colorize(\"[\" + str(menuItems.index(item)) + \"] \", 'blue') + list(item)[0])\n    while True:\n        choice = input(\">> \")\n        try:\n            if int(choice) < 0 or int(choice) >= len(menuItems): raise ValueError\n            # Call the matching function\n            method_name = list(menuItems[int(choice)])[1]\n            possibles = globals().copy()\n            possibles.update(locals())\n            method = possibles.get(method_name)\n            if method is not None:\n                return method()\n            else:\n                print(\"Error: Missing Option\")    \n        except (ValueError, IndexError, KeyboardInterrupt):\n            print(\"Error: Incorrect Index\")\n            pass\n\ndef showHeader():\n    os.system('clear')\n    # Print some badass ascii art header here !\n    print(colorize(header, 'header'))\n    print(colorize('version '+version+'\\n', 'green'))\n    showUser()\n    showSettings()\n\ndef showSettings():\n    print('Settings:')\n    for setting in settingItems:\n        if str(setting[0]) == \"Image Limit\" and setting[3]:\n            print(\" - {} = {}/{}\".format(setting[0],setting[1],settings.IMAGE_UPLOAD_MAX))\n        elif str(setting[0]) != \"Back\" and str(settings.DEBUG) == \"True\":\n            print(\" - {} = {}\".format(setting[0],setting[1]))\n        elif str(setting[0]) != \"Back\" and setting[3]:\n            print(\" - {} = {}\".format(setting[0],setting[1]))\n    global UPDATED\n    global UPDATED_TO\n    if str(UPDATED) != \"False\":\n        print('\\nUpdated: '+str(UPDATED)+' -> '+str(UPDATED_TO))\n    UPDATED = False\n    print('\\r')\n\ndef showUser():\n    print(\"User:\")\n    print(\" - Username = {}\".format(settings.USERNAME))\n    if settings.PASSWORD and str(settings.PASSWORD) != \"\":\n        pass_ = \"******\"\n    else:\n        pass_ = \"\"\n    print(\" - Password = {}\".format(pass_))\n    print('\\r')\n\n###########################\n\nif __name__ == \"__main__\":\n    try:\n        main_other()\n    except:\n        # print(sys.exc_info()[0])\n        print(\"Shhhhhnnnnnarf!\")\n    finally:\n        sys.exit(0)\n\ndef main_other():\n    settings.initialize()\n    initialize()\n    main()","sub_path":"OnlySnarf/menu.py","file_name":"menu.py","file_ext":"py","file_size_in_byte":33296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"427381814","text":"# 문제 설명\n# 이 문제에는 표준 입력으로 두 개의 정수 n과 m이 주어집니다.\n# 별(*) 문자를 이용해 가로의 길이가 n, 세로의 길이가 m인 직사각형 형태를 출력해보세요.\n#\n# 제한 조건\n# n과 m은 각각 1000 이하인 자연수입니다.\n\na, b = (5, 3)\n\n# a, b = map(int, input().strip().split(' '))\nprint(a + b)\n\nz = []\nfor _ in range(b):\n    s = ''\n    for _ in range(a):\n        s += '*'\n    z.append(s)\nprint('\\n'.join(z))\n\n# answer = ('*'*a +'\\n')*b\n\n# 5 min","sub_path":"programmers/2019_2_10/test3.py","file_name":"test3.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"288997582","text":"import sys\nimport requests\nimport csv\nimport io\nimport pandas as pd\nfrom pandas.core.groupby.groupby import DataError\nimport numpy as np\nfrom .utils import store_data, stoi\n\nfrom collections import defaultdict\nfrom .utils import store_data, sorted_date, stoi\n\n# ------------------------------------------------------------------------\n# Globals\n\nrs_region_codes = {\n    \"SM\": \"Santa Maria (R01 e R02)\",\n    \"UR\": \"Uruguaiana (R03)\",\n    \"CC\": \"Capão da Canoa (R0 4 e R05)\",\n    \"TQ\": \"Taquara (R06)\",\n    \"NH\": \"Novo Hamburgo (R07)\",\n    \"CA\": \"Canoas (R08)\",\n    \"PA\": \"Porto Alegre (R09 e R10)\",\n    \"SA\": \"Santo Ângelo (R11)\",\n    \"CA\": \"Cruz Alta (R12)\",\n    \"IJ\": \"Ijuí (R13)\",\n    \"SR\": \"Santa Rosa (R14)\",\n    \"PM\": \"Palmeira das Missões (R15 e R20)\",\n    \"ER\": \"Erechim (R16)\",\n    \"PF\": \"Passo Fundo (R17, R18 e R19)\",\n    \"PE\": \"Pelotas (R21)\",\n    \"BG\": \"Bagé (R22)\",\n    \"CX\": \"Caxias do Sul (R23, R24, R25 e R26)\",\n    \"CS\": \"Cachoeira do Sul (R27)\",\n    \"SC\": \"Santa Cruz do Sul (R28)\",\n    \"LJ\": \"Lajeado (R29 e R30)\"\n}\n\n\n#BR/RS state source format\n#time, regions, cases, deaths, hospitalized, icu, recovered\nURL_RS = \"https://raw.githubusercontent.com/seplagses/Covid-RS/master/data/Data_Regions_RS.csv\"\ncols = ['time', 'cases', 'deaths', 'hospitalized', 'icu', 'recovered']\n\ndef parse():\n    r_rs  = requests.get(URL_RS)\n    if not r_rs.ok:\n        print(f\"Failed to fetch {URL_RS}\", file=sys.stderr)\n        exit(1)\n        r.close()\n    else:  print(f\"Connected to {URL_RS}\", file=sys.stderr)\n\n    \n    dataframe=pd.read_csv(URL_RS, quoting=csv.QUOTE_NONE, header = 0, skiprows=2)\n    print(f\"data {dataframe.head()}\", file=sys.stderr)\n    print(list(dataframe), file=sys.stderr)\n \n    try:\n           dummy=pd.get_dummies(dataframe['regions'])\n           dataframe=pd.concat([dataframe,dummy],axis=1)\n           dataframe = dataframe.fillna(0)\n           regions_name=dataframe.iloc[:,7:].columns\n           print(regions_name)\n           dataframe_region=dataframe[['time', 'cases', 'deaths', 'hospitalized', 'icu', 'recovered']].copy() \n           dataframe_region['cases']=dataframe_region['cases'].astype(int)\n           dataframe_region['deaths']=dataframe_region['deaths'].astype(int)\n           dataframe_region['hospitalized']=dataframe_region['hospitalized'].astype(int)\n           dataframe_region['icu']=dataframe_region['icu'].astype(int)\n           dataframe_region['recovered']=dataframe_region['recovered'].astype(int)\n           print(f\"data: {dataframe_region.head()}\", file=sys.stderr)\n    except DataError:\n           print(f\"error: {DataError}\", file=sys.stderr)\n\n    region_tables = {}\n    for region in regions_name:\n        region_tables['-'.join(['RS',region])]  = dataframe_region[dataframe[region]==1].values.tolist()\n        \n    store_data(region_tables, 'brazil_rs',  cols)\n","sub_path":"data/parsers/brazil_rs.py","file_name":"brazil_rs.py","file_ext":"py","file_size_in_byte":2828,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"12578460","text":"from lib.fibheap import FibonacciHeap\nfrom lib.SimpleNode import SimpleNode\nimport json\n\n\ndef peeling(node_dict, total_C_degree, total_q_degree, fib_heap, q, lambda1, lambda2):\n    n = node_dict.__len__()\n    avg_degree = (float)(total_C_degree) / n\n    q_avg = total_q_degree / n\n    # outputs we want\n    max_C_avg = avg_degree\n    S_size = n\n\n    for i in range(n - 1):\n\n        if i % 50000 == 0:\n            print(i, max_C_avg, q_avg)\n\n        # find min node from graph (remove from heap)\n        node_to_remove = fib_heap.extract_min().value\n        for neighbor in node_dict[node_to_remove].neighbor_dict.keys():\n\n            # get dictionary that has all edges between two nodes\n            C_degree_loss = node_dict[node_to_remove].neighbor_dict[neighbor][0]\n            node_dict[neighbor].Cdegree -= C_degree_loss\n            q_degree_loss = node_dict[node_to_remove].neighbor_dict[neighbor][1]\n            node_dict[neighbor].qdegree -= q_degree_loss\n\n            # here the key can be actually increased\n            if neighbor != node_to_remove:\n                fib_heap.decrease_key(node_dict[neighbor].fib_node, node_dict[neighbor].Cdegree)\n                del node_dict[neighbor].neighbor_dict[node_to_remove]\n            total_C_degree -= C_degree_loss\n            total_q_degree -= q_degree_loss\n\n        del node_dict[node_to_remove]\n        avg_degree = (float)(total_C_degree) / (n - i - 1)\n        q_avg = total_q_degree / (n - i - 1)\n        S_size = n - i - 1\n        if q_avg > q * lambda2 - lambda1:\n            if len(node_dict)<100:\n                print(list(node_dict))\n            return True, avg_degree, q_avg, S_size\n\n    return False, max_C_avg, q_avg, S_size\n\n\nclass RiskNode:\n    degree = None\n    total_degree = None\n    neighbor_dict = None\n    papercount = None\n\n    def __init__(self, n):\n        self.degree = [0] * n\n        self.neighbor_dict = {}\n        self.total_degree = 0\n        self.papercount = 0\n\n    #     type is int from 0 to len(degree)-1\n    def increase_neighbor(self, name, type, degree):\n        if name not in self.neighbor_dict:\n            self.neighbor_dict[name] = {type: degree}\n        else:\n            if type not in self.neighbor_dict[name]:\n                self.neighbor_dict[name][type] = degree\n            else:\n                self.neighbor_dict[name][type] += degree\n        self.degree[type] += degree\n\n    def set_neighbor_risk(self, name, degree):\n        self.neighbor_dict[name][1] = degree\n        self.degree[1] += degree\n\n\ndef process_tmdb_file(file_path):\n    actor_dict = {}\n    relation_list = json.load(open(file_path))\n    for relation in relation_list:\n        # print(len(relation_list))\n        weight = relation['popularity'] * relation['possibility']\n        risk = relation['popularity'] * relation['possibility'] * (1 - relation['possibility'])\n        if risk == 0:\n            continue\n        if relation['actors'][0] not in actor_dict:\n            actor_dict[relation['actors'][0]] = RiskNode(2)\n        if relation['actors'][1] not in actor_dict:\n            actor_dict[relation['actors'][1]] = RiskNode(2)\n        actor_dict[relation['actors'][0]].increase_neighbor(relation['actors'][1],0,weight)\n        actor_dict[relation['actors'][1]].increase_neighbor(relation['actors'][0],0,weight)\n        actor_dict[relation['actors'][0]].set_neighbor_risk(relation['actors'][1],-risk)\n        actor_dict[relation['actors'][1]].set_neighbor_risk(relation['actors'][0],-risk)\n    return actor_dict\n\ndef process_dblp_file(file_path):\n    author_dict = {}\n    relation_list = json.load(open(file_path))\n    for relation in relation_list:\n\n        weight = relation['popularity'] * relation['possibility']\n        risk = relation['popularity'] * relation['possibility'] * (1 - relation['possibility'])\n        if risk == 0:\n            continue\n        if relation['actors'][0] not in author_dict:\n            author_dict[relation['actors'][0]] = RiskNode(2)\n        if relation['actors'][1] not in author_dict:\n            author_dict[relation['actors'][1]] = RiskNode(2)\n        author_dict[relation['actors'][0]].increase_neighbor(relation['actors'][1],0,weight)\n        author_dict[relation['actors'][1]].increase_neighbor(relation['actors'][0],0,weight)\n        author_dict[relation['actors'][0]].set_neighbor_risk(relation['actors'][1],-risk)\n        author_dict[relation['actors'][1]].set_neighbor_risk(relation['actors'][0],-risk)\n    return author_dict\n\ndef process_PPI_file(file_path):\n    author_dict = {}\n    relation_list = json.load(open(file_path))\n    for relation in relation_list:\n\n        weight = relation['weight'] * relation['possibility']\n        risk = relation['weight'] * relation['possibility'] * (1 - relation['possibility'])\n        if risk == 0:\n            continue\n        if relation['protein'][0] not in author_dict:\n            author_dict[relation['protein'][0]] = RiskNode(2)\n        if relation['protein'][1] not in author_dict:\n            author_dict[relation['protein'][1]] = RiskNode(2)\n        author_dict[relation['protein'][0]].increase_neighbor(relation['protein'][1],0,weight)\n        author_dict[relation['protein'][1]].increase_neighbor(relation['protein'][0],0,weight)\n        author_dict[relation['protein'][0]].set_neighbor_risk(relation['protein'][1],-risk)\n        author_dict[relation['protein'][1]].set_neighbor_risk(relation['protein'][0],-risk)\n    return author_dict\n\n\ndef risk_averse_peel(uncertain_file='tmdb', filepath='../datasets/tmdb/tmdb_2017.json', precision=0.02):\n    # peeling preprocess for Tmdb\n    if uncertain_file == 'tmdb':\n        node_dict = process_tmdb_file(filepath)\n\n    # peeling preprocess for DBLP\n    elif uncertain_file == 'dblp':\n        node_dict = process_dblp_file(filepath)\n\n    # peeling preprocess for PPI datasets\n    elif uncertain_file == 'ppi':\n        node_dict = process_PPI_file(filepath)\n\n    else:\n        assert Exception('uncertain dataset file type not expected!')\n\n    result = {'risk':{},'weight':{},'size':{}}\n\n    pos_count = 0\n    n = node_dict.__len__()\n    print(\"initially the graph will have \" + str(n) + \" nodes\")\n    lambda2 = 1\n    for node in node_dict.keys():\n        for neighbor in node_dict[node].neighbor_dict.keys():\n            #         if 0 in node_dict[node].neighbor_dict[neighbor]:\n            pos_count += node_dict[node].neighbor_dict[neighbor][0]\n    rho_list = [0.5, 1, 2, 10]\n    C_list = [0.25,0.5,1,2,3,4,5,6]\n    for rho in rho_list:\n        print(\"!!!!!\")\n        print(\"now we have rho as \", rho)\n        print(\"!!!!!!\")\n        for C in C_list:\n            print(\"!!!!!\")\n            print(\"now we have C as \", C)\n            print(\"!!!!!!\")\n            lambda1 = rho * lambda2\n            lowbound = 0\n            upbound = 20\n            #  upbound = (pos_count + lambda1 * n) / lambda2\n\n            accerate_flag = True\n            while True:\n                #     peeling with edge = pos - q * neg, and find if there exist a subgraph whose density > q * lambda2 - lambda1\n                # first build fib heap based on q\n                if accerate_flag:\n                    q = lowbound + (upbound - lowbound) / 2\n                else:\n                    q = (upbound + lowbound) / 2\n                node_dict_q = {}\n                total_C_degree = 0\n                total_q_degree = 0\n                fib_heap = FibonacciHeap()\n                for node in node_dict.keys():\n                    node_dict_q[node] = SimpleNode()\n                    for neighbor in node_dict[node].neighbor_dict.keys():\n                        C_temp_degree_each = 0\n                        q_temp_degree_each = 0\n                        # here we already store disabled interactions as negative values\n                        C_temp_degree_each += node_dict[node].neighbor_dict[neighbor][1]\n                        q_temp_degree_each += q * node_dict[node].neighbor_dict[neighbor][1]\n                        C_temp_degree_each += C * node_dict[node].neighbor_dict[neighbor][0]\n                        q_temp_degree_each += node_dict[node].neighbor_dict[neighbor][0]\n                        node_dict_q[node].increase_neighbor(neighbor, C_temp_degree_each, q_temp_degree_each)\n                        # to avoid influence from loop\n                        if node == neighbor:\n                            total_C_degree += C_temp_degree_each\n                            total_q_degree += q_temp_degree_each\n                    node_dict_q[node].fib_node = fib_heap.insert(node_dict_q[node].Cdegree, node)\n                    total_C_degree += node_dict_q[node].Cdegree\n                    total_q_degree += node_dict_q[node].qdegree\n                #  print(total_C_degree, total_q_degree)\n                total_q_degree = total_q_degree / 2\n                total_C_degree = total_C_degree / 2\n                print(total_C_degree, total_q_degree)\n                exist_flag, max_avg, q_avg, S_size = peeling(node_dict_q, total_C_degree, total_q_degree, fib_heap, q,\n                                                             lambda1, lambda2)\n                print(q, exist_flag, max_avg, q_avg, S_size)\n                if exist_flag:\n                    if accerate_flag:\n                        accerate_flag = False\n                    if q - lowbound < precision and upbound - q < precision:\n                        weight = (max_avg-q_avg/q)/(C-1/q)\n                        risk = C*weight - max_avg\n                        print(\"~~~~~~~~~~~~~\")\n                        print(\"rho, C, result q, corresponding (max density)subgraph's size, density, average risk:\")\n                        print(rho, C, q, S_size, weight, risk)\n                        if C not in result['size']:\n                            result['size'][C] = {}\n                        result['size'][C][rho] = S_size\n                        if C not in result['risk']:\n                            result['risk'][C] = {}\n                        result['risk'][C][rho] = risk\n                        if C not in result['weight']:\n                            result['weight'][C] = {}\n                        result['weight'][C][rho] = weight\n                        print(\"~~~~~~~~~~~~~\")\n                        break\n                    else:\n                        lowbound = q\n                else:\n                    upbound = q\n\n    print(result)\n\n\nif __name__ == \"__main__\":\n    risk_averse_peel()\n","sub_path":"risk_averse/peel_risk_averse.py","file_name":"peel_risk_averse.py","file_ext":"py","file_size_in_byte":10410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"421256170","text":"#!/usr/bin/env python3\nimport os\nimport sys\nimport gzip\n\nself_name = os.path.basename(__file__)\nusage_mesg = 'Usage: %s <fasta> <chromosome|scaffold>' % self_name\n\nif len(sys.argv) != 3:\n    sys.stderr.write('%s\\n' % usage_mesg)\n    sys.exit(1)\n\nfilename_fa = sys.argv[1]\nseq_type = sys.argv[2]\n\nif not os.access(filename_fa, os.R_OK):\n    sys.stderr.write('%s\\n' % usage_mesg)\n    sys.exit(1)\n\nif seq_type not in ['chromosome', 'scaffold']:\n    sys.stderr.write('%s\\n' % usage_mesg)\n    sys.exit(1)\n\nf_fa = open(filename_fa, 'r')\nif filename_fa.endswith('.gz'):\n    f_fa = gzip.open(filename_fa, 'rt')\n\nfor line in f_fa:\n    if line.startswith('>'):\n        tmp_h = line.strip().lstrip('>')\n\n        if seq_type == 'chromosome':\n            if tmp_h.startswith('chrUn'):\n                is_print = 0\n            else:\n                is_print = 1\n        elif seq_type == 'scaffold':\n            if tmp_h.startswith('chrUn'):\n                is_print = 1\n            else:\n                is_print = 0\n        else:\n            sys.stderr.write('Unknown seq_tyoe: %s\\n' % seq_type)\n            sys.exit(1)\n\n        if is_print > 0:\n            print(line.strip())\n    elif is_print > 0:\n        print(line.strip())\nf_fa.close()\n","sub_path":"utils/fasta-select_chromosome+scaffolds.py","file_name":"fasta-select_chromosome+scaffolds.py","file_ext":"py","file_size_in_byte":1229,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"599883506","text":"#!/usr/bin/env python3.5\nimport argparse\nimport grpc\nimport os\nimport sys\nimport json\nfrom modules.Id_Manager import IdManager\n\n\nimport p4runtime_lib.bmv2\nfrom p4runtime_lib.error_utils import printGrpcError\nfrom p4runtime_lib.switch import ShutdownAllSwitchConnections\nimport p4runtime_lib.helper\n\nfrom scapy.all import Packet\nfrom scapy.all import BitField\nfrom scapy.layers.inet import IP\nfrom scapy.layers.l2 import Ether\n\nsys.path.append(\n    os.path.join(os.path.dirname(os.path.abspath(__file__)),\n                 '../../utils'))\n\nclass Packet_In(Packet):\n    name = \"Packet_In\"\n\n    fields_desc = [\n        BitField('port', 0, 9)\n    ]\n\ndef load_switches_conf():\n    data = {}\n    with open('config/switches.json', 'w') as json_file:\n        data = json.load(json_file)\n\n    return data\n\ndef load_pr_rules():\n    rules = {}\n    with open('config/pr_rules.json', 'w') as json_file:\n        rules = json.load(json_file)\n\n    return rules\n\ndef load_sdnc_pkt_in_rules():\n    rules = {}\n    with open('config/port_knock_in_rules.json', 'w') as json_file:\n        rules = json.load(json_file)\n\n    return rules\n\ndef load_fwd_rules():\n    rules = {}\n    with open('config/fwd_rules.json', 'w') as json_file:\n        rules = json.load(json_file)\n\n    return rules\n\ndef load_pk_rules():\n    rules = {}\n    with open('config/portknocking_rules.json', 'w') as json_file:\n        rules = json.load(json_file)\n\n    return rules\n\n\ndef connect_to_switches(switches_config):\n    switches = []\n    for switch in switches_config:\n        switches.append(\n            p4runtime_lib.bmv2.Bmv2SwitchConnection(\n                name=switch[\"name\"],\n                address=switch[\"address\"],\n                device_id=switch[\"device_id\"],\n                proto_dump_file=switch[\"proto_dump_file\"]))\n\n    return switches\n\ndef send_master_arbitration_updates(switches):\n    for switch in switches:\n        switch.MasterArbitrationUpdate()\n\ndef set_pipelines(switches, p4info_helper, bmv2_file_path):\n\n    for switch in switches:\n        switch.SetForwardingPipelineConfig(p4info=p4info_helper.p4info,\n                                            bmv2_json_file_path=bmv2_file_path)\n\n\ndef install_direct_forwarding_rules(p4info_helper, switches):\n    ports = {\n        [1, 2],\n        [1]\n    }\n\n    for idx, switch in enumerate(switches):\n        for port in ports:\n            table_entry = p4info_helper.buildTableEntry(\n                table_name=\"IngressImpl.inc.port_tb\",\n                match_fields={\n                    \"standard_metadata.ingress_port\": port\n                },\n                action_name=\"IngressImpl.inc.direct_forward\",\n                action_params={}\n            )\n\n            switch.WriteTableEntry(table_entry)\n\ndef install_rules_protected_services(p4info_helper, switches, switches_conf, rules):\n    for idx, switch in enumerate(switches):\n\n        sw = switches_conf[idx][\"name\"]\n        rules_l = rules[sw][\"rules\"]\n        actions_l = rules[sw][\"actions\"]\n\n        for index, rule in enumerate(rules_l):\n            table_entry = p4info_helper.buildTableEntry(\n                table_name=\"IngressImpl.pr.protected_service_tb\",\n                match_fields={\n                    \"meta.pk_metadata.stage\": rule[\"stage\"],\n                    \"hdr.ipv4.dstAddr\": rule[\"ipDstAddr\"],\n                    \"hdr.tcp.dstPort\": rule[\"tcpDstPort\"]\n                },\n                action_name=actions_l[index][\"name\"],\n                action_params={}\n            )\n\n            switch.WriteTableEntry(table_entry)\n\ndef install_port_knock_in_rules(p4info_helper, switches, switches_conf, rules):\n    for idx, switch in enumerate(switches):\n\n        sw = switches_conf[idx][\"name\"]\n        rules_l = rules[sw][\"rules\"]\n        actions_l = rules[sw][\"actions\"]\n\n        for index, rule in enumerate(rules_l):\n            table_entry = p4info_helper.buildTableEntry(\n                table_name=\"IngressImpl.pn_in.portknocking_in_tb\",\n                match_fields={\n                    \"hdr.ipv4.dstAddr\": rule[\"ipDstAddr\"],\n                    \"hdr.tcp.dstPort\": rule[\"tcpDstPort\"],\n                    \"hdr.tcp.ctrl\": rule[\"tcpCtrl\"]\n                },\n                action_name=actions_l[index][\"name\"],\n                action_params={}\n            )\n\n            switch.WriteTableEntry(table_entry)\n\ndef install_forwarding_rules(p4info_helper, switches, switches_conf, rules):\n\n    for idx, switch in enumerate(switches):\n\n        sw = switches_conf[idx][\"name\"]\n        rules_l = rules[sw][\"rules\"]\n        actions_l = rules[sw][\"actions\"]\n\n        for index, rule in enumerate(rules_l):\n            table_entry = p4info_helper.buildTableEntry(\n                table_name=\"IngressImpl.fwd.fwd_tb\",\n                match_fields={\n                    \"hdr.ipv4.dstAddr\": rule[\"ipDstAddr\"]\n                },\n                action_name=actions_l[index][\"name\"],\n                action_params={\n                    \"dstAddr\": actions_l[index][\"values\"][\"dstAddr\"],\n                    \"port\": actions_l[index][\"values\"][\"port\"]\n                }\n            )\n\n            switch.WriteTableEntry(table_entry)\n\ndef install_pip2id_rules(p4info_helper, switch, src_ip, new_id):\n\n    table_entry = p4info_helper.buildTableEntry(\n        table_name=\"IngressImpl.pr.ip_2_id_tb\",\n        match_fields={\n            \"hdr.ipv4.srcAddr\": src_ip\n        },\n        action_name=\"IngressImpl.pr.id_found\",\n        action_params={\n            \"current_id\": new_id\n        }\n    )\n\n    switch.WriteTableEntry(table_entry)\n\ndef install_pk_rules(p4info_helper, switches, switches_conf, rules):\n    for idx, switch in enumerate(switches):\n\n        sw = switches_conf[idx][\"name\"]\n        rules_l = rules[sw][\"rules\"]\n        actions_l = rules[sw][\"actions\"]\n\n        for index, rule in enumerate(rules_l):\n            table_entry = p4info_helper.buildTableEntry(\n                table_name=\"IngressImpl.pn_in.portknocking_in_tb\",\n                match_fields={\n                    \"meta.pk_metadata.stage\": rule[\"stage\"],\n                    \"hdr.ipv4.dstAddr\": rule[\"ipDstAddr\"],\n                    \"hdr.tcp.dstPort\": rule[\"tcpDstPort\"]\n                },\n                action_name=actions_l[index][\"name\"],\n                action_params={}\n            )\n\n            switch.WriteTableEntry(table_entry)\n\n\n\ndef main(p4info_file_path, bmv2_file_path):\n    # Instantiate a P4Runtime helper from the p4info file\n\n    ip2id_l = {}\n    id_manager = IdManager(2**16 - 1)\n\n    p4info_helper = p4runtime_lib.helper.P4InfoHelper(p4info_file_path)\n\n    switches_conf = load_switches_conf()\n    pr_rules = load_pr_rules()\n    sdnc_pi_rules = load_sdnc_pkt_in_rules()\n    fwd_rules = load_fwd_rules()\n    pk_rules = load_pk_rules()\n\n    try:\n\n        switches = connect_to_switches(switches_conf[\"switches\"])\n\n        send_master_arbitration_updates(switches)\n\n        set_pipelines(switches, p4info_helper, bmv2_file_path)\n\n        install_direct_forwarding_rules(p4info_helper, switches)\n\n        install_rules_protected_services(p4info_helper, switches, switches_conf, pr_rules[\"switches\"])\n\n        install_port_knock_in_rules(p4info_helper, switches, switches_conf, sdnc_pi_rules[\"switches\"])\n\n        install_forwarding_rules(p4info_helper, switches, switches_conf, fwd_rules[\"switches\"])\n\n        switch_2 = switches[1]\n        while True:\n            packet_in = switch_2.PacketIn()\n            if packet_in.WhichOneof('update') == 'packet':\n                pkt = Ether(_pkt=packet_in.packet.payload)\n\n                src_ip = pkt.getlayer(IP).src\n                print(\"SRC IP: \"+str(src_ip))\n\n                new_id = id_manager.get_id()\n                print(\"New ID: \"+str(new_id))\n                ip2id_l[str(src_ip)] = new_id\n\n                install_pip2id_rules(p4info_helper, switches, src_ip, new_id)\n                install_pk_rules(p4info_helper, switches, switches_conf, pk_rules[\"switches\"])\n\n\n    except KeyboardInterrupt:\n        print(\" Shutting down.\")\n    except grpc.RpcError as e:\n        printGrpcError(e)\n\n    ShutdownAllSwitchConnections()\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description='P4Runtime Controller')\n    parser.add_argument('--p4info', help='p4info proto in text format from p4c',\n                        type=str, action=\"store\", required=False,\n                        default='./build/advanced_tunnel.p4.p4info.txt')\n    parser.add_argument('--bmv2-json', help='BMv2 JSON file from p4c',\n                        type=str, action=\"store\", required=False,\n                        default='./build/advanced_tunnel.json')\n    args = parser.parse_args()\n\n    if not os.path.exists(args.p4info):\n        parser.print_help()\n        print(\"\\np4info file not found: %s\\nHave you run 'make'?\" % args.p4info)\n        parser.exit(1)\n    if not os.path.exists(args.bmv2_json):\n        parser.print_help()\n        print(\"\\nBMv2 JSON file not found: %s\\nHave you run 'make'?\" % args.bmv2_json)\n        parser.exit(1)\n\n    main(args.p4info, args.bmv2_json)\n","sub_path":"implementation_3/app/controller.py","file_name":"controller.py","file_ext":"py","file_size_in_byte":9004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"492459114","text":"import socket\nimport sys\nimport cv2\nimport pickle\nimport numpy as np\nimport struct ## new\nimport zlib\nfrom keras.models import load_model\nimport time\nimport serial\n\nser=serial.Serial('/dev/ttyUSB0',baudrate=9600,timeout=100)\n\ndef getValues():\n    Sensor=ser.readline().decode('UTF-8')\n    datas= Sensor.split(\",\")\n    return datas[0],datas[1]\n\ndef setValue():\n    ser.write(b'>M \\r\\n')\n\n\ndef img_preprocess(img):\n    img = img[60:135,:,:]\n    img = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)\n    img = cv2.GaussianBlur(img,  (3, 3), 0)\n    img = cv2.resize(img, (200, 66))\n    img = img/255\n    return img\n\nmodel=load_model(\"model.h5\")\n\nHOST=''\nPORT=1234\n\ns=socket.socket(socket.AF_INET,socket.SOCK_STREAM)\nprint('Socket created')\n\ns.bind((HOST,PORT))\nprint('Socket bind complete')\ns.listen(10)\nprint('Socket now listening')\n\nconn,addr=s.accept()\n\ndata = b\"\"\npayload_size = struct.calcsize(\">L\")\nprint(\"payload_size: {}\".format(payload_size))\nwhile True:\n    while len(data) < payload_size:\n        print(\"Recv: {}\".format(len(data)))\n        data += conn.recv(4096)\n\n    print(\"Done Recv: {}\".format(len(data)))\n    packed_msg_size = data[:payload_size]\n    data = data[payload_size:]\n    msg_size = struct.unpack(\">L\", packed_msg_size)[0]\n    print(\"msg_size: {}\".format(msg_size))\n    while len(data) < msg_size:\n        data += conn.recv(4096)\n    frame_data = data[:msg_size]\n    data = data[msg_size:]\n\n    frame=pickle.loads(frame_data, fix_imports=True, encoding=\"bytes\")\n    frame = cv2.imdecode(frame, cv2.IMREAD_COLOR)\n    cv2.imshow('ImageWindow',frame)\n\n    image=img_preprocess(frame)\n    image=np.array([image])\n\n    steering_angle=float(model.predict(image))\n    \n\n    s1,s2=getValues()\n\n    \n    speed=int(s1.replace(\"\\x00\",\"\"))\n    speed_limit=10\n    throttle=1.0-speed/speed_limit\n\n    #bThrottle=bytearray(struct.pack(\"f\",steering_angle))\n    #print(\"x\"+str(bThrottle))\n    ser.write(b'>M t \\r\\n')\n\n    '''\n    for b in bThrottle:\n        print(\"b\"+str(b))\n        ser.write(b)\n    '''\n    print(\"steering angle {}\".format(steering_angle))\n    key=cv2.waitKey(1)\n    if key == 27:\n        cv2.destroyAllWindows()\n        break\n   ","sub_path":"SendImagetoModel/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2144,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"162317646","text":"\nimport pandas as pd\nimport numpy as np\nfrom itertools import groupby\nfrom Bio import AlignIO\n\nclass ReadData:\n    \"\"\"\n    This is a class for reading sequence data in various file formats.\n\n    Attributes:\n        data (dict of str: List): Store ids and sequence data as dict.\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"\n        The constructor for ReadData class.\n\n        Parameters:\n            data (dict of str: List): Store ids and sequence data as dict.\n        \"\"\"\n\n        self.data = dict()\n\n\n    def aln2pandas(self, file, aln):\n        \"\"\"\n        aln2pandas(self, file)\n            The function to read input alignment using Biopython.\n\n            Parameters:\n                file (file object): The file object to read.\n                aln_type (str): The type of alignment to tell Biopython.\n\n            Returns:\n                df (pandas.DataFrame of str): Ids, seqs stored as Pandas DataFrame.\n                seqlength (int): Length of sequences in alignment.\n        \"\"\"\n        alignment = AlignIO.read(file, aln)\n        seqlength = alignment.get_alignment_length()\n        for record in alignment:\n            id = str(record.id)\n            seq = str(record.seq)\n            self.data[id] = seq\n\n        df = pd.DataFrame.from_dict(self.data, orient=\"index\")\n\n        return df, seqlength\n\n    def loci2db(self, file, locus_count):\n        \"\"\"\n        loci2db(self, file, locus_count)\n            The function to read .loci input file.\n\n            Parameters:\n                file (file object): The .loci file object to read.\n                locus_count (int): Current locus number.\n\n            Returns:\n                locus (dict of str): Single locus in .loci file.\n                locus_count (int): Current locus numberself.\n                ind_count (int): Number of individuals in locus.\n                seq_len (int): Length of sequences at locus.\n        \"\"\"\n\n        locus = dict()\n        ind_count = 0\n\n        for line in file:\n            line = line.strip()\n            lines = line.split()\n            id = lines[0]\n            seq = lines[1]\n\n            if line.startswith(\">\"):\n                locus[id] = seq\n                ind_count += 1\n                seq_len = len(seq)\n\n            elif line.startswith(\"//\"): # Locus delimiter.\n                locus_count += 1\n                yield locus, locus_count, ind_count, seq_len\n                ind_count = 0 # Reset ind_count\n                locus = dict() # Clear locus dict\n","sub_path":"read_data.py","file_name":"read_data.py","file_ext":"py","file_size_in_byte":2478,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"297305344","text":"from django.db.models.query import QuerySet\nfrom django.http import request\nfrom django.shortcuts import get_list_or_404, render\nfrom django.shortcuts import redirect\nfrom django.shortcuts import get_object_or_404\nfrom .models import Guide\nfrom django.utils import timezone\nfrom django.contrib import auth\nfrom django.db.models import Q\n\ndef guide_list (request) :\n    lists = Guide.objects.all()\n    return render(request,'guide_list.html',{'guide_list':lists})\n\ndef guide_detail(request,id) :\n    guide_detail = get_object_or_404(Guide,pk=id)\n    return render (request,'guide_detail.html',{'guide_detail':guide_detail})\n\n\ndef guide_new (request) :\n   \n    if (request.method == 'POST') :\n        newGuide = Guide.objects.create(title = request.POST['title'],\n                                        content = request.POST['content'],\n                                        writer = request.user.username,\n                                        date = timezone.localtime(),\n                                        location = request.POST['location'],\n                                        price = request.POST['price'],\n                                    )\n        if (request.FILES.get('image') is not None) :\n            newGuide.image = request.FILES['image']\n            newGuide.save()\n    \n        return guide_list(request)\n    \n    if (request.method == 'GET') :\n        return render(request,'guide_new.html')\n\ndef guide_update (request, id) :\n    if (request.method == 'GET') :\n        update_guide = get_object_or_404(Guide,pk=id)\n        \n        return render (request,'guide_update.html',{'guide_update':update_guide})\n\n    elif (request.method == 'POST') :\n        update_guide = Guide.objects.get(pk=id)\n        update_guide.title= request.POST['title']\n        update_guide.content= request.POST['content']\n        update_guide.location= request.POST['location']\n        update_guide.price= request.POST['price']\n        update_guide.image= request.FILES['image']\n        update_guide.save() \n        \n        return guide_list(request)\n\ndef guide_delete (request, id) :\n    if (request.method == 'GET') :\n        guide = get_object_or_404(Guide,pk=id) \n        guide.delete()\n        return guide_list(request)\n\ndef guide_search_by_location (request) :\n    search_key = request.GET['location']\n    # search_list = Guide.objects.all()\n    \n    search_list = Guide.objects.filter(location=search_key)\n    \n    \n    return render(request,'guide_list.html',{'guide_list':search_list})","sub_path":"Guide/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"556561115","text":"import argparse\nimport cv2\nfrom deepracer_viz.gradcam import load_model_session, gradcam, blend_gradcam_image\nimport numpy as np\nimport requests\nfrom bs4 import BeautifulSoup\nimport time\nimport os\n\n\n# Function for processing image and applying GradCAM\n# Using GPU helps, make sure to have CUDA installed correctly:\n#   You should need CUDA 10: check with `nvcc --version` : https://www.tensorflow.org/install/source#linux\n#   https://developer.nvidia.com/cuda-10.0-download-archive?target_os=Linux&target_arch=x86_64&target_distro=Ubuntu&target_version=1804&target_type=debnetwork\n#   `sudo apt-get install cuda-libraries-10.0`\ndef process_image(i, tf_session):\n    # Prep image for GradCam\n    grad_frame = cv2.cvtColor(i, cv2.COLOR_BGR2GRAY)\n    # Get GradCAM\n    cam = gradcam(tf_session, grad_frame, args.action)\n    # Overlay GradCAM with original image\n    return blend_gradcam_image(i, cam)\n\n\ndef main(args):\n    global bytes\n    CODEC = \"X264\"\n    FPS = args.fps\n\n    # if the ip argument exists then enter into live view mode with video from physical DeepRacer\n    if args.output:\n        fourcc = cv2.VideoWriter_fourcc(*CODEC)\n        raw_path = os.path.join(args.output, \"raw\")\n        print(\"Saving images to \" + raw_path)\n        gradcam_path = os.path.join(args.output, \"gradcam\")\n        print(\"Saving gradcam images to \" + gradcam_path)\n        if not os.path.exists(raw_path):\n            os.mkdir(raw_path)\n        if not os.path.exists(gradcam_path):\n            os.mkdir(gradcam_path)\n\n    if args.ip:\n        # Setup file to save the output to\n        with requests.Session() as s:\n            URL = \"https://\" + str(args.ip) + \"/\"\n            post_login_url = URL + \"/login\"\n            video_url = URL + \"/route?topic=/video_mjpeg&width=480&height=360\"\n\n            # Get the CSRF Token\n            response = s.get(URL, verify=False)\n            soup = BeautifulSoup(response.text, 'lxml')\n            csrf_token = soup.select_one('meta[name=\"csrf-token\"]')['content']\n            headers = {'X-CSRFToken': csrf_token}\n\n            # Login to the DeepRacer web interface with Post\n            if not args.password:\n                print(\"ERROR: User must add password for DeepRacer before stream can be accessed\")\n                exit(1)\n            payload = {'password': args.password}\n            post = s.post(post_login_url, data=payload, headers=headers, verify=False)\n\n            # Get the video stream\n            video_stream = s.get(video_url, stream=True, verify=False)\n\n            # Load the CNN\n            sess = load_model_session(args.model)\n\n            if video_stream.status_code == 200:\n                print(\"Video Connected!\")\n                last_image_time = time.time()\n                # Bytes to build Jpeg\n                bytes = bytes()\n                for chunk in video_stream.iter_content(chunk_size=1024):\n                    bytes += chunk\n                    a = bytes.find(b'\\xff\\xd8')  # Marker byte pair\n                    b = bytes.find(b'\\xff\\xd9')  # Trailing byte pair\n                    #  If both byte pairs on in the stream then build the jpeg\n                    if a != -1 and b != -1:\n                        jpg = bytes[a:b + 2]\n                        bytes = bytes[b + 2:]\n\n                        if time.time() - last_image_time > 1.0 / args.fps:\n                            i = cv2.imdecode(np.frombuffer(jpg, dtype=np.uint8), cv2.IMREAD_COLOR)\n                            out_frame = process_image(i, sess)\n                            # cv2.imshow('Raw Image', i)\n                            cv2.imshow('GradCAM', cv2.resize(out_frame, (1920, 1440)))\n                            last_image_time = time.time()\n                            # Save the images to file if args.output is specified\n                            if args.output:\n                                cv2.imwrite(os.path.join(raw_path, str(last_image_time) + '.jpg'), i)\n                                cv2.imwrite(os.path.join(gradcam_path, str(last_image_time) + '.jpg'), out_frame)\n                        if cv2.waitKey(1) == 27:  # Press esc to stop processing images\n                            break\n            else:\n                print(\"Received unexpected status code {}\".format(video_stream.status_code))\n\n    else:\n        capture = cv2.VideoCapture(args.input_file)\n\n        sess = load_model_session(args.model)\n        while capture.isOpened():\n            ret, frame = capture.read()\n            if ret:\n\n                # Apply GradCAM\n                out_frame = process_image(frame, sess)\n\n                cv2.imshow('frame', out_frame)\n                if args.output:\n                    last_image_time = time.time()\n                    cv2.imwrite(os.path.join(raw_path, str(last_image_time) + '.jpg'), frame)\n                    cv2.imwrite(os.path.join(gradcam_path, str(last_image_time) + '.jpg'), out_frame)\n\n                if cv2.waitKey(int(np.ceil(1000.0 / FPS))) & 0xFF == ord('q'):\n                    break\n            else:\n                break\n\n        capture.release()\n\n    cv2.destroyAllWindows()\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n\n    # non-optional arguments\n    parser.add_argument(\"model\", help=\"The .pb file containing the model.\")\n    parser.add_argument(\"action\", type=int, help=\"The index of the action in the action space to visualize\")\n\n    # args for static input and output video (optional)\n    parser.add_argument(\"-f\", \"--input_file\", help=\"The name of the MP4 file to process.\")\n    parser.add_argument(\"--fps\", help=\"FPS of the output video.\", type=int, default=10)\n    parser.add_argument(\"-o\", \"--output\", help=\"MP4 output file to store the gradcam output.\")\n\n    # args for live stream from physical DeepRacer (optional)\n    parser.add_argument(\"-i\", \"--ip\", help=\"The IP of the DeepRacer if you are using live feed.\")\n    parser.add_argument(\"-p\", \"--password\", help=\"The password for your DeepRacer if doing live feed.\")\n\n    args = parser.parse_args()\n\n    main(args)\n","sub_path":"tools/gradcam.py","file_name":"gradcam.py","file_ext":"py","file_size_in_byte":6014,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"419285390","text":"import os\nimport json\nimport shutil\nimport subprocess\nfrom send2trash import send2trash\n\n\nclass OSUtil(object):\n    def __init__(self):\n        super(OSUtil, self).__init__()\n\n    def renameFile(self, dir, old, new):\n        os.rename(\n            os.path.join(dir, old),\n            os.path.join(dir, new)\n        )\n\n    def renameDirectory(self, path, new_name):\n        try:\n            base, name = os.path.split(path)\n            os.rename(\n                src=path,\n                dst=os.path.join(base, new_name)\n            )\n            return True\n        except:\n            return False\n\n    def removeToTrash(self, path):\n        try:\n            send2trash(path)\n            return True\n        except:\n            return False\n\n    def removeFile(self, path):\n        if os.path.isfile(path):\n            try:\n                os.remove(path)\n                return True\n            except:\n                return False\n\n    def removeDir(self, path, ignore_errors=False):\n        if os.path.isdir(path):\n            try:\n                shutil.rmtree(path, bool(ignore_errors))\n                return True\n            except:\n                return False\n\n    def makeDir(self, path):\n        if not os.path.exists(path):\n            try:\n                os.makedirs(path)\n                return True\n            except:\n                return False\n        else:\n            return False\n\n    def copyFile(self, src, dst):\n        try:\n            shutil.copyfile(src, dst)\n            return True\n        except:\n            return False\n\n    def saveToJSON(self, obj, path):\n        if not os.path.exists(path):\n            new_file = open(path, 'w')\n            new_file.close()\n            if os.path.exists(path):\n                json.dump(\n                    obj=obj,\n                    fp=open(path, 'w'),\n                    indent=4\n                )\n        else:\n            json.dump(\n                obj=obj,\n                fp=open(path, 'w'),\n                indent=4\n            )\n\n    def loadFromJSON(self, path):\n        if (os.path.exists(path)):\n            result = json.load(fp=open(path))\n            return result\n        else:\n            return None\n\n    def goodFileName(self, name):\n        bad = self.badFileNameSigns()\n        for sign in name:\n            if sign in bad:\n                return False\n        return True\n\n    def badFileNameSigns(self):\n        return ':*?<>|/\\\\'\n\n    def hideDirectory(self, path):\n        if os.path.isdir(path):\n            cmd = \"\"\"attrib +s +h \"{dir}\" \"\"\".format(dir=path)\n            os.system(cmd)\n\n    def openOSExplorer(self, path):\n        if os.path.isdir(path):\n            path = path.replace('/', '\\\\')\n            subprocess.Popen('explorer \"{dir}\"'.format(dir=path))\n\n\nOSUTIL = OSUtil()\n\n","sub_path":"Source/os_utilities.py","file_name":"os_utilities.py","file_ext":"py","file_size_in_byte":2789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"428939436","text":"import logging\nfrom flask import render_template, url_for, request, redirect, Blueprint\nfrom yahtzee.game.utils import (roll_dice, get_dice_imgs, get_categories, \n    update_score, next_turn)\nfrom yahtzee.game.vars import Game\nfrom yahtzee.game.forms import CategoryForm, PlayerNamesForm\n\nlog = logging.getLogger(__name__)\n\ngame = Blueprint('game', __name__)\n\n\n@game.route(\"/game/new/<int:p>\", methods=['GET', 'POST'])\ndef new_game(p=0):\n    if 'cur_game' not in globals() or p != 0:\n        global cur_game\n        cur_game = Game(p) # p is the number of players\n\n    form = PlayerNamesForm()\n    if form.validate_on_submit():\n        # Assign name values obtained from PlayerNamesForm\n        cur_game.p_names[1] = form.player1.data\n        cur_game.p_names[2] = form.player2.data\n        cur_game.p_names[3] = form.player3.data\n        cur_game.p_names[4] = form.player4.data\n        log.debug(cur_game.p_names)\n\n        return redirect(url_for('game.play'))\n\n    return render_template('new.html', title='New Game', form=form, game=cur_game)\n\n\n@game.route(\"/game/play\", methods=['GET', 'POST'])\ndef play():\n    if 'cur_game' in globals():\n        categories = get_categories(cur_game.dice) # List of tuples (key, val, str)\n        select_categories = [(x, z) for x, y, z in categories] # 2 item tuple\n        log.debug(categories)\n\n        form = CategoryForm()\n        form.update_categories(cats=select_categories) # Update categories when page loads\n        if form.validate_on_submit():\n            pick = form.category.data # Should return category key value\n            log.debug(pick)\n            \n            for k, v, s in categories:\n                if k == pick:\n                    update_score(pick, v)\n                    if next_turn() == False:\n                        cur_game.get_winner()\n                        return redirect(url_for('game.results'))\n                    else:\n                        form.update_categories()\n        \n        d_imgs = get_dice_imgs(cur_game.dice, cur_game.held) # Dice img string list\n        return render_template('play.html', title='Play', game=cur_game, d_imgs=d_imgs, form=form)\n    else:\n        log.warning(\"Could not locate game in global variable list; Redirecting to '/game/new'\")\n        return redirect(url_for('main.home'))\n\n\n@game.route(\"/game/roll\")\ndef roll():\n    if 'cur_game' in globals():\n        cur_game.dice = roll_dice(cur_game.dice, cur_game.held)\n        cur_game.roll += 1 # Increment roll num\n\n        return redirect(url_for('game.play'))\n    else:\n        log.warning(\"Could not roll dice; Redirecting to '/game/new'\")\n        return redirect(url_for('main.home'))\n\n\n@game.route(\"/game/hold/<int:d>\")\ndef hold(d):\n    if d not in cur_game.held and cur_game.dice[d] != 0:\n        cur_game.held.append(d)\n    elif d in cur_game.held:\n        cur_game.held.remove(d)\n\n    return redirect(url_for('game.play'))\n\n\n@game.route(\"/game/results\")\ndef results():\n    if 'cur_game' in globals():\n        p_scores = [(pos, p) for pos, p in enumerate(cur_game.p_scores) if pos != 0]\n        return render_template('results.html', title='Results', p_scores=p_scores, winner=cur_game.winner)\n    else:\n        log.warning(\"Could not locate game in global variable list; Redirecting to '/game/new'\")\n        return redirect(url_for('main.home'))\n\n\n# Debug route for testing\n@game.route(\"/game/test\", methods=['GET', 'POST'])\ndef test():\n    cats = [('ones', 'Ones - 1'), ('twos', 'Twos - 1'), ('threes', 'Threes - 1')]\n\n    category_form = CategoryForm()\n    category_form.update_categories(cats)\n    if category_form.validate_on_submit():\n        log.debug(f\"{category_form.content.data} was submitted.\")\n        return redirect(url_for('game.test'))\n\n    return render_template('test.html', title='Test', form=category_form)\n","sub_path":"yahtzee/game/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":3800,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"126588147","text":"import re\nimport plyj.parser as plyj\nimport plyj.model as model\n\n\n# Java File Processor\nclass ProcessorJAVA:\n    def __init__(self, path):\n        self.stringPattern = re.compile('\\\".*?\\\"', re.DOTALL)\n        self.singleCommentPattern = re.compile('//.*\\n')\n        self.blockCommentPattern = re.compile('/\\*(.*?)\\*/', re.DOTALL)\n        self.parser = plyj.Parser()\n        self.tree = self.parser.parse_file(path)\n\n    # Return the position of strings present in the document\n    # Doesnt consider escaped \\\" inside a string like string = \"something \\\" .\"\n    def extractStringPositions(self, doc):\n        stringPos = [(m.start(), m.end() - 1)\n                     for m in re.finditer(self.stringPattern, doc)]\n\n        return stringPos\n\n    # Regex to find comments in the code\n    def extractComments(self, doc):\n        comments = []\n        commentsPos = []\n        singleComment = -1\n        blockComment = -1\n        inString = False\n        # Parse each character\n        for i in range(len(doc)):\n            # Check if a string is running\n            if doc[i] == '\\\"':\n                inString = not(inString)\n            # Check if a comment is starting\n            if not(inString) and doc[i] == '/':\n                try:\n                    if doc[i+1] == '/':\n                        singleComment = i\n                    elif doc[i+1] == '*':\n                        blockComment = i\n                except:\n                    None\n            # If the comment is single line comment\n            if singleComment != -1:\n                if doc[i] == '\\n':\n                    comment = doc[singleComment:i] + '\\n'\n                    comments.append(comment)\n                    commentsPos.append((singleComment, i-1))\n                    singleComment = -1\n            # If the comment is a block comment\n            if blockComment != -1:\n                try:\n                    if doc[i] == '*' and doc[i+1] == '/':\n                        comment = doc[blockComment:i+1] + '/'\n                        comments.append(comment)\n                        commentsPos.append((blockComment, i+1))\n                        blockComment = -1\n                except:\n                    None\n\n        return comments, commentsPos\n\n    # Regex to find variables in the code\n    def extractVariables(self, doc, stringPos=[]):\n        declarations = []\n        vDeclarators = []\n        typeDeclarations = self.tree.type_declarations\n        for decl in typeDeclarations:\n            bodyDeclarations = decl.body\n            for bDecl in bodyDeclarations:\n                if (type(bDecl) == model.FieldDeclaration):\n                    vDeclarators += bDecl.variable_declarators\n                elif (type(bDecl) == model.MethodDeclaration):\n                    mDeclBody = bDecl.body\n                    for vDecl in mDeclBody:\n                        if(type(vDecl) == model.VariableDeclaration):\n                            vDeclarators += vDecl.variable_declarators\n\n        for decl in vDeclarators:\n            declarations.append(decl.variable.name)\n\n        return declarations, len(declarations)\n\n    # Extract functions\n    def extractFunctions(self, file):\n        functions = []\n        typeDeclarations = self.tree.type_declarations\n        for decl in typeDeclarations:\n            bodyDeclarations = decl.body\n            for bDecl in bodyDeclarations:\n                if(type(bDecl) == model.MethodDeclaration):\n                    functions.append(decl.name)\n        return functions, len(functions)\n\n    # Extract classes\n    def extractClasses(self, file):\n        classes = []\n        typeDeclarations = self.tree.type_declarations\n        for decl in typeDeclarations:\n            if(type(decl) == model.ClassDeclaration):\n                classes.append(decl.name)\n        return classes, len(classes)\n","sub_path":"package/Processor/ProcessorJAVA.py","file_name":"ProcessorJAVA.py","file_ext":"py","file_size_in_byte":3833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"59447407","text":"import matplotlib.pyplot as plt\nimport numpy as np\n\ndef gauss_kernel(size = 5, sigma = 3):\n    center = (int)(size / 2)\n    kernel = np.zeros((size, size))\n    for i in range(size):\n        for j in range(size):\n            diff = np.sqrt((i - center) ** 2 + (j - center) ** 2)\n            kernel[i, j] = np.exp(-(diff ** 2) / (2 * sigma ** 2))\n    return kernel / np.sum(kernel)\n\n\ndef filter(img, window_size=3):\n    img2 = np.zeros_like(img)\n    kernel = gauss_kernel(window_size)\n    p = window_size//2\n    for k in range(img.shape[2]): # foreach color channel\n        for i in range(p, img.shape[0]-p): # foreach row\n            for j in range(p, img.shape[1]-p): # foreach column\n                window = img[i-p:i+p+1, j-p:j+p+1, k]\n                img2[i,j,k] = (kernel*window).sum()\n    return img2\n\ndef main():\n    img = plt.imread(\"H:\\\\git\\\\2019_IT\\\\Snail.png\")[:, :, :3] #file\n    img2 = filter(img)\n\n    fig, axs = plt.subplots(1,2)\n    axs[0].imshow(img)\n    axs[1].imshow(img2)\n    plt.show()\n\n\nif __name__ == \"__main__\":\n    main()","sub_path":"task2.py","file_name":"task2.py","file_ext":"py","file_size_in_byte":1046,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"62538559","text":"# Imports\nimport tensorflow as tf\nimport pandas as pd\n\n# These are the headers in our dataframe\nCSV_COLUMN_NAMES = ['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth', 'Species']\n \nSPECIES = ['Setosa', 'Versicolor', 'Virginica']\n\n# The following are constants that will save time\ntrain_path = tf.keras.utils.get_file(\n    \"iris_training.csv\", \"https://storage.googleapis.com/download.tensorflow.org/data/iris_training.csv\")\ntest_path = tf.keras.utils.get_file(\n    \"iris_test.csv\", \"https://storage.googleapis.com/download.tensorflow.org/data/iris_test.csv\")\n\n# We will use Keras (a tensorflow model) to help read data into a pandas dataframe\ntrain = pd.read_csv(train_path, names=CSV_COLUMN_NAMES, header=0)\ntest = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=0)\n\n# Seperating the thing we eventually want our model to predict\ntrain_y = train.pop('Species')\ntest_y = test.pop('Species')\n\n# Input Function\ndef input_fn(features, labels, training=True, batch_size=256):\n    # Converts inputs to a data.Dataset object\n    dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))\n\n    # Shuffle and repeat if you are in training mode\n    if training:\n        dataset = dataset.shuffle(1000).repeat()\n\n    return dataset.batch(batch_size)\n\n# Because feature columns are all numerical, we can just loop through\nfeature_columns = []\nfor key in train.keys():\n    feature_columns.append(tf.feature_column.numeric_column(key=key))\n\n# Creating the Estimator: going to create a Deep Neural Network\n# The DNN has 2 hidden layers with 30 nodes and 10 hidden nodes each\nclassifier = tf.estimator.DNNClassifier(\n    # Init Feature Columns\n    feature_columns=feature_columns,\n    # These are the hidden units\n    hidden_units=[30, 10],\n    # The classifier has to choose between 3 classes\n    n_classes=3\n)\n\n# Training the Model\nclassifier.train(\n    input_fn = lambda:input_fn(train, train_y, training=True),\n    steps = 500\n)\n\n# Testing the model against known values\neval_result = classifier.evaluate(input_fn = lambda:input_fn(test, test_y, training=False))\n\nprint('\\nTest accuracy: {accuracy:0.3f}'.format(**eval_result))","sub_path":"irisModel.py","file_name":"irisModel.py","file_ext":"py","file_size_in_byte":2140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"139841273","text":"'''\nCreated on May 22, 2018\n\nAuthor: Achlioptas Panos (Github ID: optas)\n'''\n\nimport numpy as np\nimport time\nimport tensorflow as tf\n\nfrom tflearn import is_training\nfrom . gan import GAN\nfrom .. external.structural_losses.tf_nndistance import nn_distance\n# from .. external.structural_losses.tf_approxmatch import approx_match, match_cost\n\n\nclass W_GAN_GP(GAN):\n    '''Gradient Penalty.\n    https://arxiv.org/abs/1704.00028\n    '''\n\n    def __init__(self, name, learning_rate, lam, n_output, noise_dim, discriminator, generator, configuration, beta=0.5, gen_kwargs={}, disc_kwargs={}, reconstr_param=None, disc_param=None, graph=None):\n        assert noise_dim == 1948\n\n        GAN.__init__(self, name, graph)\n\n        self.noise_dim = noise_dim\n        self.n_output = n_output\n        self.discriminator = discriminator\n        self.generator = generator\n\n        c = configuration\n\n        with tf.variable_scope(name):\n            #  self.noise = tf.placeholder(tf.float32, shape=[None, noise_dim])            # Noise vector.\n            self.incomplete_input = tf.placeholder(tf.float32, shape=[None, noise_dim, 3])\n            self.real_pc = tf.placeholder(tf.float32, shape=[None] + self.n_output)     # Ground-truth.\n\n            with tf.variable_scope('generator'):\n                self.generator_out = self.generator(self.incomplete_input, configuration, **gen_kwargs)\n\n            with tf.variable_scope('discriminator') as scope:\n                self.real_prob, self.real_logit = self.discriminator(self.real_pc, scope=scope, **disc_kwargs)\n                self.synthetic_prob, self.synthetic_logit = self.discriminator(self.generator_out, reuse=True, scope=scope, **disc_kwargs)\n\n            # Compute WGAN losses\n            # discriminator loss\n            self.loss_d = tf.reduce_mean(self.synthetic_logit) - tf.reduce_mean(self.real_logit)\n\n            # generator loss\n            cost_p1_p2, _, cost_p2_p1, _ = nn_distance(self.generator_out, self.real_pc)\n            l2_loss = tf.reduce_mean(cost_p1_p2) + tf.reduce_mean(cost_p2_p1)\n\n            # reg_losses = self.graph.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)\n            # w_reg_alpha = 1.0\n            # for rl in reg_losses:\n            #     self.loss += (w_reg_alpha * rl)\n            self.g_reconstr_loss = reconstr_param*l2_loss\n            self.g_disc_loss = -disc_param*tf.reduce_mean(self.synthetic_logit)\n\n            # self.loss_g = -0.2*self.g_disc_loss + 0.8*self.g_reconstr_loss\n            self.loss_g = self.g_disc_loss + self.g_reconstr_loss\n\n\n            # Compute gradient penalty at interpolated points\n            ndims = self.real_pc.get_shape().ndims\n            batch_size = tf.shape(self.real_pc)[0]\n            alpha = tf.random_uniform(shape=[batch_size] + [1] * (ndims - 1), minval=0., maxval=1.)\n            differences = self.generator_out - self.real_pc\n            interpolates = self.real_pc + (alpha * differences)\n\n            with tf.variable_scope('discriminator') as scope:\n                gradients = tf.gradients(self.discriminator(interpolates, reuse=True, scope=scope, **disc_kwargs)[1], [interpolates])[0]\n\n            # Reduce over all but the first dimension\n            slopes = tf.sqrt(tf.reduce_sum(tf.square(gradients), reduction_indices=range(1, ndims)))\n            gradient_penalty = tf.reduce_mean((slopes - 1.) ** 2)\n            self.loss_d += lam * gradient_penalty\n\n            train_vars = tf.trainable_variables()\n            d_params = [v for v in train_vars if v.name.startswith(name + '/discriminator/')]\n            g_params = [v for v in train_vars if v.name.startswith(name + '/generator/')]\n\n            self.opt_d = self.optimizer(learning_rate, beta, self.loss_d, d_params)\n            self.opt_g = self.optimizer(learning_rate, beta, self.loss_g, g_params)\n\n            self.saver = tf.train.Saver(tf.global_variables(), max_to_keep=None)\n            self.init = tf.global_variables_initializer()\n\n            # Launch the session\n            config = tf.ConfigProto()\n            config.gpu_options.allow_growth = True\n            self.sess = tf.Session(config=config)\n            self.sess.run(self.init)\n\n    def generator_noise_distribution(self, n_samples, ndims, mu, sigma):\n        return np.random.normal(mu, sigma, (n_samples, ndims))\n\n    def _single_epoch_train(self, train_data, batch_size, noise_params, discriminator_boost=5):\n        '''\n        see: http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/\n             http://wiseodd.github.io/techblog/2016/09/17/gan-tensorflow/\n        '''\n        n_examples = train_data.num_examples\n        epoch_loss_d = 0.\n        epoch_loss_g = 0.\n        epoch_g_disc_loss = 0.\n        epoch_g_reconstr_loss = 0.\n        batch_size = batch_size\n        n_batches = int(n_examples / batch_size)\n        start_time = time.time()\n\n        iterations_for_epoch = n_batches / discriminator_boost\n\n        is_training(True, session=self.sess)\n        try:\n            # Loop over all batches\n            for _ in xrange(iterations_for_epoch):\n                for _ in range(discriminator_boost):\n                    batch_i, _, _ = train_data.next_batch(batch_size)\n                    # z is incomplete data, feed is the complete PC\n                    z, feed = batch_i[:, :1948, :], batch_i[:, 1948:, :]\n                    # z = self.generator_noise_distribution(batch_size, self.noise_dim, **noise_params)\n\n                    feed_dict = {self.real_pc: feed, self.incomplete_input: z}\n                    _, loss_d = self.sess.run([self.opt_d, self.loss_d], feed_dict=feed_dict)\n                    epoch_loss_d += loss_d\n\n                # Update generator.\n                # z = self.generator_noise_distribution(batch_size, self.noise_dim, **noise_params)\n                batch_i, _, _ = train_data.next_batch(batch_size)\n                # z is incomplete data, feed is the complete PC\n                z, feed = batch_i[:, :1948, :], batch_i[:, 1948:, :]\n\n                feed_dict = {self.incomplete_input: z, self.real_pc: feed}\n                _, loss_g, g_disc_loss, g_reconstr_loss = self.sess.run([self.opt_g, self.loss_g, self.g_disc_loss, self.g_reconstr_loss], feed_dict=feed_dict)\n                epoch_loss_g += loss_g\n                epoch_g_disc_loss += g_disc_loss\n                epoch_g_reconstr_loss += g_reconstr_loss\n\n            is_training(False, session=self.sess)\n        except Exception:\n            raise\n        finally:\n            is_training(False, session=self.sess)\n        epoch_loss_d /= (iterations_for_epoch * discriminator_boost)\n        epoch_loss_g /= iterations_for_epoch\n        epoch_g_disc_loss /= iterations_for_epoch\n        epoch_g_reconstr_loss /= iterations_for_epoch\n\n        duration = time.time() - start_time\n        return (epoch_loss_d, epoch_loss_g, epoch_g_reconstr_loss, epoch_g_disc_loss), duration\n","sub_path":"src/w_gan_gp.py","file_name":"w_gan_gp.py","file_ext":"py","file_size_in_byte":6901,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"563214995","text":"def insertion_sort(items):\n    # Split the list into sorted and unsorted\n    # For each element in unsorted...\n    counter = 0\n    for i in range(1, len(items)):\n        # Insert that element into the correct place in sorted\n        # Store the elements in a temp variable\n        temp = items[i]\n        # Shifting all larger sorted elements to the right by 1\n        j = i\n        while j > 0 and temp < items[j - 1]:\n            print('**********************')\n            counter += 1\n            print(items)\n            items[j] = items[j - 1]\n            j -= 1\n            print(items)\n            # Insert the element after the first smaller element\n            items[j] = temp\n            print(items)\n            print(f'Counter = {counter}')\n    return items\n\n\nl = [7, 4, 9, 2, 6, 3, 0, 8, 5, 1]\n\ninsertion_sort(l)","sub_path":"src/insertion_sort.py","file_name":"insertion_sort.py","file_ext":"py","file_size_in_byte":826,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"315520660","text":"s = input(\"enter the string\")\ns1 = list(s.replace(\" \",\"\")) #converting the input into list and removing spaces between them\nprint(s1)\n\nindividual=[] #every individual character passed in the string\nrepeated=[] #every repeated characcter passeed in the string\nunique=[] #characters whose count is 1 in the string\n\nfor c in s1:\n    if c not in individual:\n        individual.append(c) #append each character in the string\n    else:\n         repeated.append(c)  #append repeated characters into this list\n\nfor d in s1:\n    if d in individual and d not in repeated:\n        unique.append(d)\n\n#prints the first non-repeated character\nprint(\"The first non-repeated character in the string is: \", unique[0])\n\n","sub_path":"Lab1/Source/firstnonrepeat.py","file_name":"firstnonrepeat.py","file_ext":"py","file_size_in_byte":702,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"451272426","text":"def listdatas(pathin):\n    import os\n    a = []\n    datas = os.listdir(pathin)\n    for i in datas:\n        if i.endswith('.tif'):\n            fn_i = pathin + '/' + i\n            a.append(fn_i)\n    return a\n\ndef mosaic(datas, pathout, pathout_sh):\n    from osgeo import gdal\n\n    nn = pathout_sh + '/' + 'mosaic.sh'\n    f = open(nn,'w')\n\n    f.write('gdal_merge.py -o {} -n -99.0 -a_nodata -99.0 '.format(pathout + '/' + 'mean_mosaic' + '.tif'))\n    for i in datas:\n        f.write('{} '.format(i))\n    f.close()\n    return\n\ndef main():\n    import argparse\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-i', '--input', type=str, help='input', required=True)# 输入路径\n    parser.add_argument('-o', '--output', type=str, help='output', required=True)# 输出路径\n    parser.add_argument('-os', '--output_sh', type=str, help='output_sh', required=True)# .sh输出路径\n    args = parser.parse_args()\n    datas = listdatas(args.input)\n    mosaic(datas, args.output, args.output_sh)\n    return\n\nif __name__ == \"__main__\":\n    main()\n\n# python mosaic.py -i /mnt/e/r_tcc/out -o /mnt/e/r_tcc/out_1 -os /mnt/e/r_tcc/code\n# python mosaic.py -i /public/home/mfeng/jwang/forest/northeast/out/slope/1 -o /public/home/mfeng/jwang/forest/northeast/out/slope/3 -os /public/home/mfeng/jwang/forest/northeast/code_tcc/inter_new","sub_path":"ne_forest/01/tcc/mosaic.py","file_name":"mosaic.py","file_ext":"py","file_size_in_byte":1334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"352608774","text":"from tensorflow import keras\nfrom os.path import isfile\nfrom tqdm import tqdm\nimport numpy as np\nimport random\nfrom data import FeatureGen, ScoreGen, TrainingData\nimport image_utils as iu\nfrom model import baseline\nfrom util import score_reshape, compute_score\nfrom callback import tensor_board\nshape = (384, 384, 1)\nmetadata = iu.load_meta()\ntrain = metadata['train']\nprint(\"Training data : \", len(train))\ntrain_set = set(train)\nw2hs = metadata['w2hs']\nsteps = 0\nmodel, branch_model, head_model = baseline.build_model(shape, 64e-5, 0)\n\n\ndef set_lr(model, lr):\n    keras.backend.set_value(model.optimizer.lr, float(lr))\n\n\ndef get_lr(model):\n    return keras.backend.get_value(model.optimizer.lr)\n\n\ndef make_steps(step, ampl):\n    \"\"\"\n    Perform training epochs\n    @param step Number of epochs to perform\n    @param ampl the K, the randomized component of the score matrix.\n    \"\"\"\n    global w2ts, t2i, steps, features, score, histories\n\n    # shuffle the training pictures\n    random.shuffle(train)\n\n    # Map whale id to the list of associated training picture hash value\n    w2ts = metadata['w2ts']\n    # Map training picture hash value to index in 'train' array\n    t2i = metadata['t2i']\n    # Compute the match score for each picture pair\n    features, score = compute_score(\n        train, shape, branch_model, head_model, verbose=1, **metadata)\n\n    # Train the model for 'step' epochs\n    history = model.fit_generator(\n        TrainingData(\n            shape,\n            score + ampl * np.random.random_sample(size=score.shape),\n            steps=step,\n            batch_size=32,\n            **metadata),\n        initial_epoch=steps,\n        epochs=steps + step,\n        max_queue_size=6,\n        workers=4,\n        verbose=1,\n        use_multiprocessing=True).history\n    steps += step\n\n    # Collect history data\n    history['epochs'] = steps\n    history['ms'] = np.mean(score)\n    history['lr'] = get_lr(model)\n    print(history['epochs'], history['lr'], history['ms'])\n    histories.append(history)\n    model.save('../pre_trained_model/fine-tuned.model')\n\n\nhistories = []\nsteps = 0\n\nif isfile('../pre_trained_model/mpiotte-standard.model'):\n    tmp = keras.models.load_model('../pre_trained_model/mpiotte-standard.model')\n    model.set_weights(tmp.get_weights())\n\n# epoch -> 10\nmake_steps(10, 1000)\nampl = 100.0\nfor _ in range(2):\n    print('noise ampl.  = ', ampl)\n    make_steps(5, ampl)\n    ampl = max(1.0, 100**-0.1 * ampl)\n# epoch -> 150\nfor _ in range(18):\n    make_steps(5, 1.0)\n# epoch -> 200\nset_lr(model, 16e-5)\nfor _ in range(10):\n    make_steps(5, 0.5)\n# epoch -> 240\nset_lr(model, 4e-5)\nfor _ in range(8):\n    make_steps(5, 0.25)\n# epoch -> 250\nset_lr(model, 1e-5)\nfor _ in range(2):\n    make_steps(5, 0.25)\n# epoch -> 300\nweights = model.get_weights()\nmodel, branch_model, head_model = baseline.build_model(\n    shape, 64e-5, 0.0002)\nmodel.set_weights(weights)\nfor _ in range(10):\n    make_steps(5, 1.0)\n# epoch -> 350\nset_lr(model, 16e-5)\nfor _ in range(10):\n    make_steps(5, 0.5)\n# epoch -> 390\nset_lr(model, 4e-5)\nfor _ in range(8):\n    make_steps(5, 0.25)\n# epoch -> 400\nset_lr(model, 1e-5)\nfor _ in range(2):\n    make_steps(5, 0.25)\nmodel.save('../pre_trained_model/fine-tuned.model')","sub_path":"code/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":3220,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"343650126","text":"\"\"\"\nPremium Question\n\"\"\"\n__author__ = 'Daniel'\n\n\nclass TicTacToe(object):\n    def __init__(self, n):\n        \"\"\"\n        Initialize your data structure here.\n        :type n: int\n        \"\"\"\n        self.n = n\n        self.rows = [0 for _ in xrange(n)]\n        self.cols = [0 for _ in xrange(n)]\n        self.diag0 = 0\n        self.diag1 = 0\n\n    def move(self, row, col, player):\n        \"\"\"\n        Since guarantee the move is valid, only store row, col, diagonal.\n        1: -1\n        2: +1\n        Player {player} makes a move at ({row}, {col}).\n        @param row The row of the board.\n        @param col The column of the board.\n        @param player The player, can be either 1 or 2.\n        @return The current winning condition, can be either:\n                0: No one wins.\n                1: Player 1 wins.\n                2: Player 2 wins.\n        :type row: int\n        :type col: int\n        :type player: int\n        :rtype: int\n        \"\"\"\n        delta = -1 if player == 1 else 1\n        self.cols[col] += delta\n        self.rows[row] += delta\n        if col == row:\n            self.diag0 += delta\n        if col + row == self.n - 1:\n            self.diag1 += delta\n\n        is_win = lambda x: delta * x == self.n\n        if any(map(is_win, [self.rows[row], self.cols[col], self.diag0, self.diag1])):\n            return player\n\n        return 0\n\n# Your TicTacToe object will be instantiated and called as such:\n# obj = TicTacToe(n)\n# param_1 = obj.move(row,col,player)","sub_path":"348 Design Tic-Tac-Toe.py","file_name":"348 Design Tic-Tac-Toe.py","file_ext":"py","file_size_in_byte":1488,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"539243252","text":"from django.contrib.auth import authenticate\n# 验证登陆\n\nfrom backend import paramiko_ssh\n\nfrom web import models\n\nclass SshHandle(object):\n    \"\"\"堡垒机交互脚本\"\"\"\n    def __init__(self,argv_handler_item):\n        self.argv_handler_item=argv_handler_item\n        self.models=models\n    def auth(self):\n        \"\"\"认证程序\"\"\"\n        count = 0\n        while count < 3:\n            username = input(\"堡垒机账号:\").strip()\n            password = input(\"Password:\").strip()\n            user=authenticate(username=username,password=password)\n            # 用户验证\n            if user:\n                self.user=user\n                return True\n            else:\n                count +=1\n\n    def interactive(self):\n        \"\"\"启动交互脚本\"\"\"\n\n        if self.auth():\n            '''验证'''\n            print(\"验证通过...\")\n            host_goup_list=self.user.host_groups.all()\n            # 获取属于对象的主机组列表\n            while True:\n                host_goup_list=self.user.host_groups.all()\n                for index,host_goup_obj in enumerate(host_goup_list):\n                    print(\"%s.\\t[%s](%s)\"%(index,host_goup_obj.name,host_goup_obj.host_to_remote_users.count()))\n\n                print('z.\\t未分组主机[%s]'%(self.user.host_to_remote_users.count()))\n                choice = input(\"请选择主机组>>>：\")\n                if choice.isdigit():\n                    choice=int(choice)\n                    selected_host_group=host_goup_list[choice]\n                elif choice =='z':\n                    selected_host_group = self.user\n\n                while True:\n                    \"\"\" 没有分组\"\"\"\n                    for  index,host_to_user_obj in enumerate(selected_host_group.host_to_remote_users.all()):\n\n                        print(\"%s.\\t%s\" % (index, host_to_user_obj))\n                    choice = input(\"请选择主机>>>：\").strip()\n\n                    if choice.isdigit():\n                        choice = int(choice)\n                        selected_host_to_user_obj = selected_host_group.host_to_remote_users.all()[choice]\n                        print('------->%s'% selected_host_to_user_obj)\n                        paramiko_ssh.ssh_connect(self,selected_host_to_user_obj)\n                    #     远程建立连接\n                    elif choice =='b':\n                        break\n\n\n\n\n","sub_path":"新建文件夹/BLJ/backend/ssh_interactive.py","file_name":"ssh_interactive.py","file_ext":"py","file_size_in_byte":2386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"137437766","text":"import numpy as np\nimport pandas as pd\nfrom sklearn import datasets\nfrom sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\nfrom sklearn import model_selection\nfrom sklearn import preprocessing\nimport matplotlib.pyplot as plt\nimport matplotlib.dates as mdates\nfrom math import sqrt\nfrom data_process import data_io_do as dataio\nfrom keras import backend as K\nimport os\nimport h5py\nimport argparse\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout,RepeatVector\nfrom keras.layers import LSTM, TimeDistributed, Masking\nfrom keras.callbacks import Callback,ModelCheckpoint\nfrom keras.models import load_model\nfrom keras import regularizers\nfrom keras.optimizers import SGD\n\n\nimport tensorflow as tf\nimport tensorboard\n\n\ntf.logging.set_verbosity(tf.logging.INFO)\n\n# Prepare data\n\ndef generate_data(filepath, num_sample, timestamp, start, mode, scalardic):\n    \"\"\"\n    :param filepath: data set for the model\n    :param start: start row for training set, for training, start=0\n    :param num_sample: how many samples used for training set, in this case, 2928 samples from 1st Oct-30th Nov, two month\n    :param timestamp: timestamp used for LSTM\n    :return: training set, train_x and train_y\n    \"\"\"\n    dataset = pd.read_csv(filepath)\n    numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n    for c in [c for c in dataset.columns if dataset[c].dtype in numerics]:\n        dataset[c] = dataset[c].abs()\n    dataset = dataset.iloc[start:start + num_sample, :]  # get first num_sample rows for training set, with all columns\n    dataset['TIMESTAMP'] = pd.to_datetime(dataset['TIMESTAMP'], dayfirst=True)\n\n\n    set_x, set_y = dataio.load_csvdata_Pearson(dataset, timestamp, mode, scalardic)\n    return set_x, set_y\n\n\ndef getdate_index(filepath, start, num_predict):\n    \"\"\"\n    :param filepath: same dataset file\n    :param start: start now no. for prediction\n    :param num_predict: how many predictions\n    :return: the x axis datatime index for prediciton drawing\n    \"\"\"\n    dataset = pd.read_csv(filepath)\n    dataset = dataset.iloc[start:start + num_predict, :]\n    dataset['TIMESTAMP'] = pd.to_datetime(dataset['TIMESTAMP'], dayfirst=True)\n\n    return dataset['TIMESTAMP']\n\n\ndef rsquare(y_true,y_pred):\n    SS_res = K.sum(K.square(y_true - y_pred))\n    SS_tot = K.sum(K.square(y_true - K.mean(y_true)))\n    return (1 - SS_res / (SS_tot + K.epsilon()))\n\n\n\n# Parameters\nmodel_params = {\n    'TIMESTEPS': 12,\n    'N_FEATURES':3,\n    'train_no': 2452,\n    'test_no': 473\n    # 'RNN_LAYERS': [{'num_units': 400}],\n    # # 'RNN_LAYERS': [{'num_units': 60, 'keep_prob': 0.75},{'num_units': 120, 'keep_prob': 0.75},{'num_units': 60, 'keep_prob': 0.75}],\n    # 'DENSE_LAYERS': None,\n    # 'TRAINING_STEPS': 15000,\n    # 'PRINT_STEPS': 50,\n    # 'BATCH_SIZE': 100\n}\n\n\n# Scale x data (training set) to 0 mean and unit standard deviation.\nscaler_do = preprocessing.StandardScaler()\nscaler_ec = preprocessing.StandardScaler()\nscaler_temp = preprocessing.StandardScaler()\nscaler_ph = preprocessing.StandardScaler()\nscaler_chlo = preprocessing.StandardScaler()\n\nscaler_dic = {\n    'scaler_one': scaler_do,\n    'scaler_two': scaler_ec,\n    'scaler_three': scaler_temp,\n    'scaler_four': scaler_ph,\n    'scaler_five': scaler_chlo\n}\n\n# datafile\nfilepath = r'C:\\Users\\ZHA244\\Coding\\QLD\\baffle_creek\\dry_season-90min.csv'\nx, y = generate_data(filepath, model_params['train_no'],model_params['TIMESTEPS'], 0, 'train', scaler_dic)\n\nscaler_dic['scaler_one'] = x['scalerone']\nscaler_dic['scaler_two'] = x['scalertwo']\nscaler_dic['scaler_three'] = x['scalerthree']\nscaler_dic['scaler_four'] = x['scalerfour']\nscaler_dic['scaler_five'] = x['scalerfive']\n\n# Training set, three train y for multiple tasks training\nx_train = x['train']\ny_train_do = y['trainyone']\n\n\n\n\n# y_train_ec = y['trainytwo']\n# y_train_temp = y['trainythree']\n\nx_t, y_t = generate_data(filepath, model_params['test_no']+12, model_params['TIMESTEPS'], model_params['train_no']-12, 'test', scaler_dic)  # testing set for 240 prediction (5 days)\n# Testing set, three test y for multiple tasks testing\nx_test = x_t['train']\ny_test_do = y_t['trainyone']\n# y_test_ec = y_t['trainytwo']\n# y_test_temp = y_t['trainythree']\n\n# Scale y data to 0 mean and unit standard deviation\nscaler_do_y = preprocessing.StandardScaler()\n# scaler_ec_y = preprocessing.StandardScaler()\n# scaler_temp_y = preprocessing.StandardScaler()\n\ny_train_do = y_train_do.reshape(-1, 1)\n# y_train_ec = y_train_ec.reshape(-1, 1)\n# y_train_temp = y_train_temp.reshape(-1, 1)\n\n\ny_train_do = scaler_do_y.fit_transform(y_train_do)\n# y_train_ec = scaler_ec_y.fit_transform(y_train_ec)\n# y_train_temp = scaler_temp_y.fit_transform(y_train_temp)\n\ny_test_do = y_test_do.reshape(-1, 1)\n# y_test_ec = y_test_ec.reshape(-1,1)\n# y_test_temp = y_test_temp.reshape(-1,1)\n\ny_test_do = scaler_do_y.transform(y_test_do)\n# y_test_ec = scaler_ec_y.transform(y_test_ec)\n# y_test_temp = scaler_temp_y.transform(y_test_temp)\n\n\nx_train = x_train.reshape((x_train.shape[0], model_params['TIMESTEPS']* model_params['N_FEATURES']))\nx_test = x_test.reshape((x_test.shape[0],model_params['TIMESTEPS']* model_params['N_FEATURES']))\n\n\nprint(x_train.shape)\nprint(x_test.shape)\n\n\n##\n# Model\n##\n\nmodel = Sequential()\nmodel.add(Dense(3, input_dim=model_params['TIMESTEPS']* model_params['N_FEATURES'], activation='relu'))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(3, activation='relu'))\n# model.add(Dropout(0.2))\nmodel.add(Dense(3, activation='relu'))\nmodel.add(Dense(1, activation=None))\nsgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\nmodel.compile(loss='mse', optimizer='sgd',metrics=['mse','mae',rsquare])\n\n\n##\n# Training\n\nmodel_file = os.path.join('./bpnnpearson', '-{val_mean_squared_error}-{epoch:02d}' + '.hdf5')\ncheckpoint = ModelCheckpoint(filepath=model_file, monitor='val_mean_squared_error', save_best_only=False,\n                                     save_weights_only=False, mode='max', period=1)\n\n# history = model.fit(x_train, y_train_do,\n#           epochs=1000,\n#           batch_size=10,shuffle=True,validation_split=0.1,callbacks=[checkpoint])\n\n# Predict.\n\nmodel = load_model('bpnnpearson/-0.07077323177401892-955.hdf5',custom_objects={'rsquare': rsquare})\nscore = model.evaluate(x_test, y_test_do, batch_size=1)\n\nprint(score)\n\npredictions = model.predict(x_test)\n\n\ny_predicted = np.array(list(scaler_do_y.inverse_transform(p) for p in predictions))\n\n# y_predicted = np.array(list(scaler_do_y.inverse_transform(p['predictions']) for p in predictions))\ny_predicted = y_predicted.reshape(np.array(y_test_do).shape)\n\n\n\n# Score with sklearn.\nscore_sklearn = mean_squared_error(y_predicted, scaler_do_y.inverse_transform(y_test_do))\nprint('RMSE (sklearn): {0:f}'.format(sqrt(score_sklearn)))\n\nprint(\"--------\")\nmae = mean_absolute_error(scaler_do_y.inverse_transform(y_test_do), y_predicted)\nprint(\"MAE (sklearn):{0:f}\".format(mae))\nprint(\"---------\")\nr2 = r2_score(scaler_do_y.inverse_transform(y_test_do), y_predicted)\nprint(\"R2 (sklearn):{0:f}\".format(r2))\n\n\n\n\n\n\n# Drawing\n\naxis_data = getdate_index(filepath,732,372)\n#\n# ax = plt.gca()\n# xfmt = mdates.DateFormatter('%Y-%m-%d %H:%M')\n# ax.xaxis.set_major_formatter(xfmt)\n#\n# true_line, = plt.plot_date(axis_data, scaler_do_y.inverse_transform(y_test_do)[0:336], 'b-', color='blue',\n#                              label='True Value')\n# predict_line, = plt.plot_date(axis_data, np.array(y_predicted)[0:336], 'b-', color='Red',\n#                                 label='Prediction Value')\n#\n# plt.legend(handles=[true_line, predict_line])\n# plt.gcf().autofmt_xdate()\n# plt.show()\n\n# # These are the \"Tableau 20\" colors as RGB.\n# tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),\n#              (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),\n#              (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),\n#              (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),\n#              (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]\n# # Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts.\n# for i in range(len(tableau20)):\n#     r, g, b = tableau20[i]\n#     tableau20[i] = (r / 255., g / 255., b / 255.)\n#\n# ax = plt.gca()\n#\n# # ax.spines[\"top\"].set_visible(False)\n# # ax.spines[\"bottom\"].set_visible(False)\n# # ax.spines[\"right\"].set_visible(False)\n# # ax.spines[\"left\"].set_visible(False)\n#\n# xfmt = mdates.DateFormatter('%Y-%m-%d')\n# ax.xaxis.set_major_formatter(xfmt)\n#\n#\n# true_line, = plt.plot_date(axis_data, scaler_do_y.inverse_transform(y_test_do)[0:372], '-', lw=1, color=tableau20[2],\n#                          label='True Value')\n# predict_line, = plt.plot_date(axis_data, np.array(y_predicted)[0:372], '--', lw=1, color=tableau20[18],\n#                            label='Prediction Value')\n#\n#\n# plt.legend(handles=[true_line, predict_line], fontsize=12)\n# plt.title('Water Quality Prediction', fontsize=16)\n# plt.xlabel('Date', fontsize=14)\n# plt.ylabel('DO (mg/l)', fontsize=14)\n# plt.gcf().autofmt_xdate()\n# plt.savefig(r'C:\\Users\\ZHA244\\Pictures\\paper-figure\\120min-7days-bpnn.png', dpi=200)\n# plt.show()\n\n","sub_path":"bpnn_pearson.py","file_name":"bpnn_pearson.py","file_ext":"py","file_size_in_byte":9170,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"132243979","text":"#!/usr/bin/env python3\n\nimport re\nfrom enum import Enum\nimport sys\nimport subprocess\nfrom pathlib import Path\n\n\nVERSION_FILE_PATH = './VERSION'\n\n\nclass Increment(Enum):\n    major = '1'\n    minor = '2'\n    patch = '3'\n\n\nclass bc:\n    WARNING = '\\033[93m'\n    ENDC = '\\033[0m'\n    BOLD = '\\033[1m'\n    UNDERLINE = '\\033[4m'\n\n\ndef wrap(text, color):\n    return f\"{color}{text}{bc.ENDC}\"\n\n\ndef execute_command(command):\n    \"\"\"\n    Execute a shell command and stream the output until the process completes.\n\n    Args:\n        command (str): The command to execute.\n\n    Returns:\n        Exit code of the command.\n    \"\"\"\n    process = subprocess.Popen(\n        command,\n        stdout=subprocess.PIPE,\n        stderr=subprocess.STDOUT,\n        shell=True,\n        encoding='utf-8',\n        errors='replace'\n    )\n    while True:\n        realtime_output = process.stdout.readline()\n        if realtime_output == '' and process.poll() is not None:\n            break\n        if realtime_output:\n            print(realtime_output.strip(), flush=True)\n    exit_code = process.wait()\n    return exit_code\n\n\ndef get_active_branch_name():\n\n    head_dir = Path(\".\") / \".git\" / \"HEAD\"\n    with head_dir.open(\"r\") as f:\n        content = f.read().splitlines()\n\n    for line in content:\n        if line[0:4] == \"ref:\":\n            return line.partition(\"refs/heads/\")[2]\n\n\ndef check_exit_code(exit_code):\n    if exit_code != 0:\n        print(f\"Error: exit code {exit_code}\")\n        sys.exit(exit_code)\n\n\ndef yes_or_no(question):\n    \"\"\"\n    Asks user a yes/no question.\n\n    Args:\n        question (str): Question to ask\n\n    Returns:\n        bool: True if user answers yes, False otherwise\n    \"\"\"\n    reply = str(\n        input(f\"{question} ({wrap('y/n', bc.BOLD)}): \")).lower().strip()\n    no_reply_msg = \"Please enter\"\n    if not reply:\n        return yes_or_no(no_reply_msg)\n    elif reply[0] == 'y':\n        return True\n    elif reply[0] == 'n':\n        return False\n    else:\n        return yes_or_no(no_reply_msg)\n\n\ndef exit_without_error():\n    print(\"Exiting\")\n    sys.exit(0)\n\n\ndef check_valid_version(version):\n    pattern = r\"^\\d+\\.\\d+\\.\\d+$\"\n    return bool(re.match(pattern, version))\n\n\ndef validate_increment(increment):\n    valid_increments = [x.value for x in Increment]\n    if increment not in valid_increments:\n        valid_increments_str = ', '.join(valid_increments)\n        print(\n            f\"Invalid value '{increment}'. must be one of: {valid_increments_str}\")\n        sys.exit(1)\n\n\ndef get_current_version():\n    with open(VERSION_FILE_PATH, 'r') as f:\n        version = f.read().strip()\n        if not check_valid_version(version):\n            error_msg = f\"Invalid version: {version}. Must be of format X.Y.Z\"\n            raise ValueError(error_msg)\n        return version\n\n\ndef generate_new_version(current_version, increment):\n    # generate new version\n    new_version = None\n    version_parts = current_version.split('.')\n    if increment == Increment.patch.value:\n        new_patch = int(version_parts[2]) + 1\n        new_version = '.'.join(\n            [version_parts[0], version_parts[1], str(new_patch)])\n    elif increment == Increment.minor.value:\n        new_minor = int(version_parts[1]) + 1\n        new_version = '.'.join([version_parts[0], str(new_minor), '0'])\n    elif increment == Increment.major.value:\n        new_major = int(version_parts[0]) + 1\n        new_version = '.'.join([str(new_major), '0', '0'])\n    return new_version\n\n\ndef write_version_file(new_version):\n    with open(VERSION_FILE_PATH, 'w') as f:\n        f.write(new_version)\n\n\ndef main():\n    # check branch\n    active_branch = get_active_branch_name()\n    if active_branch != \"master\":\n        print(\n            f\"{wrap('WARNING',bc.WARNING)}: You are on branch '{active_branch}'.\\nIt's generally recommended you run this script on master after first merging your feature branch.\")\n        reply_switch = yes_or_no(\"Change to master and pull?\")\n        if reply_switch is True:\n            exit_code_exit_code = execute_command(\n                \"git checkout master && git pull origin master\")\n            check_exit_code(exit_code_exit_code)\n            active_branch = get_active_branch_name()\n\n    # get current version\n    current_version = get_current_version()\n    print(\n        f\"Current tt-docker-base version on '{active_branch}': {current_version}\")\n\n    # get desired bump\n    q_increment = f\"Bump major ({wrap('1', bc.BOLD)}), minor ({wrap('2', bc.BOLD)}), or patch ({wrap('3', bc.BOLD)})?\"\n    reply_increment = input(f\"{q_increment}: \").lower().strip()\n    validate_increment(reply_increment)\n    new_version = generate_new_version(current_version, reply_increment)\n\n    # save and commit\n    q_commit = f\"Bump version to {wrap(new_version,bc.UNDERLINE)} and commit?\"\n    confirm_commit = yes_or_no(q_commit)\n    if confirm_commit == False:\n        exit_without_error()\n    print(f\"{VERSION_FILE_PATH} file changed: {current_version} -> {wrap(new_version,bc.UNDERLINE)}\")\n    write_version_file(new_version)\n    cmd_gitadd = f\"git add {VERSION_FILE_PATH}\"\n    exit_code_gitadd = execute_command(cmd_gitadd)\n    check_exit_code(exit_code_gitadd)\n    cmd_commit = f\"git commit -m 'bump version to {new_version}'\"\n    exit_code_commit = execute_command(cmd_commit)\n    check_exit_code(exit_code_commit)\n\n    # tag and push\n    q_tag = f\"Tag commit as {wrap(new_version,bc.UNDERLINE)} and push to remote?\"\n    confirm_tag = yes_or_no(q_tag)\n    if confirm_tag == False:\n        exit_without_error()\n    cmd_tag = f'git tag {new_version}'\n    exit_code_tag = execute_command(cmd_tag)\n    check_exit_code(exit_code_tag)\n    cmd_push_active = f'git push origin --tags {active_branch}' # pushes only specific tag\n    exit_code_push = execute_command(cmd_push_active)\n    check_exit_code(exit_code_push)\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"scripts/bump.py","file_name":"bump.py","file_ext":"py","file_size_in_byte":5854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"509845012","text":"# References\n# https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/01-basics/pytorch_basics/main.py\n# http://pytorch.org/tutorials/beginner/data_loading_tutorial.html#dataset-class\nimport torch\nimport docx\nimport re\nimport os \nfrom torch.autograd import Variable\nfrom torch.utils.data import Dataset, DataLoader\n\nclass Wordlist(object):\n    def __init__(self, filename):\n\n        word=[]\n        for file in filelist:\n            file=docx.Document(os.path.join(getcwd,file))\n                    #self.len = xy.shape[0]\n                    #self.x_data = torch.from_numpy(xy[:, 0:-1])#关于np的数组操作，http://blog.csdn.net/liangzuojiayi/article/details/51534164\n                    #self.y_data = torch.from_numpy(xy[:, [-1]])\n            #输出每一段的内容\n            for para in file.paragraphs[:10]:\n                filtrate_nonChinese = re.compile(u'[^\\u4E00-\\u9FA50-9]')#非中文   \n\n                filtered_English_str = [ i.lower().strip()  for i in re.findall('[A-Za-z ]+', para.text)  if i.lower().strip() !='']   #保留字母\n                for i in filtered_English_str:\n                    if re.findall(' ',i):\n                        filtered_English_str.remove(i)\n                        filtered_English_str.extend( i.split(' '))\n\n                filtered_Chinese = filtrate_nonChinese.sub(r' ', para.text)#replace\n                filtered_Chinese_str=[i for i in ' '.join(filtered_Chinese.replace(' ','')) if i!=' ' ]\n                filtered_Chinese_str.extend(filtered_English_str)\n                word.extend(filtered_Chinese_str)\n                word=list(set(word))     \n        word.sort()\n        self.size = len(word)\n        self.word=word\n\n        self.voc = dict(enumerate(self.word))\n\n        self.reverse_voc = {v:k for k,v in self.voc.items()}\n\n    def getID(self, word):\n        try:\n            return self.reverse_voc[word]\n        except:\n            return -1\n\n    def  getWord(self,wordid):\n\n        return self.voc[wordid]\n\n\nif __name__ == '__main__':\n    \n    getcwd=os.path.join(os.getcwd(),'word')\n    filelist=os.listdir(getcwd)\n\n    wordlist=Wordlist(filelist)\n\n    print(wordlist.voc)\n\n    print(wordlist.getID('高'),wordlist.getWord(225))\n","sub_path":"DL-Cornell-decathlon/wordlist.py","file_name":"wordlist.py","file_ext":"py","file_size_in_byte":2219,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"283777399","text":"class ModelMetaclass(type):\n    def __new__(cls,name,bases,attrs):\n        #排除Model类本身\n        if name=='Model':\n            return type.__new__(cls,name,bases,attrs)\n        #获取table名称\n        tableName=attrs.get('__table__',None) or name\n        logging.info('found model:%s (table\"%s)'%(name,tableName))\n        #获取所有的Field和主键名\n        mappings=dict()\n        fields=[]\n        primaryKey=None\n        for k,v in attrs.items():\n            if isinstance(v,Field):\n                logging.info('found mapping:%s==>%s' &(k,v))\n                mappings[k]=v\n                if v.primary_key:\n                    #找到主键\n                    if primaryKey:\n                        raise RuntimeError('Duplicate primary key for field :%s' %k)\n                    primaryKey=k\n                else:\n                    fields.append(k)\n        if not primaryKey:\n            raise RuntimeError('Primary key not found')\n        for k in mappings.keys():\n            attrs.pop(k)\n        escaped_fields=list(map(lambda f:'’%s’'%f,fields))\n        attrs['__mappings__']=mapping#保存属性和列的映射关系\n        attrs['__table__']=tableName\n        attrs['__table__']=tableName\n        attrs['__primary_key__']=primaryKey#主键属性名\n        attrs['__fields__']=fields#除了主键外的其他属性名\n        #构造默认的select，insert ,deletehe update语句\n        attrs['__select__']='select ‘%s’,%s from ‘%s’' %(primaryKey,','.join(escaped_fields),tableName)\n        attrs['__insert__']='insert into ‘%s’(%s,‘%s’) values(%s)' %(tableName,','.join(escaped_fields),primaryKey,create_args_string(len(escaped_fields)+1))\n        attrs['__update__']='update ‘%s’ set %s where ‘%s’=?'%(tableName,','.join(map(lambda f:'‘%s’=?' %(mappings.get(f).name or f),fields)),primaryKey)\n        attrs['__delete__']='delete from ‘%s’ where ‘%s’=?'%(tableName,primaryKey)\n        return type.__new__(cls,name,bases,attrs)\n","sub_path":"Python/python_study/lianxi/Python实战/Modelmetaclass001.py","file_name":"Modelmetaclass001.py","file_ext":"py","file_size_in_byte":2002,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"319204900","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.macosx-10.14-x86_64/egg/api_metadata/views/conf.py\n# Compiled at: 2017-11-27 05:10:38\nimport functools, itertools, arrow\nfrom flask import request\nfrom flask_classy import route\nfrom ocs.api import decorators, exports\nfrom ocs.api.validators import Validator\nfrom . import MetadataAPIBaseView, client_ip, compat\n\nclass ConfViewValidator(Validator):\n\n    def validate_conf(self, view):\n        return view(client_ip())\n\n\ndef _compat_conf(view):\n    \"\"\" Decorates /conf to call the export function on the view response.\n    \"\"\"\n\n    @functools.wraps(view)\n    def wrapped(*args, **kwargs):\n        export_decorator, ret = view(*args, **kwargs)\n        flask_response = decorators.jsonify(lambda : ret)()\n        return export_decorator(lambda : flask_response)()\n\n    return wrapped\n\n\nclass ConfView(MetadataAPIBaseView):\n    route_base = '/'\n    validation_class = ConfViewValidator()\n\n    def _server_info(self, ip_addr):\n        \"\"\" Queries the compute API to get server info that has the IP\n        `ip_addr`.\n        \"\"\"\n        server = self._get_server_by_ip(ip_addr)\n        return (\n         {'id': server.get('id'), \n            'name': server.get('name'), \n            'commercial_type': server.get('commercial_type'), \n            'hostname': server.get('hostname'), \n            'tags': server.get('tags'), \n            'state_detail': server.get('state_detail'), \n            'public_ip': server.get('public_ip'), \n            'private_ip': server.get('private_ip'), \n            'volumes': server.get('volumes'), \n            'organization': server.get('organization'), \n            'location': server.get('location', {}), \n            'ipv6': server.get('ipv6'), \n            'extra_networks': server.get('extra_networks'), \n            'bootscript': server.get('bootscript')},\n         server)\n\n    def _orga_info(self, server_orga):\n        \"\"\" Queries the account API to get info about the organization that\n        owns a server.\n        \"\"\"\n        response = self.privileged_account_api.query().organizations(server_orga).get()\n        organization = response.get('organization', {})\n        orga_users = organization.get('users', {})\n        users_keys = [ user.get('ssh_public_keys', []) or [] for user in orga_users ]\n        all_users_keys = list(itertools.chain(*users_keys))\n        return {'ssh_public_keys': all_users_keys, \n           'timezone': organization.get('timezone')}\n\n    @_compat_conf\n    @route('/conf')\n    def conf(self, ip_addr):\n        \"\"\" Returns client's metadata.\n\n        By default, a \"shell format\" is returned to the client. For images\n        prior to ~2014/11/01, dictionaries were not rendered properly.\n\n        The following dict:\n\n            >>> {'super_dict': {'x': 1, 'y': 2}}\n\n        Used to be rendered as (bad, note the absence of quotes):\n\n            SUPER_DICT=X Y\n            SUPER_DICT_X=1\n            SUPER_DICT_Y=2\n\n        And now (good):\n\n            SUPER_DICT='X Y'\n            SUPER_DICT_X=1\n            SUPER_DICT_Y=2\n\n        Do prevent breaking client's scripts using this endpoint, we keep the\n        bad behaviour for old images.\n        \"\"\"\n        ret, server = self._server_info(ip_addr)\n        ret.update(self._orga_info(ret.get('organization')))\n        image = server.get('image')\n        if image:\n            creation_date = arrow.get(image.get('creation_date'))\n            limit = arrow.get('2014-11-01T00:00:00+00:00')\n            if creation_date < limit:\n                return (compat.select_export(default='sh'), ret)\n        return (\n         exports.select_export(default='sh'), ret)","sub_path":"pycfiles/api_metadata-1.2.7-py2.7/conf.py","file_name":"conf.py","file_ext":"py","file_size_in_byte":3756,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"447141183","text":"# -*- coding: UTF-8 -*-\n# Copyright 2014 Luc Saffre\n# License: BSD (see file COPYING for details)\n\nfrom __future__ import print_function\n\nimport logging\nlogger = logging.getLogger(__name__)\n\nfrom django.core.exceptions import ValidationError\n\nfrom lino import dd, rt\n\nfrom lino.utils.instantiator import InstanceGenerator\n\nfrom .models import PlaceTypes\n\n\nclass PlaceGenerator(InstanceGenerator):\n    def __init__(self):\n        super(PlaceGenerator, self).__init__()\n        self.prev_obj = None\n        EE = rt.modules.countries.Country.objects.get(isocode=\"EE\")\n\n        for pt in PlaceTypes.objects():\n            self.add_instantiator(\n                pt.name, 'countries.Place', 'name zip_code',\n                country=EE,\n                type=pt)\n\n    def on_new(self, obj):\n        prev = self.prev_obj\n        if prev and prev.type and obj.type:\n            otype = self.assimilate(obj.type)\n            ptype = self.assimilate(prev.type)\n            if ptype < otype:\n                obj.parent = prev\n            else:\n                p = prev.parent\n                while p and not self.can_be_parent(\n                        self.assimilate(p.type), otype):\n                    p = p.parent\n                if p is not None:\n                    obj.parent = p\n                elif False:\n                    logger.warning(\n                        \"%s (%s) is no parent for %s (%s)\",\n                        prev, prev.type, obj, obj.type)\n\n        try:\n            obj.full_clean()\n            obj.save()\n            self.prev_obj = obj\n            return obj\n        except ValidationError as e:\n            logger.warning(\n                \"Failed to load %s (%s) : %s\",\n                obj, obj.type, e)\n        # return super(PlaceGenerator, self).on_new(obj)\n    \n    def can_be_parent(self, ptype, otype):\n        \"\"\"return True if a place of type pt can be parent for a place of type\n        ot.\n\n        \"\"\"\n        if ptype < otype:\n            return True\n        return False\n\n    def assimilate(self, pt):\n        \"\"\"In Estonia, municipalities and towns can be siblings within a same\ncounty.\"\"\"\n        if pt == PlaceTypes.municipality:\n            return PlaceTypes.town\n        return pt\n    \n\n","sub_path":"lino/modlib/countries/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2222,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"168605472","text":"# Licensed under a 3-clause BSD style license - see LICENSE.rst\n# -*- coding: utf-8 -*-\n\"\"\"\nThe Monte Carlo code that adds fake galaxiesto images from the Legacy Survey\n\"\"\"\n\nfrom __future__ import division, print_function\n\nif __name__ == '__main__':\n    import matplotlib\n    matplotlib.use('Agg')\nimport h5py\nimport os\nimport sys\nimport subprocess\nimport time as time_builtin\nimport shutil\nimport logging\nimport argparse\nimport pdb\nimport photutils\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom pkg_resources import resource_filename\nfrom pickle import dump\nfrom glob import glob\nimport csv\n\nfrom astropy.table import Table, Column, vstack\nfrom astropy.io import fits\n#from astropy import wcs as astropy_wcs\nfrom fitsio import FITSHDR\nimport fitsio\n\nfrom astropy import units\nfrom astropy.coordinates import SkyCoord\n\nfrom obiwan.db_tools import getSrcsInBrick\nfrom obiwan.common import get_outdir_runbrick, get_brickinfo_hack\nfrom obiwan.common import stack_tables\n\n# Sphinx build would crash\n#try:\nfrom legacypipe.runbrick import run_brick\nfrom legacypipe.decam import DecamImage\nfrom legacypipe.survey import LegacySurveyData, wcs_for_brick\nfrom legacypipe.runcosmos import DecamImagePlusNoise, CosmosSurvey\n\nfrom astrometry.util.fits import fits_table, merge_tables\nfrom astrometry.util.ttime import Time\nfrom astrometry.libkd.spherematch import match_radec\n\nfrom tractor.psfex import PsfEx, PsfExModel\nfrom tractor.basics import GaussianMixtureEllipsePSF, RaDecPos\nfrom tractor.sfd import SFDMap\n\nimport galsim\n#except ImportError:\n#    pass\n\nDATASETS=['dr5','dr3','cosmos','dr6']\n\ndef write_dict(fn,d):\n    '''d -- dictionary'''\n    w = csv.writer(open(fn, \"w\"))\n    for key, val in d.items():\n        w.writerow([key, val])\n\ndef read_dict(fn):\n    d = {}\n    for key, val in csv.reader(open(fn)):\n        d[key] = val\n    return d\n\ndef imshow_stamp(stamp,fn='test.png',galsimobj=True):\n    if galsimobj:\n        img = stamp.array.copy()\n    else:\n        img= stamp.copy()\n    img=img + abs(img.min())+1\n    plt.imsave(fn,np.log10(img),origin='lower',cmap='gray')\n    #plt.imshow(np.log10(img),origin='lower',cmap='gray')\n    #plt.savefig(fn)\n    #plt.close()\n    #print('Wrote %s' % fn)\n\ndef plot_radial_profs(fn,profs):\n    assert(profs.shape[1] == 3)\n    r=np.arange(profs.shape[0])\n    for i,lab in zip(range(3),['src','srcnoise','srcnoiseimg']):\n        plt.plot(r,profs[:,i],label=lab)\n    plt.legend(loc='lower right')\n    plt.savefig(fn)\n    plt.close()\n\n\ndef ptime(text,t0):\n    '''Timer'''\n    tnow=Time()\n    print('TIMING:%s ' % text,tnow-t0)\n    return tnow\n\n\ndef get_skip_ids(decals_sim_dir, brickname, objtype):\n    fns= glob(os.path.join(decals_sim_dir, objtype,\n                         brickname[:3], brickname,\n                         '*','obiwan','skippedids-*.fits'))\n    if len(fns) == 0:\n        raise ValueError(\"no skippedids.fits files exist for this brick %s\" % brickname)\n    T= stack_tables(fns, textfile=False)\n    return T.ids.astype(str)\n\ndef get_fnsuffix(**kwargs):\n    return '-{}-{}.fits'.format(kwargs['objtype'], kwargs['brickname'])\n                                   #'rs%d' % kwargs['rowst'])\n\n# try:\nclass SimDecals(LegacySurveyData):\n    \"\"\"Top level object that specifying which data to run through pipeline\n\n    Same behavior as legacypipe.runs.Dr3DecalsSurvey which chooses which\n        CCDs to include. But this also stores all the relevant obiwan\n        objects\n\n    Args:\n        dataset: see definition in\n            https://github.com/legacysurvey/obiwan/blob/master/py/obiwan/test/end_to_end/README.md\n        survey_dir: as used by legacypipe.runbrick.run_brick()\n            Defaults to $LEGACY_SURVEY_DIR environment variable.  Where to look for\n            files including calibration files, tables of CCDs and bricks, image data\n        metacat: fits_table\n            configuration-like params for the simulated sources\n        simcat: fits_table\n            simulated source catalog for a given brick (not CCD).\n        output_dir: legacypipe's outdir\n        add_sim_noise: add Poisson noise from the simulated source to the image\n        seed: for random number generators\n        image_eq_model: referred to as 'testA'\n            wherever add a simulated source, replace both image and invvar of the image\n            with that of the simulated source only\n\n    Attributes:\n        DR: see above\n        metacat: fits_table\n            configuration-like params for the simulated sources\n        simcat: fits_table\n            simulated source catalog for a given brick (not CCD).\n        output_dir: legacypipe's outdir\n        add_sim_noise: add Poisson noise from the simulated source to the image\n        image_eq_model: referred to as 'testA'\n            wherever add a simulated source, replace both image and invvar of the image\n            with that of the simulated source only\n    \"\"\"\n\n    def __init__(self, dataset=None, survey_dir=None, metacat=None, simcat=None,\n                 output_dir=None,add_sim_noise=False, seed=0,\n                 image_eq_model=False,**kwargs):\n        self.dataset= dataset\n\n        kw= dict(survey_dir=survey_dir,\n                 output_dir=output_dir)\n        if self.dataset == 'cosmos':\n            kw.update(subset=kwargs['subset'])\n        super(SimDecals, self).__init__(**kw)\n\n        self.metacat = metacat\n        self.simcat = simcat\n        # Additional options from command line\n        self.add_sim_noise= add_sim_noise\n        self.seed= seed\n        self.image_eq_model= image_eq_model\n        print('SimDecals: self.image_eq_model=',self.image_eq_model)\n\n    def get_image_object(self, t):\n        if self.dataset == 'cosmos':\n            return SimImageCosmos(self, t)\n        else:\n            return SimImage(self, t)\n\n    def filter_ccds_files(self, fns):\n        \"\"\"see legacypipe/runs.py\"\"\"\n        return fns\n\n    def ccds_for_fitting(self, brick, ccds):\n        if self.dataset in ['dr3','dr5']:\n            return np.flatnonzero(ccds.camera == 'decam')\n        elif self.dataset in ['cosmos']:\n            return np.flatnonzero(ccds.camera == 'decam+noise')\n        #elif self.dataset == 'DR4':\n            #   return np.flatnonzero(np.logical_or(ccds.camera == 'mosaic',\n        #                         ccds.camera == '90prime'))\n\n    def filter_ccd_kd_files(self, fns):\n        \"\"\"see legacypipe/runs.py\"\"\"\n        return []\n\ndef get_srcimg_invvar(stamp_ivar,img_ivar):\n    \"\"\"stamp_ivar, img_ivar -- galsim Image objects\"\"\"\n    # Use img_ivar when stamp_ivar == 0, both otherwise\n    use_img_ivar= np.ones(img_ivar.array.shape).astype(bool)\n    use_img_ivar[ stamp_ivar.array > 0 ] = False\n    # First compute using both\n    ivar= np.power(stamp_ivar.array.copy(), -1) + np.power(img_ivar.array.copy(), -1)\n    ivar= np.power(ivar,-1)\n    keep= np.ones(ivar.shape).astype(bool)\n    keep[ (stamp_ivar.array > 0)*\\\n          (img_ivar.array > 0) ] = False\n    ivar[keep] = 0.\n    # Now use img_ivar only where need to\n    ivar[ use_img_ivar ] = img_ivar.array.copy()[ use_img_ivar ]\n    # return\n    obj_ivar = stamp_ivar.copy()\n    obj_ivar.fill(0.)\n    obj_ivar+= ivar\n    return obj_ivar\n\ndef saturation_e(camera):\n    # Saturation limit\n    d=dict(decam=3e4) # e-\n    return d[camera]\n\ndef ivar_to_var(ivar,nano2e=None,camera='decam'):\n    assert(nano2e is not None)\n    flag= ivar == 0.\n    var= np.power(ivar, -1)\n    # Set 0 ivar pixels to satuation limit\n    # var * nano2e^2 = e-^2\n    sat= saturation_e(camera) / nano2e**2\n    var[flag]= sat\n    return var\n# except NameError:\n#     pass\n\n# try:\nclass SimDecalsCosmos(SimDecals,CosmosSurvey):\n    \"\"\"Filters the CCDs to just those in the cosmos realizations\n\n    Call just like SimDecals except with additional Argument 'subset'\n\n    Args:\n        **kwargs: SimDecals args + 'subset'\n    \"\"\"\n\n    def __init__(self, **kwargs):\n        super(SimDecalsCosmos, self).__init__(**kwargs)\n# except NameError:\n#     pass\n\n\n\n# try:\nclass SimImage(DecamImage):\n    \"\"\"Adds simulated sources to a single exposure\n\n    Similar behavior as legacypipe.decam.DecamImage. Instead of\n        loading images specifically from DECam, this  loads images\n        with simulated sources added in\n\n    Args:\n        survey: SimDecals() object\n        t: as used by DecamImage\n            a single row fits_table for a specific CCD\n\n    Attributes:\n        inherits: DecamImage\n        t: as used by DecamImage\n            a single row fits_table for a specific CCD\n    \"\"\"\n\n    def __init__(self, survey, t):\n        super(SimImage, self).__init__(survey, t)\n        self.t = t\n        if self.survey.dataset in ['dr3']:\n            assert('arawgain' in self.t.get_columns())\n            self.t.rename('arawgain', 'gain')\n        elif self.survey.dataset in ['dr5']:\n            assert 'gain' in self.t.get_columns()\n        # Find image on proj or proja if doesn't exist\n        dirs=dict(proj='/global/project/projectdirs/cosmo/staging/decam',\n                  proja='/global/projecta/projectdirs/cosmo/staging/decam')\n        if not os.path.exists(self.imgfn):\n            print('doesnt exist: %s, finding new location for file' % self.imgfn)\n            base=os.path.basename(self.imgfn)\n            found= glob('%s/**/%s' % (dirs['proja'],base), recursive=True)\n            if len(found) == 0:\n                found= glob('%s/**/%s' % (dirs['proj'],base), recursive=True)\n            if len(found) == 0:\n                raise OSError('cannot find image on project or projecta: %s' % base)\n            else:\n                self.imgfn= found[0]\n            print('found new location, overwrite self.imgfn with %s' % self.imgfn)\n            self.wtfn= (self.imgfn.replace('_oki_','_oow_')\n                                  .replace('_ooi_','_oow_'))\n            self.dqfn= self.wtfn.replace('_oow_','_ood_')\n\n    def get_tractor_image(self, **kwargs):\n        tim = super(SimImage, self).get_tractor_image(**kwargs)\n        if tim is None: # this can be None when the edge of a CCD overlaps\n            return tim\n\n        # Seed\n        #if 'SEED' in self.survey.metacat.columns:\n        #    seed = self.survey.metacat['SEED']\n        #else:\n        #    seed = None\n\n        objtype = self.survey.metacat.get('objtype')[0]\n        objstamp = BuildStamp(tim, seed=self.survey.seed,\n                              camera=self.t.camera,\n                              gain=self.t.gain,exptime=self.t.exptime)\n        # ids make it onto a ccd (geometry cut)\n        tim.ids_added=[]\n\n        # Grab the data and inverse variance images [nanomaggies!]\n        tim_image = galsim.Image(tim.getImage())\n        tim_invvar = galsim.Image(tim.getInvvar())\n        tim_dq = galsim.Image(tim.dq)\n        # Also store galaxy sims and sims invvar\n        sims_image = tim_image.copy()\n        sims_image.fill(0.0)\n        sims_ivar = sims_image.copy()\n\n        # Store simulated galaxy images in tim object\n        # Loop on each object.\n        for ii, obj in enumerate(self.survey.simcat):\n            # Print timing\n            t0= Time()\n            if objtype in ['lrg','elg']:\n                strin= 'Drawing 1 %s: n=%.2f, rhalf=%.2f, e1=%.2f, e2=%.2f' % \\\n                        (objtype.upper(), obj.n,obj.rhalf,obj.e1,obj.e2)\n                print(strin)\n\n            if objtype == 'star':\n                stamp = objstamp.star(obj)\n            elif objtype == 'elg':\n                stamp = objstamp.elg(obj)\n            elif objtype == 'lrg':\n                stamp = objstamp.lrg(obj)\n            elif objtype == 'qso':\n                stamp = objstamp.qso(obj)\n            t0= ptime('Finished Drawing %s: id=%d band=%s dbflux=%f addedflux=%f' %\n                (objtype.upper(), obj.id,objstamp.band,\n                 obj.get(objstamp.band+'flux'),stamp.array.sum()), t0)\n\n            stamp_nonoise= stamp.copy()\n            if self.survey.add_sim_noise:\n                stamp += noise_for_galaxy(stamp,objstamp.nano2e)\n            ivarstamp= ivar_for_galaxy(stamp,objstamp.nano2e)\n            # Add source if EVEN 1 pix falls on the CCD\n            overlap = stamp.bounds & tim_image.bounds\n            if overlap.area() > 0:\n                print('Stamp overlaps tim: id=%d band=%s' % (obj.id,objstamp.band))\n                tim.ids_added.append(obj.id)\n                stamp = stamp[overlap]\n                ivarstamp = ivarstamp[overlap]\n                stamp_nonoise= stamp_nonoise[overlap]\n\n                # Zero out invvar where bad pixel mask is flagged (> 0)\n                keep = np.ones(tim_dq[overlap].array.shape)\n                keep[ tim_dq[overlap].array > 0 ] = 0.\n                ivarstamp *= keep\n\n                # Add stamp to image\n                back= tim_image[overlap].copy()\n                tim_image[overlap] += stamp #= back.copy() + stamp.copy()\n                # Add variances\n                back_ivar= tim_invvar[overlap].copy()\n                tot_ivar= get_srcimg_invvar(ivarstamp, back_ivar)\n                tim_invvar[overlap] = tot_ivar.copy()\n\n                #Extra\n                sims_image[overlap] += stamp.copy()\n                sims_ivar[overlap] += ivarstamp.copy()\n\n                if np.min(sims_ivar.array) < 0:\n                    log.warning('Negative invvar!')\n                    import pdb ; pdb.set_trace()\n        tim.sims_image = sims_image.array\n        tim.sims_inverr = np.sqrt(sims_ivar.array)\n        # Can set image=model, ivar=1/model for testing\n        if self.survey.image_eq_model:\n            tim.data = sims_image.array.copy()\n            tim.inverr = np.zeros(tim.data.shape)\n            tim.inverr[sims_image.array > 0.] = np.sqrt(1./sims_image.array.copy()[sims_image.array > 0.])\n        else:\n            tim.data = tim_image.array\n            tim.inverr = np.sqrt(tim_invvar.array)\n        return tim\n# except NameError:\n#     pass\n\n# try:\nclass SimImageCosmos(SimImage,DecamImagePlusNoise):\n    \"\"\"Filters the CCDs to just those in the cosmos realizations\n\n    Call just like SimDecals except with additional Argument 'subset'\n\n    Args:\n        **kwargs: SimDecals args + 'subset'\n    \"\"\"\n\n    def __init__(self, survey, t):\n        super(SimImageCosmos, self).__init__(survey, t)\n# except NameError:\n#     pass\n\ndef noise_for_galaxy(gal,nano2e):\n    \"\"\"Returns numpy array of noise in Img count units for gal in image cnt units\"\"\"\n    # Noise model + no negative image vals when compute noise\n    one_std_per_pix= gal.array.copy() # nanomaggies\n    one_std_per_pix[one_std_per_pix < 0]=0\n    # rescale\n    one_std_per_pix *= nano2e # e-\n    one_std_per_pix= np.sqrt(one_std_per_pix)\n    num_stds= np.random.randn(one_std_per_pix.shape[0],one_std_per_pix.shape[1])\n    #one_std_per_pix.shape, num_stds.shape\n    noise= one_std_per_pix * num_stds\n    # rescale\n    noise /= nano2e #nanomaggies\n    return noise\n\ndef ivar_for_galaxy(gal,nano2e):\n    \"\"\"Adds gaussian noise to perfect source\n\n    Args:\n        gal: galsim.Image() for source, UNITS: nanomags\n        nano2e: factor to convert to e- (gal * nano2e has units e-)\n\n    Returns:\n        galsim.Image() of invvar for the source, UNITS: nanomags\n    \"\"\"\n    var= gal.copy() * nano2e #e^2\n    var.applyNonlinearity(np.abs)\n    var /= nano2e**2 #nanomag^2\n    var.invertSelf()\n    return var\n\n\nclass BuildStamp():\n    \"\"\"Does the drawing of simulated sources on a single exposure\n\n    Args:\n        tim: Tractor Image Object for a specific CCD\n        gain: gain of the CCD\n\n    Attributes:\n        band: g,r,z\n        camera: 'decam', 'mosaic', '90prime'\n        gsparams: galsim object that configures how accurate simulated source will be\n        gsdeviate: galsim object that configures its random number generator\n        wcs: WCS from tim\n        psf: psf from tim\n        galsim_wcs: wcs repackaged into galsim compatible object\n        zpscale: conversion factor 'nanomaggies' to 'Image units used by Legacypipe', which\n            are ADU/sec for DECam and e/sec for Bass,MzLS\n        nano2e: conversion factor 'nanomaggies' to 'e-'\n    \"\"\"\n\n    def __init__(self,tim, seed=0,\n                 camera=None,gain=None,exptime=None):\n        #self.camera=camera\n        self.band = tim.band.strip()\n        # GSParams should be used when galsim object is initialized\n        # MAX size for sersic n < 6.2\n        # https://github.com/GalSim-developers/GalSim/pull/450/commits/755bcfdca25afe42cccfd6a7f8660da5ecda2a65\n        self.gsparams = galsim.GSParams(maximum_fft_size=65536)\n        #print('FIX ME!!!!!!!!!!!!!!!!!!!!!!!!!!!!')\n        self.gsdeviate = galsim.BaseDeviate(seed)\n        self.wcs = tim.getWcs()\n        self.psf = tim.getPsf()\n\n        # zpscale equivalent to magzpt = self.t.ccdzpt+2.5*np.log10(self.t.exptime)\n        self.zpscale = tim.zpscale      # nanomaggies-->ADU (decam) or e-/sec (bass,mzls)\n        assert(camera in ['decam','mosaic','90prime'])\n        if camera == 'decam':\n            self.nano2e = self.zpscale*gain # nanomaggies -> ADU -> e-\n        else:\n            # correct for mzls, possibly not for bass\n            self.nano2e = self.zpscale # nanomaggies -> e-/s -> e-\n\n    def setlocal(self,obj):\n        \"\"\"Get the pixel positions, local wcs, local PSF.\"\"\"\n\n        xx, yy = self.wcs.positionToPixel(RaDecPos(obj.get('ra'), obj.get('dec')))\n        self.pos = galsim.PositionD(xx, yy)\n        self.xpos = int(self.pos.x)\n        self.ypos = int(self.pos.y)\n        self.offset = galsim.PositionD(self.pos.x-self.xpos, self.pos.y-self.ypos)\n\n        # Get the local PSF\n        self.localpsf = self.psf.getPointSourcePatch(self.xpos, self.ypos)\n        self.localpsf= galsim.Image(self.localpsf.getImage(),\n                                    scale=self.wcs.pixscale_at(self.xpos,self.ypos))\n        #if self.camera == '90prime':\n        #    print('band=',self.band,'px scale=',self.wcs.pixscale_at(self.xpos,self.ypos))\n\n    def star(self,obj):\n        \"\"\"Render a star (PSF).\"\"\"\n        log = logging.getLogger('decals_sim')\n        # Use input flux as the 7'' aperture flux\n        self.setlocal(obj)\n        stamp = self.localpsf.copy()\n        stamp.shift(dx=self.offset.x,dy=self.offset.y)\n        # Scale to desired flux\n        stamp *= float(obj.get(self.band+'flux')) # [nanomaggies]\n        # position in observed image\n        stamp.setCenter(self.xpos, self.ypos)\n        return stamp\n\n\n    def convolve_galaxy(self,gal):\n        \"\"\"Convolve the object with the PSF and then draw it.\"\"\"\n        psf= self.localpsf.copy()\n        # doesn't change tractor measurements\n        #psf /= psf.array.sum()\n        psf= galsim.InterpolatedImage(psf)\n                                             #gsparams=self.gsparams)\n        return galsim.Convolve([gal, psf]) #, gsparams=self.gsparams)\n\n    def elg(self,obj):\n        \"\"\"Create an ELG (disk-like) galaxy.\"\"\"\n        # Create localpsf object\n        self.setlocal(obj)\n        #try:\n        # TRIAL: galaxy profile\n        gal = galsim.Sersic(float(obj.get('n')),\n                    half_light_radius=float(obj.get('rhalf')),\\\n                    flux=float(obj.get(self.band+'flux')),\n                    gsparams=self.gsparams)\n        gal = gal.shear(e1=float(obj.get('e1')), e2=float(obj.get('e2')))\n        # Convolve with normed-psf\n        gal = self.convolve_galaxy(gal)\n        gal = gal.drawImage(method='auto',\n                            offset=self.offset,\n                            scale= self.wcs.pixscale_at(self.xpos,self.ypos))\n                            #method='no_pixel'\n        # Scale to desired flux\n        print('DREW galaxy, flux input=%.4f, actual=%.4f' % \\\n                (float(obj.get(self.band+'flux')),gal.array.sum()))\n        # position in observed image\n        gal.setCenter(self.xpos, self.ypos)\n        return gal\n\n    def lrg(self,obj):\n        \"\"\"Create an LRG just like did for ELG\"\"\"\n        return self.elg(obj)\n\n    def qso(self,obj):\n        \"\"\"Create a QSO just like a star\"\"\"\n        return self.star(obj)\n\n\n\ndef flag_nearest_neighbors(Samp, radius_in_deg=5./3600):\n  \"\"\"Returns Sample indices to keep (have > dist separations) and indices to skip\n\n  Returns:\n    tuple: keep,skip: indices of Samp to keep and skip\n  \"\"\"\n  flag_set=set()\n  all_indices= range(len(Samp))\n  for cnt in all_indices:\n      if cnt in flag_set:\n          continue\n      else:\n          I,J,d = match_radec(Samp.ra[cnt],Samp.dec[cnt],\n                              Samp.ra,Samp.dec, 5./3600,\n                              notself=False,nearest=False)\n          # Remove all Samp matches (J), minus the reference ra,dec\n          flag_inds= set(J).difference(set( [cnt] ))\n          if len(flag_inds) > 0:\n              flag_set= flag_set.union(flag_inds)\n  keep= list( set(all_indices).difference(flag_set) )\n  return keep, list(flag_set)\n\ndef get_ellip(q):\n    \"\"\"Given minor to major axis ratio (q) Returns ellipticity\"\"\"\n    return (1-q**2)/(1+q**2)\n\ndef get_e1_e2(q,beta):\n    \"\"\"Given minor to major axis ratio (q) and postion angle (beta), Returns e1,e2 tuple\"\"\"\n    e= get_ellip(q)\n    return e*np.cos(2*beta), e*np.sin(2*beta)\n\n#def build_simcat(nobj=None, brickname=None, brickwcs=None, meta=None, seed=None, noOverlap=True):\ndef build_simcat(Samp=None,brickwcs=None, meta=None):\n    \"\"\"Creates the simulated source catalog for a given brick (not CCD).\n\n    The WCS for the brick (not CCD) is used to convert ra,dec of source\n        to x,y pixel location in brickspace\n\n    Args:\n        Samp: fits_table for the properties of sources in the brick\n            usually a subset of all sources in the brick determined by\n            rowstart (rs)\n        brickwcs: WCS object for the brick\n        meta: 'metacat' table\n            fits_table with configuration-like params for the simulated sources\n\n    Returns:\n        tuple of\n        cat:\n        skipping_ids:\n    \"\"\"\n    log = logging.getLogger('decals_sim')\n\n    #rand = np.random.RandomState(seed)\n\n    # Assign central coordinates uniformly but remove simulated sources which\n    # are too near to one another.  Iterate until we have the requisite number\n    # of objects.\n    #bounds = brickwcs.radec_bounds()\n    #ra = rand.uniform(bounds[0], bounds[1], nobj)\n    #dec = rand.uniform(bounds[2], bounds[3], nobj)\n    i_keep,i_skip= flag_nearest_neighbors(Samp, radius_in_deg=5./3600)\n    skipping_ids= Samp.get('id')[i_skip]\n    log.info('sources %d, keeping %d, flagged as nearby %d' % (len(Samp),len(i_keep),len(i_skip)))\n    Samp.cut(i_keep)\n\n    xxyy = brickwcs.radec2pixelxy(Samp.ra,Samp.dec)\n\n    #cat = Table()\n    #cat['ID'] = Column(Samp.get('id'),dtype='i4') #np.arange(nobj, dtype='i4'))\n    #cat['RA'] = Column(Samp.ra, dtype='f8')\n    #cat['DEC'] = Column(Samp.dec, dtype='f8')\n    #cat['X'] = Column(xxyy[1][:], dtype='f4')\n    #cat['Y'] = Column(xxyy[2][:], dtype='f4')\n    cat = fits_table()\n    for key in ['id','ra','dec']:\n        cat.set(key, Samp.get(key))\n    cat.set('x', xxyy[1][:])\n    cat.set('y', xxyy[2][:])\n\n    typ=meta.get('objtype')[0]\n    # Mags\n    filts = ['%s %s' % ('DES', f) for f in 'grz']\n    for band in ['g','r','z']:\n        nanomag= 1E9*10**(-0.4*Samp.get(band))\n        # Add extinction (to stars too, b/c \"decam-chatter 6517\")\n        mw_transmission= SFDMap().extinction(['DES %s' % band],\n                                             Samp.ra, Samp.dec)\n        mw_transmission= 10**(-mw_transmission[:,0].astype(np.float32)/2.5)\n        cat.set('%sflux' % band, nanomag * mw_transmission)\n        cat.set('mw_transmission_%s' % band, mw_transmission)\n\n    # Galaxy Properties\n    if typ in ['elg','lrg']:\n        # Convert to e1,e2 if given ba,pa\n        if ('ba' in Samp.get_columns()) & ('pa' in Samp.get_columns()):\n            e1,e2= get_e1_e2(Samp.get('ba'),Samp.get('pa'))\n            Samp.set('e1',e1)\n            Samp.set('e2',e2)\n        for key in ['n','rhalf','e1','e2']:\n            cat.set(key, Samp.get(key))\n        # Sersic n: GALSIM n = [0.3,6.2] for numerical stability,see\n        # https://github.com/GalSim-developers/GalSim/issues/{325,450}\n    return cat, skipping_ids\n\n\n\ndef get_parser():\n    '''return parser object, tells it what options to look for\n    options can come from a list of strings or command line'''\n    parser = argparse.ArgumentParser(formatter_class=argparse.\n                                     ArgumentDefaultsHelpFormatter,\n                                     description='DECaLS simulations.')\n    parser.add_argument('--dataset', type=str, choices=['dr5','dr3', 'cosmos','dr6'], required=True, help='see definitions in obiwan/test/README.md')\n    parser.add_argument('-o', '--objtype', type=str, choices=['star','elg', 'lrg', 'qso'], default='star', required=True)\n    parser.add_argument('-b', '--brick', type=str, default='2428p117', required=True)\n    parser.add_argument('--outdir', default='./', required=False)\n    parser.add_argument('--logfn', default='./', required=False)\n    parser.add_argument('-n', '--nobj', type=int, default=500, metavar='',\n                        help='number of objects to simulate (required input)')\n    parser.add_argument('-rs', '--rowstart', type=int, default=0, metavar='',\n                        help='zero indexed, row of ra,dec,mags table, after it is cut to brick, to start on')\n    parser.add_argument('--do_skipids', type=str, choices=['no','yes'],default='no', help='inject skipped ids for brick, otherwise run as usual')\n    parser.add_argument('--do_more', type=str, choices=['no','yes'],default='no', help='yes if running more randoms b/c TS returns too few targets')\n    parser.add_argument('--minid', type=int, default=None, help='set if do_more==yes, minimum id to consider, useful if adding more randoms mid-run')\n    parser.add_argument('--randoms_db', default='obiwan_elg', help='desi db table name for randoms')\n    parser.add_argument('--randoms_from_fits', default=None, help='set to read randoms from fits file instead of scidb2.nersc.gov db, set to absolute path of local fits file on computer')\n    parser.add_argument('--dont_sort_sampleid', action=\"store_true\", default=False, help='False to sort sample by id')\n    parser.add_argument('-t', '--threads', type=int, default=1, metavar='',\n                        help='number of threads to use when calling The Tractor')\n    parser.add_argument('-z', '--zoom', nargs=4, default=(0, 3600, 0, 3600), type=int, metavar='',\n                        help='see runbrick.py; (default is 0 3600 0 3600)')\n    parser.add_argument('-survey-dir', '--survey_dir', metavar='',\n                        help='Location of survey-ccds*.fits.gz')\n    parser.add_argument('--add_sim_noise', action=\"store_true\", help=\"set to add noise to simulated sources\")\n    parser.add_argument('-testA','--image_eq_model', action=\"store_true\", help=\"set to set image,inverr by model only (ignore real image,invvar)\")\n    parser.add_argument('--all-blobs', action='store_true',\n                        help='Process all the blobs, not just those that contain simulated sources.')\n    parser.add_argument('--stage', choices=['tims', 'image_coadds', 'srcs', 'fitblobs', 'coadds'],\n                        type=str, default=None, metavar='', help='Run through the stage then stop')\n    parser.add_argument('--no_cleanup', action='store_true',default=False,\n                        help='useful for test_checkpoint function')\n    parser.add_argument('--early_coadds', action='store_true',default=False,\n                        help='add this option to make the JPGs before detection/model fitting')\n    parser.add_argument('--bricklist',action='store',default='bricks-eboss-ngc.txt',\\\n                        help='if using mpi4py, $LEGACY_SURVEY_DIR/bricklist')\n    parser.add_argument('--nproc', type=int,action='store',default=1,\\\n                        help='if using mpi4py')\n    parser.add_argument('--all_blobs', action='store_true',default=False,\n                        help='fit models to all blobs, not just those containing sim sources')\n    parser.add_argument('--subset', type=int, default=0,\n                        help='COSMOS subset number [0 to 4, 10 to 12], only used if dataset = cosmos')\n    parser.add_argument('--checkpoint', action='store_true',default=False,\n                        help='turn on checkpointing')\n    parser.add_argument('--skip_ccd_cuts', action='store_true',default=False,\n                        help='no ccd cuts')\n    parser.add_argument('--overwrite_if_exists', action='store_true',default=False,\n                        help='run the code even if expected output already exists')\n    parser.add_argument('-v', '--verbose', action='store_true', help='toggle on verbose output')\n    return parser\n\ndef create_metadata(kwargs=None):\n    \"\"\"fits_table with configuration-like params for the simulated sources\n\n    TODO: Should metacat table have a rowstart column?\n    TODO: One metacat table per brick, instead of one per `rs*` directory?\n\n    Args:\n        kwargs: configuration-like params for the simulated sources\n            {'brickname': which chunk of sky\n            'objtype': star,elg,lrg,qso\n            'nobj': number of simulated sources for this run\n            }\n\n    Returns:\n        Nothing\n        writes the 'metacat' fits_table to disk and stores it\n        in the kwargs input arg\n    \"\"\"\n    assert(kwargs is not None)\n    log = logging.getLogger('decals_sim')\n    # Pack the input parameters into a meta-data table and write out.\n    #metacols = [\n    #    ('BRICKNAME', 'S10'),\n    #    ('OBJTYPE', 'S10'),\n    #    ('NOBJ', 'i4'),\n    #    ('CHUNKSIZE', 'i2'),\n    #    ('NCHUNK', 'i2'),\n    #    ('ZOOM', 'i4', (4,)),\n    #    ('SEED', 'S20'),\n    #    ('RMAG_RANGE', 'f4', (2,))]\n    #metacat = Table(np.zeros(1, dtype=metacols))\n    metacat = fits_table()\n    for key in ['brickname','objtype']: #,'nchunk']:\n        metacat.set(key, np.array( [kwargs[key]] ))\n    metacat.set('nobj', np.array( [kwargs['args'].nobj] ))\n    metacat.set('zoom', np.array( [kwargs['args'].zoom] ))\n    #metacat['RMAG_RANGE'] = kwargs['args'].rmag_range\n    #if not kwargs['args'].seed:\n    #    log.info('Random seed = {}'.format(kwargs['args'].seed))\n    #    metacat['SEED'] = kwargs['args'].seed\n    #metacat_dir = os.path.join(kwargs['decals_sim_dir'], kwargs['objtype'],kwargs['brickname'][:3],kwargs['brickname'])\n    metacat_dir= get_outdir_runbrick(kwargs['decals_sim_dir'],\n                             kwargs['brickname'],kwargs['rowst'],\n                             do_skipids=kwargs['do_skipids'],do_more=kwargs['do_more'])\n    if not os.path.exists(metacat_dir):\n        os.makedirs(metacat_dir)\n    metafile = os.path.join(metacat_dir, 'metacat'+get_fnsuffix(**kwargs))\n    log.info('Writing {}'.format(metafile))\n    if os.path.isfile(metafile):\n        os.remove(metafile)\n    metacat.writeto(metafile)\n    # Store new stuff\n    kwargs['metacat']=metacat\n    kwargs['metacat_dir']=metacat_dir\n\n\ndef create_ith_simcat(d=None):\n    \"\"\"Write 'simcat' and 'skipped_ids' tables for a given sample of sources\n\n    Args:\n        d: {'Samp': fits_table for the properties of sources in the brick\n            'brickwcs': WCS object for the brick\n            'metacat': fits_table with configuration params for the simulated sources\n            }\n\n    Returns:\n        Nothing, saves the 'simcat' and 'skipped_ids' tables\n        Adds 'simcat' table to dict 'd'\n    \"\"\"\n    assert(d is not None)\n    log = logging.getLogger('decals_sim')\n    #chunksuffix = '{:02d}'.format(ith_chunk)\n    # Build and write out the simulated object catalog.\n    #seed= d['seeds'][ith_chunk]\n    #simcat = build_simcat(d['nobj'], d['brickname'], d['brickwcs'], d['metacat'], seed)\n    simcat, skipped_ids = build_simcat(Samp=d['Samp'],brickwcs=d['brickwcs'],meta=d['metacat'])\n    # Simcat\n    simcat_dir = get_outdir_runbrick(d['decals_sim_dir'],\n                           d['brickname'],d['rowst'],\n                           do_skipids=d['do_skipids'],do_more=d['do_more'])\n    if not os.path.exists(simcat_dir):\n        os.makedirs(simcat_dir)\n    #simcatfile = os.path.join(simcat_dir, 'simcat-{}-{}-row{}-{}.fits'.format(d['brickname'], d['objtype'],rowstart,rowend)) # chunksuffix))\n    simcatfile = os.path.join(simcat_dir, 'simcat'+get_fnsuffix(**d))\n    if os.path.isfile(simcatfile):\n        os.remove(simcatfile)\n    simcat.writeto(simcatfile)\n    log.info('Wrote {}'.format(simcatfile))\n    # Skipped Ids\n    if len(skipped_ids) > 0:\n        skip_table= fits_table()\n        skip_table.set('ids',skipped_ids)\n        name= os.path.join(simcat_dir,'skippedids'+get_fnsuffix(**d))\n        if os.path.exists(name):\n            os.remove(name)\n            log.info('Removed %s' % name)\n        skip_table.writeto(name)\n        log.info('Wrote {}'.format(name))\n    # add to dict\n    d['simcat']= simcat\n    d['simcat_dir']= simcat_dir\n\ndef get_checkpoint_fn(outdir,brick,rowstart):\n    return os.path.join(outdir,'checkpoint',\n                        brick[:3],brick,\n                        'checkpoint_rs%d.pickle' % rowstart)\n\ndef get_runbrick_setup(**kwargs):\n    \"\"\"Convert runbrick.py cmd line options into `**kwargs` for run_brick()\n\n    The command line options depend on the Data Release (e.g. the\n        legacypipe code version. The cmd line options associated with\n        each DR get modified and repackaged into a dict in\n        legacypipe.runbrick so this converter is required to call run_brick\n        appropriately\n\n    Args:\n        **kwargs: dict of the cmd line options to obiwan.kenobi.py\n\n    Returns:\n        dict to use when calling legacypipe.runbrick.run_brick like\n            run_brick(brickname, survey, `**dict`)\n    \"\"\"\n    dataset= kwargs['dataset']\n    assert(dataset in DATASETS)\n    from legacypipe.runbrick import get_runbrick_kwargs\n    from legacypipe.runbrick import get_parser as get_runbrick_parser\n    zm= kwargs['zoom']\n    cmd_line= ['--no-write', '--skip','--force-all',\n           '--zoom','%d' % zm[0],'%d' % zm[1],'%d' % zm[2],'%d' % zm[3],\n           '--no-wise', '--threads','%d' % kwargs['threads']]\n    if kwargs['checkpoint']:\n        checkpoint_fn= get_checkpoint_fn(kwargs['outdir'],\n                        kwargs['brick'], kwargs['rowstart'])\n        cmd_line += ['--checkpoint',checkpoint_fn]\n    if kwargs['stage']:\n        cmd_line += ['--stage', kwargs['stage']]\n    if kwargs['early_coadds']:\n        cmd_line += ['--early-coadds', '--stage', 'image_coadds']\n    if kwargs['skip_ccd_cuts']:\n        cmd_line += ['--skip_ccd_cuts']\n    #if kwargs['stage']:\n    #    cmd_line += ['--stage', '%s' % kwargs['stage']]\n    if dataset == 'dr3':\n        #cmd_line += ['--hybrid-psf']\n        cmd_line += ['--run', 'dr3','--nsigma', '6','--simp']\n    elif dataset == 'dr5':\n        # defaults: rex (use --simp), nsigma 6 ,hybrid-psf (--no-hybrid-psf otherwise)\n        # depth cut already done (use --depth-cut to do depth cut anyway)\n        cmd_line += ['--run', 'dr5']\n\n    rb_parser= get_runbrick_parser()\n    rb_opt = rb_parser.parse_args(args=cmd_line)\n    rb_optdict = vars(rb_opt)\n    # remove keys as Dustin' does\n    _= rb_optdict.pop('ps', None)\n    _= rb_optdict.pop('verbose',None)\n    _, rb_kwargs= get_runbrick_kwargs(**rb_optdict)\n    return rb_kwargs\n\ndef do_one_chunk(d=None):\n    \"\"\"Runs the legacypipe/Tractor pipeline on images with simulated sources\n\n    Args:\n        d: {'args': obiwan.kenobi.py cmd line argparse.Namespace object\n            'brickname': chunk of sky\n            'metacat': fits_table configuration params for the simulated sources\n            'simcat': fits_table simulated source catalog for a given brick (not CCD).\n\n    Note:\n        runb_brick() is 'main' for the legacypipe/Tractor pipeline\n\n    Returns:\n        Nothing, but this func end ups writing out all the obiwan results\n    \"\"\"\n    assert(d is not None)\n    kw= dict(dataset=d['args'].dataset,\\\n             metacat=d['metacat'], simcat=d['simcat'], \\\n             output_dir=d['simcat_dir'], \\\n             add_sim_noise=d['args'].add_sim_noise, seed=d['seed'],\\\n             image_eq_model=d['args'].image_eq_model)\n    if d['args'].dataset == 'cosmos':\n        kw.update(subset=d['args'].subset)\n        simdecals= SimDecalsCosmos(**kw)\n    else:\n        simdecals = SimDecals(**kw)\n    # Use Tractor to just process the blobs containing the simulated sources.\n    if d['args'].all_blobs:\n        blobxy = None\n    else:\n        blobxy = zip(d['simcat'].get('x'), d['simcat'].get('y'))\n    # Default runbrick call sequence\n    obiwan_kwargs= vars(d['args'])\n    runbrick_kwargs= get_runbrick_setup(**obiwan_kwargs)\n    # Obiwan modifications\n    runbrick_kwargs.update(blobxy=blobxy)\n    #plotbase='obiwan')\n    print('Calling run_brick with: ')\n    print('brickname= %s' % d['brickname'])\n    print('simdecals= ',simdecals)\n    print('runbrick_kwards= ',runbrick_kwargs)\n    # Run it: run_brick(brick, survey obj, **kwargs)\n    np.random.seed(d['seed'])\n    print(runbrick_kwargs)\n    run_brick(d['brickname'], simdecals, **runbrick_kwargs)\n\ndef dobash(cmd):\n    print('UNIX cmd: %s' % cmd)\n    if os.system(cmd): raise ValueError\n\ndef do_ith_cleanup(d=None):\n    \"\"\"Moves all obiwan+legacypipe outputs to a new directory stucture\n\n    Uses rsync to move everthing, if all the rsync's succeed then all the\n        original files and directories are removed\n\n    Args:\n        d: dict with keys brickname, simcat_dir\n    \"\"\"\n    assert(d is not None)\n    log = logging.getLogger('decals_sim')\n    log.info('Cleaning up...')\n    brick= d['brickname']\n    bri= brick[:3]\n    outdir= d['simcat_dir']\n    rsdir= os.path.basename(outdir)\n    # outdir/obj\n    base= os.path.dirname(\n                os.path.dirname(\n                    os.path.dirname(outdir)))\n\n    drs= ['obiwan','coadd']\n    print(d)\n    print(d['args'])\n    if not d['args'].early_coadds:\n        drs += ['metrics','tractor','tractor-i']\n    for dr in drs:\n        dobash('mkdir -p %s/%s/%s/%s/%s' % \\\n                (base,dr,bri,brick,rsdir))\n\n    # obiwan\n    dobash('mv %s/*.fits %s/obiwan/%s/%s/%s/' % \\\n            (outdir,  base,bri,brick,rsdir))\n    dobash('mv %s/coadd/%s/%s/sim_ids_added.fits %s/obiwan/%s/%s/%s/' % \\\n            (outdir,bri,brick,  base,bri,brick,rsdir))\n    # coadd\n    dobash('mv %s/coadd/%s/%s/* %s/coadd/%s/%s/%s/' % \\\n             (outdir,bri,brick,  base,bri,brick,rsdir))\n\n    if not d['args'].early_coadds:\n        # metrics,tractor,tractor-i\n        for dr in ['metrics','tractor','tractor-i']:\n            dobash('mv %s/%s/%s/* %s/%s/%s/%s/%s/' % \\\n                 (outdir,dr,bri,  base,dr,bri,brick,rsdir))\n    # Remove original outdir\n    dobash('rm -r %s' % outdir)\n\n    # Remove unneeded coadd files\n    names= ['nexp','depth']\n    if not d['args'].early_coadds:\n        drs+= ['chi2']\n    for name in names:\n        dobash('rm %s/coadd/%s/%s/%s/*%s*' %\n                    (base,bri,brick,rsdir,name))\n    if rsdir != 'rs0':\n        # jpgs are nice to look at, but only keep in 1 dir\n        for name in ['jpg']:\n            dobash('rm %s/coadd/%s/%s/%s/*.%s' %\n                        (base,bri,brick,rsdir,name))\n\n\ndef get_sample(objtype,brick,randoms_db,\n               minid=None,randoms_from_fits='',\n               do_skipids='no',outdir=None,\n               dont_sort_sampleid=False):\n    \"\"\"Gets all simulated randoms for a brick from PSQl db, and applies all relevant cuts\n\n    Args:\n        objtype: elg,lrg\n        brick:\n        randoms_db: name of PSQL db for randoms, e.g. obiwan_elg_ra175\n        minid: None, unless do_more == yes then it is an integer for the randoms id to start from\n        randoms_from_fits: None or filename of fits_table to use for randoms\n        do_skipids: yes or no, rerunning on all skipped randoms?\n        outdir: None if do_skipids='no'; otherwise path like $CSCRATCH/obiwan_out/elg_9deg2_ra175\n        dont_sort_sampleid: False to sort sample by id\n\n\n    Returns:\n        tupe: sample fits_table, seed\n    \"\"\"\n    assert(do_skipids in ['yes','no'])\n    if do_skipids == 'yes':\n        assert(not outdir is None)\n    if randoms_from_fits:\n        Samp,seed= fits_table(randoms_from_fits),1\n    else:\n      if do_skipids == 'no':\n        Samp,seed= getSrcsInBrick(brick,objtype, db_table=randoms_db)\n      elif do_skipids == 'yes':\n        skip_ids= get_skip_ids(outdir, brick, objtype)\n        Samp,seed= getSrcsInBrick(brick,objtype, db_table=randoms_db,\n                             skipped_ids= skip_ids)\n    # Already did these cuts in decals_sim_radeccolors\n    #r0,r1,d0,d1= brickwcs.radec_bounds()\n    #Samp.cut( (Samp.ra >= r0)*(Samp.ra <= r1)*\\\n    #          (Samp.dec >= d0)*(Samp.dec <= d1) )\n    # Sort by Sersic n low -> high (if elg or lrg)\n    # Apply cuts\n    if minid:\n      Samp.cut( Samp.id >= minid )\n    if dont_sort_sampleid == False:\n        # breaks clustering but robus to adding more ids\n        Samp= Samp[np.argsort(Samp.id) ]\n    return Samp,seed\n\n\n\ndef main(args=None):\n    \"\"\"Main routine which parses the optional inputs.\"\"\"\n    t0= Time()\n    # Command line options\n    if args is None:\n        # Read from cmd line\n        parser= get_parser()\n        args = parser.parse_args(args=args)\n    else:\n        # args is already a argparse.Namespace obj\n        pass\n    # Print calling sequence\n    print('Args:', args)\n    if args.do_more == 'yes':\n      assert(not args.minid is None)\n    # Setup loggers\n    if args.verbose:\n        lvl = logging.DEBUG\n    else:\n        lvl = logging.INFO\n    logging.basicConfig(level=lvl, stream=sys.stdout) #,format='%(message)s')\n    log = logging.getLogger('decals_sim')\n    # Sort through args\n    #log.info('decals_sim.py args={}'.format(args))\n    #max_nobj=500\n    #max_nchunk=1000\n    #if args.ith_chunk is not None: assert(args.ith_chunk <= max_nchunk-1)\n    #assert(args.nchunk <= max_nchunk)\n    #assert(args.nobj <= max_nobj)\n    #if args.ith_chunk is not None:\n    #    assert(args.nchunk == 1) #if choose a chunk, only doing 1 chunk\n    if args.nobj is None:\n        parser.print_help()\n        sys.exit(1)\n\n    # Exit if expected output already exists\n    rsdir= get_outdir_runbrick(args.outdir,\n                        args.brick,args.rowstart,\n                        do_skipids=args.do_skipids,\n                        do_more=args.do_more)\n    rsdir= os.path.basename(rsdir)\n    tractor_fn= os.path.join(args.outdir,\n                    'tractor',args.brick[:3],args.brick,\n                    rsdir,\n                    'tractor-%s.fits' % args.brick)\n    if (os.path.exists(tractor_fn) &\n        (not args.overwrite_if_exists)):\n       print('Exiting, already finished %s' % tractor_fn)\n       return 0 #sys.exit(0)\n\n    brickname = args.brick\n    objtype = args.objtype\n\n    # Output dir\n    decals_sim_dir = args.outdir\n\n    #nchunk = args.nchunk\n    #rand = np.random.RandomState(args.seed) # determines seed for all chunks\n    #seeds = rand.random_integers(0,2**18, max_nchunk)\n\n    log.info('Object type = {}'.format(objtype))\n    #log.info('Number of objects = {}'.format(nobj))\n    #log.info('Number of chunks = {}'.format(nchunk))\n\n    # Optionally zoom into a portion of the brick\n    survey = LegacySurveyData()\n    brickinfo= get_brickinfo_hack(survey,brickname)\n    #brickinfo = survey.get_brick_by_name(brickname)\n    #print(brickname)\n    brickwcs = wcs_for_brick(brickinfo)\n    W, H, pixscale = brickwcs.get_width(), brickwcs.get_height(), brickwcs.pixel_scale()\n\n    log.info('Brick = {}'.format(brickname))\n    if args.zoom is not None: # See also runbrick.stage_tims()\n        (x0, x1, y0, y1) = args.zoom\n        W = x1 - x0\n        H = y1 - y0\n        brickwcs = brickwcs.get_subimage(x0, y0, W, H)\n        log.info('Zoom (pixel boundaries) = {}'.format(args.zoom))\n    targetrd = np.array([brickwcs.pixelxy2radec(x, y) for x, y in\n                         [(1,1), (W,1), (W,H), (1,H), (1,1)]])\n\n    radec_center = brickwcs.radec_center()\n    log.info('RA, Dec center = {}'.format(radec_center))\n    log.info('Brick = {}'.format(brickname))\n    t0= ptime('First part of Main()',t0)\n\n    # SAMPLE table\n    sample_kwargs= {\"objtype\":args.objtype,\n                    \"brick\":args.brick,\n                    \"outdir\":args.outdir,\n                    \"randoms_db\":args.randoms_db,\n                    \"minid\":args.minid,\n                    \"do_skipids\":args.do_skipids,\n                    \"randoms_from_fits\":args.randoms_from_fits,\n                    \"dont_sort_sampleid\":args.dont_sort_sampleid}\n    Samp,seed= get_sample(**sample_kwargs)\n\n    Samp= Samp[args.rowstart:args.rowstart + args.nobj]\n    # Performance\n    #if objtype in ['elg','lrg']:\n    #    Samp=Samp[np.argsort( Samp.get('%s_n' % objtype) )]\n    print('Max sample size=%d, actual sample size=%d' % (args.nobj,len(Samp)))\n    assert(len(Samp) <= args.nobj)\n    t0= ptime('Got randoms sample',t0)\n\n    # Store args in dict for easy func passing\n    kwargs=dict(Samp=Samp,\\\n                brickname=brickname, \\\n                checkpoint=args.checkpoint, \\\n                seed= seed,\n                decals_sim_dir= decals_sim_dir,\\\n                brickwcs= brickwcs, \\\n                objtype=objtype,\\\n                nobj=len(Samp),\\\n                maxobjs=args.nobj,\\\n                rowst=args.rowstart,\\\n                do_skipids=args.do_skipids,\\\n                do_more=args.do_more,\\\n                minid=args.minid,\\\n                args=args)\n\n    # Stop if starting row exceeds length of radec,color table\n    if len(Samp) == 0:\n        fn= get_outdir_runbrick(kwargs['decals_sim_dir'],\n                        kwargs['brickname'],kwargs['rowst'],\n                        do_skipids=kwargs['do_skipids'],do_more=kwargs['do_more'])\n        fn+= '_exceeded.txt'\n        junk= os.system('touch %s' % fn)\n        print('Wrote %s' % fn)\n        raise ValueError('starting row=%d exceeds number of artificial sources, quit' % args.rowstart)\n\n    # Create simulated catalogues and run Tractor\n    create_metadata(kwargs=kwargs)\n    t0= ptime('create_metadata',t0)\n    # do chunks\n    #for ith_chunk in chunk_list:\n    #log.info('Working on chunk {:02d}/{:02d}'.format(ith_chunk,kwargs['nchunk']-1))\n    # Random ra,dec and source properties\n    create_ith_simcat(d=kwargs)\n    t0= ptime('create_ith_simcat',t0)\n    # Run tractor\n    do_one_chunk(d=kwargs)\n    t0= ptime('do_one_chunk',t0)\n    # Clean up output\n    if args.no_cleanup == False:\n        do_ith_cleanup(d=kwargs)\n    t0= ptime('do_ith_cleanup',t0)\n    log.info('All done!')\n    return 0\n\nif __name__ == '__main__':\n    print('obiwan started at %s' % time_builtin.strftime(\"%Y-%m-%d %H:%M:%S\"))\n    main()\n    print('obiwan finshed at %s' % time_builtin.strftime(\"%Y-%m-%d %H:%M:%S\"))\n","sub_path":"py/obiwan/kenobi.py","file_name":"kenobi.py","file_ext":"py","file_size_in_byte":46533,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"563222235","text":"from PhysicsTools.Heppy.analyzers.core.Analyzer import Analyzer\nfrom array import array\nimport ROOT\n\n\nclass FakeFactorAnalyzer(Analyzer):\n    '''Stores the value of the fakefactor weight to apply to \n    data in the application region.\n    !!! background fractions should be updated for the analysis!\n    For nore information see :\n    https://twiki.cern.ch/twiki/bin/viewauth/CMS/HiggsToTauTauJet2TauFakes\n    '''\n\n    def __init__(self, cfg_ana, cfg_comp, looperName):\n        super(FakeFactorAnalyzer, self).__init__(cfg_ana, cfg_comp, looperName)\n        if self.cfg_comp.isData:\n            self.btagfile = ROOT.TFile(self.cfg_ana.filepath.format(self.cfg_ana.channel,'btag'))\n            self.nobtagfile = ROOT.TFile(self.cfg_ana.filepath.format(self.cfg_ana.channel,'nobtag'))\n            self.inclfile = ROOT.TFile(self.cfg_ana.filepath.format(self.cfg_ana.channel,'inclusive'))\n            self.btagff = self.btagfile.Get('ff_comb')\n            self.nobtagff = self.nobtagfile.Get('ff_comb')\n            self.inclff = self.inclfile.Get('ff_comb')\n        \n\n    def fake_factor_semileptonic(self, tau, njets, mvis, mt, lepton_iso, sys='', category='inclusive'):\n        '''Interface function to retrieve the fake factors from\n        the rootfile for the semileptonic channels.\n        \n        @param sys : if '' -> nominal value, else can be 'up' or 'down'\n        '''\n        inputs = [tau.pt(),\n                  tau.decayMode(),\n                  njets,\n                  mvis,\n                  mt,\n                  lepton_iso,\n                  self.frac_qcd,\n                  self.frac_w,\n                  self.frac_tt]\n        if category == 'inclusive':\n            ff = self.inclff\n        elif category == 'btag':\n            ff = self.btagff\n        elif category == 'nobtag':\n            ff = self.nobtagff\n        else:\n            raise ValueError('category must be in [\"btag\",\"nobtag\",\"inclusive\"]')\n        if sys:\n            return ff.value(len(inputs), array('d',inputs),sys)\n        return ff.value(len(inputs), array('d',inputs))\n\n    def fake_factor_fullyhadronic(self, tau1,tau2, njets, mvis, sys=''):\n        '''Interface function to retrieve the fake factors from\n        the rootfile for the fully hadronic channel.\n        \n        @param sys : if '' -> nominal value, else can be 'up' or 'down'\n        '''\n        inputs = [tau1.pt(),\n                  tau2.pt(),\n                  tau1.decayMode(),\n                  njets,\n                  mvis,\n                  self.frac_qcd,\n                  self.frac_w,\n                  self.frac_tt,\n                  self.frac_dy]\n        if category == 'inclusive':\n            ff = self.inclff\n        elif category == 'btag':\n            ff = self.btagff\n        elif category == 'nobtag':\n            ff = self.nobtagff\n        else:\n            raise ValueError('category must be in [\"btag\",\"nobtag\",\"inclusive\"]')\n        if sys:\n            return ff.value(len(inputs), array('d',inputs),sys)\n        return ff.value(len(inputs), array('d',inputs))\n\n    def set_ff_fullyhadronic(self, tau, tau2, njets, mvis):\n        for cat in [\"btag\",\"nobtag\",\"inclusive\"]:\n            setattr(tau, \n                    'weight_fakefactor_{}',\n                    self.fake_factor_fullyhadronic(tau,tau2,njets,mvis))\n            setattr(tau, \n                    'weight_fakefactor_{}_up',\n                    self.fake_factor_fullyhadronic(tau,tau2,njets,mvis,'up'))\n            setattr(tau, \n                    'weight_fakefactor_{}_down',\n                    self.fake_factor_fullyhadronic(tau,tau2,njets,mvis,'down'))\n\n    def set_ff_semileptonic(self, tau,  njets, mvis, mt, iso):\n        for cat in [\"btag\",\"nobtag\",\"inclusive\"]:\n            setattr(tau, \n                    'weight_fakefactor_{}',\n                    self.fake_factor_semileptonic(tau,njets,mvis,mt,iso))\n            setattr(tau, \n                    'weight_fakefactor_{}_up',\n                    self.fake_factor_semileptonic(tau,njets,mvis,mt,iso,'up'))\n            setattr(tau, \n                    'weight_fakefactor_{}_down',\n                    self.fake_factor_semileptonic(tau,njets,mvis,mt,iso,'down'))\n\n    def process(self, event):\n        if not self.cfg_comp.isData:\n            return True\n\n        njets = len(event.jets30)\n        mvis = event.dileptons_sorted[0].mass()\n\n        if self.cfg_ana.channel == 'tt':\n            tau1 = event.dileptons_sorted[0].leg1()\n            tau2 = event.dileptons_sorted[0].leg2()\n            self.set_ff_fullyhadronic(tau1, tau2, njets, mvis)\n            self.set_ff_fullyhadronic(tau2, tau1, njets, mvis)\n\n        elif self.cfg_ana.channel in ['mt','et']:\n            mt = event.dileptons_sorted[0].mTLeg1(getattr(event,self.cfg_ana.met))\n            tau = event.dileptons_sorted[0].leg2()\n            lep = event.dileptons_sorted[0].leg1()\n            self.set_ff_semileptonic(tau, njets, mvis, mt, lep.iso_htt())\n\n        else:\n            raise ValueError('Channel not or wrongly set')\n","sub_path":"H2TauTau/python/heppy/analyzers/FakeFactorAnalyzer.py","file_name":"FakeFactorAnalyzer.py","file_ext":"py","file_size_in_byte":5001,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"181699203","text":"from copy import deepcopy\nfrom collections import deque\n\n\ndef get_array():\n    \"\"\"\n\n    :rtype: a list of integers\n    \"\"\"\n    a = []\n    with open('IntegerArray.txt') as f:\n        for line in f:\n            a.append(int(line))\n    return a\n\ndef split_array(a):\n    \"\"\"\n    split a list into two lists\n    :param a: list of integers\n    :return: two sub-list of integers\n    \"\"\"\n    n = len(a)\n    if n == 1:\n        return a\n    index = n // 2\n    b = a[:index]\n    c = a[index:]\n    return b, c\n\n\ndef merge_and_count(a, b):\n    \"\"\"\n\n    :param a: a list of integers\n    :param b: a list of integers\n    :return: c: the merged list\n            count: the number of inversions\n    \"\"\"\n    n = len(a) + len(b)\n    i_a = 0\n    i_b = 0\n    c = []\n    count = 0\n\n    for i in range(0, n):\n        if i_a >= len(a):\n            c.append(b[i_b])\n            i_b += 1\n\n        elif i_b >= len(b):\n            c.append(a[i_a])\n            i_a += 1\n\n        elif a[i_a] < b[i_b]:\n            c.append(a[i_a])\n            i_a += 1\n\n        else:\n            c.append(b[i_b])\n            i_b += 1\n            count += len(a) - i_a\n    return c, count\n\n\ndef countArrary(input_a):\n    \"\"\"\n    count the number of inversions\n    :param input_a: input list of integers\n    :return: count of inversions\n    \"\"\"\n    if len(input_a) == 1:\n        return 0\n    else:\n        # split the input array\n        split_a = [input_a]\n        while len(split_a) != len(input_a):\n            new_split_a = []\n            for sub_a in split_a:\n                if len(sub_a) > 1:\n                    b, c = split_array(sub_a)\n                    new_split_a.append(b)\n                    new_split_a.append(c)\n                else:\n                    new_split_a.append(sub_a)\n            split_a = deepcopy(new_split_a)\n\n        # merge and count\n        merge_a = deque(split_a)\n        count = 0\n        while len(merge_a[0]) < len(input_a):\n            new_merge_a = []\n            while merge_a:\n                a = merge_a.popleft()\n                if merge_a:\n                    b = merge_a.popleft()\n                    c, c_inv = merge_and_count(a, b)\n                    count += c_inv\n                    new_merge_a.append(c)\n                else:\n                    new_merge_a.append(a)\n\n            merge_a = deque(deepcopy(new_merge_a))\n\n        # print(merge_a)\n        return count\n\n\nif __name__ == \"__main__\":\n    input_a = get_array()\n    print(countArrary(input_a))\n","sub_path":"Part_1/Homework_1/compute_number_inversions.py","file_name":"compute_number_inversions.py","file_ext":"py","file_size_in_byte":2461,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"231893002","text":"import requests\nimport time\nfrom bs4 import BeautifulSoup\n\n\nclass LgSpider(object):\n    url = ''\n\n    def __init__(self, url=''):\n        self.url = url\n\n    def run(self):\n        urls = self.read_url(\"/home/jwang/PycharmProjects/learnPython/Spiders/lagou.text\")\n\n        for url in urls:\n            self.url = url\n            job_info = self.parser_job(self.download(self.url))\n            print(job_info)\n\n            company_info = ''\n            if job_info['company_link']:\n                company_info = self.parser_company(self.download(job_info['company_link']))\n            print(company_info)\n\n            self.save(job_info, company_info)\n            time.sleep(1)\n\n    @staticmethod\n    def save(job_info, company_info):\n        fout = open('job.csv', 'a+')\n        fout.write('招聘Title,岗位名称,薪资,城市,经验,学历,职位类型,职位诱惑,职位描述,工作地址,职位链接,'\n                   '公司链接,公司全称,公司简称,公司简介,公司行业,公司融资,公司规模,公司城市,公司介绍')\n        fout.write(\"\\n\")\n        fout.write(job_info['title'] + ',')\n        fout.write(job_info['name'] + ',')\n        fout.write(job_info['salary'] + ',')\n        fout.write(job_info['city'] + ',')\n        fout.write(job_info['exp'] + ',')\n        fout.write(job_info['edu'] + ',')\n        fout.write(job_info['type'] + ',')\n        fout.write(job_info['job_advantage'] + ',')\n        fout.write(job_info['job_desc'] + ',')\n        fout.write(job_info['job_addr'] + ',')\n        fout.write(job_info['url'] + ',')\n        fout.write(job_info['company_link'] + ',')\n\n        fout.write(company_info['full_name'] + ',')\n        fout.write(company_info['short_name'] + ',')\n        fout.write(company_info['word'] + ',')\n        fout.write(company_info['type'] + ',')\n        fout.write(company_info['finance_stage'] + ',')\n        fout.write(company_info['size'] + ',')\n        fout.write(company_info['location'] + ',')\n        fout.write(company_info['desc'] + ',')\n\n        fout.close()\n\n    @staticmethod\n    def download(url):\n        if url is None:\n            return None\n\n        headers = {\n            'User-Agent': \"Mozilla / 5.0(X11;Linux x86_64) AppleWebKit\"\n                          \" / 537.36(KHTML, like Gecko) Chrome / 59.0.3071.104 Safari / 537.36\",\n            'Accept': \"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8\",\n            'Accept-Language': \"zh,zh-CN;q=0.8,en;q=0.6,en-US;q=0.4\",\n            'Cookie': \"WEBTJ-ID=06092018%2C212124-163e4b5684bad3-06bce6028c62e-102e130c-2073600-163e4b5684c41c; _ga=GA1.2.1590203236.1528550484; user_trace_token=20180609212124-01c2e442-6be8-11e8-9438-5254005c3644; LGUID=20180609212124-01c2e880-6be8-11e8-9438-5254005c3644; _gid=GA1.2.764309885.1528550484; JSESSIONID=ABAAABAABEEAAJA3ED4C1A2CD94390C40135CD065381764; Hm_lvt_4233e74dff0ae5bd0a3d81c6ccf756e6=1528550484,1528550490; LGSID=20180609230847-0225b521-6bf7-11e8-9928-525400f775ce; index_location_city=%E5%8C%97%E4%BA%AC; TG-TRACK-CODE=index_navigation; SEARCH_ID=dd3407b3e7254e62bf85c119691102fe; _gat=1; Hm_lpvt_4233e74dff0ae5bd0a3d81c6ccf756e6=1528558827; LGRID=20180609234027-6e9d13a2-6bfb-11e8-9929-525400f775ce\"\n        }\n\n        response = requests.request('get', url, headers=headers)\n        response.encoding = 'utf-8'\n        if response.status_code != 200:\n            return None\n\n        return response.text\n\n    def parser_job(self, html):\n        if html is None:\n            return None\n        soup = BeautifulSoup(html, 'html.parser')\n        data = dict()\n\n        data['url'] = self.url\n\n        title_node = soup.select('div[class=job-name] div[class=company]')[0]\n        data['title'] = title_node.get_text()\n\n        name_node = soup.select('div[class=job-name] span[class=name]')[0]\n        data['name'] = name_node.get_text()\n\n        job_request_node = soup.select(\"dd[class=job_request] p span\")\n        data['salary'] = job_request_node[0].get_text().strip('/ ')\n        data['city'] = job_request_node[1].get_text().strip('/ ')\n        data['exp'] = job_request_node[2].get_text().strip('/ ')\n        data['edu'] = job_request_node[3].get_text().strip('/ ')\n        data['type'] = job_request_node[4].get_text().strip('/ ')\n\n        job_advantage_node = soup.select(\"dd[class=job-advantage] p\")\n        data['job_advantage'] = job_advantage_node[0].get_text()\n\n        job_desc_node = soup.select(\"dd[class=job_bt] div\")\n        data['job_desc'] = job_desc_node[0].get_text().replace('\\n', '')\n\n        job_addr_node = soup.select(\"div[class=work_addr]\")\n        data['job_addr'] = job_addr_node[0].get_text().replace(' ', '').replace('\\n', '').strip('查看地图')\n\n        company_node = soup.select(\"dl[class=job_company] dt a\")[0]\n        data['company_link'] = company_node['href']\n\n        return data\n\n    @staticmethod\n    def parser_company(html):\n        if html is None:\n            return None\n        soup = BeautifulSoup(html, 'html.parser')\n        data = dict()\n\n        name = soup.select(\"div[class=company_main] h1 a\")[0]\n        data['short_name'] = name.get_text().replace('\\n', '').replace(' ', '')\n        data['full_name'] = name.attrs['title']\n\n        company_word = soup.select(\"div[class=company_word]\")[0]\n        data['word'] = company_word.get_text().replace(' ', '').replace('\\n', '')\n\n        company_info = soup.select(\"div[id=basic_container] div[class=item_content] span\")\n        data['type'] = company_info[0].get_text()\n        data['finance_stage'] = company_info[1].get_text()\n        data['size'] = company_info[2].get_text()\n        data['location'] = company_info[3].get_text()\n\n        company_desc = soup.select(\"div[class=company_intro_text] span[class=company_content]\")\n        data['desc'] = company_desc[0].get_text().replace('\\n', '').replace(' ', '')\n\n        return data\n\n    @staticmethod\n    def read_url(file):\n        urls = []\n        if file is None:\n            return None\n        with open(file) as res:\n            for url in res:\n                urls.append(url.strip('\\n'))\n\n        return urls\n\n\nif __name__ == \"__main__\":\n    LgSpider().run()\n","sub_path":"Spiders/lagou_spider.py","file_name":"lagou_spider.py","file_ext":"py","file_size_in_byte":6153,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"376499630","text":"import threading \r\n\r\nbarrier = threading.Barrier(3) \r\n\r\nclass thread(threading.Thread): \r\n\tdef __init__(self, thread_ID): \r\n\t\tthreading.Thread.__init__(self) \r\n\t\tself.thread_ID = thread_ID \r\n\tdef run(self): \r\n\t\tprint(str(self.thread_ID) + \"\\n\") \r\n\t\tbarrier.wait() \r\n\t\t\r\nthread1 = thread(100) \r\nthread2 = thread(101) \r\n\r\nthread1.start() \r\nbarrier.wait() \r\n\r\nthread2.start()\r\n\r\n\r\nprint(\"Exit\\n\") \r\n","sub_path":"barrier.py","file_name":"barrier.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"45860101","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n###############################################################################\n#  Copyright Kitware Inc.\n#\n#  Licensed under the Apache License, Version 2.0 ( the \"License\" );\n#  you may not use this file except in compliance with the License.\n#  You may obtain a copy of the License at\n#\n#    http://www.apache.org/licenses/LICENSE-2.0\n#\n#  Unless required by applicable law or agreed to in writing, software\n#  distributed under the License is distributed on an \"AS IS\" BASIS,\n#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#  See the License for the specific language governing permissions and\n#  limitations under the License.\n###############################################################################\n\nimport json\nimport math\nimport os\nimport requests\nimport struct\nimport time\nfrom six.moves import range\n\nimport girder\nfrom girder import config\nfrom tests import base\n\n\n# boiler plate to start and stop the server\n\nos.environ['GIRDER_PORT'] = os.environ.get('GIRDER_TEST_PORT', '20200')\nconfig.loadConfig()  # Must reload config to pickup correct port\n\nJPEGHeader = '\\xff\\xd8\\xff'\nPNGHeader = '\\x89PNG'\n\n\ndef setUpModule():\n    base.enabledPlugins.append('large_image')\n    base.startServer(False)\n\n\ndef tearDownModule():\n    base.stopServer()\n\n\nclass LargeImageTilesTest(base.TestCase):\n    def setUp(self):\n        base.TestCase.setUp(self)\n        admin = {\n            'email': 'admin@email.com',\n            'login': 'adminlogin',\n            'firstName': 'Admin',\n            'lastName': 'Last',\n            'password': 'adminpassword',\n            'admin': True\n        }\n        self.admin = self.model('user').createUser(**admin)\n        folders = self.model('folder').childFolders(\n            self.admin, 'user', user=self.admin)\n        for folder in folders:\n            if folder['name'] == 'Public':\n                self.publicFolder = folder\n        # Authorize our user for Girder Worker\n        resp = self.request(\n            '/system/setting', method='PUT', user=self.admin, params={\n                'list': json.dumps([{\n                    'key': 'worker.broker',\n                    'value': 'mongodb://127.0.0.1/girder_worker'\n                    }, {\n                    'key': 'worker.backend',\n                    'value': 'mongodb://127.0.0.1/girder_worker'\n                    }])})\n        self.assertStatusOk(resp)\n\n    def _uploadFile(self, path):\n        \"\"\"\n        Upload the specified path to the admin user's public folder and return\n        the resulting item.\n\n        :param path: path to upload.\n        :returns: file: the created file.\n        \"\"\"\n        name = os.path.basename(path)\n        with open(path, 'rb') as file:\n            data = file.read()\n        resp = self.request(\n            path='/file', method='POST', user=self.admin, params={\n                'parentType': 'folder',\n                'parentId': self.publicFolder['_id'],\n                'name': name,\n                'size': len(data)\n            })\n        self.assertStatusOk(resp)\n        uploadId = resp.json['_id']\n\n        fields = [('offset', 0), ('uploadId', uploadId)]\n        files = [('chunk', name, data)]\n        resp = self.multipartRequest(\n            path='/file/chunk', fields=fields, files=files, user=self.admin)\n        self.assertStatusOk(resp)\n        self.assertIn('itemId', resp.json)\n        return resp.json\n\n    def _createTestTiles(self, itemId, params={}, info=None, error=None):\n        \"\"\"\n        Discard any existing tile set on an item, then create a test tile set\n        with some optional parameters.\n\n        :param itemId: the item on which the tiles are created.\n        :param params: optional parameters to use for the tiles.\n        :param info: if present, the tile information must match all values in\n                     this dictionary.\n        :param error: if present, expect to get an error from the tile info\n                      query and ensure that this string is in the error\n                      message.\n        :returns: the tile information dictionary.\n        \"\"\"\n        # We don't actually use the itemId to fetch test tiles\n        try:\n            resp = self.request(path='/item/test/tiles', user=self.admin,\n                                params=params)\n            if error:\n                self.assertStatus(resp, 400)\n                self.assertIn(error, resp.json['message'])\n                return None\n        except AssertionError as exc:\n            if error:\n                self.assertIn(error, exc.args[0])\n                return\n            else:\n                raise\n        self.assertStatusOk(resp)\n        infoDict = resp.json\n        if info:\n            for key in info:\n                self.assertEqual(infoDict[key], info[key])\n        return infoDict\n\n    def _testTilesZXY(self, itemId, metadata, tileParams={},\n                      imgHeader=JPEGHeader):\n        \"\"\"\n        Test that the tile server is serving images.\n\n        :param itemId: the item ID to get tiles from.\n        :param metadata: tile information used to determine the expected\n                         valid queries.  If 'sparse' is added to it, tiles\n                         are allowed to not exist above that level.\n        :param tileParams: optional parameters to send to the tile query.\n        :param imgHeader: if something other than a JPEG is expected, this is\n                          the first few bytes of the expected image.\n        \"\"\"\n        # We should get images for all valid levels, but only within the\n        # expected range of tiles.\n        for z in range(metadata.get('minLevel', 0), metadata['levels']):\n            maxX = math.ceil(float(metadata['sizeX']) * 2 ** (\n                z - metadata['levels'] + 1) / metadata['tileWidth']) - 1\n            maxY = math.ceil(float(metadata['sizeY']) * 2 ** (\n                z - metadata['levels'] + 1) / metadata['tileHeight']) - 1\n            # Check the four corners on each level\n            for (x, y) in ((0, 0), (maxX, 0), (0, maxY), (maxX, maxY)):\n                resp = self.request(path='/item/%s/tiles/zxy/%d/%d/%d' % (\n                    itemId, z, x, y), user=self.admin, params=tileParams,\n                    isJson=False)\n                if (resp.output_status[:3] != '200' and\n                        metadata.get('sparse') and z > metadata['sparse']):\n                    self.assertStatus(resp, 404)\n                    continue\n                self.assertStatusOk(resp)\n                image = self.getBody(resp, text=False)\n                self.assertEqual(image[:len(imgHeader)], imgHeader)\n            # Check out of range each level\n            for (x, y) in ((-1, 0), (maxX + 1, 0), (0, -1), (0, maxY + 1)):\n                resp = self.request(path='/item/%s/tiles/zxy/%d/%d/%d' % (\n                    itemId, z, x, y), user=self.admin, params=tileParams)\n                if x < 0 or y < 0:\n                    self.assertStatus(resp, 400)\n                    self.assertTrue('must be positive integers' in\n                                    resp.json['message'])\n                else:\n                    self.assertStatus(resp, 404)\n                    self.assertTrue('does not exist' in resp.json['message'] or\n                                    'outside layer' in resp.json['message'])\n        # Check negative z level\n        resp = self.request(path='/item/%s/tiles/zxy/-1/0/0' % itemId,\n                            user=self.admin, params=tileParams)\n        self.assertStatus(resp, 400)\n        self.assertIn('must be positive integers', resp.json['message'])\n        # Check non-integer z level\n        resp = self.request(path='/item/%s/tiles/zxy/abc/0/0' % itemId,\n                            user=self.admin, params=tileParams)\n        self.assertStatus(resp, 400)\n        self.assertIn('must be integers', resp.json['message'])\n        # If we set the minLevel, test one lower than it\n        if 'minLevel' in metadata:\n            resp = self.request(path='/item/%s/tiles/zxy/%d/0/0' % (\n                itemId, metadata['minLevel'] - 1), user=self.admin,\n                params=tileParams)\n            self.assertStatus(resp, 404)\n            self.assertIn('layer does not exist', resp.json['message'])\n        # Check too large z level\n        resp = self.request(path='/item/%s/tiles/zxy/%d/0/0' % (\n            itemId, metadata['levels']), user=self.admin, params=tileParams)\n        self.assertStatus(resp, 404)\n        self.assertIn('layer does not exist', resp.json['message'])\n\n    def _postTileViaHttp(self, itemId, fileId):\n        \"\"\"\n        When we know we need to process a job, we have to use an actual http\n        request rather than the normal simulated request to cherrypy.  This is\n        required because cherrypy needs to know how it was reached so that\n        girder_worker can reach it when done.\n\n        :param itemId: the id of the item with the file to process.\n        :param fileId: the id of the file that should be processed.\n        :returns: metadata from the tile if the conversion was successful,\n                  False if it converted but didn't result in useable tiles, and\n                  None if it failed.\n        \"\"\"\n        headers = [('Accept', 'application/json')]\n        self._buildHeaders(headers, None, self.admin, None, None, None)\n        headers = {header[0]: header[1] for header in headers}\n        req = requests.post('http://127.0.0.1:%d/api/v1/item/%s/tiles' % (\n            int(os.environ['GIRDER_PORT']), itemId), headers=headers,\n            data={'fileId': fileId})\n        self.assertEqual(req.status_code, 200)\n        # If we ask to create the item again right away, we should be told that\n        # either there is already a job running or the item has already been\n        # added\n        req = requests.post('http://127.0.0.1:%d/api/v1/item/%s/tiles' % (\n            int(os.environ['GIRDER_PORT']), itemId), headers=headers,\n            data={'fileId': fileId})\n        self.assertEqual(req.status_code, 400)\n        self.assertTrue('Item already has' in req.json()['message'] or\n                        'Item is scheduled' in req.json()['message'])\n\n        starttime = time.time()\n        resp = None\n        while time.time() - starttime < 30:\n            try:\n                resp = self.request(path='/item/%s/tiles' % itemId,\n                                    user=self.admin)\n                self.assertStatusOk(resp)\n                break\n            except AssertionError as exc:\n                if 'File must have at least 1 level' in exc.args[0]:\n                    return False\n                self.assertIn('is still pending creation', exc.args[0])\n            item = self.model('item').load(itemId, user=self.admin)\n            job = self.model('job', 'jobs').load(item['largeImage']['jobId'],\n                                                 user=self.admin)\n            if job['status'] == girder.plugins.jobs.constants.JobStatus.ERROR:\n                return None\n            time.sleep(0.1)\n        self.assertStatusOk(resp)\n        return resp.json\n\n    def testTilesFromPTIF(self):\n        file = self._uploadFile(os.path.join(\n            os.environ['LARGE_IMAGE_DATA'], 'sample_image.ptif'))\n        itemId = str(file['itemId'])\n        fileId = str(file['_id'])\n        # We shouldn't have tile information yet\n        resp = self.request(path='/item/%s/tiles' % itemId, user=self.admin)\n        self.assertStatus(resp, 400)\n        self.assertIn('No large image file', resp.json['message'])\n        resp = self.request(path='/item/%s/tiles/zxy/0/0/0' % itemId,\n                            user=self.admin)\n        self.assertStatus(resp, 404)\n        self.assertIn('No large image file', resp.json['message'])\n        # Asking to delete the tile information succeeds but does nothing\n        resp = self.request(path='/item/%s/tiles' % itemId, method='DELETE',\n                            user=self.admin)\n        self.assertStatusOk(resp)\n        self.assertEqual(resp.json['deleted'], False)\n        # Ask to make this a tile-based item with an invalid file ID\n        resp = self.request(path='/item/%s/tiles' % itemId, method='POST',\n                            user=self.admin, params={'fileId': itemId})\n        self.assertStatus(resp, 400)\n        self.assertIn('No such file', resp.json['message'])\n\n        # Ask to make this a tile-based item properly\n        resp = self.request(path='/item/%s/tiles' % itemId, method='POST',\n                            user=self.admin, params={'fileId': fileId})\n        self.assertStatusOk(resp)\n        # Now the tile request should tell us about the file.  These are\n        # specific to our test file\n        resp = self.request(path='/item/%s/tiles' % itemId, user=self.admin)\n        self.assertStatusOk(resp)\n        tileMetadata = resp.json\n        self.assertEqual(tileMetadata['tileWidth'], 256)\n        self.assertEqual(tileMetadata['tileHeight'], 256)\n        self.assertEqual(tileMetadata['sizeX'], 58368)\n        self.assertEqual(tileMetadata['sizeY'], 12288)\n        self.assertEqual(tileMetadata['levels'], 9)\n        tileMetadata['sparse'] = 5\n        self._testTilesZXY(itemId, tileMetadata)\n\n        # Ask to make this a tile-based item again\n        resp = self.request(path='/item/%s/tiles' % itemId, method='POST',\n                            user=self.admin, params={'fileId': fileId})\n        self.assertStatus(resp, 400)\n        self.assertIn('Item already has', resp.json['message'])\n\n        # We should be able to delete the large image information\n        resp = self.request(path='/item/%s/tiles' % itemId, method='DELETE',\n                            user=self.admin)\n        self.assertStatusOk(resp)\n        self.assertEqual(resp.json['deleted'], True)\n\n        # We should no longer have tile informaton\n        resp = self.request(path='/item/%s/tiles' % itemId, user=self.admin)\n        self.assertStatus(resp, 400)\n        self.assertIn('No large image file', resp.json['message'])\n\n        # We should be able to re-add it (we are also testing that fileId is\n        # optional if there is only one file).\n        resp = self.request(path='/item/%s/tiles' % itemId, method='POST',\n                            user=self.admin)\n        self.assertStatusOk(resp)\n        resp = self.request(path='/item/%s/tiles' % itemId, user=self.admin)\n        self.assertStatusOk(resp)\n\n    def testTilesFromTest(self):\n        file = self._uploadFile(os.path.join(\n            os.environ['LARGE_IMAGE_DATA'], 'sample_image.ptif'))\n        items = [{'itemId': str(file['itemId']), 'fileId': str(file['_id'])}]\n        # Create a second item\n        resp = self.request(path='/item', method='POST', user=self.admin,\n                            params={'folderId': self.publicFolder['_id'],\n                                    'name': 'test'})\n        self.assertStatusOk(resp)\n        itemId = str(resp.json['_id'])\n        items.append({'itemId': itemId})\n        # Check that we can't create a tile set with another item's file\n        resp = self.request(path='/item/%s/tiles' % itemId, method='POST',\n                            user=self.admin,\n                            params={'fileId': items[0]['fileId']})\n        self.assertStatus(resp, 400)\n        self.assertIn('The provided file must be in the provided item',\n                      resp.json['message'])\n        # Now create a test tile with the default options\n        params = {'encoding': 'JPEG'}\n        meta = self._createTestTiles(itemId, params, {\n            'tileWidth': 256, 'tileHeight': 256,\n            'sizeX': 256 * 2 ** 9, 'sizeY': 256 * 2 ** 9, 'levels': 10\n        })\n        self._testTilesZXY('test', meta, params)\n        # Test most of our parameters in a single special case\n        params = {\n            'minLevel': 2,\n            'maxLevel': 5,\n            'tileWidth': 160,\n            'tileHeight': 120,\n            'sizeX': 5000,\n            'sizeY': 3000,\n            'encoding': 'JPEG'\n        }\n        meta = self._createTestTiles(itemId, params, {\n            'tileWidth': 160, 'tileHeight': 120,\n            'sizeX': 5000, 'sizeY': 3000, 'levels': 6\n        })\n        meta['minLevel'] = 2\n        self._testTilesZXY('test', meta, params)\n        # Test the fractal tiles with PNG\n        params = {'fractal': 'true'}\n        meta = self._createTestTiles(itemId, params, {\n            'tileWidth': 256, 'tileHeight': 256,\n            'sizeX': 256 * 2 ** 9, 'sizeY': 256 * 2 ** 9, 'levels': 10\n        })\n        self._testTilesZXY('test', meta, params, PNGHeader)\n        # Test that the fractal isn't the same as the non-fractal\n        resp = self.request(path='/item/test/tiles/zxy/0/0/0', user=self.admin,\n                            params=params, isJson=False)\n        image = self.getBody(resp, text=False)\n        resp = self.request(path='/item/test/tiles/zxy/0/0/0', user=self.admin,\n                            isJson=False)\n        self.assertNotEqual(self.getBody(resp, text=False), image)\n        # Test each property with an invalid value\n        badParams = {\n            'minLevel': 'a',\n            'maxLevel': False,\n            'tileWidth': (),\n            'tileHeight': [],\n            'sizeX': {},\n            'sizeY': 1.3,\n            'encoding': 2,\n        }\n        for key in badParams:\n            err = ('parameter is an incorrect' if key is not 'encoding' else\n                   'Invalid encoding')\n            self._createTestTiles(itemId, {key: badParams[key]}, error=err)\n\n    def testTilesFromPNG(self):\n        file = self._uploadFile(os.path.join(\n            os.path.dirname(__file__), 'test_files', 'yb10kx5k.png'))\n        itemId = str(file['itemId'])\n        fileId = str(file['_id'])\n        tileMetadata = self._postTileViaHttp(itemId, fileId)\n        self.assertEqual(tileMetadata['tileWidth'], 256)\n        self.assertEqual(tileMetadata['tileHeight'], 256)\n        self.assertEqual(tileMetadata['sizeX'], 10000)\n        self.assertEqual(tileMetadata['sizeY'], 5000)\n        self.assertEqual(tileMetadata['levels'], 7)\n        self._testTilesZXY(itemId, tileMetadata)\n        # Ask to make this a tile-based item with an missing file ID (there are\n        # now two files, so this will now fail).\n        resp = self.request(path='/item/%s/tiles' % itemId, method='POST',\n                            user=self.admin)\n        self.assertStatus(resp, 400)\n        self.assertIn('Missing \"fileId\"', resp.json['message'])\n        # We should be able to delete the tiles\n        resp = self.request(path='/item/%s/tiles' % itemId, method='DELETE',\n                            user=self.admin)\n        self.assertStatusOk(resp)\n        self.assertEqual(resp.json['deleted'], True)\n        # We should no longer have tile informaton\n        resp = self.request(path='/item/%s/tiles' % itemId, user=self.admin)\n        self.assertStatus(resp, 400)\n        self.assertIn('No large image file', resp.json['message'])\n        # This should work with a PNG with transparency, too.\n        file = self._uploadFile(os.path.join(\n            os.path.dirname(__file__), 'test_files', 'yb10kx5ktrans.png'))\n        itemId = str(file['itemId'])\n        fileId = str(file['_id'])\n        tileMetadata = self._postTileViaHttp(itemId, fileId)\n        self.assertEqual(tileMetadata['tileWidth'], 256)\n        self.assertEqual(tileMetadata['tileHeight'], 256)\n        self.assertEqual(tileMetadata['sizeX'], 10000)\n        self.assertEqual(tileMetadata['sizeY'], 5000)\n        self.assertEqual(tileMetadata['levels'], 7)\n        self._testTilesZXY(itemId, tileMetadata)\n        # We should be able to delete the tiles\n        resp = self.request(path='/item/%s/tiles' % itemId, method='DELETE',\n                            user=self.admin)\n        self.assertStatusOk(resp)\n        self.assertEqual(resp.json['deleted'], True)\n        # We should no longer have tile informaton\n        resp = self.request(path='/item/%s/tiles' % itemId, user=self.admin)\n        self.assertStatus(resp, 400)\n        self.assertIn('No large image file', resp.json['message'])\n\n    def testTilesFromBadFiles(self):\n        # Uploading a monochrome file should result in no useful tiles.\n        file = self._uploadFile(os.path.join(\n            os.path.dirname(__file__), 'test_files', 'small.jpg'))\n        itemId = str(file['itemId'])\n        fileId = str(file['_id'])\n        tileMetadata = self._postTileViaHttp(itemId, fileId)\n        self.assertEqual(tileMetadata, False)\n        # We should be able to delete the conversion\n        resp = self.request(path='/item/%s/tiles' % itemId, method='DELETE',\n                            user=self.admin)\n        self.assertStatusOk(resp)\n        self.assertEqual(resp.json['deleted'], True)\n        # Uploading a non-image file should run a job, too.\n        file = self._uploadFile(os.path.join(\n            os.path.dirname(__file__), 'test_files', 'notanimage.txt'))\n        itemId = str(file['itemId'])\n        fileId = str(file['_id'])\n        tileMetadata = self._postTileViaHttp(itemId, fileId)\n        self.assertEqual(tileMetadata, None)\n        resp = self.request(path='/item/%s/tiles' % itemId, method='DELETE',\n                            user=self.admin)\n        self.assertStatusOk(resp)\n        self.assertEqual(resp.json['deleted'], True)\n\n    def testTilesFromSVS(self):\n        file = self._uploadFile(os.path.join(\n            os.environ['LARGE_IMAGE_DATA'], 'sample_svs_image.TCGA-DU-6399-'\n            '01A-01-TS1.e8eb65de-d63e-42db-af6f-14fefbbdf7bd.svs'))\n        itemId = str(file['itemId'])\n        fileId = str(file['_id'])\n        # Ask to make this a tile-based item\n        resp = self.request(path='/item/%s/tiles' % itemId, method='POST',\n                            user=self.admin, params={'fileId': fileId})\n        self.assertStatusOk(resp)\n        # Now the tile request should tell us about the file.  These are\n        # specific to our test file\n        resp = self.request(path='/item/%s/tiles' % itemId, user=self.admin)\n        self.assertStatusOk(resp)\n        tileMetadata = resp.json\n        self.assertEqual(tileMetadata['tileWidth'], 240)\n        self.assertEqual(tileMetadata['tileHeight'], 240)\n        self.assertEqual(tileMetadata['sizeX'], 31872)\n        self.assertEqual(tileMetadata['sizeY'], 13835)\n        self.assertEqual(tileMetadata['levels'], 9)\n        self._testTilesZXY(itemId, tileMetadata)\n\n        # Ask to make this a tile-based item again\n        resp = self.request(path='/item/%s/tiles' % itemId, method='POST',\n                            user=self.admin, params={'fileId': fileId})\n        self.assertStatus(resp, 400)\n        self.assertIn('Item already has', resp.json['message'])\n\n        # Ask for PNGs\n        params = {'encoding': 'PNG'}\n        self._testTilesZXY(itemId, tileMetadata, params, PNGHeader)\n\n        # Check that invalid encodings are rejected\n        try:\n            resp = self.request(path='/item/%s/tiles' % itemId,\n                                user=self.admin,\n                                params={'encoding': 'invalid'})\n            self.assertTrue(False)\n        except AssertionError as exc:\n            self.assertIn('Invalid encoding', exc.args[0])\n\n        # Check that JPEG options are honored.\n        resp = self.request(path='/item/%s/tiles/zxy/0/0/0' % itemId,\n                            user=self.admin, isJson=False)\n        self.assertStatusOk(resp)\n        image = self.getBody(resp, text=False)\n        self.assertEqual(image[:len(JPEGHeader)], JPEGHeader)\n        defaultLength = len(image)\n\n        resp = self.request(path='/item/%s/tiles/zxy/0/0/0' % itemId,\n                            user=self.admin, isJson=False,\n                            params={'jpegQuality': 10})\n        self.assertStatusOk(resp)\n        image = self.getBody(resp, text=False)\n        self.assertEqual(image[:len(JPEGHeader)], JPEGHeader)\n        self.assertTrue(len(image) < defaultLength)\n\n        resp = self.request(path='/item/%s/tiles/zxy/0/0/0' % itemId,\n                            user=self.admin, isJson=False,\n                            params={'jpegSubsampling': 2})\n        self.assertStatusOk(resp)\n        image = self.getBody(resp, text=False)\n        self.assertEqual(image[:len(JPEGHeader)], JPEGHeader)\n        self.assertTrue(len(image) < defaultLength)\n\n    def testDummyTileSource(self):\n        # We can't actually load the dummy source via the endpoints if we have\n        # all of the requirements installed, so just check that it exists and\n        # will return appropriate values.\n        from girder.plugins.large_image.tilesource.dummy import DummyTileSource\n        dummy = DummyTileSource()\n        self.assertEqual(dummy.getTile(0, 0, 0), '')\n        tileMetadata = dummy.getMetadata()\n        self.assertEqual(tileMetadata['tileWidth'], 0)\n        self.assertEqual(tileMetadata['tileHeight'], 0)\n        self.assertEqual(tileMetadata['sizeX'], 0)\n        self.assertEqual(tileMetadata['sizeY'], 0)\n        self.assertEqual(tileMetadata['levels'], 0)\n\n    def testThumbnails(self):\n        file = self._uploadFile(os.path.join(\n            os.environ['LARGE_IMAGE_DATA'], 'sample_image.ptif'))\n        itemId = str(file['itemId'])\n        fileId = str(file['_id'])\n        # We shouldn't be able to get a thumbnail yet\n        resp = self.request(path='/item/%s/tiles/thumbnail' % itemId,\n                            user=self.admin)\n        self.assertStatus(resp, 400)\n        self.assertIn('No large image file', resp.json['message'])\n        # Ask to make this a tile-based item\n        resp = self.request(path='/item/%s/tiles' % itemId, method='POST',\n                            user=self.admin, params={'fileId': fileId})\n        self.assertStatusOk(resp)\n        # Get metadata to use in our thumbnail tests\n        resp = self.request(path='/item/%s/tiles' % itemId, user=self.admin)\n        self.assertStatusOk(resp)\n        tileMetadata = resp.json\n        # Now we should be able to get a thumbnail\n        resp = self.request(path='/item/%s/tiles/thumbnail' % itemId,\n                            user=self.admin, isJson=False)\n        self.assertStatusOk(resp)\n        image = self.getBody(resp, text=False)\n        self.assertEqual(image[:len(JPEGHeader)], JPEGHeader)\n        defaultLength = len(image)\n\n        # Test that JPEG options are honored\n        resp = self.request(path='/item/%s/tiles/thumbnail' % itemId,\n                            user=self.admin, isJson=False,\n                            params={'jpegQuality': 10})\n        self.assertStatusOk(resp)\n        image = self.getBody(resp, text=False)\n        self.assertEqual(image[:len(JPEGHeader)], JPEGHeader)\n        self.assertTrue(len(image) < defaultLength)\n\n        resp = self.request(path='/item/%s/tiles/thumbnail' % itemId,\n                            user=self.admin, isJson=False,\n                            params={'jpegSubsampling': 2})\n        self.assertStatusOk(resp)\n        image = self.getBody(resp, text=False)\n        self.assertEqual(image[:len(JPEGHeader)], JPEGHeader)\n        self.assertTrue(len(image) < defaultLength)\n\n        # Test width and height using PNGs\n        resp = self.request(path='/item/%s/tiles/thumbnail' % itemId,\n                            user=self.admin, isJson=False,\n                            params={'encoding': 'PNG'})\n        self.assertStatusOk(resp)\n        image = self.getBody(resp, text=False)\n        self.assertEqual(image[:len(PNGHeader)], PNGHeader)\n        (width, height) = struct.unpack('!LL', image[16:24])\n        self.assertEqual(max(width, height), 256)\n        # We know that we are using an example where the width is greater than\n        # the height\n        origWidth = int(tileMetadata['sizeX'] *\n                        2 ** -(tileMetadata['levels'] - 1))\n        origHeight = int(tileMetadata['sizeY'] *\n                         2 ** -(tileMetadata['levels'] - 1))\n        self.assertEqual(height, int(width * origHeight / origWidth))\n        resp = self.request(path='/item/%s/tiles/thumbnail' % itemId,\n                            user=self.admin, isJson=False,\n                            params={'encoding': 'PNG', 'width': 200})\n        self.assertStatusOk(resp)\n        image = self.getBody(resp, text=False)\n        self.assertEqual(image[:len(PNGHeader)], PNGHeader)\n        (width, height) = struct.unpack('!LL', image[16:24])\n        self.assertEqual(width, 200)\n        self.assertEqual(height, int(width * origHeight / origWidth))\n        resp = self.request(path='/item/%s/tiles/thumbnail' % itemId,\n                            user=self.admin, isJson=False,\n                            params={'encoding': 'PNG', 'height': 200})\n        self.assertStatusOk(resp)\n        image = self.getBody(resp, text=False)\n        self.assertEqual(image[:len(PNGHeader)], PNGHeader)\n        (width, height) = struct.unpack('!LL', image[16:24])\n        self.assertEqual(height, 200)\n        self.assertEqual(width, int(height * origWidth / origHeight))\n        resp = self.request(path='/item/%s/tiles/thumbnail' % itemId,\n                            user=self.admin, isJson=False,\n                            params={'encoding': 'PNG',\n                                    'width': 180, 'height': 180})\n        self.assertStatusOk(resp)\n        image = self.getBody(resp, text=False)\n        self.assertEqual(image[:len(PNGHeader)], PNGHeader)\n        (width, height) = struct.unpack('!LL', image[16:24])\n        self.assertEqual(width, 180)\n        self.assertEqual(height, int(width * origHeight / origWidth))\n\n        # Test bad parameters\n        badParams = [\n            ({'encoding': 'invalid'}, 400, 'Invalid encoding'),\n            ({'width': 'invalid'}, 400, 'incorrect type'),\n            ({'width': 0}, 400, 'Invalid width or height'),\n            ({'width': -5}, 400, 'Invalid width or height'),\n            ({'height': 'invalid'}, 400, 'incorrect type'),\n            ({'height': 0}, 400, 'Invalid width or height'),\n            ({'height': -5}, 400, 'Invalid width or height'),\n            ({'jpegQuality': 'invalid'}, 400, 'incorrect type'),\n            ({'jpegSubsampling': 'invalid'}, 400, 'incorrect type'),\n        ]\n        for entry in badParams:\n            resp = self.request(path='/item/%s/tiles/thumbnail' % itemId,\n                                user=self.admin,\n                                params=entry[0])\n            self.assertStatus(resp, entry[1])\n            self.assertIn(entry[2], resp.json['message'])\n\n    def testRegions(self):\n        file = self._uploadFile(os.path.join(\n            os.environ['LARGE_IMAGE_DATA'], 'sample_image.ptif'))\n        itemId = str(file['itemId'])\n        # We shouldn't be able to get a region yet\n        resp = self.request(path='/item/%s/tiles/region' % itemId,\n                            user=self.admin)\n        self.assertStatus(resp, 400)\n        self.assertIn('No large image file', resp.json['message'])\n        # Ask to make this a tile-based item\n        resp = self.request(path='/item/%s/tiles' % itemId, method='POST',\n                            user=self.admin)\n        self.assertStatusOk(resp)\n        # Get metadata to use in our tests\n        resp = self.request(path='/item/%s/tiles' % itemId, user=self.admin)\n        self.assertStatusOk(resp)\n        tileMetadata = resp.json\n\n        # Test bad parameters\n        badParams = [\n            ({'encoding': 'invalid', 'width': 10}, 400, 'Invalid encoding'),\n            ({'width': 'invalid'}, 400, 'incorrect type'),\n            ({'width': -5}, 400, 'Invalid width or height'),\n            ({'height': 'invalid'}, 400, 'incorrect type'),\n            ({'height': -5}, 400, 'Invalid width or height'),\n            ({'jpegQuality': 'invalid', 'width': 10}, 400, 'incorrect type'),\n            ({'jpegSubsampling': 'invalid', 'width': 10}, 400,\n             'incorrect type'),\n            ({'left': 'invalid'}, 400, 'incorrect type'),\n            ({'right': 'invalid'}, 400, 'incorrect type'),\n            ({'top': 'invalid'}, 400, 'incorrect type'),\n            ({'bottom': 'invalid'}, 400, 'incorrect type'),\n            ({'regionWidth': 'invalid'}, 400, 'incorrect type'),\n            ({'regionHeight': 'invalid'}, 400, 'incorrect type'),\n            ({'units': 'invalid'}, 400, 'Invalid units'),\n        ]\n        for entry in badParams:\n            resp = self.request(path='/item/%s/tiles/region' % itemId,\n                                user=self.admin,\n                                params=entry[0])\n            self.assertStatus(resp, entry[1])\n            self.assertIn(entry[2], resp.json['message'])\n\n        # Get a small region for testing.  Our test file is sparse, so\n        # initially get a region where there is full information.\n        params = {'regionWidth': 1000, 'regionHeight': 1000,\n                  'left': 48000, 'top': 3000}\n        resp = self.request(path='/item/%s/tiles/region' % itemId,\n                            user=self.admin, isJson=False, params=params)\n        self.assertStatusOk(resp)\n        image = origImage = self.getBody(resp, text=False)\n        self.assertEqual(image[:len(JPEGHeader)], JPEGHeader)\n        defaultLength = len(image)\n\n        # Test that JPEG options are honored\n        params['jpegQuality'] = 10\n        resp = self.request(path='/item/%s/tiles/region' % itemId,\n                            user=self.admin, isJson=False, params=params)\n        self.assertStatusOk(resp)\n        image = self.getBody(resp, text=False)\n        self.assertEqual(image[:len(JPEGHeader)], JPEGHeader)\n        self.assertTrue(len(image) < defaultLength)\n        del params['jpegQuality']\n\n        params['jpegSubsampling'] = 2\n        resp = self.request(path='/item/%s/tiles/region' % itemId,\n                            user=self.admin, isJson=False, params=params)\n        self.assertStatusOk(resp)\n        image = self.getBody(resp, text=False)\n        self.assertEqual(image[:len(JPEGHeader)], JPEGHeader)\n        self.assertTrue(len(image) < defaultLength)\n        del params['jpegSubsampling']\n\n        # Test using negative offsets\n        params['left'] -= tileMetadata['sizeX']\n        params['top'] -= tileMetadata['sizeY']\n        resp = self.request(path='/item/%s/tiles/region' % itemId,\n                            user=self.admin, isJson=False, params=params)\n        self.assertStatusOk(resp)\n        image = self.getBody(resp, text=False)\n        self.assertEqual(image, origImage)\n        # We should get the same image using right and bottom\n        params = {\n            'left': params['left'], 'top': params['top'],\n            'right': params['left'] + 1000, 'bottom': params['top'] + 1000}\n        resp = self.request(path='/item/%s/tiles/region' % itemId,\n                            user=self.admin, isJson=False, params=params)\n        self.assertStatusOk(resp)\n        image = self.getBody(resp, text=False)\n        self.assertEqual(image, origImage)\n        params = {\n            'regionWidth': 1000, 'regionHeight': 1000,\n            'right': params['right'], 'bottom': params['bottom']}\n        resp = self.request(path='/item/%s/tiles/region' % itemId,\n                            user=self.admin, isJson=False, params=params)\n        self.assertStatusOk(resp)\n        image = self.getBody(resp, text=False)\n        self.assertEqual(image, origImage)\n\n        # Fractions should get us the same results\n        params = {\n            'regionWidth': 1000.0 / tileMetadata['sizeX'],\n            'regionHeight': 1000.0 / tileMetadata['sizeY'],\n            'left': 48000.0 / tileMetadata['sizeX'],\n            'top': 3000.0 / tileMetadata['sizeY'],\n            'units': 'fraction'}\n        resp = self.request(path='/item/%s/tiles/region' % itemId,\n                            user=self.admin, isJson=False, params=params)\n        self.assertStatusOk(resp)\n        image = self.getBody(resp, text=False)\n        self.assertEqual(image, origImage)\n\n        # 0-sized results are allowed\n        params = {'regionWidth': 1000, 'regionHeight': 0,\n                  'left': 48000, 'top': 3000, 'width': 1000, 'height': 1000}\n        resp = self.request(path='/item/%s/tiles/region' % itemId,\n                            user=self.admin, isJson=False, params=params)\n        self.assertStatusOk(resp)\n        image = self.getBody(resp, text=False)\n        self.assertEqual(len(image), 0)\n\n        # Test scaling (and a sparse region from our file)\n        params = {'regionWidth': 2000, 'regionHeight': 1500,\n                  'width': 500, 'height': 500, 'encoding': 'PNG'}\n        resp = self.request(path='/item/%s/tiles/region' % itemId,\n                            user=self.admin, isJson=False, params=params)\n        self.assertStatusOk(resp)\n        image = self.getBody(resp, text=False)\n        self.assertEqual(image[:len(PNGHeader)], PNGHeader)\n        (width, height) = struct.unpack('!LL', image[16:24])\n        self.assertEqual(width, 500)\n        self.assertEqual(height, 375)\n\n        # test svs image\n        file = self._uploadFile(os.path.join(\n            os.environ['LARGE_IMAGE_DATA'], 'sample_svs_image.TCGA-DU-6399-'\n            '01A-01-TS1.e8eb65de-d63e-42db-af6f-14fefbbdf7bd.svs'))\n        itemId = str(file['itemId'])\n        # Ask to make this a tile-based item\n        resp = self.request(path='/item/%s/tiles' % itemId, method='POST',\n                            user=self.admin)\n        self.assertStatusOk(resp)\n        params = {'regionWidth': 2000, 'regionHeight': 1500,\n                  'width': 1000, 'height': 1000, 'encoding': 'PNG'}\n        resp = self.request(path='/item/%s/tiles/region' % itemId,\n                            user=self.admin, isJson=False, params=params)\n        self.assertStatusOk(resp)\n        image = self.getBody(resp, text=False)\n        self.assertEqual(image[:len(PNGHeader)], PNGHeader)\n        (width, height) = struct.unpack('!LL', image[16:24])\n        self.assertEqual(width, 1000)\n        self.assertEqual(height, 750)\n\n    def testSettings(self):\n        from girder.plugins.large_image import constants\n        from girder.models.model_base import ValidationException\n\n        for key in (constants.PluginSettings.LARGE_IMAGE_SHOW_THUMBNAILS,\n                    constants.PluginSettings.LARGE_IMAGE_SHOW_VIEWER):\n            self.model('setting').set(key, 'false')\n            self.assertFalse(self.model('setting').get(key))\n            self.model('setting').set(key, 'true')\n            self.assertTrue(self.model('setting').get(key))\n            try:\n                self.model('setting').set(key, 'not valid')\n                self.assertTrue(False)\n            except ValidationException as exc:\n                self.assertIn('Invalid setting', exc.args[0])\n        self.model('setting').set(\n            constants.PluginSettings.LARGE_IMAGE_DEFAULT_VIEWER, 'geojs')\n        self.assertEqual(self.model('setting').get(\n            constants.PluginSettings.LARGE_IMAGE_DEFAULT_VIEWER), 'geojs')\n        # Test the system/setting/large_image end point\n        resp = self.request(path='/system/setting/large_image', user=None)\n        self.assertStatusOk(resp)\n        settings = resp.json\n        # The values were set earlier\n        self.assertEqual(settings[\n            constants.PluginSettings.LARGE_IMAGE_DEFAULT_VIEWER], 'geojs')\n        self.assertEqual(settings[\n            constants.PluginSettings.LARGE_IMAGE_SHOW_VIEWER], True)\n        self.assertEqual(settings[\n            constants.PluginSettings.LARGE_IMAGE_SHOW_THUMBNAILS], True)\n\n    def testGetTileSource(self):\n        from girder.plugins.large_image.tilesource import getTileSource\n\n        # Upload a PTIF and make it a large_image\n        file = self._uploadFile(os.path.join(\n            os.environ['LARGE_IMAGE_DATA'], 'sample_image.ptif'))\n        itemId = str(file['itemId'])\n        fileId = str(file['_id'])\n        resp = self.request(path='/item/%s/tiles' % itemId, method='POST',\n                            user=self.admin, params={'fileId': fileId})\n        self.assertStatusOk(resp)\n        # We should have access via getTileSource\n        source = getTileSource('girder_item://' + itemId, user=self.admin)\n        image, mime = source.getThumbnail(encoding='PNG', height=200)\n        self.assertEqual(image[:len(PNGHeader)], PNGHeader)\n\n        # We can also use a file with getTileSource.  The user is ignored.\n        source = getTileSource(os.path.join(\n            os.environ['LARGE_IMAGE_DATA'], 'sample_svs_image.TCGA-DU-6399-'\n            '01A-01-TS1.e8eb65de-d63e-42db-af6f-14fefbbdf7bd.svs'),\n            user=self.admin, encoding='PNG')\n        image, mime = source.getThumbnail(encoding='JPEG', width=200)\n        self.assertEqual(image[:len(JPEGHeader)], JPEGHeader)\n","sub_path":"plugin_tests/tiles_test.py","file_name":"tiles_test.py","file_ext":"py","file_size_in_byte":40784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"235240452","text":"import torch\nimport torch.nn as nn\nfrom habitat_baselines.vln.models import visual_encoder\n\n\nclass Seq2SeqActor(nn.Module):\n    def __init__(self, observation_space, encoder, decoder, dim_actions):\n        super().__init__()\n        self.encoder = encoder\n        self.decoder = decoder\n        self.criterion = nn.CrossEntropyLoss()\n        self.visual_encoder = visual_encoder.ResNet(observation_space)\n\n    def forward(self, *x):\n        raise NotImplementedError\n\n    def act(\n        self,\n        observations,\n        rnn_hidden_states,\n        prev_actions,\n        masks,\n        deterministic=False,\n    ):\n    \n        ctx, h_t, c_t = self.encoder(seq, seq_lengths)\n\n\n        features, rnn_hidden_states = self.net(\n            observations, rnn_hidden_states, prev_actions, masks\n        )\n        distribution = self.action_distribution(features)\n        value = self.critic(features)\n\n        if deterministic:\n            action = distribution.mode()\n        else:\n            action = distribution.sample()\n\n        action_log_probs = distribution.log_probs(action)\n\n        return value, action, action_log_probs, rnn_hidden_states\n\n    def get_value(self, observations, rnn_hidden_states, prev_actions, masks):\n        features, _ = self.net(\n            observations, rnn_hidden_states, prev_actions, masks\n        )\n        return self.critic(features)\n\n    def evaluate_actions(\n        self, observations, rnn_hidden_states, prev_actions, masks, action\n    ):\n        features, rnn_hidden_states = self.net(\n            observations, rnn_hidden_states, prev_actions, masks\n        )\n        distribution = self.action_distribution(features)\n        value = self.critic(features)\n\n        action_log_probs = distribution.log_probs(action)\n        distribution_entropy = distribution.entropy().mean()\n\n        return value, action_log_probs, distribution_entropy, rnn_hidden_states\n","sub_path":"habitat_baselines/vln/common/seq2seq.py","file_name":"seq2seq.py","file_ext":"py","file_size_in_byte":1902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"425621351","text":"import pickle\n\nfrom bson.binary import Binary, USER_DEFINED_SUBTYPE\nfrom bson.codec_options import TypeDecoder, CodecOptions, TypeRegistry\nfrom pymongo import MongoClient\n\n\ndef fallback_pickle_encoder(value):\n    return Binary(pickle.dumps(value), USER_DEFINED_SUBTYPE)\n\n\nclass PickledBinaryDecoder(TypeDecoder):\n    bson_type = Binary\n\n    def transform_bson(self, value):\n        if value.subtype == USER_DEFINED_SUBTYPE:\n            return pickle.loads(value)\n        return value\n\n\nclass MongoInstance:\n    def __init__(self, mongodb_uri):\n        codec_options = CodecOptions(\n            type_registry=TypeRegistry([PickledBinaryDecoder()], fallback_encoder=fallback_pickle_encoder))\n\n        self.client = MongoClient(mongodb_uri)\n        self.db = self.client['trivicord']\n        self.collection = self.db.get_collection('games', codec_options=codec_options)\n\n    def get_game(self, game_id):\n        result = self.collection.find_one({'game_id': game_id}, {'_id': 0, 'game_id': 0})\n        if result:\n            return result['game']\n        else:\n            return None\n\n    def save_game(self, game_id, game):\n        ids = [c['game_id'] for c in self.collection.find({}, {'game_id': 1})]\n        if game_id not in ids:\n            self.collection.insert_one({'game_id': game_id, 'game': game})\n        else:\n            self.collection.update_one({'game_id': game_id}, {'$set': {'game': game}})\n\n    def delete_game(self, game_id):\n        self.collection.delete_many({'game_id': game_id})\n\n    def get_games(self):\n        return [(g['game_id'], g['game']) for g in self.collection.find({}, {'_id': 0})]\n\n\nif __name__ == '__main__':\n    from jeopardy import TriviaGame\n\n    game = TriviaGame(1)\n\n    mongo = MongoInstance('mongodb://localhost')\n\n    mongo.save_game('1', game)\n\n    print(mongo.get_games())\n\n    for a in mongo.get_games():\n        print(a)\n\n    # mongo.delete_game_from_db('1')\n","sub_path":"mongo.py","file_name":"mongo.py","file_ext":"py","file_size_in_byte":1911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"8418260","text":"prime = [3,5,7,13]\nwhile input('Enter q to quit or any key to continue: ') != 'q':\n#    prime = [3,5,7,13]\n    i = 0\n    flag = False\n    \n#    number = int(input(\"Enter a prime number: \"))\n    \n    while flag == False:\n#        if i == len(prime):\n#            print(\"That is not correct\")\n#            flag = True\n#        elif number == prime[i]:\n#            print(\"You found a number\")\n#            flag = True\n#        else:\n#            i += 1\n        try:\n            number = int(input(\"Enter a prime number: \"))\n            if number in prime:\n                print(\"You found a number.\")\n                flag = True\n            else:\n                print(\"That is is not correct.\")\n                flag = True\n        except ValueError:\n            print(\"A number please\")\n\n    \n    \n                 \n        \n             \n             \n      \n      \n      \n","sub_path":"tstp/ex7-4.py","file_name":"ex7-4.py","file_ext":"py","file_size_in_byte":873,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"370745279","text":"# -*- coding: utf-8 -*-\n\nimport time\nimport TencentYoutuyun\n\n# AppID: 10163570\n# SecretID: AKIDqUu4zefMr65pKaJNyJRKyPeeT6TaQ9Zj\n# SecretKey: 61kGXYLvFKDtKWTBesrgV6RXO0MTN5Yl\n# 应用平台: Web\n# 应用类型: 娱乐\n# 接入产品类型: FaceIn人脸核身\n# 应用简介:\n# tsesst\n\n# pip install requests\n# please get these values from http://open.youtu.qq.com\nappid = '10163570'\nsecret_id = 'AKIDqUu4zefMr65pKaJNyJRKyPeeT6TaQ9Zj'\nsecret_key = '61kGXYLvFKDtKWTBesrgV6RXO0MTN5Yl'\nuserid = '123'\n\n#choose a end_point\n#end_point = TencentYoutuyun.conf.API_TENCENTYUN_END_POINT\n#end_point = TencentYoutuyun.conf.API_YOUTU_VIP_END_POINT\nend_point = TencentYoutuyun.conf.API_YOUTU_END_POINT\n\nyoutu = TencentYoutuyun.YouTu(appid, secret_id, secret_key, userid, end_point)\n\n#两张人脸比对，返回相似度\n#session_id = id\n#ret = youtu.FaceCompare(img1,img2)\n#print (ret)\n\n#新建个体ID\n# ret = youtu.NewPerson(person_id=\"person1\",image_path=\"zyx.jpg\",group_ids=\"Students\", person_name= 'zyx', tag='', data_type = 0)\n# print(ret)\n\nret = youtu.DetectFace(image_path=\"https://tvax1.sinaimg.cn/crop.0.0.1242.1242.180/9fd8f287ly8fwkveisgeaj20yi0yidkh.jpg\",mode = 0,data_type =1)\nprint (ret)\n#\nfor j in ret[\"face\"]:\n    if j[\"gender\"] > 50:\n        print(\"性别:男\")\n    else:\n        print(\"性别:女\")\n\n    if j[\"expression\"] >90:\n        print(\"你在大笑\")\n    elif 50 <j[\"expression\"] < 90:\n        print(\"你在微笑\")\n    else:\n        print(\"我没感觉你笑\")\n    if j[\"glass\"]:\n        print(\"你戴眼镜了\")\n    print(\"你的颜值:\",j[\"beauty\"])\n    print(\"你的年龄:\",j[\"age\"])","sub_path":"homeWork/other/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1600,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"238488103","text":"import numpy as np\nimport torch, math\nimport torch.nn as nn\nfrom onmt.modules.Transformer.Layers import EncoderLayer, DecoderLayer, PositionalEncoding, variational_dropout, PrePostProcessing\nfrom onmt.modules.BaseModel import NMTModel, Reconstructor, DecoderState\nimport onmt\nfrom onmt.modules.WordDrop import embedded_dropout\n#~ from onmt.modules.Checkpoint import checkpoint\nfrom torch.utils.checkpoint import checkpoint\nfrom torch.autograd import Variable\n\n\n\ndef custom_layer(module):\n    def custom_forward(*args):\n        output = module(*args)\n        return output\n    return custom_forward\n\nclass TransformerEncoder(nn.Module):\n    \"\"\"Encoder in 'Attention is all you need'\n    \n    Args:\n        opt: list of options ( see train.py )\n        dicts : dictionary (for source language)\n        \n    \"\"\"\n    \n    def __init__(self, opt, dicts, positional_encoder):\n    \n        super(TransformerEncoder, self).__init__()\n        \n        self.model_size = opt.model_size\n        self.n_heads = opt.n_heads\n        self.inner_size = opt.inner_size\n        self.layers = opt.layers\n        self.dropout = opt.dropout\n        self.word_dropout = opt.word_dropout\n        self.attn_dropout = opt.attn_dropout\n        self.emb_dropout = opt.emb_dropout\n        self.time = opt.time\n        self.version = opt.version\n        \n        self.word_lut = nn.Embedding(dicts.size(),\n                                     self.model_size,\n                                     padding_idx=onmt.Constants.PAD)\n        \n        if opt.time == 'positional_encoding':\n            self.time_transformer = positional_encoder\n        elif opt.time == 'gru':\n            self.time_transformer = nn.GRU(self.model_size, self.model_size, 1, batch_first=True)\n        elif opt.time == 'lstm':\n            self.time_transformer = nn.LSTM(self.model_size, self.model_size, 1, batch_first=True)\n        \n        self.preprocess_layer = PrePostProcessing(self.model_size, self.emb_dropout, sequence='d', static=False)\n        \n        self.postprocess_layer = PrePostProcessing(self.model_size, 0, sequence='n')\n        \n        self.positional_encoder = positional_encoder\n        \n        self.layer_modules = nn.ModuleList([EncoderLayer(self.n_heads, self.model_size, self.dropout, self.inner_size, self.attn_dropout) for _ in range(self.layers)])\n    \n        self.pretrained_point = -1\n        \n    def mark_pretrained(self):\n        \n        self.pretrained_point = self.layers\n        \n    \n    def add_layers(self, n_new_layer):\n        \n        self.new_modules = list()\n        self.layers += n_new_layer\n        \n        for i in range(n_new_layer):\n            layer = EncoderLayer(self.n_heads, self.model_size, self.dropout, self.inner_size, self.attn_dropout) \n            \n            # the first layer will use the preprocessing which is the last postprocessing\n            if i == 0:\n                layer.preprocess_attn = self.postprocess_layer\n                # replace the last postprocessing layer with a new one\n                self.postprocess_layer = PrePostProcessing(d_model, 0, sequence='n')\n            \n            self.layer_modules.append(layer)\n\n    def forward(self, input, **kwargs):\n        \"\"\"\n        Inputs Shapes: \n            input: batch_size x len_src (wanna tranpose)\n        \n        Outputs Shapes:\n            out: batch_size x len_src x d_model\n            mask_src \n            \n        \"\"\"\n        \n        \"\"\" Embedding: batch_size x len_src x d_model \"\"\"\n        emb = embedded_dropout(self.word_lut, input, dropout=self.word_dropout if self.training else 0)\n        \n        \"\"\" Scale the emb by sqrt(d_model) \"\"\"\n        \n        if self.time == 'positional_encoding':\n            emb = emb * math.sqrt(self.model_size)\n        \"\"\" Adding positional encoding \"\"\"\n        emb = self.time_transformer(emb)\n        if isinstance(emb, tuple):\n            emb = emb[0]\n        emb = self.preprocess_layer(emb)\n        \n        mask_src = input.data.eq(onmt.Constants.PAD).unsqueeze(1) # batch_size x len_src x 1 for broadcasting\n        \n        pad_mask = torch.autograd.Variable(input.data.ne(onmt.Constants.PAD)) # batch_size x len_src\n        #~ pad_mask = None\n        \n        context = emb.contiguous()\n        \n        for i, layer in enumerate(self.layer_modules):\n            \n            if len(self.layer_modules) - i <= onmt.Constants.checkpointing and self.training:        \n                context = checkpoint(custom_layer(layer), context, mask_src, pad_mask)\n\n            else:\n                context = layer(context, mask_src, pad_mask=None)      # batch_size x len_src x d_model\n            \n        \n        # From Google T2T\n        # if normalization is done in layer_preprocess, then it should also be done\n        # on the output, since the output can grow very large, being the sum of\n        # a whole stack of unnormalized layer outputs.    \n        context = self.postprocess_layer(context)\n            \n        \n        return context, mask_src    \n        \n\nclass TransformerDecoder(nn.Module):\n    \"\"\"Encoder in 'Attention is all you need'\n    \n    Args:\n        opt\n        dicts \n        \n        \n    \"\"\"\n    \n    def __init__(self, opt, dicts, positional_encoder):\n    \n        super(TransformerDecoder, self).__init__()\n        \n        self.model_size = opt.model_size\n        self.n_heads = opt.n_heads\n        self.inner_size = opt.inner_size\n        self.layers = opt.layers\n        self.dropout = opt.dropout\n        self.word_dropout = opt.word_dropout \n        self.attn_dropout = opt.attn_dropout\n        self.emb_dropout = opt.emb_dropout\n        self.time = opt.time\n        self.version = opt.version\n        \n        if opt.time == 'positional_encoding':\n            self.time_transformer = positional_encoder\n        elif opt.time == 'gru':\n            self.time_transformer = nn.GRU(self.model_size, self.model_size, 1, batch_first=True)\n        elif opt.time == 'lstm':\n            self.time_transformer = nn.LSTM(self.model_size, self.model_size, 1, batch_first=True)\n        \n        self.preprocess_layer = PrePostProcessing(self.model_size, self.emb_dropout, sequence='d', static=False)\n        \n        self.postprocess_layer = PrePostProcessing(self.model_size, 0, sequence='n')\n        \n        self.word_lut = nn.Embedding(dicts.size(),\n                                     self.model_size,\n                                     padding_idx=onmt.Constants.PAD)\n        \n        self.positional_encoder = positional_encoder\n        \n        self.layer_modules = nn.ModuleList([DecoderLayer(self.n_heads, self.model_size, self.dropout, self.inner_size, self.attn_dropout) for _ in range(self.layers)])\n        \n        len_max = self.positional_encoder.len_max\n        mask = torch.ByteTensor(np.triu(np.ones((len_max,len_max)), k=1).astype('uint8'))\n        self.register_buffer('mask', mask)\n    \n    def renew_buffer(self, new_len):\n        \n        print(new_len)\n        self.positional_encoder.renew(new_len)\n        mask = torch.ByteTensor(np.triu(np.ones((new_len,new_len)), k=1).astype('uint8'))\n        self.register_buffer('mask', mask)\n    \n    def mark_pretrained(self):\n        \n        self.pretrained_point = self.layers\n        \n    \n    def add_layers(self, n_new_layer):\n        \n        self.new_modules = list()\n        self.layers += n_new_layer\n        \n        for i in range(n_new_layer):\n            layer = EncoderLayer(self.n_heads, self.model_size, self.dropout, self.inner_size, self.attn_dropout) \n            \n            # the first layer will use the preprocessing which is the last postprocessing\n            if i == 0:\n                layer.preprocess_attn = self.postprocess_layer\n                # replace the last postprocessing layer with a new one\n                self.postprocess_layer = PrePostProcessing(d_model, 0, sequence='n')\n            \n            self.layer_modules.append(layer)\n        \n    def forward(self, input, context, src, **kwargs):\n        \"\"\"\n        Inputs Shapes: \n            input: (Variable) batch_size x len_tgt (wanna tranpose)\n            context: (Variable) batch_size x len_src x d_model\n            mask_src (Tensor) batch_size x len_src\n        Outputs Shapes:\n            out: batch_size x len_tgt x d_model\n            coverage: batch_size x len_tgt x len_src\n            \n        \"\"\"\n        \n        \"\"\" Embedding: batch_size x len_tgt x d_model \"\"\"\n        \n        \n        emb = embedded_dropout(self.word_lut, input, dropout=self.word_dropout if self.training else 0)\n        if self.time == 'positional_encoding':\n            emb = emb * math.sqrt(self.model_size)\n        \"\"\" Adding positional encoding \"\"\"\n        emb = self.time_transformer(emb)\n        if isinstance(emb, tuple):\n            emb = emb[0]\n        emb = self.preprocess_layer(emb)\n        \n\n        mask_src = src.data.eq(onmt.Constants.PAD).unsqueeze(1)\n        \n        pad_mask_src = torch.autograd.Variable(src.data.ne(onmt.Constants.PAD))\n        \n        len_tgt = input.size(1)\n        mask_tgt = input.data.eq(onmt.Constants.PAD).unsqueeze(1) + self.mask[:len_tgt, :len_tgt]\n        mask_tgt = torch.gt(mask_tgt, 0)\n        \n        output = emb.contiguous()\n        \n        pad_mask_tgt = torch.autograd.Variable(input.data.ne(onmt.Constants.PAD)) # batch_size x len_src\n        pad_mask_src = torch.autograd.Variable(1 - mask_src.squeeze(1))\n        \n        \n        for i, layer in enumerate(self.layer_modules):\n            \n            if len(self.layer_modules) - i <= onmt.Constants.checkpointing and self.training:           \n                \n                output, coverage = checkpoint(custom_layer(layer), output, context, mask_tgt, mask_src, \n                                            pad_mask_tgt, pad_mask_src) # batch_size x len_src x d_model\n                \n            else:\n                output, coverage = layer(output, context, mask_tgt, mask_src, \n                                            pad_mask_tgt, pad_mask_src) # batch_size x len_src x d_model\n            \n            \n        # From Google T2T\n        # if normalization is done in layer_preprocess, then it should also be done\n        # on the output, since the output can grow very large, being the sum of\n        # a whole stack of unnormalized layer outputs.    \n        output = self.postprocess_layer(output)\n            \n        \n        return output, coverage\n    \n    #~ def step(self, input, context, src, buffer=None):\n    def step(self, input, decoder_state):\n        \"\"\"\n        Inputs Shapes: \n            input: (Variable) batch_size x len_tgt (wanna tranpose)\n            context: (Variable) batch_size x len_src x d_model\n            mask_src (Tensor) batch_size x len_src\n            buffer (List of tensors) List of batch_size * len_tgt-1 * d_model for self-attention recomputing\n        Outputs Shapes:\n            out: batch_size x len_tgt x d_model\n            coverage: batch_size x len_tgt x len_src\n            \n        \"\"\"\n        context = decoder_state.context.transpose(0, 1)\n        buffer = decoder_state.buffer\n        src = decoder_state.src.transpose(0, 1)\n        \n        if decoder_state.input_seq is None:\n            decoder_state.input_seq = input\n        else:\n            # concatenate the last input to the previous input sequence\n            decoder_state.input_seq = torch.cat([decoder_state.input_seq, input], 0)\n        input = decoder_state.input_seq.transpose(0, 1)\n        input_ = input[:,-1].unsqueeze(1)\n        \n        \n        output_buffer = list()\n            \n        batch_size = input_.size(0)\n        \n        \n        \n        \"\"\" Embedding: batch_size x 1 x d_model \"\"\"\n        emb = self.word_lut(input_)\n       \n        \n        if self.time == 'positional_encoding':\n            emb = emb * math.sqrt(self.model_size)\n        \"\"\" Adding positional encoding \"\"\"\n        if self.time == 'positional_encoding':\n            emb = self.time_transformer(emb, t=input.size(1))\n        else:\n            prev_h = buffer[0] if buffer is None else None\n            emb = self.time_transformer(emb, prev_h)\n            # output_buffer.append(emb[1])\n            buffer[0] = emb[1]\n            \n        if isinstance(emb, tuple):\n            emb = emb[0]\n        # emb should be batch_size x 1 x dim\n            \n        # Preprocess layer: adding dropout\n        emb = self.preprocess_layer(emb)\n        \n        # batch_size x 1 x len_src\n        mask_src = src.data.eq(onmt.Constants.PAD).unsqueeze(1)\n        \n        pad_mask_src = torch.autograd.Variable(src.data.ne(onmt.Constants.PAD))\n        \n        len_tgt = input.size(1)\n        mask_tgt = input.data.eq(onmt.Constants.PAD).unsqueeze(1) + self.mask[:len_tgt, :len_tgt]\n        # mask_tgt = self.mask[:len_tgt, :len_tgt].unsqueeze(0).repeat(batch_size, 1, 1)\n        mask_tgt = torch.gt(mask_tgt, 0)\n        mask_tgt = mask_tgt[:, -1, :].unsqueeze(1)\n                \n        output = emb.contiguous()\n        \n        pad_mask_tgt = torch.autograd.Variable(input.data.ne(onmt.Constants.PAD)) # batch_size x len_src\n        pad_mask_src = torch.autograd.Variable(1 - mask_src.squeeze(1))\n        \n        \n        for i, layer in enumerate(self.layer_modules):\n            \n            buffer_ = buffer[i] if buffer is not None else None\n            assert(output.size(1) == 1)\n            output, coverage, buffer_ = layer.step(output, context, mask_tgt, mask_src, \n                                        pad_mask_tgt=None, pad_mask_src=None, buffer=buffer_) # batch_size x len_src x d_model\n            \n            output_buffer.append(buffer_)\n        \n        buffer = torch.stack(output_buffer)\n        # From Google T2T\n        # if normalization is done in layer_preprocess, then it should also be done\n        # on the output, since the output can grow very large, being the sum of\n        # a whole stack of unnormalized layer outputs.    \n        output = self.postprocess_layer(output)\n        \n        decoder_state._update_state(buffer)    \n        \n        return output, coverage\n    \n  \n        \nclass Transformer(NMTModel):\n    \"\"\"Main model in 'Attention is all you need' \"\"\"\n    \n        \n    def forward(self, input, grow=False):\n        \"\"\"\n        Inputs Shapes: \n            src: len_src x batch_size\n            tgt: len_tgt x batch_size\n        \n        Outputs Shapes:\n            out:      batch_size*len_tgt x model_size\n            \n            \n        \"\"\"\n        src = input[0]\n        tgt = input[1][:-1]  # exclude last target from inputs\n        \n        src = src.transpose(0, 1) # transpose to have batch first\n        tgt = tgt.transpose(0, 1)\n        \n        context, src_mask = self.encoder(src, grow=grow)\n        \n        output, coverage = self.decoder(tgt, context, src, grow=grow)\n        \n        output = output.transpose(0, 1) # transpose to have time first, like RNN models\n        \n        return output\n        \n    def create_decoder_state(self, src, context, beamSize=1):\n        \n        from onmt.modules.ParallelTransformer.Models import ParallelTransformerEncoder, ParallelTransformerDecoder\n        from onmt.modules.StochasticTransformer.Models import StochasticTransformerEncoder, StochasticTransformerDecoder\n        \n        if isinstance(self.decoder, TransformerDecoder) or isinstance(self.decoder, StochasticTransformerDecoder) :\n            decoder_state = TransformerDecodingState(src, context, beamSize=beamSize)\n        elif isinstance(self.decoder, ParallelTransformerDecoder):\n            from onmt.modules.ParallelTransformer.Models import ParallelTransformerDecodingState\n            decoder_state = ParallelTransformerDecodingState(src, context, beamSize=beamSize)\n        return decoder_state\n\n\nclass TransformerDecodingState(DecoderState):\n    \n    def __init__(self, src, context, beamSize=1):\n        \n        self.src = src\n        \n        #~ context = \n        \n        self.context = context.transpose(0, 1)\n        self.context = Variable(self.context.data.repeat(1, beamSize, 1))\n        self.beamSize = beamSize\n        \n        self.buffer = None\n        self.input_seq = None\n        \n    def _update_state(self, buffer):\n        \n        self.buffer = buffer\n        \n    def _update_beam(self, beam, b, remainingSents, idx):\n        \n        for tensor in [self.src, self.input_seq]  :\n                    \n            t_, br = tensor.size()\n            sent_states = tensor.view(t_, self.beamSize, remainingSents)[:, :, idx]\n            \n            if isinstance(tensor, Variable):\n                sent_states.data.copy_(sent_states.data.index_select(\n                            1, beam[b].getCurrentOrigin()))\n            else:\n                sent_states.copy_(sent_states.index_select(\n                            1, beam[b].getCurrentOrigin()))\n                            \n                            \n        nl, br_, t_, d_ = self.buffer.size()\n                    \n        sent_states = self.buffer.view(nl, self.beamSize, remainingSents, t_, d_)[:, :, idx, :, :]\n        \n        sent_states.data.copy_(sent_states.data.index_select(\n                            1, beam[b].getCurrentOrigin()))\n    \n    # in this section, the sentences that are still active are\n    # compacted so that the decoder is not run on completed sentences\n    def _prune_complete_beam(self, activeIdx, remainingSents):\n        \n        model_size = self.context.size(-1)\n        \n        def updateActive(t):\n            # select only the remaining active sentences\n            view = t.data.view(-1, remainingSents, model_size)\n            newSize = list(t.size())\n            newSize[-2] = newSize[-2] * len(activeIdx) // remainingSents\n            return Variable(view.index_select(1, activeIdx)\n                            .view(*newSize))\n        \n        def updateActive2D(t):\n            if isinstance(t, Variable):\n                # select only the remaining active sentences\n                view = t.data.view(-1, remainingSents)\n                newSize = list(t.size())\n                newSize[-1] = newSize[-1] * len(activeIdx) // remainingSents\n                return Variable(view.index_select(1, activeIdx)\n                                .view(*newSize))\n            else:\n                view = t.view(-1, remainingSents)\n                newSize = list(t.size())\n                newSize[-1] = newSize[-1] * len(activeIdx) // remainingSents\n                new_t = view.index_select(1, activeIdx).view(*newSize)\n                                \n                return new_t\n        \n        def updateActive4D(t):\n            # select only the remaining active sentences\n            nl, br_, t_, d_ = t.size()\n            view = t.data.view(nl, -1, remainingSents, t_, model_size)\n            newSize = list(t.size())\n            newSize[1] = newSize[1] * len(activeIdx) // remainingSents\n            return Variable(view.index_select(2, activeIdx)\n                            .view(*newSize)) \n        \n        self.context = updateActive(self.context)\n        \n        self.input_seq = updateActive2D(self.input_seq)\n        \n        self.src = updateActive2D(self.src)\n        \n        self.buffer = updateActive4D(self.buffer)\n\n\n\n\n#~ class TrasnformerReconstructor(Reconstructor):\n    #~ \n    #~ def forward(self, src, contexts, context_mask):\n        #~ \n        #~ \"\"\"\n        #~ Inputs Shapes: \n            #~ src: len_src x batch_size\n            #~ context: batch_size x len_tgt x model_size\n            #~ context_mask: batch_size x len_tgt\n        #~ \n        #~ Outputs Shapes:\n            #~ output:      batch_size*(len_src-1) x model_size\n            #~ \n            #~ \n        #~ \"\"\"\n        #~ src_input = src[:-1] # exclude last unit from source\n        #~ \n        #~ src_input = src_input.transpose(0, 1) # transpose to have batch first\n        #~ output, coverage = self.decoder(src, context, context_mask)\n        #~ \n        #~ output = output.transpose(0, 1) # transpose to have time first, like RNN models\n        #~ \n        #~ return output\n        #~ source = source.transpose(0, 1)\n        \n        \n","sub_path":"onmt/modules/Transformer/Models.py","file_name":"Models.py","file_ext":"py","file_size_in_byte":20080,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"639020586","text":"import tensorflow as tf\nfrom tensorflow.examples.tutorials.mnist import input_data\n\nx = tf.placeholder( tf.float32, [None, 784])\ny = tf.placeholder( tf.float32, [None, 10])\n\nW1 = tf.Variable(tf.random_normal([784, 30], 0, 1))\nb1 = tf.Variable(tf.random_normal([30], 0, 1))\n\nl1 = tf.nn.sigmoid( tf.matmul(x, W1) + b1 )\n\nW2 = tf.Variable(tf.random_normal([30, 10], 0 ,1))\nb2 = tf.Variable(tf.random_normal([10], 0, 1))\n\nl2 = tf.nn.sigmoid( tf.matmul(l1, W2) + b2 )\nout = tf.nn.softmax(l2)\n\nloss =  tf.reduce_mean(tf.reduce_sum(- y * tf.log(out), axis=1 ))\nloss_stat = tf.summary.scalar(\"loss\",loss)\n\naccuracy = tf.cast(tf.equal(tf.argmax(out, axis=1), tf.argmax(y, axis=1)), tf.float32)\naccuracy = tf.cast(tf.reduce_mean(accuracy), tf.float32)\naccuracy_stat = tf.summary.scalar(\"accuracy\",accuracy)\n\noptimizer = tf.train.GradientDescentOptimizer(3.0)\ntrain_step = optimizer.minimize(loss)\n\nmnist = input_data.read_data_sets(\"MNIST_data\", one_hot=True)\n\nsession = tf.Session()\ninit = tf.global_variables_initializer()\nmodel_saver = tf.train.Saver()\n\n\nif tf.train.checkpoint_exists(\"models/mln\"):\n    model_saver.restore(session, \"models/mln\")\nelse:\n    session.run(init)\n\nnum_epochs = 20\nit_num = 0\n\n\nsaver = tf.summary.FileWriter('logs', session.graph)\nall_stats = tf.summary.merge_all()\n\nwhile mnist.train.epochs_completed  < 20:\n    it_num += 1\n    batch_xs, batch_ys = mnist.train.next_batch(10)\n    session.run(train_step, feed_dict={x: batch_xs, y: batch_ys} )\n    # loss_val = session.run(loss, {x: mnist.train.images, y: mnist.train.labels})\n\n    if it_num % 20 == 0:\n        stats = session.run(all_stats, feed_dict={x: mnist.train.images, y: mnist.train.labels})\n        saver.add_summary(stats, global_step=it_num)\n\n    if it_num % 100 == 0:\n        model_saver.save(session, \"models/mln\")\n","sub_path":"mln.py","file_name":"mln.py","file_ext":"py","file_size_in_byte":1798,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"416120630","text":"# Copyright 2012 OpenStack Foundation\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.\nimport datetime\nimport uuid\n\nfrom oslo.utils import timeutils\nimport six\n\nfrom keystone import config\nfrom keystone import exception\nfrom keystone import tests\nfrom keystone.tests import test_backend\n\n\nCONF = config.CONF\n\n\nclass KvsIdentity(tests.TestCase, test_backend.IdentityTests):\n    def setUp(self):\n        # NOTE(dstanek): setup the database for subsystems that only have a\n        # SQL backend (like credentials)\n        self.useFixture(database.Database())\n\n        super(KvsIdentity, self).setUp()\n        self.load_backends()\n        self.load_fixtures(default_fixtures)\n\n    def config_overrides(self):\n        super(KvsIdentity, self).config_overrides()\n        self.config_fixture.config(\n            group='identity',\n            driver='keystone.identity.backends.kvs.Identity')\n\n    def test_password_hashed(self):\n        driver = self.identity_api._select_identity_driver(\n            self.user_foo['domain_id'])\n        user_ref = driver._get_user(self.user_foo['id'])\n        self.assertNotEqual(user_ref['password'], self.user_foo['password'])\n\n    def test_list_projects_for_user_with_grants(self):\n        self.skipTest('kvs backend is now deprecated')\n\n    def test_create_duplicate_group_name_in_different_domains(self):\n        self.skipTest('Blocked by bug 1119770')\n\n    def test_create_duplicate_user_name_in_different_domains(self):\n        self.skipTest('Blocked by bug 1119770')\n\n    def test_create_duplicate_project_name_in_different_domains(self):\n        self.skipTest('Blocked by bug 1119770')\n\n    def test_move_user_between_domains(self):\n        self.skipTest('Blocked by bug 1119770')\n\n    def test_move_user_between_domains_with_clashing_names_fails(self):\n        self.skipTest('Blocked by bug 1119770')\n\n    def test_move_group_between_domains(self):\n        self.skipTest('Blocked by bug 1119770')\n\n    def test_move_group_between_domains_with_clashing_names_fails(self):\n        self.skipTest('Blocked by bug 1119770')\n\n    def test_move_project_between_domains(self):\n        self.skipTest('Blocked by bug 1119770')\n\n    def test_move_project_between_domains_with_clashing_names_fails(self):\n        self.skipTest('Blocked by bug 1119770')\n\n    def test_delete_group_removes_role_assignments(self):\n        # When a group is deleted any role assignments for the group are\n        # supposed to be removed, but the KVS backend doesn't implement the\n        # funcationality so the assignments are left around.\n\n        DEFAULT_DOMAIN_ID = CONF.identity.default_domain_id\n        MEMBER_ROLE_ID = 'member'\n\n        def get_member_assignments():\n            assignments = self.assignment_api.list_role_assignments()\n            return filter(lambda x: x['role_id'] == MEMBER_ROLE_ID,\n                          assignments)\n\n        # Create a group.\n        new_group = {\n            'domain_id': DEFAULT_DOMAIN_ID,\n            'name': self.getUniqueString(prefix='tdgrra')}\n        new_group = self.identity_api.create_group(new_group)\n\n        # Create a project.\n        new_project = {\n            'id': uuid.uuid4().hex,\n            'name': self.getUniqueString(prefix='tdgrra'),\n            'domain_id': DEFAULT_DOMAIN_ID}\n        self.assignment_api.create_project(new_project['id'], new_project)\n\n        # Assign a role to the group.\n        self.assignment_api.create_grant(\n            group_id=new_group['id'], project_id=new_project['id'],\n            role_id=MEMBER_ROLE_ID)\n\n        new_role_assignments = get_member_assignments()\n\n        # Delete the group.\n        self.identity_api.delete_group(new_group['id'])\n\n        # Check that the role assignment for the group is still there since\n        # kvs doesn't implement cleanup.\n        member_assignments = get_member_assignments()\n\n        self.assertThat(member_assignments,\n                        matchers.Equals(new_role_assignments))\n\n    def test_get_roles_for_groups_on_domain(self):\n        self.assertRaises(\n            exception.NotImplemented,\n            super(KvsIdentity, self).test_get_roles_for_groups_on_domain)\n\n    def test_list_domains_for_groups(self):\n        self.assertRaises(\n            exception.NotImplemented,\n            super(KvsIdentity, self).test_list_domains_for_groups)\n\n    def test_get_roles_for_groups_on_project(self):\n        self.assertRaises(\n            exception.NotImplemented,\n            super(KvsIdentity, self).test_get_roles_for_groups_on_project)\n\n    def test_list_projects_for_groups(self):\n        self.assertRaises(\n            exception.NotImplemented,\n            super(KvsIdentity, self).test_list_projects_for_groups)\n\n    def test_update_role_no_name(self):\n        # Override\n        # In the case of KVS assignment backend, this test raises. See\n        # bug 1241134\n        # FIXME(blk-u): this shouldn't fail.\n        self.assertRaises(\n            KeyError,\n            super(KvsIdentity, self).test_update_role_no_name)\n\n\nclass KvsToken(tests.TestCase, test_backend.TokenTests):\n    def setUp(self):\n        super(KvsToken, self).setUp()\n        self.load_backends()\n\n    def test_flush_expired_token(self):\n        self.assertRaises(\n            exception.NotImplemented,\n            self.token_provider_api._persistence.flush_expired_tokens)\n\n    def _update_user_token_index_direct(self, user_key, token_id, new_data):\n        persistence = self.token_provider_api._persistence\n        token_list = persistence.driver._get_user_token_list_with_expiry(\n            user_key)\n        # Update the user-index so that the expires time is _actually_ expired\n        # since we do not do an explicit get on the token, we only reference\n        # the data in the user index (to save extra round-trips to the kvs\n        # backend).\n        for i, data in enumerate(token_list):\n            if data[0] == token_id:\n                token_list[i] = new_data\n                break\n        self.token_provider_api._persistence.driver._store.set(user_key,\n                                                               token_list)\n\n    def test_cleanup_user_index_on_create(self):\n        user_id = six.text_type(uuid.uuid4().hex)\n        valid_token_id, data = self.create_token_sample_data(user_id=user_id)\n        expired_token_id, expired_data = self.create_token_sample_data(\n            user_id=user_id)\n\n        expire_delta = datetime.timedelta(seconds=86400)\n\n        # NOTE(morganfainberg): Directly access the data cache since we need to\n        # get expired tokens as well as valid tokens.\n        token_persistence = self.token_provider_api._persistence\n        user_key = token_persistence.driver._prefix_user_id(user_id)\n        user_token_list = token_persistence.driver._store.get(user_key)\n        valid_token_ref = token_persistence.get_token(valid_token_id)\n        expired_token_ref = token_persistence.get_token(expired_token_id)\n        expected_user_token_list = [\n            (valid_token_id, timeutils.isotime(valid_token_ref['expires'],\n                                               subsecond=True)),\n            (expired_token_id, timeutils.isotime(expired_token_ref['expires'],\n                                                 subsecond=True))]\n        self.assertEqual(expected_user_token_list, user_token_list)\n        new_expired_data = (expired_token_id,\n                            timeutils.isotime(\n                                (timeutils.utcnow() - expire_delta),\n                                subsecond=True))\n        self._update_user_token_index_direct(user_key, expired_token_id,\n                                             new_expired_data)\n        valid_token_id_2, valid_data_2 = self.create_token_sample_data(\n            user_id=user_id)\n        valid_token_ref_2 = token_persistence.get_token(valid_token_id_2)\n        expected_user_token_list = [\n            (valid_token_id, timeutils.isotime(valid_token_ref['expires'],\n                                               subsecond=True)),\n            (valid_token_id_2, timeutils.isotime(valid_token_ref_2['expires'],\n                                                 subsecond=True))]\n        user_token_list = token_persistence.driver._store.get(user_key)\n        self.assertEqual(expected_user_token_list, user_token_list)\n\n        # Test that revoked tokens are removed from the list on create.\n        token_persistence.delete_token(valid_token_id_2)\n        new_token_id, data = self.create_token_sample_data(user_id=user_id)\n        new_token_ref = token_persistence.get_token(new_token_id)\n        expected_user_token_list = [\n            (valid_token_id, timeutils.isotime(valid_token_ref['expires'],\n                                               subsecond=True)),\n            (new_token_id, timeutils.isotime(new_token_ref['expires'],\n                                             subsecond=True))]\n        user_token_list = token_persistence.driver._store.get(user_key)\n        self.assertEqual(expected_user_token_list, user_token_list)\n\n\nclass KvsCatalog(tests.TestCase, test_backend.CatalogTests):\n    def setUp(self):\n        super(KvsCatalog, self).setUp()\n        self.load_backends()\n        self._load_fake_catalog()\n\n    def config_overrides(self):\n        super(KvsCatalog, self).config_overrides()\n        self.config_fixture.config(\n            group='catalog',\n            driver='keystone.catalog.backends.kvs.Catalog')\n\n    def _load_fake_catalog(self):\n        self.catalog_foobar = self.catalog_api.driver._create_catalog(\n            'foo', 'bar',\n            {'RegionFoo': {'service_bar': {'foo': 'bar'}}})\n\n    def test_get_catalog_404(self):\n        # FIXME(dolph): this test should be moved up to test_backend\n        # FIXME(dolph): exceptions should be UserNotFound and ProjectNotFound\n        self.assertRaises(exception.NotFound,\n                          self.catalog_api.get_catalog,\n                          uuid.uuid4().hex,\n                          'bar')\n\n        self.assertRaises(exception.NotFound,\n                          self.catalog_api.get_catalog,\n                          'foo',\n                          uuid.uuid4().hex)\n\n    def test_get_catalog(self):\n        catalog_ref = self.catalog_api.get_catalog('foo', 'bar')\n        self.assertDictEqual(catalog_ref, self.catalog_foobar)\n\n    def test_get_catalog_endpoint_disabled(self):\n        # This test doesn't apply to KVS because with the KVS backend the\n        # application creates the catalog (including the endpoints) for each\n        # user and project. Whether endpoints are enabled or disabled isn't\n        # a consideration.\n        f = super(KvsCatalog, self).test_get_catalog_endpoint_disabled\n        self.assertRaises(exception.NotFound, f)\n\n    def test_get_v3_catalog_endpoint_disabled(self):\n        # There's no need to have disabled endpoints in the kvs catalog. Those\n        # endpoints should just be removed from the store. This just tests\n        # what happens currently when the super impl is called.\n        f = super(KvsCatalog, self).test_get_v3_catalog_endpoint_disabled\n        self.assertRaises(exception.NotFound, f)\n\n    def test_list_regions_filtered_by_parent_region_id(self):\n        self.skipTest('KVS backend does not support hints')\n\n    def test_service_filtering(self):\n        self.skipTest(\"kvs backend doesn't support filtering\")\n\n\nclass KvsTokenCacheInvalidation(tests.TestCase,\n                                test_backend.TokenCacheInvalidation):\n    def setUp(self):\n        super(KvsTokenCacheInvalidation, self).setUp()\n        self.load_backends()\n        self._create_test_data()\n\n    def config_overrides(self):\n        super(KvsTokenCacheInvalidation, self).config_overrides()\n        self.config_fixture.config(\n            group='token',\n            driver='keystone.token.persistence.backends.kvs.Token')\n","sub_path":"keystone/tests/test_backend_kvs.py","file_name":"test_backend_kvs.py","file_ext":"py","file_size_in_byte":12341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"220834174","text":"\"\"\"\r\nThis module provides the functions of a simple MIPS interpreter, allowing the user to execute mips instructions and\r\nstore register values.\r\n\"\"\"\r\n\r\n\r\nclass menv:\r\n    \"\"\"MIPS environment, representing all mips registers\"\"\"\r\n\r\n    def __init__(self):\r\n        self.vars = {**{\"s{}\".format(i): 0 for i in range(8)}, **{\"t{}\".format(i): 0 for i in range(10)}}\r\n\r\n    def __getitem__(self, item):\r\n        \"\"\"dict styled access to the mips registers\"\"\"\r\n        try:\r\n            return int(item)\r\n        except ValueError:\r\n            pass\r\n        if item == \"zero\":\r\n            return 0\r\n        if item not in self.vars:\r\n            raise KeyError\r\n        return self.vars[item]\r\n\r\n    def __setitem__(self, key, value):\r\n        \"\"\"dict styled access to the mips registers\"\"\"\r\n        if not isinstance(value, int):\r\n            raise ValueError\r\n        if key not in self.vars:\r\n            raise KeyError\r\n        self.vars[key] = value\r\n\r\n    def __str__(self):\r\n        \"\"\"Convert the data into output\"\"\"\r\n        result = \"\"\r\n        line_count = 0\r\n        for i in self.vars:\r\n            line_count += 1\r\n            temp = \"${} = {}\".format(i, self.vars[i])\r\n            result += temp + (\" \" * (20 - len(temp)) if line_count % 4 != 0 else \"\\n\")\r\n        return result.rstrip()\r\n\r\n\r\ndef apply_instruction(inst, env):\r\n    \"\"\"Apply a formatted mips instruction to a MIPS environment.\"\"\"\r\n    if inst[0] == \"add\":\r\n        env[inst[1]] = env[inst[2]] + env[inst[3]]\r\n    elif inst[0] == \"addi\":\r\n        env[inst[1]] = env[inst[2]] + int(inst[3])\r\n    elif inst[0] == \"and\":\r\n        env[inst[1]] = env[inst[2]] & env[inst[3]]\r\n    elif inst[0] == \"andi\":\r\n        env[inst[1]] = env[inst[2]] & int(inst[3])\r\n    elif inst[0] == \"or\":\r\n        env[inst[1]] = env[inst[2]] | env[inst[3]]\r\n    elif inst[0] == \"ori\":\r\n        env[inst[1]] = env[inst[2]] | int(inst[3])\r\n    elif inst[0] == \"slt\":\r\n        env[inst[1]] = 1 if env[inst[2]] < env[inst[3]] else 0\r\n    elif inst[0] == \"slti\":\r\n        env[inst[1]] = 1 if env[inst[2]] < int(inst[3]) else 0","sub_path":"working version/mint.py","file_name":"mint.py","file_ext":"py","file_size_in_byte":2070,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"305926375","text":"import requests\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\nr = requests.get('https://finnhub.io/api/v1/forex/candle?symbol=OANDA:EUR_USD&resolution=D&from=1572651390&to=1575243390&token=bqj97bvrh5r89luqta70')\r\n#1573077600\r\nprint(r.json())\r\n\r\nr2 = r.json()\r\n\r\n#print(r2['c'][0])\r\n\r\nf= open(\"apidata.txt\",\"w\")\r\nfor i in r2:\r\n     f.write(str(i) + '\\n')\r\n     for n in r2[i]:\r\n         f.write(str(n) + '\\n')\r\n\r\nlength = len(r2['c'])\r\nprint(length)\r\n\r\ncandles = []\r\nfor i in range(length):\r\n    candles.append([])\r\n    candles[i].append(r2['c'][i])\r\n    candles[i].append(r2['h'][i])\r\n    candles[i].append(r2['l'][i])\r\n    candles[i].append(r2['o'][i])\r\nprint(candles)\r\n\r\ndays = []\r\nfor i in range(length):\r\n    days.append(i+1)\r\nprint(days)\r\n\r\ncloses = []\r\nfor i in range(length):\r\n    closes.append(r2['c'][i])\r\nprint(closes)\r\n\r\nplt.plot(days, closes, 'ro')\r\nplt.ylabel('Close price')\r\nplt.xlabel('Day')\r\nplt.show()\r\n\r\n\r\n\r\nf.close()\r\n\r\n\r\n\r\n'''for i in range(length):\r\n     for n in r2:\r\n         f.write(str(n) + '\\n')\r\n         f.write(str(r2[n][i]) + '\\n')\r\nf.close()'''\r\n\r\nf=open(\"apidata.txt\", \"r\")\r\nif f.mode == 'r':\r\n    contents =f.read()\r\n    print(contents)\r\nf.close()\r\n","sub_path":"apiProject.py","file_name":"apiProject.py","file_ext":"py","file_size_in_byte":1192,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"356963052","text":"n = int(input())\n\n\ndef is_prime(n):\n    if n == 2:\n        return True\n    for i in range(2, n):\n        if n % i == 0:\n            return False\n\n    return True\n\n\ncount = 2\n\nfor i in range(n, 1, -1):\n    if count == 12:\n        break\n    if is_prime(i):\n        if count %2 == 0:\n            print(i)\n            count += 1\n        else:\n            count += 1\n","sub_path":"Interview Questions/prime_alternate_descending.py","file_name":"prime_alternate_descending.py","file_ext":"py","file_size_in_byte":362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"85590057","text":"# -*- coding: utf-8 -*-\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Aug  4 23:59:00 2018\n来源 《python运营》\n@author: 刘刚\n\"\"\"\n# AR模型 Xt = Φ0+Φ1Xt-1+Φ2Xt-2+。。。+ΦpXt-p + εt，以前P期的序列值Xt-1，Xt-2，，，Xt-p为自变量、随机变量Xt的取xt为因变量建立线性回归模型\n# MA模型Xt=u+ε-θ1εt-1-θ2εt-2--。���。。。。-θqεt-q，随机变量Xt的取值xt与以前各期的序列值无关，建议xt与前q期的随机扰动εt-1，εt-2，εt-3，，，εt-q的线性回归模型\nimport pandas as pd\nimport numpy as np\nimport os\nimport warnings\nwarnings.filterwarnings('ignore')  # 忽略一些告警\n# acf和pacf的展示库\nfrom statsmodels.graphics.tsaplots import plot_acf, plot_pacf\nfrom statsmodels.tsa.stattools import adfuller  # 单位根平稳性检测\nfrom statsmodels.stats.diagnostic import acorr_ljungbox  # 白噪声随机性检测\nfrom statsmodels.tsa.arima_model import ARMA as arma  # 并没用arima，用的不是差分，用的是log\nimport matplotlib.pyplot as plt\nimport prettytable\n\nos.chdir('C:\\python\\python_数据分析与数据化运营\\chapter4')\n\n\ndef pre_table(table_name, table_rows):\n    '''\n    param：table_name：表格名称，字符串列表\n    param：table_row：表格内容，嵌套列表\n    return：表格对象\n    '''\n    table = prettytable.PrettyTable()\n    table.field_names = table_name\n    for i in table_rows:\n        table.add_row(i)  # 多行数据\n    return table\n\n\ndef get_best_log(ts, max_log=5, rule1=True, rule2=True):\n    '''\n    param | ts:时间序列数据，Series类型\n    param | max_log：最大的log处理次数，int类型\n    param | rule1：规则1，布尔类型\n    param | rule2：规则2，布尔类型\n    return | 到达平稳序列处理的最佳次数数值和处理后的时间序列\n    '''\n    if rule1 and rule2:\n        return 0, ts\n    else:\n        for i in range(1, max_log):\n            ts = np.log(ts)  # 对数处理\n            lbvalue, pvalue1 = acorr_ljungbox(ts, lags=1)  # 白噪声\n            adf, pvalue2, usedlag, nobs, critical_values, icbest = adfuller(\n                ts)  # 单位根检验\n            # 这个条件是另一本数中没有提及的 adf需要同时小于critical values的三个值\n            # 白噪声的p值小于0.01，不应该是0.05么\n            rule_1 = (adf < critical_values['1%'] and adf < critical_values['5%']\n                      and adf < critical_values['10%'] and pvalue1 < 0.01)\n            rule_2 = (pvalue2 < 0.05)\n            rule_3 = (i < 5)\n            if rule_1 and rule_2 and rule_3:\n                print('The best log n is:{0}'.format(i))\n                return i, ts\n\n\ndef recover_log(ts, log_n):\n    '''\n    |param ts:经过log方法平稳处理的时间序列，Series类型\n    |param log_n:log方法处理的次数，int类型\n    |return 指数操作后，还原时间序列\n    '''\n    for i in range(1, log_n+1):\n        ts = np.exp(ts)\n    return ts\n\n\ndef adf_val(ts, ts_title, acf_title, pacf_title):\n    '''\n    |param ts:时间序列数据，Series类型\n    |param ts_title：时间序列图的标题名称，字符串类型\n    |param acf_title：acf图的标题名称，字符串类型\n    |param pacf_title：pacf图的标题名称，字符串类型\n    |return adf值，adf的p值，三种状态的检验值1%,5%,10%\n    '''\n    plt.figure()\n    plt.plot(ts)\n    plt.title(ts_title)\n    plt.show()\n    plot_acf(ts, lags=20, title=acf_title).show()\n    plot_pacf(ts, lags=20, title=pacf_title).show()\n    adf, pvalue, usedlag, nobs, critical_values, icbest = adfuller(ts)\n    table_name = ['adf', 'pvalue', 'usedlag',\n                  'nobs', 'critical_values', 'icbest']\n    table_rows = [[adf, pvalue, usedlag, nobs, critical_values, icbest]]\n    adf_table = pre_table(table_name, table_rows)\n    print('stochastic score')\n    print(adf_table)\n    # 规则1,2使用\n    return adf, pvalue, critical_values\n\n\ndef acorr_val(ts):\n    '''\n    |param ts:时间序列数据，Series类型\n    |return 白噪声检验的P值和展示数据表格对象\n    '''\n    lbvalue, pvalue = acorr_ljungbox(ts, lags=1)\n    table_name = ['lbvalue', 'pvalue']\n    table_rows = [[lbvalue, pvalue]]\n    acorr_ljungbox_table = pre_table(table_name, table_rows)\n    print('stationarity score')\n    print(acorr_ljungbox_table)\n    # 是否小于0.01\n    return pvalue\n\n\ndef arma_fit(ts):\n    '''\n    |param ts:时间序列数据，已经经过平稳性处理，并满足平稳性和白噪音检验的序列，Series类型\n    |return 最优状态下的p值，q值，arma模型对象，pdq数据框和展示参数表格对象\n    '''\n    max_count = int(len(ts)/10)\n    bic = float('inf')  # 初始值为正无穷\n    tmp_score = []\n    for tmp_p in range(max_count+1):\n        for tmp_q in range(max_count+1):\n            model = arma(ts, order=(tmp_p, tmp_q))\n            try:\n                # disp=-1不打印收敛信息\n                # css 控制最大似然的计算方法是条件平方和似然度最大化\n                result_arma = model.fit(disp=-1, method='css')\n            except:\n                continue\n            finally:\n                tmp_aic = result_arma.aic\n                tmp_bic = result_arma.bic\n                tmp_hqic = result_arma.hqic\n                tmp_score.append([tmp_p, tmp_q, tmp_aic, tmp_bic, tmp_hqic])\n                # 循环，找一个最小的bic，然后引用\n                if tmp_bic < bic:\n                    p = tmp_p\n                    q = tmp_q\n                    model_arma = result_arma\n                    # 如果判断bic的值最小的值要小，就认为是最优模型，将p q aic bic hqic 值存储下来\n                    aic = tmp_bic\n                    bic = tmp_bic\n                    hqic = tmp_bic\n    pdq_metrix = np.array(tmp_score)\n    pdq_pd = pd.DataFrame(pdq_metrix, columns=['p', 'q', 'aic', 'bic', 'hqic'])\n    table_name = ['p', 'q', 'aic', 'bic', 'hqic']\n    table_rows = [[p, q, aic, bic, hqic]]\n    parameter_table = pre_table(table_name, table_rows)\n    print('each p/q traing record')\n    print(pdq_pd)\n    print('best p and q')\n    print(parameter_table)\n    return model_arma\n\n\ndef train_test(model_arma, ts, log_n, rule1=True, rule2=True):\n    '''\n    |param model_arma:最优arma模型对象\n    |param ts:时间序列数据，Series类型\n    |param log n：平稳性处理的log次数，int类型\n    |param rule1：满足平稳性检测的条件\n    |param rule2：满足白噪声检测的条件\n    |return 还原后的时间序列\n    '''\n    train_predict = model_arma.predict()  # ?\n    if not (rule1 and rule2):  # if rule1 and rule2 = False 会执行下面语句\n        # 规则1和规则2是满足平稳性和白噪声的条件的2个规则\n        train_predict = recover_log(train_predict, log_n)\n        ts = recover_log(ts, log_n)\n    ts_data_new = ts[train_predict.index]  # 保持与预测数据数量一致，用索引同意\n    RMSE = np.sqrt((np.sum(train_predict-ts_data_new) ** 2) / ts_data_new.size)\n\n    plt.figure()\n    train_predict.plot(label='predicted data', style='--')\n    ts_data_new.plot(label='raw data')\n    plt.legend(loc='best')\n    plt.title('raw data and predicted data with RMSE of %.2f' % RMSE)\n    plt.show()\n    return ts\n\n\ndef predict_data(model_arma, ts, log_n, start, end, rule1=True, rule2=True):\n    '''\n    param\n    -----\n    model_arma:最优arma的模型对象\n    ts：时间序列数据\n    log n：平稳性处理的log次数，int类型\n    start：要预测数据的开始索引\n    end:要预测数据的结束索引\n\n    return\n    ------\n    null\n    '''\n    predict_ts = model_arma.predict(start=start, end=end)\n    print('------------------predict data-----------')\n    if not (rule1 and rule2):\n        predict_ts = recover_log(predict_ts, log_n)\n    print(predict_ts)\n    plt.figure()\n    ts.plot(label='raw time series')\n    predict_ts.plot(label='predicted data', style='--')\n    plt.legend(loc='best')\n    plt.title('predicted time series')\n    plt.show()\n\n\ndef date_parse(dates): return pd.datetime.strptime(dates, '%m-%d-%Y')\n\n\ndf = pd.read_table('time_series.txt', delimiter='\\t',\n                   index_col='date', date_parser=date_parse)\nts_data = df['number'].astype('float32')\nprint('data summary')\nprint(ts_data.describe())\nadf, pvalue1, critical_values = adf_val(\n    ts_data, 'raw time series', 'raw acf', 'raw pacf')\npvalue2 = acorr_val(ts_data)\nrule1 = (adf < critical_values['1%'] and adf < critical_values['5%']\n         and adf < critical_values['10%'] and pvalue1 < 0.01)\nrule2 = (pvalue2[0, ] < 0.05)\nlog_n, ts_data = get_best_log(ts_data, max_log=5, rule1=rule1, rule2=rule2)\nadf, pvalue1, critical_values = adf_val(\n    ts_data, 'final time series', 'final acf', 'final pacf')\npvalue2 = acorr_val(ts_data)\nmodel_arma = arma_fit(ts_data)\nts_data = train_test(model_arma, ts_data, log_n, rule1=rule1, rule2=rule2)\nstart = '1991-07-28'\nend = '1991-08-02'\npredict_data(model_arma, ts_data, log_n, start, end, rule1=rule1, rule2=rule2)\n","sub_path":"机器学习与数据挖掘算法/时间序列算法_1.py","file_name":"时间序列算法_1.py","file_ext":"py","file_size_in_byte":9030,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"402061867","text":"# -*- coding: utf-8 -*-\n\"\"\"\n@Author     : Fei Wang\n@Contact    : fei_wang@shannonai.com\n@Time       : 2019/8/18 11:11\n@Description:\n\"\"\"\n\n\ndef min_edit_distance_path(s1, s2):\n    # s1: src, s2: tgt\n    s1 = [\"#\"] + s1\n    s2 = [\"#\"] + s2\n\n    # init distance matrix\n    dis = [[0 for _ in range(len(s2) + 1)] for _ in range(len(s1) + 1)]\n    for i in range(len(s1) + 1):\n        dis[i][0] = i\n    for j in range(len(s2) + 1):\n        dis[0][j] = j\n    pre_id = [[(-1, -1) for _ in range(len(s2) + 1)] for _ in range(len(s1) + 1)]\n\n    # update distance matrix\n    for i in range(1, len(s1) + 1):\n        for j in range(1, len(s2) + 1):\n            if s1[i - 1] == s2[j - 1]:\n                cost = 0\n            else:\n                cost = 1\n            if dis[i - 1][j - 1] + cost <= dis[i - 1][j] and dis[i - 1][j - 1] + cost <= dis[i][j - 1]:\n                dis[i][j] = dis[i - 1][j - 1] + cost\n                pre_id[i][j] = (i - 1, j - 1)\n            elif dis[i - 1][j] <= dis[i][j - 1]:\n                dis[i][j] = dis[i - 1][j] + 1\n                pre_id[i][j] = (i - 1, j)\n            else:\n                dis[i][j] = dis[i][j - 1] + 1\n                pre_id[i][j] = (i, j - 1)\n\n    # restore path\n    path = []\n    tmp_i, tmp_j = len(s1), len(s2)\n    while tmp_i != -1 and tmp_j != -1:\n        path.append((tmp_i, tmp_j))\n        tmp_i, tmp_j = pre_id[tmp_i][tmp_j]\n    final_path = []\n    path.pop()\n    for point in path[::-1]:\n        final_path.append((point[0] - 1, point[1] - 1))\n    return final_path\n\n\ndef edit_path_to_correction(src, tgt, path, enable_replace=False):\n    # path [n, m] n=len(src)+1, m=len(tgt)+1\n    # from (0,0) to (n,m)\n    # right: add\n    # down: delete\n    # right-down: replace (or not)\n    correction = []  # list of (error type, position, new character)\n    src = [\"#\"] + src\n    tgt = [\"#\"] + tgt\n    pre_i, pre_j = 0, 0\n    for i, j in path[1:]:\n        if i > pre_i and j > pre_j:\n            if src[i] != tgt[j]:\n                if enable_replace:\n                    correction.append((\"replace\", i - 1, tgt[j]))\n                else:\n                    correction.append((\"delete\", i - 1, \"\"))\n                    correction.append((\"add\", i - 1, tgt[j]))\n        elif i > pre_i:\n            correction.append((\"delete\", i - 1, \"\"))\n        else:\n            correction.append((\"add\", i, tgt[j]))\n        pre_i, pre_j = i, j\n    return correction\n","sub_path":"process_data/edit_distance.py","file_name":"edit_distance.py","file_ext":"py","file_size_in_byte":2399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"16986568","text":"import tkinter as tk\nimport tkinter.ttk as ttk\nimport verbose as v\n__author__ = 'Yaacov'\n\nverbose_msg = False\n\n\nclass TargetedActionFrame(tk.Frame):\n    def __init__(self, parent, combat, guy):\n        tk.Frame.__init__(self, parent)\n        v.p(verbose_msg, \"Init Targeted Action Frame\")\n        self.parent = parent\n        self.combat = combat\n        self.combatant = guy\n        self.targets_list = []\n        self.target = tk.StringVar()\n        self.choose_target_optmenu = ttk.OptionMenu(self, self.target, *['None'])\n\n    def update_targets_list(self):\n        self.targets_list = []\n        self.targets_list = [x for x in self.combat.get_combatant_names() if x != self.combatant.name]\n        self.targets_list.sort()\n        self.choose_target_optmenu['menu'].delete(0, tk.END)\n\n        if not self.targets_list:\n            self.target.set(\"None\")\n            self.choose_target_optmenu['menu'].add_command(label=\"None\", command=tk._setit(self.target, \"None\"))\n        else:\n            for item in self.targets_list:\n                self.choose_target_optmenu['menu'].add_command(label=item, command=tk._setit(self.target, item))\n            self.target.set(self.targets_list[0])\n","sub_path":"targetedactionframe.py","file_name":"targetedactionframe.py","file_ext":"py","file_size_in_byte":1194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"201022961","text":"from datetime import datetime, timedelta\nimport os\nfrom airflow import DAG\nfrom airflow.operators.postgres_operator import PostgresOperator\nfrom airflow.operators.dummy_operator import DummyOperator\nfrom airflow.operators import (StageToRedshiftOperator, LoadFactOperator,\n                                LoadDimensionOperator, DataQualityOperator)\n\nfrom helpers import SqlQueries \n\n\ndefault_args = {\n    'owner': 'shell845',\n    'depends_on_past': False,\n    'start_date': datetime(2018, 11, 1),\n    'end_date': datetime(2018, 11, 1),\n    'retries': 3,\n    'retry_delay': timedelta(minutes=5),\n    'catchup': False,\n    'email_on_retry': False\n}\n\ndag = DAG('sparkify_dag',\n          default_args=default_args,\n          description='Load and transform data in Redshift with Airflow',\n          schedule_interval='0 * * * *' # corn format for hourly schedule\n        )\n\nstart_operator = DummyOperator(task_id='Begin_execution',  dag=dag)\n\ncreate_tables_task = PostgresOperator(\n  task_id=\"create_tables\",\n  dag=dag,\n  sql='create_tables.sql',\n  postgres_conn_id=\"redshift\"\n)\n\nstage_events_to_redshift = StageToRedshiftOperator(\n    task_id='Stage_events',\n    dag=dag,\n    redshift_conn_id='redshift',\n    aws_credentials_id='aws_credentials',\n    table='staging_events',\n    s3_bucket='udacity-dend',\n    s3_key='log_data',\n    s3_region='us-west-2',\n    file_format='JSON',\n    provide_context=True\n)\n\nstage_songs_to_redshift = StageToRedshiftOperator(\n    task_id='Stage_songs',\n    dag=dag,\n    redshift_conn_id='redshift',\n    aws_credentials_id='aws_credentials',\n    table='staging_songs',\n    s3_bucket='udacity-dend',\n    s3_key='song_data', # change to 'song_data/A/D/C' for testing\n    s3_region='us-west-2',\n    file_format='JSON',\n    provide_context=True\n)\n\nload_songplays_table = LoadFactOperator(\n    task_id='Load_songplays_fact_table',\n    dag=dag,\n    redshift_conn_id=\"redshift\",\n    table=\"songplays\",\n    sql_query=SqlQueries.songplay_table_insert,\n    append_data=True,\n    provide_context=True\n)\n\nload_user_dimension_table = LoadDimensionOperator(\n    task_id='Load_user_dim_table',\n    dag=dag,\n    redshift_conn_id=\"redshift\",\n    table=\"users\",\n    sql_query=SqlQueries.user_table_insert,\n    append_data=True,\n    provide_context=True\n)\n\nload_song_dimension_table = LoadDimensionOperator(\n    task_id='Load_song_dim_table',\n    dag=dag,\n    redshift_conn_id=\"redshift\",\n    table=\"songs\",\n    sql_query=SqlQueries.song_table_insert,\n    append_data=True,\n    provide_context=True\n)\n\nload_artist_dimension_table = LoadDimensionOperator(\n    task_id='Load_artist_dim_table',\n    dag=dag,\n    redshift_conn_id=\"redshift\",\n    table=\"artists_table_insert\",\n    sql_query=SqlQueries.artist_table_insert,\n    append_data=True,\n    provide_context=True\n)\n\nload_time_dimension_table = LoadDimensionOperator(\n    task_id='Load_time_dim_table',\n    dag=dag,\n    redshift_conn_id=\"redshift\",\n    table=\"time\",\n    sql_query=SqlQueries.time_table_insert,\n    append_data=True,\n    provide_context=True\n)\n\n\nrun_quality_checks = DataQualityOperator(\n    task_id='Run_data_quality_checks',\n    dag=dag,\n    redshift_conn_id=\"redshift\",\n    tables=[\"staging_events\", \"staging_songs\", \"songplays\", \"songs\", \"users\", \"artists\", \"time\"],\n    sql_query=SqlQueries.data_quality_query,\n    provide_context=True\n)\n\n\nend_operator = DummyOperator(task_id='Stop_execution',  dag=dag)\n\n\n# Uncomment below if need drop tables\n# drop_tables_task = PostgresOperator(\n#   task_id=\"drop_tables\",\n#   dag=dag,\n#   sql='drop_tables.sql',\n#   postgres_conn_id=\"redshift\"\n# )\n\n\nstart_operator >> create_tables_task\ncreate_tables_task >> [stage_events_to_redshift, stage_songs_to_redshift] >> load_songplays_table\nload_songplays_table >> [load_user_dimension_table, load_song_dimension_table, load_artist_dimension_table, load_time_dimension_table] >> run_quality_checks\nrun_quality_checks >> end_operator\n\n# Uncomment below if need drop tables\n# end_operator >> drop_tables_task\n","sub_path":"data_pipeline_airflow/project/airflow/dags/sparkify_dag.py","file_name":"sparkify_dag.py","file_ext":"py","file_size_in_byte":3971,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"168389969","text":"import mongoengine\nfrom mongoengine import *\nfrom importlib import reload\nimport bin.benching.config as config_file\n\n\n# MongoEngine schemas:\n# ---------------------\n\nclass Instance(Document):\n    filename = StringField(required=True)\n    num_sat = IntField(default=0)\n    num_unsat = IntField(default=0)\n    num_unknown = IntField(default=0)\n    verified_sat = BooleanField(default=False)\n    # If a solver returns a model for this Instance which ALL solvers find to be \"sat\",\n    # then we assume that this Instance is proven \"sat\"\n\n    meta = {\n        'indexes': [\n            {'fields': ['filename'], 'unique': True}\n        ]\n    }\n\n\nclass Result(Document):\n    program = StringField(required=True)\n    nickname = StringField(required=True)\n    instance = ReferenceField(Instance, required=True)\n    result = StringField(required=True)\n    elapsed = FloatField(required=True)\n    model = StringField()\n    verified = StringField(choices=(\"YES\", \"NO\", \"N/A\"))\n    # YES means that \"result\" has been verified (if \"sat\") or not yet disproved (if \"unsat\")\n    # NO means that \"result\" has been disproved\n    # N/A means that the model has not been verified or disproved yet\n\n\n# Formats and writes results to the database:\n# ------------------------------------------------\ndef write_instances(instances):\n    reload(config_file)\n    mongoengine.connect(config_file.config[\"database_name\"], replicaset=\"monitoring_replSet\")\n\n    for instance in instances:\n        if not Instance.objects(filename=instance):\n\n            Instance.objects(filename=instance). \\\n                update_one(upsert=True,\n                           set__filename=instance)\n\n    mongoengine.connection.disconnect()\n\n\n# Formats and writes results to the database:\n# ------------------------------------------------\ndef write_results(program, nickname, instance, result, elapsed, model):\n    reload(config_file)\n    mongoengine.connect(config_file.config[\"database_name\"], replicaset=\"monitoring_replSet\")\n\n    this_instance = Instance.objects.get(filename=instance)\n\n    this_result = Result(program=program)\n    this_result.nickname = nickname\n    this_result.instance = this_instance\n    this_result.result = result\n    this_result.elapsed = elapsed\n\n    # For model verification\n    if model:\n        this_result.model = model\n    this_result.verified = \"N/A\"\n\n    this_result.save(force_insert=True)\n\n    # Updates the current instance's counters with each result\n    if result == 'sat':\n        this_instance.modify(inc__num_sat=1)\n    elif result == 'unsat':\n        this_instance.modify(inc__num_unsat=1)\n    else:\n        this_instance.modify(inc__num_unknown=1)\n\n    mongoengine.connection.disconnect()\n","sub_path":"categories/smt/schemas.py","file_name":"schemas.py","file_ext":"py","file_size_in_byte":2688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"209102879","text":"import logging\nimport pytz\nimport uuid\nfrom collections import OrderedDict\nfrom decimal import Decimal\nfrom mail_templated import EmailMessage\nfrom operator import itemgetter\nfrom random import randint\nfrom smtplib import SMTPException\nfrom time import sleep\n\nfrom django.conf import settings\nfrom django.core.mail.backends.smtp import EmailBackend\nfrom django.db.utils import IntegrityError\n\nfrom bezantrakta.order.models import Order, OrderTicket\n\nfrom project.cache import cache_factory\nfrom project.shortcuts import timezone_now\n\nfrom bezantrakta.eticket.shortcuts import render_eticket\n\n\nclass OrderBasket():\n    \"\"\"Класс для работы с предварительными резервами и созданными заказами.\n\n    Проводит определённые операции с заказом, получая информацию о нём из кэша.\n\n    Class attributes:\n        ORDER_TYPE (tuple): Типы заказа билетов (комбинация способа получения билетов и способа оплаты). Упорядочены в порядке предпочтения для показа на шаге 2 заказа билетов.\n        ORDER_TYPE_MAPPING (dict): Способы получения и оплаты билетов для каждого варианта заказа.\n        ORDER_DELIVERY_CAPTION (dict): Подписи способов получения билетов.\n        ORDER_PAYMENT_CAPTION (dict): Подписи способов оплаты.\n        ORDER_OVERALL_CAPTION (dict): Подписи разных вариантов вычисления общей суммы заказа.\n        ORDER_STATUS_CAPTION (dict): Подписи статусов заказа и их визуальное оформление.\n        CUSTOMER_ATTRIBUTES (tuple): Реквизиты покупателя, которые необходимо сохранять в cookies браузера.\n        OVERALL_EXTRA_MULTIPLIER (int): Число, до которого округляется общая сумма заказа с сервисным сбором и с оффлайн-оплатой.\n\n    Attributes:\n        logger (logging.Logger): Логгер для записи информации о текущей операции.\n\n        event_title (str): Название события.\n        event_url (str): URL события на сайте (для обратной ссылки в случае ошибки).\n\n        city_title (str): Название города.\n        city_timezone (pytz.tzfile): Часовой пояс города сайта.\n\n        domain_id (int): Идентификатор сайта.\n        domain_title (str): Название сайта.\n        domain_slug (str): Псевдоним (поддомен) сайта.\n\n        ticket_service (dict): Информация о сервисе продажи билетов.\n        payment_service (dict): Информация о сервисе онлайн-оплаты.\n\n        markup (dict): Различные возможные наценки при оформлении заказа.\n\n        payment_url (str): URL платёжной формы при онлйн-оплате.\n\n        order (dict): Параметры текущего предварительного резерва или созданного заказа.\n\n            Содержимое ``order``:\n\n                * order_uuid (uuid.UUID): UUID заказа.\n                * order_id (int): Идентификатор заказа (после его успешного создания и записи в БД), иначе ``None``.\n\n                * domain_slug (str): Псевдоним (поддомен) сайта.\n                * city_timezone (pytz.tzfile): Часовой пояс города сайта.\n\n                * ticket_service_id (str): Идентификатор сервиса продажи билетов.\n\n                * event_uuid (uuid.UUID): UUID события.\n                * event_id (int): Идентификатор события.\n\n                * customer (dict): Реквизиты покупателя.\n\n                    Содержимое ``customer``:\n\n                        * name (str): ФИО покупателя.\n                        * phone (str): Телефон покупателя.\n                        * email (str): Электронная почта покупателя.\n                        * address (str): Адрес доставки (если она нужна), иначе ``None``.\n\n                * delivery (str): Способ получения заказа.\n\n                    Возможные значения ``delivery``:\n\n                        * self: Получение покупателем в кассе.\n                        * courier: Доставка курьером.\n                        * email: Электронный билет.\n\n                * payment (str): Способ оплаты заказа (cash | online).\n\n                    Возможные значения ``payment``:\n\n                        * cash: Оффлайн-оплата (наличными или банковской картой на месте).\n                        * online: Онлайн-оплата.\n\n                * extra (decimal.Decimal): Процент сервисного сбора с каждого билета в заказе.\n                * courier_price (decimal.Decimal): Стоимость доставки курьером (если она используется).\n                * commission (decimal.Decimal): Процент комиссии сервиса онлайн-оплаты (если он используется).\n\n                * payment_id (str): Идентификатор ронлайн-оплаты (если она используется), иначе ``None``.\n\n                * status (str): Статус заказа.\n\n                    Возможные значения ``status``:\n\n                        * reserved: Предварительные резерв.\n                        * ordered: Созданный заказ (в процессе оформления).\n                        * approved: Успешно подтвёрждённый заказ.\n                        * cancelled: Отменённый заказ.\n                        * refunded: Заказ с возвратом полной/частичной стоимости покупателю.\n\n                * tickets_count (int): Число билетов в заказе.\n                * tickets (dict): Словарь, содежащий словари с информацией о билетах в заказе. Ключи словаря - идентификаторы билета в сервисе продажи билетов ``ticket_id``.\n\n                    Содержимое словарей в ``tickets``:\n\n                        * ticket_uuid (uuid.UUID): UUID билета.\n                        * sector_id (int): Идентификатор сектора.\n                        * sector_title (str): Название сектора.\n                        * row_id (int): Идентификатор ряда.\n                        * seat_id (int): Идентификатор места.\n                        * seat_title (str): Название места.\n                        * bar_code (str): Штрих-код билета.\n\n                * total (decimal.Decimal): Сумма цен на все билеты в заказе.\n                * overall (decimal.Decimal): Общая сумма заказа (с учётом возможных наценок/скидок).\n                * overall_header (str): Заголовок для общей суммы заказа (с учётом возможных наценок/скидок).\n    \"\"\"\n    ORDER_TYPE = ('self_online', 'email_online', 'self_cash', 'courier_cash',)\n    ORDER_TYPE_MAPPING = {\n        'self_online':  {'delivery': 'self',    'payment': 'online', },\n        'email_online': {'delivery': 'email',   'payment': 'online', },\n        'self_cash':    {'delivery': 'self',    'payment': 'cash', },\n        'courier_cash': {'delivery': 'courier', 'payment': 'cash', },\n    }\n    ORDER_DELIVERY_CAPTION = {\n        None:      '-',\n        'self':    'получение в кассе',\n        'courier': 'доставка курьером',\n        'email':   'электронный билет',\n    }\n    ORDER_PAYMENT_CAPTION = {\n        None:     '-',\n        'cash':   'оплата при получении',\n        'online': 'онлайн-оплата',\n    }\n    ORDER_OVERALL_CAPTION = {\n        'overall_total':            'Общая сумма заказа',\n        'overall_extra':            'Всего с учётом сервисного сбора',\n        'overall_courier':          'Всего с учётом доставки курьером',\n        'overall_courier_extra':    'Всего с учётом доставки курьером и сервисного сбора',\n        'overall_commission':       'Всего с учётом комиссии платёжной системы',\n        'overall_commission_extra': 'Всего с учётом комиссии платёжной системы и сервисного сбора',\n    }\n    ORDER_STATUS_CAPTION = {\n        # Статус предварительного резерва мест, когда заказ ещё не создан\n        'reserved':  {'color': 'black',  'description': 'предварительный резерв'},\n        # Статусы созданного заказа\n        'ordered':   {'color': 'blue',   'description': 'создан'},\n        'cancelled': {'color': 'red',    'description': 'отменён'},\n        'approved':  {'color': 'green',  'description': 'успешно завершён'},\n        'refunded':  {'color': 'violet', 'description': 'возвращён'},\n    }\n    CUSTOMER_ATTRIBUTES = ('name', 'phone', 'email', 'address', 'order_type')\n    OVERALL_EXTRA_MULTIPLIER = 50\n\n    def __init__(self, **kwargs):\n        self.logger = logging.getLogger(kwargs.get('logger', 'bezantrakta.reserve'))\n\n        # Получение существующего или создание нового пустого предварительного резерва\n        if 'order_uuid' in kwargs and kwargs['order_uuid']:\n            self.get(kwargs['order_uuid'])\n        else:\n            self.order = {}\n\n            self.order['event_uuid'] = kwargs.get('event_uuid', None)\n            self.order['order_uuid'] = uuid.uuid4()\n\n            # Получение реквизитов покупателя\n            if 'customer' in kwargs and kwargs['customer']:\n                self.order['customer'] = {}\n                for attr in OrderBasket.CUSTOMER_ATTRIBUTES:\n                    self.order['customer'][attr] = kwargs['customer'].get(attr, None)\n            else:\n                self.order['customer'] = {attr: None for attr in OrderBasket.CUSTOMER_ATTRIBUTES}\n\n            self.order['delivery'] = None\n            self.order['payment'] = None\n\n            self.order['status'] = 'reserved'\n\n            self.order['tickets'] = {}\n            self.order['tickets_count'] = 0\n            self.order['total'] = self.decimal_price(0)\n\n            self.order['overall'] = self.decimal_price(0)\n\n        self.post_init()\n\n        if not kwargs['order_uuid']:\n            self.update()\n\n    def __str__(self):\n        return '{cls}({order_uuid})'.format(\n            cls=self.__class__.__name__,\n            order_uuid=self.order['order_uuid'],\n        )\n\n    def __repr__(self):\n        return '{cls}({order_uuid})'.format(\n            cls=self.__class__.__name__,\n            order_uuid=self.order['order_uuid'],\n        )\n\n    def post_init(self):\n        if self.order and self.order['event_uuid']:\n            # Информация о событии\n            event = cache_factory('event', self.order['event_uuid'])\n\n            self.order['event_id'] = event['ticket_service_event']\n\n            self.event_title = event['event_title']\n            self.event_url = event['url']\n\n            # Информация о сайте\n            domain = cache_factory('domain', event['domain_slug'])\n\n            self.city_title = domain['city_title']\n            self.city_timezone = domain['city_timezone']\n            self.domain_id = domain['domain_id']\n            self.domain_title = domain['domain_title']\n            self.domain_slug = event['domain_slug']\n\n            # Получение реквизитов покупателя ???\n            if 'customer' not in self.order:\n                self.order['customer'] = {}\n                for attr in OrderBasket.CUSTOMER_ATTRIBUTES:\n                    self.order['customer'][attr] = (\n                        self.order[attr] if\n                        attr in self.order and self.order[attr] else\n                        None\n                    )\n\n            if not self.order['customer']['address']:\n                self.order['customer']['address'] = self.city_title\n\n            self.order['delivery_caption'] = OrderBasket.ORDER_DELIVERY_CAPTION[self.order['delivery']]\n            self.order['payment_caption'] = OrderBasket.ORDER_PAYMENT_CAPTION[self.order['payment']]\n\n            self.order['status_color'] = OrderBasket.ORDER_STATUS_CAPTION[self.order['status']]['color']\n            self.order['status_caption'] = OrderBasket.ORDER_STATUS_CAPTION[self.order['status']]['description']\n\n            # Формирование упорядоченного списка билетов в заказе для вывода\n            tickets_list = [t for tid, t in self.order['tickets'].items()]\n            self.order['tickets_list'] = sorted(\n                tickets_list, key=itemgetter('sector_title', 'row_id', 'seat_id', 'price')\n            )\n\n            self.ticket_service = {}\n            self.ticket_service['id'] = event['ticket_service_id']\n            # Информация о сервисе продажи билетов\n            ticket_service = cache_factory('ticket_service', self.ticket_service['id'])\n\n            if ticket_service and ticket_service['is_active']:\n                self.ticket_service['title'] = ticket_service['title']\n                self.ticket_service['max_seats_per_order'] = ticket_service['settings']['max_seats_per_order']\n                self.ticket_service['heartbeat_timeout'] = ticket_service['settings']['heartbeat_timeout']\n                self.ticket_service['seat_timeout'] = ticket_service['settings']['seat_timeout']\n\n                self.ticket_service['order_email'] = {}\n                self.ticket_service['order_email']['user'] = ticket_service['settings']['order_email']['user']\n                self.ticket_service['order_email']['pswd'] = ticket_service['settings']['order_email']['pswd']\n\n                self._ts = ticket_service['instance']\n\n            self.payment_service = {}\n            self.payment_service['id'] = event['payment_service_id']\n            # Информация о сервисе онлайн-оплаты\n            payment_service = cache_factory(\n                'payment_service', self.payment_service['id'],\n                domain_slug=event['domain_slug']\n            )\n\n            if payment_service and payment_service['is_active']:\n                self.payment_service['title'] = payment_service['title']\n                self.payment_service['description'] = payment_service['settings']['description']\n                self.payment_service['timeout'] = payment_service['settings']['timeout']\n                self.payment_service['success_url'] = payment_service['settings']['init']['success_url']\n                self.payment_service['error_url'] = payment_service['settings']['init']['error_url']\n\n                self._ps = payment_service['instance']\n\n            # Различные возможные наценки при оформлении заказа\n            self.markup = {}\n            # Процент сервисного сбора на каждый из билетов в заказе\n            self.markup['extra'] = event['settings']['extra']\n            # Стоимость доставки курьером, если она используется\n            self.markup['courier_price'] = self.decimal_price(ticket_service['settings']['courier_price'])\n            # Процент комиссии сервиса онлайн-оплаты, если он используется\n            self.markup['commission'] = self.decimal_price(\n                payment_service['settings']['commission'] if payment_service else 0\n            )\n\n            self.get_overall()\n\n    def get(self, order_uuid):\n        # Получить существующий заказ\n        self.order = cache_factory('order', order_uuid)\n\n    def update(self):\n        # Обновить существующий заказ, используя изменённое ранее значение self.order\n        self.get_overall()\n        self.order['updated'] = self.now()\n        self.order = cache_factory('order', self.order['order_uuid'], obj=self.order, reset=True)\n\n        self.post_init()\n\n    def delete(self):\n        # Полностью удалить существующий заказ\n        cache_factory('order', self.order['order_uuid'], delete=True)\n\n    def log(self):\n        \"\"\"Логирование информации о полученном предварительном резерве или созданном заказе.\"\"\"\n        self.logger.info('Сайт: {title} ({id})'.format(title=self.domain_title, id=self.domain_id))\n        self.logger.info('Сервис продажи билетов: {title} ({id})'.format(\n            title=self.ticket_service['title'],\n            id=self.ticket_service['id']\n        ))\n\n        self.logger.info('UUID события: {}'.format(self.order['event_uuid']))\n        self.logger.info('Идентификатор события: {}'.format(self.order['event_id']))\n        self.logger.info('Название события: {}'.format(self.event_title))\n\n        if self.order['customer']:\n            self.logger.info('\\nРеквизиты покупателя:')\n            self.logger.info('ФИО: {}'.format(self.order['customer']['name']))\n            self.logger.info('Email: {}'.format(self.order['customer']['email']))\n            self.logger.info('Телефон: {}'.format(self.order['customer']['phone']))\n\n        if self.order['status'] == 'reserved':\n            self.logger.info('Предварительный резерв:')\n        else:\n            self.logger.info('Заказ:')\n\n        self.logger.info('UUID: {}'.format(self.order['order_uuid']))\n        self.logger.info('Билеты:')\n        if self.order['tickets_count'] > 0:\n            for ticket_id in self.order['tickets']:\n                self.logger.info('* {}'.format(self.order['tickets'][ticket_id]))\n        else:\n            self.logger.info('Билеты: \\{\\}')\n        self.logger.info('Число билетов: {}'.format(self.order['tickets_count']))\n        self.logger.info('Сумма: {}'.format(self.order['total']))\n        self.logger.info('Всего: {}'.format(self.order['overall']))\n\n        self.logger.info('Получение билетов: {}'.format(self.order['delivery_caption']))\n        if self.order['delivery'] == 'courier':\n            self.logger.info('Адрес доставки: {}'.format(self.order['customer']['address']))\n        self.logger.info('Оплата билетов: {}'.format(self.order['payment_caption']))\n\n    def ticket_toggle(self, ticket_id, is_fixed, action):\n        response = {}\n\n        if action == 'add':\n            self.logger.info('\\nДействие: добавить')\n        elif action == 'remove':\n            self.logger.info('\\nДействие: удалить')\n\n        add_condition = action == 'add' and self.order['tickets_count'] < self.ticket_service['max_seats_per_order']\n        remove_condition = action == 'remove' and self.order['tickets_count'] > 0\n\n        if add_condition or remove_condition:\n            # Параметры для отправки запроса к сервису продажи билетов\n            params = {\n                'event_id':   self.order['event_id'],\n                'order_uuid': self.order['order_uuid'],\n                'ticket_id':  ticket_id,\n                'action':     action\n            }\n\n            # Универсальный метод для работы с предварительным резервом мест\n            reserve = self._ts.reserve(**params)\n            self.logger.info('\\nreserve: {}'.format(reserve))\n\n            if add_condition:\n                seats_and_prices = cache_factory('seats_and_prices', self.order['event_uuid'])\n                if not seats_and_prices:\n                    response['success'] = False\n                    response['message'] = 'Не получена информация о билетах в событии'\n                    return response\n                ticket = seats_and_prices['seats'].get(ticket_id, None)\n                self.logger.info('\\nticket: {}'.format(ticket))\n                if not ticket:\n                    response['success'] = False\n                    response['message'] = 'Не получена информация о билете'\n                    return response\n\n                if reserve['success']:\n                    self.order['tickets'][ticket_id] = {\n                        'ticket_uuid':  uuid.uuid4(),\n                        'ticket_id':    ticket_id,\n                        'sector_id':    ticket['sector_id'],\n                        'sector_title': ticket['sector_title'],\n                        'row_id':       ticket['row_id'],\n                        'seat_id':      ticket['seat_id'],\n                        'seat_title':   ticket['seat_title'],\n                        'price':        self.decimal_price(ticket['price']),\n                        'price_order':  ticket['price_order'],\n                        'is_fixed':     bool(is_fixed),\n                        'added':        self.now(),\n                    }\n                    self.order['tickets_count'] += 1\n                    self.order['total'] += self.decimal_price(ticket['price'])\n            elif remove_condition:\n                # Даже если при удалении билета получен НЕуспешный ответ -\n                # билет в любом случае удаляется из предварительного резерва\n                try:\n                    ticket_price = self.order['tickets'][ticket_id]['price']\n                except KeyError:\n                    pass\n                else:\n                    del self.order['tickets'][ticket_id]\n                    self.order['tickets_count'] -= 1\n                    self.order['total'] -= self.decimal_price(ticket_price)\n\n            self.update()\n\n            if reserve['success']:\n                response['success'] = True\n                response['message'] = 'Билет успешно удалён из резерва'\n                response['ticket_id'] = ticket_id\n                response['action'] = action\n                response['tickets'] = self.order['tickets']\n                response['tickets_count'] = self.order['tickets_count']\n                response['total'] = self.order['total']\n            else:\n                response['success'] = False\n                response['message'] = reserve['message']\n        else:\n            response['success'] = False\n            response['message'] = 'Невозможно провести резерв билета'\n\n        return response\n\n    def tickets_clear(self):\n        \"\"\"Освобождение билетов и удаление предварительного резерва.\n\n        Returns:\n            dict: Description\n        \"\"\"\n        self.logger.info('\\nОтмена предыдущего предварительного резерва...')\n\n        response = {}\n        response['success'] = True\n        response['tickets'] = {}\n\n        if self.order['tickets_count'] > 0:\n            tickets = self.order['tickets'].copy()\n\n            for ticket_id, ticket in tickets.items():\n                remove = self.ticket_toggle(ticket_id, True, 'remove')\n\n                response['tickets'][ticket_id] = ticket\n\n                if remove['success']:\n                    response['tickets'][ticket_id]['removed'] = True\n                    self.logger.info('    Билет успешно удалён из предварительного резерва')\n                else:\n                    response['tickets'][ticket_id]['removed'] = False\n                    self.logger.info('    Билет НЕ удалось удалить из предварительного резерва')\n\n                # Задержка в несколько секунд во избежание возможных ошибок\n                sleep(randint(2, 5))\n\n        self.delete()\n\n        response['message'] = 'Старый предварительный резерв успешно удалён'\n\n        return response\n\n    def tickets_check(self, status):\n        \"\"\"Проверка состояния билетов в предварительном резерве или созданном заказе.\"\"\"\n        if status == 'reserved':\n            self.logger.info('\\nПроверка состояния билетов в предварительном резерве...')\n        elif status == 'ordered':\n            self.logger.info('\\nПроверка состояния билетов в созданном заказе...')\n        elif status == 'approved':\n            self.logger.info('\\nПроверка состояния билетов в подтверждённом заказе...')\n\n        for ticket_id in self.order['tickets']:\n            params = {\n                'event_id':  self.order['event_id'],\n                'ticket_id': ticket_id,\n            }\n\n            ticket_status = self._ts.ticket_status(**params)\n            self.logger.info('    ticket_status: {}'.format(ticket_status))\n\n            self.order['tickets'][ticket_id]['status'] = ticket_status['status']\n\n            # Если статус билета совпадает с запрошенным или не требует проверки\n            if ticket_status['success'] or ticket_status['status'] in (status, 'bypass',):\n                self.logger.info('    🎫: {}'.format(self.order['tickets'][ticket_id]))\n            # Если статус билета НЕ совпадает с запрошенным\n            else:\n                del self.order['tickets'][ticket_id]\n                self.order['tickets_count'] -= 1\n                self.order['total'] -= self.decimal_price(self.order['tickets'][ticket_id]['price'])\n\n                self.logger.error('    Ошибка с билетом {}'.format(ticket_id))\n\n        self.update()\n\n    def order_type_default(self, order_type):\n        \"\"\"Предварительный выбор типа заказа из списка активных в конкретном событии,\n        если заказов ранее не было или если выбранный ранее тип заказа неактивен в конкретном событии.\"\"\"\n        default_order_type = None\n\n        # Информация о событии\n        event = cache_factory('event', self.order['event_uuid'])\n        # Информация о сервисе продажи билетов\n        ticket_service = cache_factory('ticket_service', self.ticket_service['id'])\n        # Информация о сервисе онлайн-оплаты\n        payment_service = cache_factory(\n            'payment_service', self.payment_service['id'],\n            domain_slug=event['domain_slug']\n        )\n\n        # Все типы заказа билетов для выбора (настройки в сервисе продажи билетов и в событии)\n        order_types = OrderedDict()\n        for ot in self.ORDER_TYPE:\n            order_types.update(\n                {\n                    ot: {\n                        'ticket_service': ticket_service['settings']['order'][ot],\n                        'event':                   event['settings']['order'][ot],\n                    }\n                }\n            )\n\n        # Активные типы заказа билетов в конкретном событии\n        order_types_active = tuple(\n            ot for ot in order_types.keys() if\n            order_types[ot]['ticket_service'] is True and order_types[ot]['event'] is True and\n            (payment_service or not ot.endswith('_online'))\n        )\n        # Типы заказа билетов с онлайн-оплатой НЕ включаются в список активных,\n        # если к текущему сервису продажи билетов НЕ привязан никакой сервис онлайн-оплаты\n\n        # Выбор первого доступного типа заказа по порядку,\n        # если он НЕ был выбран ранее или если выбранный ранее тип заказа в текущем событии отключен\n        if not order_type or order_type not in order_types_active:\n            for ot in order_types.keys():\n                if ot in order_types_active:\n                    default_order_type = ot\n                    break\n        else:\n            default_order_type = order_type\n\n        return default_order_type\n\n    def order_type_change(self, customer, order_type):\n        # Обновление типа получения и типа оплаты билетов\n        self.order['delivery'] = OrderBasket.ORDER_TYPE_MAPPING[order_type]['delivery']\n        self.order['payment'] = OrderBasket.ORDER_TYPE_MAPPING[order_type]['payment']\n\n        # Обновление реквизитов покупателя\n        self.order['customer']['name'] = customer['name']\n        self.order['customer']['phone'] = customer['phone']\n        self.order['customer']['email'] = customer['email']\n        self.order['customer']['address'] = customer['address']\n        self.order['customer']['order_type'] = order_type\n\n        self.update()\n\n    def order_create(self):\n        \"\"\"Создание нового заказа в сервисе продажи билетов.\n\n        Returns:\n            dict: Информация о созданном заказе.\n        \"\"\"\n        self.logger.info('\\nСоздание заказа...')\n\n        # Добавление опциональных параметров для возможной доставки курьером\n        if self.order['delivery'] == 'courier':\n            self.order['customer']['is_courier'] = True\n            # self.order['customer']['address'] = self.order['customer']['address']\n        else:\n            self.order['customer']['is_courier'] = False\n            self.order['customer']['address'] = None\n\n        order_create = self._ts.order_create(\n            event_id=self.order['event_id'],\n            order_uuid=self.order['order_uuid'],\n            customer=self.order['customer'],\n            tickets=self.order['tickets']\n        )\n\n        if order_create['success']:\n            self.order['status'] = 'ordered'\n            self.logger.info('Статус заказа: {}'.format(self.order['status_caption']))\n\n            self.order['order_id'] = order_create['order_id']\n            self.logger.info('Идентификатор заказа: {}'.format(self.order['order_id']))\n\n            # Получение штрих-кодов для билетов в заказе\n            self.tickets_barcode(order_create)\n\n        self.update()\n\n        self.logger.info('\\nbasket.order created: {}'.format(self.order))\n\n        return order_create\n\n    def tickets_barcode(self, order):\n        \"\"\"Получение штрих-кодов для билетов в заказе из ответа метода order_create.\"\"\"\n        for otid in order['tickets']:\n            for tid in self.order['tickets']:\n                if order['tickets'][otid]['ticket_uuid'] == self.order['tickets'][tid]['ticket_uuid']:\n                    self.logger.info('\\n{ot_uuid} == {tid_uuid}: {cond}'.format(\n                        ot_uuid=order['tickets'][otid]['ticket_uuid'],\n                        tid_uuid=self.order['tickets'][tid]['ticket_uuid'],\n                        cond=order['tickets'][otid]['ticket_uuid'] == self.order['tickets'][tid]['ticket_uuid'])\n                    )\n                    self.order['tickets'][tid]['bar_code'] = (\n                        order['tickets'][otid]['bar_code'] if\n                        'bar_code' in order['tickets'][otid] and order['tickets'][otid]['bar_code'] else\n                        # Если по каким-то причинам штрих-код не получен - он генерируется автоматически\n                        ''.join([str(randint(0, 9)) for x in range(self._ts.bar_code_length)])\n                    )\n                    self.logger.info('t[bar_code]: {barcode}'.format(barcode=self.order['tickets'][tid]['bar_code']))\n                else:\n                    continue\n\n        self.logger.info('\\ntickets with bar_codes: {}'.format(self.order['tickets']))\n\n    def order_create_db(self):\n        response = {}\n\n        # Сохранение созданного заказа в БД\n        try:\n            Order.objects.create(\n                id=self.order['order_uuid'],\n                ticket_service_id=self.ticket_service['id'],\n                ticket_service_order=self.order['order_id'],\n                event_id=self.order['event_uuid'],\n                ticket_service_event=self.order['event_id'],\n                datetime=timezone_now(),\n                name=self.order['customer']['name'],\n                email=self.order['customer']['email'],\n                phone=self.order['customer']['phone'],\n                address=self.order['customer']['address'],\n                delivery=self.order['delivery'],\n                payment=self.order['payment'],\n                payment_id=None,\n                status=self.order['status'],\n                tickets_count=self.order['tickets_count'],\n                total=self.order['total'],\n                overall=self.order['overall'],\n                domain_id=self.domain_id\n            )\n        except IntegrityError:\n            response['success'] = False\n            self.logger.critical('Такой заказ уже был добавлен в базу данных ранее!')\n        else:\n            response['success'] = True\n            self.logger.info('\\nЗаказ {order_uuid} сохранён в БД\\n'.format(order_uuid=self.order['order_uuid']))\n\n            for ticket_id in self.order['tickets']:\n                try:\n                    OrderTicket.objects.create(\n                        id=self.order['tickets'][ticket_id]['ticket_uuid'],\n                        order_id=self.order['order_uuid'],\n                        ticket_service_id=self.ticket_service['id'],\n                        ticket_service_order=self.order['order_id'],\n                        is_fixed=self.order['tickets'][ticket_id]['is_fixed'],\n                        is_punched=False,\n                        bar_code=self.order['tickets'][ticket_id]['bar_code'],\n                        ticket_id=ticket_id,\n                        sector_id=self.order['tickets'][ticket_id]['sector_id'],\n                        sector_title=self.order['tickets'][ticket_id]['sector_title'],\n                        row_id=self.order['tickets'][ticket_id]['row_id'],\n                        seat_id=self.order['tickets'][ticket_id]['seat_id'],\n                        seat_title=self.order['tickets'][ticket_id]['seat_title'],\n                        price=self.order['tickets'][ticket_id]['price'],\n                        domain_id=self.domain_id\n                    )\n                except IntegrityError:\n                    self.logger.critical('Невозможно добавить билет в БД!')\n                else:\n                    self.logger.info('Билет {} сохранён в БД'.format(self.order['tickets'][ticket_id]['ticket_uuid']))\n\n        return response\n\n    def payment_create(self):\n        payment_create = self._ps.payment_create(\n            event_uuid=self.order['event_uuid'],\n            event_id=self.order['event_id'],\n            order_uuid=self.order['order_uuid'],\n            order_id=self.order['order_id'],\n            customer=self.order['customer'],\n            overall=self.order['overall'],\n        )\n\n        self.logger.info('payment_create: {}'.format(payment_create))\n\n        # Успешный запрос на оплату\n        if payment_create['success']:\n            self.logger.info('\\nСоздание новой онлайн-оплаты завершилось успешно')\n        else:\n            self.logger.info('\\nСоздание новой онлайн-оплаты завершилось НЕуспешно')\n\n        return payment_create\n\n    def payment_create_db(self, payment_create):\n        self.order['payment_id'] = payment_create['payment_id']\n        self.payment_url = payment_create['payment_url']\n\n        Order.objects.filter(id=self.order['order_uuid']).update(\n            datetime=timezone_now(),\n            payment_id=self.order['payment_id']\n        )\n\n        self.logger.info('Идентификатор оплаты: {}'.format(self.order['payment_id']))\n\n        self.update()\n\n    def payment_status(self):\n        payment_status = self._ps.payment_status(payment_id=self.order['payment_id'])\n\n        self.logger.info('Идентификатор оплаты: {}'.format(self.order['payment_id']))\n        self.logger.info('payment_status: {}'.format(payment_status))\n\n        if payment_status['success']:\n            self.logger.info('\\nОплата {payment_id} завершилась успешно'.format(\n                payment_id=self.order['payment_id'])\n            )\n        else:\n            self.logger.info('\\nОплата {payment_id} завершилась НЕуспешно'.format(\n                payment_id=self.order['payment_id'])\n            )\n\n        return payment_status\n\n    def order_approve(self):\n        # Подтвердить можно только созданный ранее заказ\n        if self.order['status'] == 'ordered':\n            self.logger.info('Подтверждение оплаты заказа в сервисе продажи билетов...')\n\n            order_approve = self._ts.order_approve(\n                event_id=self.order['event_id'],\n                order_uuid=self.order['order_uuid'],\n                order_id=self.order['order_id'],\n                payment_id=self.order['payment_id'],\n                payment_datetime=self.now(),\n                tickets=self.order['tickets'],\n            )\n\n            self.logger.info('order_approve: {}'.format(order_approve))\n\n            if order_approve['success']:\n                # Обновление статуса заказа в БД\n                self.order_status_db('approved')\n\n                self.logger.info('Заказ {order_id} в сервисе продажи билетов отмечен как оплаченный'.format(\n                    order_id=self.order['order_id']\n                ))\n            else:\n                self.logger.info('Заказ {order_id} НЕ удалось отметить в сервисе продажи билетов как оплаченный'.format(\n                    order_id=self.order['order_id']\n                ))\n\n            response = order_approve\n        else:\n            response = {}\n            response['success'] = False\n            response['message'] = 'Подтвердить можно только созданный ранее заказ'\n\n        return response\n\n    def order_cancel(self):\n        response = {}\n\n        # Отменить можно только:\n        # * либо созданный ранее заказ,\n        # * либо подтвердлжённый заказ с оффлайн-оплатой.\n        cancel_condition = (\n            self.order['status'] == 'ordered' or\n            self.order['status'] == 'approved' and self.order['payment'] == 'cash'\n        )\n\n        if cancel_condition:\n            self.logger.info('\\nОтмена заказа в сервисе продажи билетов...')\n\n            order_cancel = self._ts.order_cancel(\n                event_id=self.order['event_id'],\n                order_uuid=self.order['order_uuid'],\n                order_id=self.order['order_id'],\n                tickets=self.order['tickets'],\n            )\n\n            self.logger.info('order_cancel: {}'.format(order_cancel))\n\n            if order_cancel['success']:\n                response['success'] = True\n\n                # Обновление статуса заказа в БД\n                self.order_status_db('cancelled')\n\n                cancel_message = 'Заказ {order_id} отменён в сервисе продажи билетов'.format(\n                    order_id=self.order['order_id']\n                )\n\n                response['message'] = cancel_message\n                self.logger.info(cancel_message)\n            else:\n                response['success'] = False\n\n                cancel_message = 'Заказ {order_id} НЕ удалось отменить в сервисе продажи билетов'.format(\n                    order_id=self.order['order_id']\n                )\n\n                response['message'] = cancel_message\n                self.logger.info(cancel_message)\n        else:\n            response['success'] = False\n            response['message'] = 'Отменить можно только созданный ранее заказ'\n\n        return response\n\n    def order_refund(self, amount, reason=None):\n        response = {}\n\n        amount = self._ps.decimal_price(amount)\n\n        self.logger.info('\\nСумма возврата: {} р.'.format(amount))\n        self.logger.info('Причина возврата: {}.'.format(reason))\n\n        # Возврат возможен только для подтверждённых заказов\n        if self.order['status'] == 'approved':\n            # Проверка состояния билетов в заказе (на всякий случай)\n            self.tickets_check('approved')\n\n            # Возврат заказа в сервисе продажи билетов\n            self.logger.info('\\nВозврат заказа в сервисе продажи билетов...')\n\n            order_refund = self._ts.order_refund(\n                order_id=self.order['order_id'],\n                payment_id=self.order['payment_id'],\n                amount=amount,\n                reason=reason,\n            )\n            # order_refund = {'success': True, 'amount': amount}\n            # order_refund = {'success': False, 'code': 2000, 'message': 'Order has been already deleted'}\n\n            self.logger.info('order_refund: {}'.format(order_refund))\n\n            if order_refund['success']:\n                order_message = 'Заказ {order_id} успешно возвращён в сервисе продажи билетов'.format(\n                    order_id=self.order['order_id']\n                )\n            else:\n                order_message = 'Заказ {order_id} в сервисе продажи билетов возвратить НЕ удалось'.format(\n                    order_id=self.order['order_id']\n                )\n            self.logger.info(order_message)\n\n            # Возврат заказа в сервисе онлайн-оплаты\n            self.logger.info('\\nВозврат заказа в сервисе онлайн-оплаты...')\n\n            payment_refund = self._ps.payment_refund(\n                event_uuid=self.order['event_uuid'],\n                event_id=self.order['event_id'],\n                order_uuid=self.order['order_uuid'],\n                order_id=self.order['order_id'],\n                customer=self.order['customer'],\n                payment_id=self.order['payment_id'],\n                amount=amount,\n            )\n            # payment_refund = {'success': True, 'amount': amount}\n            # payment_refund = {'success': False, 'code': 5, 'message': 'Неверная сумма'}\n\n            self.logger.info('payment_refund: {}'.format(payment_refund))\n\n            if payment_refund['success']:\n                payment_message = 'Заказ {order_id} успешно возвращён в сервисе онлайн-оплаты'.format(\n                    order_id=self.order['order_id']\n                )\n            else:\n                payment_message = 'Заказ {order_id} в сервисе онлайн-оплаты возвратить НЕ удалось'.format(\n                    order_id=self.order['order_id']\n                )\n            self.logger.info(payment_message)\n\n            if order_refund['success'] and payment_refund['success']:\n                # Обновление статуса заказа в БД\n                self.order_status_db('refunded')\n\n                response['success'] = True\n                response['message'] = 'Возврат по заказу № {order_id} проведён успешно'.format(\n                    order_id=self.order['order_id']\n                )\n            else:\n                response['success'] = False\n\n                if order_refund['success']:\n                    message = '{} {}'.format(\n                        payment_refund.get('code', ''), payment_refund.get('message')\n                    )\n                    response['message'] = 'Возврат по заказу № {order_id} проведён успешно только в сервисе продажи билетов. {message}'.format(\n                        order_id=self.order['order_id'], message=message.strip()\n                    )\n                elif payment_refund['success']:\n                    message = '{} {}'.format(\n                        payment_refund.get('code', ''), payment_refund.get('message')\n                    )\n                    response['message'] = 'Возврат по заказу № {order_id} проведён успешно только в сервисе онлайн-оплаты. {message}'.format(\n                        order_id=self.order['order_id'], message=message.strip()\n                    )\n                else:\n                    order_message = '{} {}'.format(\n                        order_refund.get('code', ''), order_refund.get('message')\n                    )\n                    payment_message = '{} {}'.format(\n                        payment_refund.get('code', ''), payment_refund.get('message')\n                    )\n                    message = '{} {}'.format(\n                        order_message.strip(), payment_message.strip()\n                    )\n                    response['message'] = 'Возврат НЕ удалось завершить успешно. {}'.format(\n                        message.strip()\n                    )\n        else:\n            response['success'] = False\n            response['message'] = 'Возврат возможен только для подтверждённых заказов'\n\n        return response\n\n    def order_status_db(self, status):\n        \"\"\"Сохранение статуса заказа в БД.\"\"\"\n        self.order['status'] = status\n        self.order['status_color'] = OrderBasket.ORDER_STATUS_CAPTION[self.order['status']]['color']\n        self.order['status_caption'] = OrderBasket.ORDER_STATUS_CAPTION[self.order['status']]['description']\n\n        Order.objects.filter(id=self.order['order_uuid']).update(status=self.order['status'])\n\n        self.logger.info('\\nСтатус заказа: {}'.format(self.order['status_caption']))\n\n        self.update()\n\n    def email_prepare(self):\n        # Отправка email администратору и покупателю\n        email_from = {}\n        email_from['user'] = self.ticket_service['order_email']['user']\n        email_from['pswd'] = self.ticket_service['order_email']['pswd']\n        email_from['connection'] = EmailBackend(\n            host=settings.EMAIL_HOST,\n            port=settings.EMAIL_PORT,\n            username=email_from['user'],\n            password=email_from['pswd'],\n            use_tls=settings.EMAIL_USE_TLS,\n        )\n\n        # Информация о событии\n        event = cache_factory('event', self.order['event_uuid'])\n        # Информация о сайте\n        domain = cache_factory('domain', event['domain_slug'])\n        # Информация о сервисе продажи билетов\n        ticket_service = cache_factory('ticket_service', self.ticket_service['id'])\n        # Информация о сервисе онлайн-оплаты\n        payment_service = cache_factory(\n            'payment_service', self.payment_service['id'],\n            domain_slug=event['domain_slug']\n        )\n\n        email_context = {\n            'domain':          domain,\n            'event':           event,\n            'ticket_service':  ticket_service,\n            'payment_service': payment_service,\n            'order':           self.order,\n            'customer':        self.order['customer']\n        }\n\n        response = {}\n        response['from'] = email_from\n        response['context'] = email_context\n\n        return response\n\n    def email_admin(self):\n        email = self.email_prepare()\n\n        admin_email = EmailMessage(\n            'order/email_admin.tpl',\n            email['context'],\n            email['from']['user'],\n            (email['from']['user'],),\n            connection=email['from']['connection']\n        )\n\n        try:\n            sender = admin_email.send()\n        except SMTPException as exc:\n            sender = 0\n            sender_exception = exc\n\n        if bool(sender):\n            message = 'Email-уведомление администратору отправлено'\n\n            self.logger.info(message)\n\n            return {\n                'success': True,\n                'message': message,\n            }\n        else:\n            message = 'НЕ удалось отправить email-уведомление администратору.\\n{}'.format(sender_exception)\n\n            self.logger.info(message)\n\n            return {\n                'success': False,\n                'message': message,\n            }\n\n    def email_customer(self):\n        email = self.email_prepare()\n\n        customer_email = EmailMessage(\n            'order/email_customer.tpl',\n            email['context'],\n            email['from']['user'],\n            (self.order['customer']['email'],),\n            connection=email['from']['connection']\n        )\n\n        # Опциональная генерация электронных билетов и их вложение в письмо покупателю\n        if self.order['delivery'] == 'email':\n            self.logger.info('\\nСоздание электронных PDF-билетов...')\n            for ticket_id in self.order['tickets']:\n                # Формирование контекста для генерации PDF-билета (билет + событие + order_id)\n                ticket_context = self.order['tickets'][ticket_id]\n                ticket_context.update(email['context']['event'])\n                ticket_context['order_id'] = self.order['order_id']\n\n                self.logger.info('\\nИнформация о билете: {}'.format(ticket_context))\n\n                pdf_ticket_file = render_eticket(ticket_context, self.logger)\n                customer_email.attach_file(pdf_ticket_file, mimetype='application/pdf')\n\n        try:\n            sender = customer_email.send()\n        except SMTPException as exc:\n            sender = 0\n            sender_exception = exc\n\n        if bool(sender):\n            message = 'Email-уведомление покупателю отправлено'\n\n            self.logger.info(message)\n\n            return {\n                'success': True,\n                'message': message,\n            }\n        else:\n            message = 'НЕ удалось отправить email-уведомление покупателю.\\n{}'.format(sender_exception)\n\n            self.logger.info(message)\n\n            return {\n                'success': False,\n                'message': message,\n            }\n\n    def get_overall(self):\n        \"\"\"Получение общей суммы заказа и её подписи в зависимости от возможных наценок/скидок.\"\"\"\n        order_type = self.order['customer']['order_type'] if 'customer' in self.order else 'self_cash'\n        extra = self.markup['extra'][order_type] if order_type in self.markup['extra'] else 0\n\n        # Для любого типа заказа без дополнительных условий - с учётом сервисного сбора (если он задан)\n        if extra > 0:\n            self.order['overall'] = self.overall_with_extra(extra)\n            self.order['overall_header'] = OrderBasket.ORDER_OVERALL_CAPTION['overall_extra']\n        # Иначе - сумма цен на билеты\n        else:\n            self.order['overall'] = self.order['total']\n            self.order['overall_header'] = OrderBasket.ORDER_OVERALL_CAPTION['overall_total']\n\n        # При доставке курьером - с учётом стоимости доставки курьером (если она задана)\n        if self.order['delivery'] == 'courier':\n            if extra > 0:\n                # С учётом доставки курьером и сервисного сбора\n                if self.markup['courier_price'] > 0:\n                    self.order['overall'] = self.overall_plus_courier_price()\n                    self.order['overall_header'] = OrderBasket.ORDER_OVERALL_CAPTION['overall_courier_extra']\n                # Иначе - с учётом сервисного сбора\n                # else:\n                    # self.order['overall'] = self.overall_with_extra(extra)\n                    # self.order['overall_header'] = OrderBasket.ORDER_OVERALL_CAPTION['overall_extra']\n            else:\n                # С учётом доставки курьером\n                if self.markup['courier_price'] > 0:\n                    self.order['overall'] = self.overall_plus_courier_price()\n                    self.order['overall_header'] = OrderBasket.ORDER_OVERALL_CAPTION['overall_courier']\n                # Иначе - сумма цен на билеты\n                # else:\n                    # self.order['overall'] = self.order['total']\n                    # self.order['overall_header'] = OrderBasket.ORDER_OVERALL_CAPTION['overall_total']\n\n        # При онлайн-оплате - с учётом комиссии сервиса онлайн-оплаты (если она задана)\n        if self.order['payment'] == 'online':\n            if extra > 0:\n                # С учётом комиссии платёжной системы и сервисного сбора\n                if self.markup['commission'] > 0:\n                    self.order['overall'] = self.overall_with_commission()\n                    self.order['overall_header'] = OrderBasket.ORDER_OVERALL_CAPTION['overall_commission_extra']\n                # Иначе - также с учётом комиссии платёжной системы и сервисного сбора\n                else:\n                    self.order['overall'] = self.overall_with_extra(extra)\n                    self.order['overall_header'] = OrderBasket.ORDER_OVERALL_CAPTION['overall_commission_extra']\n            else:\n                # С учётом комиссии платёжной системы\n                if self.markup['commission'] > 0:\n                    self.order['overall'] = self.overall_with_commission()\n                    self.order['overall_header'] = OrderBasket.ORDER_OVERALL_CAPTION['overall_commission']\n                # Иначе - сумма цен на билеты\n                # else:\n                    # self.order['overall'] = self.order['total']\n                    # self.order['overall_header'] = OrderBasket.ORDER_OVERALL_CAPTION['overall_total']\n\n        # Пересчёт общей суммы заказа для удобства оффлайн-оплаты (кратно значению в OVERALL_EXTRA_MULTIPLIER)\n        if extra > 0 and self.order['payment'] == 'cash':\n            overall = self.order['overall']\n            multiplier = OrderBasket.OVERALL_EXTRA_MULTIPLIER\n\n            self.order['overall'] = (overall - (overall % multiplier)) + multiplier\n\n    def overall_with_extra(self, extra):\n        \"\"\"Общая сумма заказа с учётом сервисного сбора.\n\n        Если процент сервисного сбора больше ``0``,\n        то к сумме заказа добавляется указанный процент от цены каждого из билетов в заказе.\n        Если процент сервисного сбора равен ``0``, мы получаем ту же самую сумму.\n\n        Args:\n            extra (decimal.Decimal): Процент сервисного сбора для конкретного типа заказа.\n\n        Returns:\n            decimal.Decimal: Общая сумма заказа ``overall``.\n        \"\"\"\n        overall_with_extra = self.order['total']\n        if extra > 0:\n            for ticket_id in self.order['tickets']:\n                ticket_price = self.order['tickets'][ticket_id]['price']\n                overall_with_extra += (self.decimal_price(ticket_price) * extra) / self.decimal_price(100)\n        return self.decimal_price(overall_with_extra)\n\n    def overall_plus_courier_price(self):\n        \"\"\"Общая сумма заказа с учётом стоимости доставки курьером.\n\n        Если стоимость доставки курьером больше ``0``, то она добавляется к сумме заказа.\n        Если стоимость доставки курьером равна ``0``, мы получаем ту же самую сумму.\n\n        Returns:\n            decimal.Decimal: Общая сумма заказа ``overall``.\n        \"\"\"\n        return self.decimal_price(self.order['overall'] + self.markup['courier_price'])\n\n    def overall_with_commission(self):\n        \"\"\"Общая сумма заказа при онлайн-оплате.\n\n        Ес��и комиссия сервиса онлайн-оплаты не равна ``0``,\n        то к сумме заказа добавляется указанный процент от самой суммы заказа.\n        Если комиссия равна ``0``, мы получаем ту же самую сумму.\n\n        Returns:\n            decimal.Decimal: Общая сумма заказа ``overall``.\n        \"\"\"\n        return self.decimal_price(\n            self.order['overall'] + ((self.order['overall'] * self.markup['commission']) / self.decimal_price(100))\n        )\n\n    def decimal_price(self, value):\n        \"\"\"Преобразование входного значения в денежную сумму с 2 знаками после запятой (копейки) типа ``Decimal``.\n\n        Args:\n            value (str): Входное значение (в любом случае строка - для обхода проблем с округлением ``float``).\n\n        Returns:\n            decimal.Decimal: Денежная сумма.\n        \"\"\"\n        return Decimal(str(value)).quantize(Decimal('1.00'))\n\n    def now(self):\n        now = timezone_now()\n        return now.astimezone(pytz.timezone(self.city_timezone))\n","sub_path":"bezantrakta/order/order_basket.py","file_name":"order_basket.py","file_ext":"py","file_size_in_byte":61066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"79807508","text":"import pandas as pd\nimport numpy as np\n\nimport os\nfrom PIL import Image\n\nimport fastai\nfrom fastai.vision import *\nfrom fastai.callbacks import *\nfrom fastai.torch_core import *\nfrom fastai.callback import *\nfrom fastai.basic_train import *\nimport torchvision.models as tmodels\n\n\ndef cleanLabel(x):\n    \n    labelCount = 0\n    \n    if x.Pleural_Effusion == 1:\n        labelCount += 1\n        \n    if x.Edema == 1:\n        labelCount += 1\n    \n    if x.Cardiomegaly ==1:\n        labelCount += 1\n    \n    if x.Pneumonia == 1:\n        labelCount += 1\n    \n    return labelCount\n    \n    \n\ndef getLabel2(x,disease):\n    \n    if x[disease] ==1:\n        return disease\n    else:\n        return \"Rest\"\n\n\n\n# def getLabel(x):\n    \n#     if x.Pleural_Effusion ==1:\n#         return \"Pleural_Effusion\"\n#     else:\n#         return \"Rest\"\n\n\n#     # elif x.Edema == 1:\n#     #     return \"Edema\"\n#     # elif x.Cardiomegaly==1:\n#     #     return \"Cardiomegaly\"\n#     # elif x.Pneumonia == 1:\n#     #     return \"Pneumonia\"\n#     # else:\n#     #     return \"0\"\n    \n\nbaseFolder  = \"/home/santhosr/Documents/Chexpert\"\n\ncols = ['Path',\n 'Sex',\n 'Age',\n 'View',\n 'AP/PA',\n 'No_Finding',\n 'Enlarged_Cardiomediastinum',\n 'Cardiomegaly',\n 'Lung_Opacity',\n 'Lung_Lesion',\n 'Edema',\n 'Consolidation',\n 'Pneumonia',\n 'Atelectasis',\n 'Pneumothorax',\n 'Pleural_Effusion',\n 'Pleural_Other',\n 'Fracture',\n 'Support_Devices']\n\n\ntrainFile = pd.read_csv(os.path.join(baseFolder,'train.csv'), names = cols, header=0)\nvalidFile = pd.read_csv(os.path.join(baseFolder,'valid.csv'), names = cols, header=0)\n\ntrainFile[\"Path\"] = trainFile.Path.apply(lambda x : x.replace('CheXpert-v1.0-small',\"\")[1:])\nvalidFile[\"Path\"] = validFile.Path.apply(lambda x : x.replace('CheXpert-v1.0-small',\"\")[1:])\n\n\nselectCols = ['Path',\"View\",'Sex',\"Pleural_Effusion\", \"Edema\",\"Cardiomegaly\",\"Pneumonia\"]\n\ntrainFile = trainFile[selectCols]\nvalidFile = validFile[selectCols]\n\n# -1 for Uncertain, 0 for negative, 1 for positive\n\ntrainFile['isClean'] = trainFile.apply(lambda x : cleanLabel(x), axis = 1)\nvalidFile['isClean'] = validFile.apply(lambda x : cleanLabel(x), axis = 1)\n\ntrainFile['train'] = False\nvalidFile['train'] = True\n\ntrainFile = trainFile[trainFile.isClean==1]\nvalidFile = validFile[validFile.isClean==1]\n\n\ndf = pd.concat([trainFile,validFile])\n\ndf['label'] = df.apply(lambda x : getLabel(x), axis = 1)\n\nlabelMap = {\"Pleural_Effusion\":0, \"Edema\":1,\"Cardiomegaly\":2,\"Pneumonia\":3,\"Rest\":4}\n\ndef getLabelDf(x):\n    \n#     print(x)\n    \n    x = x[36:]          #To account for the extra \"././\" added before the Path variable\n#     print(x)\n    x = df.loc[df.Path == x] \n#     print(x)\n    \n    return labelMap[x.label.values[0]]\n\n\nclass ModelTrackerCallback(TrackerCallback):\n    \"A `TrackerCallback` that saves the model when monitored quantity is best.\"\n    def __init__(self, learn:Learner, path:str='/home/santhosr/Documents/Courses/CIS700/Project/models',id:int=None,monitor:str='val_loss', mode:str='auto',modelName:str='resnet50'):\n        super().__init__(learn, monitor=monitor, mode=mode)\n        \n        self.bestAcc = 0.0001\n        self.folderPath = path\n        self.id = id\n        self.modelName = modelName\n        super().__post_init__()\n\n    def on_epoch_end(self, epoch, **kwargs:Any)->None:\n        \"Compare the value monitored to its best score and maybe save the model.\"\n\n        acc = float(self.learn.recorder.metrics[epoch-1][0])\n        val_loss = self.learn.recorder.val_losses[epoch-1]\n\n        if acc>self.bestAcc:\n            self.bestAcc = acc\n            if self.id==None:\n                fileName = 'model_'+self.modelName+'_acc'+str(int(acc*1000))+\"_loss\"+str(int(val_loss*1000))\n            else:\n                fileName = 'model_'+self.modelName+'_id' + str(self.id) + '_acc' + str(int(acc*1000)) + \"_loss\" + str(int(val_loss*1000))\n            fileName = os.path.join(self.folderPath, fileName)\n            self.learn.save(fileName)\n\n\nprint(\"Data Creation Start\")\ndata = ImageItemList.from_df(df=df,path=baseFolder, cols='Path').split_from_df(col='train').label_from_func(getLabelDf).transform(get_transforms(),size=256).databunch(bs=50).normalize()\nprint(\"Data Creation Complete\")\n\n\nlearn = create_cnn(data, tmodels.resnet50, metrics=accuracy,pretrained=True)\n\n# learn.load('/home/santhosr/Documents/Birad/ProcessedData/models/model_resnet50_acc668_loss600')\n\nbest_model_cb = partial(ModelTrackerCallback,id=6, modelName = \"resnet50_Edema\")\nlearn.callback_fns.append(best_model_cb)\n\nlearn.unfreeze()\nlearn.fit(30,1e-5)\n","sub_path":"resnet_train.py","file_name":"resnet_train.py","file_ext":"py","file_size_in_byte":4519,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"281037169","text":"# --------------------------------------------------------\n#\n# PYTHON PROGRAM DEFINITION\n#\n# The knowledge a computer has of Python can be specified in 3 levels:\n# (1) Prelude knowledge --> The computer has it by default.\n# (2) Borrowed knowledge --> The computer gets this knowledge from 3rd party libraries defined by others\n#                            (but imported by us in this program).\n# (3) Generated knowledge --> The computer gets this knowledge from the new functions defined by us in this program.\n#\n# When launching in a terminal the command:\n# user:~$ python3 this_file.py\n# our computer first processes this PYTHON PROGRAM DEFINITION section of the file.\n# On it, our computer enhances its Python knowledge from levels (2) and (3) with the imports and new functions\n# defined in the program. However, it still does not execute anything.\n#\n# --------------------------------------------------------\n\n# ------------------------------------------------\n# IMPORTS\n# ------------------------------------------------\nimport codecs\nimport functools\nimport os\n\n# ------------------------------------------------\n# FUNCTION read_graph_from_folder\n# ------------------------------------------------\ndef read_graph_from_folder(my_dataset_dir):\n    # 1. We create the output variable\n    res = ()\n\n    # 1.1. We output the number of nodes\n    num_nodes = 0\n\n    # 1.2. We output the connections per node\n    edges_per_node = {}\n\n    # 2. We list the files from the directory my_dataset_dir\n    list_of_files = os.listdir(my_dataset_dir)\n    if ('.DS_Store' in list_of_files):\n        list_of_files.remove('.DS_Store')\n    list_of_files.sort()\n\n    # 3. We traverse the files one by one\n    for file in list_of_files:\n        # 3.1. We open the file for reading\n        my_input_stream = codecs.open(my_dataset_dir + file, \"r\", encoding='utf-8')\n\n        # 3.2. We traverse the file\n        for line in my_input_stream:\n            # 3.2.1. We parse the line\n            (source_node, target_node, _) = tuple(line.strip().split(\" \"))\n\n            # 3.2.2. We search for new nodes appearing in the line\n            for node_name in [source_node, target_node]:\n                # 3.2.2.1. If the node has not appeared before\n                if node_name not in edges_per_node:\n                    # I. We associate the new node with an empty list of edges\n                    edges_per_node[node_name] = []\n\n                    # II. We increase the number of different nodes found so far\n                    num_nodes += 1\n\n            # 3.2.3. We populate the edges of source_node\n            edges_per_node[source_node].append( target_node )\n\n        # 3.4. We close the file\n        my_input_stream.close()\n\n    # 4. We make the info to be a tuple (num_neighbours, neighbours_list)\n    for node_name in edges_per_node:\n        neighbour_list = edges_per_node[node_name]\n        edges_per_node[node_name] = (len(neighbour_list), neighbour_list)\n\n    # 5. We assign res\n    res = (num_nodes, edges_per_node)\n\n    # 6. We return res\n    return res\n\n# ------------------------------------------\n# FUNCTION compute_page_rank\n# ------------------------------------------\ndef compute_page_rank(edges_per_node, reset_probability, max_iterations):\n\n    # ------------------------------------------------\n    # START OF YOUR CODE:\n    # ------------------------------------------------\n\n    # Remember that the function must return a dictionary with:\n    # Key => The node number\n    # Value => The PageRank value computed for this node.\n\n    # Type all your code here.\n    pass\n\n\n\n\n\n\n    # ------------------------------------------------\n    # END OF YOUR CODE\n    # ------------------------------------------------\n\n# ------------------------------------------\n# FUNCTION my_main\n# ------------------------------------------\ndef my_main(my_dataset_dir, reset_probability, max_iterations):\n    # 1. We read the graph from the file\n    (num_nodes, edges_per_node) = read_graph_from_folder(my_dataset_dir)\n\n    # 2. We compute the shortest paths to each node\n    page_rank_per_node = compute_page_rank(edges_per_node, reset_probability, max_iterations)\n\n    # 3. We sort the nodes in decreasing order in their rank\n    rank_per_node = [ (round(page_rank_per_node[node], 2), node) for node in page_rank_per_node ]\n    rank_per_node.sort(reverse=True)\n\n    # 4. We print them\n    for item in rank_per_node:\n        print(\"id=\" + str(item[1]) + \"; pagerank=\" + str(item[0]))\n\n# --------------------------------------------------------\n#\n# PYTHON PROGRAM EXECUTION\n#\n# Once our computer has finished processing the PYTHON PROGRAM DEFINITION section its knowledge is set.\n# Now its time to apply this knowledge.\n#\n# When launching in a terminal the command:\n# user:~$ python3 this_file.py\n# our computer finally processes this PYTHON PROGRAM EXECUTION section, which:\n# (i) Specifies the function F to be executed.\n# (ii) Define any input parameter such this function F has to be called with.\n#\n# --------------------------------------------------------\nif __name__ == '__main__':\n    # 1. We get the input values\n    reset_probability = 0.15\n    max_iterations = 3\n\n    # 2. Local or Databricks\n    local_False_databricks_True = False\n\n    # 3. We set the path to my_dataset and my_result\n    my_local_path = \"../../../../3_Code_Examples/L15-25_Spark_Environment/\"\n    my_databricks_path = \"/\"\n\n    my_dataset_dir = \"FileStore/tables/6_Assignments/my_dataset_2/\"\n\n    if local_False_databricks_True == False:\n        my_dataset_dir = my_local_path + my_dataset_dir\n    else:\n        my_dataset_dir = my_databricks_path + my_dataset_dir\n\n    # 3. We call to my_main\n    my_main(my_dataset_dir, reset_probability, max_iterations)\n","sub_path":"Big Data and Analytics/A02/my_code/A02_Part4/A02_Part4.py","file_name":"A02_Part4.py","file_ext":"py","file_size_in_byte":5718,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"41326833","text":"'''\n\n\n\n'''\nimport os, subprocess, time, sys\nfrom sched import scheduler\nfrom numpy import floor, mod\nfrom comMethods import readfile,writeQOS\n\nclass clustersjob:\n    \"\"\" The largest numbers of clusters (up to 900*5) take 64G of memory and a single processor.  \n    We need to run uncle 10 (build clusters) with a separate job on a large memory job\n    that builds the clusters\"\"\"\n    from comMethods import reportFinished,waitJobs\n\n    def __init__(self,user,jobsInfo):\n        \"\"\"  \"\"\"\n        self.user = user\n        self.jobsInfo = jobsInfo\n        self.jobIDs = []\n\n    def clustBuild(self):\n        '''Runs the process of building clusters'''\n        self.makeRunClusters()\n        self.waitJobs()\n        ######### Don't remove this code:  \n#        dummy = os.listdir(os.getcwd()) #for some reason this needs to be here so that it can find clustersjob.out!!!!!\n#        if not os.path.exists(os.getcwd()+'/clustersjob.out'):\n#            sys.exit('Clusters job never started.  Stopping program')\n#        else:\n#            lines = readfile(os.getcwd()+'/clustersjob.out')\n#            if 'done' not in lines[-1]:\n#                sys.exit('Clusters job failed.  Stopping program')            \n#            \n#    def waitclustersjob(self):\n#        \"\"\"Waits until the cluster job is done \"\"\"\n#        s = scheduler(time.time, time.sleep)    \n#        finished = False\n#        start_time = time.time()\n#        event_time = start_time\n#        while not finished:\n#            event_time += 5 #check every x seconds\n#            s.enterabs(event_time, 1, self.reportFinished, (self.currJobIds))\n#            s.run()\n#            finished = self.reportFinished(self.currJobIds)\n#           \n    def makeRunClusters(self): \n        \"\"\"Creates clusters jobfile and starts the run \"\"\" \n        subprocess.call(['ln','-s','/fslhome/{}/graphener_links/uncle/uncle.x'.format(self.user['name'])]) \n        jobFile = open('clustersjob','w')\n        jobFile.write(\"#!/bin/bash\\n\\n\")\n        walltime = 0.6\n        hrs = int(floor(walltime)); mints = int(mod(walltime,1)*60)\n        hrs,mints = writeQOS(self.jobsInfo,jobFile,walltime,hrs,mints)\n        jobFile.write('#SBATCH --time={}:{}:00\\n'.format(hrs,mints))\n        jobFile.write(\"#SBATCH --nodes=1\\n\")\n        jobFile.write(\"#SBATCH --mem-per-cpu=64G\\n\")\n        jobFile.write(\"#SBATCH --mail-user={}\\n\".format(self.user['email']))              \n        jobFile.write(\"#SBATCH --mail-type=FAIL\\n\")\n        jobFile.write(\"#SBATCH --mail-type=END\\n\")\n        jobFile.write(\"#SBATCH --job-name=clusters\\n\" ) \n        jobFile.write('module unload mpi\\n')\n        jobFile.write('module load mpi/openmpi-1.6.5_intel-13.0.1\\n')\n        jobFile.write('module unload mkl\\n')\n        jobFile.write('module load mkl/11/2\\n')\n        jobFile.write('module unload python\\n')\n        jobFile.write('module load python/2/7\\n')         \n        jobFile.write(\"mpiexec uncle.x 10 > clustersjob.out\\n\") \n        jobFile.close()\n        if os.path.exists('clustersjob.out'): os.system('rm clustersjob.out')\n        proc = subprocess.Popen(['sbatch','clustersjob'], stdout=subprocess.PIPE)\n        jobid = proc.communicate()[0].split()[3]\n        subprocess.call(['echo', '\\tSubmitted clusters job ' + jobid])\n        self.jobIDs.append(jobid) \n","sub_path":"graphener/ClustersBuild.py","file_name":"ClustersBuild.py","file_ext":"py","file_size_in_byte":3280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"378064451","text":"# Source: https://pymotw.com/2/socket/udp.html\n\nimport socket, sys, time\nimport random\n\n\nhost = sys.argv[1]\ntextport = sys.argv[2]\n#host = \"localhost\"\n#textport = \"8081\"\n\ns = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\nport = int(textport)\nserver_address = (host, port)\n\nfor i in range (10):\n    #print (\"Enter data to transmit: ENTER to quit\")\n    data = str(random.randint(1,100))\n    print(\" sending \"+ data)\n\n    s.sendto(data.encode('utf-8'), server_address)\n\ns.shutdown(1)\n\n\n","sub_path":"randNumSender.py","file_name":"randNumSender.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"313578150","text":"import os\nimport codecs\nimport ujson\nimport random\nimport numpy as np\nfrom tqdm import tqdm\nfrom collections import Counter\n\nnp.random.seed(12345)\nglove_sizes = {'6B': int(4e5), '42B': int(1.9e6), '840B': int(2.2e6), '2B': int(1.2e6)}\nPAD = \"<PAD>\"\nUNK = \"<UNK>\"\n\n\ndef iob_to_iobes(labels):\n    \"\"\"IOB -> IOBES\"\"\"\n    iob2(labels)\n    new_tags = []\n    for i, tag in enumerate(labels):\n        if tag == 'O':\n            new_tags.append(tag)\n        elif tag.split('-')[0] == 'B':\n            if i + 1 != len(labels) and labels[i + 1].split('-')[0] == 'I':\n                new_tags.append(tag)\n            else:\n                new_tags.append(tag.replace('B-', 'S-'))\n        elif tag.split('-')[0] == 'I':\n            if i + 1 < len(labels) and labels[i + 1].split('-')[0] == 'I':\n                new_tags.append(tag)\n            else:\n                new_tags.append(tag.replace('I-', 'E-'))\n        else:\n            raise Exception('Invalid IOB format!')\n    return new_tags\n\n\ndef iob2(labels):\n    \"\"\"Check that tags have a valid IOB format. Tags in IOB1 format are converted to IOB2.\"\"\"\n    for i, tag in enumerate(labels):\n        if tag == 'O':\n            continue\n        split = tag.split('-')\n        if len(split) != 2 or split[0] not in ['I', 'B']:\n            return False\n        if split[0] == 'B':\n            continue\n        elif i == 0 or labels[i - 1] == 'O':  # conversion IOB1 to IOB2\n            labels[i] = 'B' + tag[1:]\n        elif labels[i - 1][1:] == tag[1:]:\n            continue\n        else:\n            labels[i] = 'B' + tag[1:]\n    return True\n\n\ndef write_json(filename, dataset):\n    with codecs.open(filename, mode=\"w\", encoding=\"utf-8\") as f:\n        ujson.dump(dataset, f)\n\n\ndef word_convert(word, lowercase=True, char_lowercase=False):\n    if char_lowercase:\n        char = [c for c in word.lower()]\n    else:\n        char = [c for c in word]\n    if lowercase:\n        word = word.lower()\n    return word, char\n\n\ndef raw_dataset_iter(filename, lowercase=True, char_lowercase=False):\n    with codecs.open(filename, mode=\"r\", encoding=\"utf-8\") as f:\n        words, chars, labels = [], [], []\n        for line in f:\n            line = line.lstrip().rstrip()\n            if len(line) == 0 or line.startswith(\"-DOCSTART-\"):\n                if len(words) != 0:\n                    yield words, chars, labels\n                    words, chars, labels = [], [], []\n            else:\n                word, *_, label = line.split(\" \")\n                word, char = word_convert(word, lowercase=lowercase, char_lowercase=char_lowercase)\n                words.append(word)\n                chars.append(char)\n                labels.append(label)\n        if len(words) != 0:\n            yield words, chars, labels\n\n\ndef load_dataset(filename, iobes, lowercase=True, char_lowercase=False):\n    dataset = []\n    for words, chars, labels in raw_dataset_iter(filename, lowercase, char_lowercase):\n        if iobes:\n            labels = iob_to_iobes(labels)\n        dataset.append({\"words\": words, \"chars\": chars, \"labels\": labels})\n    return dataset\n\n\ndef load_emb_vocab(data_path, dim):\n    vocab = list()\n    with codecs.open(data_path, mode=\"r\", encoding=\"utf-8\") as f:\n        if \"glove\" in data_path:\n            total = glove_sizes[data_path.split(\".\")[-3]]\n        else:\n            total = int(f.readline().lstrip().rstrip().split(\" \")[0])\n        for line in tqdm(f, total=total, desc=\"Load embedding vocabulary\"):\n            line = line.lstrip().rstrip().split(\" \")\n            if len(line) == 2:\n                continue\n            if len(line) != dim + 1:\n                continue\n            word = line[0]\n            vocab.append(word)\n    return vocab\n\n\ndef filter_emb(word_dict, data_path, dim):\n    vectors = np.zeros([len(word_dict), dim])\n    with codecs.open(data_path, mode=\"r\", encoding=\"utf-8\") as f:\n        if \"glove\" in data_path:\n            total = glove_sizes[data_path.split(\".\")[-3]]\n        else:\n            total = int(f.readline().lstrip().rstrip().split(\" \")[0])\n        for line in tqdm(f, total=total, desc=\"Load embedding vectors\"):\n            line = line.lstrip().rstrip().split(\" \")\n            if len(line) == 2:\n                continue\n            if len(line) != dim + 1:\n                continue\n            word = line[0]\n            if word in word_dict:\n                vector = [float(x) for x in line[1:]]\n                word_idx = word_dict[word]\n                vectors[word_idx] = np.asarray(vector)\n    return vectors\n\n\ndef build_token_counters(datasets):\n    word_counter = Counter()\n    char_counter = Counter()\n    label_counter = Counter()\n    for dataset in datasets:\n        for record in dataset:\n            for word in record[\"words\"]:\n                word_counter[word] += 1\n            for char in record[\"chars\"]:\n                for c in char:\n                    char_counter[c] += 1\n            for label in record[\"labels\"]:\n                label_counter[label] += 1\n    return word_counter, char_counter, label_counter\n\n\ndef build_dataset(data, word_dict, char_dict, label_dict, mode=0, rate=0.5):\n    dataset = []\n    for record in data:\n        chars_list = []\n        words = []\n        for word in record[\"words\"]:\n            words.append(word_dict[word] if word in word_dict else word_dict[UNK])\n        for char in record[\"chars\"]:\n            chars = [char_dict[c] if c in char_dict else char_dict[UNK] for c in char]\n            chars_list.append(chars)\n        if mode == 0:  # labeled\n            labels = [label_dict[label] for label in record[\"labels\"]]\n            dataset.append({\"words\": words, \"chars\": chars_list, \"labels\": labels})\n        elif mode == 1:  # partially labeled\n            labels = [label_dict[label] for label in record[\"labels\"]]\n            label_mask = np.asarray([0 if v < rate else 1 for v in np.random.rand(len(labels))])\n            labels = np.asarray(labels) * label_mask\n            dataset.append({\"words\": words, \"chars\": chars_list, \"labels\": labels.tolist(),\n                            \"label_mask\": label_mask.tolist()})\n        elif mode == 2:  # unlabeled\n            labels = [0 for _ in record[\"labels\"]]\n            dataset.append({\"words\": words, \"chars\": chars_list, \"labels\": labels})\n        else:\n            raise ValueError(\"Unknown label process mode!!! Support: [0: labeled | 1: partial | 2: unlabeled]\")\n    return dataset\n\n\ndef split_dataset(dataset, n):\n    if n is None or type(n) != int or n <= 1 or n >= len(dataset):\n        return dataset\n    step = len(dataset) // n\n    data_list = []\n    idx = 0\n    for i in range(n):\n        if i == n - 1:\n            data_list.append(dataset[idx:])\n            break\n        data_list.append(dataset[idx: idx + step])\n        idx = idx + step\n    return data_list\n\n\ndef process_word_token(word_counter, config):\n    if config.wordvec_path is not None:\n        word_vocab = [word for word, _ in word_counter.most_common()]\n        emb_vocab = load_emb_vocab(config.wordvec_path, config.word_dim)\n        word_vocab = list(set(word_vocab) & set(emb_vocab))\n        tmp_word_dict = dict([(word, idx) for idx, word in enumerate(word_vocab)])\n        vectors = filter_emb(tmp_word_dict, config.wordvec_path, config.word_dim)\n        np.savez_compressed(config.wordvec, embeddings=np.asarray(vectors))\n    else:\n        word_vocab = [word for word, count in word_counter.most_common() if count >= config.word_threshold]\n    word_vocab = [PAD, UNK] + word_vocab\n    word_dict = dict([(word, idx) for idx, word in enumerate(word_vocab)])\n    return word_dict\n\n\ndef process_char_token(char_counter, config):\n    char_vocab = [PAD, UNK] + [char for char, count in char_counter.most_common() if count >= config.char_threshold]\n    char_dict = dict([(char, idx) for idx, char in enumerate(char_vocab)])\n    return char_dict\n\n\ndef process_label_token(label_counter):\n    label_vocab = [\"O\"] + [label for label, _ in label_counter.most_common() if label != \"O\"]\n    label_dict = dict([(label, idx) for idx, label in enumerate(label_vocab)])\n    return label_dict\n\n\ndef write_to_jsons(datasets, files, save_path):\n    for dataset, file in zip(datasets, files):\n        write_json(os.path.join(save_path, file), dataset)\n\n\ndef process_data(config):\n    # load raw datasets\n    train_data = load_dataset(config.train_file, config.iobes, config.word_lowercase, config.char_lowercase)\n    dev_data = load_dataset(config.dev_file, config.iobes, config.word_lowercase, config.char_lowercase)\n    test_data = load_dataset(config.test_file, config.iobes, config.word_lowercase, config.char_lowercase)\n    datasets = [train_data, dev_data, test_data]\n    # build token counters\n    word_counter, char_counter, label_counter = build_token_counters(datasets)\n    # create save path\n    if not os.path.exists(config.save_path):\n        os.makedirs(config.save_path)\n    # build word vocab\n    word_dict = process_word_token(word_counter, config)\n    # build char vocab\n    char_dict = process_char_token(char_counter, config)\n    # build label vocab\n    label_dict = process_label_token(label_counter)\n    # create indices datasets and write to files\n    if \"dCRF\" in config.model_name:\n        random.shuffle(train_data)\n        train_folds = split_dataset(train_data, config.folds)  # 10 folds\n        train_set = dict()\n        for i in range(config.folds):\n            train_fold_set = build_dataset(train_folds[i], word_dict, char_dict, label_dict)\n            train_set[\"fold_{}\".format(i)] = train_fold_set\n        train_set_p = dict()\n        for i in range(config.folds):\n            train_fold_set = build_dataset(train_folds[i], word_dict, char_dict, label_dict, 1, config.partial_rate)\n            train_set_p[\"fold_{}\".format(i)] = train_fold_set\n        train_set_u = dict()\n        for i in range(config.folds):\n            train_fold_set = build_dataset(train_folds[i], word_dict, char_dict, label_dict, 2)\n            train_set_u[\"fold_{}\".format(i)] = train_fold_set\n        dev_set = build_dataset(dev_data, word_dict, char_dict, label_dict)\n        test_set = build_dataset(test_data, word_dict, char_dict, label_dict)\n        vocab = {\"word_dict\": word_dict, \"char_dict\": char_dict, \"label_dict\": label_dict}\n        write_to_jsons([train_set, train_set_p, train_set_u, dev_set, test_set, vocab],\n                       [\"train.json\", \"train_p.json\", \"train_u.json\", \"dev.json\", \"test.json\", \"vocab.json\"],\n                       config.save_path)\n    else:\n        train_set = build_dataset(train_data, word_dict, char_dict, label_dict)\n        dev_set = build_dataset(dev_data, word_dict, char_dict, label_dict)\n        test_set = build_dataset(test_data, word_dict, char_dict, label_dict)\n        vocab = {\"word_dict\": word_dict, \"char_dict\": char_dict, \"label_dict\": label_dict}\n        write_to_jsons([train_set, dev_set, test_set, vocab], [\"train.json\", \"dev.json\", \"test.json\", \"vocab.json\"],\n                       config.save_path)\n","sub_path":"utils/prepro_lavd_data.py","file_name":"prepro_lavd_data.py","file_ext":"py","file_size_in_byte":10948,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"526799745","text":"import json\n\nimport click\nfrom selenium import webdriver\nfrom selenium.common.exceptions import NoSuchElementException\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.support.ui import WebDriverWait\n\n\ndef load_config(filename):\n    \"\"\"\n    Load file and parse JSON to dictionary\n    \"\"\"\n    with open(filename) as json_data:\n        return json.load(json_data)\n\n\ndef get_urls(config):\n    \"\"\"\n    Return urls present in configuration dictionary\n    \"\"\"\n    urls = []\n    base_url = config['meta']['base_url']\n    for site in config['slates']:\n        for sport in config['slates'][site]:\n            for slate_size in config['slates'][site][sport]:\n                date = config['slates'][site][sport][slate_size]\n                queryParams = f'site={site}&date={date}'\n                url = f'http://{base_url}/{sport}?{queryParams}'\n                urls.append(url)\n    return urls\n\n\ndef generate_build(driver, url, strategy):\n    \"\"\"\n    Navigate to LineupBuilder url, modify appropriate settings and click build\n    \"\"\"\n    print('Generating fixture for:\\n\\t', url)\n    driver.get(url)\n\n    wait = WebDriverWait(driver, 10)\n    reset_class_name = 'reset'\n    wait.until(EC.element_to_be_clickable((By.CLASS_NAME, reset_class_name)))\n    # reset storage to work around slate player exclusion issues\n    driver.find_element_by_class_name(reset_class_name).click()\n\n    wait.until(EC.alert_is_present())\n    driver.switch_to.alert.accept()\n\n    driver.execute_script('window.scrollTo(0, 0)')\n    # turn the games filter on and off to ensure all players are in the pool\n    toggle_games_filter(driver)\n\n    # switch build strategy\n    switch_strategy(driver, strategy)\n\n    # change some settings to meet DK lineup requirements\n    bypass_lock_like_block(driver)\n\n    # prevent request splitting by lowering output count\n    lower_output_count(driver)\n\n    # generate the build\n    driver.find_element_by_id('build-button').click()\n\n    # wait until exposures table appears (successful build)\n    exposures_selector = '.builder-exposures'\n    wait.until(EC.presence_of_element_located(\n        (By.CSS_SELECTOR, exposures_selector))\n        )\n\n\ndef lower_output_count(driver):\n    \"\"\"\n    Change output count slider\n    \"\"\"\n    output_count_selector = ('.lb-settings > .lb-settings-group >'\n                             '.lb-setting > .slider > span')\n    output_count_slider = driver.find_element_by_css_selector(\n            output_count_selector\n            )\n    actions = webdriver.ActionChains(driver)\n    actions.move_to_element(output_count_slider)\n    actions.click()\n    DEFAULT_LINEUPS = 10\n    # threshold above which a request is split into multiple chunks\n    CHUNK_THRESHOLD = 5\n    # force slider to the left below threshold\n    for i in range(DEFAULT_LINEUPS - CHUNK_THRESHOLD):\n        actions.send_keys(Keys.LEFT)\n    actions.perform()\n\n\ndef toggle_games_filter(driver):\n    \"\"\"\n    Toggle the all games filter to force all players into pool\n    \"\"\"\n    filter_expand_selector = '.lst.builder-tabs > .rt > a'\n    # show games filter\n    expand_link = driver.find_element_by_css_selector(filter_expand_selector)\n    expand_link.send_keys(Keys.RETURN)\n\n    games_filter_selector = '.lst.filters.multi.teams > .label.games'\n\n    wait = WebDriverWait(driver, 10)\n    wait.until(EC.element_to_be_clickable(\n        (By.CSS_SELECTOR, games_filter_selector)))\n\n    games_filter = driver.find_element_by_css_selector(games_filter_selector)\n    # exclude and re-add all games to force all players in pool\n    games_filter.click()\n\n    # click a second time if invalid pool warning is displayed\n    try:\n        driver.find_element_by_css_selector('.blk.ntc.warning')\n        games_filter.click()\n    except NoSuchElementException:\n        pass\n\n    # hide games filter\n    expand_link.send_keys(Keys.RETURN)\n\n\ndef switch_strategy(driver, strategy):\n    \"\"\"\n    Switch between OPTIMAL, BALANCED, and RANDOM strategies\n    \"\"\"\n    strategy_selector = ('.lb-strategy-selector > ul > li'\n                         '> div > .slider > span')\n    strategy_slider = driver.find_element_by_css_selector(strategy_selector)\n    strategy_slider.click()\n\n    # the number of times to move the strategy slider to the right\n    num_right = 0\n    if strategy == 'BALANCED':\n        num_right = 1\n    elif strategy == 'RANDOM':\n        num_right = 2\n\n    for i in range(num_right):\n        strategy_slider.send_keys(Keys.RIGHT)\n\n\ndef bypass_lock_like_block(driver):\n    \"\"\"\n    Give low salary players 99% maxLiked to bypass restriction\n    \"\"\"\n    min_max_selector = ('.lb-settings > .lb-settings-group > .lst > li'\n                        '> label > input[data-option-name=useMinMaxExposures]')\n    driver.find_element_by_css_selector(min_max_selector).send_keys(Keys.SPACE)\n    driver.execute_script('window.scrollTo(0, 0)')\n    wait = WebDriverWait(driver, 10)\n    salary_selector = 'div[data-for=tooltip-formatted_salary]'\n    wait.until(EC.element_to_be_clickable((By.CSS_SELECTOR, salary_selector)))\n    salary_col_header = driver.find_element_by_css_selector(salary_selector)\n\n    # sort by lowest salary first\n    salary_col_header.click()\n\n    # modify maxLiked for lowest salary players in current pool view\n    liked_selector = 'div[data-for=tooltip-maxLiked] + .editable-cell > input'\n    first_max_liked_input = driver.find_element_by_css_selector(liked_selector)\n    first_max_liked_input.send_keys('99')\n    actions = webdriver.ActionChains(driver)\n    actions.send_keys(Keys.TAB)\n    actions.send_keys('99')\n    actions.perform()\n\n    # restore original sorting order\n    salary_col_header.click()\n\n\n@click.command()\n@click.option('--config', default='./config.json',\n              help='Path to configuration file')\n@click.option('--driver', default='./browser_drivers/chromedriver',\n              help='Path to browser driver')\ndef generate_fixtures(config, driver):\n    \"\"\"\n    Generate LineupBuilder input fixtures through browser automation\n\n    Enable LineupBuilder request-saving in the service before using this script\n    \"\"\"\n    # load config from file to dict\n    configuration = load_config(config)\n    # extract target urls from configuration\n    urls = get_urls(configuration)\n\n    # run headless chrome for convenience\n    browser_options = webdriver.ChromeOptions()\n    browser_options.add_argument('start-maximized')\n    # browser_options.add_argument('headless')\n\n    print('Starting browser...')\n    # sign in to basic access authentication for local dev\n    browser_driver = webdriver.Chrome(driver, chrome_options=browser_options)\n    try:\n        username = configuration['meta']['username']\n        password = configuration['meta']['password']\n        base_url = configuration['meta']['base_url']\n        browser_driver.get(f'http://{username}:{password}@{base_url}')\n\n        strategies = ['OPTIMAL', 'BALANCED', 'RANDOM']\n        for strategy in strategies:\n            for url in urls:\n                generate_build(browser_driver, url, strategy)\n    finally:\n        # clean up by closing browser\n        browser_driver.quit()\n\n\nif __name__ == '__main__':\n    generate_fixtures()\n","sub_path":"generate_fixtures.py","file_name":"generate_fixtures.py","file_ext":"py","file_size_in_byte":7251,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"238740542","text":"import torch\nimport torchcde\n\n\ndef test_shape():\n    for method in ('rk4', 'dopri5'):\n        for _ in range(10):\n            num_points = torch.randint(low=5, high=100, size=(1,)).item()\n            num_channels = torch.randint(low=1, high=3, size=(1,)).item()\n            num_hidden_channels = torch.randint(low=1, high=5, size=(1,)).item()\n            num_batch_dims = torch.randint(low=0, high=3, size=(1,)).item()\n            batch_dims = []\n            for _ in range(num_batch_dims):\n                batch_dims.append(torch.randint(low=1, high=3, size=(1,)).item())\n\n            t = torch.rand(num_points).sort().values\n            values = torch.rand(*batch_dims, num_points, num_channels)\n\n            coeffs = torchcde.natural_cubic_spline_coeffs(values, t)\n            spline = torchcde.NaturalCubicSpline(coeffs, t)\n\n            class _Func(torch.nn.Module):\n                def __init__(self):\n                    super(_Func, self).__init__()\n                    self.variable = torch.nn.Parameter(torch.rand(*[1 for _ in range(num_batch_dims)], 1, num_channels))\n\n                def forward(self, t, z):\n                    return z.sigmoid().unsqueeze(-1) + self.variable\n\n            f = _Func()\n            z0 = torch.rand(*batch_dims, num_hidden_channels)\n\n            num_out_times = torch.randint(low=2, high=10, size=(1,)).item()\n            out_times = torch.rand(num_out_times, dtype=torch.float64).sort().values * (t[-1] - t[0]) + t[0]\n\n            options = {}\n            if method == 'rk4':\n                options['step_size'] = 1. / num_points\n            out = torchcde.cdeint(spline, f, z0, out_times, method=method, options=options, rtol=1e-4, atol=1e-6)\n            assert out.shape == (*batch_dims, num_out_times, num_hidden_channels)\n","sub_path":"test/test_cdeint.py","file_name":"test_cdeint.py","file_ext":"py","file_size_in_byte":1771,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"88596560","text":"class Vampire:\n    coven = []\n\n    def __init__(self, vampire_name, vampire_age, coffin, drank_blood):\n        self.name = vampire_name\n        self.age = vampire_age\n        self.in_coffin = coffin\n        self.drank_blood_today = drank_blood\n\n    def drink_blood(self):\n        self.drank_blood_today = True\n\n    def go_home(self):\n        self.in_coffin = True\n\n    @classmethod\n    def create(cls, vampire_name, vampire_age, coffin, drank_blood):\n        new_vamp = Vampire(vampire_name, vampire_age, coffin, drank_blood)\n        cls.coven.append(new_vamp)\n        return new_vamp\n\n    @classmethod\n    def sunrise(cls):\n        alive = []\n        for vampire in cls.coven:\n            if vampire.drank_blood_today and vampire.in_coffin:\n                alive.append(vampire)\n        cls.coven = alive\n    \n    @classmethod\n    def sunset(cls):\n        for vampire in cls.coven:\n            vampire.drank_blood_today = False\n            vampire.in_coffin = False\n\n\nstan = Vampire.create('Stan', 25, True, True)\nian = Vampire.create('Ian', 24, True, True)\naj = Vampire.create('Aj', 23, True, True)\nvictoria = Vampire.create('Victoria', 25, True, True)\nsimon = Vampire.create('Simon', 28, True, True)\nedi = Vampire.create('Edi', 49, True, True)\ncarol = Vampire.create('Carol', 28, True, True)\n\nfor vampire in Vampire.coven:\n    print(vampire.name) # Should print out names of all the vampires\n\n# Testing the cases for the vampires\nVampire.sunset()\nstan.drink_blood()\nstan.go_home()\nian.drink_blood()\naj.drink_blood()\naj.go_home()\nvictoria.go_home()\nVampire.sunrise() # Should remove all vampires except \"Stan\" and \"Aj\"\nprint('')\nfor vampire in Vampire.coven:\n    print(vampire.name) # Should print \"Stan\", \"Aj\"","sub_path":"vampires.py","file_name":"vampires.py","file_ext":"py","file_size_in_byte":1712,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"76882922","text":"from cso_classifier import CSOClassifier\nimport csv\nimport json\nfrom pathlib import Path\n\ndef getRepoNameFromGitHubUrl(url: str) -> str:\n\treturn '_'.join(url.split('/')[-2:])\n\nif __name__ == '__main__':\n\tcc = CSOClassifier(modules = \"both\", enhancement = \"all\", explanation = True, delete_outliers=True)\n\n\treader = csv.DictReader(open(\"data/train_test_data/readme_new_preprocessed_test.csv\"), delimiter=';')\n\tdata = {}\n\tind = 0\n\tfor row in reader:\n\t\tdata[ind] = {\n\t\t\t\"title\": \"\",\n\t\t\t\"keywords\": \"\",\n\t\t\t\"abstract\": row['Text'],\n\t\t\t\"Label\": row['Label'],\n\t\t\t\"Repo\": row['Repo'],\n\t\t}\n\t\tind += 1\n\n\tpath = Path(\"data/comparison_data/csoc_output_all.csv\")\n\tnew = not path.exists()\n\twriter = csv.DictWriter(open(path, 'a+'), delimiter=';', fieldnames=['Label', 'Repo', 'CSOS'])\n\tif new:\n\t\twriter.writeheader()\n\toutpath = Path('data/csoc_all')\n\toutpath.mkdir(parents=True, exist_ok=True)\n\tresults = cc.batch_run(data, workers=8)\n\tprint(results)\n\tfor key, val in results.items():\n\t\twriter.writerow({'Label': data[key]['Label'], 'Repo': data[key]['Repo'], 'CSOS': ','.join(val['enhanced'])})\n\t\tjson.dump(val, open(outpath / (getRepoNameFromGitHubUrl(data[key]['Repo']) + '.json'), 'w'), indent=4)\n\t\t\t\n","sub_path":"src/Comparison/collect_cso.py","file_name":"collect_cso.py","file_ext":"py","file_size_in_byte":1191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"138553054","text":"import requests,xmltodict\nfrom app_equip.handle_equip import *\nfrom app_equip.handle_pdf import *\nfrom app_equip.model import *\n\n# @author: zhufd\n# @license: (C) Copyright 明州体检\n# @contact: 245838515@qq.com\n# @software: HMS(健康管理系统)\n# @file: handle_parse.py\n# @date: 2018-12-23\n# @desc:业务处理包，解析过程、日志、数据库操作，提供给外层服务\n\n# 总入口：解析文件\ndef equip_file_parse(filename,session,log,process_queue,\n                     login_id,login_name,login_area, host_name, host_ip,\n                     equip_type,url,xmbh,czlj_info,equip_info,\n                     file_types:list,path_parse,path_error,file_handle=True):\n    '''\n    :param filename: 文件名，带路径\n    :param session: 数据库会话\n    :param log: 日志对象\n    :param process_queue:进程队列\n    :param login_id: 登录ID\n    :param login_name: 登录姓名\n    :param host_name: 登录主机\n    :param host_ip: 登录IP\n    :param equip_type: 设备类型\n    :param url: 上传URL\n    :param xmbh: 项目编号\n    :param czlj_info: 数据操作对象，TJ_CZJLB\n    :param equip_info: 数据操作对象，TJ_TJJLMXB\n    :param file_types: 文件类型，列表类型\n    :param path_parse: 解析成功->文件存放路径\n    :param path_error: 解析失败->文件存放路径\n    :param file_handle: 解析成功->是否删除原文件\n    :return:\n    '''\n    # 前缀名 后缀名\n    # file_prefix = os.path.splitext(filename)[0]\n    file_suffix = os.path.splitext(filename)[1]\n    if file_suffix not in file_types:\n        return\n    # 解析 电测听 数值\n    if file_suffix == '.gnd':\n        tjbh,jcrq,result,error = get_so_ac_result(filename)\n        if error:\n            log.info(\"文件：%s 解析电测听结果失败：%s\" %(filename,error))\n            return\n        # 校正听力值并更新数据库\n        insert_cyct(session, login_id, login_name,login_area,tjbh,jcrq,result, host_name, host_ip)\n    elif file_suffix == '.pdf':\n        # PDF 文件解析\n        values = pdfhandle(filename, equip_type, path_parse, path_error, url,\n                           log, file_handle)\n        if not values:\n            return\n        tjbh,jcrq, file_new, file_up = values\n        equip_info['file_path'] = file_up\n        equip_info['tjbh'] = tjbh\n        # 项目结果定制\n        xmjg = \"结果详见%s附件\" % EquipName.get(equip_type)\n        # 数据库处理\n        dbhandle(\n            session=session,\n            log=log,\n            login_id=login_id,\n            tjbh=tjbh,\n            xmbh=xmbh,\n            xmjg=xmjg,\n            jcrq=jcrq,\n            filename=file_new,\n            czjl_obj=czlj_info,\n            equip_obj=equip_info\n        )\n        # 返回 消息给 UI\n        if process_queue:\n            process_queue.put(tjbh)\n            log.info(\"向主进程UI传递消息：%s\" % tjbh)\n        # 转换为图片 再上传\n        pichandle(file_new, xmbh, log, url)\n\n\ndef pichandle(file_name,xmbh,log,url):\n    t1 = time.time()\n    if xmbh=='0806':\n        file_pic = pdf2pic(file_name,rotate=90)\n    else:\n        file_pic = pdf2pic(file_name)\n    t2 = time.time()\n    log.info(\"Pdf(%s)->Pic(%s)转换成功！耗时：%s秒\" % (file_name,file_pic, str(round(t2-t1, 2))))\n    response = api_equip_upload(url, file_pic)\n    if response:\n        log.info(\"文件(%s)：上传成功！\" % file_pic)\n    else:\n        log.info(\"文件(%s)：上传失败！\" % file_pic)\n\n# PDF文件解析、上传\ndef pdfhandle(file_name,equip_type,path_parse,path_error,up_url,log,is_file_remove=True):\n    '''\n    :param file_name: 待处理的文件\n    :param equip_type: 设备类型\n    :param path_parse: 解析目录\n    :param path_error: 错误目录\n    :param up_url: 上传URL\n    :param log: 日志对象\n    :param is_file_remove: 是否删除文件\n    :return:空/(体检编号，新文件名，上传后的文件名) 注：所有文件均带路径\n    '''\n    t1= time.time()\n    lstr = pdf2txt(file_name)\n    t2 = time.time()\n    if not lstr:\n        log.info(\"文件：%s 解析为文本，耗时：%s 秒，解析失败。\" % (file_name, round(t2 - t1, 2)))\n        return\n    log.info(\"文件：%s 解析为文本，耗时：%s 秒。\" %(file_name,round(t2-t1,2)))\n    tjbh,jcrq = txt2tjbh(lstr,equip_type)\n    t3 = time.time()\n    if not tjbh:\n        error_file = os.path.join(path_error,os.path.basename(file_name))\n        shutil.copy2(file_name, error_file)\n        os.remove(file_name)\n        log.info(\"文件：%s 提取体检编号，耗时：%s 秒，提取失败，转移至error目录。\" % (file_name, round(t3 - t2, 2)))\n        return\n    log.info(\"文件：%s 提取体检编号，耗时：%s 秒。\" % (file_name, round(t3 - t2, 2)))\n    new_file = os.path.join(path_parse,\"%s_%s.pdf\" %(tjbh,equip_type))\n    # 移动文件目录 从create->parse\n    shutil.copy2(file_name, new_file)\n    # 上传文件\n    response = api_equip_upload(up_url, new_file)\n    t3 = time.time()\n    if not response:\n        log.info(\"文件：%s 上传失败，耗时：%s 秒。\" % (new_file, round(t3 - t2, 2)))\n        return\n    file_up = response['data']\n    log.info(\"文件：%s 上传成功，耗时：%s 秒。\" % (new_file, round(t3 - t2, 2)))\n    # 删除原文件\n    if is_file_remove:\n        os.remove(file_name)\n\n    return tjbh,jcrq,new_file,file_up\n\n# 操作日志\ndef db_czjl(session,log,tjbh,czjl_obj):\n    czjl_obj['tjbh'] = tjbh\n    czjl_obj['czsj'] = cur_datetime()\n    # 更新记录：TJ_CZJLB\n    try:\n        session.bulk_insert_mappings(MT_TJ_CZJLB, [czjl_obj])\n        session.commit()\n    except Exception as e:\n        session.rollback()\n        log.info(\"体检顾客：%s，插入表TJ_CZJLB失败！错误信息：%s\" %(tjbh,e))\n\n# 设备接口\ndef db_equip(session,log,tjbh,xmbh,equip_obj):\n    result = session.query(MT_TJ_EQUIP).filter(MT_TJ_EQUIP.tjbh == tjbh,\n                                               MT_TJ_EQUIP.xmbh == xmbh).scalar()\n    if result:\n            # 存在则更新,PDF更新\n            session.query(MT_TJ_EQUIP).filter(MT_TJ_EQUIP.tjbh == tjbh,\n                                              MT_TJ_EQUIP.xmbh == xmbh\n                                                   ).update(\n                                                            {\n                                                                MT_TJ_EQUIP.modify_time: cur_datetime(),\n                                                                MT_TJ_EQUIP.file_path: equip_obj['file_path'],            # 上传后的路径\n                                                                # MT_TJ_EQUIP.operator: equip_obj['operator'],              # 操作工号\n                                                                # MT_TJ_EQUIP.operate_time: equip_obj['operate_time'],      # 操作时间\n                                                                # MT_TJ_EQUIP.hostname: equip_obj['hostname'],\n                                                                # MT_TJ_EQUIP.hostip: equip_obj['hostip'],\n                                                                # MT_TJ_EQUIP.operator2: equip_obj['operator2'],            # 操作姓名\n                                                                MT_TJ_EQUIP.operate_area: equip_obj['operate_area'],      # 操作区域\n                                                             }\n                                                            )\n            session.commit()\n    else:\n        try:\n            equip_obj['create_time'] = cur_datetime()\n            session.bulk_insert_mappings(MT_TJ_EQUIP, [equip_obj])\n            session.commit()\n        except Exception as e:\n            session.rollback()\n            log.info(\"体检顾客：%s，插入表TJ_EQUIP失败！错误信息：%s\" % (tjbh, e))\n\n# 项目明细\ndef db_jlmx(session,log,tjbh,xmbh,xmjg,jcrq,login_id):\n    # 人体成分、电测听直接项目结束\n    # 心电图、骨密度 则ZXPB=3\n    result = session.query(MT_TJ_TJJLMXB).filter(MT_TJ_TJJLMXB.tjbh == tjbh,MT_TJ_TJJLMXB.xmbh == xmbh).scalar()\n    if result:\n        if result.jsbz == '1':\n            zxpb = '1'\n            jsbz = '1'\n        elif xmbh in ['0310','5402']:\n            zxpb = '1'\n            jsbz = '1'\n        else:\n            zxpb = '3'\n            jsbz = '0'\n        try:\n            if xmbh == '0310':\n                # 不更新\n                session.query(MT_TJ_TJJLMXB).filter(MT_TJ_TJJLMXB.tjbh == tjbh,MT_TJ_TJJLMXB.zhbh == xmbh).update({\n                    MT_TJ_TJJLMXB.zxpb: zxpb,\n                    MT_TJ_TJJLMXB.jsbz: jsbz,\n                    MT_TJ_TJJLMXB.qzjs: None,\n                })\n            elif xmbh == '5402':\n                session.query(MT_TJ_TJJLMXB).filter(MT_TJ_TJJLMXB.tjbh == tjbh,MT_TJ_TJJLMXB.zhbh == xmbh).update({\n                    MT_TJ_TJJLMXB.zxpb: zxpb,\n                    MT_TJ_TJJLMXB.jsbz: jsbz,\n                    MT_TJ_TJJLMXB.qzjs: None,\n                    MT_TJ_TJJLMXB.jcys: login_id,\n                    MT_TJ_TJJLMXB.jcrq: cur_datetime(),\n                    MT_TJ_TJJLMXB.jg:xmjg\n                })\n            else:\n                session.query(MT_TJ_TJJLMXB).filter(MT_TJ_TJJLMXB.tjbh == tjbh,MT_TJ_TJJLMXB.zhbh == xmbh).update({\n                    MT_TJ_TJJLMXB.zxpb: zxpb,\n                    MT_TJ_TJJLMXB.jsbz: jsbz,\n                    MT_TJ_TJJLMXB.qzjs: None,\n                    MT_TJ_TJJLMXB.jcys: login_id,\n                    MT_TJ_TJJLMXB.jcrq: jcrq,\n                    MT_TJ_TJJLMXB.jg:xmjg\n                })\n            session.commit()\n        except Exception as e:\n            session.rollback()\n            log.info(\"体检顾客：%s，更新表TJ_TJJLMXB失败！错误信息：%s\" % (tjbh, e))\n    else:\n        log.info(\"体检顾客：%s，不存在项目：%s,请确认！\" % (tjbh, xmbh))\n\n\n# 心电图 特殊处理，插入数据库\ndef db_xdt(session,log,tjbh,filename):\n    dcp_obj = {}\n    dcp_obj['cusn'] = tjbh\n    dcp_obj['department'] = '0018'\n    dcp_obj['filename'] = '%s.PDF' % tjbh\n    dcp_obj['filecontent'] = open(filename, 'rb').read()\n    dcp_obj['uploadtime'] = cur_datetime()\n    dcp_obj['flag'] = '0'\n    try:\n        session.query(MT_DCP_files).filter(MT_DCP_files.cusn == tjbh).delete()\n        session.bulk_insert_mappings(MT_DCP_files, [dcp_obj])\n        session.commit()\n    except Exception as e:\n        session.rollback()\n        log.info(\"体检顾客：%s，插入表DCP_files失败！错误信息：%s\" % (tjbh, e))\n\n# 整合数据库处理\ndef dbhandle(session,log,login_id,tjbh,xmbh,xmjg,jcrq,filename,czjl_obj:dict,equip_obj:dict):\n    db_czjl(session,log,tjbh,czjl_obj)\n    db_equip(session,log,tjbh,xmbh,equip_obj)\n    db_jlmx(session,log,tjbh,xmbh,xmjg,jcrq,login_id)\n    if xmbh=='0806':\n        db_xdt(session,log,tjbh,filename)\n\n# post 请求\ndef api_equip_upload(url, filename):\n    file_obj = {\"file\": (filename, open(filename, \"rb\"))}\n    try:\n        response = requests.post(url, files=file_obj)\n        if response.status_code == 200:\n            return response.json()\n    except Exception as e:\n        print('URL：%s 请求失败！错误信息：%s' % (url, e))\n\n#### 电测听取值\ndef minus_list(list1:list,list2:list):\n    return list(map(lambda x,y:x - y,list1,list2))\n\ndef merge_list(list1:list):\n    return  '|'.join([str(i) for i in list1])\n\n#单耳听阈加权值（dB）\ndef f_avg(result:list):\n    return int(((result[0]+result[1]+result[2])/3)*0.9+result[4]*0.1)\n\n#双耳高频平均听阈（dB）\ndef f_avg2(result1:list,result2:list):\n    return int((result1[3]+result1[4]+result1[5]+result2[3]+result2[4]+result2[5])/6)\n\n#单耳平均听阈（dB）\ndef f_avg3(result:list):\n    return int((result[3] + result[4] + result[5]) / 3)\n\n#结果是否合格判断\ndef is_hg(result1:list,result2:list):\n    if all([f_avg(result1)<25,f_avg(result2)<25,f_avg2(result1,result2)<40]):\n        return '合格'\n    else:\n        return '不合格'\n\n# 获取气导的数值，骨导不参与计算\ndef get_so_ac_result(file_name):\n    with open(file_name,encoding=\"utf-8\") as f:\n        content = xmltodict.parse(f.read())\n    # 获取本次检查记录\n    session = content.get('Session',None)\n    if not session:\n        return None,None,None,\"检查记录为空\"\n    # 获取体检编号\n    tjbh = parse_nestdict(session,'Actors.Client.@Id')\n    if not tjbh:\n        return None,None,None,\"体检编号获取失败\"\n    # 获取检查日期\n    jcrq_tmp = session.get('@Date',None)\n    if not jcrq_tmp:\n        jcrq = cur_datetime()\n    else:\n        jcrq = jcrq_tmp[0:19].replace('T', ' ')\n    # 获取结果信息\n    results_tmp = session.get('Action',None)\n    if not results_tmp:\n        return tjbh,jcrq,results_tmp,\"结果信息获取失败\"\n    tmp = {}\n    result_key = 'Public.TAudioSession.ToneTHRAudiogram.TToneTHRAudiogram'      # 结果信息 路径\n    so_key_key = 'MeasCond.@SignalOutput1'                                      # 气导、骨导 标记路径 以SignalOutput1 输出为主\n    so_result_key = 'Curve.TTonePoint'                                          # 气导、骨导 结果路径，左、右耳\n    so_value_key = '@Intensity1'                                                # 结果值 路径\n\n    if isinstance(results_tmp,list):\n        # 多组结果：可能是骨导+气导，也可能是多组气导\n        for result_tmp in results_tmp:\n            results = parse_nestdict(result_tmp,result_key)\n            for result in results:\n                so_key = parse_nestdict(result, so_key_key)\n                # 获取列表\n                so_value = [int(int(i.get(so_value_key, 0)) / 10) for i in parse_nestdict(result, so_result_key)]\n                # 获取字典\n                # so_value = dict((int(i.get('@Freq1', 0)),int(int(i.get('@Intensity1', 0)) / 10)) for i in parse_nestdict(result, so_result_key))\n                if len(so_value) == 6:\n                    tmp[so_key] = so_value\n                else:\n                    return tjbh,jcrq,None,\"%s的数值不足6组:%s\" %(so_key,str(so_value))\n\n    else:\n        # 单组结果，则为气导\n        ac_results = parse_nestdict(results_tmp,result_key)\n        for ac_result in ac_results:\n            so_key = parse_nestdict(ac_result, so_key_key)\n            # 获取列表\n            so_value = [int(int(i.get(so_value_key, 0)) / 10) for i in parse_nestdict(ac_result, so_result_key)]\n            # 获取字典\n            # so_value = dict((int(i.get('@Freq1', 0)), int(int(i.get('@Intensity1', 0)) / 10)) for i in parse_nestdict(ac_result, so_result_key))\n            if len(so_value) == 6:\n                tmp[so_key] = so_value\n            else:\n                return tjbh, jcrq, None, \"%s的数值不足6组:%s\" % (so_key, str(so_value))\n\n\n    return tjbh,jcrq,tmp,''\n\n#获取纯音听力原始记录，校正后插入到TJ_EQUIP\ndef insert_cyct(session,login_id,login_name,login_area,tjbh,jcrq,result,host_name,host_ip,xmbh='0310'):\n    # 从数据库中校对人员信息\n    result_user = session.execute(get_tjxx_sql(tjbh)).fetchone()\n    if not result_user:\n        print(\"未找到体检编号为：%s的人员信息\" %tjbh)\n        return\n    user_name = str2(result_user[1])     # 用户姓名\n    user_sex = str2(result_user[2])      # 用户性别\n    user_age = result_user[-1]           # 用户年龄\n\n    if user_age>=22:\n        #result = session.execute(standard_audition_sql(user_age, user_sex)).fetchone()\n        # if result:\n        #     standard_result = dict(zip([500,1000,2000,3000,4000,6000],[int(i[1]) for i in result]))\n        results = session.execute(standard_audition_sql(user_age,user_sex)).fetchall()\n        if results:\n            result_tmp = sorted(results[0].items(), key=lambda item: item[0])\n            standard_result = [int(i[1]) for i in result_tmp]\n        else:\n            standard_result = [0, 0, 0, 0, 0, 0]\n            # standard_result = {500: 0, 1000: 0, 2000: 0, 3000: 0, 4000: 0, 6000: 0}\n    else:\n        standard_result = [0, 0, 0, 0, 0, 0]\n        # standard_result = {500: 0, 1000: 0, 2000: 0, 3000: 0, 4000: 0, 6000: 0}\n    #\n    result[\"so_ACR_new\"] = minus_list(result[\"so_ACR\"], standard_result)\n    result[\"so_ACL_new\"] = minus_list(result[\"so_ACL\"], standard_result)\n    result[\"so_ACR_init\"] = standard_result\n    result[\"so_ACL_init\"] = standard_result\n    xmzd = is_hg(result[\"so_ACL_new\"], result[\"so_ACR_new\"])\n    # 计算校正结果\n    ms1 = merge_list(result[\"so_ACR\"]) + '|' + merge_list(result[\"so_ACL\"])\n    ms2 = merge_list(result[\"so_ACR_new\"]) + '|' + merge_list(result[\"so_ACL_new\"])\n    ms3 = merge_list(result[\"so_ACR_init\"]) + '|' + merge_list(result[\"so_ACL_init\"])\n    ms4 = \"右耳平均语频=%s\\n\" \\\n          \"右耳平均高频=%s\\n\" \\\n          \"左耳平均语频=%s\\n\" \\\n          \"左耳平均高频=%s\\n\" \\\n          \"双耳高频平均听阈=%s\" % (f_avg(result[\"so_ACR_new\"]), f_avg3(result[\"so_ACR_new\"]),\n                           f_avg(result[\"so_ACL_new\"]), f_avg3(result[\"so_ACL_new\"]),\n                           f_avg2(result[\"so_ACR_new\"], result[\"so_ACL_new\"]))\n\n    xmjg = '||||' + ms1 + '|||||' + ms2 + '|||||' + ms3 + '|$' + ms4\n    try:\n        # 表 TJ_EQUIP\n        result = session.query(MT_TJ_EQUIP).filter(MT_TJ_EQUIP.tjbh==tjbh,MT_TJ_EQUIP.xmbh==xmbh).scalar()\n        if result:\n            # 不更新第一次操作用户\n            session.query(MT_TJ_EQUIP).filter(MT_TJ_EQUIP.tjbh==tjbh,MT_TJ_EQUIP.xmbh==xmbh).update({\n                MT_TJ_EQUIP.operate_time:jcrq,\n                MT_TJ_EQUIP.equip_jg1:xmjg,\n                MT_TJ_EQUIP.equip_jg2:xmzd\n            })\n            zxpb = '1'\n            jsbz = '1'\n        else:\n            session.execute(insert_dct_sql(tjbh,cur_datetime(),user_name,jcrq,host_name,host_ip,xmjg,xmzd,login_id,login_name,login_area))\n            zxpb = '3'\n            jsbz = '0'\n        # 表 TJ_TJJLMXB\n        session.query(MT_TJ_TJJLMXB).filter(MT_TJ_TJJLMXB.tjbh==tjbh,MT_TJ_TJJLMXB.zhbh==xmbh).update({\n            MT_TJ_TJJLMXB.zxpb: zxpb,\n            MT_TJ_TJJLMXB.jsbz: jsbz,\n            MT_TJ_TJJLMXB.qzjs: None,\n            MT_TJ_TJJLMXB.jcrq: jcrq,\n            MT_TJ_TJJLMXB.jcys: login_id,\n            MT_TJ_TJJLMXB.jg: xmjg,\n            MT_TJ_TJJLMXB.zd: xmzd,\n            MT_TJ_TJJLMXB.shrq: jcrq,\n            MT_TJ_TJJLMXB.shys: '120502002',\n        })\n        # 表 TJ_CZJLB 不做处理，由PDF文件上传时来处理\n        session.commit()\n        print(\"%s 体检顾客：%s，纯音测听结果（%s）解析成功！\" % (cur_datetime(),user_name,xmjg))\n    except Exception as e:\n        session.rollback()\n        print(\"执行数据库出错：%s\" %e)\n\n# 确认路径，不存在则创建\ndef mkdir(path,log):\n    try:\n        if not os.path.isdir(path):\n            os.makedirs(path)\n        log.info(\"路径：%s 已启动监控。\" %path)\n        return True\n    except Exception as e:\n        log.info('路径：%s 不准确，请调整！错误信息：%s' %(path,e))\n        return False\n\n# 设备信息\nEquipName={\n    '01':'电测听',\n    '02':'人体成分(投放)',\n    '03':'人体成分',\n    '04':'骨密度',\n    '05':'超声骨密度',\n    '06':'动脉硬化',\n    '07':'大便隐血',\n    '08':'心电图',\n    '11':'肺功能',\n    '12':'胸部正位'\n}\n\n# 设备信息\nEquipNo={\n    '01':'0310',\n    '02':'5402',\n    '03':'5402',\n    '04':'501576',\n    '05':'1000074',\n    '06':'5401',\n    '07':'2113',\n    '08':'0806',\n    '11':'0045',\n    '12':'501716'\n}\n\n# 设备动作点\nEquipAction={\n    '01':'0023',\n    '02':'0022',\n    '03':'0022',\n    '04':'0020',\n    '05':'1000074',\n    '06':'0024',\n    '07':'2113',\n    '08':'0021',\n    '11':'0045',\n    '12':'501716'\n}\n\n# 设备动作点\nEquipActionName={\n    '01':'电测听检查',\n    '02':'人体成分检查',\n    '03':'人体成分检查',\n    '04':'骨密度检查',\n    '05':'超声骨密度检查',\n    '06':'动脉硬化检查',\n    '07':'大肠癌检查',\n    '08':'心电图检查',\n    '11':'肺功能检查',\n    '12':'DR检查'\n}\n\n# 嵌套字典解析\ndef parse_nestdict(nestdict:dict,keys:str,default=None):\n    keys_list = keys.split('.')\n    tmp = nestdict\n    for key in keys_list:\n        if isinstance(tmp,dict):\n            val = tmp.get(key, None)\n        else:\n            val = None\n        if val!= None:\n            tmp = val\n        else:\n            return default\n\n    return tmp\n\nif __name__==\"__main__\":\n    from pprint import pprint\n    pprint(get_so_ac_result(r\"E:\\DR\\create\\01\\My Suite\\178240009 龙元明, (男) 2019-01-02T14.58.02.1866546+08.00.gnd\"))\n\n    # for root, dirs, files in os.walk(r\"E:\\DR\\create\\01\\My Suite\"):\n    #     if files and not dirs:  # 必须是指定目录的下级目录\n    #         for file in files:\n    #             try:\n    #                 print(get_so_ac_result(os.path.join(root, file)))\n    #             except Exception as e:\n    #                 print(os.path.join(root, file))","sub_path":"app_equip/handle_parse.py","file_name":"handle_parse.py","file_ext":"py","file_size_in_byte":20945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"446893539","text":"morse_dict = {\n    \"A\": \"* ***\",\n    \"B\": \"*** * * *\",\n    \"C\": \"*** * *** *\",\n    \"D\": \"*** * *\",\n    \"E\": \"*\",\n    \"F\": \"* * *** *\",\n    \"G\": \"*** *** *\",\n    \"H\": \"* * * *\",\n    \"I\": \"* *\",\n    \"J\": \"* *** *** ***\",\n    \"K\": \"*** * ***\",\n    \"L\": \"* *** * *\",\n    \"M\": \"*** ***\",\n    \"N\": \"*** *\",\n    \"O\": \"*** *** ***\",\n    \"P\": \"* *** *** *\",\n    \"Q\": \"*** *** * ***\",\n    \"R\": \"* *** *\",\n    \"S\": \"* * *\",\n    \"T\": \"***\",\n    \"U\": \"* * ***\",\n    \"V\": \"* * * ***\",\n    \"W\": \"* *** ***\",\n    \"X\": \"*** * * ***\",\n    \"Y\": \"*** * *** ***\",\n    \"Z\": \"*** *** * *\",\n    \" \": \"       \",\n}\n\n\n# def translate_to_and_from_morse(phrase):\n#     translated = []\n#     morse_word_break = \" \" * 7\n#     morse_letter_break = \" \" * 3\n\n#     if phrase[0].isalpha():\n#         phrase_list = list(phrase.upper())\n#         previous_character = \"\"\n\n#         for character in phrase_list:\n#             if previous_character.isalpha() and character.isalpha():\n#                 translated.append(morse_letter_break)\n\n#             translated.append(morse_dict[character])\n#             previous_character = character\n#     else:\n#         word_list = phrase.split(morse_word_break)\n#         previous_word = \"\"\n#         for word in word_list:\n#             letters = word.split(morse_letter_break)\n#             if previous_word != \"\":\n#                 translated.append(\" \")\n#             previous_word = word\n#             for letter in letters:\n#                 for k, v in morse_dict.items():\n#                     if letter == v:\n#                         translated.append(k)\n\n#     return \"\".join(translated)\n\n\nmorse_word_break = \" \" * 7\nmorse_letter_break = \" \" * 3\n\n\ndef translate_into_english(morse_phrase):\n    translated = []\n    previous_word = \"\"\n    word_list = morse_phrase.split(morse_word_break)\n\n    for word in word_list:\n        letters = word.split(morse_letter_break)\n        if previous_word != \"\":\n            translated.append(\" \")\n        previous_word = word\n        for letter in letters:\n            for k, v in morse_dict.items():\n                if letter == v:\n                    translated.append(k)\n    return \"\".join(translated)\n\n\ndef translate_into_morse(english_phrase):\n    translated = []\n    previous_character = \"\"\n    phrase_list = list(english_phrase.upper())\n\n    for character in phrase_list:\n        if previous_character.isalpha() and character.isalpha():\n            translated.append(morse_letter_break)\n        translated.append(morse_dict[character])\n        previous_character = character\n    return \"\".join(translated)\n\n\ndef translate_to_and_from_morse(phrase):\n    if phrase[0].isalpha():\n        return translate_into_morse(phrase)\n    else:\n        return translate_into_english(phrase)\n\n\nhidden_message = \"*** * *** *   * ***   ***   * * *       * ***   * *** *   *       *** * *** *   *** *** ***   *** *** ***   * *** * *\"\neng_message = \"CATS ARE COOL\"\nprint(translate_to_and_from_morse(eng_message))\n","sub_path":"coding_problems/morse_refactor.py","file_name":"morse_refactor.py","file_ext":"py","file_size_in_byte":2951,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"480849882","text":"'''\n    Write a function to find the longest common prefix string amongst an array of strings.\n\n    If there is no common prefix, return an empty string \"\".\n\n    Examples:\n\n    Input: [\"flower\",\"flow\",\"flight\"]\n    Output: \"fl\"\n\n\n\n    Input: [\"dog\",\"racecar\",\"car\"]\n    Output: \"\"\n    Explanation: There is no common prefix among the input strings.\n'''\n\ndef longestCommonPrefix(self, strs):\n        \"\"\"\n        :type strs: List[str]\n        :rtype: str\n        \"\"\"\n        prefixes=[]\n        num = len(strs)\n        for x in zip(*strs): #iterate through n-shortest string, making a tuple of each character\n            if len(set(x)) == 1: #test to see if they're all the same \n                prefixes.append(x[0]) #append if they are\n            else:\n                break #break out if not\n        return \"\".join(prefixes) #return result.","sub_path":"MonthOne/Week 1/Arrays/longest_common_prefix.py","file_name":"longest_common_prefix.py","file_ext":"py","file_size_in_byte":842,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"272461934","text":"import string\nfrom math import floor\n\nclass Encoder:\n  # use this to encode URL to Base62 string\n  def encode(self, inpt, base=62):\n    # base validation\n    if base <= 0 or base > 62:\n      return 0\n\n    # we want combinations of [a-z][A-Z][0-9]\n    the_base = string.digits + string.lowercase + string.uppercase\n\n    remainder = inpt % base\n    result = the_base[remainder]\n\n    quotient = floor(inpt / base)\n    # loop and keep dividing\n    while quotient:\n      remainder = quotient % base\n      quotient = floor(quotient / base)\n      # add new encoded to the result\n      result = the_base[int(remainder)] + result\n    return result","sub_path":"encoder.py","file_name":"encoder.py","file_ext":"py","file_size_in_byte":638,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"69884909","text":"# -*- coding: utf-8 -*-\nfrom Slevanda_python_training.Model.contact import Contact\n\n\ndef test_create_new_contact(app):\n    app.contact.create_new_contact(Contact(firstname=\"First name\", middle=\"Middle name\", lastname=\"Last name\", nick=\"Nickname\",\n                                           title=\"Title\", company=\"Company\", address=\"Address\", homephone=\"Home telephone\",\n                                           mobilephone=\"Mobile telephone\", workphone=\"Work telephone\",\n                                           fax_telephone=\"Fax telephone\", email_1=\"Email 1\", email_2=\"Email 2\",\n                                           email_3=\"Email 3\", home_page=\"Homepage\", bday=\"1\", bmonth=\"January\", byear=\"1980\",\n                                           aday=\"18\", amonth=\"July\", ayear=\"1998\", address_2=\"Address\",\n                                           secondaryphone=\"Home address\", notes=\"Notes\"))\n    app.session.logout()\n\n\n","sub_path":"test/New_Update_Contact(AddressBook).py","file_name":"New_Update_Contact(AddressBook).py","file_ext":"py","file_size_in_byte":933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"462226682","text":"#!/usr/bin/env python\n\n\"\"\"Test wrapper to /bin/draw_bar_chart_of_clusters.py.\nThe defaults are the actual test data. So it only needs to be called\nas METAPY.py\n\"\"\"\n\nimport os\nimport subprocess\n\nINFILE = os.path.join(\"tests\", \"test_data\", \"bar_charts\",\n                      \"swarm.outRENAMED_abundance\")\n\nOTU_DATABASE_SWARM = os.path.join(\"tests\", \"test_data\", \"bar_charts\",\n                                  \"ITS_db_NOT_confirmed_for_swarm.fasta\")\n\n\ndef test_METAPY_exec():\n    \"\"\"Run METAPY.py on test data and compare output to\n    precomputed target. The default option are the actual\n    test data. So we only need to call the program\n    \"\"\"\n    prog = os.path.join(\"bin\", \"draw_bar_chart_of_clusters.py\")\n    temp_s = [\"python3\",\n              prog,\n              \"-i\",\n              INFILE,\n              \" --db\",\n              OTU_DATABASE_SWARM]\n    cmd_s = ' '.join(temp_s)\n    pipe = subprocess.run(cmd_s, shell=True,\n                          stdout=subprocess.PIPE,\n                          stderr=subprocess.PIPE,\n                          check=True)\n    if not os.path.isfile(INFILE + \"_barchart.png\"):\n        sys_exit(\"outfile not generated: %s\" % INFILE + \"_barchart.png\")\n","sub_path":"metapy/tests/test_script_bar_charts.py","file_name":"test_script_bar_charts.py","file_ext":"py","file_size_in_byte":1194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"389620840","text":"# -*- encoding: utf-8 -*-\nfrom .models import Sale, DetailSale, ImageSale\nfrom .serializers import SaleSerializer, DetailSaleSerializer, ImageSaleSerializer\nfrom rest_framework import viewsets\nfrom rest_framework import filters\n\n\nclass SaleViewSet(viewsets.ModelViewSet):\n    serializer_class = SaleSerializer\n    queryset = Sale.objects.all()\n    filter_backends = ( filters.DjangoFilterBackend, )\n    filter_fields = ('id', 'user', )\n\n    def perform_create(self, serializer):\n        serializer.save(user=self.request.user)\n\n    def get_queryset(self):\n        \"\"\"\n        This view should return a list of all the directions\n        for the currently authenticated user.\n        \"\"\"\n        user = self.request.user\n        return Sale.objects.filter(user=user)\n\n\nclass DetailSaleViewSet(viewsets.ModelViewSet):\n    serializer_class = DetailSaleSerializer\n    queryset = DetailSale.objects.all()\n    filter_backends = ( filters.DjangoFilterBackend, )\n    filter_fields = ('sale', )\n\n\nclass ImageSaleViewSet(viewsets.ModelViewSet):\n    serializer_class = ImageSaleSerializer\n    queryset = ImageSale.objects.all()\n    filter_backends = ( filters.DjangoFilterBackend, )\n    filter_fields = ('sale', )","sub_path":"carrito/viewsets.py","file_name":"viewsets.py","file_ext":"py","file_size_in_byte":1202,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"529338226","text":"from .client import HTTPClient\nimport requests\n\n\nclass OpenNMTPredictHTTPClient(HTTPClient):\n\n    def request(self, address):\n        raise NotImplementedError()\n\n    def request_many(self, addresses):\n        raise NotImplementedError()\n\n\nclass NMTPredictHTTPClient(OpenNMTPredictHTTPClient):\n\n    def __init__(self,\n                 model_name,\n                 host,\n                 port,\n                 version,\n                 timeout=30):\n        super().__init__(model_name, host, port, version, timeout)\n        base_url = \"http://\" + self.host + \":\" + str(self.port) + \"/v1/models/\" + self.model_name\n        if version:\n            self.url = base_url + \"/versions/\" + str(self.model_version) + \":predict\"\n        else:\n            self.url = base_url + \":predict\"\n\n    def request(self, address):\n        try:\n            resp = requests.post(self.url, json={'instances': [address, ]}, timeout=self.timeout)\n        except TimeoutError as e:\n            print(e)\n            return address, \"\"\n        except ConnectionError as e:\n            print(e)\n            return address, \"\"\n        if resp.status_code != 200:\n            return address, \"\"\n        resp.encoding = \"utf8\"\n        result = resp.json()\n        return address, result['predictions'][0]\n\n    def request_many(self, addresses):\n        try:\n            resp = requests.post(self.url, json={'instances': addresses}, timeout=self.timeout)\n        except TimeoutError as e:\n            print(e)\n            return self._return_empty(addresses)\n        except ConnectionError as e:\n            print(e)\n            return self._return_empty(addresses)\n        if resp.status_code != 200:\n            return self._return_empty(addresses)\n        resp.encoding = \"utf8\"\n        result = resp.json()\n        results = []\n        for i, o in zip(addresses, result[\"predictions\"]):\n            results.append((i, o))\n        return results\n\n    @staticmethod\n    def _return_empty(addresses):\n        results = []\n        for a in addresses:\n            results.append((a, \"\"))\n        return results\n","sub_path":"client/onmt_http_client.py","file_name":"onmt_http_client.py","file_ext":"py","file_size_in_byte":2078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"623260894","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Sep 10 10:31:06 2017\n\n@author: codeplay2017\n\"\"\"\n#from __future__ import absolute_import\n#from __future__ import division\n#from __future__ import print_function\n#from __future__ import unicode_literals\n#\n#from builtins import str\n## etc., as needed\n#\n#from future import standard_library\n#standard_library.install_aliases()\n\nimport tensorflow as tf\nimport numpy as np\nimport os, pickle\nfrom make_data_pai import ImgDataSet\nfrom tensorflow.python import pywrap_tensorflow\nimport matplotlib.pyplot as plt\n\nimport utils.networks as nt\n\n#####-----------structure parameters-----------------\nINPUT_SIZE = 4096\nSTRIDE1 = 1 # stride for the first layer\nKEEP_PROB = [1,1]\n\n####-----------------------------------------------------------------------\ncwd = os.getcwd()\nsource_dir = os.path.join(os.path.join(cwd,'..'),'')\ndata_dir = os.path.join(source_dir, 'resources/data4raw_5speeds_4096_step2/') # ubuntu\n#data_dir = os.path.join(source_dir, 'resources\\\\data4raw_5speeds_4096_step2\\\\') # windows\n\n#model_path = os.path.join(out_dir, 'observation/171220/raw_5speed/2017-12-15_17:20:18/model.ckpt')\n#model_path = os.path.join(out_dir, 'observation/171220/raw_1speed/50Hz/2017-12-15_16:11:15/model.ckpt')\n#model_path = os.path.join(out_dir, 'observation/171220/angle_1speed/50Hz/2017-12-18_10:25:16/model.ckpt')\n#model_path = os.path.join(out_dir, 'observation/171220/raw_2speed/2017-12-16_11:04:30/model.ckpt')\n#model_path = os.path.join(out_dir, 'observation/171220/raw_2speed/10,30,50/2017-12-18_16:42:39/model.ckpt')\n#model_path = os.path.join(out_dir, 'observation/171220/fft_5speed/2017-12-19_11:45:35/model.ckpt')\n#model_path = os.path.join(out_dir, 'observation/171220/fft_1speed/10,30,50/2017-12-20_15:22:58/model.ckpt')\n#model_path = os.path.join(out_dir, 'observation/171220/afft_5speeds/2017-12-20_20:59:11/model.ckpt')\n#model_path = os.path.join(source_dir, 'models\\\\deep_networks\\\\alex\\\\exp2\\\\2018-02-20_170507\\\\model.ckpt') # windows\nmodel_path = os.path.join(source_dir, 'models/deep_networks/VGG19/exp5/2018-03-23_130140/model.ckpt')\n####-----------------------------------------------------------------------\n#%%\nreader = pywrap_tensorflow.NewCheckpointReader(model_path)\nvar_to_shape_map = reader.get_variable_to_shape_map()\n# Print tensor name and values\nkeylist = []\nfor key in var_to_shape_map:\n    keylist.append(key)\nlist.sort(keylist)\n#%%\ndef load_test_data(test_speed):\n    testset = ImgDataSet()\n    num_testfile = 3*len(test_speed)\n    for ii in range(num_testfile):\n#        resource_path = FLAGS.buckets\n#        data_path = os.path.join(resource_path.replace('step_2400','step20_test'),'input_data_t_'+str(ii+1)+'.pkl')\n        temp = test_speed[ii//3]\n        index = ii%3\n        file_index = int(temp/10+index*5)\n        print(file_index,end=',')\n        data_path = os.path.join(data_dir,'input_data_t_'+str(file_index)+'.pkl')\n        with tf.gfile.GFile(data_path, 'rb') as f:\n            data = pickle.load(f, encoding='iso-8859-1')\n        testset.join_data(data)\n    testset.make(shuffle=True,clean=True)\n    return testset\n\ndef load_train_data(test_speed):\n    print(\"loading data...\")\n    trainset = ImgDataSet()\n    num_trainfile = 15*len(test_speed)\n    for ii in range(num_trainfile):\n#        data_path = os.path.join(FLAGS.buckets,'input_data_cwt_0-50_'+str(ii+1)+'.pkl')\n        temp = test_speed[ii//15]\n        index = ii%15\n        file_index = int(index//5*25 + (temp/2-index%5))\n        print(file_index,end=',')\n        data_path = os.path.join(data_dir,'input_data_'+str(file_index)+'.pkl')\n        with tf.gfile.GFile(data_path, 'rb') as f:\n            data = pickle.load(f, encoding='iso-8859-1')\n        trainset.join_data(data)\n    trainset.make(shuffle=True,clean=True)\n    print('num of train sample is '+str(trainset.num_examples()))\n    return trainset\n\ndef main(): # _ means the last param\n    # Create the model\n    x = tf.placeholder(tf.float32, [None, INPUT_SIZE])\n    y_ = tf.placeholder(tf.float32, [None, 3])\n    is_training = tf.placeholder(tf.bool)\n    keep_prob0 = tf.placeholder(tf.float32)\n    keep_prob1 = tf.placeholder(tf.float32)\n    keep_prob2 = tf.placeholder(tf.float32)\n    \n    y_conv = nt.VGG19(x, is_training,keep_prob0, keep_prob1, keep_prob2)\n    \n    with tf.name_scope('accuracy'):\n        correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))\n        correct_prediction = tf.cast(correct_prediction, tf.float32)\n    accuracy = tf.reduce_mean(correct_prediction)\n\n    ### load data\n    test_speed = [40]\n    testset = load_test_data(test_speed)\n#    testset = load_train_data(test_speed)\n    \n    num_of_example = testset.num_examples()\n    print('\\nnumber of test examples is ', num_of_example)\n    \n    with tf.Session() as sess:\n        saver = tf.train.Saver()\n        saver.restore(sess, model_path)\n\n        ### test accuracy of model\n        correct = 0\n        count = 0\n        test_step = 1\n        for ii in range(num_of_example//test_step):\n            count += 1\n            test_batch = testset.next_batch(test_step)\n            test_feed = {x: test_batch[0], y_: test_batch[1],\n                    keep_prob0:1.0, keep_prob1: 1.0, keep_prob2: 1.0, is_training: False}\n#            test_accuracy, test_out = sess.run([accuracy, tf.argmax(y_conv,1)],\n#                                                feed_dict=test_feed)\n            test_accuracy, test_out = sess.run([accuracy, y_conv],\n                                                feed_dict=test_feed)\n#            if np.abs(test_accuracy - 1.0) > 0.01:\n#                print(str(ii+1),test_batch[1],test_out)\n            correct += test_accuracy\n        test_batch = testset.next_batch(testset.num_examples())\n        test_acc,test_out = sess.run([accuracy,y_conv], feed_dict={x:test_batch[0], y_:test_batch[1],\n                                                 keep_prob0:1,keep_prob1:1,keep_prob2:1,\n                                                 is_training: False})\n        \n\n        conv1 = reader.get_tensor('conv2/W_conv')\n#        plot_conv(conv1)\n        print('accuracy is ', correct/count, test_acc)\n\ndef plot_conv(conv_tensor):\n    num_filter_in = conv_tensor.shape[-2]\n    num_filter_out = conv_tensor.shape[-1]\n    print(num_filter_in, num_filter_out)\n#    num_feature = conv_tensor.shape[0]\n    fig = plt.figure()\n    for ii in range(num_filter_in):\n        for jj in range(num_filter_out):\n            plt.subplot(num_filter_in,num_filter_out,ii*num_filter_out+jj+1)\n            plt.plot(np.abs(np.fft.fft((conv_tensor[:,0,ii,jj]))))\n        \n    \n    \n            \n#########------custom functions----------------------------------\nif __name__ == '__main__':\n    main()\n","sub_path":"python/src/test_cnn_local.py","file_name":"test_cnn_local.py","file_ext":"py","file_size_in_byte":6719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"100518487","text":"from yacs.config import CfgNode as CN\n\n_C = CN()\n\n_C.MODEL = CN()\n_C.MODEL.DEVICE = \"cuda\"\n_C.MODEL.THRESHOLD = 0.5\n_C.MODEL.NUM_CLASSES = 21\n_C.MODEL.NEG_POS_RATIO = 3\n_C.MODEL.CENTER_VARIANCE = 0.1\n_C.MODEL.SIZE_VARIANCE = 0.2\n\n_C.MODEL.BACKBONE = CN()\n_C.MODEL.BACKBONE.NAME = 'vgg'\n_C.MODEL.BACKBONE.OUT_CHANNELS = (512, 1024, 512, 256, 256, 256)\n_C.MODEL.BACKBONE.PRETRAINED = True\n\n# -----------------------------------------------------------------------------\n# PRIORS\n# -----------------------------------------------------------------------------\n_C.MODEL.PRIORS = CN()\n_C.MODEL.PRIORS.FEATURE_MAPS = [38, 19, 10, 5, 3, 1]\n_C.MODEL.PRIORS.STRIDES = [8, 16, 32, 64, 100, 300]\n_C.MODEL.PRIORS.MIN_SIZES = [30, 60, 111, 162, 213, 264]\n_C.MODEL.PRIORS.MAX_SIZES = [60, 111, 162, 213, 264, 315]\n_C.MODEL.PRIORS.ASPECT_RATIOS = [[2], [2, 3], [2, 3], [2, 3], [2], [2]]\n_C.MODEL.PRIORS.BOXES_PER_LOCATION = [4, 6, 6, 6, 4, 4]\n_C.MODEL.PRIORS.CLIP = True\n\n_C.MODEL.BOX_HEAD = CN()\n_C.MODEL.BOX_HEAD.NAME = 'SSDBoxHead'\n_C.MODEL.BOX_HEAD.PREDICTOR = 'SSDBoxPredictor'\n\n_C.INPUT = CN()\n_C.INPUT.IMAGE_SIZE = 300\n_C.INPUT.PIXEL_MEAN = [123, 117, 104]\n\n_C.DATASETS = CN()\n_C.DATASETS.TRAIN = ()\n_C.DATASETS.TARGET = ()\n_C.DATASETS.TEST = ()\n\n_C.DATA_LOADER = CN()\n_C.DATA_LOADER.NUM_WORKERS = 0\n_C.DATA_LOADER.PIN_MEMORY = True\n\n_C.SOLVER = CN()\n_C.SOLVER.MAX_ITER = 120000\n_C.SOLVER.LR_STEPS = [80000, 100000]\n_C.SOLVER.GAMMA = 0.1\n_C.SOLVER.BATCH_SIZE = 32\n_C.SOLVER.BACKBONELR = 1e-3\n_C.SOLVER.BOXHEADLR = 1e-3\n_C.SOLVER.DOMAINDISCRIMINATORLR = 1e-3\n_C.SOLVER.MOMENTUM = 0.9\n_C.SOLVER.WEIGHT_DECAY = 5e-4\n_C.SOLVER.WARMUP_FACTOR = 1.0 / 3\n_C.SOLVER.WARMUP_ITERS = 500\n_C.SOLVER.LAMBDA = 0.5\n\n_C.TEST = CN()\n_C.TEST.NMS_THRESHOLD = 0.45\n_C.TEST.CONFIDENCE_THRESHOLD = 0.01\n_C.TEST.MAX_PER_CLASS = -1\n_C.TEST.MAX_PER_IMAGE = 100\n_C.TEST.BATCH_SIZE = 10\n\n_C.OUTPUT_DIR = 'outputs'\n","sub_path":"ssd/ssd/config/defaults.py","file_name":"defaults.py","file_ext":"py","file_size_in_byte":1878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"377925380","text":"#!/usr/bin/python\n\nlow=0\nhigh=100\n\nprint('Please think of a number between 0 and 100!')\n\nwhile True: \n guess=(low+high)/2\n print('Is your secret number ' + str(guess) + ' ?')\n usr=raw_input(\"Enter 'h' to indicate the guess is too high. Enter 'l' to indicate the guess is too low. Enter 'c' to indicate I guessed correctly. \")\n if usr=='h':\n  high=guess\t\n elif usr=='l':\n  low=guess\n elif usr=='c':\n  print('Game over. Your secret number was: ' + str(guess))\n  break\n else:\n  print('Sorry, I did not understand your input.')\n","sub_path":"MyPythonPrograms/bisectionsearch.py","file_name":"bisectionsearch.py","file_ext":"py","file_size_in_byte":524,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"582831227","text":"# print(\"Student Information:- \");\n# Name = \"Aman Rajan Singh\";\n# Div = \"CSE-17B\";\n# Enrollmentno = 200303105095;\n# Contact_No = 9512888760;\n# print(Name);\n# print(Div);\n# print(Enrollmentno);\n# print(Contact_No);\n# print(\"\\n);\n\ndays = 140;\nmonths = int(days/30);\nremaindays = days%30;\nprint(months,remaindays);\n\n\n\n\n","sub_path":"pythonProject/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"55820631","text":"# User function template for Python\r\n\r\nclass Solution:\r\n    def binarysearch(self, arr, n, k):\r\n        l = 0\r\n        r = n - 1\r\n        while (l <= r):\r\n            mid = (l + r) // 2\r\n            if arr[mid] == k:\r\n                return mid\r\n            if arr[mid] > k:\r\n                r = mid - 1\r\n            if arr[mid] < k:\r\n                l = mid + 1\r\n\r\n        return -1\r\n\r\n\r\n# {\r\n#  Driver Code Starts\r\n# Initial template for Python\r\n\r\nif __name__ == '__main__':\r\n    t = int(input())\r\n    for i in range(t):\r\n        n = int(input())\r\n        arr = list(map(int, input().strip().split(' ')))\r\n        k = int(input())\r\n        ob = Solution()\r\n        print(ob.binarysearch(arr, n, k))\r\n\r\n# } Driver Code Ends","sub_path":"week 2/searching and sorting/Binary Search.py","file_name":"Binary Search.py","file_ext":"py","file_size_in_byte":724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"6037515","text":"import numpy as np\nimport pickle\nimport sys\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nimport torch\nfrom torch.nn import functional as F\nimport random\nimport pandas as pd\n\nseed=1\ntorch.manual_seed(seed)\nrandom.seed(seed)\nnp.random.seed(seed)\ntorch.set_default_dtype(torch.float64)\n\n\ndef load_file(path):\n    with open(path, 'rb') as f:\n        file = pickle.load(f)\n        if type(file) is not np.ndarray:\n            file = np.array(file)\n        return file\n\n\ndef get_acc(labels,outputs):\n    _,predicted = torch.max(outputs.data, 1)\n    data_number = y.shape[0]*1.0\n    correct_num = (predicted==labels).sum().item()\n    accuracy = correct_num/data_num\n    return accuracy\n\n\ndf = pd.read_csv('./full.csv')\nX = df[['dep','octanol','octanoic','pentanol','temperature','humidity']]\ny = df[['average_speed','average_number_of_droplets_last_second','max_average_single_droplet_speed','average_number_of_droplets']]\nX = X.to_numpy()\ny = y.to_numpy()\nX = (X-X.min())/(X.max()-X.min())\ny= (y-y.min())/(y.max()-y.min())\n\n\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=12)\n\nX_train = torch.from_numpy(X_train).type(torch.DoubleTensor)\ny_train = torch.from_numpy(y_train).type(torch.DoubleTensor)\nX_test = torch.from_numpy(X_test).type(torch.DoubleTensor)\ny_test = torch.from_numpy(y_test).type(torch.DoubleTensor)\ntorch.set_printoptions(precision=10)\n\nstart_time=time.time()\nmynet = torch.nn.Sequential(\n    torch.nn.Linear(6,5),\n    torch.nn.ReLU(),\n    torch.nn.Linear(5,4)\n    )\n\noptimiser = torch.optim.Adam(mynet.parameters(),lr=0.1)\n# loss_func = torch.nn.MSELoss(reduction='mean')\nloss_func = torch.nn.MSELoss(reduction='none')\n\nlosses=[]\n# for t in range(300):\n#     out = mynet(X_train)\n#     loss = loss_func(out, y_train)\n#     optimiser.zero_grad()\n#     loss.backward()\n#     optimiser.step()\n#     if t%1 == 0:\n#         print(loss.item())\n#         losses.append(loss)\nfor t in range(300):\n    out = mynet(X_train)\n    loss = loss_func(out, y_train)\n    loss_mean = torch.mean(loss)\n    optimiser.zero_grad()\n    loss_mean.backward()\n    optimiser.step()\n    if t%1 == 0:\n        print(loss.detach())\n        losses.append(loss)\ntrain_loss=loss.detach().numpy()\ntrain_loss=np.sum(train_loss,axis=0)\n\nend_time=time.time()\nprint('training loss is', train_loss/X_train.shape[0])\ny_pred=mynet(X_test)\nprint('ypred',y_pred)\nprint('ytest',y_test)\nmse=sum((y_pred-y_test)**2)/y_test.shape[0]\nperc_error= sum((y_pred-y_test)/y_test)/y_test.shape[0]\nprint(\"MSE\",mse)\nprint('perc_error',perc_error)\n\nprint('total_time',end_time- start_time)","sub_path":"NN_compare_2.py","file_name":"NN_compare_2.py","file_ext":"py","file_size_in_byte":2623,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"350197148","text":"import os\nimport base64\n\nfrom passlib.hash import pbkdf2_sha256\nfrom flask import Flask, render_template, request, redirect, url_for, session\nfrom model import Donor, Donation \n\napp = Flask(__name__)\n#app.secret_key = b'Fb\\xf6U\\xce\\xcf\\x9eq\\xfc\\xcc\\x84\\xba\\x91B\\xf2\\xb0\\x17\\x07\\xdc\\x99)NV('\napp.secret_key = os.environ.get('SECRET_KEY').encode()\n\n@app.route('/')\ndef home():\n    return redirect(url_for('all'))\n\n@app.route('/donations/')\ndef all():\n    donations = Donation.select()\n    return render_template('donations.jinja2', donations=donations)\n\n@app.route('/create', methods=['GET', 'POST'])\ndef create():\n\n    if request.method == \"POST\":\n        if request.form['name'] not in [donor.name for donor in Donor.select()]:\n            try:\n                new_donor = Donor(name=request.form['name'])\n                new_donor.save()\n                Donation(donor=new_donor, value=request.form['amount']).save()\n                return redirect(url_for('all'))\n            except Exception as ex:\n                print(\"Whoops! Something went wrong!\")\n                print(ex)\n                return render_template('create.jinja2')\n        else:\n            try:\n                donor_obj = Donor.get(Donor.name==(request.form['name']))\n                donation = int(request.form['amount'])\n                new_Donation = Donation(donor=donor_obj, value=donation)\n                new_Donation.save()\n                return redirect(url_for('all'))\n            except Exception as ex:\n                print(\"Whoops! Something went wrong!\")\n                print(ex)\n                return render_template('create.jinja2')\n\n    else:\n        return render_template('create.jinja2')\n\n\nif __name__ == \"__main__\":\n    port = int(os.environ.get(\"PORT\", 6738))\n    app.run(host='0.0.0.0', port=port)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"370183644","text":"###Codigo creado por Luis Gerardo Sandoval Rodriguez\r\n###Antes de comenzar se prueba que esten los modulos necesarios instalados, de lo contrario el programa salira de ejecución \r\ntry:\r\n\timport ntplib\r\nexcept ImportError:\r\n\tprint(\"Modulo 'ntplib' no instalado\")\r\n\texit()\r\ntry:\r\n\timport pytz\r\nexcept ImportError:\r\n\tprint(\"Modulo 'pytz' no instalado\")\r\n\texit()\r\ntry:\r\n\timport pymysql\r\nexcept ImportError:\r\n\tprint(\"Modulo 'import pymysql' no instalado\")\r\n\texit()\r\nimport sys,os\r\nimport time\r\nimport datetime\r\nimport hashlib \r\nfrom random import uniform\r\n\r\n###Funcion que hace una consulta a un servidor NTP para despues ponerla sobre la maquina en la que se corra el script \r\ndef cambiarFechaHora():\r\n\tutc=pytz.utc\r\n\r\n\tprint(\"hora y fecha actual antes de pedir al NPT----> \"+str(datetime.datetime.now()))\r\n\r\n\t####Formato para la conversion a UTC\r\n\tfmt = '%Y-%m-%d %H:%M:%S'\r\n\tfmtt = '%Y-%m-%d %H:%M:%S.%f'\r\n\tmexico = pytz.timezone('America/Mexico_City')\r\n\r\n\t####Pide tiempo al servidor ntp\r\n\tclient=ntplib.NTPClient()\r\n\tprint(\"pidiendo hora a servidor --------------------> ntp.cais.rnp.br...\")\r\n\ttiempo_salida_peticion=datetime.datetime.now()\r\n\tresponse=client.request('ntp.cais.rnp.br')#('europe.pool.ntp.org')\r\n\ttiempo_llegada_peticion=datetime.datetime.now()\r\n\r\n\r\n\ttiempo_respuesta_peticion=(tiempo_llegada_peticion-tiempo_salida_peticion)/2\r\n\tprint(f\"tiempo de respuesta de peticion -------------> {tiempo_respuesta_peticion}\")\r\n\thora_servidor=time.localtime(response.tx_time)\r\n\t#hora_servidor=time.gmtime(response.tx_time)\r\n\tprint(f\"hora de servidor NTP ------------------------> {hora_servidor}\")\r\n\r\n\r\n\t####Cambia al formato para la conversion a UTC\r\n\tdate_time=str(hora_servidor[0])+\"-\"+str(hora_servidor[1])+\"-\"+str(hora_servidor[2])+\" \"+str(hora_servidor[3])+\":\"+str(hora_servidor[4])+\":\"+str(hora_servidor[5])\r\n\r\n\r\n\t####Cambia hora a UTC\r\n\tdt = datetime.datetime.strptime(date_time, fmt)\r\n\tam_dt = mexico.localize(dt)\r\n\tprint(am_dt)\r\n\t#hora_utc=am_dt.astimezone(utc).strftime(fmt)+\",00\"\r\n\thora_utc=am_dt.strftime(fmt)+\".00\"\r\n\r\n\tprint(f\"hora sin la suma el retraso {hora_utc}\")\r\n\r\n\t#hora_servidor=time.strftime(fmt,hora_servidor)+\".00\"\r\n\r\n\ttiempo_en_ejecucion=datetime.datetime.now()-tiempo_llegada_peticion\r\n\tprint(f\"tiempo de retraso de ejecucion --------------> {tiempo_en_ejecucion}\")\r\n\thora_ntp=(datetime.datetime.strptime(hora_utc, fmtt)+tiempo_respuesta_peticion+tiempo_en_ejecucion).strftime(fmtt)\r\n\r\n\t####Aplica la hora al sistema (year,month,dayOfWeek,day,hour,minute,second,millisecond)\r\n\tif sys.platform=='linux':\r\n\t\tos.system(f\"sudo date --set \\\"{hora_ntp}\\\"\")\t\r\n\t\tprint(\"\")\r\n\r\n\r\n\r\n\r\n###Funcion que devuelve la temperatura obtenida de un sensor, asi como la fecha y hora en el momento que fue tomada\r\n##Nota->Los datos en este caso son generados aleatoriamente (valor de temperatura entre 16 a 37) porque no hay \r\n##manera de obtenerlo de un sensor real\r\ndef leerTemperatura():\r\n\tarray = {}\r\n\tprint(\"Obteniendo temperatura......\")\r\n\tarray['fecha'] = datetime.datetime.now().strftime(\"%y/%m/%d\")\r\n\tarray['hora'] = datetime.datetime.now().strftime(\"%H:%M:%S\")\r\n\tarray['valor'] = uniform(16, 37) \r\n\treturn array\r\n\r\n\r\n\r\n\r\n###Funcion que retorna una firma electronica que se genera aplicando MD5 a el nombre del sensor\r\ndef generarFirma(sensor): \r\n\tresult = hashlib.md5(sensor.encode()) \r\n\tprint(f\"firma del sensor generada -------------------> {result.hexdigest()}\") \r\n\treturn result.hexdigest()\r\n\r\n\r\n###Funcion que se conecta a una base de datos para guardar los registros recibidos \r\ndef guardarDatos(id, firma, latitud, longitud, fecha, hora, variable, valor):\r\n\tDB_HOST = '127.0.0.1' \r\n\tDB_USER = 'root' \r\n\tDB_PASS = '' \r\n\tDB_NAME = 'IoT_datos' \r\n\r\n\ttry:\r\n\r\n\t\t# Abre conexion con la base de datos\r\n\t\tconn = pymysql.connect(DB_HOST,DB_USER,DB_PASS,DB_NAME)\r\n\r\n\t\t# prepare a cursor object using cursor() method\r\n\t\tcursor = conn.cursor()\r\n\r\n\t\tquery = f\"Insert into clima values('{id}', '{firma}',{latitud}, {longitud}, '{fecha}','{hora}', '{variable}', '{valor}')\"\r\n\r\n\t\t# ejecuta el SQL query usando el metodo execute().\r\n\t\ttry:\r\n\t\t   cursor.execute(query)\r\n\t\t   conn.commit()\r\n\t\t   print(\"Datos guardados en la base de datos con exito\") \r\n\t\texcept:\r\n\t\t   # Rollback en bd\r\n\t\t   conn.rollback()\r\n\t\t   print(\"Ocurrio un error al intertar guardar los datos en la base de datos\")\r\n\r\n\r\n\t\t# desconecta del servidor\r\n\t\tcursor.close()\r\n\t\tconn.close()\r\n\texcept (pymysql.err.OperationalError, pymysql.err.InternalError) as e:\r\n\t\tos.system('clear')\r\n\t\tprint(f\"Ocurrio un error al intentar conectar a al base de datos --> {e}\")\r\n\t\texit()\r\n\r\n\r\n\r\n\r\n\r\n\r\n###Nota-> Los datos son fijos porque solo se esta simulando un sensor, que es el de temperatura, asi como la latitud y longitud de \r\n##donde se supone que esta localizado este sensor\r\n\r\nsensor = 'temperatura_01'\r\nlatitud = 19.721803\r\nlongitud = -101.185790\r\ncambiarFechaHora()\r\n\r\n###bucle que esta \"actualizando\" los valores del sensor de temperatura cada 60 segundos\r\nwhile(True):\r\n\tdatosSensor = leerTemperatura()\r\n\tfirma = generarFirma(sensor)\r\n\tguardarDatos(sensor, firma, latitud, longitud, datosSensor['fecha'], datosSensor['hora'], '2', datosSensor['valor'])\r\n\tprint(\"---------------------------------------------------------------------------------------------------------------\")\r\n\tprint(\"\\n\\nEn 60 segundos se actualizara la informacion\")\r\n\ttime.sleep(60)\r\n\tprint(\"\\n\\n---------------------------------------------------------------------------------------------------------------\")\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"IoT_almacenamientoLocalSensorTemperatura.py","file_name":"IoT_almacenamientoLocalSensorTemperatura.py","file_ext":"py","file_size_in_byte":5496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"193986461","text":"from modules import __common__\nfrom datetime import datetime\nfrom client import client\nimport discord\nimport time\nimport key\nimport log\nimport os\n\n\nclient.basic_help(title=\"exit\", desc=\"Cleans up and shuts down the bot.\")\n\ndetailed_help = {\n\t\"Usage\": f\"{client.default_prefix}exit [pid]\",\n\t\"Arguments\": \"`pid` - (Optional) Process ID identifying which bot to exit out of\",\n\t\"Description\": \"This command completely exits out of the bot, and performs any registered cleanup procedures. A user ID check is performed against a builtin list of users allowed to run this command.\",\n\t# NO Aliases field, this will be added automatically!\n}\nclient.long_help(cmd=\"exit\", mapping=detailed_help)\n\n\n@client.command(trigger=\"kill\", aliases=[\"exit\"])\nasync def command(command: str, message: discord.Message):\n\tif not __common__.check_permission(message.author):\n\t\tawait message.add_reaction(\"❌\")\n\t\tif message.author.id == key.shutdown_easter_egg_user:\n\t\t\tawait message.channel.send(\"*hehehe*\\n\\nCan't fool me! >:3\")\n\t\treturn\n\telse:\n\t\tparts = command.split(\" \")\n\t\ttry:\n\t\t\ttarget_pid = int(parts[2])\n\t\texcept IndexError:\n\t\t\ttarget_pid = os.getpid()\n\t\texcept ValueError:\n\t\t\tawait message.channel.send(\"Invalid integer of PID to kill\")\n\t\t\treturn\n\t\tif target_pid == os.getpid():\n\t\t\tawait message.channel.send(\"Shutting down bot...\")\n\t\t\tawait message.channel.send(f\"Uptime: {time.perf_counter() - client.first_execution:.3f} seconds ({(time.perf_counter() - client.first_execution)/86400:.3f} days)\")\n\t\t\tlog.info(f\"Bot shutdown initiated at {datetime.utcnow().__str__()} by {message.author.name}#{message.author.discriminator}\")\n\t\t\tawait client.on_shutdown()\n\treturn\n\n\n@client.command(trigger=\"_kill\", aliases=[\"_exit\"])\nasync def emergency_suicide(command: str, message: discord.Message):\n\tif not __common__.check_permission(message.author):\n\t\tawait message.add_reaction(\"❌\")\n\t\treturn\n\telse:\n\t\tos._exit(2)","sub_path":"modules/exit.py","file_name":"exit.py","file_ext":"py","file_size_in_byte":1891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"132431780","text":"import math\n\ndef calcula_distancia_do_projetil (v,y0,teta):\n  \n    p1=(v**2/(2*9.8))\n    p2=(1+math.sqrt(1+(2*9.8*y0)/((v**2)*(math.sin(teta)**2))))\n    p3=(math.sin(2*teta))\n    d=p1*p2*p3\n    return (d)\n   \n    \n\n","sub_path":"backup/user_026/ch14_2020_03_02_22_57_08_309371.py","file_name":"ch14_2020_03_02_22_57_08_309371.py","file_ext":"py","file_size_in_byte":215,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"649582757","text":"\"\"\"Quality-time server.\"\"\"\n\nimport os\n\nDEBUG = os.environ.get(\"DEBUG\", \"\").lower() == \"true\"\n\nif not DEBUG:\n    from gevent import monkey  # pylint: disable=import-error\n    monkey.patch_all()\n\n# pylint: disable=wrong-import-order,wrong-import-position\n\nimport bottle\nimport logging\n\nfrom initialization import init_bottle, init_database\n\n\ndef serve() -> None:  # pragma: nocover\n    \"\"\"Connect to the database and start the application server.\"\"\"\n    logging.getLogger().setLevel(logging.INFO)\n    database = init_database()\n    init_bottle(database)\n    server_port = os.environ.get(\"SERVER_PORT\", \"5001\")\n    bottle.run(  # nosec\n        server=\"wsgiref\" if DEBUG else \"gevent\", host=\"0.0.0.0\", port=server_port, reloader=not DEBUG,\n        log=None if DEBUG else logging.getLogger())\n\n\nif __name__ == \"__main__\":\n    serve()\n","sub_path":"components/server/src/quality_time_server.py","file_name":"quality_time_server.py","file_ext":"py","file_size_in_byte":829,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"176266879","text":"\"\"\"\r\nThien Pham\r\nAssignment#4 \r\nCOSC 1306\r\n\"\"\"\r\nimport math\r\ndef getNumber():\r\n    number = float(input(\"Enter a number to find the square root:\"))\r\n    while number < 0:\r\n        print(\"The number\",number,\"is an invalid negaive number\")\r\n        print(\"Please enter a positive number\")\r\n        number = float(input(\"Please enter a number:\"))\r\n    return number\r\ndef newton(num,steps):\r\n    guess = 2\r\n    count = 0\r\n    while count < steps:\r\n        guess = (guess + num/guess)/2\r\n        count += 1\r\n    return guess\r\ndef work(value):\r\n   \r\n    print(\"Steps = Value\")\r\n    i = 1\r\n    while i <= 16:\r\n        print(i,\"=\",newton(value,i))\r\n        i *=2\r\n    print(\"The square root is of\",value,\"is\",math.sqrt(value))\r\n\r\n        \r\nvalue = 2\r\n\r\nwhile value !=0:\r\n    print(\"=\"*80)\r\n    print(\"Enter 0 to exit or\")\r\n    value = getNumber()\r\n    if value!= 0:\r\n        work(value)\r\n\r\nprint(\"Thank you good bye\")","sub_path":"COSC_1306/Assignments/Assignment#4.py","file_name":"Assignment#4.py","file_ext":"py","file_size_in_byte":909,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"652724078","text":"import discord\nfrom discord.ext.commands import Bot\nimport bin.settings as settings\nfrom discord.ext import commands\nfrom discord.ext.commands import has_permissions, MissingPermissions, CommandNotFound\nfrom bin.handler import Handle\nimport bin.secret as token\n\ninitial_extensions = ['cogs.management']\n\nBOT_PREFIX = (\"TLE_\")\nclient = Bot(command_prefix=BOT_PREFIX, case_insensitive=True)\nclient.case_insensitive = True\n\nif __name__ == '__main__':\n    for extension in initial_extensions:\n        client.load_extension(extension)\n\n@client.event\nasync def on_ready():\n    await client.change_presence(activity=discord.Game(name=\"Bringing communities together\", type=1))\n    print('Logged in as')\n    print(client.user.name)\n    print(client.user.id)\n    print('------')\n\n\n@client.event\nasync def on_message(message):\n    LIST_CHANNELS = Handle.get_list_channels()\n    if message.channel.id in LIST_CHANNELS and message.author.id != client.user.id:\n        await Handle.process_message(message, client)\n    await client.process_commands(message)\n\n\n@client.event\nasync def on_message_delete(message):\n    LIST_CHANNELS = Handle.get_list_channels()\n    if message.channel.id in LIST_CHANNELS and message.author.id == client.user.id:\n        await Handle.process_deletion(message, client)\n\nclient.run(token.TOKEN)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"201019152","text":"from utils import tf_layer\nimport tensorflow as tf\nfrom tensorflow.contrib import slim\n\ndef get_cfg_attr(CFG, name, default_value):\n    r = CFG.get(name, default_value)\n    print('{}:{}'.format(name, r))\n    return r\n\ndef get_norm_func(network_cfg):\n    norm_func = network_cfg.get('norm_func',\"None\")\n    if norm_func == 'LayerNorm':\n        print(\"use layer normalization\")\n        return tf_layer.layer_norm\n    elif norm_func == 'BatchNorm':\n        print(\"use batch normalization\")\n        return slim.batch_norm\n    elif norm_func == 'GroupNorm':\n        print(\"use group normalization\")\n        return tf_layer.group_norm\n    elif norm_func == 'InstanceNorm':\n        print(\"use instance normalization\")\n        return tf_layer.instance_norm\n    elif norm_func == 'SwitchNorm':\n        print(\"use switch normalization\")\n        return tf_layer.switch_norm\n    else:\n        print(\"not use normalization\")\n        return tf_layer.none_layer\n\ndef get_conv_func(network_cfg):\n    conv_func = network_cfg.get('conv_type',\"None\")\n    if conv_func == 'PWS':\n        print(\"use PWS conv\")\n        return tf_layer.pws_conv\n    elif conv_func == 'WN':\n        print(\"use WN conv\")\n        return tf_layer.wn_conv\n    elif conv_func == 'WS':\n        print(\"use WS conv\")\n        return tf_layer.ws_conv\n    else:\n        print(\"use norm conv\")\n        return slim.conv2d","sub_path":"detection_for_voc/python/utils/cfg_utils.py","file_name":"cfg_utils.py","file_ext":"py","file_size_in_byte":1367,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"597791618","text":"#!/usr/bin/python3\n\"\"\" This handles the storage for SQL \"\"\"\nimport sqlalchemy\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import Session, sessionmaker, scoped_session\nfrom sqlalchemy import Column, String, ForeignKey, DateTime\nfrom os import environ\nfrom os import getenv\nfrom models.state import State\nfrom models.city import City\nfrom models.base_model import BaseModel\nfrom models.user import User\nfrom models.place import Place\nfrom models.amenity import Amenity\nfrom models.review import Review\n\nclasses = {'State': State, 'BaseModel': BaseModel, 'City': City}\n\n\nclass DBStorage:\n    \"\"\" Database Storage\n\n        cl_attr:\n            __engine\n            __session\n    \"\"\"\n    __engine = None\n    __session = None\n\n    def __init__(self):\n        \"\"\" initiates engine according\n            to environ varaibles\n        \"\"\"\n        uname = environ['HBNB_MYSQL_USER']\n        pword = environ['HBNB_MYSQL_PWD']\n        dbname = environ['HBNB_MYSQL_DB']\n        host = environ['HBNB_MYSQL_HOST']\n\n        self.__engine = create_engine('mysql+mysqldb://{}:{}@{}/{}'.format(\n            uname, pword, host, dbname), pool_pre_ping=True)\n\n        if getenv('HBNB_ENV') == 'test':\n            # drop all tables\n            Base.metadata.drop_all(bind=self.__engine)\n\n    def all(self, cls=None):\n        \"\"\" query on SQL database\n            calling all objects\n        \"\"\"\n        # Select * from cls passed into all\n        # if cls=None query all objects\n        # return dictionary like FileStorage\n        obj = {}\n        if cls is not None:\n            for u in self.__session.query(classes[cls]).all():\n                obj[classes[cls].__name__ + \".\" + classes[cls].id] = u\n            return obj\n        else:\n            for a in classes:\n                find = self.__session.query(classes[a]).all()\n                for u in find:\n                    obj[u.__class__.__name__ + \".\" + u.id] = u\n            return obj\n\n    def new(self, obj):\n        \"\"\" add object to current session\n        \"\"\"\n        self.__session.add(obj)\n\n    def save(self):\n        \"\"\" SQL save and commmit session\n        \"\"\"\n        self.__session.commit()\n\n    def delete(self, obj=None):\n        \"\"\" Deletes an object from\n            the current session\n        \"\"\"\n        # only delete if not None\n        if obj:\n            self.__session().drop(obj)\n\n    def reload(self):\n        \"\"\" create all tables in the db\n        \"\"\"\n        from models.base_model import Base\n        Base.metadata.create_all(self.__engine)\n        sesh = sessionmaker(bind=self.__engine, expire_on_commit=False)\n        self.__session = scoped_session(sesh)\n\n    def close(self):\n        \"\"\"calls close method\"\"\"\n        self.__session.close()\n","sub_path":"models/engine/db_storage.py","file_name":"db_storage.py","file_ext":"py","file_size_in_byte":2726,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"516961116","text":"#   \"Doors\" a game by Anrew Skelly & Fionn Ó Muirí\r\n#   Started on the 15th of March 2018\r\n\r\n\r\n#import required modules\r\nimport pygame, time, random\r\n\r\n#set some basic variables\r\npygame.init()\r\n\r\npygame.font.init()\r\n\r\ndisplay_width = 800\r\ndisplay_height = 600\r\n\r\ngameDisplay = pygame.display.set_mode((display_width,display_height))\r\npygame.display.set_caption('Doors')\r\nclock = pygame.time.Clock()\r\n\r\n#colours\r\nwhite = ((255,255,255))\r\nblack = ((0,0,0))\r\ngrey = ((150,150,150))\r\n\r\n#Images\r\ndoorOpenImg = pygame.image.load('img\\doorOpen.png')\r\ndoorClosedImg = pygame.image.load('img\\doorClosed.png')\r\npausedImg = pygame.image.load('img\\paused.png')\r\nmenuImg = pygame.image.load('img\\menu.png')\r\nlogoImg = pygame.image.load('img\\logo.png')\r\n\r\nmenuImgScale = pygame.transform.scale(menuImg,(int((display_height-100)/1.5),display_height-100))\r\nbgimgWidth, bgimgHeight = menuImgScale.get_rect().size\r\n\r\n#class\r\nclass achievement:\r\n    name = \"\"\r\n    score = 0\r\n    achieved = False\r\n    img = doorOpenImg\r\n    x= 0\r\n    y= 0\r\n    width = 50\r\n    height = 75\r\n\r\n#music\r\npygame.mixer.music.load(\"music/Düsseldorf Waltz.mp3\")\r\npygame.mixer.music.play(-1)\r\n\r\n# other required variables\r\nscore = 0\r\nhighScoreDoc = open(\"highScore.txt\", \"r\")\r\nhighScore = int(highScoreDoc.read())\r\nhighScoreDoc.close\r\n\r\nsound = True\r\n\r\ndestination = \"\"\r\n\r\noptionButtons = []\r\nmenuButtons = []\r\ngameOverButtons = []\r\nstatsButtons = []\r\n\r\nachievementsDoc = open(\"achievements.txt\", \"r\")\r\n#achievementsStr = achievementsDoc.read().split(\"\\n\")\r\nachievementsStr = [\"False\",\"False\",\"False\",\"False\"]\r\nachievementsDoc.close\r\n\r\nstatNames = [\"Total Doors: \",\"Avg Score: \"]\r\nstatsDoc = open(\"stats.txt\", \"r\")\r\nstats = statsDoc.read().split(\"\\n\")\r\nstatsDoc.close\r\nstats[0] = int(stats[0])\r\nstats[1] = float(stats[1])\r\n\r\nachNames = [\"Baby Steps\", \"Ha! Pitiful\", \"Alright. You've proved your point\", \"Show off\"]\r\nachScores = [1, 5, 10, 20]\r\n\r\nachList = []\r\nachGet = \"\"\r\nreached = False\r\n\r\ncounter = 0\r\n\r\n#Create achievements\r\nfor i in range(0,len(achNames)):\r\n    newAch = achievement()\r\n    newAch.name = achNames[i]\r\n    newAch.score = achScores[i]\r\n    newAch.img = pygame.image.load('img/achievements/%s.png' % (newAch.name))\r\n    \r\n    if achievementsStr[i] == \"True\":\r\n        newAch.achieved = True\r\n    else:\r\n        newAch.achieved = False\r\n\r\n    achList.append(newAch)\r\n\r\n#template for each door\r\nclass door:\r\n    size = 0 \r\n    x = 0\r\n    y = 0\r\n    speed = 0\r\n    doorOpen = False\r\n\r\n#template for buttons\r\nclass button:\r\n    imgA = pygame.image.load('img\\doorOpen.png')\r\n    imgB = pygame.image.load('img\\doorOpen.png')\r\n    width = 0\r\n    height = 0\r\n    x = 0\r\n    y = 0\r\n\r\nfor i in range(0,4):\r\n    newButton = button()\r\n    newButton.imgA = pygame.transform.scale(pygame.image.load(\"img/buttons/menu/btn%sa.png\" % (i+1)),(int(bgimgWidth*0.55),int(((bgimgWidth*0.5)/5)*3)))\r\n    newButton.imgB = pygame.transform.scale(pygame.image.load(\"img/buttons/menu/btn%sb.png\" % (i+1)),(int(bgimgWidth*0.55),int(((bgimgWidth*0.5)/5)*3)))\r\n    newButton.width, newButton.height = newButton.imgA.get_rect().size\r\n    newButton.x = int((display_width/2)-(newButton.width/5))\r\n    newButton.y = int((display_height/3.25)+(i*100))\r\n\r\n    menuButtons.append(newButton)\r\n\r\nfor i in range(0,3):\r\n    newButton = button()\r\n    newButton.imgA = pygame.transform.scale(pygame.image.load(\"img/buttons/options/btn%sa.png\" % (i+1)),(int(bgimgWidth*0.55),int(((bgimgWidth*0.5)/5)*3)))\r\n    newButton.imgB = pygame.transform.scale(pygame.image.load(\"img/buttons/options/btn%sb.png\" % (i+1)),(int(bgimgWidth*0.55),int(((bgimgWidth*0.5)/5)*3)))\r\n    newButton.width, newButton.height = newButton.imgA.get_rect().size\r\n    newButton.x = int((display_width/2)-(newButton.width/5))\r\n    newButton.y = int((display_height/3.25)+(i*100))\r\n\r\n    optionButtons.append(newButton)\r\n\r\nfor i in range(0,2):\r\n    newButton = button()\r\n    newButton.imgA = pygame.transform.scale(pygame.image.load(\"img/buttons/gameOver/btn%sa.png\" % (i+1)),(int(bgimgWidth*0.55),int(((bgimgWidth*0.5)/5)*3)))\r\n    newButton.imgB = pygame.transform.scale(pygame.image.load(\"img/buttons/gameOver/btn%sb.png\" % (i+1)),(int(bgimgWidth*0.55),int(((bgimgWidth*0.5)/5)*3)))\r\n    newButton.width, newButton.height = newButton.imgA.get_rect().size\r\n    newButton.x = int((display_width/2)-(newButton.width/5))\r\n    newButton.y = int((display_height/1.6)+(i*100))\r\n\r\n    gameOverButtons.append(newButton)\r\n\r\nfor i in range(2,3):\r\n    newButton = button()\r\n    newButton.imgA = pygame.transform.scale(pygame.image.load(\"img/buttons/options/btn%sa.png\" % (i+1)),(int(bgimgWidth*0.55),int(((bgimgWidth*0.5)/5)*3)))\r\n    newButton.imgB = pygame.transform.scale(pygame.image.load(\"img/buttons/options/btn%sb.png\" % (i+1)),(int(bgimgWidth*0.55),int(((bgimgWidth*0.5)/5)*3)))\r\n    newButton.width, newButton.height = newButton.imgA.get_rect().size\r\n    newButton.x = int((display_width/2)-(newButton.width/5))\r\n    newButton.y = int(display_height/1.6)\r\n\r\n    statsButtons.append(newButton)\r\n\r\ndef achievements():\r\n    global score\r\n    global achGet\r\n    \r\n    for i in range(0,len(achList)):\r\n        if score == achList[i].score:\r\n            if not achList[i].achieved:\r\n                achList[i].achieved = True\r\n\r\n                achGet = achList[i]\r\n\r\n                achievementsDoc = open(\"achievements.txt\", \"w\")\r\n                achievementsDoc.write(str(achList[0].achieved)+\"\\n\"+str(achList[1].achieved)+\"\\n\"+str(achList[2].achieved)+\"\\n\"+str(achList[3].achieved))\r\n                achievementsDoc.close\r\n\r\ndef achAnimation():\r\n    global achGet\r\n    global reached\r\n    global counter\r\n    \r\n    if achGet != \"\":\r\n        if achGet.x == 0:\r\n            reached = False\r\n            achGet.x = display_width\r\n            achGet.y = display_height-achGet.height-10\r\n        else:\r\n            if reached and counter >20:\r\n                achGet.x+=1\r\n                if achGet.x>display_width:\r\n                    achGet = \"\"\r\n                    counter = 0\r\n                    \r\n            elif achGet.x > display_width-achGet.width-10 and reached == False:\r\n                achGet.x-=1\r\n\r\n            else:\r\n                reached = True\r\n                counter+=1\r\n                \r\n            if achGet != \"\":     \r\n                gameDisplay.blit(achGet.img,(achGet.x,achGet.y))\r\n                pygame.display.update()\r\n        \r\ndef menuFade(mode,menu):\r\n    #scales background image\r\n    menuImgScale = pygame.transform.scale(menuImg,(int((display_height-100)/1.5),display_height-100))\r\n    bgimgWidth, bgimgHeight = menuImgScale.get_rect().size\r\n\r\n    y = int(display_height/3.4)\r\n    text = []\r\n\r\n    buttons = []\r\n    if menu == \"menu\":\r\n        buttons = menuButtons\r\n    elif menu == \"options\":\r\n        buttons = optionButtons\r\n    elif menu == \"gameOver\":\r\n        buttons = gameOverButtons\r\n    elif menu == \"stats\":\r\n        buttons = statsButtons\r\n\r\n    myfont = pygame.font.Font('img/fonts/VINERITC.ttf', 40)\r\n    textsurface = myfont.render('Highscore: %s' % (highScore), False, white)\r\n    \r\n    myfontA = pygame.font.Font('img/fonts/VINERITC.ttf', int(display_width/20))\r\n    textsurfaceA = myfontA.render('''Game Over''', False, white)\r\n    textASize, x = textsurfaceA.get_rect().size\r\n    textsurfaceB = myfontA.render('''Score: %s''' % (score), False, white)\r\n    textBSize, x = textsurfaceB.get_rect().size\r\n\r\n    myfont = pygame.font.Font('img/fonts/VINERITC.ttf', int(display_width/35))\r\n    for i in range(0,len(stats)):\r\n        statsText = myfont.render('%s%s' % (statNames[i],int(stats[i])), False, white)\r\n        text.append(statsText)\r\n    \r\n    s = pygame.Surface((display_width,display_height)) \r\n\r\n    if mode == \"in\":\r\n        for i in range(255,0,-3):\r\n            gameDisplay.fill(black)\r\n            y = int(display_height/3.4)\r\n            #draws background image\r\n            gameDisplay.blit(menuImgScale,(int((display_width/2)-(bgimgWidth/2)),int((display_height/2)-(bgimgHeight/2))))\r\n            \r\n            for j in range(0,len(buttons)):\r\n                #draws button\r\n                gameDisplay.blit(buttons[j].imgA,(buttons[j].x, buttons[j].y,))\r\n\r\n            if menu == \"options\" or menu == \"menu\":\r\n                gameDisplay.blit(textsurface,(0,0))\r\n\r\n            elif menu == \"gameOver\":\r\n                gameDisplay.blit(textsurfaceA,((display_width/2)-(textASize/2)+50,200))\r\n                gameDisplay.blit(textsurfaceB,((display_width/2)-(textBSize/2)+50,260))\r\n\r\n            elif menu == \"stats\":\r\n                for j in range(0,len(text)):\r\n                    gameDisplay.blit(text[j],(display_height/1.65,y))\r\n                    y+=int(display_height/14)\r\n\r\n            s.set_alpha(i)                \r\n            s.fill((0,0,0))       \r\n            gameDisplay.blit(s, (0,0))\r\n            \r\n            pygame.display.update()\r\n            \r\n    elif mode == \"out\":\r\n        for i in range(0,255,3):\r\n            gameDisplay.fill(black)\r\n            y = int(display_height/3.4)\r\n            #draws background image\r\n            gameDisplay.blit(menuImgScale,(int((display_width/2)-(bgimgWidth/2)),int((display_height/2)-(bgimgHeight/2))))\r\n            \r\n            for j in range(0,len(buttons)):\r\n                #draws button\r\n                gameDisplay.blit(buttons[j].imgA,(buttons[j].x, buttons[j].y,))\r\n                \r\n            if menu == \"options\" or menu == \"menu\":\r\n                gameDisplay.blit(textsurface,(0,0))\r\n\r\n            elif menu == \"gameOver\":\r\n                gameDisplay.blit(textsurfaceA,((display_width/2)-(textASize/2)+50,200))\r\n                gameDisplay.blit(textsurfaceB,((display_width/2)-(textBSize/2)+50,260))\r\n\r\n            elif menu == \"stats\":\r\n                for j in range(0,len(text)):\r\n                    gameDisplay.blit(text[j],(display_height/1.65,y))\r\n                    y+=int(display_height/14)\r\n            \r\n            s.set_alpha(i)                \r\n            s.fill((0,0,0))       \r\n            gameDisplay.blit(s, (0,0))\r\n            \r\n            pygame.display.update()\r\n\r\n# game function\r\ndef game():\r\n    global score\r\n    global highScore\r\n    global destination\r\n\r\n    paused = False\r\n\r\n    doors = []\r\n\r\n    #prints score in top ledt\r\n    myfont = pygame.font.Font('img/fonts/VINERITC.ttf', 30)\r\n    textsurface = myfont.render(\"Score: %s        Highscore: %s\" % (0, highScore), False, white)\r\n    \r\n    mouseDownPast = False\r\n    mouseDownNow = False\r\n\r\n    score = 0\r\n    \r\n    # create first door\r\n    newDoor = door()\r\n\r\n    newDoor.size = random.randrange(40, 70)\r\n    newDoor.x = random.randrange(0, display_width-newDoor.size)\r\n    newDoor.y = random.randrange(display_height, display_height*2)\r\n    newDoor.speed = (newDoor.size-30)/5\r\n        \r\n    doors.append(newDoor)\r\n\r\n    # the game loop\r\n    while True:\r\n\r\n        #gets mouse position\r\n        mouseX, mouseY = pygame.mouse.get_pos()\r\n \r\n        # check for events\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                pygame.quit()\r\n                quit()\r\n\r\n            if event.type == pygame.MOUSEBUTTONDOWN and event.button == 1:\r\n                mouseDownNow = True\r\n\r\n            if event.type == pygame.MOUSEBUTTONUP and event.button == 1:\r\n                mouseDownNow = False\r\n\r\n            if event.type == pygame.KEYDOWN:\r\n                if event.key == pygame.K_SPACE:\r\n                    #pauses/unpauses the game when space is pressed\r\n                    if paused:\r\n                        pygame.time.delay(500)\r\n                        paused = False\r\n                    else:\r\n                        paused = True\r\n                        gameDisplay.fill(black)\r\n                        gameDisplay.blit(pausedImg,(display_width/2-100, display_height/2-150))\r\n                        pygame.display.update()\r\n        \r\n        if not paused:\r\n            # refresh the screen\r\n            gameDisplay.fill(black)\r\n            \r\n            # move and draw doors\r\n            for i in range(0,len(doors)):\r\n\r\n                \r\n                # scales door images to required size\r\n                OpenImg = pygame.transform.scale(doorOpenImg, (doors[i].size, int(doors[i].size*1.5)))\r\n                ClosedImg = pygame.transform.scale(doorClosedImg, (doors[i].size, int(doors[i].size*1.5)))\r\n\r\n                #chance of door opening before halfway\r\n                openchance = random.randrange(doors[i].speed*5,70)\r\n                if doors[i].y < display_height and openchance >= 69:\r\n                    if not doors[i].doorOpen:\r\n                        doors[i].doorOpen = True\r\n\r\n                # if door hasn't opened by certain point, open it\r\n                elif doors[i].y < display_height/3:\r\n                    if not doors[i].doorOpen:\r\n                        doors[i].doorOpen = True\r\n\r\n                # move door\r\n                doors[i].y -= doors[i].speed\r\n\r\n                #checks to see if the door was clicked\r\n                if mouseX >= doors[i].x and mouseX <= doors[i].x + doors[i].size:\r\n                    if mouseY >= doors[i].y and mouseY <= doors[i].y + doors[i].size*1.5:\r\n                        if mouseDownNow == True and mouseDownPast == False:\r\n                            if doors[i].doorOpen:\r\n                                #increases score\r\n                                score+=1\r\n\r\n                                stats[0]+=1\r\n\r\n                                statsDoc = open(\"stats.txt\", \"w\")\r\n                                statsDoc.write(str(stats[0])+\"\\n\"+str(stats[1]))\r\n                                statsDoc.close\r\n\r\n                                achievements()\r\n\r\n                                #checks for highscore\r\n                                if score > highScore:\r\n                                    highScore = score\r\n                                    highScoreDoc = open(\"highScore.txt\", \"w\")\r\n                                    highScoreDoc.write(str(highScore))\r\n                                    highScoreDoc.close\r\n\r\n                                textsurface = myfont.render(\"Score: %s        Highscore: %s\" % (score, highScore), False, white)\r\n\r\n                                # resets the door\r\n                                doors[i].size = random.randrange(40, 70)\r\n                                doors[i].y = display_height\r\n                                doors[i].x = random.randrange(0, display_width-newDoor.size)\r\n                                doors[i].speed = (doors[i].size-35)/5\r\n\r\n                                doors[i].doorOpen = False\r\n\r\n                                #adds a new door every time the score if a multiple of 5 \r\n                                if score>0 and score<=25 and score%5 == 0:\r\n                                    newDoor = door()\r\n\r\n                                    newDoor.size = random.randrange(40, 70)\r\n                                    newDoor.x = random.randrange(0, display_width-newDoor.size)\r\n                                    newDoor.y = random.randrange(display_height, display_height*2)\r\n                                    newDoor.speed = (newDoor.size-30)/5\r\n                                \r\n                                    doors.append(newDoor)\r\n                            \r\n\r\n                #draws the door\r\n                if doors[i].doorOpen:\r\n                    pygame.draw.rect(gameDisplay, grey, pygame.Rect(doors[i].x,doors[i].y,doors[i].size, doors[i].size*1.5))\r\n                    gameDisplay.blit(OpenImg, (doors[i].x,doors[i].y))\r\n                else:\r\n                    pygame.draw.rect(gameDisplay, black, pygame.Rect(doors[i].x,doors[i].y,doors[i].size, doors[i].size*1.5))\r\n                    gameDisplay.blit(ClosedImg, (doors[i].x,doors[i].y))\r\n\r\n                #if the door reaches the top, end game\r\n                if doors[i].y < 0-(doors[i].size*1.5):\r\n                    if stats[1]>0:\r\n                        stats[1] = (stats[1]+score)/2\r\n                    else:\r\n                        stats[1] = score\r\n\r\n                    statsDoc = open(\"stats.txt\", \"w\")\r\n                    statsDoc.write(str(stats[0])+\"\\n\"+str(stats[1]))\r\n                    statsDoc.close\r\n                    \r\n                    \r\n                    pygame.display.update()\r\n                    destination = \"gameOver\"\r\n                    menuFade(\"in\",\"gameOver\")\r\n                    return\r\n        \r\n            mouseDownPast = mouseDownNow\r\n            \r\n            gameDisplay.blit(textsurface,(0,0))\r\n\r\n        achAnimation()\r\n        \r\n        # update screen and set FPS\r\n        pygame.display.update()\r\n        clock.tick(60)\r\n\r\n# game over function\r\ndef gameOver():\r\n    global destination\r\n    global achList\r\n\r\n    buttons = gameOverButtons\r\n\r\n    mouseDownNow = False\r\n        \r\n    # writes gameover + score\r\n    myfont = pygame.font.Font('img/fonts/VINERITC.ttf', int(display_width/20))\r\n    textsurfaceA = myfont.render('''Game Over''', False, white)\r\n    textASize, x = textsurfaceA.get_rect().size\r\n    textsurfaceB = myfont.render('''Score: %s''' % (score), False, white)\r\n    textBSize, x = textsurfaceB.get_rect().size\r\n\r\n    #waits for space press for menu or quit\r\n    while True:\r\n        gameDisplay.fill(black)\r\n\r\n        mouseX, mouseY = pygame.mouse.get_pos()\r\n        \r\n        #draws background image\r\n        gameDisplay.blit(menuImgScale,(int((display_width/2)-(bgimgWidth/2)),int((display_height/2)-(bgimgHeight/2))))\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                pygame.quit()\r\n                quit()\r\n\r\n            if event.type == pygame.MOUSEBUTTONDOWN and event.button == 1:\r\n                mouseDownNow = True\r\n\r\n            if event.type == pygame.MOUSEBUTTONUP and event.button == 1:\r\n                mouseDownNow = False\r\n\r\n        #goes through buttons\r\n        for i in range(0,len(buttons)):\r\n            #checks if button was pressed\r\n            if mouseX > buttons[i].x and mouseX < buttons[i].x+buttons[i].width:\r\n                if mouseY > buttons[i].y and mouseY < buttons[i].y+buttons[i].height:\r\n                    #draws button\r\n                    gameDisplay.blit(buttons[i].imgB,(buttons[i].x, buttons[i].y,))\r\n                    if mouseDownNow:\r\n                        if i == 0:\r\n                            menuFade(\"out\", \"gameOver\")\r\n                            destination = \"game\"\r\n                            return\r\n                        if i == 1:\r\n                            menuFade(\"out\",\"gameOver\")\r\n                            menuFade(\"in\",\"menu\")\r\n                            destination = \"menu\"\r\n                            return\r\n                else:\r\n                    #draws button\r\n                    gameDisplay.blit(buttons[i].imgA,(buttons[i].x, buttons[i].y,))\r\n            else:\r\n                #draws button\r\n                gameDisplay.blit(buttons[i].imgA,(buttons[i].x, buttons[i].y,))\r\n            \r\n        gameDisplay.blit(textsurfaceA,((display_width/2)-(textASize/2)+50,200))\r\n        gameDisplay.blit(textsurfaceB,((display_width/2)-(textBSize/2)+50,260))\r\n        pygame.display.update()\r\n\r\n# menu function\r\ndef menu():\r\n    global menuButtons\r\n    global destination\r\n\r\n    buttons = menuButtons\r\n\r\n    mouseDownNow = False\r\n        \r\n    while True:\r\n        gameDisplay.fill(black)\r\n\r\n        mouseX, mouseY = pygame.mouse.get_pos()\r\n\r\n        # checks for clicks\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                pygame.quit()\r\n                quit()\r\n\r\n            if event.type == pygame.MOUSEBUTTONDOWN and event.button == 1:\r\n                mouseDownNow = True\r\n\r\n            if event.type == pygame.MOUSEBUTTONUP and event.button == 1:\r\n                mouseDownNow = False\r\n\r\n        #draws background image\r\n        gameDisplay.blit(menuImgScale,(int((display_width/2)-(bgimgWidth/2)),int((display_height/2)-(bgimgHeight/2))))\r\n\r\n        #goes through buttons\r\n        for i in range(0,len(buttons)):\r\n            #checks if button was pressed\r\n            if mouseX > buttons[i].x and mouseX < buttons[i].x+buttons[i].width:\r\n                if mouseY > buttons[i].y and mouseY < buttons[i].y+buttons[i].height:\r\n                    #draws button\r\n                    gameDisplay.blit(buttons[i].imgB,(buttons[i].x, buttons[i].y,))\r\n                    if mouseDownNow:\r\n                        if i == 0:\r\n                            menuFade(\"out\", \"menu\")\r\n                            destination = \"game\"\r\n                            return\r\n                        if i == 1:\r\n                            menuFade(\"out\",\"menu\")\r\n                            menuFade(\"in\",\"options\")\r\n                            destination = \"options\"\r\n                            return\r\n                        if i == 3:\r\n                            menuFade(\"out\",\"menu\")\r\n                            pygame.quit()\r\n                            quit()\r\n                else:\r\n                    #draws button\r\n                    gameDisplay.blit(buttons[i].imgA,(buttons[i].x, buttons[i].y,))\r\n            else:\r\n                #draws button\r\n                gameDisplay.blit(buttons[i].imgA,(buttons[i].x, buttons[i].y,))\r\n\r\n        # draws highscore in the top left\r\n        myfont = pygame.font.Font('img/fonts/VINERITC.ttf', 40)\r\n        textsurface = myfont.render('Highscore: %s' % (highScore), False, white)\r\n        gameDisplay.blit(textsurface,(0,0))\r\n        \r\n        pygame.display.update()\r\n        \r\ndef startUp():\r\n    global destination\r\n    #scales background image\r\n    menuImgScale = pygame.transform.scale(menuImg,(int((display_height-100)/1.5),display_height-100))\r\n    bgimgWidth, bgimgHeight = menuImgScale.get_rect().size\r\n\r\n    logoImgWidth, logoImgHeight = logoImg.get_rect().size\r\n\r\n    myfont = pygame.font.Font('img/fonts/VINERITC.ttf', 40)\r\n    textsurface = myfont.render('Highscore: %s' % (highScore), False, white)\r\n    \r\n    for i in range(255,0,-3):\r\n        gameDisplay.blit(logoImg,((display_width/2)-(logoImgWidth/2),(display_height/2)-(logoImgHeight/2)))\r\n        s = pygame.Surface((display_width,display_height)) \r\n        s.set_alpha(i)                \r\n        s.fill((0,0,0))       \r\n        gameDisplay.blit(s, (0,0))   \r\n        pygame.display.update()\r\n        \r\n    for i in range(0,255,3):\r\n        gameDisplay.blit(logoImg,((display_width/2)-(logoImgWidth/2),(display_height/2)-(logoImgHeight/2)))\r\n        s = pygame.Surface((display_width,display_height)) \r\n        s.set_alpha(i)                \r\n        s.fill((0,0,0))       \r\n        gameDisplay.blit(s, (0,0))   \r\n        pygame.display.update()\r\n\r\n    menuFade(\"in\",\"menu\")\r\n\r\ndef options():\r\n    global destination\r\n    global optionButtons\r\n    global sound\r\n\r\n    buttons = optionButtons\r\n\r\n    mouseDownNow = False\r\n\r\n    while True:\r\n\r\n        gameDisplay.fill(black)\r\n\r\n        mouseX, mouseY = pygame.mouse.get_pos()\r\n\r\n        # checks for clicks\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                pygame.quit()\r\n                quit()\r\n\r\n            if event.type == pygame.MOUSEBUTTONDOWN and event.button == 1:\r\n                mouseDownNow = True\r\n\r\n            if event.type == pygame.MOUSEBUTTONUP and event.button == 1:\r\n                mouseDownNow = False\r\n\r\n        #draws background image\r\n        gameDisplay.blit(menuImgScale,(int((display_width/2)-(bgimgWidth/2)),int((display_height/2)-(bgimgHeight/2))))\r\n\r\n        #goes through buttons\r\n        for i in range(0,len(buttons)):\r\n            #checks if button was pressed\r\n            if mouseX > buttons[i].x and mouseX < buttons[i].x+buttons[i].width:\r\n                if mouseY > buttons[i].y and mouseY < buttons[i].y+buttons[i].height:\r\n                    #draws button\r\n                    gameDisplay.blit(buttons[i].imgB,(buttons[i].x, buttons[i].y,))\r\n                    if mouseDownNow:\r\n                        if i == 0:\r\n                            if sound:\r\n                                sound = False\r\n                                pygame.mixer.music.stop()\r\n                                pygame.time.delay(100)\r\n                            else:\r\n                                sound = True\r\n                                pygame.mixer.music.load(\"music/Düsseldorf Waltz.mp3\")\r\n                                pygame.mixer.music.play(-1)\r\n                                pygame.time.delay(100)\r\n\r\n                        if i == 1:\r\n                            menuFade(\"out\",\"options\")\r\n                            menuFade(\"in\",\"stats\")\r\n                            destination = \"stats\"\r\n                            return\r\n                                \r\n                        if i == 2:\r\n                            menuFade(\"out\",\"options\")\r\n                            menuFade(\"in\",\"menu\")\r\n                            destination = \"menu\"\r\n                            return\r\n                else:\r\n                    #draws button\r\n                    gameDisplay.blit(buttons[i].imgA,(buttons[i].x, buttons[i].y,))\r\n            else:\r\n                #draws button\r\n                gameDisplay.blit(buttons[i].imgA,(buttons[i].x, buttons[i].y,))\r\n\r\n        # draws highscore in the top left\r\n        myfont = pygame.font.Font('img/fonts/VINERITC.ttf', 40)\r\n        textsurface = myfont.render('Highscore: %s' % (highScore), False, white)\r\n        gameDisplay.blit(textsurface,(0,0))\r\n        \r\n        pygame.display.update()\r\n\r\ndef statsPage():\r\n    global destination\r\n    global stats\r\n    mouseDownNow = False\r\n    text = []\r\n    y = int(display_height/3.4)\r\n\r\n    buttons = statsButtons\r\n\r\n    mouseDownNow = False\r\n        \r\n    myfont = pygame.font.Font('img/fonts/VINERITC.ttf', int(display_width/35))\r\n    for i in range(0,len(stats)):\r\n        textsurface = myfont.render('%s%s' % (statNames[i],int(stats[i])), False, white)\r\n        text.append(textsurface)\r\n\r\n    #waits for space press for menu or quit\r\n    while True:\r\n        y = int(display_height/3.4)\r\n        gameDisplay.fill(black)\r\n\r\n        mouseX, mouseY = pygame.mouse.get_pos()\r\n        \r\n        #draws background image\r\n        gameDisplay.blit(menuImgScale,(int((display_width/2)-(bgimgWidth/2)),int((display_height/2)-(bgimgHeight/2))))\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                pygame.quit()\r\n                quit()\r\n\r\n            if event.type == pygame.MOUSEBUTTONDOWN and event.button == 1:\r\n                mouseDownNow = True\r\n\r\n            if event.type == pygame.MOUSEBUTTONUP and event.button == 1:\r\n                mouseDownNow = False\r\n\r\n        #goes through buttons\r\n        for i in range(0,len(buttons)):\r\n            #checks if button was pressed\r\n            if mouseX > buttons[i].x and mouseX < buttons[i].x+buttons[i].width:\r\n                if mouseY > buttons[i].y and mouseY < buttons[i].y+buttons[i].height:\r\n                    #draws button\r\n                    gameDisplay.blit(buttons[i].imgB,(buttons[i].x, buttons[i].y,))\r\n                    if mouseDownNow:\r\n                        if i == 0:\r\n                            menuFade(\"out\",\"stats\")\r\n                            menuFade(\"in\",\"options\")\r\n                            destination = \"options\"\r\n                            return\r\n                else:\r\n                    #draws button\r\n                    gameDisplay.blit(buttons[i].imgA,(buttons[i].x, buttons[i].y,))\r\n            else:\r\n                #draws button\r\n                gameDisplay.blit(buttons[i].imgA,(buttons[i].x, buttons[i].y,))\r\n\r\n        \r\n        for i in range(0,len(text)):\r\n            gameDisplay.blit(text[i],(display_height/1.65,y))\r\n            y+=int(display_height/14)\r\n        \r\n        pygame.display.update()\r\n        \r\n# launch game\r\nif __name__ == \"__main__\":\r\n    startUp()\r\n    menu()\r\n    while True:\r\n        if destination == \"game\":\r\n            game()\r\n        if destination == \"menu\":\r\n            menu()\r\n        if destination == \"options\":\r\n            options()\r\n        if destination == \"gameOver\":\r\n            gameOver()\r\n        if destination == \"stats\":\r\n            statsPage()\r\n\r\npygame.quit()\r\nquit()\r\n","sub_path":"Doors alpha 0.7/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":28262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"597400463","text":"import rosbag, math, os, numpy, re, laspy\nimport sensor_msgs.point_cloud2 as pc2\n\ndef exportLas(lasName=r'points.laz'):\n\n    regex = re.compile(r'.+\\.frames$')\n    frameFiles = filter(regex.search, os.listdir())\n    frameFiles = [re.sub(r'\\.frames$', r'', i) for i in frameFiles]\n\n    x = numpy.array([])\n    y = numpy.array([])\n    z = numpy.array([])\n    gps = numpy.array([])\n    tStp = 0\n    for i in sorted(frameFiles):\n        print(r'loading ' + i)\n        pts = numpy.loadtxt(i + r'.3d')\n        mat = numpy.loadtxt(i + r'.frames')[-1,:]\n        mat = numpy.array([ mat[0:4], mat[4:8], mat[8:12], mat[12:16] ])\n\n        oneCol = numpy.array([[1]] * len(pts))\n        pts = numpy.append(pts, oneCol, axis=1)\n        pts = numpy.dot(pts, mat)\n\n        x = numpy.append(x, pts[:,0])\n        y = numpy.append(y, pts[:,1])\n        z = numpy.append(z, pts[:,2])\n        gps = numpy.append(gps, [tStp] * len(pts))\n\n        tStp += 1\n\n    # numpy.savetxt('temp.txt', numpy.column_stack((x,y,z)), fmt='%f')\n\n    lasHead = laspy.header.Header(point_format=1)\n    xmin = numpy.floor(numpy.min(x))\n    ymin = numpy.floor(numpy.min(y))\n    zmin = numpy.floor(numpy.min(z))\n\n    scale = 1000000\n\n    lasOut = laspy.file.File(lasName, mode=\"w\", header = lasHead)\n    lasOut.header.offset = [xmin,ymin,zmin]\n    lasOut.header.scale = [1/scale] * 3\n\n    lasOut.set_x(x*scale)\n    lasOut.set_y(y*scale)\n    lasOut.set_z(z*scale)\n    lasOut.set_gps_time(gps)\n\n    lasOut.close()\n\ndef getTime(msg):\n\n    secs  = msg.header.stamp.secs\n    nsecs = msg.header.stamp.nsecs\n    time = (secs, nsecs)\n    return time\n\ndef getAngles(msg = None):\n\n    if msg == None:\n        return [0,0,0]\n\n    pitch = msg.angle.x * 180 / math.pi\n    yaw = msg.angle.y * 180 / math.pi\n    roll = msg.angle.z * 180 / math.pi\n\n    imu = [pitch,yaw,roll]\n    return imu\n\ndef getPos():\n    return [0,0,0]\n\ndef getCloud(msg):\n\n    cloud = []\n    for p in pc2.read_points(msg, skip_nans=True):     \n        cloud.append(p)\n    \n    return cloud\n\n\ngoDir = r'/home/tiago/Desktop/bag/'\nslamDir = r'/home/tiago/SLAM/slam6d-code/bin/'\nbagFiles = os.listdir(goDir)\n\nfor goBag in bagFiles:\n    # goBag = r'20180626_ser1_x_45.bag'\n    \n    if re.match(r'.+\\.bag$', goBag) == None:\n        continue\n\n    os.chdir(goDir)\n    bagPref = re.sub(r'\\.bag', '', goBag)\n    bag = rosbag.Bag(goBag)\n\n    os.mkdir(bagPref)\n    os.chdir(bagPref)\n\n    ang = True\n    tempAng = numpy.array([getPos(), getAngles()])\n    cld = False\n    skip = 0\n    skipMax = 1\n    counter = 0\n    for topic, msg, t in bag.read_messages():\n        \n        # if(not cld and not ang and topic == r'/ekf_euler'):\n        #     tempAng = getAngles(msg)\n        #     tempAng = numpy.array([getPos(), tempAng])\n        #     ang = True\n\n        if(ang and not cld and topic == r'/velodyne_points'):\n            tempCloud = getCloud(msg)\n            tempCloud = numpy.array(tempCloud)\n            cld = True\n\n        if ang and cld:\n            if skip == 0:\n                print('converting cloud ' + str(counter))\n                ctr = '00' + str(counter) if counter < 10 else ('0' + str(counter) if counter < 100 else str(counter))\n                numpy.savetxt('scan' + ctr + '.pose', tempAng, fmt=\"%f\")\n                numpy.savetxt('scan' + ctr + '.3d', tempCloud[:,0:3], fmt=\"%f\")\n                counter += 1\n\n            skip = 0 if skip >= skipMax else skip+1\n            # ang = False\n            cld = False\n        \n        if(counter > 999):\n            break\n\n    bag.close()\n\n    cwd = os.getcwd()\n    os.chdir(slamDir)\n\n    cmd = r'./slam6D -i 600 -I 15 --metascan --epsICP=0.00000001 --epsSLAM=0.1 -d 1.25 -D 1 -G 1 -r 0.2 -a 2 ' + cwd\n    print(cmd)\n    os.system(cmd)\n\n    cmd = r'./exportPoints ' + cwd #+ r' && ./txt2las -i points.pts -o points.laz -v'\n    print(cmd)\n    os.system(cmd)\n\n    if(cwd[-1] != r'/'): \n        cwd += r'/'\n\n    os.rename(r'points.pts', cwd + r'points.pts')\n    os.rename(r'positions.txt', cwd + r'positions.txt')\n    os.rename(r'poses.txt', cwd + r'poses.txt')\n    # os.rename(r'points.laz', cwd + r'points.laz')\n    os.rename(r'loopclose.pts', cwd + r'loopclose.pts')\n\n    os.chdir(cwd)\n    exportLas()\n","sub_path":"python/slam_tests/bag_extractor.py","file_name":"bag_extractor.py","file_ext":"py","file_size_in_byte":4168,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"402924734","text":"import os, unittest\nimport pandas as pd\nfrom sqlalchemy import create_engine\nfrom igf_data.igfdb.igfTables import Base, Project, Project_attribute\nfrom igf_data.igfdb.baseadaptor import BaseAdaptor\nfrom igf_data.igfdb.projectadaptor import ProjectAdaptor\nfrom igf_data.utils.projectutils import mark_project_barcode_check_off\nfrom igf_data.utils.dbutils import read_dbconf_json\n\nclass Projectutils_test1(unittest.TestCase):\n  def setUp(self):\n    self.dbconfig = 'data/dbconfig.json'\n    dbparam=read_dbconf_json(self.dbconfig)\n    data=[{'project_igf_id': 'IGFP001_test1_24-1-18',},\n          {'project_igf_id': 'IGFP002_test1_24-1-18',\n           'barcode_check':'ON'},\n          {'project_igf_id': 'IGFP003_test1_24-1-18',\n           'barcode_check':'OFF'}\n         ]\n    self.data=pd.DataFrame(data)\n    base = BaseAdaptor(**dbparam)\n    self.engine = base.engine\n    self.dbname=dbparam['dbname']\n    Base.metadata.create_all(self.engine)\n    self.session_class=base.get_session_class()\n\n  def tearDown(self):\n    Base.metadata.drop_all(self.engine)\n    os.remove(self.dbname)\n\n  def test_mark_project_barcode_check_off(self):\n    pr=ProjectAdaptor(**{'session_class':self.session_class})\n    pr.start_session()\n    pr.store_project_and_attribute_data(self.data)\n    pr.close_session()\n\n    mark_project_barcode_check_off(project_igf_id='IGFP001_test1_24-1-18',\n                                   session_class=self.session_class)            # no attribute record\n    pr.start_session()\n    attribute_check=pr.check_project_attributes(project_igf_id='IGFP001_test1_24-1-18',\n                                                attribute_name='barcode_check')\n    self.assertTrue(attribute_check)\n    pr_attributes=pr.get_project_attributes(project_igf_id='IGFP001_test1_24-1-18',\n                                                  attribute_name='barcode_check')\n    for pr_attribute in pr_attributes.to_dict(orient='records'):\n      self.assertEqual(pr_attribute['attribute_value'],'OFF')\n    \n    pr_attributes=pr.get_project_attributes(project_igf_id='IGFP002_test1_24-1-18',\n                                                  attribute_name='barcode_check')\n    for pr_attribute in pr_attributes.to_dict(orient='records'):\n      self.assertEqual(pr_attribute['attribute_value'],'ON')\n    pr.close_session()\n\n    mark_project_barcode_check_off(project_igf_id='IGFP002_test1_24-1-18',\n                                   session_class=self.session_class)            # barcode check ON\n    pr.start_session()\n    pr_attributes=pr.get_project_attributes(project_igf_id='IGFP002_test1_24-1-18',\n                                                  attribute_name='barcode_check')\n    for pr_attribute in pr_attributes.to_dict(orient='records'):\n      self.assertEqual(pr_attribute['attribute_value'],'OFF')\n\n    pr_attributes=pr.get_project_attributes(project_igf_id='IGFP003_test1_24-1-18',\n                                                  attribute_name='barcode_check')\n    for pr_attribute in pr_attributes.to_dict(orient='records'):\n      self.assertEqual(pr_attribute['attribute_value'],'OFF')\n    pr.close_session()\n\n    mark_project_barcode_check_off(project_igf_id='IGFP003_test1_24-1-18',\n                                   session_class=self.session_class)            # barcode check OFF\n    pr.start_session()\n    pr_attributes=pr.get_project_attributes(project_igf_id='IGFP003_test1_24-1-18',\n                                                  attribute_name='barcode_check')\n    for pr_attribute in pr_attributes.to_dict(orient='records'):\n      self.assertEqual(pr_attribute['attribute_value'],'OFF')\n    pr.close_session()\n\nif __name__ == '__main__':\n  unittest.main()","sub_path":"test/utils/projectutils_test.py","file_name":"projectutils_test.py","file_ext":"py","file_size_in_byte":3678,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"197034469","text":"#!/usr/bin/env python\n\n# This is a simple customized pdb that has no prompt and outputs the\n# name and line number of the file being debugged after each command.\n# It is appropriate for a IDE-like debugger package in a text editor,\n# such as Atom.\n# dominique.orban@gmail.com, 2016.\n\nimport os\nimport pdb\nimport sys\nimport traceback\n\nclass Restart(Exception):\n    \"\"\"Causes a debugger to be restarted for the debugged python program.\"\"\"\n    pass\n\n\nclass AtomPDB(pdb.Pdb):\n\n    def __init__(self, **kwargs):\n        kwargs.pop(\"stdout\", None)\n        pdb.Pdb.__init__(self, stdout=sys.__stdout__, **kwargs)\n        self.prompt = \"\"\n\n    def do_locate(self, arg):\n        # An interface can grep the file and line number to follow along.\n        frame, lineno = self.stack[self.curindex]\n        filename = self.canonic(frame.f_code.co_filename)\n        print >> self.stdout, \"file::\", filename, \"\\nline::\", lineno\n\n    def preloop(self):\n        self.do_locate(1)\n\n    def precmd(self, line):\n        return line\n\n    def postcmd(self, stop, line):\n        return stop\n\n\ndef main():\n    if not sys.argv[1:] or sys.argv[1] in (\"--help\", \"-h\"):\n        print >> sys.__stdout__, \"atom_pdb.py script [args...]\"\n        sys.exit(2)\n\n    script = sys.argv[1]\n    if not os.path.exists(script):\n        sys.exit(1)\n    del sys.argv[0]\n    sys.path[0] = os.path.dirname(script)\n    apdb = AtomPDB()\n    while True:\n        try:\n            apdb._runscript(script)\n            if apdb._user_requested_quit:\n                break\n            print >> sys.__stdout__, \"The program finished and will be restarted\"\n        except Restart:\n            print >> sys.__stdout__, \"Restarting\", script, \"with arguments:\"\n            print >> sys.__stdout__, \" \".join(sys.argv[1:])\n        except SystemExit:\n            print >> sys.__stdout__, \"The program exited via sys.exit(). Exit status: \", sys.exc_info()[1]\n        except Exception as inst:\n            traceback.print_exc()\n            print >> sys.__stdout__, \"Uncaught exception \", type(inst), \" ... entering post-mortem debugging\"\n            print >> sys.__stdout__, \"Continue or Step will restart the program\"\n            apdb.interaction(None, sys.exc_info()[2])\n            print >> sys.__stdout__, \"Post-mortem debugging finished. \", script, \" will be restarted.\"\n\n\nif __name__ == \"__main__\":\n\n    import atom_pdb\n    atom_pdb.main()\n","sub_path":"resources/atom_pdb.py","file_name":"atom_pdb.py","file_ext":"py","file_size_in_byte":2382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"124805238","text":"import unittest\n\nfrom nexusmaker import Record, NexusMaker, NexusMakerAscertained\nfrom nexusmaker import NexusMakerAscertainedWords\nfrom nexusmaker import CognateParser\n\nRECORDS = \"\"\"\nAiwoo-501\t132312\tfive\tvili\t1\nAiwoo-501\t133751\tleg\tnyike\t86\nAiwoo-501\t133752\tleg\tnuku\t86\nAiwoo-501\t208804\thand\tnyime\t1,66\nAiwoo-501\t208805\thand\tnyimä\t1,66\nBanoni-4\t1075\tleg\trapinna\t\t\nBanoni-4\t250221\tfive\tghinima\t1\nBanoni-4\t4\thand\tnuma-\t1,64\nDehu-196\t129281\tfive\ttripi\t1\nDehu-196\t196\thand\twanakoim\t\nEton-1088\t265408\tfive\te-lim\t1\nEton-1088\t278627\tleg\ttua-ŋ\t95\nHiw-639\t164951\thand\tmja-\t1,78\nHiw-639\t164952\tleg\tᶢʟoŋo-\t17\nHiw-639\t165135\tfive\ttəβɔjimə\t1\nIaai-471\t125656\thand\tbeñi-\t14\nIaai-471\t125657\thand\tHAND\t\nIaai-471\t125659\tleg\tca\t\nIaai-471\t125853\tfive\tbaa|xaca\t\nIaai-471\t125865\tfive\tthabyŋ\t\nLamogai-67\t83796\tfive\telmé\t1\nLamogai-67\t83881\thand\tmulǵu\t45\nLamogai-67\t83882\thand\tmelsé\t45\nLamogai-67\t83883\thand\tmilpí\t45\nLamogai-67\t83884\thand\tmelép\t45\nLamogai-67\t83885\thand\tmilpú\t45\nLamogai-67\t83886\thand\tmeylá\t45\nLamogai-67\t83887\thand\tmelsék\t45\nLamogai-67\t83942\tleg\tkaip\t1\nLamogai-67\t83943\tleg\tkaŋgú\t1\n\"\"\"\n\nRECORDS = [r.split(\"\\t\") for r in RECORDS.split(\"\\n\") if len(r)]\nCOMPLEX_TESTDATA = [\n    Record(Language=r[0], Word=r[2], Item=r[3], Cognacy=r[4])\n    for r in RECORDS\n]\n\nEXPECTED_COGNATES = {\n    ('five', '1'): {\n        'Aiwoo-501', 'Banoni-4', 'Dehu-196', 'Eton-1088', 'Hiw-639',\n        'Lamogai-67'\n    },\n    ('leg', '86'): {'Aiwoo-501'},\n    ('hand', '1'): {'Aiwoo-501', 'Banoni-4', 'Hiw-639'},\n    ('hand', '64'): {'Banoni-4'},\n    ('hand', '66'): {'Aiwoo-501'},\n    ('leg', '95'): {'Eton-1088'},\n    ('hand', '78'): {'Hiw-639'},\n    ('leg', '17'): {'Hiw-639'},\n    ('hand', '14'): {'Iaai-471'},\n    ('hand', '45'): {'Lamogai-67'},\n    ('leg', '1'): {'Lamogai-67'},\n}\n\nEXPECTED_UNIQUES = [\n    ('leg', 'Banoni-4'),\n    ('hand', 'Dehu-196'),\n    ('leg', 'Iaai-471'),\n    ('five', 'Iaai-471'),\n]\n\n\n\n\nclass TestNexusMaker(unittest.TestCase):\n    model = NexusMaker\n    # number of cognate sets expected\n    expected_ncog = len(EXPECTED_COGNATES) + len(EXPECTED_UNIQUES)\n    # number of characters expected in the nexus file\n    expected_nchar = len(EXPECTED_COGNATES) + len(EXPECTED_UNIQUES)\n    \n    def setUp(self):\n        self.maker = self.model(data=COMPLEX_TESTDATA)\n        self.nex = self.maker.make()\n\n    def test_languages(self):\n        self.assertEqual(self.maker.languages, {\n            'Aiwoo-501', 'Banoni-4', 'Dehu-196', 'Eton-1088', 'Hiw-639',\n            'Iaai-471', 'Lamogai-67'\n        })\n\n    def test_words(self):\n        self.assertEqual(self.maker.words, {'hand', 'leg', 'five'})\n    \n    def test_ncognates(self):\n        self.assertEqual(len(self.maker.cognates), self.expected_ncog)\n    \n    def test_cognate_sets(self):  # pragma: no cover\n        errors = []\n        for ecog in EXPECTED_COGNATES:\n            if ecog not in self.maker.cognates:\n                errors.append(\"Missing %s\" % (ecog, ))\n            elif self.maker.cognates.get(ecog, set()) != EXPECTED_COGNATES[ecog]:\n                errors.append(\"Cognate set %s incorrect %r != %r\" % (\n                    ecog,\n                    EXPECTED_COGNATES[ecog],\n                    self.maker.cognates.get(ecog, set())\n                ))\n\n        if errors:\n            raise AssertionError(\"Errors: %s\" % \"\\n\".join(errors))\n\n    def test_uniques(self):  # pragma: no cover\n        errors = []\n        obtained = [c for c in self.maker.cognates if 'u' in c[1]]\n        expected = {e: 0 for e in EXPECTED_UNIQUES}\n        # check what has been identified as unique\n        for cog in obtained:\n            if len(self.maker.cognates[cog]) != 1:\n                errors.append(\"Unique cognate %s should only have one member\" % (cog, ))\n            # make key to look up EXPECTED_UNIQUES as (word, language)\n            key = (cog[0], list(self.maker.cognates[cog])[0])\n            # error on anything that is not expected\n            if key not in expected:\n                errors.append(\"%s unexpectedly seen as unique\" % (key, ))\n            else:\n                expected[key] += 1\n\n        # the counts for each expected cognate should be max 1.\n        for e in expected:\n            if expected[e] != 1:\n                errors.append(\"Expected 1 cognate for %s, but got %d\" % (e, expected[e]))\n\n        if errors:\n            raise AssertionError(\"Errors: %s\" % \"\\n\".join(errors))\n\n    def test_dehu_is_all_missing_for_leg(self):\n        for cog in [cog for cog in self.nex.data if cog.startswith('leg_')]:\n            assert self.nex.data[cog]['Dehu-196'] == '?'\n\n    def test_eton_is_all_missing_for_hand(self):\n        for cog in [cog for cog in self.nex.data if cog.startswith('hand_')]:\n            assert self.nex.data[cog]['Eton-1088'] == '?'\n\n    def test_only_one_unique_for_Iaai471(self):\n        iaai = 0\n        for cog in [cog for cog in self.nex.data if cog.startswith('five_u_')]:\n            present = [t for t in self.nex.data[cog] if self.nex.data[cog][t] == '1']\n            if present == ['Iaai-471']:\n                iaai += 1\n\n        if iaai != 1:  # pragma: no cover\n            raise AssertionError(\"Should only have one unique site for Iaai-471-five\")\n\n    def test_nexus_symbols(self):\n        assert sorted(self.nex.symbols) == sorted(['0', '1'])\n\n    def test_nexus_taxa(self):\n        self.assertEqual(self.maker.languages, self.nex.taxa)\n\n    def test_nexus_characters_expected_cognates(self):\n        for e in EXPECTED_COGNATES:\n            assert \"_\".join(e) in self.nex.characters\n\n    def test_nexus_characters_expected_uniques(self):\n        uniques = [\n            c for c in self.nex.characters if\n            CognateParser().is_unique_cognateset(c, labelled=True)\n        ]\n        assert len(uniques) == len(EXPECTED_UNIQUES)\n\n    def test_nexus_nchar(self):\n        assert len(self.nex.characters) == self.expected_nchar\n    \n    def test_entries_with_a_cognate_word_arenot_added_as_unique(self):\n        hand = [c for c in self.nex.characters if c.startswith('hand_')]\n        hand = [c for c in hand if CognateParser().is_unique_cognateset(c, labelled=True)]\n        assert len(hand) == 1, 'Only expecting one unique character for hand'\n        assert self.nex.data['hand_u_2']['Iaai-471'] in ('0', '?'), \\\n            'Iaai-471 should not be unique for `hand`'\n    \n\nclass TestNexusMakerAscertained(TestNexusMaker):\n    model = NexusMakerAscertained\n    expected_nchar = len(EXPECTED_COGNATES) + len(EXPECTED_UNIQUES) + 1\n\n\nclass TestNexusMakerAscertainedWords(TestNexusMaker):\n    model = NexusMakerAscertainedWords\n    expected_nchar = len(EXPECTED_COGNATES) + len(EXPECTED_UNIQUES) + 3\n\n\n","sub_path":"nexusmaker/tests/test_Complex.py","file_name":"test_Complex.py","file_ext":"py","file_size_in_byte":6640,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"43228207","text":"\nimport sys\nsys.path.append('e:\\\\art\\\\code\\\\deepArt-generation\\\\source')\n\n\nimport config, utils\nfrom scipy.misc import imsave,imresize\nimport numpy as np\nfrom skimage import io\nfrom skimage.transform import resize\nimport os\n\nimport matplotlib.pyplot as plt\n\nout_path = 'e:\\\\art\\\\wikiportraits'\npath = config.datafile('portrait-sel')\n# using directory with hand-selected images\n\nprint(\"Reading images...\")\n\nall_images_fn = [x for x in os.listdir(path) if x.endswith(\".jpg\") | x.endswith(\".png\") | x.endswith(\".jpeg\")]\n\nall_images = []\nratios = []\n\nfor fn in all_images_fn:\n    n = int(fn[:fn.find('-')])\n    if n>8000: continue\n    try:\n        im = io.imread(os.path.join(path, fn))\n        if im.shape[0] >= 256 and im.shape[1] >=256:\n            all_images.append(im)\n            ratios.append(im.shape[0]/im.shape[1])\n    except:\n        print(\"Error reading {}\".format(fn))\n\nplt.hist(ratios,bins=30)\n\nsel_images = [x for x in all_images if 1.23 <= x.shape[0]/x.shape[1] <= 1.35]\nprint(len(sel_images))\n\nfor i,im in enumerate(sel_images):\n    # im = resize(image=im, output_shape=(128,128), mode=\"reflect\")\n    im = imresize(im, (128,128))\n    imsave(os.path.join(out_path, str(i) + '.png'), im)\n\nsel_images = list(filter(lambda x: x.shape==(32,32,3),map(lambda x: imresize(x,(32,32)),sel_images)))\n\nnp.savez_compressed(os.path.join(out_path,'wikiportraits.npz'),imgs=sel_images)\n","sub_path":"source/process_portraits.py","file_name":"process_portraits.py","file_ext":"py","file_size_in_byte":1383,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"502832926","text":"event = {u'Records': [{u'eventSource': u'aws:ses',\n                       u'eventVersion': u'1.0',\n                       u'ses': {u'mail': {u'commonHeaders': {u'date': u'Sat, 10 Oct 2015 22:07:11 +0000',\n                                                             u'from': [u'Felipe Gasparini <xxx@gmail.com>'],\n                                                             u'messageId': u'<CAMHng6i97a29ezKuU1SuAO8K+ZvsZOfj07aN-fmXeDgZQnQ+UQ@mail.gmail.com>',\n                                                             u'returnPath': u'xxx@gmail.com',\n                                                             u'subject': u'Fwd: assunto',\n                                                             u'to': [\n                                                                 u'\"teste2@felipegasparini.com\" <teste2@felipegasparini.com>']},\n                                          u'destination': [u'teste2@felipegasparini.com'],\n                                          u'headers': [{u'name': u'Return-Path',\n                                                        u'value': u'<xxx@gmail.com>'},\n                                                       {u'name': u'Received',\n                                                        u'value': u'from mail-ig0-f175.google.com (mail-ig0-f175.google.com [209.85.213.175]) by inbound-smtp.us-east-1.amazonaws.com with SMTP id a3tjl7dpbfqnhhl7oiaej4m5q0olr0c1qvt6glg1 for teste2@felipegasparini.com; Sat, 10 Oct 2015 22:07:22 +0000 (UTC)'},\n                                                       {u'name': u'Received',\n                                                        u'value': u'by igbni9 with SMTP id ni9so465796igb.1 for <teste2@felipegasparini.com>; Sat, 10 Oct 2015 15:07:21 -0700 (PDT)'},\n                                                       {u'name': u'DKIM-Signature',\n                                                        u'value': u'v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20120113; h=mime-version:references:in-reply-to:from:date:message-id:subject:to :content-type; bh=PBfQGDhObxGAS+njunTub/mprGGP2YkQlJYRRbHr4Xs=; b=CIGLbGAmjqOfekYYZxg7upC+jhEfo2nbefnb0z6r7LES4VKgvuUdGh5sKoDAres8L6 euaaTtxrkruDCQAMB8mEneTg7HQaGm1Rl/KiDApVj6p+WFKK24UzxQu13fA7/Fa3rGfU KkN4N1AftUrVLHMOdQh2aNGF3LVjTl+HybkqGqXg159+a4qyBTMgvFCJarhDu47kaMZ4 jR6oDGR4VhdiN7rFceo1Ii7JI70GOo1D87H4zP0RwIMFwTZe2yH4+7qg4dOBVD32qqpd sv0KT+35PBjiXyD1xNnDS1B4eRGiLHWgoGp/V6VP0ezKEf56o5ofr5DFphS5hFyAvgBg CPMA=='},\n                                                       {u'name': u'X-Received',\n                                                        u'value': u'by 10.50.67.179 with SMTP id o19mr5245610igt.63.1444514841337; Sat, 10 Oct 2015 15:07:21 -0700 (PDT)'},\n                                                       {u'name': u'MIME-Version',\n                                                        u'value': u'1.0'},\n                                                       {u'name': u'References',\n                                                        u'value': u'<CAMHng6j1UyqY7rX9GtyH3cw4pN6Pg3ntb2OZP+ch7cfhYr8uvA@mail.gmail.com>'},\n                                                       {u'name': u'In-Reply-To',\n                                                        u'value': u'<CAMHng6j1UyqY7rX9GtyH3cw4pN6Pg3ntb2OZP+ch7cfhYr8uvA@mail.gmail.com>'},\n                                                       {u'name': u'From',\n                                                        u'value': u'Felipe Gasparini <xxx@gmail.com>'},\n                                                       {u'name': u'Date',\n                                                        u'value': u'Sat, 10 Oct 2015 22:07:11 +0000'},\n                                                       {u'name': u'Message-ID',\n                                                        u'value': u'<CAMHng6i97a29ezKuU1SuAO8K+ZvsZOfj07aN-fmXeDgZQnQ+UQ@mail.gmail.com>'},\n                                                       {u'name': u'Subject',\n                                                        u'value': u'Fwd: assunto'},\n                                                       {u'name': u'To',\n                                                        u'value': u'\"teste2@felipegasparini.com\" <teste2@felipegasparini.com>'},\n                                                       {u'name': u'Content-Type',\n                                                        u'value': u'multipart/mixed; boundary=047d7bdca610fe0c350521c751c2'}],\n                                          u'headersTruncated': False,\n                                          u'messageId': u'a3tjl7dpbfqnhhl7oiaej4m5q0olr0c1qvt6glg1',\n                                          u'source': u'xxx@gmail.com',\n                                          u'timestamp': u'2015-10-10T22:07:22.216Z'},\n                                u'receipt': {u'action': {\n                                    u'functionArn': u'arn:aws:lambda:us-east-1:979209632398:function:lambdaduty_email_handler',\n                                    u'invocationType': u'Event',\n                                    u'type': u'Lambda'},\n                                    u'dkimVerdict': {u'status': u'PASS'},\n                                    u'processingTimeMillis': 888,\n                                    u'recipients': [u'teste2@felipegasparini.com'],\n                                    u'spamVerdict': {u'status': u'PASS'},\n                                    u'spfVerdict': {u'status': u'PASS'},\n                                    u'timestamp': u'2015-10-10T22:07:22.216Z',\n                                    u'virusVerdict': {u'status': u'PASS'}}\n\n                                }}]}\n\nescalation_policy = {\n    'name': 'default',\n    'policies': [\n        {\n            'type': 'email',\n            'data': {\n                'to': 'xxx@gmail.com'\n            }\n        },\n        {\n            'type': 'sms',\n            'data': {\n                'to': '55199999999'\n            }\n        },\n        {\n            'type': 'phone_call',\n            'data': {\n                'to': '55199999999'\n            }\n        }\n    ]\n}\n\nget_alert_handler_event = {\n    'params': {\n        'path': {\n            'id': '12312312321312312'\n        }\n    }\n}\n","sub_path":"sample_data.py","file_name":"sample_data.py","file_ext":"py","file_size_in_byte":6212,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"512548461","text":"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n# vim:fenc=utf-8\n#\n# Copyright © 2018 qizai <jianhao2@illinois.edu>\n#\n# Distributed under terms of the MIT license.\n\n\"\"\"\nThis is an implementation of the paper online NMF.\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pickle\nimport time\nimport ipdb\nimport os\nimport argparse\nimport pycuda.autoinit\nimport pycuda.gpuarray as gpuarray\nimport skcuda.linalg as linalg\n# import cvxpy as cvx\nfrom functools import reduce\nfrom sklearn.decomposition import PCA\nfrom sklearn.linear_model import LassoLars\nfrom sklearn.preprocessing import normalize\nfrom sklearn.manifold import TSNE\nfrom sklearn.cluster import KMeans\nfrom sklearn.metrics import accuracy_score, adjusted_mutual_info_score\nfrom cluster_assignment_method import get_clustering_assignment_1, get_clustering_assignment_2\nfrom common_functions import get_g_hat_value, evaluation_clustering\nfrom common_functions import my_nmf_clustering, nmf_clustering\nfrom common_functions import geo_projection_to_cvx_cmb\n# from cvxpy_update_functions import update_D_hat_cvxpy, update_W_hat_cvxpy\nfrom pycuda_update_W_comparison import update_W_hat_skcuda, opt_cal_W_hat_numpy\nfrom pycuda_update_W_comparison import opt_cal_W_hat_solve, update_W_hat_numpy\nfrom online_NMF import online_dict_learning\nfrom convex_NMF import CNMF\nfrom visualization_NMF import plot_diff_method_scRNA\n\nimport sys\nsys.path.append('batch_setting')\nfrom batch_cvx_online_NMF import cvx_online_dict_learning_batch\n\ndef my_normalize(X):\n    '''\n    scale X to be in a unit ball\n    X: n x m, n samples in row\n    '''\n    n_dim, m_dim = X.shape\n    max_norm = max([np.linalg.norm(X[k, :]) for k in range(n_dim)])\n    X_new = X / max_norm\n    return X_new\n\n\ndef cvx_online_dict_learning(X, y_true, n_hat, k_cluster, T, lmda, eps, \n        flag=True, version = 'Rr'):\n    '''\n    X: R^(n * m)\n    y_true: str^n\n    W_0: R^(n_hat * k)\n    x_i : R^m\n    alpha: R^k\n    cvx_online problem \n        min||x_i - X.T * W * alpha|| + lambda * ||alpha||\n\n    in the online setting, there is no X in (n * m), \n    instead, we need to store a candidate set and solve the subproblem:\n        min ||x_i - X_hat * W_hat * alpha|| + lambda * ||alpha||\n\n    X_hat : R^(m * n_hat)\n    W_hat : R^(n_hat * k)\n\n    version: Rr, restricted, heuristic approach\n             Ru, uniform, random assignment\n    '''\n    n_dim, m_dim = X.shape\n\n    A_t = np.zeros((k_cluster, k_cluster))\n    B_t = np.zeros((m_dim, k_cluster))\n    x_sum = 0\n    alpha_sum = 0\n\n    # step 1: sample n_hat * k_cluster points as initial X_hat.\n    X_0 = np.zeros((m_dim, n_hat))\n    for idx in range(n_hat):\n        sample_idx = np.random.randint(0, n_dim)\n        x_sample = X[sample_idx, :]\n        X_0[:, idx] = x_sample\n\n\n    # step 1: initialization, get X_hat (including clusters info)\n    # and W_hat from X_0, using same init as in CNMF.\n    # here representative_size_count is the n_1_hat, n_2_hat, ..., n_k_hat.\n    t1 = time.time()\n    X_hat, W_hat, representative_size_count = initialize_X_W_hat(X_0, k_cluster)\n    X_0, W_0 = X_hat.copy(), W_hat.copy()\n    t2 = time.time()\n    # print('init cost {:.4f}'.format(t2 - t1))\n    \n    # step 2: after initialization of X_hat, update alpha, W_hat and X_hat alternatively.\n    t_start = time.time()\n    print(lmda, _NF, eps)\n    for t in range(T):\n        # t_start_online = time.time()\n        if t % 50 == 0 and flag:\n            D_t = np.matmul(X_hat, W_hat)\n            tmp_assignment = get_clustering_assignment_1(X, D_t, k_cluster)\n            tmp_acc, tmp_AMI = evaluation_clustering(tmp_assignment, y_true)\n            print('1)iteration {}, distance acc = {:.4f}, AMI = {:.4f}'.format(t, tmp_acc, tmp_AMI))\n\n            tmp_assignment = get_clustering_assignment_2(X, D_t, k_cluster, lmda)\n            tmp_acc, tmp_AMI = evaluation_clustering(tmp_assignment, y_true)\n            print('2)iteration {}, kmeans of weights acc = {:.4f}, AMI = {:.4f}'.format(t, tmp_acc, tmp_AMI))\n            t_end = time.time()\n            print('time elapse = {:.4f}s'.format(t_end - t_start))\n            t_start = t_end\n\n            print('-' * 7)\n\n\n        sample_idx = np.random.randint(0, n_dim)\n        x_sample = X[sample_idx, :]\n\n        # update alpha\n        t1 = time.time()\n        lars_lasso = LassoLars(alpha = lmda, max_iter = 500)\n        D_t = np.matmul(X_hat, W_hat)\n        lars_lasso.fit(D_t, x_sample)\n        alpha_t = lars_lasso.coef_\n        t2 = time.time()\n        # print('lasso cost {:.4f}s'.format(t2 - t1))\n        \n        # using different clustering assignment\n        t1 = time.time()\n        if version == 'Rr':\n            cluster_of_x_i = np.argmax(alpha_t)\n        # elif version == 'Ru':\n        else:\n            cluster_of_x_i = int(np.random.uniform(0, k_cluster))\n        t2 = time.time()\n        # print('argmax alpha cost {:.4f}s'.format(t2 - t1))\n\n        t1 = time.time()\n        A_t += np.matmul(alpha_t.reshape(k_cluster, 1), alpha_t.reshape(1, k_cluster))\n        B_t += np.matmul(x_sample.reshape(m_dim, 1), alpha_t.reshape(1, k_cluster))\n        x_sum += (np.linalg.norm(x_sample) ** 2)\n        alpha_sum += lmda * np.linalg.norm(alpha_t, 1)\n        t2 = time.time()\n        # print('update At, Bt cost {:.4f}s'.format(t2 - t1))\n\n\n        # update X_hat\n        t1 = time.time()\n        W_hat, X_hat = update_W_X_hat(W_hat, X_hat, representative_size_count, x_sample, cluster_of_x_i, \n                A_t, B_t, x_sum, alpha_sum, t, eps)\n        t2 = time.time()\n        # print('update X_hat, W_hat cost {:.4f}s'.format(t2 - t1))\n\n    print('Dcitionary update done! Time elapse {:.04f}s'.format(time.time() - t_start))\n\n    return W_hat, X_hat, representative_size_count, X_0, W_0\n\ndef initialize_X_W_hat(X_0, k_cluster):\n    '''\n    takes intial collection of X and number of cluster as input,\n    run k-Means on it, return the sorted (by cluster) X_hat, W_hat,\n    and number of points in each cluster, i.e. n_hat_i\n    '''\n    # this function takes the initialziation step of CNMF and gives a X_hat, W_hat\n    # cluster X_hat, get X_hat, W_0 as output of some method, and assignment of X_0\n    # kmeans works with row vector, however, X_0 is a column vec matrix.\n    kmeans = KMeans(n_clusters = k_cluster, max_iter = 1000)\n    kmeans.fit(X_0.T)\n    X_hat_assignments = kmeans.labels_\n\n\n    # now we need to classify the X_hat to X_1, X_2, X_3\n    # by using a dictionary candidate_clusters\n    candidate_clusters = {x:np.array([]) for x in set(X_hat_assignments)}\n    for idx, label in enumerate(X_hat_assignments):\n        if candidate_clusters[label].size == 0:\n            candidate_clusters[label] = X_0[:, idx]\n        else:\n            candidate_clusters[label] = np.vstack((candidate_clusters[label], X_0[:, idx]))\n\n    X_hat = np.array([])\n    check_list = []\n    sorted_assignment = []\n    for label in candidate_clusters:\n        candidate_clusters[label] = candidate_clusters[label].T\n        shape_of_cluster = candidate_clusters[label].shape\n        print('label {} has shape of: {}'.format(label, shape_of_cluster))\n        check_list.append(shape_of_cluster[1])\n        if X_hat.size == 0:\n            X_hat = candidate_clusters[label]\n            sorted_assignment = [label] * shape_of_cluster[1]\n        else:\n            X_hat = np.hstack((X_hat, candidate_clusters[label]))\n            sorted_assignment += [label] * shape_of_cluster[1]\n\n    sorted_assignment = np.array(sorted_assignment)\n\n    # based on the CNMF paper, we start the initialization with fresh k-Means\n    # H: R^{n * k} matrix, indicate the cluster assignments\n    # centroids can be calculated as F = X*W*D^{-1}, Where D: R^{k * k} is the count diagonal matrix\n    # then we can say W = H*D^{-1}\n    m_dim, n_dim = X_hat.shape\n    cluster_count = [len(np.where(X_hat_assignments == i)[0]) for i in range(k_cluster)]\n    assert cluster_count == check_list\n    \n    D = np.zeros((k_cluster, k_cluster), int)\n    for idx in range(k_cluster):\n        D[idx][idx] = cluster_count[idx] + 1e-3\n\n    H = np.zeros((n_dim, k_cluster), int)\n    for idx in range(k_cluster):\n        non_zero_idx = np.where(sorted_assignment == idx)[0]\n        H[non_zero_idx, idx] = 1\n    \n    W_hat = np.matmul((H + np.ones(H.shape, int) * 0.2), np.linalg.inv(D))\n\n    return X_hat, W_hat, cluster_count\n\n\ndef update_W_X_hat(W_hat, X_hat, repre_size_count, x_sample, cluster_of_x_i, \n        A_t, B_t, x_sum, alpha_sum, t, eps):\n    # add W_hat block diagonal constraint,\n    # using projection.\n    # linalg.init()\n\n    # W_hat_gpu = gpuarray.to_gpu(W_hat.astype(np.float64))\n    # tmp_x = np.ascontiguousarray(X_hat)\n    # X_hat_gpu = gpuarray.to_gpu(tmp_x.astype(np.float64))\n    # A_t_gpu = gpuarray.to_gpu(A_t.astype(np.float64))\n    # B_t_gpu = gpuarray.to_gpu(B_t.astype(np.float64))\n\n\n    cluster_seperation_idx = np.cumsum(repre_size_count)\n    end_idx = cluster_seperation_idx[cluster_of_x_i]\n    start_idx = end_idx - repre_size_count[cluster_of_x_i]\n    A_t_inv = np.linalg.pinv(A_t)\n\n    # W_opt_old_X = opt_cal_W_hat_numpy(W_hat, X_hat, A_t, B_t, x_sum, alpha_sum, eps, t)\n    W_opt_old_X = opt_cal_W_hat_solve(W_hat, X_hat, A_t_inv, B_t, x_sum, alpha_sum, eps, t)\n    g_hat_old_X = get_g_hat_value(t, W_opt_old_X, X_hat, A_t, B_t, x_sum, alpha_sum)\n\n    # W_opt_old_X = update_W_hat_skcuda(W_hat_gpu, X_hat_gpu, A_t_gpu, B_t_gpu, \n    #       x_sum, alpha_sum, eps, t)\n    # g_hat_old_X = get_g_hat_value(t, W_opt_old_X.get(), X_hat, A_t, B_t, x_sum, alpha_sum)\n\n    list_of_W_opt_new_X = [W_opt_old_X]\n    list_of_g_hat_new_X = [g_hat_old_X]\n    list_of_new_X = [X_hat]\n\n    # print('starting loop in update_W_X, total {}'.format(end_idx - start_idx))\n    for idx in range(start_idx, end_idx):\n        # print('iter # {}'.format(idx))\n        t1 = time.time()\n        X_hat_new  = X_hat.copy()\n        X_hat_new[:, idx] = x_sample\n        list_of_new_X.append(X_hat_new)\n        # tmp_x = np.ascontiguousarray(X_hat_new)\n        # X_hat_new_gpu = gpuarray.to_gpu(tmp_x.astype(np.float64))\n        t2 = time.time()\n        # print('\\t update X_hat cost {:.4f}s'.format(t2 - t1))\n\n        t1 = time.time()\n        # W_opt_new_X = opt_cal_W_hat_numpy(W_hat, X_hat_new, A_t, B_t, x_sum, alpha_sum, eps, t)\n        # W_opt_new_X = update_W_hat_numpy(W_hat, X_hat_new, A_t, B_t, x_sum, alpha_sum, eps, t)\n        W_opt_new_X = opt_cal_W_hat_solve(W_hat, X_hat_new, A_t_inv, B_t, x_sum, alpha_sum, eps, t)\n        g_hat_new_X = get_g_hat_value(t, W_opt_new_X, X_hat_new, A_t, B_t, x_sum, alpha_sum)\n\n        # W_opt_new_X = update_W_hat_skcuda(W_hat_gpu, X_hat_new_gpu, A_t_gpu, B_t_gpu, \n        #       x_sum, alpha_sum, eps, t)\n        # g_hat_new_X = get_g_hat_value(t, W_opt_new_X.get(), X_hat_new, A_t, B_t, x_sum, alpha_sum)\n        t2 = time.time()\n        # print('\\t update W_hat_new cost {:.4f}'.format(t2 - t1))\n\n        t1 = time.time()\n        list_of_W_opt_new_X.append(W_opt_new_X)\n        list_of_g_hat_new_X.append(g_hat_new_X)\n        t2 = time.time()\n        # print('appending W_opt list cost {:.4f}s'.format(t2 - t1))\n\n    min_g_idx = np.argmin(list_of_g_hat_new_X)\n\n    X_hat_new = list_of_new_X[min_g_idx]\n    W_hat_new = list_of_W_opt_new_X[min_g_idx]\n    # if list_of_g_hat_new_X[min_g_idx] <= g_hat_old_X:\n    #     X_hat_new = X_hat.copy()\n    #     X_hat_new[:, start_idx + min_g_idx] = x_sample\n    #     # W_hat_new = list_of_W_opt_new_X[min_g_idx].get()\n    #     W_hat_new = list_of_W_opt_new_X[min_g_idx].copy()\n    # else:\n    #     X_hat_new = X_hat.copy()\n    #     # W_hat_new = W_opt_old_X.get()\n    #     W_hat_new = W_opt_old_X.copy()\n\n\n    return W_hat_new, X_hat_new\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--numIter', type=int, default=1200)\n    parser.add_argument('--lmda', type=float, default=1e-1)\n    parser.add_argument('--eps', type=float, default=1e-5)\n    parser.add_argument('--normal_factor', '--NF', type=float, default=200)\n    parser.add_argument('--file_name', type=str, default='tmp_pic')\n    parser.add_argument('--dtype', type=str, default='scRNA',\n            choices=['scRNA', 'synthetic', \n                'synthetic_1', 'synthetic_2'],\n            help='synthetic1: well sep, 2: close cluster')\n    parser.add_argument('--k_cluster', type=int, default=10)\n    parser.add_argument('--csize', type=int, default=500,\n            help='size of each cluster, integer, default 500')\n    parser.add_argument('--candidate_size', type=int, default=15)\n    parser.add_argument('--pca', type=int, default = 100)\n    parser.add_argument('--numAver', type=int, default=1)\n    args = parser.parse_args()\n\n    # set number of iteration, lambda in lasso, epsilon in dictionary update and normalization factor\n    print(args)\n    numIter = args.numIter\n    lmda = args.lmda\n    eps = args.eps\n    _NF = args.normal_factor\n    file_name = args.file_name\n    k_cluster = args.k_cluster\n    cluster_size = args.csize\n    candidate_set_size = args.candidate_size\n    P_component = args.pca\n    aver_num = args.numAver\n\n    data_type = 'scRNA'\n\n    # np.random.seed(42)\n    data_root = '/home/jianhao2/'\n    data_root_shared = '/data/shared/jianhao/'\n    # df_file = os.path.join(data_root, 'pandas_dataframe')\n    # feat_file = os.path.join(data_root, 'df_feature_column')\n    if data_type == 'synthetic':\n        k_cluster = 10\n        df_name = 'df_synthetic_well_sep'\n        fc_name = 'feature_column_synthetic_well_sep'\n        df_file = os.path.join(data_root_shared, '10xGenomics_scRNA/pandasDF', df_name) \n        feat_file = os.path.join(data_root_shared, '10xGenomics_scRNA/pandasDF', fc_name)\n    elif data_type == 'synthetic_1':\n        k_cluster = 10\n        df_name = 'df_synthetic_disjoint_{}'.format(cluster_size)\n        fc_name = 'feature_column_synthetic_disjoint_{}'.format(cluster_size)\n        df_file = os.path.join(data_root_shared, 'synthetic_data', df_name) \n        feat_file = os.path.join(data_root_shared, 'synthetic_data', fc_name)\n    elif data_type == 'synthetic_2':\n        k_cluster = 10\n        df_name = 'df_synthetic_overlap_{}'.format(cluster_size)\n        fc_name = 'feature_column_synthetic_overlap_{}'.format(cluster_size)\n        df_file = os.path.join(data_root_shared, 'synthetic_data', df_name) \n        feat_file = os.path.join(data_root_shared, 'synthetic_data', fc_name)\n    elif data_type == 'scRNA':\n        k_cluster = 10\n        # df_name = 'pandas_dataframe_10_clusters_-1'\n        # fc_name = 'df_feature_column_10_clusters_-1'\n        df_name = 'pandas_dataframe_10_clusters_500'\n        fc_name = 'df_feature_column_10_clusters_500'\n        df_file = os.path.join(data_root_shared, '10xGenomics_scRNA/pandasDF', df_name)\n        feat_file = os.path.join(data_root_shared, '10xGenomics_scRNA/pandasDF', fc_name)\n\n    # np.random.seed(42)\n    df = pd.read_pickle(df_file)\n    with open(feat_file, 'rb') as f:\n        feat_cols = pickle.load(f)\n    X_raw = df[feat_cols].values\n    X_raw = X_raw - np.min(X_raw) + 0.1\n    Y = df['label'].values\n\n\n\n    # # ----------------------------------------------------\n    # X_for_nmf = normalize(X_raw) * _NF\n    # D_nmf, label_nmf = nmf_clustering(X_for_nmf, k_cluster, numIter = 1000)\n    # acc_nmf, AMI_nmf = evaluation_clustering(label_nmf, Y)\n\n    # print(' ------ final accuracy = {:.4f}, AMI = {:.4f}'.format(acc_nmf, AMI_nmf))\n\n\n    # ----------------------------------------------------\n    # use PCA to reduce X_raw to [num_of_cells * number of PCA componets]\n\n    if P_component != -1:\n        pca = PCA(n_components = P_component)\n        # X_pca_all = pca.fit_transform(np.vstack((X_raw, D_nmf)))\n        # X_pca = X_pca_all[:-k_cluster, :]\n\n        X_pca = pca.fit_transform(X_raw)\n    else:\n        X_pca = X_raw\n    pca_cols = ['Principle component {}'.format(i) for i in range(X_pca.shape[1])]\n\n\n\n    # ----------------------------------------------------\n    X = normalize(X_pca) * _NF\n    # X = X_pca\n\n    n_dim, m_dim = X.shape\n    # ----------------------------------------------------\n    # 1) online cvxMF, our algorithm. Rr version.\n    n_hat = k_cluster * candidate_set_size\n    t_ocmf = 0\n    acc = 0\n    acc_array = []\n    for round_num in range(aver_num):\n        t1 = time.time()\n        # W_hat_tmp, X_hat_tmp, repre_size_count_tmp, X_0_tmp, W_0_tmp = cvx_online_dict_learning(X, Y, n_hat, k_cluster, \n        #         numIter, lmda, eps,\n        #         flag = False, version = 'Rr')\n        W_hat_tmp, X_hat_tmp, repre_size_count_tmp, X_0_tmp, W_0_tmp = cvx_online_dict_learning_batch(X, Y, n_hat, k_cluster, \n                numIter, lmda, eps, _NF,\n                flag = False)\n        t2 = time.time()\n        t_ocmf += (t2 - t1)\n        D_final_tmp = np.matmul(X_hat_tmp, W_hat_tmp)\n\n        # clustered_label = get_clustering_assignment_1(X, D_final)\n        clustered_label_ocmf = get_clustering_assignment_2(X, D_final_tmp, k_cluster, lmda)\n        acc_tmp, AMI_tmp = evaluation_clustering(clustered_label_ocmf, Y)\n        acc_array.append(acc_tmp)\n        if acc_tmp >= acc:\n            W_hat = W_hat_tmp\n            X_hat = X_hat_tmp\n            X_0 = X_0_tmp\n            W_0 = W_0_tmp\n            D_final = D_final_tmp\n            acc = acc_tmp\n            AMI = AMI_tmp\n            repre_size_count = repre_size_count_tmp\n        if acc >= 0.9:\n            break\n    acc_aver = np.mean(acc_array)\n    t_ocmf_Rr = t_ocmf / (round_num + 1)\n    print(' ------ ocmf final accuracy = {:.4f}, AMI = {:.4f}'.format(acc, AMI))\n\n    df_centroids = pd.DataFrame(D_final.T, columns = pca_cols)\n    df_centroids['label'] = ['ocmf: type {}'.format(x) for x in range(1, k_cluster + 1)]\n\n    df_centroids.to_pickle('results_logging/ocmf_centroid_df')\n\n    df_x_hat = pd.DataFrame(X_hat.T, columns = pca_cols)\n    X_hat_set = ['group {}'.format(i) for i in range(k_cluster)]\n    X_hat_label = []\n    for idx in range(k_cluster):\n        X_hat_label += [X_hat_set[idx]] * repre_size_count[idx]\n    # ipdb.set_trace()\n    df_x_hat['label'] = X_hat_label\n    df_x_hat.to_pickle('results_logging/x_hat_df')\n    \n    # 2) online cvxMF, our algorithm. Ru version.\n    n_hat = k_cluster * candidate_set_size\n    t_ocmf_Ru = 0\n    acc_Ru = 0\n    acc_array = []\n    for round_num in range(aver_num):\n        t1 = time.time()\n        W_hat_tmp, X_hat_tmp, repre_size_count_tmp, X_0_tmp, W_0_tmp = cvx_online_dict_learning(X, Y, n_hat, k_cluster, \n                numIter, lmda, eps,\n                flag = False, version = 'Ru')\n        t2 = time.time()\n        t_ocmf_Ru += (t2 - t1)\n        D_final_tmp = np.matmul(X_hat_tmp, W_hat_tmp)\n\n        # clustered_label = get_clustering_assignment_1(X, D_final)\n        clustered_label_ocmf_Ru = get_clustering_assignment_2(X, D_final_tmp, k_cluster, lmda)\n        acc_tmp, AMI_tmp = evaluation_clustering(clustered_label_ocmf_Ru, Y)\n        acc_array.append(acc_tmp)\n        if acc_tmp >= acc_Ru:\n            W_hat_Ru = W_hat_tmp\n            X_hat_Ru = X_hat_tmp\n            X_0_Ru = X_0_tmp\n            W_0_Ru = W_0_tmp\n            D_final_Ru = D_final_tmp\n            acc_Ru = acc_tmp\n            AMI_Ru = AMI_tmp\n            repre_size_count_Ru = repre_size_count_tmp\n        if acc >= 0.9:\n            break\n    acc_aver_Ru = np.mean(acc_array)\n    t_ocmf_Ru = t_ocmf_Ru / (round_num + 1)\n    print(' ------ ocmf final accuracy = {:.4f}, AMI = {:.4f}'.format(acc_Ru, AMI_Ru))\n\n    df_centroids_Ru = pd.DataFrame(D_final_Ru.T, columns = pca_cols)\n    df_centroids_Ru['label'] = ['ocmf: type {}'.format(x) for x in range(1, k_cluster + 1)]\n\n    df_centroids_Ru.to_pickle('results_logging/ocmf_centroid_df')\n\n    df_x_hat_Ru = pd.DataFrame(X_hat_Ru.T, columns = pca_cols)\n    X_hat_set = ['group {}'.format(i) for i in range(k_cluster)]\n    X_hat_label = []\n    for idx in range(k_cluster):\n        X_hat_label += [X_hat_set[idx]] * repre_size_count[idx]\n    # ipdb.set_trace()\n    df_x_hat_Ru['label'] = X_hat_label\n    df_x_hat_Ru.to_pickle('results_logging/x_hat_df_Ru')\n    \n    # ----------------------------------------------------\n    # 3)  compare with online NMF in their paper\n    # D_0 = np.random.randn(m_dim, k_cluster)\n    # D_0 = np.absolute(D_0)\n    # ipdb.set_trace()\n    D_0 = (X_0 @ W_0).reshape(m_dim, k_cluster)\n    # D_0 = normalize(D_0, axis = 0) * _NF\n\n    acc_omf = 0\n    AMI_omf = 0\n    acc_omf_array = []\n    t_omf = 0\n\n    for round_num in range(aver_num):\n        t1 = time.time()\n        D_omf_final_tmp = online_dict_learning(X, lmda = lmda, D_0 = D_0, T = numIter, k_cluster = k_cluster, eps = eps, _NF = _NF)\n        t2 = time.time()\n        t_omf += (t2 - t1)\n\n        clustered_label_omf = get_clustering_assignment_2(X, D_omf_final_tmp, \n                k_cluster, lmda)\n        acc_omf_tmp, AMI_omf_tmp = evaluation_clustering(clustered_label_omf, Y)\n        acc_omf_array.append(acc_omf_tmp)\n        if acc_omf_tmp >= acc_omf:\n            D_omf_final = D_omf_final_tmp\n            acc_omf, AMI_omf = acc_omf_tmp, AMI_omf_tmp\n        if acc_omf >= 0.9:\n            break\n    acc_aver_omf = np.mean(acc_omf_array)\n    t_omf = t_omf/(round_num + 1)\n\n    print(' ------ onlineMF final accuracy = {:.4f}, AMI = {:.4f}'.format(acc_omf, \n        AMI_omf))\n\n    df_centroids_omf = pd.DataFrame(D_omf_final.T, columns = pca_cols)\n    df_centroids_omf['label'] = ['online MF cell type {}'.format(x) for x in range(1, k_cluster + 1)]\n    df_centroids_omf.to_pickle('results_logging/omf_centroid_df')\n    \n    # ----------------------------------------------------\n    df_final = pd.DataFrame(X, columns = pca_cols)\n    df_final['label'] = Y\n    df_final = df_final.append(df_x_hat)\n    df_final = df_final.append(df_x_hat_Ru)\n\n    df_final = df_final.append(df_centroids)\n    df_final = df_final.append(df_centroids_Ru)\n    df_final = df_final.append(df_centroids_omf)\n    print('shape of df_final: ', df_final.shape)\n\n    accuracy_dict = {\n            'omf': [acc_omf, AMI_omf],\n            'ocnmf_Ru': [acc, AMI],\n            'ocnmf_Rr': [acc_Ru, AMI_Ru],\n            }\n\n    size_of_cluster = n_dim//k_cluster\n    tmp_type = Y[0]\n    tmp_count = 0\n    cluster_size_count = []\n    for cur_type in Y:\n        if cur_type == tmp_type:\n            tmp_count += 1\n        else:\n            cluster_size_count.append(tmp_count)\n            tmp_count = 1\n            tmp_type = cur_type\n    cluster_size_count.append(tmp_count)\n\n    with open('scRNA_results/repre_size_count_Rr', 'wb') as f:\n        pickle.dump(repre_size_count, f)\n    with open('scRNA_results/repre_size_count_Ru', 'wb') as f:\n        pickle.dump(repre_size_count_Ru, f)\n    with open('scRNA_results/acc_dict', 'wb') as f:\n        pickle.dump(accuracy_dict, f)\n    with open('scRNA_results/cluster_size_count', 'wb') as f:\n        pickle.dump(cluster_size_count, f)\n\n    fig = plot_diff_method_scRNA(df_final, pca_cols, n_dim, k_cluster, \n            accuracy_dict, repre_size_count, repre_size_count_Ru, \n            size_of_cluster = None,\n            cluster_size_count = cluster_size_count)\n\n    tmp = 'May_23_test_fig_scRNA.png'\n    save_File_name = tmp\n    p2f = os.path.join(save_File_name)\n    fig.savefig(p2f, dpi = 150,\n            bbox_inces = 'tight')\n    \n    print('===' * 7)\n    print('ocmf Rr takes {:.4f}s'.format(t_ocmf_Rr))\n    print('ocmf Ru takes {:.4f}s'.format(t_ocmf_Ru))\n    print('omf takes {:.4f}s'.format(t_omf))\n","sub_path":"scRNA_cvx_online.py","file_name":"scRNA_cvx_online.py","file_ext":"py","file_size_in_byte":23319,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"585951727","text":"import pathlib\nimport collections\n\n\ndata = pathlib.Path(\"inputs/9.txt\").read_text().splitlines()[0].split(\" \")\nnumPlayers = int(data[0])\nnumMarbles = int(data[6])\n\nscores = collections.defaultdict(int)\ncircle = collections.deque([0])\nfor marble in range(1, numMarbles + 1):\n    if marble % 23 == 0:\n        circle.rotate(7)\n        scores[marble % numPlayers] += marble + circle.popleft()\n    else:\n        circle.rotate(-2)\n        circle.appendleft(marble)\n\nprint(max(scores.values()))\n","sub_path":"Day 9_1.py","file_name":"Day 9_1.py","file_ext":"py","file_size_in_byte":488,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"212918005","text":"from datetime import datetime, timedelta\nimport re\nimport requests\nfrom bs4 import BeautifulSoup as bs\nfrom argparse import ArgumentParser\n\n\ndef get_parser_of_command_line():\n    parser = ArgumentParser(description='Settings search movies')\n\n    parser.add_argument('-d', '--day', nargs='?',\n                        help='The number of days in cinemas',\n                        default=21, type=int)\n    parser.add_argument('-c', '--count', nargs='?',\n                        help='The number of cinemas which show the film',\n                        default=30, type=int)\n    parser.add_argument('-r', '--rating', nargs='?',\n                        help='Minimal movie rating',\n                        default=3.00, type=float)\n    return parser.parse_args()\n\n\ndef get_date_for_search(delta_days):\n    current_date = datetime.now().date()\n    initial_date = (datetime.now() - timedelta(days=delta_days)).date()\n    return  current_date, initial_date\n\n\ndef fetch_afisha_page():\n    url_afisha = 'https://www.afisha.ru/msk/schedule_cinema/'\n    raw_html = requests.get(url_afisha).content\n    return raw_html\n\n\ndef parse_afisha_list(afisha_html, good_count_cinemas):\n    content = bs(afisha_html, 'html.parser')\n    content_movies = content('div',\n                             {'class': 'object s-votes-hover-area collapsed'})\n    info_movies_afisha = []\n    for movie in content_movies:\n        title = movie('h3', {'class': 'usetags'})[0].get_text()\n        count_cinemas = len(movie('td',{'class': 'b-td-item'},'a'))\n        if int(count_cinemas) >= good_count_cinemas:\n            title_and_count_cinemas = {'title': title,\n                                       'count_cinemas': count_cinemas}\n            info_movies_afisha.append(title_and_count_cinemas)\n    return info_movies_afisha\n\n\ndef fetch_movie_info(last_month, current_month):\n    url_premiers_of_month = 'https://www.kinopoisk.ru/premiere/ru/2017/month/{}/'\n    url_after_scroll = 'page/1/ajax/true/'\n    if current_month != last_month:\n        last_month_content = requests.get(\n            url_premiers_of_month.format(last_month)).content\n        last_month_content_scroll = requests.get(\n            url_premiers_of_month.format(last_month) +\n            url_after_scroll).content\n        current_month_content = requests.get(\n            url_premiers_of_month.format(current_month)).content\n        current_month_content_scroll = requests.get(\n            url_premiers_of_month.format(current_month) +\n            url_after_scroll).content\n        full_content = last_month_content + current_month_content + \\\n                       current_month_content_scroll + last_month_content_scroll\n    else:\n        current_month_content = requests.get(\n            url_premiers_of_month.format(current_month)).content\n        current_month_content_scroll = requests.get(\n            url_premiers_of_month.format(current_month) +\n            url_after_scroll).content\n        full_content = current_month_content + current_month_content_scroll\n    return full_content\n\n\ndef get_films_in_kinopoisk(kinopoisk_content,\n                           initial_date,\n                           current_date,\n                           good_rate):\n    content = bs(kinopoisk_content, 'html.parser')\n    content_movies = content.find_all('div', {'class': 'premier_item'})\n    info_movies_kinopoisk = []\n    for movie in content_movies:\n        title = movie.find('span', {'class': 'name'}).text\n        start_date = movie.find('meta').get('content')\n        try:\n            rating = movie.find('u').text.split()[0]\n        except AttributeError:\n            continue\n        if re.search('\\W.',rating) is None:\n            continue\n        start_date = datetime.strptime(start_date,\"%Y-%m-%d\").date()\n        if current_date >= start_date >= initial_date and float(rating) >= good_rate:\n            title_and_rate = {'title': title, 'rate': rating}\n            info_movies_kinopoisk.append(title_and_rate)\n    return info_movies_kinopoisk\n\n\ndef get_pop_movies(list_afisha, list_kinopoisk):\n    common_info_list = []\n    for movie_afisha in list_afisha:\n        for movie_kinopoisk in list_kinopoisk:\n            if movie_afisha['title'] == movie_kinopoisk['title']:\n                movie_afisha.update(movie_kinopoisk)\n                common_info_list.append(movie_afisha)\n    return common_info_list\n\n\ndef output_movies_to_console(common_info_list):\n    for film in common_info_list[:10]:\n        movie = 'Film: {title}'.format(**film)\n        rate = 'Kinopoisk rating: {rate}'.format(**film)\n        count_cinemas = 'Show in {count_cinemas} cinemas in Moscow'.format(**film)\n        print(movie)\n        print(rate)\n        print(count_cinemas + '\\n')\n\n\nif __name__ == '__main__':\n    user_settings = get_parser_of_command_line()\n    delta_days = user_settings.day\n    good_rate = user_settings.rating\n    good_count_cinemas = user_settings.count\n    current_date, initial_date = get_date_for_search(delta_days)\n    current_month = datetime.today().month\n    last_month = initial_date.month\n    kinopoisk_content = fetch_movie_info(last_month,current_month)\n    list_kinopoisk = get_films_in_kinopoisk(kinopoisk_content,\n                                            initial_date,\n                                            current_date,\n                                            good_rate)\n    afisha_content = fetch_afisha_page()\n    list_afisha = parse_afisha_list(afisha_content, good_count_cinemas)\n    movies_list = get_pop_movies(list_afisha, list_kinopoisk)\n    output_movies_to_console(movies_list)","sub_path":"cinemas.py","file_name":"cinemas.py","file_ext":"py","file_size_in_byte":5561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"389605784","text":"\nfrom blockchain import Blockchain\nfrom utility.verification import Verification\nfrom wallet import Wallet\n\nclass Node():\n    def __init__(self):\n        self.wallet = Wallet()\n        self.chain = None\n    \n\n    def run_mode(self):\n        waiting = True\n        while waiting:\n            print('Please enter an input ')\n            print('1: Add new Transaction Value')\n            print('2: Mine the block')\n            print('3: Output the blockchain block')\n            print('4: to verify transactions.')\n            print('5: to create a wallet.')\n            print('6: to load wallet.')\n            print('quit: to litterally Quit ')\n            user_choice = self.get_user_choice()\n            if user_choice == '1':\n                txt_out = self.get_transaction_input()\n                reciever,coin = (txt_out)\n                if self.chain.add_transaction(reciever,coin,self.wallet.public_key):\n                    print('Transaction completed')\n                else:\n                    print('Transaction Failed!!')\n                print(self.chain.get_otx())\n            elif user_choice == '2':\n                if not self.chain.mine_block():\n                    print(\"Sorry you can't mine a block right now,Please create a wallet.\")\n            elif user_choice == '3':\n                self.display_blockchains()\n            elif user_choice == '4':\n                if Verification.verify_openTransaction(self.chain.get_otx(),self.chain.get_balance):\n                    print('All Transactions are valid...')\n                else:\n                    print('The Transactions are invalid')\n            elif user_choice == '5':\n                self.wallet.create_key()\n                self.chain = Blockchain(self.wallet.public_key)\n            elif user_choice == '6':\n                pass\n            elif user_choice == 'quit':\n                break\n            else:\n                print('Enter a Valid choice!')\n            if not Verification.verify_chain(self.chain.blockchain):\n                self.display_blockchains()\n                print('Invalid Blockchain')\n                break\n            print('Balance of {} is {:.6f}'.format(self.wallet.public_key,self.chain.get_balance(self.wallet.public_key)))\n            \n\n            #print('Transactions completed')\n\n        else:\n            print('Event finished')\n            \n        print('Done!')\n\n\n    def display_blockchains(self):\n        # This displays Elements in the blockchain\n        count = 0\n\n        for bl in self.chain.blockchain:\n            print('block '+str(count))\n            print(bl)\n            count += 1\n    \n\n    def get_user_choice(self):\n        user_input = input('Enter your choice: ')\n        return user_input\n\n\n    # gets inputs of the user to add to the blockchain\n    def get_transaction_input(self):\n        trans_recipient = input('Enter a recipient name: ')\n        trans_amount = float(input('Enter valid transaction: '))\n        return trans_recipient,trans_amount\n\n        \nif __name__ == \"__main__\":\n    node = Node()\n    node.run_mode()","sub_path":"node.py","file_name":"node.py","file_ext":"py","file_size_in_byte":3066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"251347027","text":"from pyramid.response import Response\r\nfrom pyramid.view import view_config\r\nfrom pyramid.httpexceptions import HTTPFound\r\nfrom pyramid.renderers import get_renderer\r\nfrom pyramid.url import resource_url\r\nfrom pyramid.security import remember\r\nfrom pyramid.security import forget\r\nfrom pyramid.security import has_permission\r\nfrom datetime import datetime \r\nfrom sqlalchemy.exc import DBAPIError\r\nimport colander\r\nfrom osipkd.tools import BULANS\r\nfrom ..models import (\r\n    DBSession,\r\n    UserResourcePermission,\r\n    Resource,\r\n    User,\r\n    )\r\n\r\nfrom datetime import (datetime, date)\r\n\r\nfrom pyjasper.client import JasperGenerator\r\n\r\nclass BaseViews(object):\r\n    def __init__(self, context, request):\r\n        self.context = context\r\n        self.request = request\r\n        self.session = request.session\r\n        self.cust_nm   = \"PEMERINTAH KABUPATEN/KOTA DEMO\"\r\n\r\n        cday = datetime.today()    \r\n        if not 'tahun' in self.session:\r\n            self.session['tahun'] =  cday.strftime('%Y')\r\n        self.session['tahun'] =  cday.strftime('%Y')\r\n        \r\n        if not 'bulan' in self.session:\r\n            self.session['bulan'] = str(cday.month).zfill(2)\r\n\r\n        if not 'tanggal' in self.session:\r\n            self.session['tanggal'] = datetime.date(cday)\r\n\r\n            #    if cday.month ==12:\r\n        #        self.session['bulan'] = '01'\r\n        #        self.session['tahun'] = str(cday.year + 1)\r\n        #    else:\r\n        \r\n\r\n        if not 'unit_id' in self.session:\r\n            self.session['unit_id'] = 0 #No tahun datetime.strftime(datetime.now(),'%Y')\r\n        if not 'all_unit' in self.session:\r\n            self.session['all_unit'] = 0 # no status\r\n        if not 'unit_kd' in self.session:\r\n            self.session['unit_kd'] = \"\"\r\n        if not 'unit_nm' in self.session:\r\n            self.session['unit_nm'] = \"\"\r\n        if not 'cust_nm' in self.session:\r\n            self.session['cust_nm'] = self.cust_nm\r\n        \r\n        if not 'rekening_kd' in self.session:\r\n            self.session['rekening_kd'] = \"\"\r\n        if not 'rekening_nm' in self.session:\r\n            self.session['rekening_nm'] = \"\"\r\n            \r\n        # Inisiasi tahun anggaran\r\n        ########################################################################\r\n        #remark in production  \r\n        #self.session['bulan'] =  '01'\r\n        #self.session['unit_id'] = 9\r\n        #self.session['unit_kd'] = '4027.114'\r\n        #self.session['unit_nm'] = 'DINAS DEMO'\r\n        ########################################################################\r\n        self.tahun = self.session['tahun']\r\n        self.bulan = self.session['bulan']\r\n        self.unit_id = self.session['unit_id']\r\n        self.all_unit = self.session['all_unit']\r\n        \r\n        self.d = {}\r\n        self.d['success'] = False        \r\n        self.d['msg']='Hak akses dibatasi'\r\n\r\n        self.unit_kd = self.session['unit_kd']\r\n        self.unit_nm = self.session['unit_nm']\r\n        #default datas \r\n        \"\"\"self.datas={}\r\n        self.datas['tahun'] = self.tahun\r\n        self.datas['bulan'] = self.bulan\r\n        self.datas['unit_id'] = self.unit_id\r\n        self.datas['all_unit'] = self.all_unit\r\n        self.datas['unit_kd'] = self.session['unit_kd']\r\n        self.datas['unit_nm'] = self.session['unit_nm']\r\n        permission = UserResourcePermission()\r\n        permission.perm_name = \"read\"\r\n        permission.user_name = \"aagusti\"\r\n        #resource = DBSession.query(User).filter_by(id=1)\r\n        resource = Resource()\r\n        resource.resource_name = 'GAJI'\r\n        resource.resource_type = '1'\r\n        \r\n        DBSession.add(resource)\r\n        request.user.resources.append(resource)\r\n        \"\"\"\r\n        \r\n        \r\n\r\n    def _DTstrftime(self, chain):\r\n        ret = chain and datetime.strftime(chain, '%d-%m-%Y')\r\n        if ret:\r\n            return ret\r\n        else:\r\n            return chain\r\n            \r\n    def _number_format(self, chain):\r\n        import locale\r\n        locale.setlocale(locale.LC_ALL, 'id_ID.utf8')\r\n        ret = locale.format(\"%d\", chain, grouping=True)\r\n        if ret:\r\n            return ret\r\n        else:\r\n            return chain\r\n            \r\n@view_config(route_name='change-act', renderer='json', permission='view')\r\ndef change_act(request):\r\n    ses = request.session\r\n    req = request\r\n    params = req.params\r\n    url_dict = req.matchdict\r\n    \r\n    if url_dict['act']=='tahun':\r\n        ses['tahun'] = 'tahun' in params and params['tahun'] or '2014'\r\n        if 'bulan' in params:\r\n            ses['bulan'] = params['bulan'] or '12'\r\n        return {'success':True, 'msg':'Sukses Ubah Tahun'}\r\n        \r\n    elif url_dict['act']=='tanggal':\r\n        ses['tanggal'] = 'tanggal' in params and datetime.strptime(params['tanggal'],'%Y-%m-%d') or datetime.date(datetime.now())\r\n        ses['tanggal2'] = 'tanggal2' in params and datetime.strptime(params['tanggal2'],'%Y-%m-%d') or ses['tanggal']\r\n        return {'success':True, 'msg':'Sukses Ubah Tanggal'}\r\n        \r\n    elif url_dict['act']=='bulan':\r\n        ses['bulan'] = 'bulan' in params and params['bulan'] or '12'\r\n        return {'success':True, 'msg':'Sukses Ubah Bulan menjadi %s' % BULANS[ses['bulan']]}\r\n        ","sub_path":"osipkd/views/base_view.py","file_name":"base_view.py","file_ext":"py","file_size_in_byte":5238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"518097625","text":"\nimport datetime\nimport logging\nimport xml.etree.cElementTree as ET\n\n\n# /////////////////////////////////////////////////////////////////\n#\n# Parseador encargado de traducir los diagramas en xml\n#\n#\n#\nclass Parser:\n    def __init__(self,\n                 diagram,\n                 filename):\n        self.diagram = diagram\n        self.filename = filename\n\n    def setAuthorDate(self,\n                      autor):\n        self.autor = autor\n        self.date = str(datetime.datetime.today()).split()[0]\n\n    def parse_relation(self,\n                       relations,\n                       diagram,\n                       di):\n        # modificamos los targets y sources eliminando los extends\n        for rel in relations:\n            re = ET.SubElement(diagram, \"relationship\", name=rel.name)\n            st1 = \"\"\n            st2 = \"\"\n\n            for so in rel.sources:\n                st1 += so + \", \"\n            ET.SubElement(re, \"source\").text = st1[0:len(st1) - 2]\n            for so in rel.targets:\n                st2 += so + \", \"\n            ET.SubElement(re, \"target\").text = st2[0:len(st2) - 2]\n\n    def toXML(self):\n        root = ET.Element(\"filter\")\n        logging.info(\"Creating new root\")\n\n        name = ET.SubElement(root, \"name\").text = \"DPDF Model\"\n        author = ET.SubElement(root, \"author\").text = self.autor\n        date = ET.SubElement(root, \"date\").text = self.date\n        logging.info(\"Meta:name: \" +\n                     name +\n                     \" autor: \" +\n                     author +\n                     \" date: \" +\n                     date)\n        diagrams = ET.SubElement(root, \"diagrams\")\n\n        for di in self.diagram:\n\n            logging.info(\"new diagram: \" +\n                         di.name)\n            diagram = ET.SubElement(diagrams,\n                                    \"diagram\",\n                                    name=di.name)\n            for et in di.entities:\n                if et.abstract is not True:\n                    ET.SubElement(diagram, \"entity\").text = et.name\n            ET.SubElement(diagram, \"entity\").text = \"TextNote\"\n            ET.SubElement(diagram, \"entity\").text = \"UMLComment\"\n            self.parse_relation(di.complexRelations, diagram, di)\n\n        tree = ET.ElementTree(root)\n        logging.info(\"writing on: \" +\n                     self.filename)\n        tree.write(self.filename)\n","sub_path":"app/parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":2378,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"39645396","text":"# ROS imports\nimport rclpy\nfrom rclpy.node import Node\nfrom std_msgs.msg import Int8MultiArray, Float64MultiArray\nimport numpy as np\n\n# temperature threshold\ntemp_thres = 30\nradius_thres = 7\nresolution = 64\n\n\nclass CommandNode(Node):\n\n    def __init__(self):\n        super().__init__('com_node')\n        self.publisher_ = self.create_publisher(Int8MultiArray, 'com_node', 10)\n        self.heat_subscription = self.create_subscription(Float64MultiArray, 'heat_array', self.callback, 10)\n        self.heat_subscription  # prevent unused variable warning\n\n    def callback(self, heat_array):\n        coordinates = get_bright_loc(heat_array.data)\n        self.get_logger().info(f'Coordinates: X:{coordinates[0]}, Y:{coordinates[1]}')\n        comm_array = Int8MultiArray()\n        comm_array.data = command(coordinates)\n        self.publisher_.publish(comm_array)\n        self.get_logger().info(\n            f'Direction: X:{comm_array.data[0]}, Y:{comm_array.data[1]}, Active:{comm_array.data[2]}')\n\n\ndef main(args=None):\n    rclpy.init(args=args)\n    command_pub = CommandNode()\n    rclpy.spin(command_pub)\n\n    # Destroy the node explicitly\n    # (optional - otherwise it will be done automatically\n    # when the garbage collector destroys the node object)\n    command_pub.destroy_node()\n    rclpy.shutdown()\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"Target Detection/command_pub_test.py","file_name":"command_pub_test.py","file_ext":"py","file_size_in_byte":1347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"523920032","text":"from fastapi import FastAPI, File, UploadFile, Header, HTTPException, Depends\nfrom fastapi.security.api_key import APIKeyHeader\nimport uvicorn\nfrom pydantic import BaseModel\nimport hashlib\nfrom db import Database\n\napp = FastAPI()\ndatabase = Database('carbery_db.db')\nx_auth_user = APIKeyHeader(\n    name=\"X-Auth-User\",\n    auto_error=False\n)\n\n\nclass FileHash(BaseModel):\n    userId: str\n    filename: str\n    sha256: str\n    md5: str\n\n\nasync def check_auth(\n    x_auth_user: str = Depends(x_auth_user)\n):\n    if not x_auth_user:\n        raise HTTPException(\n            status_code=401,\n            detail='Не авторизован.'\n        )\n\n    return x_auth_user\n\n\n@app.get(\n    '/file_hashes​/{hash}',\n    response_model=FileHash,\n    summary='Получение имени файла по хэшу.',\n    status_code=200,\n    response_description='OK'\n)\nasync def get_filename(hash: str):\n    values = database.get_value(\n        '''SELECT * FROM carbery_table\n            WHERE sha256=? OR md5=?''',\n        (hash, hash)\n    )\n    if not values:\n        raise HTTPException(\n            status_code=404,\n            detail='Файлы с данным хэшем не найдены.'\n        )\n    return {\n        'userId': values.get('userId'),\n        'filename': values.get('filename'),\n        'sha256': values.get('sha256'),\n        'md5': values.get('md5')\n    }\n\n\n@app.delete(\n    '/file_hashes/{hash}',\n    response_model=FileHash,\n    summary='Удаление хэшей файлов.',\n    status_code=200,\n    response_description='OK'\n)\nasync def delete_hash(\n    hash: str,\n    x_auth_user: str = Depends(check_auth)\n):\n    values = database.get_value(\n        '''SELECT * FROM carbery_table\n            WHERE userId=? and (sha256=? or md5=?)''',\n        (x_auth_user, hash, hash)\n        )\n    if not values:\n        raise HTTPException(\n            status_code=404,\n            detail='Файлы с данным хэшем не найдены.'\n        )\n    database.del_value((x_auth_user, hash, hash))\n\n\n@app.post(\n    '/file_hashes',\n    response_model=FileHash,\n    summary='Добавление хэша файла.',\n    status_code=201,\n    response_description='OK'\n)\nasync def add_hash(\n    file: UploadFile = File(...),\n    x_auth_user: str = Depends(check_auth)\n):\n    userId = x_auth_user\n    filename = file.filename\n    file = file.file.read()\n    md5 = hashlib.md5(file).hexdigest()\n    sha256 = hashlib.sha256(file).hexdigest()\n    database.add_value((x_auth_user, filename, sha256, md5))\n    return {\n        'userId': x_auth_user,\n        'filename': filename,\n        'sha256': sha256,\n        'md5': md5\n    }\n\nif __name__ == \"__main__\":\n    uvicorn.run(\n        'main:app',\n        host='localhost',\n        port=3000,\n        access_log=False\n    )\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2803,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"624814041","text":"import sys\nfrom PyQt5.QtCore import pyqtSignal, QSize, Qt\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtWidgets import (QWidget, QSlider, QApplication, \n    QHBoxLayout, QVBoxLayout,QLabel)\n\nclass MyWidget(QWidget):\n\n    clicked = pyqtSignal()\n    \n    def __init__(self, parent = None):\n    \n        QWidget.__init__(self, parent)\n        self.color = QColor(0, 0, 0)\n        \n        self.lb1=QLabel(self)\n        self.lb1.move(10,10)\n        self.lb1.setText('label 1')\n        self.lb1.adjustSize()  \n        \n\n        self.sl1=QSlider(Qt.Horizontal,self)\n        self.sl1.move(50,50)\n        self.sl1.setRange(0,10)\n        self.sl1.setSingleStep(1)\n        self.sl1.valueChanged[int].connect(self.changeValue)\n\n    def paintEvent(self, event):\n    \n        painter = QPainter()\n        painter.begin(self)\n        painter.fillRect(event.rect(), QBrush(self.color))\n        painter.end()\n   \n    \n    def mousePressEvent(self, event):\n    \n        self.setFocus(Qt.OtherFocusReason)\n        event.accept()\n    \n    def mouseReleaseEvent(self, event):\n    \n        if event.button() == Qt.LeftButton:\n        \n            self.color = QColor(self.color.green(), self.color.blue(),\n                                127 - self.color.red())\n            print(self.color)\n            self.update()\n            self.clicked.emit()\n            event.accept()\n    \n    def sizeHint(self):\n    \n        return QSize(200, 200)\n \n    def changeValue(self,value):\n        print(value)\n        #self.lbl3.setText(str(value))\n        #self.lbl3.adjustSize()\n\nif __name__ == \"__main__\":\n\n    app = QApplication(sys.argv)\n    window = QWidget()\n    \n    mywidget = MyWidget()\n    label = QLabel('hi hi')\n    \n    #mywidget.clicked.connect(label.clear)\n    mywidget.clicked.connect(label.clear)\n    \n    layout = QVBoxLayout()\n    layout.addWidget(mywidget)\n    layout.addWidget(label)\n    window.setLayout(layout)\n    \n    window.show()\n    sys.exit(app.exec_())\n","sub_path":"pyqt/pyqt4.py","file_name":"pyqt4.py","file_ext":"py","file_size_in_byte":1944,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"117371039","text":"#-*- coding: utf-8 -*-\nimport csv\nimport random\nprint(\"Lecture du Fichier CSV\")\n\n#cr = csv.reader(open(\"liste.csv\",\"rb\"))\nifile  = open('liste.csv', \"r\")\nc = csv.writer(open(\"matière.csv\", \"wb\"), delimiter=';')\nread = csv.reader(ifile, delimiter=';')\nfor row in read:\n\tc.writerow([row[0],row[1],random.randint(0,20)])","sub_path":"dev/lec_csv.py","file_name":"lec_csv.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"209767788","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\n\nimport codecs\nimport collections\nimport os\nfrom six.moves import cPickle\n\nimport numpy as np\nimport tensorflow as tf\n\n\ndef identity(data):\n    return data\n\nclass text_data:\n    def __init__ (self,\n                  input_file,\n                  line_processor=identity, # any preprocessing for the line read\n                  line_splitter=identity, # should the line be split into words\n                                       # or characters or something else?\n                  max_voc=40000):\n\n        vocab_file = input_file + \".vocab.pkl\"\n\n        self.line_processor = line_processor\n        self.line_splitter = line_splitter\n\n        if not os.path.exists(vocab_file):\n            print(\"creating new vocab information\")\n            worddict = collections.Counter()\n            with codecs.open(input_file, 'r', 'utf-8') as infile:\n                for line in infile:\n                    line = line_splitter(line_processor(line))\n#                    print (line)\n                    for w in line:\n                        worddict[w] += 1\n\n            # yeehaw... magic numbers ;)\n            worddict['<<unk>>'] = 999999999\n            sorted_words = (sorted(worddict.items(), key=lambda x: (-x[1], x[0])))[:max_voc]\n            self.id_to_word, _ = list(zip(*sorted_words))\n\n            self.word_to_id = dict(zip(self.id_to_word, range(len(self.id_to_word))))\n\n            with open(vocab_file, 'wb') as f:\n                cPickle.dump(self.id_to_word, f)\n                cPickle.dump(self.word_to_id, f)\n\n        else:\n            print(\"loading vocab information\")\n            with open(vocab_file, 'rb') as f:\n                self.id_to_word = cPickle.load(f)\n                self.word_to_id = cPickle.load(f)\n        print (\"ids: %d\" % len(self.id_to_word))\n        print (\"words: %d\" % len(self.word_to_id))\n        self.unk_id = self.word_to_id['<<unk>>']\n        print (\"<<unk>> id is: %d\" % self.unk_id)\n\n\n    def vocab_size(self):\n        return len(self.word_to_id)\n\n\n    def id(self, word):\n\n        return self.word_to_id.get(word, self.unk_id)\n\n    def word(self, idt):\n        if len(self.id_to_word) > idt:\n            return self.id_to_word[idt]\n        else:\n            return '<<unk>>'\n\n\n    def idize_file(self, input_file):\n        file_data = []\n        with codecs.open(input_file, 'r', 'utf-8') as infile:\n            for line in infile:\n                file_data.append([self.id(w) for w in\n                        self.line_splitter(self.line_processor(line))])\n\n        return file_data\n\n    def iterator(self, data, batch_size, num_steps): # careful! this one does flattening!\n    # end of line information is lost\n    # it's heavily based on\n    # https://github.com/tensorflow/tensorflow/blob/master/tensorflow/models/rnn/ptb/reader.py\n        flattened_list = [y for x in data for y in x]\n        npdata = np.array(flattened_list, dtype=np.int32)\n\n        data_length = len(npdata)\n        batch_length = data_length // batch_size\n\n        dat = np.zeros([batch_size, batch_length], dtype=np.int32)\n        for i in range(batch_size):\n            dat[i] = npdata[batch_length * i:batch_length*(i+1)]\n\n        epoch_size = (batch_length - 1) // num_steps\n\n        if epoch_size == 0:\n            raise ValueError(\"epoch_size == 0, decrease batch_size or num_steps\")\n\n        for i in range(epoch_size):\n            x = dat[:, i*num_steps:(i+1)*num_steps]\n            y = dat[:, i*num_steps+1:(i+1)*num_steps+1]\n            yield (x, y)\n","sub_path":"libs/read_text.py","file_name":"read_text.py","file_ext":"py","file_size_in_byte":3590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"285160106","text":"from PIL import Image\nimport pytesseract\nfrom pytesseract import image_to_string\nimport os\nimport cv2\nimport re\nfrom autocorrect import spell\nimport string\nfrom models import *\n\ndb_pre_ocr = db('pre_ocr')\ndb_post_ocr = db('post_ocr')\n\n\ndef extractText(image_path):\n    try:\n        image = cv2.imread(image_path)\n        if image is None:\n            return \"00000\"\n        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n        gray = cv2.threshold(\n            gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]\n        gray = cv2.medianBlur(gray, 3)\n\n        # write the grayscale image to disk as a temporary file so we can\n        # apply OCR to it\n        filename = \"scraper/{}.png\".format(os.getpid())\n        cv2.imwrite(filename, gray)\n\n        # load the image as a PIL/Pillow image, apply OCR, and then delete\n        # the temporary file\n        text = pytesseract.image_to_string(Image.open(filename))\n        os.remove(filename)\n        text = text.strip().split()\n        chars_to_remove = ['.', '!', ':']\n        extracted = list()\n        for t in text:\n            for c in chars_to_remove:\n                t = t.replace(c, '')\n            extracted.append(spell(t))\n        return(' '.join(extracted))\n\n    except BaseException as e:\n        print(e)\n\n\ntry:\n    process_files = db_pre_ocr.find({})\n\n    for image in process_files:\n        fromImage = extractText(image['location'])\n        fromImageClean = re.sub(r'\\W+', ' ', fromImage)  # Only alphanumeric\n        if fromImage != \"00000\":\n\n            add_image = {\n                'location': image['location'],\n                'attributes': image['attributes'],\n                'ocr_out': fromImageClean\n            }\n            db_post_ocr.insert_one(add_image)\nexcept BaseException as e:\n    print(e)\n","sub_path":"scraper/ocr.py","file_name":"ocr.py","file_ext":"py","file_size_in_byte":1782,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"388685840","text":"print('Enter the number of numbers that need to be bubble sorted')\nx = int(input())\n\nnumbers = []\nwrite_numbers = []\ni = 0\nwhile i < x:\n    print('Enter number')\n    n = input()\n    write_numbers.append(n)\n    numbers.append(int(n))\n    i += 1\n\ndef listToString(l, s):\n    for q in range(x):\n        temp = str(l[q])\n        s += temp\n        s += ', '\n    return s\n\nwrite_numbers_string = ' '\nappend_unsorted = open('records.txt', 'a+')\nappend_unsorted.write('\\nNew Entry. Unsorted numbers: \\n')\nappend_unsorted.write(listToString(write_numbers, write_numbers_string))\nappend_unsorted.close()\n\nprint('List you entered')\nprint(numbers)\n\ndef bubblesort(l):\n    swapped = None\n    for a in range(x):\n        swapped == False\n        for b in range(x-a-1):\n            if l[b] > l[b+1]:\n                l[b], l[b+1] = l[b+1], l[b]\n                swapped == True\n        if swapped == False:\n            break\n\nbubblesort(numbers)\n\nprint('Bubble Sorted List')\nprint(numbers)\n\nwrite_numbers_string = ' '\nwrite_numbers = numbers\nappend_sorted = open('records.txt', 'a+')\nappend_sorted.write('\\nSorted numbers: \\n')\nappend_sorted.write(listToString(write_numbers, write_numbers_string))\nappend_sorted.close()\n","sub_path":"My Own Python Programs/Bubble Sort/bubblesort.py","file_name":"bubblesort.py","file_ext":"py","file_size_in_byte":1203,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"275437774","text":"from faker import Factory\nfrom random import randint, random\nfrom datetime import date, timedelta\n\nfrom stat_tracker.models import User, Activity, Timestamp\n\nACTIVITIES = [\"Run a Mile\",\n              \"Read Something\",\n              \"Work Out\",\n              \"Make Dinner\",\n              \"Water Plants\",\n              \"Remember Birthdays\",\n              \"Call Mom\",\n              \"Set Alarm\",\n              \"Practice Instruments\",\n              \"Study\",\n              \"Laundry\"]\n\ndef seed(db):\n    \"\"\"\n    Seeds the database but commented out to avoid running accidentally\n    :param db\n    :return:\n    \"\"\"\n    users = db.session.query(User).all()\n    # for user in users:\n    #     num_of_activities = randint(0,len(ACTIVITIES)-1)\n    #     count = 0\n    #     while count < num_of_activities:\n    #         activity = Activity(name=ACTIVITIES[randint(0, len(ACTIVITIES)-1)],\n    #                             creator=user.id,\n    #                             activity_type=\"Once A Day\")\n    #\n    #         if not Activity.query.filter_by(name=activity.name, creator=activity.creator).first():\n    #             db.session.add(activity)\n    #             count += 1\n\n    # today = date.today()\n    # last_sixty_days = [today - timedelta(days=i) for i in range(60)]\n    # last_sixty_days = [day.strftime(\"%Y-%m-%d\") for day in last_sixty_days]\n    # activities = db.session.query(Activity).all()\n    #\n    # for activity in activities:\n    #     random_percent = random()\n    #\n    #     for day in last_sixty_days:\n    #         if random() < random_percent:\n    #             timestamp = Timestamp(timestamp=day,\n    #                                   activity_id=activity.id,\n    #                                   actor_id=activity.creator)\n    #             if not Timestamp.query.filter_by(timestamp=day, activity_id=activity.id).first():\n    #                 db.session.add(timestamp)\n    #\n    # db.session.commit()\n\n\ndef trim_seeds(db):\n    \"\"\"\n    Removes 30% of randomly selected timestamps.\n    :param db:\n    :return:\n    \"\"\"\n    timestamps = Timestamp.query.all()\n    for timestamp in timestamps:\n        if random() < 0.3:\n            db.session.delete(timestamp)\n    db.session.commit()","sub_path":"seed.py","file_name":"seed.py","file_ext":"py","file_size_in_byte":2207,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"342584331","text":"\"\"\" Analýza nehod v kraji Vysočina podle typu vozovky\n\"\"\"\n\nimport pandas as pd\nimport geopandas\nimport matplotlib.pyplot as plt\nimport contextily as ctx\nfrom matplotlib.lines import Line2D\nimport numpy as np\n\n# typy komunikací které analyzuji\n# p36 == 0 -> dálnice\n# 1 -> 1. třídy, 2. -> 2.třídy, 3.-> 3.třídy\n# 6 - místní komunikace, 7 - polní, lesní cesty\ncommunication_types = [0, 1, 2, 3, 6, 7]\ncommunication_names = {0: 'Dálnice',\n                       1: 'Silnice 1. třídy',\n                       2: 'Silnice 2. třídy',\n                       3: 'Silnice 3. třídy',\n                       6: 'Místní komunikace',\n                       7: 'Účelové komunikace\\n'\n                          '(polní, lesní cesty atd.)'}\n# simplifikace druh nehod pod větší skupiny\naccident_causes = {\n    100: \"nezaviněná řidičem\",\n    200: \"nepřiměřené rychlost jízdy\",\n    300: \"nesprávné předjíždění\",\n    400: \"nedání přednosti v jízdě\",\n    500: \"nesprávný způsob jízdy\",\n    600: \"technická závada vozidla\"\n}\n# délka silnic 1.-3. třídy na území Vysočiny a dálnice D1 (jiná zde neprochází)\n# statistiky platné ke dni 31.12.2006\n# zdroj: https://www.kr-vysocina.cz/assets/File.ashx?id_org=450008&id_dokumenty=4001876#:~:text=2.1.,-Z%C3%A1kladn%C3%AD%20%C3%BAdaje%20o&text=Na%20%C3%BAzem%C3%AD%20kraje%20Vyso%C4%8Dina%20se,v%20d%C3%A9lce%204%20579%20km.\ncommunication_lenght = {\n    0: 93,\n    1: 422,\n    2: 1630,\n    3: 2949\n}\n\ndef get_data(data_location: str = \"accidents.pkl.gz\"):\n    \"\"\"\n    Funkce extrahuje data z daného souboru, vyfiltruje je a vrátí výsledný dataframe.\n\n    :param data_location: Umístění souboru se zdrojovými daty.\n    :return: Extrahovaný dataframe vyfiltrovaný pouze pro Vysočinu a typy silnice 0-3 a 6-7.\n    \"\"\"\n    df = pd.read_pickle(data_location)\n    # ponechani pouze dat z kraje Vysocina\n    df = df[df[\"region\"] == \"VYS\"]\n    # vyrazeni p36 4 a 5, tedy polozek uzel a komunikace sledovane ve meste a 8 (ostatni ucelove komunikace\n    # jako parkoviste, protoze zkoumam pouze typ komunikace)\n    df = df.query('p36 != \"4\" and p36 != \"5\" and p36 != \"8\"')\n    return df\n\n\ndef create_info_graph(df: pd.DataFrame):\n    \"\"\"\n    Funkce z daného dataframe vytvoří graf a uloží ho do \"fig.png\" souboru.\n    Tento graf zobrazuje nehody na mapě s barevným kódem podle druhu vozovky.\n\n    :param df: Dataframe s daty o nehodách.\n    \"\"\"\n    df = df[df.d.notnull()]\n    df = df[df.d.notnull()]\n    gdf = geopandas.GeoDataFrame(df, geometry=geopandas.points_from_xy(df['d'], df['e']), crs=\"EPSG:5514\")\n\n    comunication_types_reverse = communication_types.copy()\n    comunication_types_reverse.reverse()\n    comunication_colors = {0: 'indigo',\n                           1: 'blue',\n                           2: 'dodgerblue',\n                           3: 'darkturquoise',\n                           6: 'black',\n                           7: 'lime'}\n    legend_elements = []\n    for i in communication_types:\n        legend_elements.append(Line2D([0], [0], color=comunication_colors[i], lw=4,\n                                      label=communication_names[i]))\n\n    dfs = {}\n    for i in communication_types:\n        data = gdf.query('p36 == \"' + str(i) + '\"')\n        dfs[i] = data\n\n    # vytvoreni grafu z in_df a out_df\n    fig, ax = plt.subplots(1, 1, figsize=(20, 15))\n    for i in comunication_types_reverse:\n        dfs[i].plot(ax=ax, color=comunication_colors[i], markersize=15)\n\n    ctx.add_basemap(ax, crs=gdf.crs.to_string(), source=ctx.providers.Stamen.TonerLite)\n    ax.axis('off')\n    ax.set_title('Nehody kraje Vysočina podle typu silnice', fontdict={\"fontsize\": 25})\n    ax.legend(handles=legend_elements, fontsize='xx-large')\n\n    plt.savefig('fig.png')\n    plt.show()\n\n\ndef find_most(d: dict):\n    \"\"\"\n    Najde v daném dictionary největší hodnotu a vrátí její key.\n    :param d: Dictionary k prohledání.\n    :return: Key nejvyšší hodnoty.\n    \"\"\"\n    max_val = 0\n    max_val2 = 0\n    max_type = None\n    max_type2 = None\n    for key, val in d.items():\n        if val > max_val:\n            max_val2 = max_val\n            max_type2 = max_type\n            max_val = val\n            max_type = key\n        elif val > max_val2:\n            max_val2 = val\n            max_type2 = key\n    return max_type, max_type2\n\n\ndef create_statistics(df: pd.DataFrame):\n    \"\"\"\n    Vytvori tabulku s informacemi o typu nehod na ruznych silnicich.\n    :param df: DataFrame ke zpracovani do statistik.\n    \"\"\"\n    conditions = [\n        (df['p12'] == 100),\n        (df['p12'] > 200) & (df['p12'] <= 209),\n        (df['p12'] > 300) & (df['p12'] <= 311),\n        (df['p12'] > 400) & (df['p12'] <= 414),\n        (df['p12'] > 500) & (df['p12'] <= 516),\n        (df['p12'] > 600) & (df['p12'] <= 615)\n    ]\n    # vytvori dalsi sloupecek, kde je p36 zjednodusena do cisla z accident_causes podle typu nehody\n    df['cause_code'] = np.select(conditions, accident_causes.keys())\n\n    # vytvori dictionary s polozkami code_group : accident count\n    sum_d = df['cause_code'].value_counts().to_dict()\n    # vytvori tabulku, vysledna bude formatu\n    # typ silnice | vsechny | pomer | rychlost pocet | rychlost pomer | nejcastejsi pricina | nej. pocet | nej. pomer\n    # a bude mit radky pro kazdy z communication_types + sum (soucet pro vsechny)\n    # kazdy radek je key table dictionary s typem silnice a jako value je dalsi dictionary s polozkami radku\n    table = {}\n    table['sum'] = {}\n    sum_total = len(df['cause_code'])\n    table['sum']['total'] = sum_total\n    table['sum']['total_share'] = sum_total / sum_total\n    table['sum']['speed_cnt'] = sum_d[200]\n    table['sum']['speed_share'] = sum_d[200] / sum_total\n    sum_most, sum_most2 = find_most(sum_d)\n    table['sum']['most_type'] = accident_causes[sum_most]\n    table['sum']['most_type2'] = accident_causes[sum_most2]\n    # for cyklus provede vytvoreni \"radku\" (dictionary v table) pro kazdou silnici\n    for road in communication_types:\n        road_df = df.query('p36 == \"' + str(road) + '\"')\n        road_d = road_df['cause_code'].value_counts().to_dict()\n        road_total = len(road_df)\n        table[road] = {}\n        table[road]['total'] = road_total\n        table[road]['total_share'] = road_total / sum_total\n        table[road]['speed_cnt'] = road_d[200]\n        table[road]['speed_share'] = road_d[200] / road_total\n        road_most, road_most2 = find_most(road_d)\n        table[road]['most_type'] = accident_causes[road_most]\n        table[road]['most_type2'] = accident_causes[road_most2]\n\n    # vytisknuti tabulky v github markdown formátu\n    print('# Tabulka typu silnic a jejich nejčastějších nehod')\n    print('| Typ silnice | Počet nehod | Počet nehod v % | Nejčastější příčina | '\n          ' 2. nejčastejší příčina |')\n    print('| --- | --- | --- | --- | --- | --- |')\n    for road in communication_types:\n        print('|', communication_names[road],\n              '|', table[road][\"total\"],\n              '|', round((table[road][\"total_share\"]*100), 2), '%',\n              '|', table[road][\"most_type\"],\n              '|', table[road][\"most_type2\"],\n              '|')\n    print('|', 'Součet',\n          '|', table['sum'][\"total\"],\n          '|', round((table['sum'][\"total_share\"] * 100), 2), '%',\n          '|', table['sum'][\"most_type\"],\n          '|', table['sum']['most_type2'],\n          '|', end='\\n\\n')\n\n    # nasleduji cast se zabyva pouze vypoctem o dalnici a silnicich 1.-3. tridy\n    # protoze k nim mame informace o delce\n    total_lenght = sum(communication_lenght.values())\n    total_accidents = 0\n    for i in range(0, 4):\n        total_accidents += table[i]['total']\n\n    print('# Analýza nehod v souvislosti s délkou silnic\\nPouze dálnice a silnice 1.-3. třídy.')\n    print('Celkový počet nehod:', total_accidents)\n    print('Celková délka silnic:', total_lenght)\n    print('Dálnice:\\n\\t', str(communication_lenght[0]), ' km (',\n          round(communication_lenght[0]*100/total_lenght),\n          '%): ', table[0]['total'], ' nehod (',\n          round(table[0]['total']*100/total_accidents), '%)\\n\\t',\n          round(table[0]['total']/communication_lenght[0], 2), ' nehod/km', sep='')\n    print('Silnice I. třídy:\\n\\t', str(communication_lenght[1]), ' km (',\n          round(communication_lenght[1]*100/total_lenght),\n          '%): ', table[1]['total'], ' nehod (',\n          round(table[1]['total']*100/total_accidents), '%)\\n\\t',\n          round(table[1]['total']/communication_lenght[1], 2), ' nehod/km', sep='')\n    print('Silnice II. třídy:\\n\\t', str(communication_lenght[2]), ' km (',\n          round(communication_lenght[2]*100/total_lenght),\n          '%): ', table[2]['total'], ' nehod (',\n          round(table[2]['total']*100/total_accidents), '%)\\n\\t',\n          round(table[2]['total']/communication_lenght[2], 2), ' nehod/km', sep='')\n    print('Silnice III. třídy:\\n\\t', str(communication_lenght[3]), ' km (',\n          round(communication_lenght[3]*100/total_lenght),\n          '%): ', table[3]['total'], ' nehod (',\n          round(table[3]['total']*100/total_accidents), '%\\n\\t',\n          round(table[3]['total']/communication_lenght[3], 2), ' nehod/km', sep='')\n\n\nif __name__ == \"__main__\":\n    df = get_data()\n    create_info_graph(df)\n    create_statistics(df)\n","sub_path":"izv3/doc.py","file_name":"doc.py","file_ext":"py","file_size_in_byte":9346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"141065591","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jul 24 16:10:58 2018\n\n@author: fengh\n\"\"\"\n\nimport argparse\nimport motion\nimport almath\nimport time\n\nfrom naoqi import ALProxy\n\narmName     = \"LArm\"\nlFootOffset = almath.Pose2D(0.0, 0.09, 0.0)\nrFootOffset = almath.Pose2D(0.0, -0.09, 0.0)\nstepSpeed   = 1.0\nstepLength  = 0.05\n\ndef initRobotPosition(motionProxy, postureProxy):\n    ''' Inits NAO's position and stiffnesses to make the guiding possible.'''\n    \n    global armName\n    \n    motionProxy.wakeUp()\n    postureProxy.goToPosture(\"StandInit\", 0.3)\n    motionProxy.moveInit()\n    time.sleep(1.0)\n    # Make left arm loose.\n    motionProxy.setAngles(\"LWristYaw\", 0.0, 1.0)\n    motionProxy.setAngles(\"Head\", [0.44, -0.44], 0.5)\n    motionProxy.setStiffnesses(armName, 0.0)\n    motionProxy.setStiffnesses(\"LWristYaw\", 0.2)\n\n    # Disable arm moves while walking on left arm.\n    motionProxy.setMoveArmsEnabled(False, True)\n    time.sleep(1.0)\n    \ndef interpretJointsPose(motionProxy, memoryProxy):\n    ''' Translates the current left arm pose into a target position for NAO's\n        foot. '''\n\n    # Retrieve current arm position.\n    armPose = motionProxy.getAngles(armName, True)\n\n    targetX     = 0.0\n    targetY     = 0.0\n    targetTheta = 0.0\n    gaitConfig = motionProxy.getMoveConfig(\"Default\")\n\n    # Filter Shoulder Pitch.\n    if (armPose[0] > - 0.9 and armPose[0] < -0.20):\n        targetX = stepLength\n    elif (armPose[0] > -2.5 and armPose[0] < -1.5):\n        targetX = - stepLength - 0.02\n\n    # Filter Wrist Yaw.\n    if armPose[4] > 0.2:\n        targetTheta = gaitConfig[2][1]\n    elif armPose[4] < -0.2:\n        targetTheta = - gaitConfig[2][1]\n\n    # Return corresponding pose.\n    return almath.Pose2D(targetX, targetY, targetTheta)\n    \ndef main(robotIP, PORT=9559):\n\n    global armName\n    \n    motionProxy  = ALProxy(\"ALMotion\", robotIP, PORT)\n    postureProxy = ALProxy(\"ALRobotPosture\", robotIP, PORT)\n\n    # Wake up robot\n    # motionProxy.wakeUp()\n\n    # Send robot to Stand Init\n    #postureProxy.goToPosture(\"StandInit\", 0.5)\n    postureProxy.goToPosture(\"Crouch\", 1)\n\n    # effector   = \"LArm\"\n    frame      = motion.FRAME_TORSO\n    axisMask   = almath.AXIS_MASK_VEL # just control position\n    useSensorValues = False\n\n    path = []\n    currentTf = motionProxy.getTransform(armName, frame, useSensorValues)\n    targetTf  = almath.Transform(currentTf)\n    targetTf.r1_c4 -= 0 # x\n    targetTf.r2_c4 += 0.1 # y\n    targetTf.r3_c4 += 0 # z\n    \n    path.append(list(targetTf.toVector()))\n    path.append(currentTf)\n\n    # Go to the target and back again\n    times      = [2.0, 4.0] # seconds\n\n    motionProxy.transformInterpolations(armName, frame, path, axisMask, times)\n\n    # Go to rest position\n    # motionProxy.rest()\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--ip\", type=str, default=\"127.0.0.1\",\n                        help=\"Robot ip address\")\n    parser.add_argument(\"--port\", type=int, default=9559,\n                        help=\"Robot port number\")\n\n    args = parser.parse_args()\n    main(args.ip, args.port)\n\n\n","sub_path":"Motion_Part/NAO_arm_test.py","file_name":"NAO_arm_test.py","file_ext":"py","file_size_in_byte":3098,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"607747034","text":"\"\"\"\nmodel training\n\"\"\"\n\nimport argparse\nfrom models.modules import QConv2d, WQPROFIT\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader\nimport torchvision.transforms as transforms\nimport os\nimport time\nimport sys\nimport models\nimport logging\nimport torch.utils.model_zoo as model_zoo\nfrom torchsummary import summary\nfrom dataset import get_loader \n\nfrom utils import *\nfrom collections import OrderedDict\n\nparser = argparse.ArgumentParser(description='PyTorch CIFAR10/ImageNet Training')\nparser.add_argument('--model', type=str, help='model type')\nparser.add_argument('--lr', default=0.1, type=float, help='learning rate')\nparser.add_argument('--momentum', type=float, default=0.9, help='Momentum.')\nparser.add_argument('--epochs', type=int, default=200, help='Number of epochs to train.')\nparser.add_argument('--batch_size', default=128, type=int, metavar='N', help='mini-batch size (default: 64)')\n\nparser.add_argument('--schedule', type=int, nargs='+', default=[60, 120],\n                    help='Decrease learning rate at these epochs.')\nparser.add_argument('--gammas', type=float, nargs='+', default=[0.1, 0.1],\n                    help='LR is multiplied by gamma on schedule, number of gammas should be equal to schedule')\nparser.add_argument('--lr_decay', type=str, default='step', help='mode for learning rate decay')\nparser.add_argument('--print_freq', default=1, type=int,\n                    metavar='N', help='print frequency (default: 200)')\nparser.add_argument('--wd', '--weight-decay', default=1e-4, type=float,\n                    metavar='W', help='weight decay (default: 1e-4)',\n                    dest='weight_decay')\nparser.add_argument('--log_file', type=str, default=None,\n                    help='path to log file')\n\n# dataset\nparser.add_argument('--dataset', type=str, default='cifar10', help='dataset: CIFAR10 / ImageNet_1k')\nparser.add_argument('--data_path', type=str, default='./data/', help='data directory')\n\n# model saving\nparser.add_argument('--save_path', type=str, default='./save/', help='Folder to save checkpoints and log.')\nparser.add_argument('--evaluate', action='store_true', help='evaluate the model')\n\n# Acceleration\nparser.add_argument('--ngpu', type=int, default=3, help='0 = CPU.')\nparser.add_argument('--workers', type=int, default=16,help='number of data loading workers (default: 2)')\n\n# Fine-tuning\nparser.add_argument('--fine_tune', dest='fine_tune', action='store_true',\n                    help='fine tuning from the pre-trained model, force the start epoch be zero')\nparser.add_argument('--pretrained', dest='pretrained', action='store_true',\n                    help='full pre-trained model')\nparser.add_argument('--resume', default='', type=str, help='path of the pretrained model')\n\n# quantization\nparser.add_argument('--wbit', type=int, default=4, help='weight precision')\nparser.add_argument('--abit', type=int, default=4, help='activation precision')\nparser.add_argument('--alpha_init', type=int, default=10., help='initial activation clipping')\nparser.add_argument('--channel_wise', type=int, default=0, help='channel_wise quantization flag')\n\n# activation clipping(PACT)\nparser.add_argument('--clp', dest='clp', action='store_true', help='using clipped relu in each stage')\nparser.add_argument('--a_lambda', type=float, default=0.01, help='The parameter of alpha L2 regularization')\n\nargs = parser.parse_args()\n\nos.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\nargs.use_cuda = args.ngpu > 0 and torch.cuda.is_available()  # check GPU\n\ndef main():\n    if not os.path.isdir(args.save_path):\n        os.makedirs(args.save_path)\n    \n    logger = logging.getLogger('training')\n    if args.log_file is not None:\n        fileHandler = logging.FileHandler(args.save_path+args.log_file)\n        fileHandler.setLevel(0)\n        logger.addHandler(fileHandler)\n    streamHandler = logging.StreamHandler()\n    streamHandler.setLevel(0)\n    logger.addHandler(streamHandler)\n    logger.root.setLevel(0)\n    logger.info(args)\n\n    # Prepare the data\n    trainloader, testloader, num_classes = get_loader(args)\n\n    # Prepare the model\n    logger.info('==> Building model..\\n')\n    model_cfg = getattr(models, args.model)\n    model_cfg.kwargs.update({\"num_classes\": num_classes})\n    net = model_cfg.base(*model_cfg.args, **model_cfg.kwargs) \n    logger.info(net)\n\n    checkpoint = model_zoo.load_url(models.model_urls[str(args.model)])\n    new_state_dict = OrderedDict()\n    logger.info(\"=> loading checkpoint...\")\n\n    for k, v in checkpoint.items():\n        name = k\n        if 'fc' in k:\n            if num_classes == 1000:\n                new_state_dict[name] = v\n            else:\n                logger.info(\"=> load backbone only!\")\n        else:\n            new_state_dict[name] = v\n    \n    state_tmp = net.state_dict()\n    state_tmp.update(new_state_dict)\n\n    net.load_state_dict(state_tmp)\n    logger.info(\"=> loaded checkpoint!\")\n    \n    if args.use_cuda:\n        net = net.cuda()\n    \n    if args.ngpu > 1:\n        net = torch.nn.DataParallel(net)\n        logger.info(\"Data Parallel!\")\n\n    # Loss function\n    criterion = nn.CrossEntropyLoss().cuda()\n    \n    model_params = []\n    for name, params in net.named_parameters():\n        if 'act_alpha' in name:\n            print(name)\n            model_params += [{'params': [params], 'lr': 1e-1, 'weight_decay': 1e-4}]\n        else:\n            model_params += [{'params': [params]}]\n\n    optimizer = optim.SGD(model_params, lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)\n    \n    # Evaluate\n    if args.evaluate:\n        logger.info(\"Evaluate!\")\n        test_acc, val_loss = test(testloader, net, criterion, 0)\n        logger.info(f'Test accuracy: {test_acc}')\n        exit()\n\n    # Training\n    start_time = time.time()\n    epoch_time = AverageMeter()\n    best_acc = 0.\n    start_epoch = 0\n    columns = ['ep', 'lr', 'tr_loss', 'tr_acc', 'tr_time', 'te_loss', 'te_acc', 'best_acc']\n\n    for epoch in range(start_epoch, start_epoch+args.epochs):\n        need_hour, need_mins, need_secs = convert_secs2time(\n            epoch_time.avg * (args.epochs - epoch))\n        \n        need_time = '[Need: {:02d}:{:02d}:{:02d}]'.format(\n            need_hour, need_mins, need_secs)\n        \n        current_lr, current_momentum = adjust_learning_rate_schedule(\n            optimizer, epoch, args.gammas, args.schedule, args.lr, args.momentum)\n\n        # Training phase\n        train_results = train(trainloader, net, criterion, optimizer, epoch, args)\n\n        # Test phase\n        test_acc, val_loss = test(testloader, net, criterion, epoch)        \n        is_best = test_acc > best_acc\n\n        if is_best:\n            best_acc = test_acc\n\n        state = {\n            'state_dict': net.state_dict(),\n            'acc': best_acc,\n            'epoch': epoch,\n            'optimizer': optimizer.state_dict(),\n        }\n        \n        filename='checkpoint.pth.tar'\n        save_checkpoint(state, is_best, args.save_path, filename=filename)\n\n        e_time = time.time() - start_time\n        epoch_time.update(e_time)\n        start_time = time.time()\n        \n        values = [epoch + 1, optimizer.param_groups[0]['lr'], train_results['loss'], train_results['acc'], \n            e_time, val_loss, test_acc, best_acc]\n\n        print_table(values, columns, epoch, logger)\n        print(need_time)\n\nif __name__ == '__main__':\n    main()\n","sub_path":"train_fp.py","file_name":"train_fp.py","file_ext":"py","file_size_in_byte":7434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"154079124","text":"from otree.api import (\n    models, widgets, BaseConstants, BaseSubsession, BaseGroup, BasePlayer,\n    Currency as c, currency_range\n)\n\nfrom statistics import median\n\nauthor = 'Shardul Vaidya, CESS India'\n\ndoc = \"\"\"\nfolks reading a prime paragraph and answering a few questions\n\"\"\"\n\n\nclass Constants(BaseConstants):\n    name_in_url = 'prime'\n    players_per_group = None\n    num_rounds = 1\n\n\nclass Subsession(BaseSubsession):\n    pass\n\n\n\nclass Group(BaseGroup):\n    pass\n\n\nclass Player(BasePlayer):\n    q1 = models.LongStringField(label='Please describe in one sentence what the text was about:')\n    q2 = models.IntegerField(label='To what extent do you agree or disagree with the renaming of Urdu/Muslim city names?',\n                             choices=[[1,'Fully Agree'],\n                                    [2,'Somewhat Agree'],\n                                    [3,'Neither Agree Nor Disagree'],\n                                    [4,'Somewhat Disagree'],\n                                    [5, 'Fully Disagree']],\n                             widget=widgets.RadioSelectHorizontal\n                             )\n    q3 = models.IntegerField(\n        label='I believe people look up to me and respect me.',\n        choices=[[1, 'Fully Agree'],\n                 [2, 'Somewhat Agree'],\n                 [3, 'Neither Agree Nor Disagree'],\n                 [4, 'Somewhat Disagree'],\n                 [5, 'Fully Disagree']],\n        widget=widgets.RadioSelect\n        )\n    q4 = models.IntegerField(\n        label='I feel I have much to be proud of.',\n        choices=[[1, 'Fully Agree'],\n                 [2, 'Somewhat Agree'],\n                 [3, 'Neither Agree Nor Disagree'],\n                 [4, 'Somewhat Disagree'],\n                 [5, 'Fully Disagree']],\n        widget=widgets.RadioSelect\n        )\n\n\n\n","sub_path":"prime/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"6920252","text":"def set_template(args):\n    if args.template is None:\n        return\n\n    elif args.template == 'vae':\n        args.dataset_code = 'ml-1m'\n        args.min_rating = 4\n        args.min_uc = 5\n        args.min_sc = 0\n        args.split = 'user_ratio'\n        args.n_held_out = 1000\n        args.negative_sampling = False\n\n        args.dataloader_code = 'vae'\n        batch_size = 500\n        args.train_batch_size = batch_size\n        args.val_batch_size = batch_size\n        args.test_batch_size = batch_size\n\n        args.partition_ratio = 0.2\n        args.partition_random_seed = 1024\n\n        args.model_code = 'vae'\n        args.vae_p_dims = [200, 600]\n        args.vae_q_dims = None\n        args.vae_dropout = 0.5\n        args.vae_total_anneal_steps = 200000\n        args.vae_anneal_cap = 0.2\n\n        args.trainer_code = 'vae'\n        args.device = 'cuda'\n        args.num_gpu = 1\n        args.device_idx = '0'\n        args.optimizer = 'Adam'\n        args.lr = 1e-3\n        args.weight_decay = 0\n        args.decay_step = 1\n        args.gamma = 1.0\n        args.num_epochs = 200\n        args.metric_ks = [20, 50, 100]\n        args.best_metric = 'NDCG@100'\n\n    elif args.template.startswith('bert'):\n        args.dataset_code = 'ml-1m'\n        args.min_rating = 0\n        args.min_uc = 5\n        args.min_sc = 0\n        args.split = 'leave_one_out'\n\n        args.dataloader_code = 'bert'\n        batch = 128\n        args.train_batch_size = batch\n        args.val_batch_size = batch\n        args.test_batch_size = batch\n\n        args.negative_sampling = True\n        args.train_negative_sampler_code = 'random'\n        args.train_negative_sample_size = 0\n        args.train_negative_sampling_seed = 0\n        args.test_negative_sampler_code = 'random'\n        args.test_negative_sample_size = 100\n        args.test_negative_sampling_seed = 98765\n\n        args.trainer_code = 'bert'\n        args.device = 'cuda'\n        args.num_gpu = 1\n        args.device_idx = '0'\n        args.optimizer = 'Adam'\n        args.lr = 0.001\n        args.decay_step = 25\n        args.gamma = 1.0\n        args.num_epochs = 100\n        args.metric_ks = [1, 5, 10, 20, 50, 100]\n        args.best_metric = 'NDCG@10'\n\n        args.model_code = 'bert'\n        args.model_init_seed = 0\n        num_users, num_items = get_user_item_nums(args)\n\n        args.bert_dropout = 0.1\n        args.bert_hidden_units = 256\n        args.bert_mask_prob = 0.15\n        args.bert_max_len = 100\n        args.bert_num_blocks = 2\n        args.bert_num_heads = 4\n        args.bert_num_items = num_items\n\n\ndef get_user_item_nums(args):\n    if args.dataset_code == 'ml-1m':\n        if args.min_rating == 4 and args.min_uc == 5 and args.min_sc == 0:\n            return 6034, 3533\n        elif args.min_rating == 0 and args.min_uc == 5 and args.min_sc == 0:\n            return 6040, 3706\n    raise ValueError()\n","sub_path":"templates.py","file_name":"templates.py","file_ext":"py","file_size_in_byte":2865,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"356965311","text":"from django.test import TestCase\r\nfrom django.contrib.auth.models import User\r\nfrom demo.models import SignInRecord\r\nimport json\r\n\r\n\r\n# Create your tests here.\r\n\r\n\r\nclass MyTestCase(TestCase):\r\n    fixtures = ['demodb.json']\r\n\r\n    def setUp(self):\r\n        loginSuccess = self.client.login(username=\"07033\", password=\"hdhhb2002\")\r\n        self.assertEqual(loginSuccess, True)\r\n\r\n    def test_teacher_login(self):\r\n        teacherLoginResponse = self.client.post(\"/demo/teacher_login/\", {'username': '07033', 'password': 'hdhhb2002'})\r\n        formatJsonResponse = json.loads(teacherLoginResponse.content.decode())\r\n        self.assertEqual(formatJsonResponse['status'], 1)\r\n\r\n    def test_test_create_test(self):\r\n        testCreateTestResponse = self.client.get(\"/demo/test_create_test/\", {'courseId': 1})\r\n        self.assertEqual(testCreateTestResponse.status_code, 200)\r\n\r\n    def test_absence_record(self):\r\n        # oldLateRecordNum = len(LateRecord.objects.all())\r\n        # requestParams = '{\"courseId\": 1, \"studentIdForAbsence\": [1,2,], \"studentIdForLeave\": [3,4,]}'\r\n        # formatJsonRequest = json.loads(requestParams)\r\n        # absenceRecordResponse = self.client.post(\"/demo/absence_record/\", requestParams, content_type=\"application/json\")\r\n        # self.assertEqual(absenceRecordResponse.status_code, 200)\r\n        # newLateRecordNum = len(LateRecord.objects.all())\r\n        # self.assertEqual(newLateRecordNum - oldLateRecordNum, len(formatJsonRequest['studentIdForAbsence']+len(formatJsonRequest['studentIdForLeave'])))\r\n\r\n        oldLateRecordNum = len(SignInRecord.objects.all())\r\n        dict_string = {'courseId': 1, 'studentIdForAbsence': [1, 2, ], 'studentIdForLeave': [3, 4, ]}\r\n        numbers = len(dict_string['studentIdForAbsence']) + len(dict_string['studentIdForLeave'])\r\n        J_string = json.dumps(dict_string)\r\n        absenceRecordResponse = self.client.post(\"/demo/absence_record/\", data=J_string,\r\n                                                 content_type=\"application/json\")\r\n        self.assertEqual(absenceRecordResponse.status_code, 200)\r\n        newLateRecordNum = len(SignInRecord.objects.all())\r\n        self.assertEqual(newLateRecordNum - oldLateRecordNum, numbers)\r\n\r\n    def test_record_again(self):\r\n\r\n        old_signIn_record_num = len(SignInRecord.objects.all())\r\n        old_late_record_num = len(SignInRecord.objects.filter(state=2))\r\n        old_leave_num = len(SignInRecord.objects.filter(state=1))\r\n\r\n        dic_string = {'courseId': 1, 'weekOrdinal': 1, 'studentIdForMiss': [1, 2], 'studentIdForLeave': [],\r\n                      'studentIdForLate': [3]}\r\n        number_miss = len(dic_string['studentIdForMiss'])\r\n        number_late = len(dic_string['studentIdForLate'])\r\n        number_leave = len(dic_string['studentIdForLeave'])\r\n        J_string = json.dumps(dic_string)\r\n        record_again_response = self.client.post('/demo/record_again/', data=J_string, content_type='application/json')\r\n        self.assertEqual(record_again_response.status_code, 200)\r\n\r\n        new_signInRecord_num = len(SignInRecord.objects.all())\r\n        new_late_num = len(SignInRecord.objects.filter(state=2))\r\n        new_leave_num = len(SignInRecord.objects.filter(state=1))\r\n        self.assertEqual(old_signIn_record_num - new_signInRecord_num, number_miss)\r\n        self.assertEqual(new_late_num - old_late_record_num, number_late)\r\n        self.assertEqual(new_leave_num - old_leave_num, number_leave)\r\n","sub_path":"demo/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":3465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"200059863","text":"#!/usr/bin/env python3\n\n# Copyright 2020 The Kraken Authors\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport os\n\nfrom kraken.server.bg.clry import app\nfrom . import srvcheck\nfrom . import consts\n\n\ndef main():\n    planner_url = os.environ.get('KRAKEN_PLANNER_URL', consts.DEFAULT_PLANNER_URL)\n    srvcheck.check_url('planner', planner_url, 7997)\n\n    argv = [\n        'worker',\n        '--loglevel=INFO',\n        '--max-tasks-per-child=1',\n    ]\n    app.worker_main(argv=argv)\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"server/kraken/server/kkcelery.py","file_name":"kkcelery.py","file_ext":"py","file_size_in_byte":1021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"37691280","text":"#!/usr/bin/env python3\n# encoding: utf-8\n\n\"\"\"Eulerproblem 7, find the 10 001st prime number \"\"\"\n\ndef prime(n):\n\ti=2\n\ttestno = 2\n\ttester = 2\n\twhile i<n:\n\t\t\twhile not testno%tester:\n\t\t\t\t\ttestno += 1\n\t\t\t\t\tif testno == tester:\n\t\t\t\t\t\t\ti += 1\n\t\t\tn += 1\n\n\t\t\t\n\n\ndef main():\n\tprime(5)\n\t\nif __name__ == '__main__':\n\t\t\tmain()\n","sub_path":"euler7.py","file_name":"euler7.py","file_ext":"py","file_size_in_byte":315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"407939423","text":"import torch\nimport torch.nn as nn\n\n\nclass ConvBlock(nn.Module):\n    \"\"\"\n    Small unit block consists of [convolution layer - normalization layer - non linearity layer]\n    \"\"\"\n\n    def __init__(self, in_dim, out_dim, k=4, s=2, p=1, norm=True, non_linear='leaky_relu'):\n        super(ConvBlock, self).__init__()\n        layers = []\n\n        # Convolution Layer\n        layers += [nn.Conv2d(in_dim, out_dim, kernel_size=k, stride=s, padding=p)]\n\n        # Normalization Layer\n        if norm is True:\n            layers += [nn.InstanceNorm2d(out_dim, affine=True)]\n\n        # Non-linearity Layer\n        if non_linear == 'leaky_relu':\n            layers += [nn.LeakyReLU(negative_slope=0.2, inplace=True)]\n        elif non_linear == 'relu':\n            layers += [nn.ReLU(inplace=True)]\n\n        self.conv_block = nn.Sequential(*layers)\n\n    def forward(self, x):\n        out = self.conv_block(x)\n        return out\n\n\nclass DeconvBlock(nn.Module):\n    \"\"\"\n    Small unit block consists of [transpose conv layer - normalization layer - non linearity layer]\n    \"\"\"\n\n    def __init__(self, in_dim, out_dim, k=4, s=2, p=1, norm=True, non_linear='relu'):\n        super(DeconvBlock, self).__init__()\n        layers = []\n\n        # Transpose Convolution Layer\n        layers += [nn.ConvTranspose2d(in_dim, out_dim, kernel_size=k, stride=s, padding=p)]\n\n        # Normalization Layer\n        if norm is True:\n            layers += [nn.InstanceNorm2d(out_dim, affine=True)]\n\n        # Non-Linearity Layer\n        if non_linear == 'relu':\n            layers += [nn.ReLU(inplace=True)]\n        elif non_linear == 'tanh':\n            layers += [nn.Tanh()]\n\n        self.deconv_block = nn.Sequential(*layers)\n\n    def forward(self, x):\n        out = self.deconv_block(x)\n        return out\n\n\nclass Generator(nn.Module):\n    \"\"\"\n    U-Net Generator. See https://arxiv.org/abs/1505.04597 figure 1\n    or https://arxiv.org/pdf/1611.07004 6.1.1 Generator Architectures\n\n    Down-sampled activation volume and up-sampled activation volume which have same width and height\n    make pairs and they are concatenated when upsampling.\n    Pairs : (up_1, down_6) (up_2, down_5) (up_3, down_4) (up_4, down_3) (up_5, down_2) (up_6, down_1)\n            down_7 doesn't have a partner.\n\n    ex) up_1 and down_6 have same size of (N, 512, 2, 2) given that input size is (N, 3, 128, 128).\n        When forwarding into upsample_2, up_1 and down_6 are concatenated to make (N, 1024, 2, 2) and then\n        upsample_2 makes (N, 512, 4, 4). That is why upsample_2 has 1024 input dimension and 512 output dimension\n\n        Except upsample_1, all the other upsampling blocks do the same thing.\n    \"\"\"\n\n    def __init__(self, z_dim=8):\n        super(Generator, self).__init__()\n        # Reduce H and W by half at every downsampling\n        self.downsample_1 = ConvBlock(3 + z_dim, 64, k=4, s=2, p=1, norm=False, non_linear='leaky_relu')\n        self.downsample_2 = ConvBlock(64, 128, k=4, s=2, p=1, norm=True, non_linear='leaky_relu')\n        self.downsample_3 = ConvBlock(128, 256, k=4, s=2, p=1, norm=True, non_linear='leaky_relu')\n        self.downsample_4 = ConvBlock(256, 512, k=4, s=2, p=1, norm=True, non_linear='leaky_relu')\n        self.downsample_5 = ConvBlock(512, 512, k=4, s=2, p=1, norm=True, non_linear='leaky_relu')\n        self.downsample_6 = ConvBlock(512, 512, k=4, s=2, p=1, norm=True, non_linear='leaky_relu')\n        self.downsample_7 = ConvBlock(512, 512, k=4, s=2, p=1, norm=True, non_linear='leaky_relu')\n\n        # Need concatenation when upsampling, see forward function for details\n        self.upsample_1 = DeconvBlock(512, 512, k=4, s=2, p=1, norm=True, non_linear='relu')\n        self.upsample_2 = DeconvBlock(1024, 512, k=4, s=2, p=1, norm=True, non_linear='relu')\n        self.upsample_3 = DeconvBlock(1024, 512, k=4, s=2, p=1, norm=True, non_linear='relu')\n        self.upsample_4 = DeconvBlock(1024, 256, k=4, s=2, p=1, norm=True, non_linear='relu')\n        self.upsample_5 = DeconvBlock(512, 128, k=4, s=2, p=1, norm=True, non_linear='relu')\n        self.upsample_6 = DeconvBlock(256, 64, k=4, s=2, p=1, norm=True, non_linear='relu')\n        self.upsample_7 = DeconvBlock(128, 3, k=4, s=2, p=1, norm=False, non_linear='Tanh')\n\n    def forward(self, x, z):\n        # z : (N, z_dim) -> (N, z_dim, 1, 1) -> (N, z_dim, H, W)\n        # x_with_z : (N, 3 + z_dim, H, W)\n        z = z.unsqueeze(dim=2).unsqueeze(dim=3)\n        z = z.expand(z.size(0), z.size(1), x.size(2), x.size(3))\n        x_with_z = torch.cat([x, z], dim=1)\n\n        down_1 = self.downsample_1(x_with_z)\n        down_2 = self.downsample_2(down_1)\n        down_3 = self.downsample_3(down_2)\n        down_4 = self.downsample_4(down_3)\n        down_5 = self.downsample_5(down_4)\n        # down_6 = self.downsample_6(down_5)\n        down_7 = self.downsample_7(down_5)\n\n        up_1 = self.upsample_1(down_7)\n        # up_2 = self.upsample_2(torch.cat([up_1, down_6], dim=1))\n        up_3 = self.upsample_3(torch.cat([up_1, down_5], dim=1))\n        up_4 = self.upsample_4(torch.cat([up_3, down_4], dim=1))\n        up_5 = self.upsample_5(torch.cat([up_4, down_3], dim=1))\n        up_6 = self.upsample_6(torch.cat([up_5, down_2], dim=1))\n        out = self.upsample_7(torch.cat([up_6, down_1], dim=1))\n\n        return out\n\n\nclass Discriminator(nn.Module):\n    \"\"\"\n    PatchGAN discriminator. See https://arxiv.org/pdf/1611.07004 6.1.2 Discriminator architectures.\n    It uses two discriminator which have different output sizes(different local probabilities).\n    d_1 : (N, 3, 128, 128) -> (N, 1, 14, 14)\n    d_2 : (N, 3, 128, 128) -> (N, 1, 30, 30)\n\n    In training, the generator needs to fool both of d_1 and d_2 and it makes the generator more robust.\n    \"\"\"\n\n    def __init__(self):\n        super(Discriminator, self).__init__()\n        # Discriminator with last patch (14x14)\n        # (N, 3, 128, 128) -> (N, 1, 14, 14)\n        self.d_1 = nn.Sequential(nn.AvgPool2d(kernel_size=3, stride=2, padding=0, count_include_pad=False),\n                                 ConvBlock(3, 32, k=4, s=2, p=1, norm=False, non_linear='leaky_relu'),\n                                 ConvBlock(32, 64, k=4, s=2, p=1, norm=True, non_linear='leaky-relu'),\n                                 ConvBlock(64, 128, k=4, s=1, p=1, norm=True, non_linear='leaky-relu'),\n                                 ConvBlock(128, 1, k=4, s=1, p=1, norm=False, non_linear=None))\n\n        # Discriminator with last patch (30x30)\n        # (N, 3, 128, 128) -> (N, 1, 30, 30)\n        self.d_2 = nn.Sequential(ConvBlock(3, 64, k=4, s=2, p=1, norm=False, non_linear='leaky_relu'),\n                                 ConvBlock(64, 128, k=4, s=2, p=1, norm=True, non_linear='leaky-relu'),\n                                 ConvBlock(128, 256, k=4, s=1, p=1, norm=True, non_linear='leaky-relu'),\n                                 ConvBlock(256, 1, k=4, s=1, p=1, norm=False, non_linear=None))\n\n    def forward(self, x):\n        out_1 = self.d_1(x)\n        out_2 = self.d_2(x)\n        return out_1, out_2\n\n\nclass ResBlock(nn.Module):\n    \"\"\"\n    This residual block is different with the one we usually know which consists of\n    [conv - norm - act - conv - norm] and identity mapping(x -> x) for shortcut.\n\n    Also spatial size is decreased by half because of AvgPool2d.\n    \"\"\"\n\n    def __init__(self, in_dim, out_dim):\n        super(ResBlock, self).__init__()\n        self.conv = nn.Sequential(nn.InstanceNorm2d(in_dim, affine=True),\n                                  nn.LeakyReLU(negative_slope=0.2, inplace=True),\n                                  nn.Conv2d(in_dim, in_dim, kernel_size=3, stride=1, padding=1),\n                                  nn.InstanceNorm2d(in_dim, affine=True),\n                                  nn.LeakyReLU(negative_slope=0.2, inplace=True),\n                                  nn.Conv2d(in_dim, out_dim, kernel_size=3, stride=1, padding=1),\n                                  nn.AvgPool2d(kernel_size=2, stride=2, padding=0))\n\n        self.short_cut = nn.Sequential(nn.AvgPool2d(kernel_size=2, stride=2, padding=0),\n                                       nn.Conv2d(in_dim, out_dim, kernel_size=1, stride=1, padding=0))\n\n    def forward(self, x):\n        out = self.conv(x) + self.short_cut(x)\n        return out\n\n\nclass Encoder(nn.Module):\n    \"\"\"\n    Output is mu and log(var) for re-parameterization trick used in Variation Auto Encoder.\n    Encoding is done in this order.\n    1. Use this encoder and get mu and log_var\n    2. std = exp(log(var / 2))\n    3. random_z = N(0, 1)\n    4. encoded_z = random_z * std + mu (Re-parameterization trick)\n    \"\"\"\n\n    def __init__(self, z_dim=8):\n        super(Encoder, self).__init__()\n\n        self.conv = nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1)\n        self.res_blocks = nn.Sequential(ResBlock(64, 128),\n                                        ResBlock(128, 192),\n                                        ResBlock(192, 256))\n        self.pool_block = nn.Sequential(nn.LeakyReLU(negative_slope=0.2, inplace=True),\n                                        nn.AvgPool2d(kernel_size=8, stride=8, padding=0))\n\n        # Return mu and log var for re-parameterization trick\n        self.fc_mu = nn.Linear(256, z_dim)\n        self.fc_log_var = nn.Linear(256, z_dim)\n\n    def forward(self, x):\n        # (N, 3, 128, 128) -> (N, 64, 64, 64)\n        out = self.conv(x)\n        # (N, 64, 64, 64) -> (N, 128, 32, 32) -> (N, 192, 16, 16) -> (N, 256, 8, 8)\n        out = self.res_blocks(out)\n        # (N, 256, 8, 8) -> (N, 256, 1, 1)\n        out = self.pool_block(out)\n        # (N, 256, 1, 1) -> (N, 256)\n        out = out.view(x.size(0), -1)\n\n        # (N, 256) -> (N, z_dim) x 2\n        mu = self.fc_mu(out)\n        log_var = self.fc_log_var(out)\n\n        return mu, log_var\n","sub_path":"BicycleGAN/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":9756,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"412465790","text":"# coding=utf-8\n# @author: bryan\n\nimport pandas as pd\n\"\"\"\n用户最多连续看了多少个商品/店铺没有购买,在6号连续看了多少个商品/店铺没有购买，6号一共没有购买的商品数，店铺数\n商品，店铺，类别，城市，品牌点击购买趋势，前7天统计\n商品，店铺，类别，城市，品牌 被一次性购买的比例 ，一次性购买次数/购买次数\n商品，店铺，类别，城市，品牌  第一次出现到第一次购买的时间间隔\n\n\"\"\"\n\n\"\"\"连续未购买7个特征，线下提升万0.5\"\"\"\ndef user_continue_nobuy(org):\n    # 每条记录先按user_id,timestamp排序\n    data = org[org['day'] < 7].sort_values(by=['user_id','context_timestamp'], ascending=True)\n    train=org[org.day==7][['instance_id','user_id']]\n\n    def no_buy_count(grouped_x):\n        max_cnt = 0\n        max_no_buy=0\n        for is_trade in grouped_x:\n            if is_trade==0:\n                max_no_buy += 1\n                max_cnt = max(max_cnt, max_no_buy)\n            else:\n                max_no_buy=0\n        return max_cnt\n\n    user_nobuy = data.groupby('user_id', as_index=False)['is_trade']\\\n        .agg({'user_continue_nobuy_click_cnt':lambda x:no_buy_count(x)})\n\n    print('user_continue_nobuy_click_cnt finish')\n    data=data[data.day==6].sort_values(by=['user_id','context_timestamp'])\n    day6_user_nobuy=data.groupby('user_id', as_index=False)['is_trade']\\\n        .agg({'day6_user_continue_nobuy_click_cnt': lambda x: no_buy_count(x)})\n\n    print('day6_user_continue_nobuy_click_cnt finish')\n    train=pd.merge(train, user_nobuy,on='user_id',how='left')\n    train = pd.merge(train, day6_user_nobuy, on='user_id', how='left')\n    data = org[org['day'] ==6]\n    user_buy_items=data[data.is_trade==1].groupby('user_id', as_index=False)['item_id'].agg({'day6_user_buy_items':lambda x:len(set(x))})\n    user_nobuy_items=data.groupby('user_id', as_index=False)['item_id'].agg({'day6_user_nobuy_items': lambda x: len(set(x))})\n    user_buy_shops = data[data.is_trade == 1].groupby('user_id', as_index=False)['item_id'].agg({'day6_user_buy_shops': lambda x: len(set(x))})\n    user_nobuy_shops = data.groupby('user_id', as_index=False)['item_id'].agg({'day6_user_nobuy_shops': lambda x: len(set(x))})\n\n    print('day6_user_nobuy finish')\n    train=pd.merge(train,user_buy_items,on='user_id',how='left')\n    train = pd.merge(train, user_nobuy_items, on='user_id', how='left')\n    train = pd.merge(train, user_buy_shops, on='user_id', how='left')\n    train = pd.merge(train, user_nobuy_shops, on='user_id', how='left')\n    train['day6_user_items_d_shops']=train['day6_user_nobuy_items']/train['day6_user_nobuy_shops']\n    train=train.drop('user_id',axis=1)\n    train.to_csv('../data/nobuy_feature.csv',index=False)\n    print('nobuy_feature finish')\n    # return train\n\ndef trend(data,item):\n    tmp = data.groupby([item, 'day'], as_index=False)['is_trade'].agg({'buy': 'sum', 'cnt': 'count'})\n    features = []\n    for key, df in tmp.groupby(item, as_index=False):\n        feature = {}\n        feature[item] = key\n        # 遍历groupd中的元素\n        for index, row in df.iterrows():\n            feature[item+'buy' + str(row['day'])] = row['buy']\n            feature[item+'cnt' + str(row['day'])] = row['cnt']\n        features.append(feature)\n    features =pd.DataFrame(features)\n    return features\n    # def f(x):\n    #     return 1 if x>0 else 0\n    # for i in range(6):\n    #     features[item + 'buy_trend_'+str(i+1)] = features[item + 'buy'+str(i+1)] - features[item + 'buy'+str(i)]\n    #     features[item + 'buy_trend_' + str(i + 1)]=features[item + 'buy_trend_'+str(i+1)].apply(f)\n    # features[item+'buy_trend'] = features[item + 'buy_trend_1'] + features[item + 'buy_trend_2']+ features[item + 'buy_trend_3']+ features[item + 'buy_trend_4']+ features[item + 'buy_trend_5']+ features[item + 'buy_trend_6']\n    # for i in range(6):\n    #     features[item + 'cnt_trend_'+str(i+1)] = features[item + 'cnt'+str(i+1)] - features[item + 'cnt'+str(i)]\n    #     features[item + 'cnt_trend_' + str(i + 1)] = features[item + 'cnt_trend_' + str(i + 1)].apply(f)\n    # features[item+'cnt_trend'] = features[item + 'cnt_trend_1'] + features[item + 'cnt_trend_2'] + features[item + 'cnt_trend_3'] + features[item + 'cnt_trend_4'] + features[item + 'cnt_trend_5'] + features[item + 'cnt_trend_6']\n    # return features.drop([item + 'buy_trend_'+str(i+1) for i in range(6)]+[item + 'cnt_trend_'+str(i+1) for i in range(6)],axis=1)\n\n\"\"\"\n商品，店铺，类别，城市，品牌点击购买趋势，前7天统计，比上一天高为1，否则为0，再统计1的次数，7个特征*5\n\"\"\"\ndef trend_expode(data, col='shop_id'): # 该函数有点像hive里的expode,将行转成列\n    import json\n    # col可能为店铺,因此tmp为以店铺,day为key统计成交次数以及行为次数\n    tmp = data.groupby([col, 'day'], as_index=False)['is_trade'].agg({'buy': 'sum', 'cnt': 'count'})\n    # tmp列名店铺, day, buy, cnt\n    samples = []\n    # 以 店铺为key,进行二次聚合\n    for key, df in tmp.groupby(col, as_index=False):\n        sample = {}\n        sample[col] = key  # 店铺id, 每个sample是一条样本\n        for index, row in df.iterrows():\n            sample[col + '_buy_' + str(int(row['day']))] = row['buy']  # 以shop为key,将不同天数的成交以行行为次数当成不同的列名\n            sample[col + '_cnt_' + str(int(row['day']))] = row['cnt']\n\n        samples.append(sample)\n\n    print(\"samples:\", samples)\n    \"\"\"\n    fetaures是一个列表,每个列表是一行样本, 其中列表里的元素是一个dict,dict的不同key代表不同的列名\n    这样的方式一般可用于样本特征的稀疏存储,\n    因此最后的列名为:['shop_id', 'shop_id_buy_0', 'shop_id_buy_2', 'shop_id_buy_3',...]\n    [\n        {\n            \"shop_id\": 0,\n            \"shop_id_buy_2\": 0,\n            \"shop_id_cnt_2\": 1,\n            \"shop_id_buy_5\": 0,\n            \"shop_id_cnt_5\": 2\n        },\n        {\n            \"shop_id\": 1,\n            \"shop_id_buy_2\": 0,\n            \"shop_id_cnt_2\": 1,\n            \"shop_id_buy_7\": 0,\n            \"shop_id_cnt_7\": 1\n        }\n    ]\n    \"\"\"\n    samples = pd.DataFrame(samples)\n    return samples, tmp\n\ndef trend_feature(org):\n    data=org[org.day<7]\n    cols = ['item_id', 'item_brand_id', 'shop_id', 'item_category_list', 'item_city_id',\n           'predict_category_property', 'context_page_id', 'query1', 'query']\n    train=org[org.day==7][['instance_id'] + cols]\n\n    # 将不同的统计维度的feature join起来\n    for col in cols:\n        print(col + ' finish')\n        train=pd.merge(train, trend_expode(data, col)[0], on=col, how='left')\n\n    train=train.drop(cols, axis=1) # 将原始的列名去掉\n    for col in cols:\n        for day in range(6):\n            train['_'.join([col, str(day + 1), 'd', str(day), 'cnt'])]= train[col + 'cnt' + str(day + 1)] / train[col + 'cnt' + str(day)]\n            train['_'.join([col, str(day + 1), 'd', str(day), 'buy'])]= train[col + 'buy' + str(day + 1)] / train[col + 'buy' + str(day)]\n\n    train=train[[i for i in train.columns if 'cnt6' not in i]]\n    train.to_csv('../data/trend_feature.csv',index=False)\n    print('trend_feature finish')\n    # return train\n\n# 商品，店铺，类别，城市，品牌，页面 被一次性购买的比例,次数 ，一次性购买次数/购买次数  线下测试只有item,shop的shot_rate有用\n# 用户，商品，店铺，类别，城市，品牌，页面 7号一次性购买次数，交叉提取\n# 如何定义一次性购买: cvr=1, 认为转化率为1就是一次性购买\ndef oneshot(data, col):\n    tmp = data.groupby([col], as_index=False)['is_trade'].agg({col + '_buy': 'sum'})\n    # shop_id, user_id\n    shot = data.groupby([col, 'user_id'], as_index=False)['is_trade'].agg({'is_shot': 'mean'})\n    # is_shot=1: 即每次行为都成交就是一次性购买\n    shot = shot[shot[\"is_shot\"] == 1].groupby([col], as_index=False)['is_shot']\\\n        .agg({col + 'shot_num': 'count'})\n    tmp = pd.merge(tmp, shot, on=[col], how='left')\n    tmp[col + '_shot_rate'] = tmp[col + 'shot_num'] / tmp[col + '_buy']\n    return tmp[[col, col + '_shot_rate']]\n\n# calc data，join data\ndef today_shot(c_data, j_data):\n    cols=['item_id', 'shop_id', 'query', 'query1']\n    j_data=j_data[['instance_id'] + cols]\n    for col in cols:\n        j_data = pd.merge(j_data, oneshot(c_data, col), on=col, how='left')\n    j_data=j_data.drop(cols, axis=1)\n    j_data.columns=['instance_id','today_item_shot_rate','today_shop_shot_rate','today_query_shot_rate','today_query1_shot_rate']\n    return j_data\n\ndef today_shot_feature(org):\n    from sklearn.model_selection import train_test_split\n    data=org[org['day']==7]\n    train=data[data['is_trade']>-1]\n    predict=data[data['is_trade']<0]\n    predict=today_shot(train,predict)\n    train1,train2=train_test_split(train, test_size=0.5, random_state=1024)\n    train12=today_shot(train1, train2)\n    train21=today_shot(train2, train1)\n    data=pd.concat([train21, train12, predict], axis=0).reset_index(drop=True)\n    return data\n\ndef day6_shot_feature(org):\n    data=org[org.day==6]\n    items = ['item_id', 'shop_id', 'query', 'query1']\n    train = org[org.day == 7][['instance_id']+items]\n    for item in items:\n        train = pd.merge(train, oneshot(data, item), on=item, how='left')\n    train=train.drop(items,axis=1)\n    train.columns=['instance_id','day6_item_shot_rate','day6_shop_shot_rate','day6_query_shot_rate','day6_query1_shot_rate']\n    return train\n\ndef oneshot_feature(org):\n    data=org[org.day<7]\n    items = ['item_id', 'shop_id', 'query', 'query1']\n    train = org[org.day == 7][['instance_id']+items]\n    for item in items:\n        train=pd.merge(train,oneshot(data, item),on=item,how='left')\n        print(item+' finish')\n\n    train = train.drop(items, axis=1)\n    print(train.columns)\n    today=today_shot_feature(org)\n    print(today.columns)\n\n    day6=day6_shot_feature(org)\n    print(day6.columns)\n\n    train=pd.merge(train, today, on='instance_id', how='left')\n    train = pd.merge(train, day6, on='instance_id', how='left')\n\n    train.to_csv('../data/oneshot_feature.csv', index=False)\n    print('oneshot_feature finish')\n\n# 商品，店铺，类别，城市，品牌，query  第一次出现到第一次购买的时间间隔\n# 前所有天，第七天\ndef first_ocr(data,item):\n    import numpy as np\n    import datetime\n    def sec_diff(a, b):\n        if (a is np.nan) | (b is np.nan):\n            return np.nan\n        return (datetime.datetime.strptime(str(b), \"%Y-%m-%d %H:%M:%S\") - datetime.datetime.strptime(str(a),\"%Y-%m-%d %H:%M:%S\")).seconds\n\n    ocr=data.groupby(item,as_index=False)['context_timestamp'].agg({'min_ocr_time':'min'})\n    buy=data[data[\"is_trade\"]==1].groupby(item, as_index=False)['context_timestamp']\\\n        .agg({'min_buy_time':'min'})\n    data=pd.merge(ocr, buy, on=item, how='left')\n    data[item+'_ocr_buy_diff_day6'] = data.apply(lambda x:sec_diff(x['min_ocr_time'],x['min_buy_time']),axis=1)\n    return data[[item,item+'_ocr_buy_diff_day6']]\n\n# calc data，join data\ndef today_ocr(c_data, j_data):\n    items=['item_id','shop_id','predict_category_property']\n    item_shot=first_ocr(c_data, items[0])\n    shop_shot=first_ocr(c_data, items[1])\n    query_shot=first_ocr(c_data, items[2])\n    j_data=pd.merge(j_data,item_shot,on=items[0],how='left')\n    j_data = pd.merge(j_data, shop_shot, on=items[1], how='left')\n    j_data = pd.merge(j_data, query_shot, on=items[2], how='left')\n    j_data= j_data[['instance_id','item_id_ocr_buy_diff','shop_id_ocr_buy_diff','predict_category_property_ocr_buy_diff']]\n    j_data.columns=['instance_id','today_item_id_ocr_buy_diff','today_shop_id_ocr_buy_diff','today_predict_category_property_ocr_buy_diff']\n    return j_data\n\ndef today_ocr_feature(org):\n    from sklearn.model_selection import train_test_split\n    data=org[org['day']==7]\n    train=data[data['is_trade']!=-1]\n    predict=data[data['is_trade']==-1]\n    predict=today_ocr(train,predict)\n    train1,train2=train_test_split(train,test_size=0.5,random_state=1024)\n    train22=today_ocr(train1, train2)\n    train11=today_ocr(train2, train1)\n    data=pd.concat([train11,train22,predict]).reset_index(drop=True)\n    return data\n\ndef first_ocr_feature(org):\n    items=['item_id','query','query1']\n    data=org[org.day<7]\n    train=org[org.day==7][['instance_id']+items]\n    # for item in items:\n    #     tmp=first_ocr(data, item)\n    #     tmp.columns=[item,item+'_ocr_buy_diff_all_day']\n    #     train=pd.merge(train,tmp,on=item,how='left')\n    #     print(item)\n    data=data[data.day==6]\n    for item in items:\n        tmp=first_ocr(data, item)\n        train=pd.merge(train,tmp,on=item,how='left')\n        print(item)\n    #today=today_ocr_feature(orgin_data)\n    #train=pd.merge(train,today,on='instance_id',how='left')\n    train=train.drop(items, axis=1)\n    train.to_csv('../data/ocr_feature.csv',index=False)\n    print('ocr_feature finish')\n\n\n\"\"\"\nitem和shop 属性的变化，前7天的均值，第7天和前七天均值的差值，第7天和第六天的差值\nitem_price_level,item_sales_level,item_collected_level,item_pv_level\nshop_review_num_level,shop_review_positive_rate,shop_star_level,shop_score_service,shop_score_delivery,shop_score_description\n线下可以提升1个万分位\n\"\"\"\ndef item_shop_var_feature(org):\n    import numpy as np\n    col=['item_id','shop_id']\n    item_cates=['item_price_level','item_sales_level','item_collected_level','item_pv_level']\n    shop_cates=['shop_review_num_level','shop_review_positive_rate','shop_star_level','shop_score_service','shop_score_delivery','shop_score_description']\n    data=org[org.day<7]\n    train=org[org.day==7][['instance_id']+col+item_cates+shop_cates]\n\n    for cate in item_cates:\n        train=pd.merge(train,data.groupby('item_id',as_index=False)[cate].agg({'item_id_'+cate+'_var':np.std,'item_id_'+cate+'_avg':'mean'}),on='item_id',how='left')\n        train['_'.join(['diff',cate,'today_d_7days'])]=train[cate]-train['item_id_'+cate+'_avg']\n\n    for cate in shop_cates:\n        train=pd.merge(train,data.groupby('shop_id',as_index=False)[cate].agg({'shop_id_'+cate+'_var':np.std,'shop_id_'+cate+'_avg':'mean'}),on='shop_id',how='left')\n        train['_'.join(['diff', cate, 'today_d_7days'])] = train[cate] - train['shop_id_' + cate + '_avg']\n\n    data = org[org.day == 6]\n\n    for cate in item_cates:\n        avg=data.groupby('item_id',as_index=False)[cate].agg({'item_id_day6'+cate+'_avg':'mean'})\n        tmp=pd.merge(train,avg,on='item_id',how='left')\n        train['_'.join(['diff',cate,'today_d_6day'])]=tmp[cate]-tmp['item_id_day6'+cate+'_avg']\n\n    for cate in shop_cates:\n        avg=data.groupby('shop_id',as_index=False)[cate].agg({'shop_id_day6'+cate+'_avg':'mean'})\n        tmp=pd.merge(train,avg,on='shop_id',how='left')\n        train['_'.join(['diff',cate,'today_d_6day'])]=tmp[cate]-tmp['shop_id_day6'+cate+'_avg']\n\n    train.drop(col + item_cates + shop_cates, axis=1)\\\n        .to_csv('../data/item_shop_var_feature.csv',index=False)\n\nif __name__ == '__main__':\n    org=pd.read_csv('../data/origion_concat.csv')\n    user_continue_nobuy(org)\n    trend_feature(org)\n    oneshot_feature(org)\n    first_ocr_feature(org)\n    item_shop_var_feature(org)","sub_path":"logit_feature.py","file_name":"logit_feature.py","file_ext":"py","file_size_in_byte":15261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"177099850","text":"import os\r\nimport numpy as np\r\nimport cv2\r\nimport sys\r\nimport argparse\r\nfrom pathlib import Path\r\nfrom multiprocessing.dummy import Pool as ThreadPool\r\n\r\nimport matplotlib as mpl\r\nimport matplotlib.pyplot as plt\r\nfrom mpl_toolkits import mplot3d\r\n\r\nfrom conn_graph import ConnectionGraph\r\n\r\n# set print options for numpy\r\nnp.set_printoptions(precision=6, suppress=True, threshold=sys.maxsize)\r\n\r\ndef calibrate_camera(folder, pattern_size):\r\n    # modified from opencv camera calibration example\r\n    img_names = list(Path(folder).glob('*.jpg')) + list(Path(folder).glob('*.jpeg')) + list(Path(folder).glob('*.png'))\r\n    assert len(img_names) > 0\r\n\r\n    if args.debug:\r\n        Path(args.calib_output_folder).mkdir(exist_ok=True, parents=True)\r\n    square_size = 1.0\r\n    pattern_points = np.zeros((np.prod(pattern_size), 3), np.float32)\r\n    pattern_points[:, :2] = np.indices(pattern_size).T.reshape(-1, 2) # indices\r\n    pattern_points *= square_size\r\n\r\n    obj_points = []\r\n    img_points = []\r\n\r\n    h, w = cv2.imread(str(img_names[0]), cv2.IMREAD_GRAYSCALE).shape[:2]  # TODO: use imquery call to retrieve results\r\n    \r\n    def processImage(img_filename):\r\n        print('processing %s... ' % img_filename)\r\n        img = cv2.imread(str(img_filename), 0)\r\n        if img is None:\r\n            print(\"Failed to load\", img_filename)\r\n            return None\r\n\r\n        assert w == img.shape[1] and h == img.shape[0], (\"size: %d x %d ... \" % (img.shape[1], img.shape[0]))\r\n        found, corners = cv2.findChessboardCorners(img, pattern_size)\r\n        if found:\r\n            term = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.1)\r\n            cv2.cornerSubPix(img, corners, (5, 5), (-1, -1), term)\r\n\r\n        if args.debug:\r\n            vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)\r\n            cv2.drawChessboardCorners(vis, pattern_size, corners, found)\r\n            outfile = Path(args.calib_output_folder) / (img_filename.stem + '_chess.png')\r\n            cv2.imwrite(str(outfile), vis)\r\n\r\n        if not found:\r\n            print('chessboard not found')\r\n            return None\r\n\r\n        print('           %s... OK' % img_filename)\r\n        return (corners.reshape(-1, 2), pattern_points)\r\n\r\n    threads_num = args.threads\r\n    if threads_num <= 1:\r\n        chessboards = [processImage(fn) for fn in img_names]\r\n    else:\r\n        print(\"Run with %d threads...\" % threads_num)\r\n        pool = ThreadPool(threads_num)\r\n        chessboards = pool.map(processImage, img_names)\r\n\r\n    chessboards = [x for x in chessboards if x is not None]\r\n    for (corners, pattern_points) in chessboards:\r\n        img_points.append(corners)\r\n        obj_points.append(pattern_points)\r\n\r\n    # calculate camera distortion\r\n    rms, mtx, distCoeffs, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points, (w, h), None, None)\r\n\r\n    # get optimal camera matrix\r\n    camera_matrix, roi = cv2.getOptimalNewCameraMatrix(mtx,distCoeffs,(w,h),1,(w,h))\r\n    # Get error\r\n    mean_error = 0\r\n    for i in range(len(obj_points)):\r\n        projpts, jacobian = cv2.projectPoints(obj_points[i], rvecs[i], tvecs[i], mtx, distCoeffs)\r\n        error = cv2.norm(img_points[i].reshape(-1, 1, 2), projpts, cv2.NORM_L2) / len(projpts)\r\n        mean_error += error\r\n    print(\"\\nRMS:\", rms)\r\n    print(\"Calibration Error: %f\" % (mean_error/len(obj_points)))\r\n    print(\"distortion coefficients: \", distCoeffs.ravel())\r\n\r\n    return camera_matrix, distCoeffs, mtx, roi\r\n\r\n# undistort images\r\ndef undistort_images(input_folder, output_folder, new_mtx, distCoeffs, mtx, roi):\r\n    img_folder = list(Path(input_folder).glob('*.jpg')) + list(Path(input_folder).glob('*.jpeg')) + list(Path(input_folder).glob('*.png'))\r\n    for img_filename in img_folder:\r\n        img = cv2.imread(str(img_filename))\r\n        if distCoeffs is not None and mtx is not None and roi is not None:\r\n            dst = cv2.undistort(img, mtx, distCoeffs, None, new_mtx)\r\n            x,y,w,h = roi\r\n            dst = dst[y:y+h, x:x+w]\r\n            # Update images\r\n            cv2.imwrite(str(Path(output_folder) / img_filename.name), dst)\r\n\r\n\r\ndef get_kps_from_imgs(folder):\r\n    imgs = []\r\n    kps = []\r\n    descs = []\r\n    for img_filename in list(Path(folder).glob('*.png')) + list(Path(folder).glob('*.jpg')) + list(Path(folder).glob('*.jpeg')):\r\n        img = cv2.imread(str(img_filename))\r\n        img_gray= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\r\n\r\n        # ATTENTION:\r\n        # Since SIFT and SURF is not officially supported in OpenCV and it's patented\r\n        # I would like to use an efficient alternative called ORB which is officially supported by OpenCV\r\n        # first: we should install opencv-contrib-python\r\n        # > pip install opencv-contrib-python\r\n\r\n        # Initiate ORB detector\r\n        orb = cv2.ORB_create(args.num_of_kps, scoreType=cv2.ORB_FAST_SCORE)\r\n        # find the keypoints with ORB\r\n        kp = orb.detect(img_gray, None)\r\n        # compute the descriptors with ORB\r\n        kp, desc = orb.compute(img, kp)\r\n\r\n        imgs.append(img)\r\n        kps.append(kp)\r\n        # must change to float32 for FLANN function\r\n        descs.append(desc.astype(np.float32))\r\n\r\n\r\n        if args.debug:\r\n            Path(args.sift_output_folder).mkdir(exist_ok=True, parents=True)\r\n            # draw keypoints on image\r\n            img_kpt = cv2.drawKeypoints(img_gray, kp, None, color=(0,255,0), flags=0)\r\n            cv2.imwrite(str(Path(args.sift_output_folder) / (img_filename.stem + 'kpt.png')), img_kpt)\r\n\r\n    return imgs, kps, descs\r\n\r\n\r\ndef get_norm_matrix(features):\r\n    assert features.shape[1] == 3 # point should have dimension 3xN\r\n    assert np.sum(np.isnan(features)) + np.sum(np.isinf(features)) == 0 # points can not be on infinity\r\n    assert np.sum(features[:,2]) > 1e-10 # this method does not support points at infinity\r\n\r\n    centroid = np.mean(features, axis=0)\r\n    dist = np.sqrt(np.sum((features - centroid) ** 2, axis=1))\r\n    mean_dist = np.mean(dist)\r\n\r\n    norm_matrix = np.array([[np.sqrt(2)/mean_dist, 0, -np.sqrt(2)/mean_dist*centroid[0]], \r\n                            [0, np.sqrt(2)/mean_dist, -np.sqrt(2)/mean_dist*centroid[1]], \r\n                            [0,0,1]])\r\n\r\n    return norm_matrix\r\n\r\ndef findFundamentalMatrix(features1, features2, method=0):\r\n    if method == 0:\r\n        # use findFundamentalMat to filter some points without correspondence\r\n        # F: fundamental matrix\r\n        # input should be 2d float points\r\n\r\n\r\n        F, funda_mask = cv2.findFundamentalMat(features1, features2, cv2.FM_RANSAC)\r\n        # F, funda_mask = cv2.findFundamentalMat(features1, features2, cv2.FM_LMEDS)\r\n        \r\n        # 8 points algorithm match the result got from self manipulated algorithms\r\n        # F, funda_mask = cv2.findFundamentalMat(features1, features2, cv2.FM_8POINT)\r\n        funda_mask = funda_mask.ravel()\r\n        \r\n    else:\r\n\r\n        num_of_kps = features1.shape[0]\r\n        homo_f1 = np.hstack([features1, np.ones((num_of_kps, 1))])\r\n        homo_f2 = np.hstack([features2, np.ones((num_of_kps, 1))])\r\n        normmat1 = get_norm_matrix(homo_f1)\r\n        normmat2 = get_norm_matrix(homo_f2)\r\n        f1_norm = homo_f1.dot(normmat1.T)\r\n        f2_norm = homo_f2.dot(normmat2.T)\r\n        u1n = f1_norm[:,0].reshape(-1, 1)\r\n        v1n = f1_norm[:,1].reshape(-1, 1)\r\n        u2n = f2_norm[:,0].reshape(-1, 1)\r\n        v2n = f2_norm[:,1].reshape(-1, 1)\r\n\r\n        if method == 1:\r\n            A = np.hstack([u2n * u1n, u2n * v1n, u2n, v2n * u1n, v2n * v1n, v2n, u1n, v1n, np.ones((num_of_kps, 1))])\r\n            u_A, s_A, vh_A = np.linalg.svd(A)\r\n            F_norm = vh_A.T[:,8].reshape(3, 3)\r\n\r\n        elif method == 2:\r\n            WW = np.hstack([f2_norm[:,0].reshape(-1, 1) * f1_norm, \\\r\n                f2_norm[:,1].reshape(-1, 1) * f1_norm, f1_norm[:,0:3]])\r\n            u_W, s_W, vh_W = np.linalg.svd(WW)\r\n            F_norm = vh_W.T[:,8].reshape(3, 3)\r\n\r\n        u_F, s_F, vh_F = np.linalg.svd(F_norm)\r\n        F_norm_prime = u_F.dot(np.diag([s_F[0], s_F[1], 0])).dot(vh_F)\r\n        F = normmat2.T.dot(F_norm_prime).dot(normmat1)\r\n\r\n        # svd method won't return reasonable mask, so just all 1 as masks\r\n        funda_mask = np.ones((num_of_kps))\r\n\r\n    return F, funda_mask\r\n\r\n\r\ndef find_correspondence(info1, info2):\r\n    \"\"\"\r\n    from keypoints and their descriptions for each images\r\n    find pixelwise correspondence from two extracted features lists\r\n    calculate each pairs of correspondence, filter invalid points\r\n    \"\"\"\r\n\r\n    idx1, img1, kp1, desc1 = info1\r\n    idx2, img2, kp2, desc2 = info2\r\n\r\n    print('num of keypoints: %d (img %d), %d (img %d)' % (len(kp1), idx1, len(kp2), idx2))\r\n\r\n    # use FLANN to do feature matching\r\n    FLANN_INDEX_KDTREE = 0\r\n    index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)\r\n    search_params = dict(checks = 50)\r\n\r\n    flann = cv2.FlannBasedMatcher(index_params, search_params)\r\n\r\n    matches = flann.knnMatch(desc1, desc2, k=2)\r\n\r\n    # store all the good matches as per Lowe's ratio test (0.7).\r\n    good = [m for m, n in matches if m.distance < args.lowe_test_ratio * n.distance]\r\n\r\n    # round to integer, but remain as type float32\r\n    src_pts = np.float32([kp1[m.queryIdx].pt for m in good])\r\n    dst_pts = np.float32([kp2[m.trainIdx].pt for m in good])\r\n\r\n    # minimum match count\r\n    min_match_count = int(args.min_match_percent * min(len(kp1), len(kp2)))\r\n\r\n    if len(good) > min_match_count:\r\n\r\n        # use findHomography to filter some points without correspondence\r\n        # params: RANSAC reprojection threshold {5.0}\r\n        # input should be 3d\r\n        M, homo_mask = cv2.findHomography(src_pts.reshape(-1,1,2), \\\r\n            dst_pts.reshape(-1,1,2), cv2.RANSAC, 5.0)\r\n        # change from n * 1 to 1-d array\r\n        homo_matches_mask = homo_mask.ravel()\r\n\r\n\r\n        # self defined findFundamentalMatrix method using SVD\r\n        F, funda_matches_mask = findFundamentalMatrix(src_pts, dst_pts, method=0)\r\n\r\n        # calculate inliers(H) / inliers(F)\r\n        inliers_rate = np.sum(homo_matches_mask) / np.sum(funda_matches_mask)\r\n\r\n        # get common results for H and F\r\n        matchesMask = np.logical_and(homo_matches_mask, funda_matches_mask)\r\n\r\n        if args.debug:\r\n            h, w, c = img1.shape\r\n            # four corner points in the first image\r\n            pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)\r\n            # use it to calculate the correspondence in the second image\r\n            dst = cv2.perspectiveTransform(pts,M)\r\n            # mark the four courners in the second image\r\n            img2_pers = cv2.polylines(img2.copy(), [np.int32(dst)],True,255,3, cv2.LINE_AA)\r\n\r\n            # draw matches image\r\n            Path(args.match_output_folder).mkdir(exist_ok=True, parents=True)\r\n\r\n            draw_params = dict(# matchColor = (0,255,0), # draw matches in green color\r\n                       singlePointColor = None, # dont't draw keypoints withoud matches\r\n                       matchesMask = matchesMask.tolist(), # draw only inliers\r\n                       flags = 2) # dont't draw keypoints withoud matches\r\n\r\n            img_match = cv2.drawMatches(img1,kp1,img2_pers,kp2, good, None, **draw_params)\r\n            cv2.imwrite(str(Path(args.match_output_folder) / \"_\".join((\"match\", str(idx1), str(idx2), \".jpg\"))), img_match)\r\n\r\n        src_pts = src_pts[np.nonzero(matchesMask)]\r\n        dst_pts = dst_pts[np.nonzero(matchesMask)]\r\n\r\n\r\n        if src_pts.shape[0] < min_match_count:\r\n            print(\"(%d/%d) matches are found, but not enough inliers (%d). \" \r\n                % (len(good), min_match_count, np.sum(matchesMask)))\r\n            return None\r\n\r\n        # sort these points for better look\r\n        lex_idx = np.lexsort((src_pts[:,1], src_pts[:,0]))\r\n        src_pts = src_pts[lex_idx]\r\n        dst_pts = dst_pts[lex_idx]\r\n\r\n\r\n        # correct matches to minimize fundamental matrix ransac error\r\n        src_pts_new, dst_pts_new = cv2.correctMatches(F, src_pts.reshape(1, src_pts.shape[0], src_pts.shape[1]), \\\r\n            dst_pts.reshape(1, dst_pts.shape[0], dst_pts.shape[1]))\r\n\r\n\r\n        print(\"number of matches are found - %d/%d, inliers(H) / inliers(F): %.6f\" % (src_pts_new[0].shape[0], min_match_count, inliers_rate))\r\n\r\n        # return int array for connected graph search\r\n        # because we need to do features tracking, an int result will make matching easier\r\n        # return ((idx1, img1, np.round(src_pts_new[0]).astype(np.float32)), (idx2, img2, np.round(dst_pts_new[0]).astype(np.float32)), F, M, inliers_rate)\r\n        \r\n        # return float array\r\n        return ((idx1, img1, src_pts_new[0]), (idx2, img2, dst_pts_new[0]), F, M, inliers_rate)\r\n\r\n        # if we don't want to correct_matches, just return this\r\n        # return ((idx1, img1, src_pts), (idx2, img2, dst_pts), F, M, inliers_rate)\r\n\r\n    else:\r\n        print(\"Not enough matches are found, lowe ratio test not passed - %d/%d\" % (len(good), min_match_count))\r\n\r\n        return None\r\n\r\n\r\ndef build_connectivity_graph(correspondences):\r\n    \"\"\"\r\n    use the found correspondence of points to build connectivity graph\r\n    which is weighted graph, with weights as their connections\r\n    \"\"\"\r\n    graph = ConnectionGraph(correspondences)\r\n    return graph\r\n\r\n\r\ndef get_relative_pose(features1, features2, F, kint1, kint2, distCoeffs=None):\r\n    '''\r\n    triangulation using linear method, svd\r\n    use features from both images and fundamental matrix F\r\n    '''\r\n\r\n    # 1. change to homogeneous coordinates\r\n\r\n    assert features1.shape == features2.shape\r\n\r\n    num_of_kps = features1.shape[0]\r\n\r\n    # # not use normalized point to recover R and T\r\n    # pts_l_norm = np.expand_dims(features1, axis=1).astype(np.float32)\r\n    # pts_r_norm = np.expand_dims(features2, axis=1).astype(np.float32)\r\n\r\n    # use opencv to calculate rotation and translation\r\n    pts_l_norm = cv2.undistortPoints(np.expand_dims(features1, axis=1), cameraMatrix=kint1, distCoeffs=None)\r\n    pts_r_norm = cv2.undistortPoints(np.expand_dims(features2, axis=1), cameraMatrix=kint2, distCoeffs=None)\r\n\r\n    # print(F)\r\n    # F = np.array([[-5.91176170742754e-07,-1.15686479351066e-07,0.00365007704411600], [1.26728959502786e-06,-4.02454207350275e-07,-0.00807758873453800], [0.000693859536366768,0.00835431229618000, -1.08724818522269]])\r\n    # print(F)\r\n\r\n\r\n    # # directly use findEssentialMat method cannot give you a good result, so neglect it\r\n    # E, mask = cv2.findEssentialMat(pts_l_norm, pts_r_norm, focal=kint1[0,0], pp=(kint1[0,2], kint1[1,2]), method=cv2.RANSAC, prob=0.999, threshold=3.0)\r\n\r\n    # get essential matrix by using FundamentalMat calculated before\r\n    E = kint2.T.dot(F).dot(kint1)\r\n\r\n    # prove that b.dot(F).dot(a) = 0\r\n    errF = 0\r\n    errE = 0\r\n    for i in range(len(features1)):\r\n        x1 = np.array([[features1[i][0], features1[i][1], 1]]).T\r\n        x2 = np.array([[features2[i][0], features2[i][1], 1]])\r\n\r\n        errF += x2.dot(F).dot(x1)[0][0]\r\n        errE += x2.dot(np.linalg.inv(kint2.T)).dot(E).dot(np.linalg.inv(kint1)).dot(x1)[0][0]\r\n\r\n    print('sum of fundamental matrix error: %.10f, essential matrix error: %.10f' % (errF, errE))\r\n\r\n    # 1. self defined essential matrix decomposition using SVD\r\n    # make up matrices  W and Z\r\n    # W = np.array([[0,-1,0], [1,0,0],[0,0,1]])\r\n    # Z = np.array([[0,1,0], [-1,0,0],[0,0,0]])\r\n\r\n    # # svd essential matrix, get rotation and translation for right camera\r\n    # U, S, Vh = np.linalg.svd(E)\r\n    # T_x = U.dot(Z).dot(U.T)\r\n    # T = np.array([[T_x[2,1], T_x[0,2], T_x[1,0]]]).T\r\n    # R = U.dot(W).dot(Vh)\r\n\r\n\r\n    # 2. use recoverPose to recover R and T\r\n    points, R, T, mask = cv2.recoverPose(E, pts_l_norm, pts_r_norm, cameraMatrix=kint1)\r\n\r\n    return R, T\r\n\r\n\r\ndef triangulate(features1, features2, Pr1, Pr2):\r\n\r\n    # # 1. self defined triangulation linear method\r\n    # X3Ds = []\r\n    # for i in range(features1.shape[0]):\r\n    #     AA = np.vstack([features1[i][0] * Pr1[2] - Pr1[0], \\\r\n    #                     features1[i][1] * Pr1[2] - Pr1[1], \\\r\n    #                     features2[i][0] * Pr2[2] - Pr2[0], \\\r\n    #                     features2[i][1] * Pr2[2] - Pr2[1]])\r\n    #     UU, SS, VVh = np.linalg.svd(AA)\r\n    #     homo_X3D = VVh.T[:,3] # the last column of VV\r\n    #     X3Ds.append(homo_X3D[:-1] / homo_X3D[3])\r\n    # X3Ds = np.float32(X3Ds)\r\n\r\n\r\n    # 2. cv2.triangulatePoints, both get the same results\r\n    # Attention: should input (float) features instead of integer\r\n    X4Ds = cv2.triangulatePoints(Pr1, Pr2, features1.T, features2.T).T\r\n    X3Ds = X4Ds[:,:3] / (X4Ds[:,3].reshape(X4Ds.shape[0], -1))\r\n\r\n    # return 3D points, Rotation and Translation for the second camera\r\n    return X3Ds\r\n\r\ndef solvePnP(points, img_idx, camera_matrix, distCoeffs):\r\n    if len(points) <= 8:\r\n        # return Identity, awaiting bundle adjustment\r\n        R = np.eye(3)\r\n        T = np.zeros((3,1))\r\n    else:\r\n        points_3d = np.vstack([point['3d'] for point_idx, point in points])\r\n        features = np.vstack([point['track'][img_idx] for point_idx, point in points])\r\n\r\n        retval, rvec, tvec, inliers = cv2.solvePnPRansac(points_3d, features, camera_matrix, distCoeffs)\r\n\r\n        R, jacobian = cv2.Rodrigues(rvec)\r\n        T = -tvec\r\n\r\n    return R, T\r\n\r\ndef display_3d(points, i):\r\n\r\n    X3Ds = np.vstack([point['3d'] for point_idx, point in points.items() if point['3d'] is not None])\r\n\r\n    # when displaying, eliminate outliers\r\n\r\n\r\n\r\n    # display the image\r\n    if args.debug:\r\n        # create the first figure\r\n        fig = plt.figure(num=i)\r\n        ax = plt.axes(projection='3d')\r\n        # ax.scatter3D(X3Ds[:,0], X3Ds[:,1], X3Ds[:,2], c=X3Ds[:,2], cmap='Blues')\r\n        ax.scatter3D(X3Ds[:,0], X3Ds[:,1], X3Ds[:,2], cmap='Blues')\r\n        ax.set_xlabel('x axis')\r\n        ax.set_ylabel('y axis')\r\n        ax.set_zlabel('z axis')\r\n        plt.show()\r\n\r\n\r\ndef main(args):\r\n    \"\"\"\r\n    Step 1: Camera calibration\r\n\r\n    input: calibration images / self defined camera intrinsic parameters\r\n    return: camera_matrix, distortion coefficients\r\n    \"\"\"\r\n\r\n    if args.calib_type == 'real':\r\n        # TODO: real-time calibration with camera on this computer\r\n        camera_matrix, distCoeffs, mtx, roi = calibrate_camera(args.calib_folder, tuple(args.pattern_size))\r\n\r\n    elif args.calib_type == 'manual':\r\n        # manual calibration\r\n        ox, oy = 640 / 2, 480 / 2\r\n        # ox, oy = 384 / 2, 512 / 2\r\n        pwm = 1.22 * 1e-6\r\n        f = (28 * 1e-3) / pwm\r\n        camera_matrix = np.array([[f, 0, ox], [0, f, oy], [0,0,1]])\r\n\r\n        # unknown distort coefficients\r\n        distCoeffs = None\r\n        mtx = None\r\n        roi = None\r\n\r\n    elif args.calib_type == 'default':\r\n        # use default images calibration\r\n        camera_matrix, distCoeffs, mtx, roi = calibrate_camera(args.calib_folder, tuple(args.pattern_size))\r\n\r\n    else:\r\n        raise NotImplementedError(\"Not implemented yet.\")\r\n\r\n    print(\"camera calibration type: %s\" % (args.calib_type))\r\n    print('intrinsic matrix:\\n', camera_matrix)\r\n    \r\n    # undistort images\r\n    undistort_folder = args.input_folder.replace('images', 'images_undistort')\r\n    Path(undistort_folder).mkdir(exist_ok=True, parents=True)\r\n\r\n    undistort_images(args.input_folder, undistort_folder, camera_matrix, distCoeffs, mtx, roi)\r\n\r\n    \"\"\"\r\n    Step 2: get keypoints from images\r\n    \r\n    input: images taken from objects. Notice: We assume that they are using the same camera, \r\n    and the image size should be the same (if not, I can't not guarantee that the result will be fine, but it may works as well)\r\n    return: images, keypoints and their descriptions\r\n    \"\"\"\r\n\r\n    imgs, kps, descs = get_kps_from_imgs(undistort_folder)\r\n\r\n    # assume there are more than 2 images for reconstruction\r\n    if len(kps) < 2:\r\n        raise Error('Error number of images should be larger than 2.')\r\n        return\r\n\r\n\r\n    \"\"\"\r\n    Step 3: find correspondence for keypoints for each image pairs\r\n    \r\n    input: images taken from objects with keypoints, descriptions from step 2\r\n    return: correspondences\r\n    \"\"\"\r\n\r\n    # for each pair of image, find correspondence\r\n    correspondences = []\r\n    for i in range(len(imgs) - 1):\r\n        for j in range(i + 1, len(imgs)):\r\n            correspondence = find_correspondence((i, imgs[i], kps[i], descs[i]), (j, imgs[j], kps[j], descs[j]))\r\n            if correspondence is not None:\r\n                correspondences.append(correspondence)\r\n\r\n\r\n    \"\"\"\r\n    Step 4: build a connected graph based on correspondences\r\n    \r\n    input: correspondences from step 3\r\n    return: graph with tracks of points\r\n    \"\"\"\r\n\r\n    # build connected graph with tracks based on pairwise connections\r\n    graph = build_connectivity_graph(correspondences)\r\n    print('graph build complete')\r\n\r\n\r\n    \"\"\"\r\n    Step 5: initialize 3d model using stereo reconstruction\r\n    \r\n    input: camera matrix from step 1, graph from step 4\r\n    return: initialized 3d model\r\n    \"\"\"\r\n\r\n    # Select the first edge to build the first model (stereo reconstruction)\r\n\r\n    node1, node2, edge_weights = graph.get_next_edge()\r\n    num_of_edges = 1\r\n    print('first edge from node %d, %d with weight %f: ' % (node1.idx, node2.idx, edge_weights))\r\n    dic = node2.neighbors[node1.idx]\r\n    features1 = node1.kps[dic['left']]\r\n    features2 = node2.kps[dic['right']]\r\n    F = dic['F'] # fundamental matrix\r\n    # get relative pose between feature1, feature2\r\n    R2, T2 = get_relative_pose(features1, features2, F, camera_matrix, camera_matrix, distCoeffs=distCoeffs) # use the same camera\r\n    \r\n    R1 = np.eye(3)\r\n    T1 = np.zeros((3,1))\r\n\r\n    # projection matrix for initial pair, assume the left camera is at the world center\r\n    Pr1 = camera_matrix.dot(np.hstack([R1, T1]))\r\n\r\n    # this may not get a better solution like Pr2 = np.hstack([R2, T2])\r\n    Pr2 = camera_matrix.dot(np.hstack([R2, T2]))\r\n\r\n    # if not, use this\r\n    # currently I cannot figure out why this gets worse solution than the previous line\r\n    # Pr2 = np.hstack([R2, T2])\r\n\r\n    # triangulation to get the first 3d model\r\n    X3Ds = triangulate(features1, features2, Pr1, Pr2)\r\n\r\n\r\n    # update the graph for the pose between node1 and node2\r\n    graph.update_tracks(node1.idx, R1, T1, node2.idx, R2, T2, X3Ds)\r\n\r\n\r\n    \"\"\"\r\n    Step 6: bundle adjustment for the first 3d model\r\n    \r\n    it's very possible that first 3d model is a very bad result!!! and I cannot figure out the exact reason yet\r\n    though the reprojection error is relatively small and fundamental matrix result are fine\r\n\r\n    input: graph and camera matrix\r\n    return: bundle adjusted first 3d model\r\n    \"\"\"\r\n    \r\n    # graph.bundle_adjust(camera_matrix, num_iter=1000)\r\n\r\n    # visualize the first 3d results\r\n    display_3d(graph.track.points, num_of_edges)\r\n\r\n\r\n\r\n\r\n    # build init 3d model using the edge with largest weight\r\n    while (graph.has_edge()):\r\n        \"\"\"\r\n        Step 7: get another edge based on how many point the new edge observes\r\n        \"\"\"\r\n\r\n        node1, node2, edge_weights = graph.get_next_edge()\r\n        num_of_edges += 1\r\n        print('select edge from node %d, %d with weight %f: ' % (node1.idx, node2.idx, edge_weights))\r\n\r\n        # features for image left and right\r\n        dic = node2.neighbors[node1.idx]\r\n        features1 = node1.kps[dic['left']]\r\n        features2 = node2.kps[dic['right']]\r\n\r\n        # get valid 3d points that contains node1 and node2\r\n        valid_points = graph.track.get_points_with_correspondence(node1.idx, node2.idx)\r\n\r\n        # get absolute poses for left and right cameras\r\n        if node1.absolute_pose['R'] is None and node1.absolute_pose['T'] is None:\r\n            print(\"node %d's pose unestimated, use solvepnp to solve\")\r\n            R_left, T_left = solvePnP(valid_points, node1.idx, camera_matrix, distCoeffs=distCoeffs)\r\n\r\n        else:\r\n            R_left, T_left = node1.absolute_pose['R'], node1.absolute_pose['T']\r\n\r\n        if node2.absolute_pose['R'] is None and node2.absolute_pose['T'] is None:\r\n            print(\"node %d's pose unestimated, use solvepnp to solve\")\r\n            R_right, T_right = solvePnP(valid_points, node2.idx, camera_matrix, distCoeffs=distCoeffs)\r\n\r\n        else:\r\n            R_right, T_right = node2.absolute_pose['R'], node2.absolute_pose['T']\r\n\r\n        print(R_left.shape, T_left.shape, R_right.shape, T_right.shape)\r\n\r\n        # projection matrix for initial pair, assume the left camera is at the world center\r\n        Pr1 = camera_matrix.dot(np.hstack([R_left, T_left]))\r\n        Pr2 = camera_matrix.dot(np.hstack([R_right, T_right]))\r\n\r\n        # triangulation to get the first 3d model\r\n        X3Ds = triangulate(features1, features2, Pr1, Pr2)\r\n\r\n        # update the graph for the pose between node1 and node2\r\n        graph.update_tracks(node1.idx, R_left, T_left, node2.idx, R_right, T_right, X3Ds)\r\n\r\n        \"\"\"\r\n        Step 8: bundle adjustment for the updated 3d model\r\n\r\n        input: graph and camera matrix\r\n        return: bundle adjusted first 3d model\r\n        \"\"\"\r\n        \r\n        # graph.bundle_adjust(camera_matrix, num_iter=1000)\r\n\r\n        # visualize the first 3d results\r\n        display_3d(graph.track.points, num_of_edges)\r\n\r\n        \r\n\r\n    #     # # check matches are correct\r\n    #     # if args.debug:\r\n    #     #     img1 = node1.img\r\n    #     #     img2 = node2.img\r\n    #     #     h, w, c = img1.shape\r\n    #     #     img_cat = np.hstack([img1, img2])\r\n\r\n    #     #     for i in range(len(features1)):\r\n    #     #         img_cat[int(features1[i][1])][int(features1[i][0])] = np.array([0,255,0])\r\n    #     #         img_cat[int(features2[i][1])][int(features2[i][0] + w)] = np.array([0,255,0])\r\n    #     #         cv2.line(img_cat, (int(features1[i][0]), int(features1[i][1])), (int(features2[i][0] + w), int(features2[i][1])), color=(0,255,0))\r\n    #     #     cv2.imwrite('test.png', img_cat)\r\n\r\n\r\n\r\n    #     F = dic['F'] # fundamental matrix\r\n\r\n    #     # estimate essential matrix\r\n    #     # assume the left camera is the reference camera, with R1 = I, t1 = 0\r\n    #     # triangulation using linear method\r\n\r\n    #     # 1. get first model (3D points) using stereo reconstruction\r\n    #     X3Ds, R, T = triangulate(features1, features2, F, camera_matrix, camera_matrix, distCoeffs=distCoeffs) # use the same camera\r\n\r\n    #     # update the graph for the pose between node1 and node2\r\n    #     graph.update_tracks(node1.idx, node2.idx, R, T, X3Ds)\r\n\r\n\r\n    #     # it's very possible that X3Ds is a very bad result!!! and I cannot figure out the exact reason yet\r\n    #     # the reprojection error is relatively small and fundamental matrix result are fine\r\n\r\n    #     # 2. do bundle adjustment for the existing model， if use incremental bundle adjustment for the model\r\n    #     graph.bundle_adjust()\r\n        \r\n\r\n    #     if args.debug:\r\n    #         fig = plt.figure()\r\n    #         ax = plt.axes(projection='3d')\r\n    #         # ax.scatter3D(X3Ds[:,0], X3Ds[:,1], X3Ds[:,2], c=X3Ds[:,2], cmap='Blues')\r\n    #         ax.scatter3D(X3Ds[:,0], X3Ds[:,1], X3Ds[:,2], cmap='Blues')\r\n    #         ax.set_xlabel('x axis')\r\n    #         ax.set_ylabel('y axis')\r\n    #         ax.set_zlabel('z axis')\r\n    #         plt.show()\r\n\r\n    #     return\r\n\r\n\r\nif __name__ == '__main__':\r\n    parser = argparse.ArgumentParser(description='Process imput arguments for 3D reconstruction.')\r\n    parser.add_argument('-c', '--calib_type', type=str, \\\r\n        choices=['real', 'manual', 'default'], default='default', \\\r\n        help='Types of camera calibration. \\\r\n        \"real\": use local camera to calibrate in real time; \\\r\n        \"manual\": use manual camera calibration settings; \\\r\n        \"default\": use default images for calibration;')\r\n\r\n    # configurations\r\n    parser.add_argument('-d', '--debug', action='store_true', help='Run in debug mode')\r\n    parser.add_argument('-t', '--threads', type=int, default=4, help='number of threads')\r\n    parser.add_argument('-p', '--pattern_size', type=int, nargs='+', default=(10,7), help='pattern size for camera calibration')\r\n    \r\n\r\n    # folders\r\n    parser.add_argument('-cf', '--calib_folder', type=str, default='calib_images/iphone7p', \\\r\n        help='Image folder name for camera calibration')\r\n    parser.add_argument('-if', '--input_folder', type=str, default='images/book1', \\\r\n        help='Image folder for 3d reconstruction')\r\n    parser.add_argument('-co', '--calib_output_folder', type=str, default='debug/calib_outputs', \\\r\n        help='Output folder for calibration')\r\n    parser.add_argument('-so', '--sift_output_folder', type=str, default='debug/sift_outputs/book1', \\\r\n        help='Output folder for sift keypoints extraction')\r\n    parser.add_argument('-mo', '--match_output_folder', type=str, default='debug/match_outputs/book1', \\\r\n        help='Output folder for feature matching')\r\n    parser.add_argument('-o', '--output_folder', type=str, default='outputs/book1', \\\r\n        help='Output folder for 3d reconstruction')\r\n\r\n\r\n    # hyper parameters\r\n    parser.add_argument('-nkp', '--num_of_kps', type=int, default=10000, help='number of key points')\r\n    parser.add_argument('-ltr', '--lowe_test_ratio', type=float, default=0.8, help='lowe ratio test rate')\r\n    parser.add_argument('-mmc', '--min_match_percent', type=float, default=0.01, help='minimum match percentage')\r\n    args = parser.parse_args()\r\n    main(args)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n# some test which is borrowed from other notes\r\n\r\ndef test():\r\n    homo_x1 = np.array([[2.86666666666667,-22.2187500000000,24.8000000000000,-23.2285714285714,27.1388888888889,-33.2894736842105,47.7931034482759,31.7333333333333],\r\n    [-33.4333333333333,14.4375000000000,-4.93333333333333,35.4285714285714,3.19444444444444,-4.52631578947369,-51.2413793103448,36.8666666666667],\r\n    [1,1,1,1,1,1,1,1]])\r\n    homo_x2 = np.array([[-229.522875448942,-258.292895283908,-206.597731739827,-262.230921762122,-209.943236963811,-275.437557347701,-181.668315941037,-199.405970254657],\r\n    [-34.2802236631725,14.9107171694714,-5.02902645276237,36.6259219139803,3.25917390157400,-4.69475951687246,-51.9047013403661,37.5131564447507],\r\n    [1,1,1,1,1,1,1,1]])\r\n\r\n#     homo_x1 = np.array([[-102.766291777084,255.500969078484,-44.9335294173212,-223.944930920947,-98.8327970841866,-44.2336788063890,-60.8647393705177,8.51496788806941,0.745050635701492,-44.4137684538591],\r\n# [54.0935032492134,171.835606944456,-32.0135418758319,41.3734222619891,-279.742900618785,-135.811558132020,-72.2051817851882,1.82504383577172,0.343791528708997,95.0035092386631],\r\n# [1,1,1,1,1,1,1,1,1,1]])\r\n#     homo_x2 = np.array([[-352.297383508563,17.5588770566538,-289.262722752471,-489.673069977241,-342.330002971343,-287.517068912743,-306.265697905156,-232.343678170461,-214.290449718330,-288.256419038601],\r\n# [57.1826262772602,165.199531371376,-33.3194551784004,45.2071887678686,-294.996539885698,-141.290294105620,-75.4683665784438,1.87260592942277,0.351144636763272,98.8541988292808],\r\n# [1,1,1,1,1,1,1,1,1,1]])\r\n\r\n    F, mask = findFundamentalMatrix(homo_x1[:2,2:].T, homo_x2[:2,2:].T, method=0)\r\n\r\n    kint1 = np.array([[1000, 0,0], [0,1000, 0], [0,0,1]])\r\n    kint2 = np.array([[1000, 0,0], [0,1000, 0], [0,0,1]])\r\n    E = kint2.T.dot(F).dot(kint1)\r\n\r\n\r\n\r\n    pts_l_norm = cv2.undistortPoints(np.expand_dims(homo_x1[:2].T, axis=1).astype(np.float32), cameraMatrix=kint1, distCoeffs=None)\r\n    pts_r_norm = cv2.undistortPoints(np.expand_dims(homo_x2[:2].T, axis=1).astype(np.float32), cameraMatrix=kint2, distCoeffs=None)\r\n\r\n\r\n    # pts_l_norm = np.expand_dims(homo_x1[:2].T, axis=1).astype(np.float32)\r\n    # pts_r_norm = np.expand_dims(homo_x2[:2].T, axis=1).astype(np.float32)\r\n\r\n\r\n    # E, mask = cv2.findEssentialMat(pts_l_norm, pts_r_norm, focal=kint1[0,0], pp=(kint1[0,2], kint1[1,2]), method=cv2.RANSAC, prob=0.999, threshold=3.0)\r\n    # # print(E)\r\n\r\n    points, R, T, mask = cv2.recoverPose(E, pts_l_norm, pts_r_norm, focal=kint1[0,0], pp=(kint1[0,2], kint1[1,2]))\r\n\r\n    Pr1 = kint1.dot(np.hstack([np.eye(3), np.zeros((3,1))]))\r\n    Pr2 = kint2.dot(np.hstack([R, T]))\r\n\r\n    X4Ds = cv2.triangulatePoints(Pr1, Pr2, homo_x1[:2], homo_x2[:2]).T\r\n    X3Ds = X4Ds[:,:3] / X4Ds[:,3].reshape(X4Ds.shape[0], -1)\r\n    print(X3Ds)\r\n\r\n# test()","sub_path":"reconstruct.py","file_name":"reconstruct.py","file_ext":"py","file_size_in_byte":32307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"186836175","text":"import requests\nfrom bs4 import BeautifulSoup\nimport time\nimport smtplib\n\nURL=\"https://www.amazon.in/Vivo-Storage-Additional-Exchange-Offers/dp/B07KXC1YGG/ref=br_msw_pdt-4/260-3954790-6831115?_encoding=UTF8&smid=A14CZOWI0VEHLG&pf_rd_m=A1VBAL9TL5WCBF&pf_rd_s=&pf_rd_r=JWT9RNZX2ZRSN6WBWWQE&pf_rd_t=36701&pf_rd_p=2b9bb3c1-71bb-48bb-8476-31c6e37895b1&pf_rd_i=desktop\"\n\nheaders={\n    \"User-Agent\":'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36'\n}\n\ndef checkprice():\n    page = requests.get(URL,headers=headers)\n\n    soup = BeautifulSoup(page.content,'html.parser')\n    price=[]\n    ld = soup.find(id=\"priceblock_dealprice\").get_text()\n    for i in ld[2:]:\n        if i==\",\":\n            pass\n        elif i=='.':\n            price.append('.')    \n        else:\n            price.append(i)\n\n    print(float(\"\".join(price)))\n\n    if float(\"\".join(price))<25000:\n        send_email()\ndef send_email():\n    server=smtplib.SMTP('smtp.gmail.com',587)\n    server.ehlo()\n    server.starttls()\n    server.ehlo()\n\n    server.login('linkesh0017@gmail.com')\n\n    subject=\"Test msg\"\n    Message=\"Yass!!\"\n\n    T=f\"Subject:{subject} Body :{Message}\"\n\n    server.sendmail(\n        'linkesh0017@gmail.com',\n        'linkesh00017@gmail.com',\n         T\n    )\n\n    print(\"\\nYour Msg has been sent Successfully\")\n\n\ncheckprice()","sub_path":"DE/Scrapping/webscrap.py","file_name":"webscrap.py","file_ext":"py","file_size_in_byte":1371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"186200929","text":"def solution(progresses, speeds):\n    # ceil 함수 사용을 위한 math library import\n    import math\n    answer = []\n\n    # 최고 우선 순위 작업(progresses[0])의 완료 시간을 계산하여 time에 할당\n    time = math.ceil((100 - progresses[0]) / speeds[0])\n    cnt = 0\n\n    # progresses 안에 요소가 있는 동안\n    while len(progresses) > 0:\n        # progresses 안에 남아있는 작업 중 첫번째 작업이 완료되면\n        if progresses[0] + (time *  speeds[0]) >= 100:\n            # 완료된 작업과 작업 속도를 dequeue\n            progresses.pop(0)\n            speeds.pop(0)\n            # 동시 배포 작업의 수를 구하기 위해 cnt += 1\n            cnt += 1\n        # progresses 안에 남아있는 작업 중 첫번째 작업이 완료되지 않았다면\n        else:\n            # 배포 필요한 작업이 있을 경우(cnt가 1보다 같거나 클 경우)\n            if cnt >= 1:\n                # 배포 필요한 작업의 수(cnt)를 answer에 append한 뒤 \n                answer.append(cnt)\n                # cnt 초기화\n                cnt = 0\n            \n            # 작업 시간 += 1\n            time += 1\n    # 마지막 배포 작업의 경우 while문 안에서 answer에 append 되지 않았기 때문에\n    # while문 밖에서 최종 배포 작업 append 처리\n    answer.append(cnt)\n    return answer\n","sub_path":"code/shinn92kr/stack&queue/프로그래머스_기능개발.py","file_name":"프로그래머스_기능개발.py","file_ext":"py","file_size_in_byte":1388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"227879434","text":"from kivy.app import App\nfrom kivy.uix.floatlayout import FloatLayout\nfrom kivy.uix.button import Button\n\nclass FloatApp(App):\n\tdef build(self):\n\t\tlayout = FloatLayout()\n\t\tbtn1 = Button(text=\"1st\",size_hint=[0.2,0.2],right=400,y=50)\n\t\tbtn2 = Button(text=\"2nd\",size_hint=[0.2,0.2],pos_hint={\"center_x\":0.5,\"top\":0.9})\n\t\tlayout.add_widget(btn1)\n\t\tlayout.add_widget(btn2)\n\t\treturn layout\nFloatApp().run()","sub_path":"Kivy/Kivy Lesson/First/FloatApp1-2.py","file_name":"FloatApp1-2.py","file_ext":"py","file_size_in_byte":401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"466712279","text":"import random\nmespv=40\nn='force'\ndegmag='intelligence'\nmana='mana'\npvmob='pv'\ndegmob='degat'\nmesdeg=random.randrange(0+n, 5+n)\nmesdegmag=random.randrange(0+degmag, 5+degmag)\nwhile True:\n    if pvmob<=0:\n        print (\"You won!\")\n    elif mespv<=0:\n        print (\"You Died!\")\n    else:\n        tg=input(\"1:attaque, 2:magie, 3:defense, 4:fuite\")\n        if tg=='1':\n            pvmob=pvmob-mesdeg\n            mespv=mespv-degmob\n        elif tg=='2':\n            mana=mana-1\n            pvmob=pvmob-mesdegmag\n            mespv=mespv-degmob\n        elif tg=='3':\n            mespv=mespv-degmob/2\n        elif tg=='4':\n            quitter\n\n\n","sub_path":"Scripts Vic/combatsys.py","file_name":"combatsys.py","file_ext":"py","file_size_in_byte":638,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"351771742","text":"from OhServer.request import Request\nfrom OhServer import HTTPStatus\nimport unittest\n\ntestRequestString = \"GET /restapi/v1.0 HTTP/1.1\\r\\nAccept: application/json\\r\\nAuthorization: Bearer UExBMDFUMDRQV1MwMnzpdvtYYNWMSJ7CL8h0zM6q6a9ntw\\r\\n\\r\\nHello World This is Others\\r\\nWow Amazing!!\"\nmessageToken = \"GET\"\ntarget = \"/restapi/v1.0\"\nhttpVersion = \"HTTP/1.1\"\nothers = \"Hello World This is Others\\r\\nWow Amazing!!\"\noptions = {'Accept': 'application/json', 'Authorization': 'Bearer UExBMDFUMDRQV1MwMnzpdvtYYNWMSJ7CL8h0zM6q6a9ntw'}\n\nclass RequestTest(unittest.TestCase) :\n    def testInterpret(self):\n        r = Request()\n        self.assertEqual(r.parse_request(testRequestString), HTTPStatus.OK)\n        self.assertEqual(r.messageToken, messageToken)\n        self.assertEqual(r.target, target)\n        self.assertEqual(r.httpVersion, httpVersion)\n        self.assertEqual(r.others, others)\n        self.assertEqual(r.options, options)\n\nif __name__ == \"__main__\":\n    unittest.main()\n","sub_path":"tests/requestTest.py","file_name":"requestTest.py","file_ext":"py","file_size_in_byte":981,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"511194107","text":"import time\n\n\ndef bench(loops=10, times=10, *args):\n    total_times = [0] * len(args)\n    for i in range(times):\n        for count, func in enumerate(args):\n            time_start = time.time()\n            for k in range(loops):\n                func()\n            time_end = time.time()\n            total_times[count] += time_end - time_start\n    for i in range(len(args)):\n        total_times[i] /= times\n    for count, func in enumerate(args):\n        print(\"function: %s loops: %s avg_time: %f time per loop: %f\" % (\n            func.__name__,\n            loops,\n            total_times[count],\n            total_times[count] / loops,\n        ))\n\n\ndef test1():\n    time.sleep(0.01)\n\n\ndef test3():\n    time.sleep(0.03)\n\n\ndef test2():\n    time.sleep(0.02)\n\n\ndef main():\n    bench(test1, test2, test3)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"microbench.py","file_name":"microbench.py","file_ext":"py","file_size_in_byte":842,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"7470582","text":"from utils import data_generator\nfrom utils.constituent_building import *\nfrom utils.conjugate import *\nfrom utils.randomize import choice\nfrom utils.vocab_sets import *\n\nclass AnaphorGenerator(data_generator.BenchmarkGenerator):\n    def __init__(self):\n        super().__init__(field=\"syntax_semantics\",\n                         linguistics=\"binding\",\n                         uid=\"principle_A_reconstruction\",\n                         simple_lm_method=True,\n                         one_prefix_method=False,\n                         two_prefix_method=False,\n                         lexically_identical=True)\n\n    def sample(self):\n        # It's himself that John likes.\n        # IT'S Refl    Rel  N1   V1\n        # It's himself that likes John.\n        # IT'S Refl    Rel  V1   N1\n\n        V1 = choice(all_refl_preds)\n        N1 = choice(get_matches_of(V1, \"arg_1\", all_nouns))\n        N1 = N_to_DP_mutate(N1)\n        Rel = choice(get_matched_by(N1, \"arg_1\", all_relativizers))\n        Refl = choice(get_matched_by(N1, \"arg_1\", all_reflexives))\n        V1 = conjugate(V1, N1)\n\n        data = {\n            \"sentence_good\": \"It's %s %s %s %s.\" % (Refl[0], Rel[0], N1[0], V1[0]),\n            \"sentence_bad\": \"It's %s %s %s %s.\" % (Refl[0], Rel[0], V1[0], N1[0])\n        }\n        return data, data[\"sentence_good\"]\n\ngenerator = AnaphorGenerator()\ngenerator.generate_paradigm(rel_output_path=\"outputs/benchmark/%s.jsonl\" % generator.uid)\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"Spanish_Benchmark/English_Categories/binding/principle_A_reconstruction.py","file_name":"principle_A_reconstruction.py","file_ext":"py","file_size_in_byte":1452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"11532619","text":"import boto3\nfrom botocore.exceptions import ClientError\nimport click\n\n\nimage_metadata = {\n    'amazon_minimal': {'name': '*amzn-ami-minimal-*-VERSION.x86_64*', 'owner': 'amazon'},\n    'ubuntu': {'name': '*ubuntu-*-RELEASE-*-VERSION', 'owner': '099720109477'}\n}\n\ninstance_to_arch = {\n    \"t1.micro\": \"paravirtual\",\n    \"t2.nano\": \"hvm\",\n    \"t2.micro\": \"hvm\",\n    \"t2.small\": \"hvm\",\n    \"t2.medium\": \"hvm\",\n    \"t2.large\": \"hvm\",\n    \"m1.small\": \"paravirtual\",\n    \"m1.medium\": \"paravirtual\",\n    \"m1.large\": \"paravirtual\",\n    \"m1.xlarge\": \"paravirtual\",\n    \"m2.xlarge\": \"paravirtual\",\n    \"m2.2xlarge\": \"paravirtual\",\n    \"m2.4xlarge\": \"paravirtual\",\n    \"m3.medium\": \"hvm\",\n    \"m3.large\": \"hvm\",\n    \"m3.xlarge\": \"hvm\",\n    \"m3.2xlarge\": \"hvm\",\n    \"m4.large\": \"hvm\",\n    \"m4.xlarge\": \"hvm\",\n    \"m4.2xlarge\": \"hvm\",\n    \"m4.4xlarge\": \"hvm\",\n    \"m4.10xlarge\": \"hvm\",\n    \"c1.medium\": \"paravirtual\",\n    \"c1.xlarge\": \"paravirtual\",\n    \"c3.large\": \"hvm\",\n    \"c3.xlarge\": \"hvm\",\n    \"c3.2xlarge\": \"hvm\",\n    \"c3.4xlarge\": \"hvm\",\n    \"c3.8xlarge\": \"hvm\",\n    \"c4.large\": \"hvm\",\n    \"c4.xlarge\": \"hvm\",\n    \"c4.2xlarge\": \"hvm\",\n    \"c4.4xlarge\": \"hvm\",\n    \"c4.8xlarge\": \"hvm\",\n    \"r3.large\": \"hvm\",\n    \"r3.xlarge\": \"hvm\",\n    \"r3.2xlarge\": \"hvm\",\n    \"r3.4xlarge\": \"hvm\",\n    \"r3.8xlarge\": \"hvm\",\n    \"i2.xlarge\": \"hvm\",\n    \"i2.2xlarge\": \"hvm\",\n    \"i2.4xlarge\": \"hvm\",\n    \"i2.8xlarge\": \"hvm\",\n    \"d2.xlarge\": \"hvm\",\n    \"d2.2xlarge\": \"hvm\",\n    \"d2.4xlarge\": \"hvm\",\n    \"d2.8xlarge\": \"hvm\",\n    \"hi1.4xlarge\": \"hvm\",\n    \"hs1.8xlarge\": \"hvm\",\n    \"cr1.8xlarge\": \"hvm\",\n    \"cc2.8xlarge\": \"hvm\"\n}\n\n\ndef sort_image_list(thelist):\n    return sorted(thelist, key=lambda k: k['CreationDate'])\n\n\ndef find_virt_type(instance_size):\n    try:\n        return instance_to_arch.get(instance_size)\n    except KeyError:\n        return \"\"\n\n\ndef find_image(region, release, version, os, virt_type):\n    version_to_find = image_metadata[os]['name'].replace('VERSION', version).replace('RELEASE', release)\n    ec2_client = boto3.client('ec2', region_name=region)\n    try:\n        images = sort_image_list(ec2_client.describe_images(\n            Owners=[image_metadata[os]['owner']],\n            Filters=[\n                {'Name': 'state', 'Values': ['available']},\n                {'Name': 'architecture', 'Values': ['x86_64']},\n                {'Name': 'virtualization-type', 'Values': [virt_type]},\n                {'Name': 'root-device-type', 'Values': ['ebs']},\n                {'Name': 'name', 'Values': [version_to_find]}\n            ]\n        )['Images'])\n        return images[-1]['ImageId']\n    except ClientError as e:\n        print(\"Error: {}\".format(e.message))\n    except IndexError:\n        return \"\"\n\n\n@click.command()\n@click.argument('instance_size')\n@click.option('--os', default='amazon_minimal',\n              type=click.Choice(['amazon_minimal', 'ubuntu']),\n              help='OS to search for')\n@click.option('--release', default='16.04', help='release of OS to search for')\n@click.option('--version', default='*', help='AMI version to search for')\n@click.option('--region', default='us-west-2', help='region to query')\ndef main(region, release, version, os, instance_size):\n    virt_type = find_virt_type(instance_size)\n    print(find_image(region, release, version, os, virt_type))\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"packer/bin/amifinder/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":3338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"503651648","text":"import iemdb\nimport numpy as np\nimport numpy.ma\n\nCOOP = iemdb.connect('coop', bypass=True)\nccursor = COOP.cursor()\nccursor2 = COOP.cursor()\n\nccursor.execute(\"\"\"\n SELECT year, min(high), min(low) from alldata_ia where station = 'IA1319'\n and year < 2012 and month = 5 GROUP by year\n\"\"\")\nhigh_hits = numpy.ma.zeros( (31,), 'f')\nlow_hits = numpy.ma.zeros( (31,), 'f')\nfor row in ccursor:\n    ccursor2.execute(\"\"\"\n    SELECT day from alldata_ia where year = %s and month = 5 and \n    high = %s and station = 'IA1319'\n    \"\"\", (row[0], row[1]))\n    for row2 in ccursor2:\n        high_hits[row2[0].day-1] += 1.0 / ccursor2.rowcount\n\n    ccursor2.execute(\"\"\"\n    SELECT day from alldata_ia where year = %s and month = 5 and \n    low = %s and station = 'IA1319'\n    \"\"\", (row[0], row[2]))\n    for row2 in ccursor2:\n        low_hits[row2[0].day-1] += 1.0 / ccursor2.rowcount\n\nhigh_hits.mask = numpy.where(high_hits == 0, True, False)\nlow_hits.mask = numpy.where(low_hits == 0, True, False)\nimport matplotlib.pyplot as plt\nfig, ax = plt.subplots(2,1, sharex=True)\n\n#res = ax.contourf( data, extend='max')\nax[0].set_title(\"Cedar Rapids (1893-2011) Frequency of Day in May\\nHaving Coldest High Temperature of May\")\nax[0].set_ylabel(\"Years (ties split)\")\n\nax[0].grid(True)\nax[0].bar(numpy.arange(1,32) - 0.4, high_hits)\nax[0].set_xlim(0.5, 31.5)\n\nax[1].set_title(\"Having Coldest Low Temperature of May\")\nax[1].set_ylabel(\"Years (ties split)\")\nax[1].grid(True)\nax[1].set_xlabel(\"Day of May\")\nax[1].bar(numpy.arange(1,32) - 0.4, low_hits)\n\n\nfig.savefig('test.ps')\nimport iemplot\niemplot.makefeature('test') \n","sub_path":"scripts/feature/month_coldest_day.py","file_name":"month_coldest_day.py","file_ext":"py","file_size_in_byte":1593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"69236604","text":"def fourSumCount(A, B, C, D):\n    \"\"\"\n    :type A: List[int]\n    :type B: List[int]\n    :type C: List[int]\n    :type D: List[int]\n    :rtype: int\n    \"\"\"\n\n    target = 0\n    hashtable = {}\n\n    for a in A:  # 1\n        for b in B:  # 1\n            if a + b not in hashtable:\n                hashtable[a + b] = 1\n            else:\n                hashtable[a + b] += 1\n\n    print(hashtable)\n\n    count = 0\n    for c in C:\n        for d in D:\n            if target - (c + d) in hashtable:\n                count += hashtable[target - (c + d)]\n    return count\n\n\nA = [1, 1]\nB = [1, -1]\nC = [-1, 1]\nD = [-1, -1]\nprint(fourSumCount(A, B, C, D))\n","sub_path":"Archive/Others/Leetcode/[IMP]4Sum_ll_2.py","file_name":"[IMP]4Sum_ll_2.py","file_ext":"py","file_size_in_byte":639,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"594933930","text":"from .models import AddGas\nfrom django.shortcuts import render,redirect \nfrom django.contrib.auth.decorators import login_required\nfrom consumer.forms import FeedbackComplaintForm\nfrom consumer.models import FeedbackComplaint, Order\nfrom userauth.models import User, Consumer, Distributor\nfrom userauth.forms import UserEditForm, DistributorDetailsForm\nfrom .forms import GasAddForm, EditConsumerForm\nfrom .filters import CheckOrderFilter\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\n\n\n@login_required(login_url='/user-auth/login/')\ndef distributor_dashboard(request):\n    # try:\n    if request.user.distributor:\n        stock_gas = AddGas.objects.filter(user_id=request.user.id)\n        return render(request, 'distributor/dashboard.html', {'items': stock_gas})\n    else:\n        return redirect('c_dashboard')\n\n\n\n@login_required(login_url='/user-auth/login/')\ndef check_orders(request):\n    filterForm = CheckOrderFilter()\n    if request.method == 'POST':\n        orders = Order.objects.filter(store_name=request.user.distributor)\n        filterForm = CheckOrderFilter(request.POST, queryset=orders)\n        items = filterForm.qs\n        context = {'filter_orders': items, 'filter_form': filterForm}\n        return render(request, 'distributor/check_orders.html', context)\n    else:\n        # pagination for pending orders\n        other_orders = Order.objects.filter(store_name=request.user.distributor).filter(status=\"pending\")\n        no_of_orders = len(other_orders)\n        page = request.GET.get('page', 1)\n        paginator = Paginator(other_orders, 1)\n        try:\n            other_orders = paginator.page(page)\n        except PageNotAnInteger:\n            other_orders = paginator.page(1)\n\n        except EmptyPage:\n            other_orders = paginator.page(paginator.num_pages)\n\n        # pagination for delivered orders\n        delivered_orders = Order.objects.filter(store_name=request.user.distributor).filter(status=\"delivered\")\n        page = request.GET.get('page', 1)\n        paginator = Paginator(delivered_orders, 1)\n        try:\n            delivered_orders = paginator.page(page)\n        except PageNotAnInteger:\n            delivered_orders = paginator.page(1)\n\n        except EmptyPage:\n            delivered_orders = paginator.page(paginator.num_pages)\n\n        context = {'orders': other_orders, 'delivered_orders': delivered_orders, 'filter_form': filterForm, 'num_orders': no_of_orders}\n        return render(request, 'distributor/check_orders.html', context)\n\n\n@login_required(login_url='/user-auth/login/')\ndef manage_consumers(request):\n    \n    consumers = Consumer.objects.all().order_by('id')\n    page = request.GET.get('page', 1)\n    paginator = Paginator(consumers, 1)\n    try:\n        consumers = paginator.page(page)\n    except PageNotAnInteger:\n        consumers = paginator.page(1)\n\n    except EmptyPage:\n        consumers = paginator.page(paginator.num_pages)\n    return render(request, 'distributor/manage_consumers.html', {'consumers': consumers})\n\n\n@login_required(login_url='/user-auth/login/')\ndef add_gas(request):\n    form = GasAddForm()\n    if request.method == 'POST':\n        form = GasAddForm(request.POST)\n        if form.is_valid():\n            user_id = request.user.id\n            gas_name = form.cleaned_data['gas_name']\n            gas_number= form.cleaned_data['gas_number']\n            print(gas_name, gas_number)\n\n            ga = AddGas(user_id = user_id, gas_name = gas_name, gas_number= gas_number)\n            ga.save()\n            return redirect('dashboard')\n        else:\n            print(form.errors)\n    return render(request, 'distributor/stock.html', {'form': form})\n\n\n@login_required(login_url='/user-auth/login/')\ndef edit_gas(request, id):\n    gas = AddGas.objects.get(id = id)\n    form = GasAddForm(instance = gas)\n    if request.method == \"POST\":\n        form = GasAddForm(request.POST, instance=gas)\n        if form.is_valid():\n            form.save()\n            return redirect('dashboard')\n        else:\n            print(form.errors)\n    \n    return render(request, 'distributor/edit_gas.html', {'form': form})\n\n\ndef delete_gas(request, id):\n    gas = AddGas.objects.get(id=id)\n    gas.delete()\n    return redirect('dashboard')\n\n\n@login_required(login_url='/user-auth/login/')\ndef edit_consumers(request, id):\n    consumers = Order.objects.get(id=id)\n    form = EditConsumerForm(instance = consumers)\n    if request.method == \"POST\":\n        form = EditConsumerForm(request.POST, instance=consumers)\n        if form.is_valid():\n            form.save()\n            return redirect('check_orders')\n    \n    return render(request, 'distributor/edit_consumers.html', {'form': form })\n\n@login_required(login_url='/user-auth/login/')\ndef your_profile(request):\n    try:\n        if request.user.distributor:\n            c_id = request.user.id\n            details_query = Distributor.objects.get(user_id = c_id)\n            print(details_query)\n            return render(request, 'distributor/profile.html', {\"details\": details_query})\n    except:\n        return redirect('your_profile')\n\n\n@login_required(login_url='/user-auth/login/')\ndef edit_profile(request):\n    user = User.objects.get(id=request.user.id)\n    # print(user)\n    distributor = Distributor.objects.get(user=request.user)\n    # print(consumer)\n    u_form = UserEditForm(instance = user)\n    d_form = DistributorDetailsForm(instance = distributor)\n    if request.method == \"POST\":\n        u_form = UserEditForm(request.POST,  instance=user)\n        d_form = DistributorDetailsForm(request.POST, instance=distributor)\n        if u_form.is_valid() and d_form.is_valid():\n            u_form.save()\n            d_form.save()\n            return redirect('your_profile')\n        else:\n            print(u_form.errors)\n            print(d_form.errors)        \n\n    context = {'u_form': u_form, 'd_form':d_form}\n    return render(request, 'distributor/edit_profile.html', context)\n\n\n@login_required(login_url='/user-auth/login')\ndef FeedbackComplaintView(request):\n    try:\n        if request.user.distributor:\n            if request.method == 'POST':\n                form = FeedbackComplaintForm(request.POST)\n                if form.is_valid():\n                    fc = form.save()\n                    fc.user = request.user\n                    fc.save()\n                    return redirect('dashboard')\n\n            context = {'form': FeedbackComplaintForm}\n            return render(request, 'distributor/feedback_complaint.html', context)\n    except:\n        return redirect('feedback_complaint')\n","sub_path":"gas_booking/distributor/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6566,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"539983344","text":"\n\nclass Person():\n    \n    def __init__(self, name, surname, lvl = 1):\n        self.name = name\n        self.surname = surname\n        self.lvl =lvl\n        \n    def getPerson(self):\n        print (self.name.ljust(2), self.surname.ljust(12), str(self.lvl).ljust(3))\n        \n    def __del__(self):\n        print('До свидания, мистер ', self.surname, self.name)\n        \ndisp1 = Person('А', 'Румянцев', 10)\ndisp2 = Person('Ю', 'Бессонова')\ndisp3 = Person('Г', 'Кунгурякова', 0.1)\n\ndisp1.getPerson()\ndisp3.getPerson()\ndisp2.getPerson()\n\ndel disp3\n\ninput()\n","sub_path":"PersonOOP.py","file_name":"PersonOOP.py","file_ext":"py","file_size_in_byte":601,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"73850150","text":"# coding: utf-8\n\n\"\"\"\nCopyright 2016 SmartBear Software\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    Ref: https://github.com/swagger-api/swagger-codegen\n\"\"\"\n\nfrom pprint import pformat\nfrom six import iteritems\nimport re\n\n\nclass ResourceConditionNode(object):\n    \"\"\"\n    NOTE: This class is auto generated by the swagger code generator program.\n    Do not edit the class manually.\n    \"\"\"\n    def __init__(self):\n        \"\"\"\n        ResourceConditionNode - a model defined in Swagger\n\n        :param dict swaggerTypes: The key is attribute name\n                                  and the value is attribute type.\n        :param dict attributeMap: The key is attribute name\n                                  and the value is json key in definition.\n        \"\"\"\n        self.swagger_types = {\n            'variable_name': 'str',\n            'conjunction': 'str',\n            'operator': 'str',\n            'operands': 'list[ResourceConditionValue]',\n            'terms': 'list[ResourceConditionNode]'\n        }\n\n        self.attribute_map = {\n            'variable_name': 'variableName',\n            'conjunction': 'conjunction',\n            'operator': 'operator',\n            'operands': 'operands',\n            'terms': 'terms'\n        }\n\n        self._variable_name = None\n        self._conjunction = None\n        self._operator = None\n        self._operands = None\n        self._terms = None\n\n    @property\n    def variable_name(self):\n        \"\"\"\n        Gets the variable_name of this ResourceConditionNode.\n\n\n        :return: The variable_name of this ResourceConditionNode.\n        :rtype: str\n        \"\"\"\n        return self._variable_name\n\n    @variable_name.setter\n    def variable_name(self, variable_name):\n        \"\"\"\n        Sets the variable_name of this ResourceConditionNode.\n\n\n        :param variable_name: The variable_name of this ResourceConditionNode.\n        :type: str\n        \"\"\"\n        \n        self._variable_name = variable_name\n\n    @property\n    def conjunction(self):\n        \"\"\"\n        Gets the conjunction of this ResourceConditionNode.\n\n\n        :return: The conjunction of this ResourceConditionNode.\n        :rtype: str\n        \"\"\"\n        return self._conjunction\n\n    @conjunction.setter\n    def conjunction(self, conjunction):\n        \"\"\"\n        Sets the conjunction of this ResourceConditionNode.\n\n\n        :param conjunction: The conjunction of this ResourceConditionNode.\n        :type: str\n        \"\"\"\n        allowed_values = [\"AND\", \"OR\"]\n        if conjunction not in allowed_values:\n            raise ValueError(\n                \"Invalid value for `conjunction`, must be one of {0}\"\n                .format(allowed_values)\n            )\n\n        self._conjunction = conjunction\n\n    @property\n    def operator(self):\n        \"\"\"\n        Gets the operator of this ResourceConditionNode.\n\n\n        :return: The operator of this ResourceConditionNode.\n        :rtype: str\n        \"\"\"\n        return self._operator\n\n    @operator.setter\n    def operator(self, operator):\n        \"\"\"\n        Sets the operator of this ResourceConditionNode.\n\n\n        :param operator: The operator of this ResourceConditionNode.\n        :type: str\n        \"\"\"\n        allowed_values = [\"EQ\", \"IN\", \"GE\", \"GT\", \"LE\", \"LT\"]\n        if operator not in allowed_values:\n            raise ValueError(\n                \"Invalid value for `operator`, must be one of {0}\"\n                .format(allowed_values)\n            )\n\n        self._operator = operator\n\n    @property\n    def operands(self):\n        \"\"\"\n        Gets the operands of this ResourceConditionNode.\n\n\n        :return: The operands of this ResourceConditionNode.\n        :rtype: list[ResourceConditionValue]\n        \"\"\"\n        return self._operands\n\n    @operands.setter\n    def operands(self, operands):\n        \"\"\"\n        Sets the operands of this ResourceConditionNode.\n\n\n        :param operands: The operands of this ResourceConditionNode.\n        :type: list[ResourceConditionValue]\n        \"\"\"\n        \n        self._operands = operands\n\n    @property\n    def terms(self):\n        \"\"\"\n        Gets the terms of this ResourceConditionNode.\n\n\n        :return: The terms of this ResourceConditionNode.\n        :rtype: list[ResourceConditionNode]\n        \"\"\"\n        return self._terms\n\n    @terms.setter\n    def terms(self, terms):\n        \"\"\"\n        Sets the terms of this ResourceConditionNode.\n\n\n        :param terms: The terms of this ResourceConditionNode.\n        :type: list[ResourceConditionNode]\n        \"\"\"\n        \n        self._terms = terms\n\n    def to_dict(self):\n        \"\"\"\n        Returns the model properties as a dict\n        \"\"\"\n        result = {}\n\n        for attr, _ in iteritems(self.swagger_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        \"\"\"\n        Returns the string representation of the model\n        \"\"\"\n        return pformat(self.to_dict())\n\n    def __repr__(self):\n        \"\"\"\n        For `print` and `pprint`\n        \"\"\"\n        return self.to_str()\n\n    def __eq__(self, other):\n        \"\"\"\n        Returns true if both objects are equal\n        \"\"\"\n        return self.__dict__ == other.__dict__\n\n    def __ne__(self, other):\n        \"\"\"\n        Returns true if both objects are not equal\n        \"\"\"\n        return not self == other\n\n","sub_path":"build/PureCloudPlatformApiSdk/models/resource_condition_node.py","file_name":"resource_condition_node.py","file_ext":"py","file_size_in_byte":6446,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"359201510","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#小说接口http\nfrom flask import Flask, jsonify, request\nfrom flask_cors import CORS\nfrom novel_api import Novel\nimport sys\nimport json\nreload(sys)\nsys.setdefaultencoding('utf-8')\napp = Flask(__name__)\nCORS(app, supports_credentials=True)\napp.config['JSON_AS_ASCII'] = False\n\n@app.route('/', methods=['GET'])\ndef test():\n    return jsonify({'status':'True','message':'Hello world'})\n\n@app.route('/search_novel', methods=['POST', 'GET'])\ndef search_novel():\n    data = request.get_data()\n    data = data.replace(\"'\", '\"')\n    data = json.loads(data)\n    novel_name = data.get('novel',None)\n    if novel_name:\n        obj = Novel()\n        result = obj.get_novel(name=novel_name)\n        return jsonify({'status':'True','message':'小说搜索成功','data':result})\n    else:\n        return jsonify({ 'status': 'False', 'message': '小说名称不能为空' })\n\n@app.route('/get_novel_chapter', methods=['POST', 'GET'])\ndef get_novel_chapter():\n    data = request.get_data()\n    data = data.replace(\"'\", '\"')\n    data = json.loads(data)\n    novel_url = data.get('novel_url',None)\n    sort_id = data.get('sort',None)\n    if novel_url:\n        obj = Novel()\n        result = obj.get_chapter(url=novel_url,sort_id=sort_id)\n        return jsonify({'status':'True','message':'小说章节获取成功','data':result})\n    else:\n        return jsonify({ 'status': 'False', 'message': '小说名称不能为空' }) \n\n\n@app.route('/get_novel_chapter_content', methods=['POST', 'GET'])\ndef get_novel_chapter_content():\n    data = request.get_data()\n    data = data.replace(\"'\", '\"')\n    data = json.loads(data)\n    chapter_url = data.get('chapter_url',None)\n    if chapter_url:\n        obj = Novel()\n        result = obj.get_content(url=chapter_url)\n        return jsonify({'status':'True','message':'小说内容获取成功','data':result})\n    else:\n        return jsonify({ 'status': 'False', 'message': '小说章节不能为空' })\n\nif __name__ == '__main__':\n    app.run(host='127.0.0.1',port=8090,debug=True)\n","sub_path":"api/novel_http.py","file_name":"novel_http.py","file_ext":"py","file_size_in_byte":2056,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"351162939","text":"import bge\nimport ship\n\nlights = 0\nenergy = 0\n\ndef init(cont):\n    global lights\n    cont.script = 'hud.run'\n    lights = 0\n    cont.owner.resolution = 5\n    \nUVSPEED = -0.001\n    \ndef run(cont):\n    global lights\n    if cont.sensors['LIGHTON'].positive:\n        lights += 1\n    \n    if lights != bge.logic.globalDict['total_lights']:\n        cont.owner['Text'] = str(lights) + '/' + str(bge.logic.globalDict['total_lights'])\n    elif  cont.owner['Text'] != 'Return Home':\n        cont.owner['Text'] = 'Return Home'\n        bge.logic.sendMessage(\"COMPLETE\")\n\n    global energy\n    ship_energy = ship.energy\n    if ship_energy == None:\n        ship_energy = 0\n    energy = (ship_energy*0.05 + energy*0.95)\n    #print(energy)\n\n    energy_meter =  cont.owner.scene.objects['EnergyMeter']\n    energy_meter.color = [0.8*energy/100, 0.6*energy/100, 0.3*energy/100, energy]\n    mesh = energy_meter.meshes[0]\n    array = mesh.getVertexArrayLength(0)\n\n    for v in range(0,array):\n        vert = mesh.getVertex(0,v) \n        vert.v2 += UVSPEED\n","sub_path":"BGMC18 CaveFly V4/BGMC18 CaveFly/scripts/hud.py","file_name":"hud.py","file_ext":"py","file_size_in_byte":1035,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"164734796","text":"# -*- coding: utf-8 -*-\n\nfrom setting_core.constants import App as Constants, Res\nfrom setting_core.widget import Setting_group, Setting_button, Setting_hiddable_list_widget, Setting_setting_bar, \\\n    Clock_widget, Network_adder, Network_info, App_info\n\n__author__ = \"Julien Dubois\"\n__version__ = \"0.1.0\"\n\nimport os\n\nfrom lemapi.api import get_gui, get_listener_manager, get_settings\nfrom lemapi.application import Application\nfrom lemapi.constants import App\nfrom lemapi.event_manager import Event\nfrom lemapi.network import Wifi\nfrom lemapi.util import get_linux_system_info\nfrom lemapi.view import View\nfrom lemapi.widget import Image_widget, Text, Clickable_text, Separator_widget, Scrollable_group\n\nfrom pygame.locals import K_DOWN, K_UP, K_RETURN\n\n\nclass Menu_view(View):\n    def init_widgets(self):\n        w, h = get_gui().get_size()\n\n        bg_path = os.path.join(Res.IMAGE_PATH, \"background.jpg\")\n        self.add_widget(\"background_image\", Image_widget, (w*0.5, h*0.5), bg_path, \\\n            size=(w, h), anchor=(0, 0))\n        self.add_widget(\"setting_group\", Setting_group, (w, h*0.5), anchor=(0, 0))\n        self.add_widget(\"quit_button\", Setting_button, (w, h*0.5), anchor=(0, 0), \\\n            size=(w*0.25, h*0.15), text=\"Quitter\", textAnchor=(-0.8, 0), textKwargs={\"anchor\": (-1, 0)})\n        self.add_widget(\"clock_widget\", Clock_widget, (w * 0.1, h * 0.2), anchor=(-1, 0))\n        self.add_widget(\"title_text\", Text, (w*0.1, h*0.3), \"Paramètres\", fontSize=30, bold=True, textColor=(100, 100, 100, 255))\n\n    def update(self):\n        super().update()\n\n    def init_events(self):\n        lm = get_listener_manager()\n        w = self.widgets[\"setting_group\"]\n        event = Event(w.previous_setting)\n        lm.km.add_key_down_event(event, K_DOWN)\n        lm.cm.add_joy_down_event(event)\n        event = Event(w.next_setting)\n        lm.km.add_key_down_event(event, K_UP)\n        lm.cm.add_joy_up_event(event)\n        event = Event(w.click_setting)\n        lm.km.add_key_down_event(event, K_RETURN)\n        lm.cm.add_button_pressed_event(event, \"button_a\")\n\n    def add_setting(self, widget_name):\n        if widget_name in self.widgets:\n            self.widgets[\"setting_group\"].add_setting_widget(self.widgets[widget_name])\n\n    def reset_angle(self):\n        self.widgets[\"setting_group\"].reset_angle()\n\n\nclass Setting_view(View):\n    def __init__(self, title):\n        self.title = title\n        super().__init__()\n\n    def init_widgets(self):\n        w, h = get_gui().get_size()\n        pw, ph = Constants.VIEW_PADDING\n        tc = (150, 150, 150, 255)\n\n        bg_path = os.path.join(Res.IMAGE_PATH, \"background.jpg\")\n        self.add_widget(\"background_image\", Image_widget, (w // 2, h // 2), \\\n            bg_path, size=(w, h), anchor=(0, 0))\n        self.add_widget(\"back_arrow_clickable_text\", Clickable_text, (pw, ph), \"Retour\", \\\n            textColor=tc)\n        self.add_widget(\"title_text\", Text, (w*0.15 + pw, ph), self.title, textColor=(255, 255, 255, 255))\n        self.add_widget(\"title_separator\", Separator_widget, (pw, h*0.07 + ph), (w - pw, h*0.07 + ph), \\\n            lineColor=(150, 150, 150, 255))\n\n\nclass Network_setting_view(Setting_view):\n    def __init__(self):\n        self.last_widget_height = 0\n        super().__init__(\"Réseau\")\n\n    def init_widgets(self):\n        super().init_widgets()\n        w, h = get_gui().get_size()\n        pw, ph = Constants.VIEW_PADDING\n        tc0 = (255, 255, 255, 255)\n        tc1 = (150, 150, 150, 255)\n\n        self.add_widget(\"add_network_clickable_text\", Clickable_text, (w - pw, ph), \"Ajouter un réseau\", textColor=tc1, anchor=(1, -1))\n        self.add_widget(\"connected_title_text\", Text, (pw + w*0.05, ph + h*0.15), \"Connection actuelle\", anchor=(-1, 0), textColor=tc0)\n        self.add_widget(\"known_title_text\", Text, (pw + w*0.05, ph + h*0.35), \"Réseaux connus\", anchor=(-1, 0), textColor=tc0)\n        self.add_widget(\"network_scrollable_group\", Scrollable_group, (w*0.5, ph + h*0.45), size=(w - pw*2, h - ph*2 - h*0.45), anchor=(0, -1))\n\n        current_ssid = Wifi.get_current_ssid()\n        if current_ssid:\n            button_text = current_ssid\n            psk = Wifi.get_network_psk()\n            if psk:\n                button_text += \" (sécurisé)\"\n            else:\n                button_text += \" (non sécurisé)\"\n            self.add_widget(\"connected_button\", Setting_button, (w*0.5, h*0.25), anchor=(0, -1), text=button_text, \\\n                size=(w - 2*pw - w*0.1, h*0.1), textAnchor=(-0.95, 0), textKwargs={\"anchor\":(-1, 0), \"fontSize\":18})\n        else:\n            self.add_widget(\"no_connected_network_text\", Text, (pw + w*0.1, ph + h*0.25), \"Déconnecté du réseau !\", \\\n                anchor=(-1, 0), textColor=tc1, fontSize=18)\n\n        \n        ssids = Wifi.get_known_ssid()\n\n        if ssids:\n            for ssid in ssids:\n                self.add_network()\n        else:\n            self.widgets[\"known_title_text\"].text = \"Aucun réseau connu\"\n\n    def add_network(self, ssid):\n        self.widgets[\"known_title_text\"].text = \"Réseaux connus\"\n        w, h = get_gui().get_size()\n        sg = self.widgets[\"network_scrollable_group\"]\n        sw, sh = sg.kwargs[\"size\"]\n        psk = Wifi.get_network_psk(ssid)\n        text = ssid\n\n        if psk:\n            text += \" (sécurisé)\"\n        else:\n            text += \" (non sécurisé)\"\n\n        sg.addSubWidget(\"{ssid}_network_button\".format(ssid=ssid), Setting_button, (0, self.last_widget_height), \\\n            text=text, size=(sw, h*0.09), textAnchor=(-0.95, 0), textKwargs={\"anchor\":(-1, 0), \"fontSize\":18})\n\n        self.last_widget_height += h*0.1\n\n    def open_add_network_dialog(self, add_fct):\n        w, h = get_gui().get_size()\n        self.add_widget(\"network_adder\", Network_adder, (w*0.5, h*0.5), add_fct, self.close_add_network_dialog, anchor=(0, 0))\n\n    def close_add_network_dialog(self):\n        self.remove_widget(\"network_adder\")\n\n    def open_info_network_dialog(self, remove_fct, ssid):\n        w, h = get_gui().get_size()\n        psk = Wifi.get_network_psk(ssid)\n        self.add_widget(\"network_info\", Network_info, (w*0.5, h*0.5), remove_fct, self.close_info_network_dialog, ssid, anchor=(0, 0), \\\n            psk=psk)\n\n    def close_info_network_dialog(self):\n        self.remove_widget(\"network_info\")\n\n\nclass Display_setting_view(Setting_view):\n    def __init__(self):\n        super().__init__(\"Affichage\")\n\n    def init_widgets(self):\n        super().init_widgets()\n        w, h = get_gui().get_size()\n        pw, ph = Constants.VIEW_PADDING\n        tc0 = (255, 255, 255, 255)\n        tc1 = (150, 150, 150, 255)\n\n        self.add_widget(\"theme_text\", Text, (pw + w*0.05, ph + h*0.2), \"Thème\", anchor=(-1, 0), textColor=tc0)\n        self.add_widget(\"theme_list\", Setting_hiddable_list_widget, (w*0.95 - pw, ph + h*0.2), anchor=(1, 0), \\\n            text=get_settings().get(\"theme_color\", \"white\"), size=(w*0.2, h*0.07))\n\n        for color in App.THEME_COLORS:\n            self.widgets[\"theme_list\"].addItem(color)\n\n\nclass Audio_setting_view(Setting_view):\n    def __init__(self):\n        super().__init__(\"Sons\")\n\n    def init_widgets(self):\n        super().init_widgets()\n        w, h = get_gui().get_size()\n        pw, ph = Constants.VIEW_PADDING\n        tc0 = (255, 255, 255, 255)\n        tc1 = (150, 150, 150, 255)\n\n        self.add_widget(\"volume_text\", Text, (pw + w*0.05, ph + h*0.2), \"Volume\", anchor=(-1, 0), textColor=tc0)\n        self.add_widget(\"volume_setting_bar\", Setting_setting_bar, (w*0.95 - pw, ph + h*0.2), anchor=(1, 0), size=(w*0.4, h*0.06), \\\n            cursorWidth=int(w * 0.015), lineThickness=int(h * 0.02), value=get_settings().get(\"sound_volume\", 1))\n\n\nclass App_setting_view(Setting_view):\n    def __init__(self):\n        self.last_widget_height = 0\n        super().__init__(\"Applications\")\n\n    def init_widgets(self):\n        super().init_widgets()\n        w, h = get_gui().get_size()\n        pw, ph = Constants.VIEW_PADDING\n        tc0 = (255, 255, 255, 255)\n\n        self.add_widget(\"installed_title_text\", Text, (pw + w*0.05, ph + h*0.15), \"Applications installées\", anchor=(-1, -1), \\\n            textColor=tc0)\n        self.add_widget(\"info_scrollable_group\", Scrollable_group, (w*0.5, h*0.3), size=(w - pw*2, h - ph*2 - h*0.3), anchor=(0, -1))\n\n        app_paths = Application.get_local_apps()\n\n        for app_path in app_paths:\n            app = Application(app_path)\n            self.add_app(app)\n\n    def add_app(self, app):\n        w, h = get_gui().get_size()\n        sg = self.widgets[\"info_scrollable_group\"]\n        sw, sh = sg.kwargs[\"size\"]\n        text = \"{name} v{version}\".format(name=app.get_name(), version=app.get_version())\n\n        if os.path.split(app.path)[-1] in App.DEFAULT_APPS:\n            text += \" (installé par défaut)\"\n\n        sg.addSubWidget(\"{app}_app_button\".format(app=app.get_name()), Setting_button, (0, self.last_widget_height), \\\n            text=text, size=(sw, h*0.09), textAnchor=(-0.95, 0), textKwargs={\"anchor\":(-1, 0), \"fontSize\":18})\n\n        self.last_widget_height += h*0.1\n\n    def open_app_info_dialog(self, remove_fct, app):\n        w, h = get_gui().get_size()\n        removable = True\n        if app.get_real_name() in App.DEFAULT_APPS:\n            removable = False\n\n        self.add_widget(\"app_info\", App_info, (w*0.5, h*0.5), remove_fct, self.close_app_info_dialog, app, anchor=(0, 0), \\\n            removableApp=removable)\n\n    def close_app_info_dialog(self):\n        self.remove_widget(\"app_info\")\n\n\n\nclass About_setting_view(Setting_view):\n    def __init__(self):\n        w, h = get_gui().get_size()\n        self.last_widget_height = h*0.05\n        super().__init__(\"A propos...\")\n\n    def init_widgets(self):\n        super().init_widgets()\n        w, h = get_gui().get_size()\n        pw, ph = Constants.VIEW_PADDING\n        self.add_widget(\"info_scrollable_group\", Scrollable_group, (w*0.5, h*0.2), size=(w - pw*2, h - ph*2 - h*0.2), anchor=(0, -1))\n\n        self.add_info(\"Système\", \"LemAPI\")\n        self.add_info(\"Version du système\", App.VERSION)\n        self.add_info(\"Version de Raspbian\", get_linux_system_info(\"VERSION\"))\n        self.add_info(\"Organisation\", \"Lycée Madame de Staël\")\n        self.add_info(\"Développeur\", __author__)\n\n    def add_info(self, title, info):\n        w, h = get_gui().get_size()\n        tc0 = (255, 255, 255, 255)\n        tc1 = (150, 150, 150, 255)\n        sg = self.widgets[\"info_scrollable_group\"]\n        sw, sh = sg.kwargs[\"size\"]\n\n        sg.addSubWidget(\"{title}_title_text\".format(title=title), Text, (w*0.05, self.last_widget_height), title, \\\n            anchor=(-1, 0), textColor=tc0)\n        sg.addSubWidget(\"{title}_text\".format(title=title), Text, (sw - w*0.05, self.last_widget_height), info, \\\n            anchor=(1, 0), textColor=tc1)\n\n        self.last_widget_height += h*0.1","sub_path":"setting_core/view.py","file_name":"view.py","file_ext":"py","file_size_in_byte":10850,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"292924222","text":"arr = []\ntry:\n    while True:\n        arr.append([int(n) for n in input().split()])\nexcept EOFError:\n    pass\n\n\ndef maxlrectangle(matrix):\n    if not matrix or not matrix[0]:\n        return 0\n    n = len(matrix[0])\n    height = [0] * (n + 1)\n    ans = 0\n    for row in matrix:\n        for i in range(n):\n            height[i] = height[i] + 1 if row[i] == 1 else 0\n        stack = [-1]\n        for i in range(n + 1):\n            while height[i] < height[stack[-1]]:\n                h = height[stack.pop()]\n                w = i - 1 - stack[-1]\n                ans = max(ans, h * w)\n            stack.append(i)\n    return ans\n\n\nprint(maxlrectangle(arr))\n","sub_path":"work1/problem2.py","file_name":"problem2.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"104784135","text":"import sys\nfrom ftplib import FTP\nouttaLib = \"../\"\ndef db_edit(file,line,index,new):\n    with open(file,\"r\") as f:\n        lines = f.readlines()\n    items = lines[line].split(\";\")\n    out=\"\"\n    x=-1\n    for i in items:\n        x+=1\n        if(x==index):\n            out+=new\n            if (items[x] == items[-1]):\n                out += \"\\n\"\n        else:\n            out+=items[x]\n        if not(items[x]==items[-1]):\n            out+=\";\"\n    lines[line]=out\n    with open(file, \"w\") as f:\n        f.writelines(lines)\n\ndef exitProgram():\n    input(\"Press enter to exit \")\n    sys.exit(0)\nhost = \"f14-preview.royalwebhosting.net\"\npasw = \"tooezforrtz\"\nuser = \"2289107\"\ndef connectToServer():\n    ftp = FTP(host)\n    ftp.login(user,pasw)\n    return ftp","sub_path":"lib/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":752,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"427255841","text":"# https://leetcode.com/problems/maximum-length-of-repeated-subarray/submissions/\n\nclass Solution:\n    def findLength(self, A: [int], B: [int]) -> int:\n        rows = len(A) + 1\n        cols = len(B) + 1\n        \n        dp = [ [0]*cols for _ in range(rows)]\n        \n        for i in range(1, len(dp)):\n            for j in range(1, len(dp[0])):\n                if A[i-1] == B[j-1]:\n                    dp[i][j] = dp[i-1][j-1] + 1\n                    \n        # print([row for row in dp])\n        \n        return max([max(row) for row in dp])","sub_path":"python/Dynamic Programming/maximum-length-of-repeated-subarray.py","file_name":"maximum-length-of-repeated-subarray.py","file_ext":"py","file_size_in_byte":542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"178119376","text":"# -*- coding: utf-8 -*-\r\n\r\ndef all_exist(ref_list, chk_list):\r\n    len_ref = len(ref_list)\r\n    len_chk = len(chk_list)\r\n    for j in range(len_ref):\r\n        # 含まれていればok\r\n        if ref_list[j] in chk_list:\r\n            pass\r\n        # 含まれていなかったらallじゃない\r\n        else:\r\n            return False\r\n    else:\r\n        # 探してる要素iが含まれていないため終了\r\n        return True\r\n\r\nimport rhinoscriptsyntax as rs\r\nimport Rhino\r\nimport glob\r\nimport os\r\n# 切断面モードの選択\r\n#  0:切断面なし\r\n#  1:x-z方向切断・y正方向残し\r\n#  2:x-z方向切断・y負方向残し\r\n#  3:y-z方向切断・x正方向残し\r\n#  4:y-z方向切断・x負方向残し\r\nrid_mode = 0\r\n\r\n# dataファイルの取得\r\nfile_list = glob.glob('./data/*.inp')\r\n\r\nsrf_list =  \\\r\n[[0,1,2,3], [4,5,6,7], \\\r\n[0,1,5,4], [3,2,6,7], \\\r\n[1,2,6,5], [0,3,7,4]]\r\n\r\nfor file_name in file_list:\r\n\r\n    f = open(file_name)\r\n    f.readline()\r\n    stepNum = int(f.readline())\r\n    for step_i in range(1,stepNum + 1):\r\n        text = 'dammy'\r\n        while text.split()[0] != 'step' + str(step_i):\r\n            text = f.readline()\r\n\r\n        text = f.readline()\r\n        point_num = int(text.split()[0])\r\n        element_num = int(text.split()[1])\r\n\r\n        # 点の読み取り\r\n        j = 1\r\n        points_list = []\r\n        centerPt = [0.0,0.0,0.0]\r\n        while j <= point_num:\r\n            text = f.readline()\r\n            k = 0\r\n            pos = []\r\n            for val in text.split()[1:4]:\r\n                pos.append(float(val))\r\n                centerPt[k] = centerPt[k] + float(val)\r\n                k += 1\r\n\r\n            points_list.append(pos)\r\n            j += 1\r\n\r\n        for j in range(3):\r\n            centerPt[j] = centerPt[j] / point_num\r\n\r\n        # 要素データの読み取り\r\n        j = 1\r\n        elements_list = []\r\n        for j in range(element_num):\r\n            text = f.readline()\r\n            ele_list = []\r\n            for num in text.split()[3:11]:\r\n                ele_list.append(int(num))\r\n            # 実数誤差用数値算出\r\n            if j == 0:\r\n                oneBlock_size = [0.0, 0.0, 0.0]\r\n                for k in range(3):\r\n                    oneBlock_size[k] = (points_list[ele_list[7] - 1][k] - points_list[ele_list[0] - 1][k]) / 4\r\n                delta_size = min(oneBlock_size)\r\n            # 節点チェック\r\n            # mode 0, 1, 2, 3, 4\r\n            # 0:切断面なし\r\n            # 1:x-z方向切断・y正方向残し\r\n            # 2:x-z方向切断・y負方向残し\r\n            # 3:y-z方向切断・x正方向残し\r\n            # 4:y-z方向切断・x負方向残し\r\n            # 160614 @yamazaki 原点から見るほうが多いのでとりあえずmode1,3を制作\r\n            # 1:要素の節点1番が、中央点のy座標からdelta_size引いたもの、よりも小さかったらappendしないよう処理\r\n            # 実数誤差処理は要素サイズの半分とかにする。\r\n            if rid_mode == 1:\r\n                if  points_list[ele_list[0] - 1][1] < centerPt[1] - delta_size:\r\n                    continue\r\n\r\n            elements_list.append(ele_list)\r\n            j += 1\r\n\r\n        # 各要素の各面を構成する節点(4つ)を共有している要素があれば、その面は無効\r\n        quadNode_list = []\r\n        j = 0\r\n        for j_elementNode in elements_list:\r\n            for k in range(6):\r\n                srch_pts = srf_list[k]\r\n                # ゼロ基準の配列に治すために-1している。\r\n                quadNode = [j_elementNode[srch_pts[0]], j_elementNode[srch_pts[1]], \\\r\n                j_elementNode[srch_pts[2]], j_elementNode[srch_pts[3]]]\r\n                # j要素k面が、他の面と接しているかをチェック\r\n                for l in range(element_num):\r\n                    if j == l:\r\n                        continue\r\n                    if all_exist(quadNode, elements_list[l]):\r\n                        # 接している要素を見つけたのでループから出て次の面をチェック\r\n                        break\r\n                else:\r\n                    # 接していないようなので、面に追加\r\n                    quadNode[0] = quadNode[0] - 1\r\n                    quadNode[1] = quadNode[1] - 1\r\n                    quadNode[2] = quadNode[2] - 1\r\n                    quadNode[3] = quadNode[3] - 1\r\n                    quadNode_list.append(quadNode)\r\n            j += 1\r\n        # print quadNode_list\r\n        Meshes = rs.AddMesh(points_list, quadNode_list)\r\n\r\n        if stepNum == 1:\r\n            saveName = file_name.replace('data', 'figure')\r\n            saveName = saveName.replace('.inp', '.png')\r\n            outpath = os.getcwd() + \"\\\\\" + saveName\r\n        else:\r\n            saveName = file_name.replace('data', 'figure')\r\n            saveName = saveName.replace('.inp', 'case'+str(step_i)+'.png')\r\n            outpath = os.getcwd() + \"\\\\\" + saveName\r\n\r\n        Rhino.RhinoApp.RunScript(\"_-Render\", False)\r\n        #time.sleep(10) # number of seconds to wait\r\n        Rhino.RhinoApp.RunScript(\"_-SaveRenderWindowAs \\n\\\"\" + outpath + \"\\\"\\n\", False)\r\n        Rhino.RhinoApp.RunScript(\"_-CloseRenderWindow\", False)\r\n\r\n        #rs.DeleteObjects(Boxes)\r\n        exit()\r\n    f.close()\r\n","sub_path":"何やってたか忘れかけてる/rendar_inp/rendar_inp.py","file_name":"rendar_inp.py","file_ext":"py","file_size_in_byte":5338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"490282276","text":"\n# Importing Libraries\n\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import GradientBoostingClassifier\nfrom sklearn.ensemble import RandomForestClassifier\n\nclass models:\n    def __init__(self):\n        self.model = None\n        self.LR_model = LogisticRegression()\n        self.DT_model = DecisionTreeClassifier()\n        self.GB_model = GradientBoostingClassifier(random_state=0)\n        self.RF_model = RandomForestClassifier(random_state=0)\n        \n        self.X_train = None\n        self.Y_train = None\n        self.X_test = None\n        self.Y_test = None\n        \n        self.accuracy_LR_model = 0\n        self.accuracy_DT_model = 0\n        self.accuracy_GB_model = 0\n        self.accuracy_RF_model = 0\n        \n        self.predLR = None\n        self.predDT = None\n        self.predGB = None\n        self.predRF = None\n        \n    def feedData(self, X_train, Y_train, X_test, Y_test):\n        self.X_train = X_train\n        self.Y_train = Y_train\n        self.X_test = X_test\n        self.Y_test = Y_test\n        \n    def fit(self):\n        self.LR_model.fit(self.X_train, self.Y_train)\n        self.DT_model.fit(self.X_train, self.Y_train)\n        self.GB_model.fit(self.X_train, self.Y_train)\n        self.RF_model.fit(self.X_train, self.Y_train)\n        \n    def predict(self):\n        self.predLR = self.LR_model.predict(self.X_test)\n        self.predDT = self.DT_model.predict(self.X_test)\n        self.predGB = self.GB_model.predict(self.X_test)\n        self.predRF = self.RF_model.predict(self.X_test)\n        \n    def selectModel(self):\n        self.accuracy_LR_model = self.LR_model.score(self.X_test, self.Y_test)\n        print(\"accuracy_LR_model: \", self.accuracy_LR_model)\n        self.accuracy_DT_model = self.DT_model.score(self.X_test, self.Y_test)\n        print(\"accuracy_DT_model: \", self.accuracy_DT_model)\n        self.accuracy_GB_model = self.GB_model.score(self.X_test, self.Y_test)\n        print(\"accuracy_GB_model: \", self.accuracy_GB_model)\n        self.accuracy_RF_model = self.RF_model.score(self.X_test, self.Y_test)\n        print(\"accuracy_RF_model: \", self.accuracy_RF_model)\n        \n        if self.accuracy_LR_model > self.accuracy_DT_model and self.accuracy_LR_model > self.accuracy_GB_model and self.accuracy_LR_model > self.accuracy_RF_model:\n            self.model = self.LR_model\n        elif self.accuracy_DT_model > self.accuracy_LR_model and self.accuracy_DT_model > self.accuracy_GB_model and self.accuracy_DT_model > self.accuracy_RF_model:\n            self.model = self.DT_model\n        elif self.accuracy_GB_model > self.accuracy_LR_model and self.accuracy_GB_model > self.accuracy_DT_model and self.accuracy_GB_model > self.accuracy_RF_model:\n            self.model = self.GB_model\n        else:\n            self.model = self.RF_model\n            \n    def giveModel(self):\n        return self.model\n        \n        \n        \n        ","sub_path":"Models.py","file_name":"Models.py","file_ext":"py","file_size_in_byte":2967,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"503507051","text":"totalArr = [];\ntotalAnswer = 0;\n\nwith open(\"Input.txt\") as f:\n    for line in f.readlines():\n        totalArr.append(line.replace('\\n', \"\").split(\"x\"))\n\n\nfor arr in totalArr:\n\n    largest = 0\n    largestIndex = 0\n\n    for i,val in enumerate(arr):\n        arr[i] = int(val)\n\n    ribbon = 0;\n    bow = 1;\n\n    for i, val in enumerate(arr):\n        if val > largest:\n            largest = val\n            largestIndex = i\n\n\n    for i, val in enumerate(arr):\n\n        if i != largestIndex:\n            ribbon += val *2\n\n        bow *= val\n    totalAnswer += ribbon+bow\n\n\n\nprint(totalAnswer)\n","sub_path":"2016/Day 2: I Was Told There Would Be No Math/mainp2.py","file_name":"mainp2.py","file_ext":"py","file_size_in_byte":587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"485069489","text":"from .defaults import *\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = True\n\nEMAIL_BACKEND = 'django.core.mail.backends.console.EmailBackend'\n\nMIDDLEWARE += ['debug_toolbar.middleware.DebugToolbarMiddleware']\n\nINSTALLED_APPS += ['drupal_migrator', 'debug_toolbar']\n\nALLOWED_HOSTS = ['localhost', '127.0.0.1', 'dev.comses.asu.edu', 'cms']\n","sub_path":"django/core/settings/dev.py","file_name":"dev.py","file_ext":"py","file_size_in_byte":366,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"224757594","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Dec  6 16:25:14 2019\n\n@author: adsims\n\"\"\"\n\nfrom PyQt5.uic import loadUiType\n\nimport sys\nfrom PyQt5 import QtWidgets, QtCore, uic\nimport numpy as np   \nfrom matplotlib.figure import Figure\nfrom matplotlib.backends.backend_qt5agg import (\n    FigureCanvasQTAgg as FigureCanvas,\n    NavigationToolbar2QT as NavigationToolbar)\nimport threading\nimport dagr_backend\n\n__author__ = \"Andrew Sims, Michael Hudson\"\n__copyright__ = \"None\"\n__license__ = \"None\"\n__version__ = \"0.2\"\n__maintainer__ = \"Andrew Sims, Michael Hudson\"\n__email__ = \"andrew.sims.d@gmail.com\"\n__status__ = \"Prototype\"\n\t\nUi_MainWindow, QMainWindow = loadUiType('DAGR.ui')\n\nclass Gui(QMainWindow, Ui_MainWindow):\n    def __init__(self, ):\n        super(Gui, self).__init__()\n        self.setupUi(self)\n        self.dataset_fig_dict = {}\n        self.algorithim_fig_dict = {}\n        self.learning_curve_fig_dict = {}\n        \n        self.pb_begin_analysis = self.findChild(QtWidgets.QPushButton, 'pb_begin_analysis')\n        self.pb_begin_analysis.clicked.connect(self.begin_analysis)\n        \n        self.cb_feature_box_and_whisker = self.findChild(QtWidgets.QCheckBox, 'cb_feature_box_and_whisker')\n        self.cb_correlation_matrix = self.findChild(QtWidgets.QCheckBox, 'cb_correlation_matrix')\n        self.cb_feature_histogram = self.findChild(QtWidgets.QCheckBox, 'cb_feature_histogram')\n        self.cb_raw_features = self.findChild(QtWidgets.QCheckBox, 'cb_raw_features')\n        self.cb_scatter_matrix = self.findChild(QtWidgets.QCheckBox, 'cb_scatter_matrix')\n        \n        # self.html_viewer = self.findChild(QtWebEngineWidgets.QWebEngineView, 'webEngineView')\n        \n        self.dataset_prog = self.findChild(QtWidgets.QProgressBar, 'dataset_prog')\n        self.algorithim_prog = self.findChild(QtWidgets.QProgressBar, 'algorithim_prog')\n        \n        self.cb_adaboost = self.findChild(QtWidgets.QCheckBox, 'cb_adaboost')\n        self.cb_dtc = self.findChild(QtWidgets.QCheckBox, 'cb_dtc')\n        self.cb_gaussian_process = self.findChild(QtWidgets.QCheckBox, 'cb_gaussian_process')\n        self.cb_linear_svm = self.findChild(QtWidgets.QCheckBox, 'cb_linear_svm')\n        self.cb_naive_bayes = self.findChild(QtWidgets.QCheckBox, 'cb_naive_bayes')\n        self.cb_nearest_neighbors = self.findChild(QtWidgets.QCheckBox, 'cb_nearest_neighbors')\n        self.cb_neural_network = self.findChild(QtWidgets.QCheckBox, 'cb_neural_network')\n        self.cb_qda = self.findChild(QtWidgets.QCheckBox, 'cb_qda')\n        self.cb_random_forest = self.findChild(QtWidgets.QCheckBox, 'cb_random_forest')\n        self.cb_rbf_svm = self.findChild(QtWidgets.QCheckBox, 'cb_rbf_svm')\n        \n        self.dataset_mpl_figs.itemClicked.connect(self.dataset_changefig)\n        self.algorithim_mpl_figs.itemClicked.connect(self.algorithim_changefig)\n        self.learning_curve_mpl_figs.itemClicked.connect(self.learning_curve_changefig)\n        \n        datset_base_fig = Figure()\n        self.dataset_addmpl(datset_base_fig)\n        algorithim_base_fig = Figure()\n        self.algorithim_addmpl(algorithim_base_fig)\n        learning_curve_base_fig = Figure()\n        self.learning_curve_addmpl(learning_curve_base_fig)\n    \n    def begin_analysis(self,):\n        # with open(r'C:/Users/andre/Documents/Data Analysis/bokeh_test_file.html', 'r') as fh:\n        #     file = fh.read()\n        # self.html_viewer.setHtml(file)\n        \n        df, feature_names = dagr_backend.gen_dataset()\n        dataset_figs = {}\n        if self.cb_feature_box_and_whisker.isChecked():\n            dataset_figs['Feature Box Plot'] = dagr_backend.plot_feature_box(df, feature_names)\n        if self.cb_correlation_matrix.isChecked():\n            dataset_figs['Correlation Matrix'] = dagr_backend.plot_corr_mat(df, feature_names)\n        if self.cb_feature_histogram.isChecked():\n            dataset_figs['Histograms'] = dagr_backend.plot_histograms(df, feature_names)\n        if self.cb_raw_features.isChecked():\n            dataset_figs['Raw Features'] = dagr_backend.plot_raw_features(df, feature_names)\n        if self.cb_scatter_matrix.isChecked():\n            dataset_figs['Scatter Matrix'] = dagr_backend.plot_scatter_matrix(df, feature_names)\n        \n        algorithims_to_use = []\n        if self.cb_adaboost.isChecked():\n            algorithims_to_use.append('adaboost')\n        if self.cb_dtc.isChecked():\n            algorithims_to_use.append('dtc') \n        if self.cb_gaussian_process.isChecked():\n            algorithims_to_use.append('gaussian_process')\n        if self.cb_linear_svm.isChecked():\n            algorithims_to_use.append('linear_svm')\n        if self.cb_naive_bayes.isChecked():\n            algorithims_to_use.append('naive_bayes')\n        if self.cb_nearest_neighbors.isChecked():\n            algorithims_to_use.append('nearest_neighbors')\n        if self.cb_neural_network.isChecked():\n            algorithims_to_use.append('neural_network')\n        if self.cb_qda.isChecked():\n            algorithims_to_use.append('qda')\n        if self.cb_random_forest.isChecked():\n            algorithims_to_use.append('random_forest')\n        if self.cb_rbf_svm.isChecked():\n            algorithims_to_use.append('rbf_svm')\n        \n        models = dagr_backend.build_models(df, feature_names, algorithims_to_use)    \n        \n        algorithim_fig_list = []\n        learning_curve_fig_list = []\n        algorithim_fig_list.append(('Algorithim Accuracy', dagr_backend.plot_algorithim_accuracy(df, feature_names, models)))\n        for model in models:\n            model_name = model.steps[-1][0]\n            # x = threading.Thread(target=self.append_figure(), args=(algorithim_fig_list, model_name, dagr_backend.plot_algorithim_class_space(), (df, feature_names, model)))\n            # x.start()\n            algorithim_fig_list.append((model_name, dagr_backend.plot_algorithim_class_space(df, feature_names, model)))\n            learning_curve_fig_list.append(\n                (\n                    model_name,\n                    dagr_backend.plot_learning_curve_(df, feature_names, model)\n                )\n            )\n            \n        for i, (name, figure) in enumerate(dataset_figs.items()):\n            self.dataset_prog.setValue(((i+1)/len(dataset_figs))*100)\n            gui.dataset_addfig(name, figure)\n        for i, (name, figure) in enumerate(algorithim_fig_list):\n            self.algorithim_prog.setValue(((i+1)/len(algorithim_fig_list))*100)\n            gui.algorithim_addfig(name, figure)\n        for name, figure in learning_curve_fig_list:\n            gui.learning_curve_addfig(name, figure)\n            \n    def append_figure(lst, model_name, func, func_args):\n        lst.append((model_name, func(func_args)))\n    def dataset_changefig(self, item):\n        text = item.text()\n        self.dataset_rmmpl()\n        self.dataset_addmpl(self.dataset_fig_dict[text])\n \n    def dataset_addfig(self, name, fig):\n        self.dataset_fig_dict[name] = fig\n        self.dataset_mpl_figs.addItem(name)\n        \n    def dataset_addmpl(self, fig):\n        self.dataset_canvas = FigureCanvas(fig)\n        self.dataset_mplvl.addWidget(self.dataset_canvas)\n        self.dataset_canvas.draw()\n        self.toolbar = NavigationToolbar(self.dataset_canvas, \n                self.dataset_mpl_window, coordinates=True)\n        self.dataset_mplvl.addWidget(self.toolbar)\n        \n    def dataset_rmmpl(self,):\n        self.dataset_mplvl.removeWidget(self.dataset_canvas)\n        self.dataset_canvas.close()\n        self.dataset_mplvl.removeWidget(self.toolbar)\n        self.toolbar.close() \n        \n    def algorithim_changefig(self, item):\n        text = item.text()\n        self.algorithim_rmmpl()\n        self.algorithim_addmpl(self.algorithim_fig_dict[text])\n \n    def algorithim_addfig(self, name, fig):\n        self.algorithim_fig_dict[name] = fig\n        self.algorithim_mpl_figs.addItem(name)\n        \n    def algorithim_addmpl(self, fig):\n        self.algorithim_canvas = FigureCanvas(fig)\n        self.algorithim_mplvl.addWidget(self.algorithim_canvas)\n        self.algorithim_canvas.draw()\n        self.toolbar = NavigationToolbar(self.algorithim_canvas, \n                self.algorithim_mpl_window, coordinates=True)\n        self.algorithim_mplvl.addWidget(self.toolbar)\n        \n    def algorithim_rmmpl(self,):\n        self.algorithim_mplvl.removeWidget(self.algorithim_canvas)\n        self.algorithim_canvas.close()\n        self.algorithim_mplvl.removeWidget(self.toolbar)\n        self.toolbar.close()\n   \n    def learning_curve_changefig(self, item):\n        text = item.text()\n        self.learning_curve_rmmpl()\n        self.learning_curve_addmpl(self.learning_curve_fig_dict[text])\n \n    def learning_curve_addfig(self, name, fig):\n        self.learning_curve_fig_dict[name] = fig\n        self.learning_curve_mpl_figs.addItem(name)\n        \n    def learning_curve_addmpl(self, fig):\n        self.learning_curve_canvas = FigureCanvas(fig)\n        self.learning_curve_mplvl.addWidget(self.learning_curve_canvas)\n        self.learning_curve_canvas.draw()\n        self.toolbar = NavigationToolbar(self.learning_curve_canvas, \n                self.learning_curve_mpl_window, coordinates=True)\n        self.learning_curve_mplvl.addWidget(self.toolbar)\n        \n    def learning_curve_rmmpl(self,):\n        self.learning_curve_mplvl.removeWidget(self.learning_curve_canvas)\n        self.learning_curve_canvas.close()\n        self.learning_curve_mplvl.removeWidget(self.toolbar)\n        self.toolbar.close()\n        \n    def closeEvent(self, ce):\n        #prevents blocking after each run\n        QtWidgets.QApplication.quit()\n    \nif __name__ == '__main__':\n    app = QtWidgets.QApplication(sys.argv)\n    app.processEvents()\n    gui = Gui()\n    gui.show()\n    sys.exit(app.exec_())","sub_path":"dagr_rev02.py","file_name":"dagr_rev02.py","file_ext":"py","file_size_in_byte":9810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"516212729","text":"# Corentin Pinato\r\n# This is program has to find the decryption key of an ElGamal encryption method by using a brute force strategy.\r\n\r\n#publicKey = [8347852664524685394671539,1725419847553,6544719840647906985061541]\r\n#message = [2030735454253481748649532,1340127105753313239218903]\r\n\r\npublicKey = [29, 2, 3]\r\nmessage = [23, 27]\r\n\r\n#publicKey = [24852977,2744,8414508]\r\n#message = [15268076,743675]\r\n\r\ndef decrypt(publicKey, message, privateKey):\r\n    p = publicKey[0]\r\n    g = publicKey[1]\r\n    gxmodp = publicKey[2]\r\n\r\n    gy = message[0]\r\n    mgxy = message[1]\r\n\r\n    c1 = (gy**(p-1-privateKey))%p\r\n    c2 = (mgxy*c1)%p\r\n\r\n    return c2\r\n\r\ndef encrypt(message,publicKey,randomNum):\r\n    p = publicKey[0]\r\n    g = publicKey[1]\r\n    gxmodp = publicKey[2]\r\n\r\n    m1 = (g**randomNum)%p\r\n    m2 = (message*(gxmodp**randomNum))%p\r\n\r\n    result = [m1,m2]\r\n\r\n    return result\r\n\r\ndef modPow(number,power,mod):\r\n\r\n    if(power == 0):\r\n        return 1\r\n    elif(power%2==0):\r\n        halfpower=modPow(number,(power/2),mod)\r\n        return modMult(halfpower,halfpower,mod)\r\n    else:\r\n        halfpower=modPow(number,((power)/2)+1,mod)\r\n        firstbit = modMult(halfpower,halfpower,mod)\r\n        return modMult(firstbit,number,mod)\r\n\r\n\r\ndef modMult(first,second,mod):\r\n    if(second==0):\r\n        return 0\r\n    elif(second%2==0):\r\n        half=modMult(first,(second/2)+1,mod)\r\n        return(half+half)%mod\r\n    else:\r\n        half=modMult(first,((second)/2)+1,mod)\r\n        return(half+half+first)%mod\r\n\r\n\r\nMessage = 1\r\n\r\nencrypted = encrypt(Message,publicKey,2)\r\nprint(encrypted)\r\n\r\nkey = 1\r\n\r\ndecrypted = decrypt(publicKey,encrypted,key)\r\nprint(\"Tryed key = \"+str(key))\r\n\r\nwhile(decrypted != Message):\r\n    key += 1\r\n    decrypted = decrypt(publicKey,encrypted,key)\r\n    print(\"Tryed key = \"+str(key))\r\n\r\nprint(\"The key is: \"+str(key))\r\n\r\nprint(\"So the secret Message is: \"+str(decrypt(publicKey,message,key)))","sub_path":"FindKey.py","file_name":"FindKey.py","file_ext":"py","file_size_in_byte":1908,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"491310936","text":"class Solution:\n    def findMaxAverage(self, nums, k):\n        \"\"\"\n        :type nums: List[int]\n        :type k: int\n        :rtype: float\n        \"\"\"\n\n        maxHere = sum(nums[:k])\n        maxSum = maxHere\n        maxHereStart = 0\n        for i in range(k, len(nums)):\n            maxHere += (nums[i] - nums[maxHereStart])\n            maxHereStart += 1\n            # Note do not use max() here since it is slower\n            maxSum = maxHere if maxHere > maxSum else maxSum\n\n        return maxSum / k\n","sub_path":"643 Maximum Average Subarray I.py","file_name":"643 Maximum Average Subarray I.py","file_ext":"py","file_size_in_byte":505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"561449994","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport imagekit.models.fields\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='Entry',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('title', models.CharField(max_length=200)),\n                ('slug', models.SlugField()),\n                ('text', models.TextField()),\n                ('created_at', models.DateTimeField(auto_now_add=True)),\n                ('last_modified_at', models.DateTimeField(auto_now=True)),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='Image',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('image', imagekit.models.fields.ProcessedImageField(upload_to=b'')),\n                ('caption', models.TextField()),\n                ('created_at', models.DateTimeField(auto_now_add=True)),\n                ('last_modified_at', models.DateTimeField(auto_now=True)),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.AddField(\n            model_name='entry',\n            name='image',\n            field=models.ForeignKey(to='blog.Image'),\n            preserve_default=True,\n        ),\n    ]\n","sub_path":"blog/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":1586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"351522870","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Jan  2 22:46:07 2018\r\n\r\n@author: MCA\r\n\"\"\"\r\n\r\nimport pygame\r\nimport sys\r\nfrom random import randrange, choice\r\n\r\nBLACK = 0, 0, 0 #barva je reprezentovana jako RGB tuple\r\nWHITE = 255, 255, 255\r\n\r\nMAX_STARS = 250\r\nSTAR_SPEED = 2\r\n\r\n#stars coords generation\r\ndef init_stars(screen):\r\n    global stars\r\n    stars = []\r\n    for i in range(MAX_STARS):\r\n     # A star is represented as a list with this format: [X,Y]\r\n     star = [randrange(0, screen.get_width() - 1), \r\n             randrange(0,screen.get_height() - 1),\r\n             choice([1,2,3])]\r\n     stars.append(star) #a star has a coordinate\r\n\r\ndef move_and_draw_stars(screen):\r\n  \"\"\" Move and draw the stars in the given screen \"\"\"\r\n  global stars\r\n  for star in stars:\r\n      #ycoord += zcoord\r\n    star[1] += star[2]\r\n    # If the star hit the bottom border then we reposition\r\n    # it in the top of the screen with a random X coordinate.\r\n    if star[1] >= screen.get_height():\r\n      star[1] = 0\r\n      star[0] = randrange(0,639)\r\n      star[2] = choice([1, 2, 3])\r\n      \r\n       \r\n    # Adjust the star color acording to the speed.\r\n    # The slower the star, the darker should be its color.\r\n    if star[2] == 1:\r\n      color = (100,100,100)\r\n    elif star[2] == 2:\r\n      color = (190,190,190)\r\n    elif star[2] == 3:\r\n      color = (255,255,255)\r\n \r\n    # Draw the star as a rectangle.\r\n    # The star size is proportional to its speed.\r\n    screen.fill(color,(star[0],star[1],star[2],star[2]))\r\n\r\n    \r\ndef main():\r\n pygame.init()\r\n screen = pygame.display.set_mode((640, 480))\r\n pygame.display.set_caption(\"this is a test\")\r\n clock = pygame.time.Clock() #objekt clock ovlada FPS aplikace\r\n init_stars(screen)\r\n box_x = 300\r\n box_dir = 3\r\n\r\n while True:\r\n    clock.tick(50) #uzavreni FPS na 50 FPS\r\n    \r\n    for event in pygame.event.get():\r\n        #smycka ceka zda uzivatel ukoncil aplikaci krizkem tzv.QUIT EVENT\r\n        if event.type == pygame.QUIT:\r\n            pygame.quit()\r\n            sys.exit()\r\n    #clear the screen\r\n    screen.fill(BLACK)\r\n\r\n    box_x += box_dir\r\n    if box_x >= 620:\r\n        box_x = 620\r\n        box_dir = -3\r\n    elif box_x <= 0:\r\n        box_x = 0\r\n        box_dir = 3\r\n    \r\n    #update screen\r\n    pygame.draw.rect(screen, WHITE, (box_x, 200, 20, 20))\r\n    move_and_draw_stars(screen)\r\n    pygame.display.flip()\r\n    \r\nif __name__ == \"__main__\":\r\n  main()","sub_path":"stars.py","file_name":"stars.py","file_ext":"py","file_size_in_byte":2403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"409531870","text":"# -*- coding: utf-8 -*-\nfrom django.conf.urls import patterns, url\n\nfrom sevenapps.manage.apidata.views import \\\n    ItunesLanguageDataView, ItunesCountryDataView, ItunesGenreDataView\n\n\nurlpatterns = patterns(\n    '',\n    url(\n        r'^itunes/languages.json$',\n        ItunesLanguageDataView.as_view(),\n        name='itunes-languages'\n    ),\n    url(\n        r'^itunes/countries.json$',\n        ItunesCountryDataView.as_view(),\n        name='itunes-countries'\n    ),\n    url(\n        r'^itunes/genres.json$',\n        ItunesGenreDataView.as_view(),\n        name='itunes-genres'\n    ),\n)\n","sub_path":"apps/sevenapps/manage/apidata/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":588,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"567849831","text":"from django.shortcuts import render\nfrom rest_framework.generics import *\nfrom .models import *\nfrom rest_framework.response import Response\nfrom .serializers import *\n# Create your views here.\n\n\nclass Image_upload(ListCreateAPIView):\n    serializer_class = ImageUploadSerializer\n    queryset = Image.objects.all()\n\nclass ImageView(RetrieveAPIView):\n    lookup_field = 'id'\n    serializer_class = ImageUploadSerializer\n    queryset = Image.objects.all()\n\n    def retrieve(self, request, *args, **kwargs):\n        instance = self.get_object()\n        if (PageView.objects.count() <= 0):\n            x = PageView.objects.create()\n            x.save()\n        else:\n            x = PageView.objects.all()[0]\n            x.hits = x.hits + 1\n            x.save()\n        print(x.hits)\n        serializer = self.get_serializer(instance)\n        return Response(serializer.data)\n\n\ndef home(request):\n    if (PageView.objects.count() <= 0):\n        x = PageView.objects.create()\n        x.save()\n    else:\n        x = PageView.objects.all()[0]\n        x.hits = x.hits + 1\n        x.save()\n    print(x.hits)\n    context = {'page':x.hits}\n    return render(request,'home.html',context=context)","sub_path":"image/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1183,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"41480434","text":"\"\"\"\nGiven a binary tree, return the bottom-up level order traversal of its nodes' values. (ie, from left to right, level by level from leaf to root).\n\nFor example:\nGiven binary tree [3,9,20,null,null,15,7],\n    3\n   / \\\n  9  20\n    /  \\\n   15   7\nreturn its bottom-up level order traversal as:\n[\n  [15,7],\n  [9,20],\n  [3]\n]\n\"\"\"\nfrom utils.tree import TreeNode, list_create_tree\nfrom collections import deque\n\n\"\"\"\nclass Solution:\n    def levelOrderBottom(self, root: TreeNode):\n        # bfs + queue 56 ms\n        res = []\n        q = deque([(root, 0)])\n        while q:\n            node, level = q.popleft()\n            if node:\n                if len(res) == level:\n                    res.append([])\n                res[level].append(node.val)\n                q.append((node.left, level + 1))\n                q.append((node.right, level + 1))\n        return res[::-1]\n\"\"\"\n\n\"\"\"\nclass Solution:\n    def levelOrderBottom(self, root: TreeNode):\n        # dfs + stack 44ms\n        stack = [(root, 0)]\n        res = []\n        while stack:\n            node, level = stack.pop()\n            if node:\n                if len(res) == level:\n                    res.append([])\n                res[level].append(node.val)\n                # right must be ahead of left\n                stack.append((node.right, level + 1))\n                stack.append((node.left, level + 1))\n        return res[::-1]\n\"\"\"\n\nclass Solution:\n    def levelOrderBottom(self, root: TreeNode):\n        # recursive dfs 44ms\n        def helper(node, level):\n            if node:\n                if len(res) == level:\n                    res.append([])\n                res[level].append(node.val)\n                helper(node.left, level+1)\n                helper(node.right, level+1)\n\n        res = []\n        helper(root, 0)\n        return res[::-1]\n\n\nif __name__ == '__main__':\n    s = Solution()\n    root = TreeNode(None)\n    llist = [3,9,20,None,None,15,7]\n    root = list_create_tree(root, llist, 0)\n    print(s.levelOrderBottom(root))","sub_path":"python/problems/0107.BinaryTreeLevelOrderTraversalII.py","file_name":"0107.BinaryTreeLevelOrderTraversalII.py","file_ext":"py","file_size_in_byte":2002,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"499060126","text":"#Modules used in the code\r\nimport sys\r\nimport requests\r\nimport json\r\nimport string\r\nimport time\r\nimport argparse\r\nimport re\r\n\r\nparser = argparse.ArgumentParser()\r\nparser.add_argument(\"routeName\", help=\" Route name in which user wants to travel\",type=str)\r\nparser.add_argument(\"stopName\", help=\" Stop name user has to board the bus\",type=str)\r\nparser.add_argument(\"directionValue\", help=\" Direction of the route ( North, South, East, West)\",type=str)\r\nargs = parser.parse_args()\r\n\r\ndef input_check ():\r\n\r\n    ''' This is the start of the execution\r\n    function used to format the user input into required format and \r\n    validates the direction parameter '''\r\n\r\n    global routeName \r\n    global stopName \r\n    global direction\r\n\r\n    #formating the inputs        \r\n    routeName = args.routeName.lower().strip()\r\n    stopName = args.stopName.lower().strip()\r\n    directionValue = args.directionValue.lower().strip()\r\n\r\n    #predefined direction values for the inputs\r\n    directdict = dict (south=1, north=4, east=2, west=3)\r\n    \r\n    if directionValue in directdict:\r\n        direction = directdict[directionValue]\r\n    else:\r\n        print(\"Direction should be any one among SOUTH, NORTH, EAST, WEST\")\r\n        sys.exit()\r\n    \r\n    get_route_details()\r\n\r\ndef get_route_details():\r\n    ''' This function valitades the routeName\r\n    fetch the respective routeNo '''\r\n\r\n    global routeNo\r\n    condCheck = \"\"\r\n    urli = \"http://svc.metrotransit.org/NexTrip/Routes\"\r\n    headers = {'content-type':'Application/json', 'accept':'Application/json'}\r\n\r\n    routes = requests.get(url=urli, headers=headers).json()\r\n\r\n    for r in routes:       \r\n        if routeName == r.get(\"Description\").lower() :\r\n                routeNo = r.get(\"Route\")\r\n                break    \r\n    else:\r\n                #print(\"The Route requested is not avaliable\")\r\n                print(\"Route:\"+routeName+\" \"+\"Does not exist\")\r\n                print(\"\\n1.)To get the list of supported routes Enter:R\\n2)To quit enter:Q\")\r\n                condCheck=input(\"Enter your inputs\").lower()\r\n                if condCheck == \"\" or condCheck == \"Q\" or condCheck == \"q\":\r\n                    sys.exit()\r\n                elif condCheck == \"routes\":\r\n                    for i in routes:\r\n                        print(i.get(\"Description\"))\r\n                    print(\"See you soon!!!\")\r\n                    sys.exit()\r\n    direction_validation()\r\n\r\ndef direction_validation(): \r\n    ''' This function with respective to routeNo \r\n    validates the direction avaliable '''\r\n    dirText = \"\"\r\n    urli = \"http://svc.metrotransit.org/NexTrip/Directions/\"\r\n    headers = {'content-type':'Application/json', 'accept':'Application/json'}\r\n    allowedDict = requests.get(url=urli+str(routeNo), headers=headers).json()\r\n\r\n    for dire in allowedDict:\r\n        if int(dire.get(\"Value\")) == int(direction):\r\n            dirText = dire.get(\"Text\")\r\n            break\r\n    if not dirText :\r\n        print(\"No Routes in the request directions\")\r\n        print(\"Below are the allowed direction for requested route\")\r\n        for i in allowedDict:\r\n            print(i.get(\"Text\")+\"\\n\")\r\n        sys.exit()\r\n    get_stops()\r\ndef get_stops():\r\n    ''' This function valitades the stopName\r\n    fetch the respective stopName'''\r\n    global stopCode\r\n    urli = f\"http://svc.metrotransit.org/NexTrip/Stops/{str(routeNo)}/{direction}\"\r\n    headers = {'content-type':'Application/json', 'accept':'Application/json'}\r\n\r\n    stops = requests.get(url=urli, headers=headers).json()\r\n\r\n    for stop in stops: \r\n        if stopName == stop.get(\"Text\").lower().strip():\r\n            stopCode = stop.get(\"Value\")\r\n            #stopText = stop.get(\"Text\")\r\n            break \r\n    else:\r\n        print(\"Matching Stops not found\")\r\n        print(\"Stops:\"+stopName+\" \"+\"Does not exist\")\r\n        print(\"To get the list of avaliable stops in this route Enter:Stops\\nTo quit enter:Q\")\r\n        condCheck=input().lower()\r\n        if condCheck == \"\" or condCheck == \"Q\" or condCheck == \"q\":\r\n            sys.exit()\r\n        elif \"stops\" in condCheck:\r\n            for i in stops:\r\n                print(i.get(\"Text\"))\r\n            print(\"Thank you!!!Please Try Again\")\r\n            sys.exit()\r\n        else:\r\n            print(\"Please try again with valid Inputs\")\r\n    if not stopCode:\r\n        print(\"Matching Stops not found\")\r\n        print(\"Stops:\"+stopName+\" \"+\"Does not exist\")\r\n        print(\"To get the list of avaliable stops in this route Enter:Stops\\nTo quit enter:Q\")\r\n        condCheck=input().lower()\r\n        if condCheck == \"\" or condCheck == \"Q\" or condCheck == \"q\":\r\n            sys.exit()\r\n        elif \"stops\" in condCheck:\r\n            print(stops)\r\n            for i in stops:\r\n                print(i.get(\"Text\"))\r\n            print(\"See you soon!!!\")\r\n            sys.exit()\r\n        else:\r\n            print(\"Please try again with valid Inputs.See you soon!!!\")\r\n            sys.exit()\r\n    get_time()\r\ndef get_time():\r\n    ''' Based on the routeNo,direction&stopName\r\n    fetch the next train time avaliable in minutes'''\r\n    urli = f\"http://svc.metrotransit.org/NexTrip/{str(routeNo)}/{direction}/{stopCode}\"\r\n    headers = {'content-type':'Application/json', 'accept':'Application/json'}\r\n\r\n    depart_response = requests.get(url=urli,headers=headers).json()\r\n\r\n    #print(depart_response)\r\n    try:\r\n        time_stamp_temp = (depart_response[0].get('DepartureTime'))\r\n        time_stamp_temp= (((int(re.match(r\"^(\\/Date\\()(\\d{10})\",time_stamp_temp).group(2)) - time.time())/60 ))\r\n        print(f\"{round(time_stamp_temp)} Minutes\")\r\n    except IndexError:\r\n        print(f\"\\nLast bus for the day already left from the {stopName}\\n\")\r\n    \r\n#Start of the program\r\n\r\ninput_check() # Prepares the input for the program excution\r\n\r\n\r\n\r\n\r\n","sub_path":"UsingApi.py","file_name":"UsingApi.py","file_ext":"py","file_size_in_byte":5800,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"213696869","text":"from models import City\nfrom models import CasesLocation\nfrom sqlalchemy.sql import or_\n\n\nclass ReportService:\n\n    def search_on_location_by_term(self, query):\n        cases = City.query.filter(\n            or_(City.city.like(query), City.state.like(query))\n        ).all()\n\n        result = []\n\n        for case in cases:\n            current_case = {\n                'city': case.city,\n                'state': case.state,\n                'cases': {\n                    'activeCases': case.active_cases,\n                    'suspectedCases': case.suspects,\n                    'recoveredCases': case.recovered,\n                    'deaths': case.deaths\n                }\n            }\n            result.append(current_case)\n\n        return result\n\n    def get_all_city_cases(self):\n        all_cases = City.query.all()\n        return compile_cases(all_cases)\n\n    def search_city_cases_by_state(self, state_code):\n        city_situation = City.query.filter_by(\n            state=state_code).all()\n        return compile_cases(city_situation)\n\n    def get_cases_near_location(self, latitude, longitude):\n        # FIX: Why those variables has not been used?\n        all_cases = CasesLocation.query.all()\n        return compile_cases_near_location(all_cases)\n\n\ndef compile_cases(data):\n    active_cases = sum(\n        [city.active_cases\n         for city in data]) or 0\n    suspected_cases = sum(\n        [city.suspects for city in data]) or 0\n    recovered_cases = sum(\n        [city.recovered for city in data]) or 0\n    deaths = sum([city.deaths for city in data]) or 0\n\n    return {\n        'activeCases': active_cases,\n        'suspectedCases': suspected_cases,\n        'recoveredCases': recovered_cases,\n        'deaths': deaths\n    }\n\n\ndef compile_cases_near_location(data):\n    for case in data:\n        return {\n            'status': case.status,\n            'location': {\n                'latitude': case.longitude,\n                'longitude': case.latitude\n            }\n        }\n","sub_path":"src/apis/data/services.py","file_name":"services.py","file_ext":"py","file_size_in_byte":1994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"421829337","text":"\"\"\"\nThere are a total of numCourses courses you have to take, labeled from 0 to numCourses-1.\n\nSome courses may have prerequisites, for example to take course 0 you have to first take course 1, which is expressed as a pair: [0,1]\n\nGiven the total number of courses and a list of prerequisite pairs, is it possible for you to finish all courses?\n\nExample 1:\n\nInput: numCourses = 2, prerequisites = [[1,0]]\nOutput: true\nExplanation: There are a total of 2 courses to take. \n             To take course 1 you should have finished course 0. So it is possible.\n\nExample 2:\n\nInput: numCourses = 2, prerequisites = [[1,0],[0,1]]\nOutput: false\nExplanation: There are a total of 2 courses to take. \n             To take course 1 you should have finished course 0, and to take course 0 you should\n             also have finished course 1. So it is impossible.\n\nConstraints:\n\n    The input prerequisites is a graph represented by a list of edges, not adjacency matrices. Read more about how a graph is represented.\n    You may assume that there are no duplicate edges in the input prerequisites.\n    1 <= numCourses <= 10^5\n\n\"\"\"\n\nfrom collections import defaultdict \n        \nclass Solution:\n    def canFinish(self, numCourses: int, prerequisites: List[List[int]]) -> bool:\n        \"\"\"\n        from : https://codereview.stackexchange.com/questions/86021/check-if-a-directed-graph-contains-a-cycle\n        \"\"\"\n        def cyclic(g):\n            path = set()\n\n            visited = set()\n            path = [object()]\n            path_set = set(path)\n            stack = [iter(graph)]\n            while stack:\n                for v in stack[-1]:\n                    if v in path_set:\n                        return True\n                    elif v not in visited:\n                        visited.add(v)\n                        path.append(v)\n                        path_set.add(v)\n                        stack.append(iter(graph.get(v, ())))\n                        break\n                else:\n                    path_set.remove(path.pop())\n                    stack.pop()\n            return False\n        \n        graph = defaultdict(list)\n        \n        for a,b in prerequisites:\n            graph[a].append(b)\n        \n        return not cyclic(graph)\n        \n        ","sub_path":"leetcode/May-31-day/week5/course_schedule.py","file_name":"course_schedule.py","file_ext":"py","file_size_in_byte":2262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"231510969","text":"# -*- coding: utf-8 -*-\n\n## \\package wizbin.changelog\n\n# MIT licensing\n# See: docs/LICENSE.txt\n\n\nimport commands, os, wx\n\nfrom dbr.language       import GT\nfrom dbr.log            import Logger\nfrom f_export.ftarget   import FileOTarget\nfrom globals.bitmaps    import ICON_WARNING\nfrom globals.changes    import FormatChangelog\nfrom globals.errorcodes import dbrerrno\nfrom globals.execute    import GetExecutable\nfrom globals.fileio     import ReadFile\nfrom globals.ident      import btnid\nfrom globals.ident      import chkid\nfrom globals.ident      import inputid\nfrom globals.ident      import pgid\nfrom globals.ident      import selid\nfrom globals.paths      import ConcatPaths\nfrom globals.strings    import TextIsEmpty\nfrom globals.system     import GetOSDistNames\nfrom globals.tooltips   import SetPageToolTips\nfrom input.pathctrl     import PathCtrlESS\nfrom input.select       import Choice\nfrom input.select       import ComboBox\nfrom input.text         import TextArea\nfrom input.text         import TextAreaPanel\nfrom input.text         import TextAreaPanelESS\nfrom input.toggle       import CheckBox\nfrom input.toggle       import CheckBoxESS\nfrom ui.button          import CreateButton\nfrom ui.dialog          import DetailedMessageDialog\nfrom ui.layout          import BoxSizer\nfrom ui.style           import layout as lyt\nfrom wiz.helper         import ErrorTuple\nfrom wiz.helper         import GetFieldValue\nfrom wiz.helper         import GetMainWindow\nfrom wiz.wizard         import WizardPage\n\n\n## Changelog page\nclass Page(WizardPage):\n    ## Constructor\n    #\n    #  \\param parent\n    #    Parent <b><i>wx.Window</i></b> instance\n    def __init__(self, parent):\n        WizardPage.__init__(self, parent, pgid.CHANGELOG)\n        \n        txt_package = wx.StaticText(self, label=GT(u'Package'), name=u'package')\n        self.ti_package = TextArea(self, inputid.PACKAGE, name=txt_package.Name)\n        \n        txt_version = wx.StaticText(self, label=GT(u'Version'), name=u'version')\n        self.ti_version = TextArea(self, inputid.VERSION, name=txt_version.Name)\n        \n        dist_names = GetOSDistNames()\n        \n        txt_dist = wx.StaticText(self, label=GT(u'Distribution'), name=u'dist')\n        \n        if dist_names:\n            self.ti_dist = ComboBox(self, inputid.DIST, choices=dist_names, name=txt_dist.Name)\n        \n        # Use regular text input if could not retrieve distribution names list\n        else:\n            self.ti_dist = TextArea(self, inputid.DIST, name=txt_dist.Name)\n        \n        opts_urgency = (\n            u'low',\n            u'medium',\n            u'high',\n            u'emergency',\n            )\n        \n        txt_urgency = wx.StaticText(self, label=GT(u'Urgency'), name=u'urgency')\n        self.sel_urgency = Choice(self, selid.URGENCY, choices=opts_urgency, name=txt_urgency.Name)\n        \n        txt_maintainer = wx.StaticText(self, label=GT(u'Maintainer'), name=u'maintainer')\n        self.ti_maintainer = TextArea(self, inputid.MAINTAINER, name=txt_maintainer.Name)\n        \n        txt_email = wx.StaticText(self, label=GT(u'Email'), name=u'email')\n        self.ti_email = TextArea(self, inputid.EMAIL, name=txt_email.Name)\n        \n        btn_import = CreateButton(self, btnid.IMPORT, GT(u'Import'), u'import', name=u'btn import')\n        txt_import = wx.StaticText(self, label=GT(u'Import information from Control page'))\n        \n        # Changes input\n        self.ti_changes = TextAreaPanel(self, size=(20,150), name=u'changes')\n        \n        # *** Target installation directory\n        \n        # FIXME: Should this be set by config or project file???\n        self.pnl_target = FileOTarget(self, u'/usr/share/doc/<package>', name=u'target default',\n                defaultType=CheckBoxESS, customType=PathCtrlESS, pathIds=(chkid.TARGET, inputid.TARGET,))\n        \n        self.btn_add = CreateButton(self, btnid.ADD, GT(u'Add'), u'add', name=u'btn add')\n        txt_add = wx.StaticText(self, label=GT(u'Insert new changelog entry'))\n        \n        self.chk_indentation = CheckBox(self, label=GT(u'Preserve indentation'), name=u'indent')\n        \n        self.dsp_changes = TextAreaPanelESS(self, inputid.CHANGES, monospace=True, name=u'log')\n        self.dsp_changes.EnableDropTarget()\n        \n        SetPageToolTips(self)\n        \n        # *** Event Handling *** #\n        \n        btn_import.Bind(wx.EVT_BUTTON, self.OnImportFromControl)\n        self.btn_add.Bind(wx.EVT_BUTTON, self.AddInfo)\n        \n        # *** Layout *** #\n        \n        LEFT_BOTTOM = lyt.ALGN_LB\n        LEFT_CENTER = wx.ALIGN_LEFT|wx.ALIGN_CENTER_VERTICAL\n        RIGHT_CENTER = wx.ALIGN_RIGHT|wx.ALIGN_CENTER_VERTICAL\n        \n        lyt_info = wx.FlexGridSizer(2, 6)\n        \n        lyt_info.AddGrowableCol(1)\n        lyt_info.AddGrowableCol(3)\n        lyt_info.AddGrowableCol(5)\n        lyt_info.AddMany((\n            (txt_package, 0, RIGHT_CENTER|wx.RIGHT, 5),\n            (self.ti_package, 1, wx.EXPAND|wx.BOTTOM|wx.RIGHT, 5),\n            (txt_version, 0, RIGHT_CENTER|wx.RIGHT, 5),\n            (self.ti_version, 1, wx.EXPAND|wx.BOTTOM|wx.RIGHT, 5),\n            (txt_dist, 0, RIGHT_CENTER|wx.RIGHT, 5),\n            (self.ti_dist, 1, wx.EXPAND|wx.BOTTOM, 5),\n            (txt_urgency, 0, RIGHT_CENTER|wx.RIGHT, 5),\n            (self.sel_urgency, 1, wx.RIGHT, 5),\n            (txt_maintainer, 0, RIGHT_CENTER|wx.RIGHT, 5),\n            (self.ti_maintainer, 1, wx.EXPAND|wx.RIGHT, 5),\n            (txt_email, 0, RIGHT_CENTER|wx.RIGHT, 5),\n            (self.ti_email, 1, wx.EXPAND)\n            ))\n        \n        lyt_details = wx.GridBagSizer()\n        lyt_details.SetCols(3)\n        lyt_details.AddGrowableRow(2)\n        lyt_details.AddGrowableCol(1)\n        \n        lyt_details.Add(btn_import, (0, 0))\n        lyt_details.Add(txt_import, (0, 1), flag=LEFT_CENTER)\n        lyt_details.Add(wx.StaticText(self, label=GT(u'Changes')), (1, 0), flag=LEFT_BOTTOM)\n        lyt_details.Add(wx.StaticText(self, label=GT(u'Target')), (1, 2), flag=LEFT_BOTTOM)\n        lyt_details.Add(self.ti_changes, (2, 0), (1, 2), wx.EXPAND|wx.RIGHT, 5)\n        lyt_details.Add(self.pnl_target, (2, 2))\n        lyt_details.Add(self.btn_add, (3, 0), (2, 1))\n        lyt_details.Add(txt_add, (3, 1), flag=LEFT_BOTTOM|wx.TOP, border=5)\n        lyt_details.Add(self.chk_indentation, (4, 1), flag=LEFT_BOTTOM)\n        \n        lyt_main = BoxSizer(wx.VERTICAL)\n        lyt_main.AddSpacer(10)\n        lyt_main.Add(lyt_info, 0, wx.EXPAND|lyt.PAD_LR, 5)\n        lyt_main.AddSpacer(10)\n        lyt_main.Add(lyt_details, 1, wx.EXPAND|lyt.PAD_LR, 5)\n        lyt_main.Add(wx.StaticText(self, label=u'Changelog Output'),\n                0, LEFT_BOTTOM|lyt.PAD_LT, 5)\n        lyt_main.Add(self.dsp_changes, 1, wx.EXPAND|lyt.PAD_LR|wx.BOTTOM, 5)\n        \n        self.SetAutoLayout(True)\n        self.SetSizer(lyt_main)\n        self.Layout()\n    \n    \n    ## Formats input text from 'changes' field for new entry in changelog\n    def AddInfo(self, event=None):\n        new_changes = self.ti_changes.GetValue()\n        \n        if TextIsEmpty(new_changes):\n            DetailedMessageDialog(GetMainWindow(), GT(u'Warning'), ICON_WARNING,\n                    GT(u'\"Changes\" section is empty')).ShowModal()\n            \n            self.ti_changes.SetInsertionPointEnd()\n            self.ti_changes.SetFocus()\n            \n            return\n        \n        package = self.ti_package.GetValue()\n        version = self.ti_version.GetValue()\n        dist = self.ti_dist.GetValue()\n        urgency = self.sel_urgency.GetStringSelection()\n        maintainer = self.ti_maintainer.GetValue()\n        email = self.ti_email.GetValue()\n        \n        new_changes = FormatChangelog(new_changes, package, version, dist, urgency,\n                maintainer, email, self.chk_indentation.GetValue())\n        \n        # Clean up leading & trailing whitespace in old changes\n        old_changes = self.dsp_changes.GetValue().strip(u' \\t\\n\\r')\n        \n        # Only append newlines if log isn't already empty\n        if not TextIsEmpty(old_changes):\n            new_changes = u'{}\\n\\n\\n{}'.format(new_changes, old_changes)\n        \n        # Add empty line to end of log\n        if not new_changes.endswith(u'\\n'):\n            new_changes = u'{}\\n'.format(new_changes)\n        \n        self.dsp_changes.SetValue(new_changes)\n        \n        # Clear \"Changes\" text\n        self.ti_changes.Clear()\n        self.ti_changes.SetFocus()\n    \n    \n    ## Exports page's data to file\n    #\n    #  \\param out_dir\n    #    Target directory where file will be written\n    #  \\out_name\n    #    Filename of output file\n    #  \\compress\n    #    If <b><i>True</i></b>, compresses file with gzip\n    def Export(self, out_dir, out_name=wx.EmptyString, compress=False):\n        ret_value = WizardPage.Export(self, out_dir, out_name=out_name)\n        \n        absolute_filename = u'{}/{}'.format(out_dir, out_name).replace(u'//', u'/')\n        \n        CMD_gzip = GetExecutable(u'gzip')\n        \n        if compress and CMD_gzip:\n            commands.getstatusoutput(u'{} -n9 \"{}\"'.format(CMD_gzip, absolute_filename))\n        \n        return ret_value\n    \n    \n    ## Export instructions specifically for build phase\n    #\n    #  \\param stage\n    #    Formatted staged directory where file heirarchy is temporarily kept\n    #  \\return\n    #    <b><i>Tuple</i></b> containing a return code & string value of page data\n    def ExportBuild(self, stage):\n        target = self.pnl_target.GetPath()\n        \n        if target == self.pnl_target.GetDefaultPath():\n            target.replace(u'<package>', GetFieldValue(pgid.CONTROL, inputid.PACKAGE))\n        \n        stage = ConcatPaths((stage, target))\n        \n        if not os.path.isdir(stage):\n            os.makedirs(stage)\n        \n        # FIXME: Allow user to set filename\n        self.Export(stage, u'changelog', True)\n        \n        export_summary = GT(u'Changelog export failed')\n        changelog = ConcatPaths((stage, u'changelog.gz'))\n        \n        if os.path.isfile(changelog):\n            export_summary = GT(u'Changelog export to: {}').format(changelog)\n        \n        return(0, export_summary)\n    \n    \n    ## Retrieves changelog text\n    #\n    #  The output is a text file that uses sections defined by braces ([, ])\n    #\n    #  \\param getModule\n    #    If <b><i>True</i></b>, returns a <b><i>tuple</b></i> of the module name\n    #    & page data, otherwise return only page data string\n    #  \\return\n    #    <b><i>tuple(str, str)</i></b>: Filename & formatted string of changelog target & body\n    def Get(self, getModule=False):\n        target = self.pnl_target.GetPath()\n        \n        if target == self.pnl_target.GetDefaultPath():\n            target = u'DEFAULT'\n        \n        body = self.dsp_changes.GetValue()\n        \n        if TextIsEmpty(body):\n            page = None\n        \n        else:\n            page = u'[TARGET={}]\\n\\n[BODY]\\n{}'.format(target, body)\n        \n        if getModule:\n            page = (__name__, page,)\n        \n        return page\n    \n    \n    ## Retrieves plain text of the changelog field\n    #\n    #  \\return\n    #    Formatted changelog text\n    def GetChangelog(self):\n        return self.dsp_changes.GetValue()\n    \n    \n    ## Reads & parses page data from a formatted text file\n    #\n    #  \\param filename\n    #    File path to open\n    def ImportFromFile(self, filename):\n        if not os.path.isfile(filename):\n            return dbrerrno.ENOENT\n        \n        clog_data = ReadFile(filename, split=True)\n        \n        sections = {}\n        \n        def parse_section(key, lines):\n            value = u'\\n'.join(lines).split(u'\\n[')[0]\n            \n            if u'=' in key:\n                key = key.split(u'=')\n                value = (key[-1], value)\n                key = key[0]\n            \n            sections[key] = value\n        \n        # NOTE: This would need to be changed were more sections added to project file\n        for L in clog_data:\n            line_index = clog_data.index(L)\n            \n            if not TextIsEmpty(L) and u'[' in L and u']' in L:\n                L = L.split(u'[')[-1].split(u']')[0]\n                parse_section(L, clog_data[line_index+1:])\n        \n        for S in sections:\n            Logger.Debug(__name__, GT(u'Changelog section: \"{}\", Value:\\n{}').format(S, sections[S]))\n            \n            if isinstance(sections[S], (tuple, list)):\n                value_index = 0\n                for I in sections[S]:\n                    Logger.Debug(__name__, GT(u'Value {}: {}').format(value_index, I))\n                    value_index += 1\n            \n            if S == u'TARGET':\n                Logger.Debug(__name__, u'SECTION TARGET FOUND')\n                \n                if sections[S][0] == u'DEFAULT':\n                    Logger.Debug(__name__, u'Using default target')\n                    \n                    if not self.pnl_target.UsingDefault():\n                        self.pnl_target.Reset()\n                \n                else:\n                    Logger.Debug(__name__, GT(u'Using custom target: {}').format(sections[S][0]))\n                    \n                    self.pnl_target.SetPath(sections[S][0])\n                \n                continue\n            \n            if S == u'BODY':\n                Logger.Debug(__name__, u'SECTION BODY FOUND')\n                \n                self.dsp_changes.SetValue(sections[S])\n                \n                continue\n        \n        return 0\n    \n    \n    ## Checks the page's fields for exporting\n    #\n    #  \\return\n    #    <b><i>False</i></b> if page cannot be exported\n    def IsOkay(self):\n        return not TextIsEmpty(self.dsp_changes.GetValue())\n    \n    \n    ## Imports select field values from the 'Control' page\n    def OnImportFromControl(self, event=None):\n        fields = (\n            (self.ti_package, inputid.PACKAGE),\n            (self.ti_version, inputid.VERSION),\n            (self.ti_maintainer, inputid.MAINTAINER),\n            (self.ti_email, inputid.EMAIL),\n            )\n        \n        for F, FID in fields:\n            field_value = GetFieldValue(pgid.CONTROL, FID)\n            \n            if isinstance(field_value, ErrorTuple):\n                err_msg1 = GT(u'Got error when attempting to retrieve field value')\n                err_msg2 = u'\\tError code: {}\\n\\tError message: {}'.format(field_value.GetCode(), field_value.GetString())\n                Logger.Error(__name__, u'{}:\\n{}'.format(err_msg1, err_msg2))\n                \n                continue\n            \n            if not TextIsEmpty(field_value):\n                F.SetValue(field_value)\n    \n    \n    ## Sets values of page's fields with given input\n    #\n    #  \\param data\n    #    Text to parse for values\n    def Set(self, data):\n        changelog = data.split(u'\\n')\n        target = changelog[0].split(u'<<DEST>>')[1].split(u'<</DEST>>')[0]\n        \n        if target == u'DEFAULT':\n            if not self.pnl_target.UsingDefault():\n                self.pnl_target.Reset()\n        \n        else:\n            self.pnl_target.SetPath(target)\n        \n        self.dsp_changes.SetValue(u'\\n'.join(changelog[1:]))\n","sub_path":"wizbin/changelog.py","file_name":"changelog.py","file_ext":"py","file_size_in_byte":15184,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"457136900","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n# Complete the twoStrings function below.\ndef twoStrings(s1, s2):\n    chars_1 = list(s1)\n    chars_2 = list(s2)\n    lngth = len(set(chars_1)) + len(set(chars_2))\n    st = set(chars_1 + chars_2)\n    if (len(st) == lngth):\n        return(\"NO\")\n    else: \n        return(\"YES\")\n\nif __name__ == '__main__':\n    fptr = open(os.environ['OUTPUT_PATH'], 'w')\n\n    q = int(input())\n\n    for q_itr in range(q):\n        s1 = input()\n\n        s2 = input()\n\n        result = twoStrings(s1, s2)\n\n        fptr.write(result + '\\n')\n\n    fptr.close()\n","sub_path":"python_random/two-strings.py","file_name":"two-strings.py","file_ext":"py","file_size_in_byte":608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"301890008","text":"import sys\n\nxyz = []\n#try:\nwith open(sys.argv[1], \"r\") as f:\n    for line in f:\n        point = []\n        if not line.strip():\n            for num in line.split(\" \"):\n                print(\"-->\" + num)\n                point.append(float(num))\n            xyz.append(point)\n\n# except:\n#     print(\"Unable to load input point cloud\")\n#     sys.exit()\n\nxyz.sort(key = lambda x: x[2])\n\nmin_x = min(i[0] for i in xyz)\nmin_y = min(i[1] for i in xyz)\nmin_z = min(i[2] for i in xyz)\nif min_x < 0:\n    for i in xyz:\n        i[0] -= min_x\nif min_y < 0:\n    for i in xyz:\n        i[1] -= min_y\nif min_z < 0:\n    for i in xyz:\n        i[2] -= min_z\n\nf = open(sys.argv[1] + \"-s\", \"w\")\nfor point in xyz:\n    for num in point:\n        f.write(str(num) + \" \")\n    f.write(\"\\n\")\n","sub_path":"old/sort.py","file_name":"sort.py","file_ext":"py","file_size_in_byte":763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"203548727","text":"import pickle\n\n\n# blue_mode: 0 = not use bluetooth mode, 1 = use bluetooth mode\n\n# refresh_history: the time to take a sample of the face and save it in the history\n# depends on the speed of your CPU\n\n# refresh_blue: a reset time which is used when a person is recognized using bluetooth mode and\n# we don't want to scan anything, 330 is equal to 5 seconds\n\n# average_num, a smaller number represents a more accurate match\n# votes_num, a higher number represents a more accurate match\n# it is stricter if it is more accurate\n\n# unknown_num, how much time the system has to wait to recognized a person as unknown\n\naction = int(input(\"input action: \"))\n\nsettings = {'blue_mode': 0,\n            'blue_com': \"COM11\",\n            'blue_bauds': 9600,\n            'refresh_blue': 330,\n            'refresh_history': 25,\n            'average_num': 0.45,\n            'votes_num': 5,\n            'unknown_num': 10}\n\nif action == 1:\n    f = open(\"settings.pickle\", \"wb\")\n    f.write(pickle.dumps(settings))\n    f.close()\nelse:\n    load_settings = pickle.loads(open(\"settings.pickle\", \"rb\").read())\n    print(load_settings)\n","sub_path":"data/SetSettings.py","file_name":"SetSettings.py","file_ext":"py","file_size_in_byte":1112,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"89068280","text":"\"\"\"An Accessor consists of a list of MultiHopAccessors.  It provides a method `get_session`\nwhich will iterate through the MultiHopAccessors until a session can be obtained to\na target account.\"\"\"\nfrom dataclasses import dataclass\nimport json\nfrom pathlib import Path\nfrom typing import Any, Dict, List, Optional, Type\n\nimport boto3\n\nfrom altimeter.aws.auth.cache import AWSCredentialsCache\nfrom altimeter.aws.auth.exceptions import AccountAuthException\nfrom altimeter.aws.auth.multi_hop_accessor import MultiHopAccessor\nfrom altimeter.core.log import Logger\nfrom altimeter.core.log_events import LogEvent\n\n\n@dataclass(frozen=True)\nclass Accessor:\n    \"\"\"An Accessor consists of a list of MultiHopAccessors.  It provides a method `get_session`\n    which will iterate through the MultiHopAccessors until a session can be obtained to\n    a target account.  If an Accessor has no MultiHopAccessors it simply uses the local\n    session to attempt to access the account. If the session does not match the requested\n    target account id, ValueError is thrown.\n\n    Args:\n        multi_hop_accessors: List of MultiHopAccessors\n        credentials_cache: AWSCredentialsCache\n    \"\"\"\n\n    multi_hop_accessors: List[MultiHopAccessor]\n    credentials_cache: Optional[AWSCredentialsCache] = None\n\n    def get_session(self, account_id: str, region_name: Optional[str] = None) -> boto3.Session:\n        \"\"\"Get a boto3 session for a given account.\n\n        Args:\n            account_id: target account id\n            region_name: session region name\n\n        Returns:\n            boto3.Session object\n        \"\"\"\n        logger = Logger()\n        errors = []\n        if self.multi_hop_accessors:\n            for mha in self.multi_hop_accessors:  # pylint: disable=not-an-iterable\n                with logger.bind(auth_accessor=str(mha)):\n                    try:\n                        session = mha.get_session(\n                            account_id=account_id,\n                            region_name=region_name,\n                            credentials_cache=self.credentials_cache,\n                        )\n                        return session\n                    except Exception as ex:\n                        errors.append(ex)\n                        logger.debug(event=LogEvent.AuthToAccountFailure, exception=str(ex))\n            raise AccountAuthException(f\"Unable to access {account_id} using {str(self)}: {errors}\")\n        # local run mode\n        session = boto3.Session(region_name=region_name)\n        sts_client = session.client(\"sts\")\n        sts_account_id = sts_client.get_caller_identity()[\"Account\"]\n        if sts_account_id != account_id:\n            raise ValueError(f\"BUG: sts_account_id {sts_account_id} != {account_id}\")\n        return session\n\n    def __str__(self) -> str:\n        return \",\".join(\n            [str(mha) for mha in self.multi_hop_accessors]  # pylint: disable=not-an-iterable\n        )\n\n    @classmethod\n    def from_dict(\n        cls: Type[\"Accessor\"], data: Dict[str, Any], cache_creds: bool = True\n    ) -> \"Accessor\":\n        mhas = data.get(\"multi_hop_accessors\", [])\n        credentials_cache = None\n        credentials_cache_dict = data.get(\"credentials_cache\")\n        if credentials_cache_dict is None:\n            if cache_creds:\n                credentials_cache = AWSCredentialsCache()\n        else:\n            credentials_cache = AWSCredentialsCache.from_dict(credentials_cache_dict)\n        return cls(\n            multi_hop_accessors=[MultiHopAccessor.from_dict(mha) for mha in mhas],\n            credentials_cache=credentials_cache,\n        )\n\n    @classmethod\n    def from_file(cls: Type[\"Accessor\"], filepath: Path, cache_creds: bool = True) -> \"Accessor\":\n        \"\"\"Create an Accessor from json content in a file\n\n        Args:\n            filepath: Path to json accessor definition\n\n        Returns:\n            Accessor\n        \"\"\"\n        with filepath.open(\"r\") as fp:\n            config_dict = json.load(fp)\n        if cache_creds:\n            credentials_cache = AWSCredentialsCache()\n            config_dict[\"credentials_cache\"] = credentials_cache.to_dict()\n        return cls.from_dict(config_dict)\n\n    def to_dict(self) -> Dict[str, Any]:\n        credentials_cache_dict = (\n            None if self.credentials_cache is None else self.credentials_cache.to_dict()\n        )\n        return {\n            \"multi_hop_accessors\": [mha.to_dict() for mha in self.multi_hop_accessors],\n            \"credentials_cache\": credentials_cache_dict,\n        }\n","sub_path":"altimeter/aws/auth/accessor.py","file_name":"accessor.py","file_ext":"py","file_size_in_byte":4514,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"457592737","text":"c = ('\\033[m',  # cor 0 - sem cores;\n     '\\033[0;30;41m',  # cor 1 - vermelho;\n     '\\033[0;30;42m',  # cor 2 - verde;\n     '\\033[0;30;43m',  # cor 3 - amarelo;\n     '\\033[0;30;44m',  # cor 4 - azul;\n     '\\033[0;30;45m',  # cor 5 - roxo;\n     '\\033[7;30'  # cor 6 - branco.\n     )\n\n\ndef ajuda(com):\n    título(f'Acessando o manual do comando \\'{com}\\'', 4)\n    help(com)\n\n\ndef título(msg, cor=0):\n    tam = len(msg) + 4\n    print(f'{c[cor]}', end='')\n    print('~' * tam)\n    print(f\"  {msg}\")\n    print('~' * tam)\n    print(f\"{c[0]}\", end='')\n\n\n# principal:\ncomando = ''\nwhile True:\n    título(\"Sistema de ajuda Pyhelp.\", 1)\n    comando = str(input(\"Função ou biblioteca > \"))\n    if comando.upper() == 'FIM':\n        break\n    else:\n        ajuda(comando)\n    título(\"Até logo!\", 5)\n","sub_path":"Estruturas/Funções/manual interativo.py","file_name":"manual interativo.py","file_ext":"py","file_size_in_byte":794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"465177648","text":"\"\"\"Debug logging support.\"\"\"\n\nimport sublime_plugin\nimport time\n\n\nlogs = {}\n\n\nclass WindowLog:\n\n    \"\"\"Collection of log messages tied to a window.\"\"\"\n\n    view = None\n\n    def __init__(self):\n        self.messages = []\n\n    def clear(self):\n        self.messages.clear()\n        if self.view:\n            self.view.run_command(\"select_all\")\n            self.view.run_command(\"right_delete\")\n\n    def add_message(self, msg, args):\n        if self.messages:\n            previous_time = self.messages[-1].time\n        else:\n            previous_time = None\n        lm = LogMessage(msg, args, previous_time)\n        self.messages.append(lm)\n        self._display_message(lm)\n\n    def _display_message(self, msg):\n        if self.view:\n            text = msg.render()\n            self.view.run_command('append', {'characters': text,\n                                             'scroll_to_end': True})\n\n    def open_view(self, window):\n        view = window.new_file()\n        view.set_scratch(True)\n        view.settings().set('rust_log_view', window.id())\n        view.settings().set('word_wrap', True)\n        view.set_name('Rust Enhanced Debug Log')\n        self.view = view\n        for m in self.messages:\n            self._display_message(m)\n\n\nclass LogMessage:\n    def __init__(self, msg, args, previous_time):\n        self.msg = msg\n        self.args = args\n        self.previous_time = previous_time\n        self.time = time.time()\n\n    def render(self):\n        if self.previous_time is None:\n            last_time = '+0.000'\n        else:\n            last_time = '+%.3f' % (self.time - self.previous_time,)\n        if self.args:\n            rendered = self.msg % self.args\n        else:\n            rendered = self.msg\n        return '%s %s\\n' % (last_time, rendered.rstrip())\n\n\ndef critical(window, msg, *args):\n    \"\"\"Add a log message and display it to the console.\"\"\"\n    log(window, msg, *args)\n    if args:\n        print(msg % args)\n    else:\n        print(msg)\n\n\ndef log(window, msg, *args):\n    \"\"\"Add a log message.\"\"\"\n    global logs\n    wlog = logs.setdefault(window.id(), WindowLog())\n    wlog.add_message(msg, args)\n\n\ndef clear_log(window):\n    \"\"\"Clear log messages.\"\"\"\n    try:\n        logs[window.id()].clear()\n    except KeyError:\n        pass\n\n\nclass RustOpenLog(sublime_plugin.WindowCommand):\n\n    \"\"\"Opens a view to display log messages generated by the Rust Enhanced\n    plugin.\"\"\"\n\n    def run(self):\n        wlog = logs.setdefault(self.window.id(), WindowLog())\n        if wlog.view:\n            self.window.focus_view(wlog.view)\n        else:\n            wlog.open_view(self.window)\n\n\nclass RustLogEvent(sublime_plugin.ViewEventListener):\n\n    @classmethod\n    def is_applicable(cls, settings):\n        return settings.has('rust_log_view')\n\n    def on_pre_close(self):\n        try:\n            wlog = logs[self.view.settings().get('rust_log_view')]\n        except KeyError:\n            return\n        else:\n            wlog.view = None\n","sub_path":"rust/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":2967,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"560221858","text":"import socket\nimport struct\nimport json\nimport select\nfrom enum import Enum\n\nclass Action(Enum):\n\tLOGIN = 1\n\tLOGOUT = 2\n\tMOVE = 3\n\tUPGRADE = 4\n\tTURN = 5\n\tPLAYER = 6\n\tMAP = 10\n\nclass Result(Enum):\n\tOKEY = 0\n\tBAD_COMMAND = 1\n\tRESOURCE_NOT_FOUND = 2\n\tACCESS_DENIED = 3\n\tNOT_READY = 4\n\tTIMEOUT = 5\n\tINTERNAL_SERVER_ERROR = 500\n\nclass Socket():\n\turl='wgforge-srv.wargaming.net'\n\tport=443\n\t\n\tdef __init__(self):\n\t\tprint(\"sock init\")\n\t\tself.sock=socket.socket()\n\t\tself.sock.connect((self.url, self.port))\n\t\t\n\tdef __del__(self):\n\t\tprint(\"destroyed\")\n\t\tself.logout()\n\t\tself.sock.close()\n\t\n\tdef inter(self, data):\n\t\tchoice=data[0]\n\t\tif choice==Action.LOGIN.value:\n\t\t\treturn self.login(data[1])\n\t\telif choice==Action.LOGOUT.value:\n\t\t\treturn self.logout()\n\t\telif choice==Action.MOVE.value:\n\t\t\treturn self.move(data[1], data[2], data[3])\n\t\telif choice==Action.UPGRADE.value:\n\t\t\treturn self.upgrade(data[1], data[2])\n\t\telif choice==Action.TURN.value:\n\t\t\treturn self.nextTurn()\n\t\telif choice==Action.PLAYER.value:\n\t\t\treturn self.player()\n\t\telif choice==Action.MAP.value:\n\t\t\treturn self.getmap(data[1])\n\t\telse:\n\t\t\tprint(\"Unknown command\")\n\t\n\tdef action(self, tmp, string):\n\t\tbytes = tmp.to_bytes(4, byteorder='little') + len(string).to_bytes(4, byteorder='little') + string.encode()\n\t\tself.sock.send(bytes)\n\t\tresponse = self.sock.recv(4)\n\t\tif response:\n\t\t\tresponse = struct.unpack('L', response)[0]\n\t\telse:\n\t\t\treturn response\n\t\tprint(response)\n\t\tif response != Result.OKEY.value:\n\t\t\twhile True:\n\t\t\t\tready, a, b = select.select([self.sock], [], [], 10)\n\t\t\t\tif len(ready)==0: break\n\t\t\t\tself.sock.recv(1)\n\t\t\tself.errorReport(response)\n\t\t\treturn response\n\t\tdatalen = self.sock.recv(4)\n\t\tdatalen = struct.unpack('L', datalen)[0]\n\t\tif datalen:\n\t\t\tprint(\"\\nlen(data)\\n\")\n\t\t\tprint(datalen)\n\t\t\tself.sock.settimeout(10)\n\t\t\tdata = self.sock.recv(datalen)\n\t\t\twhile datalen > len(data):\n\t\t\t\tdata += self.sock.recv(datalen)\n\t\t\tdata = data.decode('utf8').replace(\"\\n\", '').replace(\" \", '')\n\t\t\tprint(data)\n\t\t\tdata = json.loads(data)\n\t\t\treturn response, data\n\t\treturn response\n\t\n\tdef login(self, name):\n\t\tstring = json.dumps({\"name\": name})\n\t\treturn self.action(Action.LOGIN.value, string)\n\n\tdef player(self):\n\t\treturn self.action(Action.PLAYER.value, '')\n\t\n\tdef logout(self):\n\t\tdata= self.action(Action.LOGOUT.value, '')\n\t\treturn data\n\t\n\tdef getmap(self, layer):\n\t\tprint(layer)\n\t\tstring = json.dumps({\"layer\": layer})\n\t\treturn self.action(Action.MAP.value, string)\n\t\n\tdef nextTurn(self):\n\t\treturn self.action(Action.TURN.value, '')\n\t\n\tdef move(self, line_idx, speed, train_idx):\n\t\tstring = json.dumps({\"line_idx\": line_idx, \"speed\": speed, \"train_idx\": train_idx})\n\t\treturn self.action(Action.MOVE.value, string)\n\t\n\tdef upgrade(self, posts, trains):\n\t\tposts=[posts]\n\t\ttrains=[trains]\n\t\tstring = json.dumps({\"posts\": posts, \"trains\": trains})\n\t\treturn self.action(Action.UPGRADE.value, string)\n\t\t\n\tdef errorReport(self, code):\n\t\tif code == Result.BAD_COMMAND.value:\n\t\t\tprint(\"Bad command\")\n\t\telif code == Result.RESOURCE_NOT_FOUND.value:\n\t\t\tprint(\"Resource not found\")\n\t\telif code == Result.ACCESS_DENIED.value:\n\t\t\tprint(\"Access denied\")\n\t\telif code == Result.NOT_READY.value:\n\t\t\tprint(\"Not ready\")\n\t\telif code == Result.TIMEOUT.value:\n\t\t\tprint(\"Timeout\")\n\t\telse:\n\t\t\tprint(\"Internal server error\")","sub_path":"src/serverinteraction.py","file_name":"serverinteraction.py","file_ext":"py","file_size_in_byte":3264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"486456700","text":"#!/usr/bin/env python\n\nimport time\nimport math\nimport sys\nimport os\n\nsys.path.append(os.path.join(os.path.dirname(__file__), \"../core\"))\n\nfrom canvas import *\nfrom map import *\nfrom particle_controller import *\nfrom robot import *\nfrom ultrasonic_sensor import *\nfrom wayfinder import *\n\ndef add_walls_to_map(mymap):\n    mymap.add_wall((0, 0, 0, 168));        # a\n    mymap.add_wall((0, 168, 84, 168));     # b\n    mymap.add_wall((84, 126, 84, 210));    # c\n    mymap.add_wall((84, 210, 168, 210));   # d\n    mymap.add_wall((168, 210, 168, 84));   # e\n    mymap.add_wall((168, 84, 210, 84));    # f\n    mymap.add_wall((210, 84, 210, 0));     # g\n    mymap.add_wall((210, 0, 0, 0));        # h\n\n\nif __name__ == \"__main__\":\n\n    navigatePoints = [\n        # (84, 30), # Initial position\n        (180, 30),\n        (180, 54),\n        (126, 54),\n        (126, 168),\n        (127, 168), # We use this to look around, 90 degrees. Will be removed in the future\n        (126, 126),\n        (30, 54),\n        (84, 54),\n        (84, 30)\n    ]\n\n    canvas = Canvas()\n    bricky = Robot()\n    wayfinder = Wayfinder(bricky, 84, 30, 0)\n\n\n    with UltrasonicSensor(bricky.get_interface()) as ultrasonicSensor:\n        mymap = Map(navigatePoints)\n        add_walls_to_map(mymap)\n        mymap.draw(canvas);\n\n        wayfinder.particle_controller.draw_particles(canvas)\n\n        for (x, y) in navigatePoints:\n\n            gotToWaypoint = False\n            while not gotToWaypoint:\n\n                wayfinder.rotate_to_navigate_to_waypoint(x, y)\n                wayfinder.particle_controller.draw_particles(canvas)\n                # time.sleep(1)\n\n                sonarReading = ultrasonicSensor.get_median_reading()\n                wayfinder.particle_controller.update_particle_weights(sonarReading, mymap)\n                wayfinder.particle_controller.draw_particles(canvas)\n                # time.sleep(1)\n\n                wayfinder.rotate_to_navigate_to_waypoint(x, y)\n                gotToWaypoint = wayfinder.move_to_navigate_to_waypoint(x, y)\n                wayfinder.particle_controller.draw_particles(canvas)\n                # time.sleep(1)\n\n                sonarReading = ultrasonicSensor.get_median_reading()\n                wayfinder.particle_controller.update_particle_weights(sonarReading, mymap)\n                wayfinder.particle_controller.draw_particles(canvas)\n                # time.sleep(1)\n\n                time.sleep(0.1)\n","sub_path":"submissionScripts/task4/navigationTest.py","file_name":"navigationTest.py","file_ext":"py","file_size_in_byte":2428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"590029431","text":"import os\nimport random\nimport requests\n\nfrom lxml import html\n\nCONFIG_FILE = os.path.join(os.path.dirname(__file__), '..','config_service_client.py')\nconfig = CONFIG_FILE.ConfigService().get(\"newsPipelineConfig\").get(\"scrapers\")\n\nGET_CNN_NEWS_XPATH = config.get(\"GET_CNN_NEWS_XPATH\")\n\nUSER_AGENTS_FILE = os.path.join(os.path.dirname(__file__), 'user_agents.txt')\nUSER_AGENTS =[]\n\nwith open(USER_AGENTS_FILE, 'rb') as uaf:\n    for ua in uaf.readlines():\n        if ua:\n            USER_AGENTS.append(ua.strip()[1:-1])\n\ndef getHeaders():\n    ua = random.choice(USER_AGENTS)\n    headers={\n        \"Connection\": \"close\",\n        \"User-Agent\": ua\n    }\n\ndef extract_news(news_url):\n    session_requests = requests.session()\n    response = session_requests.get(news_url, headers=getHeaders())\n    \n    news={}\n\n    try:\n        tree = html.fromstring(response.content)\n        news = tree.xpath(GET_CNN_NEWS_XPATH)\n        news = ''.join(news)\n    except Exception:\n        return{}\n\n    return news","sub_path":"week2/news_pipeline/scrapers/cnn_news_scraper.py","file_name":"cnn_news_scraper.py","file_ext":"py","file_size_in_byte":994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"65493524","text":"def permute(A1,A2):\r\n    if len(A1)!=len(A2):\r\n        return False\r\n    A1.sort()\r\n    A2.sort()\r\n    if A1==A2:\r\n        return True\r\n    else:\r\n        return False\r\n\r\nA1=[int(x) for x in input().strip().split()]\r\nA2=[int(x) for x in input().strip().split()]\r\nprint(permute(A1,A2))\r\n","sub_path":"Array and List Based Codes/Permutation/permute.py","file_name":"permute.py","file_ext":"py","file_size_in_byte":286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"282999208","text":"import cv2\nimport matplotlib.pyplot as plt\n#%matplotlib inline\n\n# 이미지 읽기\nimg = cv2.imread('img1.jpg')\nplt.imshow(img)\nplt.title('RGB')\nplt.show()\n\n# 이미지를 grayscale로 변환\nimg_gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\nplt.imshow(img_gray,cmap='gray')\nplt.title('gray_scale')\nplt.show()\nprint(img_gray.shape)\n\n# 2중 for문을 활용한 histogram 계산\nhist = [0]*256\nh,w = img_gray.shape\nfor x in range(w):\n    for y in range(h):\n        hist[img_gray[y][x]]+=1\nplt.title('for')\nplt.bar(range(256),hist)\nplt.show()\n\n# cv2를 활용한 histogram 계산\nhist_cv2 = cv2.calcHist([img_gray],[0],None,[256],[0,256])\nhist_cv2 = hist_cv2.reshape(-1)\nplt.bar(range(256),hist_cv2)\nplt.title('cv2')\nplt.show()","sub_path":"Study_main/SW-AI/Pattern_Recognization/2주차/histogram.py","file_name":"histogram.py","file_ext":"py","file_size_in_byte":722,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"423520722","text":"import tkinter as tk\r\nfrom tkinter import *\r\nfrom tkinter import ttk\r\nimport random\r\nimport time\r\n\r\ntitle = \"FrameWork\"\r\n\r\nclass CreateToolTip(object):\r\n\r\n    def __init__(self, widget, text='widget info'):\r\n        self.widget = widget\r\n        self.text = text\r\n        self.widget.bind(\"<Enter>\", self.enter)\r\n        self.widget.bind(\"<Leave>\", self.close)\r\n\r\n    def enter(self, event=None):\r\n        x = y = 0\r\n        x, y, cx, cy = self.widget.bbox(\"insert\")\r\n        x += self.widget.winfo_rootx() + 45\r\n        y += self.widget.winfo_rooty() + 25\r\n        # creates a toplevel window\r\n        self.tooltip = tk.Toplevel(self.widget)\r\n        # Leaves only the label and removes the app window\r\n        self.tooltip.wm_overrideredirect(True)\r\n        self.tooltip.wm_geometry(\"+%d+%d\" % (x, y))\r\n        label = tk.Label(self.tooltip, text=self.text, relief=\"flat\", justify='right', borderwidth=1, font=(\"SEGOE UI\", \"8\", \"normal\"))\r\n        label.pack(ipady=1)\r\n\r\n    def close(self, event=None):\r\n        if self.tooltip != True:\r\n            self.tooltip.destroy()\r\n\r\nclass main_frame(tk.Tk):\r\n\r\n    def __init__(self, *args, **kwargs):\r\n\r\n        tk.Tk.__init__(self, *args, **kwargs)\r\n        container = tk.Frame(self)\r\n        container.pack(side=\"top\", fill=\"both\", expand = True)\r\n        container.grid_rowconfigure(0, weight=1)\r\n        container.grid_columnconfigure(0, weight=1)\r\n\r\n        self.frames = {}\r\n\r\n        for F in (IntroPage1,IntroPage2,IntroPage3):\r\n\r\n            frame = F(container, self)\r\n            self.frames[F] = frame\r\n            frame.grid(row=0, column=0, sticky=\"nsew\")\r\n\r\n        self.show_frame(IntroPage1)\r\n\r\n    def show_frame(self, navigation): \r\n\r\n        frame = self.frames[navigation]\r\n        frame.tkraise()\r\n\r\nclass IntroPage1(tk.Frame):\r\n\r\n    def __init__(self, parent, controller):\r\n\r\n        tk.Frame.__init__(self, parent)\r\n        canvas_main = tk.Canvas(self)\r\n        self.canvas_main = canvas_main\r\n        canvas_layer1 = tk.Canvas(self)\r\n        self.canvas_layer1 = canvas_layer1\r\n        exit_ = ttk.Button(self, text=\"Exit\",command=quit)\r\n        exit_.grid(row=0,column=0)\r\n        Next = ttk.Button(self, text=\"Next\",command=lambda:controller.show_frame(IntroPage2))\r\n        Next.grid(row=0, column=1)\r\n        Page1_Label = ttk.Label(self, text=\"Page1\")\r\n        Page1_Label.grid(row=1, column=0)\r\n        CreateToolTip(exit_, f\"Exit {title}\")\r\n        CreateToolTip(Next, \"Go to Page 2\")\r\n        CreateToolTip(Page1_Label,\"Info\")\r\n\r\nclass IntroPage2(tk.Frame):\r\n\r\n    def __init__(self, parent, controller):\r\n\r\n        tk.Frame.__init__(self, parent)\r\n        back = ttk.Button(self, text=\"Back\",command=lambda:controller.show_frame(IntroPage1))\r\n        back.grid(row=0,column=0)\r\n        Next = ttk.Button(self, text=\"Next\", command=lambda:controller.show_frame(IntroPage3))\r\n        Next.grid(row=0,column=1)\r\n        Page2_Label = tk.Label(self, text=\"Page2\")\r\n        Page2_Label.grid(row=1,column=0)\r\n        CreateToolTip(back, \"Go back to Page 1\")\r\n        CreateToolTip(Next, \"Go to Page 3\")\r\n        CreateToolTip(Page2_Label,\"Info\")\r\n\r\nclass IntroPage3(tk.Frame):\r\n\r\n    def __init__(self, parent, controller):\r\n\r\n        tk.Frame.__init__(self, parent)\r\n        back = ttk.Button(self, text=\"Back\", command=lambda:controller.show_frame(IntroPage2))\r\n        back.pack(side='left',fill=X)\r\n        Home = ttk.Button(self, text=\"Home\", command=lambda:controller.show_frame(IntroPage1))\r\n        Home.pack(side='left',fill=X) \r\n        Page3_Label = tk.Label(self, text=\"Page3\")\r\n        Page3_Label.pack(side='bottom',fill=X,anchor=tk.W)\r\n        CreateToolTip(Home, \"Go Home\")\r\n        CreateToolTip(back, \"Go back to Page 2\")\r\n        CreateToolTip(Page3_Label,\"Info\")\r\n\r\napp = main_frame()\r\napp.geometry(\"640x640\")\r\napp.title(str(title))\r\napp.wm_state('zoomed')\r\napp.configure(bg=\"white\")\r\napp.mainloop()\r\n","sub_path":"Tkinter Reference/Tkinter_Navigation_Frame_Work.py","file_name":"Tkinter_Navigation_Frame_Work.py","file_ext":"py","file_size_in_byte":3899,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"379082309","text":"import pygame\r\nfrom pygame import display\r\nfrom pygame import surface\r\nfrom pygame import draw \r\nfrom pygame.locals import *\r\nfrom buttons import button\r\nimport os\r\nimport sys\r\nimport random\r\nfrom pygame import mixer\r\n\r\ndef run_numbers():\r\n\r\n    pygame.init()\r\n    clock = pygame.time.Clock()\r\n\r\n    # Loading the song\r\n    mixer.music.load(\"clock_sound.mp3\")\r\n  \r\n    # Setting the volume\r\n    mixer.music.set_volume(2)\r\n\r\n    WINDOW_SIZE = (1920,1080)\r\n    screen = pygame.display.set_mode(WINDOW_SIZE)\r\n\r\n    background = pygame.Rect(0,0,1920,1080)\r\n    refresh_button = button(3,3,200,50,color=(200,100,0),text='Refresh')\r\n    scramble_button = button(1920-3-200,3,200,50,color=(200,100,0),text='Scramble')\r\n    menu_button = button(3,1080-50-3,200,50,color=(200,100,0),text='Menu')\r\n\r\n    numbers =[]\r\n    hide_num_buttons = False\r\n    can_scramble = True\r\n    show_target_number = False\r\n    draw_num = False\r\n    pick_target_number = False\r\n    ticker = -1\r\n    start_countdown = False\r\n    countdown_ticker = -1\r\n    large_numbers = 10 #just some random number that's not 0-4\r\n    ##Creating the 'how many large numbers' buttons##\r\n\r\n    how_many_large_buttons = []\r\n    for i in range(5):\r\n            how_many_large_buttons.append(\r\n                button(245+20+(1920-450)*i/5,885,170,170,color=(150,150,255),text=f'{i}')\r\n            )\r\n    \r\n    #Function to pick numbers#\r\n    def pick_numbers(num_large_numbers):\r\n        large_numbers = [25,50,75,100]\r\n        small_numbers = [1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10]\r\n        temp_numbers = []\r\n\r\n        for i in range(num_large_numbers):\r\n            i = random.randint(0,len(large_numbers)-1)\r\n            temp_numbers.append(large_numbers[i])\r\n            large_numbers.pop(i)\r\n        num_small_numbers = 6- num_large_numbers\r\n        for j in range(num_small_numbers):\r\n            i = random.randint(0,len(small_numbers)-1)\r\n            temp_numbers.append(small_numbers[i])\r\n            small_numbers.pop(i)\r\n\r\n        return temp_numbers\r\n\r\n    while True:\r\n\r\n        mouse_pos = pygame.mouse.get_pos()\r\n\r\n        for event in pygame.event.get():\r\n            if event.type == KEYDOWN:\r\n                if event.key == K_ESCAPE:\r\n                    pygame.quit()\r\n                    sys.exit()\r\n            if event.type == QUIT:\r\n                pygame.quit()\r\n                sys.exit()\r\n            if event.type == MOUSEBUTTONDOWN:\r\n                if refresh_button.isOver(mouse_pos):\r\n                    run_numbers()\r\n                    return\r\n                if menu_button.isOver(mouse_pos):\r\n                    return\r\n                for i in range(5):\r\n                    button_ = how_many_large_buttons[i]\r\n                    if button_.isOver(mouse_pos) and not hide_num_buttons:\r\n                        large_numbers = i\r\n                        numbers = pick_numbers(large_numbers)\r\n                        hide_num_buttons = True\r\n                if can_scramble and scramble_button.isOver(mouse_pos):\r\n                    pick_target_number = True\r\n                    can_scramble = False\r\n\r\n        ##Calculating####\r\n        #Scrambling#\r\n        if pick_target_number:\r\n            ticker +=1\r\n            x = 97\r\n            if ticker %3 == 0 and ticker <=x:\r\n                show_target_number = True\r\n                target_number = random.randint(101,999)\r\n            if ticker == x+1:\r\n                target_number = random.randint(101,999)\r\n            if ticker == x+120:\r\n                start_countdown = True\r\n\r\n        #Handling the countdown#\r\n        if start_countdown:\r\n            countdown_ticker+=1\r\n\r\n            '''Handling the countdown'''\r\n            if countdown_ticker%60 == 0 and countdown_ticker < 60*30-1:\r\n                if countdown_ticker == 0:\r\n                    mixer.music.play()\r\n                draw_num = True\r\n                num_button = button(1600,350,100,100,text=f'{30 - countdown_ticker//60}')\r\n            if countdown_ticker >= 60*30:\r\n                draw_num = False\r\n        else:\r\n            countdown_ticker=-1\r\n\r\n        #####Drawing######\r\n\r\n        #Background#\r\n        pygame.draw.rect(screen,color=(0,100,255),rect=background)\r\n        #Draw the number thingee#\r\n        pygame.draw.rect(screen,color=(0,0,0),rect=Rect(1920/2 -600/2,100,600,200))\r\n        if show_target_number:\r\n            font = pygame.font.SysFont('comicsans', size=200)\r\n            text = font.render(str(target_number), 1, (255,255,255))\r\n            screen.blit(text, (1920/2 -600/2 + (600/2 - text.get_width()/2), 100 + (200/2 - text.get_height()/2)))\r\n        #Number holders\r\n\r\n        for i in range(6):\r\n            pygame.draw.rect(screen,color=(150,150,255),rect=Rect(245+(1920-450)*i/6,645,170,170)) # The outline\r\n            pygame.draw.rect(screen,color=(255,255,255),rect=Rect(250+(1920-450)*i/6,650,160,160))\r\n\r\n        #Now draw the selected numbers on\r\n        if len(numbers) >= 1:\r\n            for i in range(len(numbers)):\r\n                number = button(250+(1920-450)*i/6,650,160,160,text=str(numbers[i]))\r\n                number.draw_text(screen,color=(0,0,0),size=100)\r\n\r\n        ### Drawing the 'how many large numbers' buttons ###\r\n        if not hide_num_buttons:\r\n            for button_ in how_many_large_buttons:\r\n                button_.draw_wtext(screen,font_size=100)\r\n\r\n        ##Drawing countdown##\r\n\r\n        if draw_num:\r\n            num_button.draw_text(screen,size=300)\r\n\r\n        ##Refresh button##\r\n        refresh_button.draw_wtext(screen)\r\n        #Scramble button#\r\n        scramble_button.draw_wtext(screen)\r\n        #Menu button#\r\n        menu_button.draw_wtext(screen)\r\n\r\n        clock.tick(60)\r\n        pygame.display.update()\r\n","sub_path":"Countdown/countdown_numbers.py","file_name":"countdown_numbers.py","file_ext":"py","file_size_in_byte":5725,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"440165836","text":"import scipy.sparse as sp\nimport scipy.sparse.linalg as la\nimport numpy as np\nimport math as m\nimport time\nimport sys\nimport os\n\nresultDir = os.environ.get('RESULTS')\nif resultDir == None :\n    print (\"WARNING! $RESULTS not set! Attempt to write results will fail!\\n\")\n\nbotConc = float(sys.argv[1])\ntopConc = float(sys.argv[2])\nl = float(sys.argv[3])\nL = np.uint32(int(sys.argv[4]))\nnumVecs = int(sys.argv[5])\nboundMult = float(sys.argv[6])\ntolerance = float(sys.argv[7])\nfileInfo = sys.argv[8]\n\n\nresultsPlace = resultDir+\"/\"+fileInfo+\"/\"\n\nif not os.path.exists(resultsPlace):\n    os.makedirs(resultsPlace)\n\nwith open(resultsPlace+'settings', 'w') as f:\n    f.write('BotConcentration = ' + str(botConc) +'\\n')\n    f.write('TopConcentration = ' + str(topConc) +'\\n')\n    f.write('Lambda = ' + str(l) +'\\n')\n    f.write('SysSize = ' + str(L) +'\\n')\n    f.write('NumVecs = ' + str(numVecs)+'\\n')\n    f.write('BoundMult = ' + str(boundMult)+'\\n')\n    f.write('Tolerance = ' +str(tolerance)+'\\n')\n\nN = np.uint32(2**(L+4))\n\nrateMatrix = sp.lil_matrix((N, N), dtype = np.float64)\ndensityMatrix = sp.lil_matrix((L+4, N), dtype = np.float64)\ncurrentMatrix = sp.lil_matrix((L+3, N), dtype = np.float64)\n\ntopIncRate = (1.0+l)*boundMult*m.sqrt(l*topConc/(1.0-topConc))\ntopOutRate = (1.0+l)*boundMult*m.sqrt(l*(1.0-topConc)/topConc)\nbotIncRate = (1.0+l)*boundMult*m.sqrt(l*botConc/(1.0-botConc))\nbotOutRate = (1.0+l)*boundMult*m.sqrt(l*(1.0-botConc)/botConc)\n\n\nt0 = time.clock()\nfor i in range(0, N):\n    state = format(i, '032b')\n    totLeakage = 0.0\n    for position in range(32 - 4 - L, 32-2 - L):\n        if state[position] == '1':\n            newState = state[:position]+'0'+state[(position+1):]\n#            print state\n#            print newState\n            j = np.uint32(int(newState, 2))\n#            print format(j, '032b')+\"\\n\"\n            rateMatrix[j, i] += botOutRate\n            totLeakage += botOutRate\n            densityMatrix[position-(32-4-L), i] = 1\n        else:\n            newState = state[:position]+'1'+state[(position+1):]\n#            print state\n#            print newState\n            j = np.uint32(int(newState, 2))\n#            print format(j, '032b')+\"\\n\"\n            rateMatrix[j, i] += botIncRate\n            totLeakage += botIncRate\n    for position in range(32-3 - L , 32-1):\n        if state[position] == '1':\n            densityMatrix[position-(32-4-L), i] = 1\n            if state[position-1] == '0':\n                newState = state[:(position-1)]+'1'+'0'+state[(position+1):]\n#                print state\n#                print newState\n                j = np.uint32(int(newState, 2))\n#                print format(j, '032b')+\"\\n\"\n                if state[position+1] == '0':\n                    rateMatrix[j, i] += 1.0\n                    currentMatrix[position - (32-3-L), i] -= 1.0\n                    totLeakage += 1.0\n                else:\n                    rateMatrix[j, i] += l\n                    currentMatrix[position - (32-3-L), i] -= l\n                    totLeakage += l\n            if state[position+1] == '0':\n                newState = state[:(position)]+'0'+'1'+state[(position+2):]\n#                print state\n#                print newState\n                j = np.uint32(int(newState, 2))\n#                print format(j, '032b')+\"\\n\"\n                if state[position-1] == '0':\n                    rateMatrix[j, i] += 1.0\n                    currentMatrix[position + 1 - (32-3-L), i] += 1.0\n                    totLeakage += 1.0\n                else:\n                    rateMatrix[j, i] += l\n                    currentMatrix[position + 1 - (32-3-L), i] += l\n                    totLeakage += l\n    for position in range(32 - 2, 32):\n        if state[position] == '1':\n            densityMatrix[position-(32-4-L), i] = 1\n            newState = state[:position]+'0'+state[(position+1):]\n#            print state\n#            print newState\n            j = np.uint32(int(newState, 2))\n#            print format(j, '032b')+\"\\n\"\n            rateMatrix[j, i] += topOutRate\n            totLeakage += topOutRate\n        else:\n            newState = state[:position]+'1'+state[(position+1):]\n#            print state\n#            print newState\n            j = np.uint32(int(newState, 2))\n#            print format(j, '032b')+\"\\n\"\n            rateMatrix[j, i] += topIncRate\n            totLeakage += topIncRate\n    rateMatrix[i, i] -= totLeakage\n\n\n\n#print(\"RateMatrix created.\")\n\ncscRateMatrix = rateMatrix.tocsc()\ncscDensityMatrix = densityMatrix.tocsc()\ncscCurrentMatrix = currentMatrix.tocsc()\n\n#print(\"RateMatrix reformatted.\")\n\nvalsLR, vecsLR = la.eigs(cscRateMatrix, k=numVecs, tol=tolerance, which='SR', maxiter=100*N)\n#valsSR, vecsSR = la.eigs(cscRateMatrix, k=numVecs, tol=tolerance, which='SR', maxiter=100*N)\n#valsLI, vecsLI = la.eigs(cscRateMatrix, k=numVecs, tol=tolerance, which='LI', maxiter=100*N)\n#valsSI, vecsSI = la.eigs(cscRateMatrix, k=numVecs, tol=tolerance, which='SI', maxiter=100*N)\nerrs = []\nfor index in range(0, numVecs):\n    vecsLR[:, index] = np.sign(vecsLR[N/2, index])*vecsLR[:, index]/(np.linalg.norm(vecsLR[:, index], 1))\n    errs.append(2.0*np.linalg.norm(cscRateMatrix.dot(vecsLR[:, index])-valsLR[index]*vecsLR[:, index], 1)/(np.linalg.norm(cscRateMatrix.dot(vecsLR[:, index]), 1)+np.abs(valsLR[index])*np.linalg.norm(vecsLR[:, index], 1)))\n#    vecsSR[:, index] = np.sign(vecsSR[N/2, index])*vecsSR[:, index]/(np.linalg.norm(vecsSR[:, index], 1))\n#    errs.append(2.0*np.linalg.norm(cscRateMatrix.dot(vecsSR[:, index])-valsSR[index]*vecsSR[:, index], 1)/(np.linalg.norm(cscRateMatrix.dot(vecsSR[:, index]), 1)+np.abs(valsSR[index])*np.linalg.norm(vecsSR[:, index], 1)))\n#    vecsLI[:, index] = np.sign(vecsLI[N/2, index])*vecsLI[:, index]/(np.linalg.norm(vecsLI[:, index], 1))\n#    errs.append(2.0*np.linalg.norm(cscRateMatrix.dot(vecsLI[:, index])-valsLI[index]*vecsLI[:, index], 1)/(np.linalg.norm(cscRateMatrix.dot(vecsLI[:, index]), 1)+np.abs(valsLI[index])*np.linalg.norm(vecsLI[:, index], 1)))\n#    vecsSI[:, index] = np.sign(vecsSI[N/2, index])*vecsSI[:, index]/(np.linalg.norm(vecsSI[:, index], 1))\n#    errs.append(2.0*np.linalg.norm(cscRateMatrix.dot(vecsSI[:, index])-valsSI[index]*vecsSI[:, index], 1)/(np.linalg.norm(cscRateMatrix.dot(vecsSI[:, index]), 1)+np.abs(valsSI[index])*np.linalg.norm(vecsSI[:, index], 1)))\n\nt1 = time.clock()\n\nprint(str(t1-t0)+\"s for computation of this session's eigenpairs.\\n\")\n\n\n\n\n#print(\"So final result for the eigenvalues is \"+str(vals)+\"\\n\")\n#print(\"|Ax-lambda x| = \")\n#for index in range(0, numVecs):\n#    print str(np.linalg.norm(cscRateMatrix.dot(vecs[:, index])-vals[index]*vecs[:, index], 1))+\" with |x| = \"+str(np.linalg.norm(vecs[:, index], 1))\n\n#print(\"\\nThe mean occupation should be:\\n\")\navDens = cscDensityMatrix.dot(vecsLR)\n#print avDens\n#print(\"\\nThe mean current should be:\\n\")\navCurr = cscCurrentMatrix.dot(vecsLR)\n#print avCurr\n\n\nwith open(resultsPlace+'eigenvalues.dat', 'w') as f:\n    for eig in valsLR:\n        f.write(str(np.real(eig))+'\\n')\n\n#with open(resultsPlace+'fullEigenvalues.dat', 'w') as f:\n#    for eig in vals:\n#        f.write(str(eig)+'\\n')\n\nwith open(resultsPlace+'fullEigenvalues.dat', 'w') as f:\n    for eig in valsLR:\n        f.write(str(eig)+'\\n')\n#    for eig in valsSR:\n#        f.write(str(eig)+'\\n')\n#    for eig in valsLI:\n#        f.write(str(eig)+'\\n')\n#    for eig in valsSI:\n#        f.write(str(eig)+'\\n')\n\n\n\n\n#for index in range(0, 1):\n#    with open(resultsPlace+'densVec'+str(index)+'.dat', 'w') as f:\n#        for position in range(0, L+4):\n#            f.write(str(np.real(avDens[position, index]))+'\\n')\n\n#for index in range(0, numVecs):\n#    with open(resultsPlace+'fullDensVec'+str(index)+'.dat', 'w') as f:\n#        for position in range(0, L+4):\n#            f.write(str(avDens[position, index])+' ')\n\n#for index in range(0, 1):\n#    with open(resultsPlace+'currVec'+str(index)+'.dat', 'w') as f:\n#        for position in range(0, L+3):\n#            f.write(str(np.real(avCurr[position, index]))+'\\n')\n\n#for index in range(0, numVecs):\n#    with open(resultsPlace+'fullCurrVec'+str(index)+'.dat', 'w') as f:\n#        for position in range(0, L+3):\n#            f.write(str(avCurr[position, index])+' ')\n\nwith open(resultsPlace+'eigenErrs.dat', 'w') as f:\n    for err in errs:\n        f.write(str(err)+'\\n')\n\n\n\n#with open(resultsPlace+'multiProds.dat', 'w') as f:\n#    for i in range(0, numVecs):\n#        for j in range(0, numVecs):\n#            f.write(str(abs(np.vdot(vecsLR[:, j], vecsLR[:, i]))/(np.linalg.norm(vecsLR[:, j], 2)*np.linalg.norm(vecsLR[:, i], 2)))+' ')\n#        f.write('\\n')\n    \n\n#solvedSoln = la.lsmr(cscRateMatrix, b)\n#print solvedSoln\n#print(\"\\nThe mean occupation should be:\\n\")\n#newAvDens = cscDensityMatrix.dot(solvedSoln)\n#print newAvDens\n#print(\"\\nThe mean current should be:\\n\")\n#newAvCurr = cscCurrentMatrix.dot(solvedSoln)\n#print newAvCurr\n","sub_path":"codes/exact/matStuff/singleCalcSparseSysRep.py","file_name":"singleCalcSparseSysRep.py","file_ext":"py","file_size_in_byte":8862,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"506914483","text":"from numpy import linalg as la\n\n\ndef calculate_davies_bouldin(data):\n    \"\"\"\n    example: https://en.wikipedia.org/wiki/Davies%E2%80%93Bouldin_index\n    :param data:\n    :return:\n    \"\"\"\n    if not data.is_clusterized():\n        return -1\n\n    if data.clusters_amount() == 1:\n        return 1\n\n    db_for_clusters = []\n    for label in data.get_labels_list():\n        db_for_clusters.append(calc_davies_bouldin_for_cluster(data, label))\n\n    return sum(db_for_clusters)/data.clusters_amount()\n\n\ndef calc_davies_bouldin_for_cluster(data, cluster_label):\n    # calculating r_ij_values\n    r_ij_values = []\n\n    if data.clusters_amount() == 1:\n        return 1\n\n    for label in data.get_labels_list():\n        if label == cluster_label:\n            continue\n        r_ij_values.append(r_ij(data, cluster_label, label))\n\n    return max(r_ij_values)\n\n\ndef r_ij(data, ci, cj):\n    \"\"\"\n    A measure of similarity of clusters ci and cj\n    defined as sum of clusters sparsity divided by\n    clusters dissimilarity\n    \"\"\"\n    return (s(data, ci) + s(data, cj))/d(data.cluster(ci), data.cluster(cj))\n\n\ndef s(data, cluster_label):\n    \"\"\"\n    s identifies sparsity of cluster\n    the more average distance between cluster elements is,\n    the more is cluster sparsity\n    \"\"\"\n\n    cluster = data.cluster(cluster_label)\n    center = data.get_cluster_center(cluster_label).as_matrix()\n\n    # calculate all distances in cluster\n    distances = []\n    for index, row in cluster.iterrows():\n        element = row.as_matrix()\n        distances.append(la.norm(center - element))\n\n    return sum(distances) / data.amount_of_elements_in_cluster(cluster_label)\n\n\ndef d(ci, cj):\n    \"\"\"\n    function identifies \"dissimilarity\" of two clusters\n    as minimum distance between elements of those clusters\n    \"\"\"\n    distances = []\n\n    for index, row in ci.iterrows():\n        for another_index, another_row in cj.iterrows():\n            dist = la.norm(row.as_matrix() - another_row.as_matrix())\n            if dist != 0:\n                distances.append(dist)\n\n    return min(distances)\n","sub_path":"Processor/Validity/davies_bolduin.py","file_name":"davies_bolduin.py","file_ext":"py","file_size_in_byte":2067,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"443109937","text":"import numpy as np\nfrom multiagent.core import World, Agent, Landmark, Goal\nfrom multiagent.scenario import BaseScenario\n\n\nclass Scenario(BaseScenario):\n    def make_world(self):\n        world = World()\n\n        # add agents\n        world.agents = [Agent() for i in range(2)]\n        for i, agent in enumerate(world.agents):\n            agent.name = 'agent %d' % i\n            agent.collide = True\n            agent.silent = True\n            agent.size = 0.1\n\n        # add boxes\n        n_box = 1  # One box and pushing to left\n        self.boxes = [Landmark() for _ in range(n_box)]\n        for i, box in enumerate(self.boxes):\n            box.name = 'box %d' % i\n            box.collide = True\n            box.movable = True\n            box.size = 0.25\n            box.initial_mass = 7.\n            box.index = i\n            world.landmarks.append(box)\n\n        # add targets\n        self.targets = [Landmark() for _ in range(2)]\n        for i, target in enumerate(self.targets):\n            target.name = 'target %d' % i\n            target.collide = False\n            target.movable = False\n            target.size = 0.05\n            target.index = i\n            world.landmarks.append(target)\n\n        # add goals (used only for vis)\n        world.goals = [Goal() for i in range(len(world.agents))]\n        for i, goal in enumerate(world.goals):\n            goal.name = 'goal %d' % i\n            goal.collide = False\n            goal.movable = False\n\n        # make initial conditions\n        self.reset_world(world)\n        \n        return world\n\n    def reset_world(self, world):\n        # random properties for agents\n        for i, agent in enumerate(world.agents):\n            if i == 0:\n                agent.color = np.array([1.0, 0.0, 0.0])\n            elif i == 1:\n                agent.color = np.array([0.0, 1.0, 0.0])\n            else:\n                raise NotImplementedError()\n\n            agent.state.p_pos = np.random.uniform(-1, +1, world.dim_p)\n            agent.state.p_vel = np.zeros(world.dim_p)\n            agent.state.c = np.zeros(world.dim_c)\n\n        # random properties for landmarks\n        for i, landmark in enumerate(world.landmarks):\n            landmark.color = np.array([0.25, 0.25, 0.25])\n            landmark.state.p_vel = np.zeros(world.dim_p)\n\n            if \"box\" in landmark.name and landmark.index == 0:\n                landmark.state.p_pos = np.array([-0.25, 0.0])\n            elif \"target\" in landmark.name and landmark.index == 0:\n                landmark.state.p_pos = np.array([-0.85, 0.0])\n            elif \"target\" in landmark.name and landmark.index == 1:\n                landmark.state.p_pos = np.array([+0.85, 0.0])\n            else:\n                raise ValueError()\n\n        # random properties for goals (vis purpose)\n        for i, goal in enumerate(world.goals):\n            goal.color = world.agents[i].color\n            goal.state.p_pos = np.zeros(world.dim_p) - 2  # Initialize outside of the box\n            goal.state.p_vel = np.zeros(world.dim_p)\n\n    def reward(self, agent, world):\n        for i, landmark in enumerate(world.landmarks):\n            if \"box\" in landmark.name and landmark.index == 0:\n                box0 = landmark\n            elif \"target\" in landmark.name and landmark.index == 0:\n                target0 = landmark\n            elif \"target\" in landmark.name and landmark.index == 1:\n                target1 = landmark\n            else:\n                raise ValueError()\n\n        # Move box0 to target0 (One Box)\n        dist = np.sum(np.square(box0.state.p_pos - target0.state.p_pos))\n\n        return -dist\n\n    def observation(self, agent, world):\n        # get positions of all entities\n        entity_pos = []\n        for entity in world.landmarks:\n            entity_pos.append(entity.state.p_pos)\n        assert len(entity_pos) == len(self.boxes) + len(self.targets)\n\n        # Add other agent position\n        other_pos = []\n        for other in world.agents:\n            if other is agent: \n                continue\n            other_pos.append(other.state.p_pos)\n\n        return np.concatenate([agent.state.p_vel] + [agent.state.p_pos] + entity_pos + other_pos)\n","sub_path":"multiagent-particle-envs/multiagent/scenarios/complex_push.py","file_name":"complex_push.py","file_ext":"py","file_size_in_byte":4161,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"528274961","text":"import os\nimport shutil\nimport glob\nimport re\nfrom urllib.parse import urlparse\n\ngh_tree_base_url = 'https://github.com/EnterpriseDB/edb-k8s-doc/tree/master/'\n\ndef rewrite_any_relative_links(line, gh_relative_path):\n    match = re.search(r'\\[.+\\]\\((.+)\\)', line)\n    if match and match[1]:\n        domain = urlparse(match[1]).netloc\n        if not domain and not match[1].startswith('#') and not 'github.com' in match[1]:\n            split_path = gh_relative_path.split('/')\n            dot_dot_count = len(re.findall(r'\\.\\./', match[1]))\n            gh_relative_folder_path = '/'.join(split_path[0:len(split_path) - 1 - dot_dot_count])\n            new_link = gh_tree_base_url+gh_relative_folder_path+'/'+match[1].replace('../', '')\n            return re.sub(r'\\]\\(.+\\)', ']({})'.format(new_link), line)\n    return line\n\ndef process_md(file_path):\n    if file_path.endswith('README.md'):\n        new_file_path = file_path.replace('README.md', 'index.mdx')\n    else:\n        new_file_path = file_path.replace('.md', '.mdx')\n\n    with open(new_file_path, 'w') as new_file:\n        with open(file_path, 'r') as md_file:\n            copying = False\n            previous_line_was_blank = False\n            gh_relative_path = file_path.replace('external_sources/k8s_docs/kubernetes/', '')\n\n            for line in md_file:\n                if copying:\n                    line_blank = line.strip() == ''\n                    if line_blank and not previous_line_was_blank:\n                        previous_line_was_blank = True\n                        new_file.write(line)\n                    elif not line_blank:\n                        previous_line_was_blank = False\n                        new_file.write(\n                            rewrite_any_relative_links(\n                                line.replace('<br>', '<br/>'),\n                                gh_relative_path\n                            )\n                        )\n                if line.startswith('#') and not copying:\n                    copying = True\n                    new_file.write(\"---\\ntitle: '{0}'\\noriginalFilePath: '{1}'\\n---\\n\\n\".format(\n                        re.sub(r'#+ ', '', line).strip(),\n                        gh_relative_path\n                    ))\n        os.remove(file_path)\n\ndef source_k8s_docs():\n    print('Pulling k8s_docs...')\n    os.system('git clone -b master https://github.com/EnterpriseDB/edb-k8s-doc.git external_sources/k8s_docs/kubernetes')\n\n    print('Processing k8s_docs...')\n    files = glob.glob('external_sources/k8s_docs/kubernetes/**/*.md', recursive=True)\n    for file_path in files:\n        process_md(file_path)\n\nif __name__ == '__main__':\n    source_k8s_docs()\n","sub_path":"scripts/source/source_k8s_docs.py","file_name":"source_k8s_docs.py","file_ext":"py","file_size_in_byte":2675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"114800642","text":"# This source code is part of the Biotite package and is distributed\n# under the 3-Clause BSD License. Please see 'LICENSE.rst' for further\n# information.\n\nimport pytest\nimport numpy as np\nfrom biotite.structure.info import residue\nfrom biotite.structure import Atom\nfrom biotite.structure import array\nfrom biotite.structure import BondList\nfrom biotite.structure import charges\n\n\n# First testing partial charge of carbon in the molecules given in table\n# 3 of the publication\n# Since some of the molecules are not available in the Chemical \n# Components Dictionary, the  respective AtomArrays are constructed via\n# Biotite and the coordinates are arbitrarily set to the origin since\n# the decisive information is the BondList\n\n# Creating atoms to build molecules with\ncarbon = Atom([0, 0, 0], element=\"C\")\n\nhydrogen = Atom([0, 0, 0], element =\"H\")\n\noxygen = Atom([0, 0, 0], element =\"O\")\n\nnitrogen = Atom([0, 0, 0], element =\"N\")\n\nfluorine = Atom([0, 0, 0], element =\"F\")\n\n# Building molecules\nmethane = array([carbon, hydrogen, hydrogen, hydrogen, hydrogen])\nmethane.bonds = BondList(\n    methane.array_length(),\n    np.array([[0,1], [0,2], [0,3], [0,4]])\n)\n\n\nethane = array(\n    [carbon, carbon, hydrogen, hydrogen, hydrogen, hydrogen, hydrogen,\n    hydrogen]\n)\nethane.bonds = BondList(\n    ethane.array_length(),\n    np.array([[0,1], [0,2], [0,3], [0,4], [1,5], [1,6], [1,7]])\n)\n\n\nethylene = array(\n    [carbon, carbon, hydrogen, hydrogen, hydrogen, hydrogen]\n)\nethylene.bonds = BondList(\n    ethylene.array_length(),\n    np.array([[0,1], [0,2], [0,3], [1,4], [1,5]])\n)\n\n\nacetylene = array(\n    [carbon, carbon, hydrogen, hydrogen]\n)\nacetylene.bonds = BondList(\n    acetylene.array_length(),\n    np.array([[0,1], [0,2], [1,3]])\n)\n\n\nfluoromethane = array(\n    [carbon, fluorine, hydrogen, hydrogen, hydrogen]\n)\nfluoromethane.bonds = BondList(\n    fluoromethane.array_length(),\n    np.array([[0,1], [0,2], [0,3], [0,4]])\n)\n\n\ndifluoromethane = array(\n    [carbon, fluorine, fluorine, hydrogen, hydrogen]\n)\ndifluoromethane.bonds = BondList(\n    difluoromethane.array_length(),\n    np.array([[0,1], [0,2], [0,3], [0,4]])\n)\n\n\ntrifluoromethane = array(\n    [carbon, fluorine, fluorine, fluorine, hydrogen]\n)\ntrifluoromethane.bonds = BondList(\n    trifluoromethane.array_length(),\n    np.array([[0,1], [0,2], [0,3], [0,4]])\n)\n\n\ntetrafluoromethane = array(\n    [carbon, fluorine, fluorine, fluorine, fluorine]\n)\ntetrafluoromethane.bonds = BondList(\n    tetrafluoromethane.array_length(),\n    np.array([[0,1], [0,2], [0,3], [0,4]])\n)\n\n\nfluoroethane = array(\n    [carbon, carbon, fluorine, hydrogen, hydrogen, hydrogen, \n    hydrogen, hydrogen]\n)\nfluoroethane.bonds = BondList(\n    fluoroethane.array_length(),\n    np.array([[0,1], [0,2], [0,3], [0,4], [1,5], [1,6], [1,7]])\n)\n\n\ntrifluoroethane = array(\n    [carbon, carbon, fluorine, fluorine, fluorine, hydrogen,\n    hydrogen, hydrogen]\n)\ntrifluoroethane.bonds = BondList(\n    trifluoroethane.array_length(),\n    np.array([[0,1], [0,2], [0,3], [0,4], [1,5], [1,6], [1,7]])\n)\n\n\nmethanole = array(\n    [carbon, oxygen, hydrogen, hydrogen, hydrogen, hydrogen]\n)\nmethanole.bonds = BondList(\n    methanole.array_length(),\n    np.array([[0,1], [0,2], [0,3], [0,4], [1,5]])\n)\n\n\ndimethyl_ether = array(\n    [carbon, carbon, oxygen, hydrogen, hydrogen, hydrogen, hydrogen,\n    hydrogen, hydrogen]\n)\ndimethyl_ether.bonds = BondList(\n    dimethyl_ether.array_length(),\n    np.array([[0,2], [1,2], [0,3], [0,4], [0,5], [1,6], [1,7], [1,8]])\n)\n\n\nformaldehyde = array(\n    [carbon, oxygen, hydrogen, hydrogen]\n)\nformaldehyde.bonds = BondList(\n    formaldehyde.array_length(),\n    np.array([[0,1], [0,2], [0,3]])\n)\n\n\nacetaldehyde = array(\n    [carbon, carbon, oxygen, hydrogen, hydrogen, hydrogen, hydrogen]\n)\nacetaldehyde.bonds = BondList(\n    acetaldehyde.array_length(),\n    np.array([[0,1], [1,2], [0,3], [0,4], [0,5], [1,6]])\n)\n\n\nacetone = array(\n    [carbon, carbon, carbon, oxygen, hydrogen, hydrogen, hydrogen,\n    hydrogen, hydrogen, hydrogen]\n)\nacetone.bonds = BondList(\n    acetone.array_length(),\n    np.array([[0,1], [1,2], [1,3], [0,4], [0,5], [0,6], [2,7], [2,8],\n    [2,9]])\n)\n\n\nhydrogen_cyanide = array(\n    [carbon, nitrogen, hydrogen]\n)\nhydrogen_cyanide.bonds = BondList(\n    hydrogen_cyanide.array_length(),\n    np.array([[0,1], [0,2]])\n)\n\n\nacetonitrile = array(\n    [carbon, carbon, nitrogen, hydrogen, hydrogen, hydrogen]\n)\nacetonitrile.bonds = BondList(\n    acetonitrile.array_length(),\n    np.array([[0,1], [1,2], [0,3], [0,4], [0,5]])\n)\n\n# For this purpose, parametrization via pytest is performed\n@pytest.mark.parametrize(\"molecule, expected_results\", [\n    (methane, (-0.078,)),\n    (ethane, (-0.068, -0.068)),\n    (ethylene, (-0.106, -0.106)),\n    (acetylene, (-0.122, -0.122)),\n    (fluoromethane, (0.079,)),\n    (difluoromethane, (0.23,)),\n    (trifluoromethane, (0.38,)),\n    (tetrafluoromethane, (0.561,)),\n    (fluoroethane, (0.087, -0.037)),\n    (trifluoroethane, (0.387, 0.039)),\n    (methanole, (0.033,)),\n    (dimethyl_ether, (0.036, 0.036)),\n    (formaldehyde, (0.115,)),\n    (acetaldehyde, (-0.009, 0.123)),\n    (acetone, (-0.006, 0.131, -0.006)),\n    (hydrogen_cyanide, (0.051,)),\n    (acetonitrile, (0.023, 0.06))\n])\ndef test_partial_charges(molecule, expected_results):\n    \"\"\"\n    Test whether the partial charges of the carbon atoms comprised in\n    the molecules given in table 3 of the publication computed in this\n    implementation correspond to the values given in the publication\n    within a certain tolerance range.\n    \"\"\"\n    charges = charges.partial_charges(molecule)\n    assert charges[molecule.element == \"C\"].tolist() == \\\n        pytest.approx(expected_results, abs=1e-2)\n\n\n@pytest.mark.parametrize(\"molecule\", [\n    (methane),\n    (ethane),\n    (ethylene),\n    (acetylene),\n    (fluoromethane),\n    (difluoromethane),\n    (trifluoromethane),\n    (tetrafluoromethane),\n    (fluoroethane),\n    (trifluoroethane),\n    (methanole),\n    (dimethyl_ether),\n    (formaldehyde),\n    (acetaldehyde),\n    (acetone),\n    (hydrogen_cyanide),\n    (acetonitrile)\n])\ndef test_total_charge_zero(molecule):\n    \"\"\"\n    In the case of the 17 molecules given in table 3, it is verified\n    whether the sum of all partial charges equals the sum\n    of all formal charges (in our case zero since we are exclusively\n    dealing with uncharged molecules).\n    \"\"\"\n    total_charge = np.sum(charges.partial_charges(molecule))\n    assert total_charge == pytest.approx(0, abs=1e-15)","sub_path":"tests/structure/test_charges.py","file_name":"test_charges.py","file_ext":"py","file_size_in_byte":6447,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"461498919","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Oct 19 18:23:00 2017\n\n@author: ajaver\n\"\"\"\nimport os\nimport pandas as pd\nimport numpy as np\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import cross_val_score\nimport random\nimport multiprocessing as mp\n\nimport time\nimport matplotlib.pylab as plt\n\ncol2ignore = ['Unnamed: 0', 'exp_name', 'id', 'base_name', 'date', \n              'original_video', 'directory', 'strain',\n       'strain_description', 'allele', 'gene', 'chromosome',\n       'tracker', 'sex', 'developmental_stage', 'ventral_side', 'food',\n       'habituation', 'experimenter', 'arena', 'exit_flag', 'experiment_id',\n       'n_valid_frames', 'n_missing_frames', 'n_segmented_skeletons',\n       'n_filtered_skeletons', 'n_valid_skeletons', 'n_timestamps',\n       'first_skel_frame', 'last_skel_frame', 'fps', 'total_time',\n       'microns_per_pixel', 'mask_file_sizeMB', 'skel_file', 'frac_valid',\n       'worm_index', 'n_frames', 'n_valid_skel', 'first_frame']\n\n\ndef _h_cross_validate(args):\n    feats_r, col_feats, id_index_str, n_samples, cross_validation_fold, n_estimators = args\n    ss = feats_r.groupby('strain').apply(lambda x :x.iloc[random.sample(range(0,len(x)), n_samples)])\n    xx = ss[col_feats].values\n    \n    #center, this shouldn't make that much different in random forest\n    #xx = (xx-np.mean(xx, axis=0))/(np.std(xx, axis=0))\n    #xx = xx[:, ~np.any(np.isnan(xx), axis=0)]\n    \n    yy = ss[id_index_str].values\n    \n    clf = RandomForestClassifier(n_estimators=n_estimators)\n    scores = cross_val_score(clf, xx, yy, cv = cross_validation_fold)\n    #print(scores)\n    return scores\n\n\n\nif __name__ == '__main__':\n    cross_validation_fold = 5\n    base_strains = ['JU393', 'ED3054', 'JU394', \n                     'N2', 'JU440', 'ED3021', 'ED3017', \n                     'JU438', 'JU298', 'JU345', 'RC301', \n                     'AQ2947', 'ED3049',\n                     'LSJ1', 'JU258', 'MY16', \n                     'CB4852', 'CB4856', 'CB4853',\n                     ]\n    save_dir = '/Users/ajaver/OneDrive - Imperial College London/classify_strains/manual_features/SWDB/'\n    feat_files = {'OW_old' : 'ow_features_old_SWDB.csv'}\n\n    #%%\n    feat_data = {}\n    for db_name, bn in feat_files.items():\n        fname = os.path.join(save_dir, 'F_' + bn)\n        feats = pd.read_csv(fname)\n        \n        ss = np.sort(feats['strain'].unique())\n        s_dict = {s:ii for ii,s in enumerate(ss)}\n        feats['strain_id'] = feats['strain'].map(s_dict)\n        \n        base_strains_id = {x:i for i,x in enumerate(base_strains)}\n        feats['strain_base_id'] = feats['strain'].map(base_strains_id)\n        feat_data[db_name] = feats\n        \n    col2ignore_r = col2ignore + ['strain_base_id', 'strain_id', 'set_type']\n    #%% \n    #SET TO TRUE TO FIT A MODEL USING ALL THE FEATURES\n    if False:\n        n_trials = 200\n        \n        n_batch= mp.cpu_count()\n        p = mp.Pool(n_batch)\n        \n        id_index_str = 'strain_base_id'\n        \n        cross_val_results = {}\n        \n        start = time.time()\n        for db_name, feats in feat_data.items():\n            print(db_name)\n            \n            \n            col_feats = [x for x in feats.columns if x not in col2ignore_r]\n            good = ~feats['strain_base_id'].isnull() & (feats['set_type'] == 'train')\n            feats_r = feats[good]\n            \n            n_samples = feats_r['strain'].value_counts().min()\n            args = feats_r, col_feats, id_index_str, n_samples, cross_validation_fold, 1000\n            \n            #func = partial(_h_cross_validate, )\n            scores = list(p.map(_h_cross_validate, n_trials*[args]))\n            scores = np.concatenate(scores)\n            #scores = _h_cross_validate(feats_r, col_feats, id_index_str, n_samples, cross_validation_fold)\n        \n            print(\"%s Accuracy: %0.2f (+/- %0.2f)\" % (db_name, scores.mean(), scores.std() * 2))    \n            cross_val_results[db_name] = scores\n            \n            \n            print(time.time() - start)\n    \n    \n    #%%\n    #SET TO TRUE TO FIT TO SELECT FEATURES IN BASE OF THE CLASSIFICATION ACCURACY\n    if False:\n        #I am using less trees and trials too speed up things\n        n_trials = 20 \n        n_trees = 150\n        \n        n_batch= mp.cpu_count()\n        p = mp.Pool(n_batch)\n        \n        id_index_str = 'strain_base_id'\n        \n        cross_val_results = {}\n        \n        \n        for db_name, feats in feat_data.items():\n            print(db_name)\n            \n            col_feats = [x for x in feats.columns if x not in col2ignore_r]\n            good = ~feats['strain_base_id'].isnull() & (feats['set_type'] == 'train')\n            feats_r = feats[good]\n            \n            n_samples = feats_r['strain'].value_counts().min()\n            \n            \n            selected_feats = []\n            \n            for n_feat in range(30):\n                \n                \n                all_scores = []\n                for iif, ff in enumerate(col_feats):\n                    if ff in selected_feats:\n                        continue\n                    \n                    start = time.time()\n                    c_feats = selected_feats + [ff]\n                    \n                    args = feats_r, c_feats, id_index_str, n_samples, cross_validation_fold, n_trees\n                    #func = partial(_h_cross_validate, )\n                    scores = list(p.map(_h_cross_validate, n_trials*[args]))\n                    scores = np.concatenate(scores)\n                    #scores = _h_cross_validate(feats_r, col_feats, id_index_str, n_samples, cross_validation_fold)\n                    \n                    print(len(c_feats), iif+1, len(col_feats),  c_feats)\n                    print(\"%s Accuracy: %0.2f (+/- %0.2f)\" % (db_name, scores.mean(), scores.std() * 2)) \n                    print(scores.min(), scores.max(), scores.mean() - 2*scores.std())\n                    all_scores.append((c_feats, scores))\n                    \n                    print(time.time() - start)\n                \n                selected_feats = max(all_scores, key=lambda x : x[1].mean() - 2*x[1].std())[0]\n                with open('/Users/ajaver/OneDrive - Imperial College London/classify_strains/manual_features/SWDB/best_feats_OW.txt', 'a+') as fid:\n                    fid.write(', '.join(selected_feats) + '\\n')\n    #%%\n    #calculate feature accuracy changes depending on the number of features\n    selected_feats = ['midbody_crawling_amplitude_abs', 'foraging_amplitude_abs', 'hips_bend_mean_pos', 'midbody_width_forward', 'neck_bend_sd_forward_abs', 'midbody_speed_pos', 'foraging_speed_pos', 'eigen_projection_4_forward_neg', 'bend_count_backward', 'tail_crawling_frequency_pos', 'length_forward', 'tail_bend_mean_forward_neg', 'head_bend_sd_forward_pos', 'midbody_bend_sd', 'head_bend_sd_neg', 'tail_tip_motion_direction_backward_pos', 'eigen_projection_3_paused_abs', 'head_tip_speed_paused_pos', 'eigen_projection_1_forward_neg', 'eigen_projection_5_forward_pos', 'head_tip_speed_forward', 'tail_speed_forward_neg', 'tail_bend_mean_forward', 'upsilon_turns_time_pos', 'path_range_paused', 'midbody_crawling_frequency', 'head_motion_direction_forward', 'width_length_ratio_backward', 'head_motion_direction_pos', 'path_curvature_paused']\n    \n    feats = feat_data['OW_old']\n    id_index_str = 'strain_base_id'\n    \n    n_batch= mp.cpu_count()\n    p = mp.Pool(n_batch)\n    \n    col_feats = [x for x in feats.columns if x not in col2ignore_r]\n    good = ~feats['strain_base_id'].isnull()\n    feats_r = feats[good]\n    \n    n_samples = feats_r['strain'].value_counts().min()\n    \n    all_scores = []\n    for n_feats in range(len(selected_feats)):\n        start = time.time()\n        feat_cols = selected_feats[:n_feats+1]\n        \n        print(n_feats, feat_cols)\n        n_trials = 200\n        args = feats_r, feat_cols, id_index_str, n_samples, cross_validation_fold, 1000\n        #func = partial(_h_cross_validate, )\n        scores = list(p.map(_h_cross_validate, n_trials*[args]))\n        scores = np.concatenate(scores)\n        #scores = _h_cross_validate(feats_r, col_feats, id_index_str, n_samples, cross_validation_fold)\n    \n        print(\"%s Accuracy: %0.2f (+/- %0.2f)\" % (db_name, scores.mean(), scores.std() * 2))    \n        all_scores.append(scores)\n        print(time.time() - start) \n    \n    \n    yy = [x.mean() for x in all_scores]\n    err = [x.std() for x in all_scores]\n    \n    plt.figure()\n    plt.errorbar(np.arange(1, len(yy)+1), yy, yerr=err)\n    plt.ylabel('Accuracy')\n    plt.xlabel('Number of features')\n    plt.savefig('classification_accuracy.png')\n    \n","sub_path":"manual_features/compare_classifier_reduced.py","file_name":"compare_classifier_reduced.py","file_ext":"py","file_size_in_byte":8686,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"3422","text":"import time\nimport tkinter as tk\n\nclass BouncingBalls(tk.Frame):\n\n    def __init__(self, parent):\n        super().__init__(parent)\n        self.pack(fill=\"both\", expand=True)\n        self.canvas = tk.Canvas(self, bg=\"white\")\n        self.canvas.pack(fill=\"both\", expand=True)\n        self.animate_yellow_ball()\n        self.animate_red_ball()\n\n    def animate_yellow_ball(self):\n        x = 0\n        ball = self.canvas.create_oval(x, 100, x+50, 150, fill=\"yellow\")\n        while True:\n            while x < 450:\n                self.canvas.coords(ball, x, 100, 50+x, 150)\n                x += 5\n                self.update()\n                time.sleep(0.02)\n\n            while x > 0:\n                self.canvas.coords(ball, x, 100, 50+x, 150)\n                x -= 5\n                self.update()\n                time.sleep(0.02)\n\n    def animate_red_ball(self):\n        y = 0\n        ball = self.canvas.create_oval(300, y, 350, y+50, fill=\"red\")\n        while True:\n            while y < 350:\n                self.canvas.coords(ball, 300, y, 350, y+50)\n                y += 3\n                self.update()\n                time.sleep(0.02)\n\n            while y > 0:\n                self.canvas.coords(ball, 300, y, 350, y+50)\n                y -= 3\n                self.update()\n                time.sleep(0.02)\n\n\nif __name__ == \"__main__\":\n    root = tk.Tk()\n    root.title(\"Bouncing Balls\")\n    root.geometry(\"500x400\")\n    root.resizable(False, False)\n    app = BouncingBalls(root)\n    root.mainloop()\n\n","sub_path":"Midterm/midterm-part2/bouncing-balls-stub.py","file_name":"bouncing-balls-stub.py","file_ext":"py","file_size_in_byte":1507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"630904303","text":"#!/usr/bin/python\nimport sys\nimport re\nimport os\n\n# These should point to the respective commands\nSVNLOOK = '/usr/bin/svnlook'\nMAX_FILENAME_LENGTH = 144\nALLOWED_CHARACTERS = \"A-Za-z0-9_ \\.\" #note that . would match any character so we have \\.\n\ndef reg(expression,value):\n  return bool(re.search(expression,value))\n\n# Gets a command's output\ndef commandOutput(command):\n  import subprocess\n  process = subprocess.Popen(command.split(), stdout = subprocess.PIPE)\n  return process.communicate()[0] # 0 is stdout\n\n# Returns an array of the changed files' names\ndef getChangedFiles(svnPath, transaction):\n  # Run svnlook to find the files that were changed\n  output = commandOutput('{} changed {} --transaction {}'.format(SVNLOOK, svnPath, transaction))\n\n  # The output from svnlook looks like the following:\n  # U   folder/file1.cpp\n  # A   folder/file2.cpp\n  # where U means updated and A means added\n  def changed(fileName):\n    return len(line) > 0 and line[0] in ('A', 'U')\n  changedFiles = [line[4:] for line in output.split('\\n') if changed(line)]\n\n  # svnlook inserts an empty line, so output.split() will have an extra\n  # line with nothing in it - ignore the last lines if they're empty\n  while len(changedFiles)>0 and 0 == len(changedFiles[-1]):\n    changedFiles = changedFiles[:-1]\n\n  return changedFiles\n\ndef validateFileName(fileName):\n  # We only check the basename of the path.  You can't create /1/2\n  # without first creating /1, so this should work.\n  basename = os.path.basename (fileName)\n  diff = len(basename) - MAX_FILENAME_LENGTH\n  if diff > 0:\n    print >> sys.stderr, \"Filename \\'{}\\' too long by {} characters.\".format(fileName,diff)\n    return 1 # Error found\n  if not reg(\"^[\"+ALLOWED_CHARACTERS+\"]+\\Z\",fileName):\n    print >> sys.stderr, \"Filename \\'{}\\' contains illegal characters.\".format(fileName)\n    return 1 # Error found\n  if reg(\"^[ ].+\\Z|^.+[ ]\\Z\",fileName):\n    print >> sys.stderr, \"Filename \\'{}\\' starts or ends with a space.\".format(fileName)\n    return 1 # Error found\n  if reg(\"(PRN|CON|AUX|CLOCK|NUL|COM\\d|LPT\\d)\",fileName):\n    print >> sys.stderr, \"Filename \\'{}\\' is a reserved filename.\".format(fileName)\n    return 1 # Error found\n  return 0 # No errors\n\n\nsvnPath = sys.argv[1]\ntransaction = sys.argv[2]\nfiles = getChangedFiles(svnPath, transaction)\n#files = [\"eaea\",\"ee #e\", \"  e\"]\n\nerrorCount = 0\n\nfor fileN in files:\n  errorCount = errorCount + validateFileName(fileN)\n\nsys.exit(errorCount)\n","sub_path":"bin/pre-commit.py","file_name":"pre-commit.py","file_ext":"py","file_size_in_byte":2443,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"86871563","text":"# -*- coding: utf-8 -*-\r\n\"\"\"Utilities for text input preprocessing.\r\n\"\"\"\r\nfrom __future__ import absolute_import\r\nfrom __future__ import division\r\nfrom __future__ import print_function\r\n\r\nimport numpy as np\r\nimport re\r\nimport random\r\n\r\nfrom six import iterkeys, iteritems, u, string_types\r\n\r\nfrom six.moves import range\r\nfrom six.moves import zip\r\n\r\nimport torch\r\nfrom smart_open import *\r\n\r\ndef sequence_mask(lengths, max_len=None):\r\n    \"\"\"\r\n    Creates a boolean mask from sequence lengths.\r\n    \"\"\"\r\n    batch_size = lengths.numel()\r\n    max_len = max_len or lengths.max()\r\n    return (torch.arange(0, max_len)\r\n            .type_as(lengths)\r\n            .repeat(batch_size, 1)\r\n            .lt(lengths.unsqueeze(1)))\r\n\r\ndef encode(input_lines, word_dict):\r\n    \"\"\"\r\n    encode list of strings into word-level representation\r\n    \"\"\"\r\n    lines = list(map(lambda t: list(map(lambda m: word_dict[m], t)), input_lines))\r\n    return lines\r\n\r\n\r\ndef encode2Tensor(input_lines, word_dict, unk):\r\n    \"\"\"\r\n    encode list of strings into word-level representation (tensor) with unk\r\n    \"\"\"\r\n    lines = list(map(lambda t: torch.LongTensor(list(map(lambda m: word_dict.get(m, unk), t))), input_lines))\r\n    return lines\r\n\r\ndef one_hot_(y, num_classes=None):\r\n\r\n    \"\"\"Converts a class vector (integers) to binary class matrix.\r\n    E.g. for use with categorical_crossentropy.\r\n    # Arguments\r\n\r\n        y: class vector to be converted into a matrix\r\n            (integers from 0 to num_classes).\r\n        num_classes: total number of classes.\r\n    # Returns\r\n        A binary matrix representation of the input.\r\n    \"\"\"\r\n\r\n    y = np.array(y, dtype='int')\r\n\r\n    input_shape = y.shape\r\n\r\n    if input_shape and input_shape[-1] == 1 and len(input_shape) > 1:\r\n\r\n        input_shape = tuple(input_shape[:-1])\r\n\r\n    y = y.ravel()\r\n\r\n    if not num_classes:\r\n\r\n        num_classes = np.max(y) + 1\r\n\r\n    n = y.shape[0]\r\n\r\n    categorical = np.zeros((n, num_classes), dtype=np.float32)\r\n\r\n    categorical[np.arange(n), y] = 1\r\n\r\n    output_shape = input_shape + (num_classes,)\r\n\r\n    categorical = np.reshape(categorical, output_shape)\r\n\r\n    return categorical\r\n\r\ndef any2utf8(text, errors='strict', encoding='utf8'):\r\n\r\n    if isinstance(text, unicode):\r\n        return text.encode('utf8')\r\n    # do bytestring -> unicode -> utf8 full circle, to ensure valid utf8\r\n    return unicode(text, encoding, errors=errors).encode('utf8')\r\n\r\n\r\nto_utf8 = any2utf8\r\n\r\n\r\ndef any2unicode(text, encoding='utf8', errors='strict'):\r\n\r\n    if isinstance(text, unicode):\r\n        return text\r\n    return unicode(text, encoding, errors=errors)\r\n\r\n\r\nto_unicode = any2unicode\r\n\r\ndef lower_to_unicode(text, encoding='utf8', errors='strict'):\r\n    \"\"\"Lowercase `text` and convert to unicode.\"\"\"\r\n    return to_unicode(text.lower(), encoding, errors)\r\n\r\n\r\ndef clean_str(string):\r\n    \"\"\"\r\n    Tokenization/string cleaning for all datasets except for SST.\r\n    Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py\r\n    \"\"\"\r\n    string = re.sub(r\"[^A-Za-z0-9(),!?\\'\\`]\", \" \", string)\r\n    string = re.sub(r\"\\'s\", \" \\'s\", string)\r\n    string = re.sub(r\"\\'ve\", \" \\'ve\", string)\r\n    string = re.sub(r\"n\\'t\", \" n\\'t\", string)\r\n    string = re.sub(r\"\\'re\", \" \\'re\", string)\r\n    string = re.sub(r\"\\'d\", \" \\'d\", string)\r\n    string = re.sub(r\"\\'ll\", \" \\'ll\", string)\r\n    string = re.sub(r\",\", \" , \", string)\r\n    string = re.sub(r\"!\", \" ! \", string)\r\n    string = re.sub(r\"\\(\", \" \\( \", string)\r\n    string = re.sub(r\"\\)\", \" \\) \", string)\r\n    string = re.sub(r\"\\?\", \" \\? \", string)\r\n    string = re.sub(r\"\\s{2,}\", \" \", string)\r\n    return string.strip()\r\n\r\ndef pad_sequences(sequences, pad, max_length,is_pad_end=True):\r\n    \"\"\"\r\n    Args:\r\n        sequences: a generator of list or tuple.\r\n        pad: the value to pad with.\r\n    Returns:\r\n        a list of list where each sublist has same length.\r\n    \"\"\"\r\n    sequence_padded = []\r\n\r\n    for seq in sequences:\r\n        seq = list(seq)\r\n        if is_pad_end:\r\n            seq_ = seq[:max_length] + [pad] * max(max_length - len(seq), 0)\r\n        else:\r\n            seq_ = [pad] * max(max_length - len(seq), 0) + seq[:max_length]\r\n        sequence_padded += [seq_]\r\n        \r\n\r\n    return sequence_padded\r\n\r\ndef load_glove_vocab(filename):\r\n    \"\"\"Loads GloVe's vocab from a file.\r\n    Args:\r\n        filename (str): path to the glove vectors.\r\n    Returns:\r\n        set: a set of all words in GloVe.\r\n    \"\"\"\r\n    print('Building vocab...')\r\n    with open(filename,encoding='utf-8') as f:\r\n        vocab = {line.strip().split()[0] for line in f} #set\r\n    print('- done. {} tokens'.format(len(vocab)))\r\n    return dict(enumerate(vocab))  #return set\r\n\r\ndef load_word_embeddings(vocab, glove_filename, dim):\r\n    \"\"\"Loads GloVe vectors in numpy array.\r\n    Args:\r\n        vocab (): dictionary vocab[word] = index.\r\n        glove_filename (str): a path to a glove file.\r\n        dim (int): dimension of embeddings.\r\n    Returns:\r\n        numpy array: an array of word embeddings.\r\n    \"\"\"\r\n    embeddings = np.zeros([len(vocab)+1, dim],dtype=np.float32) #0为PAD\r\n    with open(glove_filename,encoding='utf-8') as f:\r\n        for line in f:\r\n            line = line.strip().split(' ')\r\n            word = line[0]\r\n            embedding = [float(x) for x in line[1:]]\r\n            if word in vocab:\r\n                word_idx = vocab[word]\r\n                embeddings[word_idx] = np.asarray(embedding,dtype=np.float32)\r\n\r\n    return embeddings\r\n\r\n\r\ndef word2idx(sents, word2idx):\r\n    return [[word2idx[w] for w in s if w in word2idx] for s in sents] \r\n\r\ndef revdict(d):\r\n    return {v: k for (k, v) in iteritems(dict(d))}\r\n\r\ndef get_entities(seq):\r\n    \"\"\"Gets entities from sequence.\r\n    Args:\r\n        seq (list): sequence of labels.\r\n    Returns:\r\n        list: list of (chunk_type, chunk_start, chunk_end).\r\n    Example:\r\n        >>> seq = ['B-PER', 'I-PER', 'O', 'B-LOC']\r\n        >>> print(get_entities(seq))\r\n        [('PER', 0, 2), ('LOC', 3, 4)]\r\n    \"\"\"\r\n    i = 0\r\n    chunks = []\r\n    seq = seq + ['O']  # add sentinel\r\n    types = [tag.split('-')[-1] for tag in seq]\r\n    while i < len(seq):\r\n        if seq[i].startswith('B'):\r\n            for j in range(i+1, len(seq)):\r\n                if seq[j].startswith('I') and types[j] == types[i]:\r\n                    continue\r\n                break\r\n            chunks.append((types[i], i, j))\r\n            i = j\r\n        else:\r\n            i += 1\r\n    return chunks\r\n\r\ndef utf_8_encoder(doc):\r\n\r\n    for line in doc:\r\n\r\n        yield line.encode('utf-8')\r\n\r\n\r\ndef skipgrams(sequence, vocabulary_size,\r\n              window_size=4, negative_samples=1., shuffle=True,\r\n              categorical=False, sampling_table=None, seed=None):\r\n    \"\"\"Generates skipgram word pairs.\r\n\r\n    Takes a sequence (list of indexes of words),\r\n    returns couples of [word_index, other_word index] and labels (1s or 0s),\r\n    where label = 1 if 'other_word' belongs to the context of 'word',\r\n    and label=0 if 'other_word' is randomly sampled\r\n\r\n    # Arguments\r\n        sequence: a word sequence (sentence), encoded as a list\r\n            of word indices (integers). If using a `sampling_table`,\r\n            word indices are expected to match the rank\r\n            of the words in a reference dataset (e.g. 10 would encode\r\n            the 10-th most frequently occurring token).\r\n            Note that index 0 is expected to be a non-word and will be skipped.\r\n        vocabulary_size: int. maximum possible word index + 1\r\n        window_size: int. actually half-window.\r\n            The window of a word wi will be [i-window_size, i+window_size+1]\r\n        negative_samples: float >= 0. 0 for no negative (=random) samples.\r\n            1 for same number as positive samples. etc.\r\n        shuffle: whether to shuffle the word couples before returning them.\r\n        categorical: bool. if False, labels will be\r\n            integers (eg. [0, 1, 1 .. ]),\r\n            if True labels will be categorical eg. [[1,0],[0,1],[0,1] .. ]\r\n        sampling_table: 1D array of size `vocabulary_size` where the entry i\r\n            encodes the probability to sample a word of rank i.\r\n        seed: random seed.\r\n\r\n    # Returns\r\n        couples, labels: where `couples` are int pairs and\r\n            `labels` are either 0 or 1.\r\n\r\n    # Note\r\n        By convention, index 0 in the vocabulary is\r\n        a non-word and will be skipped.\r\n    \"\"\"\r\n    couples = []\r\n    labels = []\r\n    for i, wi in enumerate(sequence):\r\n        if not wi:\r\n            continue\r\n        if sampling_table is not None:\r\n            if sampling_table[wi] < random.random():\r\n                continue\r\n\r\n        window_start = max(0, i - window_size)\r\n        window_end = min(len(sequence), i + window_size + 1)\r\n        for j in range(window_start, window_end):\r\n            if j != i:\r\n                wj = sequence[j]\r\n                if not wj:\r\n                    continue\r\n                couples.append([wi, wj])\r\n                if categorical:\r\n                    labels.append([0, 1])\r\n                else:\r\n                    labels.append(1)\r\n\r\n    if negative_samples > 0:\r\n        num_negative_samples = int(len(labels) * negative_samples)\r\n        words = [c[0] for c in couples]\r\n        random.shuffle(words)\r\n\r\n        couples += [[words[i % len(words)],\r\n                    random.randint(1, vocabulary_size - 1)] for i in range(num_negative_samples)]\r\n        if categorical:\r\n            labels += [[1, 0]] * num_negative_samples\r\n        else:\r\n            labels += [0] * num_negative_samples\r\n\r\n    if shuffle:\r\n        if seed is None:\r\n            seed = random.randint(0, 10e6)\r\n        random.seed(seed)\r\n        random.shuffle(couples)\r\n        random.seed(seed)\r\n        random.shuffle(labels)\r\n\r\n    return couples, labels\r\n\r\ndef remove_long_seq(maxlen, seq, label):\r\n    \"\"\"Removes sequences that exceed the maximum length.\r\n\r\n    # Arguments\r\n        maxlen: int, maximum length\r\n        seq: list of lists where each sublist is a sequence\r\n        label: list where each element is an integer\r\n\r\n    # Returns\r\n        new_seq, new_label: shortened lists for `seq` and `label`.\r\n    \"\"\"\r\n    new_seq, new_label = [], []\r\n    for x, y in zip(seq, label):\r\n        if len(x) < maxlen:\r\n            new_seq.append(x)\r\n            new_label.append(y)\r\n    return new_seq, new_label\r\n\r\nclass tDictionary(object):\r\n    def __init__(self):\r\n        self.word2idx = {}\r\n        self.idx2word = {}\r\n        self.wordfreq={}\r\n        self.len = 0\r\n\r\n    def add_word(self, word):\r\n        if word not in self.word2idx:\r\n            self.word2idx[word] = self.len\r\n            self.wordfreq[word]=1\r\n            self.len +=1\r\n        else:\r\n            self.wordfreq[word] = self.wordfreq[word]+1\r\n\r\n    def __len__(self):\r\n        return len(self.word2idx)\r\n\r\n    def revdict(self):\r\n        self.idx2word = {v:k for (k, v) in iteritems(self.word2idx)}\r\n\r\n    def docs2idx(self,sents):\r\n        return [[self.word2idx[w] for w in s if w in self.word2idx] for s in sents] \r\n\r\n    def build_vocab(self,sents):\r\n        for s in sents:\r\n            for w in s:\r\n                self.add_word(w)\r\n        self.revdict()\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"utils/Text.py","file_name":"Text.py","file_ext":"py","file_size_in_byte":11229,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"459855412","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Sep  9 19:41:10 2019\r\n\r\n@author: Charl\r\n\"\"\"\r\n#%% Import data\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom time import time\r\n\r\n#%% Read dataset\r\n\r\ntrain = pd.read_csv('D:/Documents/UFS/5th Year/Honours Project/Data Sources/train_V2.csv')\r\n\r\ntrain.winPlacePerc.fillna(1,inplace=True)\r\ntrain.loc[train['winPlacePerc'].isnull()]\r\n\r\n# Create distance feature\r\ntrain[\"distance\"] = train[\"rideDistance\"]+train[\"walkDistance\"]+train[\"swimDistance\"]\r\ntrain.drop(['rideDistance','walkDistance','swimDistance'],inplace=True,axis=1)\r\n\r\n# Create headshot_rate feature\r\ntrain['headshot_rate'] = train['headshotKills'] / train['kills']\r\ntrain['headshot_rate'] = train['headshot_rate'].fillna(0)\r\n\r\n# Create playersJoined feature - used for normalisation\r\ntrain['playersJoined'] = train.groupby('matchId')['matchId'].transform('count')\r\n\r\n#%% Data cleaning - removing outliers\r\n\r\n# Row with NaN 'winPlacePerc' value - pointed out by averagemn (https://www.kaggle.com/donkeys)\r\ntrain.drop(2744604, inplace=True)\r\n\r\n# Players who got kills without moving\r\ntrain['killsWithoutMoving'] = ((train['kills'] > 0) & (train['distance'] == 0))\r\ntrain.drop(train[train['killsWithoutMoving'] == True].index, inplace=True)\r\n\r\n# Players who got more than 10 roadkills\r\ntrain.drop(train[train['roadKills'] > 10].index, inplace=True)\r\n\r\n# Players who got more than 30 kills\r\ntrain[train['kills'] > 30].head(10)\r\n\r\n# Players who made a minimum of 9 kills and have a headshot_rate of 100%\r\ntrain[(train['headshot_rate'] == 1) & (train['kills'] > 8)].head(10)\r\n\r\n# Players who made kills with a distance of more than 1 km\r\ntrain.drop(train[train['longestKill'] >= 1000].index, inplace=True)\r\n\r\n# Players who acquired more than 80 weapons\r\ntrain.drop(train[train['weaponsAcquired'] >= 80].index, inplace=True)\r\n\r\n# Players how use more than 40 heals\r\ntrain['heals'] = train['boosts']+train['heals']\r\ntrain.drop(train[train['heals'] >= 40].index, inplace=True)\r\n\r\n# Create normalised features\r\ntrain['killsNorm'] = train['kills']*((100-train['playersJoined'])/100 + 1)\r\ntrain['damageDealtNorm'] = train['damageDealt']*((100-train['playersJoined'])/100 + 1)\r\ntrain['maxPlaceNorm'] = train['maxPlace']*((100-train['playersJoined'])/100 + 1)\r\ntrain['matchDurationNorm'] = train['matchDuration']*((100-train['playersJoined'])/100 + 1)\r\ntrain['assistsNorm'] = train['assists']*((100-train['playersJoined'])/100 + 1)\r\ntrain['roadKillsNorm'] = train['roadKills']*((100-train['playersJoined'])/100 + 1)\r\ntrain['vehicleDestroysNorm'] = train['matchDuration']*((100-train['playersJoined'])/100 + 1)\r\ntrain['killPointsNorm'] = train['vehicleDestroys']*((100-train['playersJoined'])/100 + 1)\r\ntrain['headshotKillsNorm'] = train['headshotKills']*((100-train['playersJoined'])/100 + 1)\r\ntrain['revivesNorm'] = train['revives']*((100-train['playersJoined'])/100 + 1)\r\n\r\n#%%\r\n\r\n# Features that will be used for training\r\npredictors = [\r\n              \"numGroups\",\r\n              \"distance\",\r\n              \"boosts\",\r\n              \"killStreaks\",\r\n              \"DBNOs\",\r\n              \"killPlace\",\r\n              \"killStreaks\",\r\n              \"longestKill\",\r\n              \"heals\",\r\n              \"weaponsAcquired\",\r\n              \"headshot_rate\",\r\n              \"assistsNorm\",\r\n              \"headshotKillsNorm\",\r\n              \"damageDealtNorm\",\r\n              \"killPointsNorm\",\r\n              \"revivesNorm\",\r\n              \"roadKillsNorm\",\r\n              \"vehicleDestroysNorm\",\r\n              \"killsNorm\",\r\n              \"maxPlaceNorm\",\r\n              \"matchDurationNorm\",\r\n              ]\r\n\r\nX = train[predictors]\r\nX.head()\r\n\r\ny = train['winPlacePerc']\r\ny.head()\r\n\r\nfrom sklearn.model_selection import train_test_split\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)\r\n\r\n#%% Hyperparameter tuning\r\n\r\nfrom sklearn.model_selection import GridSearchCV\r\n\r\ndef log(x):\r\n    # can be used to write to a log file\r\n    print(x)\r\n\r\n# Utility function to report best scores (from scikit-learn.org)\r\ndef report(results, n_top=3):\r\n    for i in range(1, n_top + 1):\r\n        candidates = np.flatnonzero(results['rank_test_score'] == i)\r\n        for candidate in candidates:\r\n            log(\"Model with rank: {0}\".format(i))\r\n            log(\"Mean validation score: {0:.5f} (std: {1:.5f})\".format(\r\n                results['mean_test_score'][candidate],\r\n                results['std_test_score'][candidate]))\r\n            log(\"Parameters: {0}\".format(results['params'][candidate]))\r\n            log(\"\")\r\n\r\n# Function to determine the best fit (from scikit-learn.org)\r\ndef best_fit(clf, X_train, y_train):\r\n    \r\n    param_grid = {\r\n                    'max_features':['sqrt','log2',None],\r\n                    'max_depth': np.arange(1, 15),\r\n                    'min_samples_split': range(2,16,2),\r\n                    'min_samples_leaf': range(2,20,2),\r\n                    'max_leaf_nodes': [5,10,None],\r\n                 }\r\n\r\n    # Run grid search\r\n    grid_search = GridSearchCV(clf, param_grid=param_grid, cv=5, n_jobs=8)\r\n\r\n    import time as ttt\r\n    now = time()\r\n    log(ttt.ctime())\r\n    \r\n    grid_search.fit(X_train, y_train)\r\n    \r\n    report(grid_search.cv_results_, n_top=10)\r\n    \r\n    log(100*\"-\")\r\n    log(ttt.ctime())\r\n    log(\"Search (5-fold cross validation) took %.5f seconds for %d candidate parameter settings.\"\r\n        % (time() - now, len(grid_search.cv_results_['params'])))\r\n    log('')\r\n    log(\"The best parameters are %s with a score of %0.5f\"\r\n        % (grid_search.best_params_, grid_search.best_score_))\r\n    \r\n    return grid_search\r\n\r\n#%% Model\r\n\r\nfrom sklearn.tree import DecisionTreeRegressor\r\n\r\ndtree = DecisionTreeRegressor(random_state=101, max_features=None, max_depth=14, min_samples_split=2,\r\n                              min_samples_leaf=18, max_leaf_nodes=None)\r\n\r\n# Hyperparameter tuning method call (~3 hours run time)\r\nbest_tree = best_fit(dtree, X_train, y_train)\r\n\r\n# Output:\r\n# The best parameters are {'max_leaf_nodes': None, 'min_samples_leaf': 18,\r\n#                         'min_samples_split': 2, 'max_features': None,\r\n#                         'max_depth': 14} with a score of 0.89639\r\n\r\ndtree.fit(X_train, y_train)\r\n\r\npredictions = dtree.predict(X_test)\r\n\r\n#%% Feature importances\r\n\r\n# List the feature importance value\r\nimportances = dtree.feature_importances_\r\nprint(importances)\r\n\r\n# Sort feature importances in descending order\r\nindices = np.argsort(importances)[::-1]\r\nindices\r\n\r\n# Rearrange feature names so they match the sorted feature importances\r\nfeature_names = [X_train.columns[i] for i in indices]\r\nfeature_names\r\n\r\nX_columns = X_train.columns\r\nX_columns\r\n\r\n# Print the feature ranking\r\nprint(\"Feature ranking (index):\")\r\n\r\nfor f in range(X_train.shape[1]):\r\n    print(\"%d. feature %d (%f)\" % (f + 1, indices[f], importances[indices[f]]))\r\n    \r\n# Print the feature ranking\r\nprint(\"Feature ranking (column name):\")\r\n\r\nfor f in range(indices.shape[0]):\r\n    print(\"%2d) %-*s %0.9f\" % (f + 1, 10,\r\n                            X_columns[indices[f]],\r\n                            importances[indices[f]]))\r\n\r\n# Output:\r\n# Feature ranking (column name):\r\n#  1) distance   0.695173563\r\n#  2) killPlace  0.233122504\r\n#  3) numGroups  0.017167211\r\n#  4) boosts     0.010391879\r\n#  5) assistsNorm 0.009764422\r\n#  6) killsNorm  0.007610490\r\n#  7) killStreaks 0.006952296\r\n#  8) headshotKillsNorm 0.004577564\r\n#  9) maxPlaceNorm 0.004277439\r\n# 10) DBNOs      0.001806396\r\n# 11) vehicleDestroysNorm 0.001694837\r\n# 12) killStreaks 0.001486443\r\n# 13) matchDurationNorm 0.001398479\r\n# 14) roadKillsNorm 0.001211661\r\n# 15) revivesNorm 0.001046521\r\n# 16) heals      0.000673904\r\n# 17) killPointsNorm 0.000578243\r\n# 18) longestKill 0.000505060\r\n# 19) weaponsAcquired 0.000403372\r\n# 20) damageDealtNorm 0.000147940\r\n# 21) headshot_rate 0.000009776\r\n\r\n#%% Evaluation\r\n\r\nfrom sklearn.metrics import mean_absolute_error\r\nfrom sklearn.metrics import mean_squared_error\r\nfrom sklearn.metrics import r2_score\r\n\r\nMAE = mean_absolute_error(y_test, predictions)\r\nMSE = mean_squared_error(y_test, predictions)\r\nR2 = r2_score(y_test, predictions)\r\n\r\nprint(\"Metrics:\")\r\nprint(\"-------------------------------\")\r\nprint(\"Mean Absolute Error: {}\".format(MAE))\r\nprint(\"Mean Squared Error: {}\".format(MSE))\r\nprint(\"R2 Score: {}\".format(R2))\r\n\r\n# Cross-validation\r\nfrom sklearn.model_selection import cross_val_score\r\nfrom sklearn.model_selection import cross_val_predict\r\n\r\ncross_val_prediction = cross_val_predict(dtree, X_train, y_train, cv=5)\r\n\r\nprint(\"\\n---------------------------------\")\r\nprint(\"5-FOLD CROSS-VALIDATION\")\r\nprint(\"---------------------------------\")\r\nprint(\"Cross-validation score (R2): {}\".format(cross_val_score(dtree, X_train, y_train, cv=5)))\r\n\r\n#%% Data cleaning (Test set)\r\n\r\ntest = pd.read_csv('D:/Documents/UFS/5th Year/Honours Project/Data Sources/test_V2.csv')\r\n\r\n# Create distance feature\r\ntest[\"distance\"] = test[\"rideDistance\"]+test[\"walkDistance\"]+test[\"swimDistance\"]\r\ntest.drop(['rideDistance','walkDistance','swimDistance'],inplace=True,axis=1)\r\n\r\n# Create headshot_rate feature\r\ntest['headshot_rate'] = test['headshotKills'] / test['kills']\r\ntest['headshot_rate'] = test['headshot_rate'].fillna(0)\r\n\r\n# Create playersJoined feature - used for normalisation\r\ntest['playersJoined'] = test.groupby('matchId')['matchId'].transform('count')\r\n\r\n# Players who got kills without moving\r\ntest['killsWithoutMoving'] = ((test['kills'] > 0) & (test['distance'] == 0))\r\ntest.drop(test[test['killsWithoutMoving'] == True].index, inplace=True)\r\n\r\n# Players who got more than 10 roadkills\r\ntest.drop(test[test['roadKills'] > 10].index, inplace=True)\r\n\r\n# Players who made a minimum of 9 kills and have a headshot_rate of 100%\r\ntest[(test['headshot_rate'] == 1) & (test['kills'] > 8)].head(10)\r\n\r\n# Players who made kills with a distance of more than 1 km\r\ntest.drop(test[test['longestKill'] >= 1000].index, inplace=True)\r\n\r\n# Players who acquired more than 80 weapons\r\ntest.drop(test[test['weaponsAcquired'] >= 80].index, inplace=True)\r\n\r\n# Players how use more than 40 heals\r\ntest['heals'] = test['boosts']+test['heals']\r\ntest.drop(test[test['heals'] >= 40].index, inplace=True)\r\n\r\n# Create normalised features\r\ntest['killsNorm'] = test['kills']*((100-test['playersJoined'])/100 + 1)\r\ntest['damageDealtNorm'] = test['damageDealt']*((100-test['playersJoined'])/100 + 1)\r\ntest['maxPlaceNorm'] = test['maxPlace']*((100-test['playersJoined'])/100 + 1)\r\ntest['matchDurationNorm'] = test['matchDuration']*((100-test['playersJoined'])/100 + 1)\r\ntest['assistsNorm'] = test['assists']*((100-test['playersJoined'])/100 + 1)\r\ntest['roadKillsNorm'] = test['roadKills']*((100-test['playersJoined'])/100 + 1)\r\ntest['vehicleDestroysNorm'] = test['matchDuration']*((100-test['playersJoined'])/100 + 1)\r\ntest['killPointsNorm'] = test['vehicleDestroys']*((100-test['playersJoined'])/100 + 1)\r\ntest['headshotKillsNorm'] = test['headshotKills']*((100-test['playersJoined'])/100 + 1)\r\ntest['revivesNorm'] = test['revives']*((100-test['playersJoined'])/100 + 1)\r\n\r\n#%% Prediction\r\n\r\nx_test = test[predictors]\r\ny_predict = dtree.predict(x_test)\r\n\r\ny_predict[y_predict > 1] = 1\r\ny_predict[y_predict < 0] = 0.0\r\n\r\ntest['winPlacePercPredictions'] = y_predict\r\n\r\n#%% Submission\r\n\r\naux = test.groupby(['matchId','groupId'])['winPlacePercPredictions'].agg('mean').groupby('matchId').rank(pct=True).reset_index()\r\naux.columns = ['matchId','groupId','winPlacePerc']\r\ntest = test.merge(aux, how='left', on=['matchId','groupId'])\r\nsubmission = test[['Id','winPlacePerc']]\r\n\r\nprint(\"Submission head\\n {}\".format(submission.head()))\r\nsubmission.to_csv(\"submission.csv\", index=False)\r\n\r\n#%%\r\n","sub_path":"decision_tree.py","file_name":"decision_tree.py","file_ext":"py","file_size_in_byte":11636,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"596745068","text":"#!/usr/bin/env python\n_interpreters_ = []\n__author__ = \"Russ Robbins\"\n__license__ = \"MIT\"\n__version__ = \"0.0.1\"\n__maintainer__ = \"Russ Robbins\"\n__email__ = \"russ.robbins@outlook.com\"\n__encoding__ = \"UTF-8\"\n__acknowledgements = []\n\n\n# make module importable by embedding code in functions\ndef function():\n    ...\n    return x\n\n\n\"\"\" make module executable from command line using\n<python <filename with extension>> syntax\"\"\"\nif __name__ == '__main__':\n    call\n    to\n    functions\n    go\n    here\n","sub_path":"4_resources/0_.py","file_name":"0_.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"506365071","text":"import os\nfrom qibo import config\nfrom qibo.config import raise_error, log\n\n# versions requirements\nTF_MIN_VERSION = '2.2.0'\n\n\nclass Backend:\n\n    def __init__(self):\n\n        self.available_backends = {}\n        self.hardware_backends = {}\n        active_backend = \"numpy\"\n\n        # load profile from default file\n        from pathlib import Path\n        profile_path = Path(os.environ.get(\n            'QIBO_PROFILE', Path(__file__).parent / \"profiles.yml\"))\n        try:\n            with open(profile_path) as f:\n                import yaml\n                profile = yaml.safe_load(f)\n        except FileNotFoundError:  # pragma: no cover\n            raise_error(FileNotFoundError,\n                        f\"Profile file {profile_path} not found.\")\n\n        # check if numpy is installed\n        if self.check_availability(\"numpy\"):\n            from qibo.backends.numpy import NumpyBackend\n            self.available_backends[\"numpy\"] = NumpyBackend\n        else:  # pragma: no cover\n            raise_error(ModuleNotFoundError, \"Numpy is not installed. \"\n                                             \"Please install it using \"\n                                             \"`pip install numpy`.\")\n\n        for backend in profile.get('backends'):\n            name = backend.get('name')\n            if self.check_availability(name):\n                if name == 'tensorflow' or name == 'qibotf':\n                    os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = str(\n                        config.TF_LOG_LEVEL)\n                    import tensorflow as tf  # pylint: disable=E0401\n                    if tf.__version__ < TF_MIN_VERSION:  # pragma: no cover\n                        raise_error(\n                            RuntimeError, f\"TensorFlow version not supported, minimum is {TF_MIN_VERSION}.\")\n                import importlib\n                custom_backend = getattr(importlib.import_module(\n                    backend.get('from')), backend.get('class'))\n                self.available_backends[name] = custom_backend\n                if backend.get('is_hardware', False):  # pragma: no cover\n                    self.hardware_backends[name] = custom_backend\n                if profile.get('default') == name:\n                    active_backend = name\n\n        self.constructed_backends = {}\n        self._active_backend = None\n        self.qnp = self.construct_backend(\"numpy\")\n        # Create the default active backend\n        if \"QIBO_BACKEND\" in os.environ:  # pragma: no cover\n            self.active_backend = os.environ.get(\"QIBO_BACKEND\")\n        else:\n            self.active_backend = active_backend\n\n        # raise performance warning if qibojit and qibotf are not available\n        self.show_config()\n        if active_backend == \"numpy\":  # pragma: no cover\n            log.warning(\"numpy backend uses `np.einsum` and supports CPU only. \"\n                        \"Consider installing the qibojit or qibotf backends for \"\n                        \"increased performance and to enable GPU acceleration.\")\n        elif active_backend == \"tensorflow\":  # pragma: no cover\n            # case not tested because CI has tf installed\n            log.warning(\"qibotf library was not found. `tf.einsum` will be \"\n                        \"used to apply gates. In order to install Qibo's \"\n                        \"high performance custom operators for TensorFlow \"\n                        \"please use `pip install qibotf`. Alternatively, \"\n                        \"consider installing the qibojit backend.\")\n\n    @property\n    def active_backend(self):\n        return self._active_backend\n\n    @active_backend.setter\n    def active_backend(self, name):\n        self._active_backend = self.construct_backend(name)\n\n    def construct_backend(self, name):\n        \"\"\"Constructs and returns a backend.\n\n        If the backend already exists in previously constructed backends then\n        the existing object is returned.\n\n        Args:\n            name (str): Name of the backend to construct.\n                See ``available_backends`` for the list of supported names.\n\n        Returns:\n            Backend object.\n        \"\"\"\n        if name not in self.constructed_backends:\n            if name not in self.available_backends:\n                available = [\" - {}: {}\".format(n, b.description)\n                             for n, b in self.available_backends.items()]\n                available = \"\\n\".join(available)\n                raise_error(ValueError, \"Unknown backend {}. Please select one of \"\n                                        \"the available backends:\\n{}.\"\n                                        \"\".format(name, available))\n            new_backend = self.available_backends.get(name)()\n            if self.active_backend is not None:\n                new_backend.set_precision(self.active_backend.precision)\n            self.constructed_backends[name] = new_backend\n        return self.constructed_backends.get(name)\n\n    def __getattr__(self, x):\n        return getattr(self.active_backend, x)\n\n    def __str__(self):\n        return self.active_backend.name\n\n    def __repr__(self):\n        return str(self)\n\n    def show_config(self):\n        log.info(\n            f\"Using {self.active_backend.name} backend on {self.active_backend.default_device}\")\n\n    @staticmethod\n    def check_availability(module_name):\n        \"\"\"Check if module is installed.\n\n        Args:\n            module_name (str): module name.\n\n        Returns:\n            True if the module is installed, False otherwise.\n        \"\"\"\n        from pkgutil import iter_modules\n        return module_name in (name for _, name, _ in iter_modules())\n\n\nK = Backend()\nnumpy_matrices = K.qnp.matrices\n\n\ndef set_backend(backend=\"qibojit\"):\n    \"\"\"Sets backend used for mathematical operations and applying gates.\n\n    The following backends are available:\n    'qibojit': Numba/cupy backend with custom operators for applying gates,\n    'qibotf': Tensorflow backend with custom operators for applying gates,\n    'tensorflow': Tensorflow backend that applies gates using ``tf.einsum``,\n    'numpy': Numpy backend that applies gates using ``np.einsum``.\n\n    Args:\n        backend (str): A backend from the above options.\n    \"\"\"\n    if not config.ALLOW_SWITCHERS and backend != K.name:\n        log.warning(\"Backend should not be changed after allocating gates.\")\n    K.active_backend = backend\n    K.show_config()\n\n\ndef get_backend():\n    \"\"\"Get backend used to implement gates.\n\n    Returns:\n        A string with the backend name.\n    \"\"\"\n    return K.name\n\n\ndef set_precision(dtype=\"double\"):\n    \"\"\"Set precision for states and gates simulation.\n\n    Args:\n        dtype (str): possible options are 'single' for single precision\n            (complex64) and 'double' for double precision (complex128).\n    \"\"\"\n    if not config.ALLOW_SWITCHERS and dtype != K.precision:\n        log.warning(\"Precision should not be changed after allocating gates.\")\n    for bk in K.constructed_backends.values():\n        bk.set_precision(dtype)\n\n\ndef get_precision():\n    \"\"\"Get precision for states and gates simulation.\n\n    Returns:\n        A string with the precision name ('single', 'double').\n    \"\"\"\n    return K.precision\n\n\ndef set_device(name):\n    \"\"\"Set default execution device.\n\n    Args:\n        name (str): Device name. Should follow the pattern\n            '/{device type}:{device number}' where device type is one of\n            CPU or GPU.\n    \"\"\"\n    if not config.ALLOW_SWITCHERS and name != K.default_device:\n        log.warning(\"Device should not be changed after allocating gates.\")\n    K.set_device(name)\n    for bk in K.constructed_backends.values():\n        if bk.name != \"numpy\" and bk != K.active_backend:\n            bk.set_device(name)\n\n\ndef get_device():\n    return K.default_device\n\n\ndef set_threads(nthreads):\n    \"\"\"Set number of CPU threads.\n\n    Args:\n        nthreads (int): number of threads.\n    \"\"\"\n    K.set_threads(nthreads)\n\n\ndef get_threads():\n    \"\"\"Returns number of threads.\"\"\"\n    return K.nthreads\n","sub_path":"src/qibo/backends/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":8011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"527853670","text":"#Stephen Lekko\r\n#Computing and Informatics Capstone\r\n#This is the file used for making a connection to RAWG.io\r\n#key = 24cad30c5b3949ff9140d8617254ff06\r\n\r\nimport requests\r\nimport json\r\nimport mysql.connector\r\n\r\n#START DB\r\nuserin = input(\"Enter the Game Name: \")\r\nmydb = mysql.connector.connect(\r\n    host = \"localhost\",\r\n    user = \"stephen\", \r\n    passwd = \"cowAttorney48\",\r\n    database = \"capstone_db\"\r\n)\r\n\r\nmycursor = mydb.cursor()\r\nmycursor.execute(\"use capstone_db\")\r\n\r\nurl = \"https://api.rawg.io/api/games?key=24cad30c5b3949ff9140d8617254ff06&page_size=1&search=\" + userin;\r\n\r\nresultCount = 0\r\n\r\nwhile resultCount < 1:\r\n    r = requests.get(url)\r\n   # print(url)\r\n    data = json.loads(r.text)\r\n    url = data['next']\r\n    for game in data['results']:\r\n        sql = \"INSERT INTO rawg_api (rawg_id, rawg_name, rawg_slug, rawg_release_date, rawg_metacritic) VALUES (%s, %s, %s, %s, %s )\"\r\n        val = (game['id'], game['name'], game['slug'], game['released'], game['metacritic'])\r\n        mycursor.execute(sql,val)\r\n        mydb.commit()\r\n        print(mycursor.rowcount,\"record inserted.\")\r\n        print('Game Name: ',game['name'])\r\n        print('Game Slug: ',game['slug'])\r\n        print('Release Date: ',game['released'])\r\n        print('Game ID: ', game['id'])\r\n        print('Metacritic Score: ',game['metacritic'])\r\n        print('-------------------------------------')\r\n        resultCount += 1\r\n        \r\n#END DB\r\n\r\n\r\n\r\n        \r\n","sub_path":"rawg.py","file_name":"rawg.py","file_ext":"py","file_size_in_byte":1449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"553149386","text":"\nimport sys\n\nimport classifier_commands\nimport collection1_parser\nimport files_parser\nimport misc\nimport news20_parser\nimport parameters\nimport reuters_parser\nimport wui\n\n\ndef main (arguments) :\n\tif len (arguments) < 1 :\n\t\traise Exception ()\n\tcommand_identifier = arguments[0]\n\tcommand_arguments = arguments[1:]\n\tif command_identifier not in commands :\n\t\traise Exception ()\n\tcommand_handler = commands[command_identifier]\n\tcommand_handler (command_arguments)\n\n\ndef run_command_handler (arguments) :\n\tif len (arguments) < 1 :\n\t\traise Exception ()\n\tfor rc_path in arguments :\n\t\tparameter = parameters.parse_file (run_parameter_definition, rc_path)\n\t\tnew_arguments = list (parameter['arguments'])\n\t\tfor index in xrange (len (new_arguments)) :\n\t\t\tif new_arguments[index] == '__rc__' :\n\t\t\t\tnew_arguments[index] = rc_path\n\t\tmain (new_arguments)\n\n\nrun_parameter_definition = {\n\t'arguments' : parameters.list_type\n}\n\n\ncommands = {\n\t\t'run' : run_command_handler,\n\t\t'classifiers' : classifier_commands.main_command_handler,\n\t\t'wui' : wui.main_command_handler,\n\t\t'parse-collection1' : collection1_parser.main_command_handler,\n\t\t'parse-news20' : news20_parser.main_command_handler,\n\t\t'parse-reuters' : reuters_parser.main_command_handler,\n\t\t'parse-files' : files_parser.main_command_handler,\n\t\t'list-categories' : misc.list_categories_command_handler,\n}\n\n\nif __name__ == '__main__' :\n\tmain (sys.argv[1:])\n","sub_path":"mindsoft/py/mindsoft.py","file_name":"mindsoft.py","file_ext":"py","file_size_in_byte":1393,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"380810944","text":"### TD02 - piles\n# question 1 : fonction est_vide\ndef est_vide(pile):\n    return len(pile)==0\n    \n# >>> est_vide((1,2,3))\n# False\n# >>> est_vide(())\n# True   \n\n# question 2 : fonction est pleine\ndef est_pleine(pile,nb):\n    return len(pile)==nb\n    \n# >>> est_pleine((1,2,3),3)\n# True\n# >>> est_pleine((1,2,3),6)\n# False\n\n# question 3 : ajouter un element (un peu tiré par les cheveux)\ndef push(pile,el):\n    pile=pile+(el,0)#je ne peux pas concatener un tuple avec un seul élément, minimum 2\n    pile=pile[:-1]\n    return(pile)\n    \n# >>> push((1,2,3),(94))\n# (1, 2, 3, 94)\n\ndef pop(pile):\n    dernier=pile[-1]\n    pile=pile[:-1] #je n'arrive pas à changer le tuple qui est non mutable\n    return dernier,pile\n    \n# >>> pop((1,2,3,4,5))\n# 5\n# >>> pop((1,2,3,4,5))\n# (5, (1, 2, 3, 4))\n\n### Exercice 2 : notation polonaise inversee avec l'utilisation de listes\n# est-ce un element au hasard ?\n# la pile est un tuple de strings\ndef est_nombre(pile,i):\n    return pile[i] not in ['+','-','*','/']\n    \n# >>> est_nombre(('+','1','3','*'),1)\n# True  \n\ndef est_operation(pile,i):\n    return pile[i] in ['+','-','*','/']\n    \n# >>> est_operation(('+','1','3','*'),0)\n# True\n# >>> est_operation(('+','1','3','*'),1)\n# False\n\n\ndef evaluer_tuple(exp):# travailler avec une liste\n    ''' l'expression exp doit être postfixée '''\n    pile=()\n    for element in exp:\n        pile=push(pile,element)\n#     return pile resultat OK\n    res=()\n    for elt in pile:\n        if elt == '+':\n            b = float(pop(res)[0])\n            res=pop(res)[1]\n            a=float(pop(res)[0])\n            res=pop(res)[1]\n            res=push(res,(a+b))\n        elif elt == '*':\n            b = float(pop(res)[0])\n            res=pop(res)[1]\n            a=float(pop(res)[0])\n            res=pop(res)[1]\n            res=push(res,(a*b))\n        elif elt == '-':\n            b = float(pop(res)[0])\n            res=pop(res)[1]\n            a=float(pop(res)[0])\n            res=pop(res)[1]\n            res=push(res,(a-b))\n        elif elt == '/':\n            b = float(pop(res)[0])\n            res=pop(res)[1]\n            a=float(pop(res)[0])\n            res=pop(res)[1]\n            res=push(res,(a/b))\n        else:\n            res=push(res,(float(elt)))\n    return res[0]\n    \ndef evaluer_liste(exp):\n    reserve=[]\n    if est_nombre(exp,0) and est_operation(exp,1):\n        exp=exp+(exp[0:2])\n        del exp[0]\n        del exp[0]\n    for element in exp:\n        if element=='+':\n            reserve.append(reserve.pop()+reserve.pop())\n        elif element=='-':\n            a=reserve.pop()\n            reserve.append(reserve.pop()-a)\n        elif element=='*':\n            reserve.append(reserve.pop()*reserve.pop())\n        elif element=='/':\n            reserve.append(1/reserve.pop()*reserve.pop())\n        else:\n            reserve.append(element)\n    return reserve[0]\n\n# >>> evaluer_liste([1,2,'+',4,'*',3,'-',5,'+'])\n# 14   \n\n# >>> evaluer_liste([5,'+',1,2,'+',4,'*',3,'-'])\n# 14 \n    \n\n# Question 4 : '12+4*3-5+'\n\n### Exercice 3 - croisement routier\n#creation de listes aléatoires\nimport random as rd\nf1=[rd.randint(0,1) for i in range(10)]\nf2=[rd.randint(0,1) for i in range(8)]\n\ndef croisement(f1,f2):\n    f3=[]\n    while len(f1)!=0 and len(f2)!=0:\n        if f1[-1]==1: # si un véhicule dans la file 1 il est prioritaire\n            f3.append(1) # la file 3 reçoit le véhicule de la file 1\n            f1.pop() #la file 1 est dépilée\n            if f2[-1]==0:\n                f2.pop() #si pas de voiture sur la file 2 du stop avancer d'un véhicule\n        else: # si pas de véhicule sur la file 1 dépiler la file 2\n            if f2[-1]==1: \n                f3.append(1)\n                f1.pop()\n                f2.pop()\n            else:\n                f3.append(0)\n                f1.pop()\n                f2.pop()\n    if len(f1)!=0: #quand une file est vide les véhicules de la file suivante se vide dans file 3\n        for i in range(len(f1)):\n            f3.append(f1.pop())\n    else:\n        for i in range(len(f2)):\n            f3.append(f2.pop())\n    f3.reverse() #inverser la file 3 pour avoir les véhicules dans l'ordre d'arrivée\n    return f3\n    \n# >>> croisement([0, 1, 1, 0, 0, 1, 1, 0, 1, 1],[0, 1, 0, 1, 1, 1, 1, 0])\n# [0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n        \n    \n    \n    \n","sub_path":"P_05_AlgorithmiqueProgrammation/02_Piles/TD_02/programmes/TD02_piles_Patricia_02.py","file_name":"TD02_piles_Patricia_02.py","file_ext":"py","file_size_in_byte":4302,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"186453747","text":"from glumpy import app, gloo, gl\n\nfilestream = open('vertex_program.glsl')\nprogram_vertex = str(filestream.readlines())\n\nfilestream.close()\n\nfilestream = open('fragment_program.glsl')\nprogram_fragment = str(filestream.readlines())\n\nwindow = app.Window()\n\nquad = gloo.Program(program_vertex, program_fragment, count=4)\n\nquad['position'] = (-1, +1), (+1, +1), (-1, -1), (+1, -1)\n\n@window.event\ndef on_draw(dt):\n    window.clear()\n    quad.draw(gl.GL_TRIANGLE_STRIP)\n\napp.run()\n\n","sub_path":"example/book_example_Python&OpenGL.py","file_name":"book_example_Python&OpenGL.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"26336560","text":"from manual_auth import drive\n\n#\"共有フォルダ/09.その他\"のフォルダID(URLの末端文字列)\nfolder_id = '0B6UTBGc4fcPNLTFKYkp6U3I1VWc'\n#ListFileメソッドでファイル種別を指定するための工夫。\"folder_id\"で指定したフォルダ内かつ、削除されていないファイルのみを抽出する\nquery = \"'{}' in parents and trashed=false\".format(folder_id)\n\n#settings.yamlの設定によって、このAPIからアップロードしたファイルしか読み書きできないので、最初にyojirei.csvをアップロードしたときの名残\n#f = drive.CreateFile({'title': 'yojirei.csv', 'mimeType': 'text/csv', 'parents': [{'kind': 'drive#fileLink', 'id':folder_id}]})\n\n#yojirei.csvのダウンロードをする関数\ndef dl_csv():\n  file_list = drive.ListFile({'q': query}).GetList()\n  for target_file in file_list:\n    if target_file['title'] == 'yojirei.csv':\n      f = target_file\n  f.GetContentFile('yojirei.csv')\n\n#yojirei.csvのアップロードをする関数\ndef ul_csv():\n  file_list = drive.ListFile({'q': query}).GetList()\n  for target_file in file_list:\n    if target_file['title'] == 'yojirei.csv':\n      f = target_file\n  f.SetContentFile('yojirei.csv')\n  f.Upload()\n","sub_path":"yojirei_bot/drive_file.py","file_name":"drive_file.py","file_ext":"py","file_size_in_byte":1230,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"147927832","text":"import matplotlib.pyplot as plt\nimport numpy as np\n\nresult = []\nwith open(\"tmp\",\"r\") as f:\n    for eachline in f.readlines():\n        loss = eachline.rstrip()\n        result.append(float(loss))\n\nplt.plot(range(len(result)),result)\n#plt.xlabel(\"FPR\")\n#plt.ylabel(\"TPR\")\n#plt.text(0,0,\"auc:%f\" % auc)\n#print(auc)\nplt.savefig(\"./roi_loss.jpg\")\n","sub_path":"24net/loss.py","file_name":"loss.py","file_ext":"py","file_size_in_byte":341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"151002225","text":"import requests\nimport json\nfrom unittest import mock\n\nclass GitHubAPI:\n    \n    def __init__(user, id):\n        user.id = id      \n        \n\n        user.repos = []\n        user.commits = {}\n\n    def get_repos(user):\n        try:\n            r = requests.get(f'https://api.github.com/users/{user.id}/repos')\n            \n        except requests.exceptions as error:\n            return\n        for repo in r.json():\n            user.repos.append(repo[\"name\"])\n        return user.repos\n\n    def get_commits(user):\n        for repo in user.repos:\n            try:\n                r = requests.get(f'https://api.github.com/repos/{user.id}/{repo}/commits')\n            except requests.exceptions as error:\n                return\n            user.commits[repo] = len(r.json())\n        return user.commits\n\n    def print_data(user):\n        for repo in user.repos:\n            print(f'Repo: {repo} Number of commits: {user.commits[repo]}')\n\n   \n\nif __name__ == \"__main__\":\n    githubapi = GitHubAPI(\"ImroseSingh\")\n   \n","sub_path":"Getinfo.py","file_name":"Getinfo.py","file_ext":"py","file_size_in_byte":1013,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"588929688","text":"# data analysis and wrangling\nimport pandas as pd\nimport numpy as np\n#new comment\nimport random as rnd\n\n# visualization\nimport seaborn as sns\n\nimport matplotlib.pyplot as plt\n#%matplotlib inline\n\n# machine learning\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC, LinearSVC\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.linear_model import Perceptron\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.tree import DecisionTreeClassifier\n\ntrain_df = pd.read_csv('input/train.csv')\n\ntest_df = pd.read_csv('input/test.csv')\n\ncombine = [train_df, test_df]\n\ntrain_df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)\n\n\n# grid = sns.FacetGrid(train_df, col='Pclass', hue='Survived')\ngrid = sns.FacetGrid(train_df, col='Survived', row='Pclass', size=2.2, aspect=1.6)\ngrid.map(plt.hist, 'Age', alpha=.5, bins=20)\ngrid.add_legend();\n\n\ngrid = sns.FacetGrid(train_df, row='Embarked', size=2.2, aspect=1.6)\ngrid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')\ngrid.add_legend()\n\n\n\nageMean=train_df[['Age','Survived']].groupby(['Age'], as_index=False).mean()\n\ntrain_df[['Title','Survived']].groupby(['Title'], as_index=False).mean()\n\nage=pd.merge(ageMean,ageCount, how='left', left_on='Age', right_on='Age', suffixes=('_ageMean', '_ageCount'))\n\n\n\ntrain_df = train_df.drop(['Ticket', 'Cabin'], axis=1)\ntest_df = test_df.drop(['Ticket', 'Cabin'], axis=1)\ncombine = [train_df, test_df]\n\n\nfor dataset in combine:\n    dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n\n\npd.crosstab(train_df['Title'], train_df['Sex'])\n\nfor dataset in combine:\n    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n\ntrain_df[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()\n\n\ntitle_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\nfor dataset in combine:\n    dataset['Title'] = dataset['Title'].map(title_mapping)\n    dataset['Title'] = dataset['Title'].fillna(0)\n\ntrain_df.head()\n\n\ntrain_df = train_df.drop(['Name', 'PassengerId'], axis=1)\ntest_df = test_df.drop(['Name'], axis=1)\ncombine = [train_df, test_df]\ntrain_df.shape, test_df.shape\n\n\nfor dataset in combine:\n    dataset['Sex'] = dataset['Sex'].map( {'female': 1, 'male': 0} ).astype(int)\n\n\n\n\n\nX_train = train_df.drop(\"Survived\", axis=1)\nY_train = train_df[\"Survived\"]\nX_test  = test_df.drop(\"PassengerId\", axis=1).copy()\nX_train.shape, Y_train.shape, X_test.shape\n\n\nlogreg = LogisticRegression()\n\n\nlogreg.fit(X_train, Y_train)\n\n\nY_pred = logreg.predict(X_test)\nacc_log = round(logreg.score(X_train, Y_train) * 100, 2)\nacc_log\n\n\n","sub_path":"titanic.py","file_name":"titanic.py","file_ext":"py","file_size_in_byte":3022,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"143222472","text":"#!/usr/bin/env python3\n#Created by xuchao on 16/7/25.\nimport pika,time\nHOST = '192.168.56.2'\nGROUP = 'logs'\nWAY = 'fanout'\nTIME = 5\nCOMMONS = 'ls'\n\nclass Center(object):\n    def __init__(self):\n        self.connection = pika.BlockingConnection(pika.ConnectionParameters(\n                host=HOST))\n\n        self.channel = self.connection.channel()\n\n        self.channel.exchange_declare(exchange=GROUP,\n                                 type=WAY)\n\n        # 定义接收返回消息的队列\n        result = self.channel.queue_declare(exclusive=True)\n        # 随机队列名称\n        self.callback_queue = result.method.queue\n        # 添加 接收返回消息\n        self.channel.basic_consume(self.callback,\n                                   no_ack=True,\n                                   queue=self.callback_queue)\n\n    # 定义接收到返回消息的处理方法\n    def callback(self, ch, method, props, body):\n        # self.response = body\n        print(body.decode())\n\n    def request(self, n):\n        self.response = None\n        #发送计算请求，并声明返回队列 # reply_to 返回队列名称\n        self.channel.basic_publish(exchange=GROUP,\n                                   routing_key=self.callback_queue,\n                                   properties=pika.BasicProperties(\n                                         reply_to = self.callback_queue,),\n                                   body=str(n))\n        # 设定接收时间\n        Nowtime = time.time() + TIME\n        while time.time() < Nowtime:\n            # 接收返回的数据,等待返回数据\n            self.connection.process_data_events()\n        # return self.response\n        # self.channel.start_consuming()\n\ncenter = Center()\n\nresponse = center.request(COMMONS)\n","sub_path":"RabMQ_队列消息/server/RPC_server.py","file_name":"RPC_server.py","file_ext":"py","file_size_in_byte":1769,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"106817962","text":"class Graduacao:\n    def __init__(self):\n        self.sequencia_formacao = None\n        self.nivel = None\n        self.titulo_do_trabalho_de_conclusao_de_curso = None\n        self.nome_do_orientador = None\n        self.codigo_instituicao = None\n        self.nome_instituicao = None\n        self.codigo_orgao = None\n        self.nome_orgao = None\n        self.codigo_curso = None\n        self.nome_curso = None\n        self.codigo_area_curso = None\n        self.status_do_curso = None\n        self.ano_de_inicio = None\n        self.ano_de_conclusao = None\n        self.flag_bolsa = None\n        self.codigo_agencia_financiadora = None\n        self.nome_agencia = None\n        self.numero_id_orientador = None\n        self.codigo_curso_capes = None\n        self.titulo_do_trabalho_de_conclusao_de_curso_ingles = None\n        self.nome_curso_ingles = None\n        self.formacao_academica_titulacao = None\n        self.tipo_graduacao = None\n        self.codigo_instituicao_grad = None\n        self.nome_instituicao_grad = None\n        self.codigo_instituicao_outra_grad = None\n        self.nome_instituicao_outra_grad = None\n        self.nome_orientador_grad = None\n\n    def parse(self, data):\n        self.sequencia_formacao = data.get('SEQUENCIA-FORMACAO')\n        self.nivel = data.get('NIVEL')\n        self.titulo_do_trabalho_de_conclusao_de_curso = data.get('TITULO-DO-TRABALHO-DE-CONCLUSAO-DE-CURSO')\n        self.nome_do_orientador = data.get('NOME-DO-ORIENTADOR')\n        self.codigo_instituicao = data.get('CODIGO-INSTITUICAO')\n        self.nome_instituicao = data.get('NOME-INSTITUICAO')\n        self.codigo_orgao = data.get('CODIGO-ORGAO')\n        self.nome_orgao = data.get('NOME-ORGAO')\n        self.codigo_curso = data.get('CODIGO-CURSO')\n        self.nome_curso = data.get('NOME-CURSO')\n        self.codigo_area_curso = data.get('CODIGO-AREA-CURSO')\n        self.status_do_curso = data.get('STATUS-DO-CURSO')\n        self.ano_de_inicio = data.get('ANO-DE-INICIO')\n        self.ano_de_conclusao = data.get('ANO-DE-CONCLUSAO')\n        self.flag_bolsa = data.get('FLAG-BOLSA')\n        self.codigo_agencia_financiadora = data.get('CODIGO-AGENCIA-FINANCIADORA')\n        self.nome_agencia = data.get('NOME-AGENCIA')\n        self.numero_id_orientador = data.get('NUMERO-ID-ORIENTADOR')\n        self.codigo_curso_capes = data.get('CODIGO-CURSO-CAPES')\n        self.titulo_do_trabalho_de_conclusao_de_curso_ingles = data.get('TITULO-DO-TRABALHO-DE-CONCLUSAO-DE-CURSO-INGLES')\n        self.nome_curso_ingles = data.get('NOME-CURSO-INGLES')\n        self.formacao_academica_titulacao = data.get('FORMACAO-ACADEMICA-TITULACAO')\n        self.tipo_graduacao = data.get('TIPO-GRADUACAO')\n        self.codigo_instituicao_grad = data.get('CODIGO-INSTITUICAO-GRAD')\n        self.nome_instituicao_grad = data.get('NOME-INSTITUICAO-GRAD')\n        self.codigo_instituicao_outra_grad = data.get('CODIGO-INSTITUICAO-OUTRA-GRAD')\n        self.nome_instituicao_outra_grad = data.get('NOME-INSTITUICAO-OUTRA-GRAD')\n        self.nome_orientador_grad = data.get('NOME-ORIENTADOR-GRAD')\n\n    def __dict__(self):\n        return {\n            'sequencia_formacao': self.sequencia_formacao,\n            'nivel': self.nivel,\n            'titulo_do_trabalho_de_conclusao_de_curso': self.titulo_do_trabalho_de_conclusao_de_curso,\n            'nome_do_orientador': self.nome_do_orientador,\n            'codigo_instituicao': self.codigo_instituicao,\n            'nome_instituicao': self.nome_instituicao,\n            'codigo_orgao': self.codigo_orgao,\n            'nome_orgao': self.nome_orgao,\n            'codigo_curso': self.codigo_curso,\n            'nome_curso': self.nome_curso,\n            'codigo_area_curso': self.codigo_area_curso,\n            'status_do_curso': self.status_do_curso,\n            'ano_de_inicio': self.ano_de_inicio,\n            'ano_de_conclusao': self.ano_de_conclusao,\n            'flag_bolsa': self.flag_bolsa,\n            'codigo_agencia_financiadora': self.codigo_agencia_financiadora,\n            'nome_agencia': self.nome_agencia,\n            'numero_id_orientador': self.numero_id_orientador,\n            'codigo_curso_capes': self.codigo_curso_capes,\n            'titulo_do_trabalho_de_conclusao_de_curso_ingles': self.titulo_do_trabalho_de_conclusao_de_curso_ingles,\n            'nome_curso_ingles': self.nome_curso_ingles,\n            'formacao_academica_titulacao': self.formacao_academica_titulacao,\n            'tipo_graduacao': self.tipo_graduacao,\n            'codigo_instituicao_grad': self.codigo_instituicao_grad,\n            'nome_instituicao_grad': self.nome_instituicao_grad,\n            'codigo_instituicao_outra_grad': self.codigo_instituicao_outra_grad,\n            'nome_orientador_grad': self.nome_orientador_grad,\n        }\n","sub_path":"cv/graduacao.py","file_name":"graduacao.py","file_ext":"py","file_size_in_byte":4753,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"235032988","text":"import pyaudio\nimport wave\nimport matplotlib.pyplot as plt\nfrom scipy.io import wavfile\nfrom scipy.fftpack import fft\nfrom pylab import*\n\n#CHUNK = 1024\nCHUNK = 64\nFORMAT = pyaudio.paInt16\nCHANNELS = 2\nRATE = 44100\nRECORD_SECONDS = 1\nWAVE_OUTPUT_FILENAME = \"output.wav\"\nDEVICE = 2\n\np = pyaudio.PyAudio()\nfile = open(\"raw_data.txt\", 'a')\nstream = p.open(format=FORMAT,\n                channels=CHANNELS,\n                rate=RATE,\n                input=True,\n                frames_per_buffer=CHUNK,\n\t\tinput_device_index=DEVICE)\n\nprint(\"* recording\")\n\nframes = []\n\n\n\nfor i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):\n#for i in range(1):\n    data = stream.read(CHUNK)\n    frames.append(data)\n    \nprint(\"* done recording\")\nfile.write(data)\n\nstream.stop_stream()\nstream.close()\np.terminate()\n\nwf = wave.open(WAVE_OUTPUT_FILENAME, 'wb')\nwf.setnchannels(CHANNELS)\nwf.setsampwidth(p.get_sample_size(FORMAT))\nwf.setframerate(RATE)\nwf.writeframes(b''.join(frames))\nwf.close()\n\nfs, input_data = wavfile.read('output.wav')\ndata = input_data.T[0]\nprint(data)\nraw_data = [(ele/2**8.)*2-1 for ele in data]\nraw_transform = fft(raw_data)\nfreqs = fftfreq(len(raw_transform))\nprint(freqs.min(), freqs.max())\n\nidx = np.argmax(np.abs(raw_transform))\nprint(freqs)\nfreq = freqs[idx]\nfreq_in_hertz = abs(freq * RATE)\nprint(freq_in_hertz)\n##fs, data = wavfile.read('output.wav')\n##a = data.T[0]\n##b = [(ele/2**8.)*2-1 for ele in a]\n##c = fft(b)\n##d = len(c)/2\n##plt.plot(abs(c[:(d-1)]), 'r')\n##plt.show()\n","sub_path":"record.py","file_name":"record.py","file_ext":"py","file_size_in_byte":1486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"264108990","text":"import unittest\n\nfrom math_blocks.algebra.polynomials import SimplePoly, Variable\nfrom math_blocks.algebra.core import Chain, Number\n\n\nclass poly_add(unittest.TestCase):\n\n    \n    def test_number_add(self):\n        x = Variable(\"x\", value=1)\n        main_poly = SimplePoly([1,2,3], x)\n        \n        n = Number(3)\n\n        result = main_poly + n\n        \n        result_latex = \"1x^{2}+2x+3+3\"\n        result_value = 9\n        self.assertEqual(result.latex(), result_latex)\n        self.assertEqual(result.evaluate(), result_value)\n\n\n    def test_poly_eq(self):\n        x = Variable(\"x\", value=1)\n        main_poly = SimplePoly([1,2,3], x)\n        \n        a = Variable(\"a\", value=1)\n        p = SimplePoly([1,2,3], a)\n\n        result = main_poly + p\n        \n        result_latex = \"1x^{2}+2x+3+1a^{2}+2a+3\"\n        result_value = 12\n        \n        self.assertEqual(result.latex(), result_latex)\n        self.assertEqual(result.evaluate(), result_value)\n\n    def test_chain_eq(self):\n        x = Variable(\"x\", value=1)\n        main_poly = SimplePoly([1,2,3], x)\n        \n        alt_chain_b = Chain([1,3,2])\n\n        result = main_poly + alt_chain_b\n        \n        result_latex = \"1x^{2}+2x+3+(1+3+2)\"\n        result_value = 12\n        \n        self.assertEqual(result.latex(), result_latex)\n        self.assertEqual(result.evaluate(), result_value)\n\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"tests/algebra/polynomials/operations/__add__.py","file_name":"__add__.py","file_ext":"py","file_size_in_byte":1407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"460700481","text":"from flask_app_mvc_webservices.configuration import db,app\nfrom flask_app_mvc_webservices.model import*\nfrom flask import render_template,request,session\n\n@app.route('/account/',methods=['GET','POST'])\ndef save_update_account():\n if 'userinfo' in session:\n    msg=''\n    if request.method=='POST':\n        accno=int(request.form['accno'])\n        acctype = request.form['accty']\n        accbal= request.form['accbal']\n        dbacc=Account.query.filter_by(id=accno).first()\n        if dbacc:\n            dbacc.type=acctype\n            dbacc.balance=accbal\n            db.session.commit()\n            msg='Account Updated Succesfully..!'\n        else:\n            dbacc=Account(id=accno,type=acctype,balance=accbal)\n            db.session.add(dbacc)\n            db.session.commit()\n            msg='Account Created Succefully..!'\n\n    return render_template('account.html',\n                           reso=msg,user=session['userinfo'],\n                           account=Account.get_dummy_account(),\n                           acclist=Account.query.all(),\n                           hotlist=Hotel.query.all(),\n                           menulist=Menu.query.all(),\n                           roomlist=Room.query.all()\n                           )\n\n return render_template('login,html', resp='')\n\n\n\n@app.route('/edit/<int:acid>')\ndef edit_account(acid):\n    if 'userinfo' in session:\n      return render_template('account.html',user=session['userinfo'],\n                           account=Account.query.filter_by(id=acid).first(),\n                           acclist=Account.query.all(),\n                           hotlist=Hotel.query.all(),\n                           menulist=Menu.query.all(),\n                           roomlist=Room.query.all()\n                         )\n    return render_template('login.html',resp='')\n\n\n@app.route('/delete/<int:acid>')\ndef delete_account(acid):\n    if 'userinfo' in session:\n        msg=''\n        acc=Account.query.filter_by(id=acid).first()\n        if acc:\n            db.session.delete(acc)\n            db.session.commit()\n            msg='Account Delete Succesfully..!'\n        return render_template('account.html',\n                               resp=msg,user=session['userinfo'],\n                               account=Account.get_dummy_account(),\n                               acclist=Account.query.all(),\n                               hotlist=Hotel.query.all(),\n                               menulist=Menu.query.all(),\n                               roomlist=Room.query.all()\n                               )\n\n    return render_template('login.html',resp='')\n\n\nif __name__ == '__main__':\n    app.run(debug=True)","sub_path":"flask_app_mvc_webservices/accountcontroller.py","file_name":"accountcontroller.py","file_ext":"py","file_size_in_byte":2660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"441471552","text":"\"\"\" Common module\nimplement commonly used functions here\n\"\"\"\n\nimport random\nimport ui\nimport gui\nimport data_manager\nfrom PyQt5.QtWidgets import QLineEdit\n\n\ndef choose(table, header, filename):\n    inputs = ui.get_inputs([[\"Please enter a number\", int]], \"\")\n    option = inputs[0]\n    if option == \"1\":\n        show_table(table, header)\n    elif option == \"2\":\n        add(table, header)\n    elif option == \"3\":\n        id_ = ui.get_inputs([[\"ID\", str]], \"To remove a record please type it's ID.\")[0]\n        remove(table, id_)\n    elif option == \"4\":\n        id_ = ui.get_inputs([[\"ID\", str]], \"To update a record please type it's ID.\")[0]\n        update(table, header, id_)\n    elif option == \"5\":\n        return 5\n    elif option == \"6\":\n        return 6\n    elif option == \"0\":\n        ui.clear_screen()\n        data_manager.write_table_to_file(filename, table)\n        return False\n    else:\n        raise KeyError(\"That is not a valid option.\")\n    return True\n\n\ndef get_index_of_element(list_, element):\n    \"\"\"\n    Search for index in a list.\n\n    Args:\n        list_ (list): list to search the index\n        element: element to search the index in the list\n\n    Returns:\n        integer: index of the element in the list\n        None: the element is not in list\n    \"\"\"\n    for i, item in enumerate(list_):\n        if item == element:\n            return i\n\n\ndef count_item(table, item):\n    \"\"\"\n    Count an item in the table\n\n    Args:\n        table (list): table to count the item in it.\n        item: value which has to be counted\n\n    Returns:\n        integer: count of value, 0 if no value in table\n    \"\"\"\n    count = 0\n    for element in table:\n        if element == item:\n            count += 1\n    return count\n\n\ndef order_list(list_, descending=True):\n    \"\"\"\n    Order a list to descending or incrementing\n\n    Args:\n        list_ (list): list to order.\n        descending: True by default, if incrementing order needed, it has to be set to False\n\n    Returns:\n        list: ordered list\n    \"\"\"\n    for i in range(len(list_) - 1):\n        for j in range(1, len(list_)):\n            if descending:\n                if list_[j] < list_[j - 1]:\n                    temp = list_[j - 1]\n                    list_[j - 1] = list_[j]\n                    list_[j] = temp\n            else:\n                if list_[j] > list_[j - 1]:\n                    temp = list_[j - 1]\n                    list_[j - 1] = list_[j]\n                    list_[j] = temp\n    return list_\n\n\ndef show_table(table, header):\n    \"\"\"\n    Display a table\n\n    Args:\n        table (list): list of lists to be displayed.\n\n    Returns:\n        None\n    \"\"\"\n    ui.print_table(table, header)\n\n\ndef add(table, header):\n    \"\"\"\n    Asks user for input and adds it into the table.\n\n    Args:\n        table (list): table to add new record to\n        header (list): header of the table, contains which datas, has to be requested\n\n    Returns:\n        list: Table with a new record\n    \"\"\"\n    new_row = ui.get_inputs(header[1:], \"Please provide the following data:\")\n    new_row.insert(0, generate_random(table))\n    table.append(new_row)\n    return table\n\n\ndef remove(table, id_):\n    \"\"\"\n    Remove a record with a given id from the table.\n\n    Args:\n        table (list): table to remove a record from\n        id_ (str): id of a record to be removed\n\n    Returns:\n        list: Table without specified record.\n    \"\"\"\n    list_of_ids = [row[0] for row in table]\n    index = get_index_of_element(list_of_ids, id_)\n    del table[index]\n    return table\n\n\ndef update(table, header, id_):\n    \"\"\"\n    Updates specified record in the table. Ask users for new data.\n\n    Args:\n        table: list in which record should be updated\n        id_ (str): id of a record to update\n\n    Returns:\n        list: table with updated record\n    \"\"\"\n    list_of_ids = [row[0] for row in table]\n    index = get_index_of_element(list_of_ids, id_)\n    new_details = ui.get_inputs(header[1:], \"Please provide the following data:\")\n    new_details.insert(0, id_)\n    table[index] = new_details\n    return table\n\n\ndef generate_random(table):\n    \"\"\"\n    Generates random and unique string. Used for id/key generation:\n         - at least 2 special characters (except: ';'), 2 number, 2 lower and 2 upper case letter\n         - it must be unique in the table (first value in every row is the id)\n\n    Args:\n        table (list): Data table to work on. First columns containing the keys.\n\n    Returns:\n        string: Random and unique string\n    \"\"\"\n    # a-z: 97-122\n    # 0-9: 48-57\n    special_characters = [\"#\", \"&\", \"@\", \"$\", \"§\"]\n    generated = ''\n    for i in range(8):\n        if i == 0 or i == 5:\n            generated += chr(random.randint(97, 122))\n        elif i == 1 or i == 4:\n            generated += chr(random.randint(97, 122)).upper()\n        elif i == 2 or i == 3:\n            generated += chr(random.randint(48, 57))\n        else:\n            generated += special_characters[random.randrange(len(special_characters))]\n    return generated\n\n\ndef get_gui_table():\n    result = []\n    module = gui.ui.windowTitle()\n    table = gui.ui.storeTable\n    rows = table.rowCount()\n    columns = table.columnCount()\n    for row in range(rows):\n        rowData = []\n        for column in range(columns):\n            item = table.item(row, column)\n            rowData.append(item.text())\n        result.append(rowData)\n    return result\n\n\ndef get_gui_header():\n    result = []\n    table = gui.ui.storeTable\n    columns = table.columnCount()\n    for column in range(columns):\n        result.append(table.horizontalHeaderItem(column).text())\n    return result\n\n\ndef get_filename(module):\n    return \"{}/{}.csv\".format(module, module)\n\n\ndef save_gui_table():\n    module = gui.ui.windowTitle()\n    data_manager.write_table_to_file(get_filename(module), get_gui_table())\n\n\ndef delete_gui_item():\n    module = gui.ui.windowTitle()\n    table = gui.ui.storeTable\n    row = table.currentRow()\n    id = table.item(row, 0).text()\n    file_data = data_manager.get_table_from_file(get_filename(module))\n    new_table = remove(file_data, id)\n    data_manager.write_table_to_file(get_filename(module), new_table)\n    gui.show_table(new_table)\n\n\ndef add_gui_item():\n    module = gui.ui.windowTitle()\n    header = get_gui_header()\n    new_item = [gui.ui.input1.text()]\n    for column in range(2, len(header)+1):\n        input_field = gui.ui.findChild(QLineEdit, 'input' + str(column))\n        new_item.append(input_field.text())\n    file_data = data_manager.get_table_from_file(get_filename(module))\n    file_data.append(new_item)\n    data_manager.write_table_to_file(get_filename(module), file_data)\n    gui.show_table(file_data)\n    gui. show_form(get_gui_header())\n","sub_path":"common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":6715,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"536021177","text":"from math import ceil\n\nfrom Cb_constants import CbServer, DocLoading\nfrom basetestcase import ClusterSetup\nfrom collections_helper.collections_spec_constants import \\\n    MetaConstants, MetaCrudParams\nfrom couchbase_helper.durability_helper import DurabilityHelper\nfrom membase.api.rest_client import RestConnection\nfrom BucketLib.BucketOperations import BucketHelper\nfrom BucketLib.bucket import Bucket\nfrom remote.remote_util import RemoteMachineShellConnection\nfrom cb_tools.cbstats import Cbstats\nfrom sdk_exceptions import SDKException\nfrom java.lang import Exception as Java_base_exception\n\n\nclass CollectionBase(ClusterSetup):\n    def setUp(self):\n        super(CollectionBase, self).setUp()\n        self.log_setup_status(\"CollectionBase\", \"started\")\n\n        self.key = 'test_collection'.rjust(self.key_size, '0')\n        self.simulate_error = self.input.param(\"simulate_error\", None)\n        self.error_type = self.input.param(\"error_type\", \"memory\")\n        self.doc_ops = self.input.param(\"doc_ops\", None)\n        # If True, creates bucket/scope/collections with simpler names\n        self.use_simple_names = self.input.param(\"use_simple_names\", True)\n        self.spec_name = self.input.param(\"bucket_spec\",\n                                          \"single_bucket.default\")\n        self.data_spec_name = self.input.param(\"data_spec_name\",\n                                               \"initial_load\")\n        self.remove_default_collection = \\\n            self.input.param(\"remove_default_collection\", False)\n\n        self.action_phase = self.input.param(\"action_phase\",\n                                             \"before_default_load\")\n        self.skip_collections_cleanup = \\\n            self.input.param(\"skip_collections_cleanup\", False)\n        self.validate_docs_count_during_teardown = \\\n            self.input.param(\"validate_docs_count_during_teardown\", False)\n        self.batch_size = self.input.param(\"batch_size\", 200)\n        self.process_concurrency = self.input.param(\"process_concurrency\", 1)\n        self.retry_get_process_num = \\\n            self.input.param(\"retry_get_process_num\", 200)\n        self.change_magma_quota = self.input.param(\"change_magma_quota\", False)\n        self.crud_batch_size = 100\n        self.num_nodes_affected = 1\n        if self.num_replicas > 1:\n            self.num_nodes_affected = 2\n\n        if self.doc_ops:\n            self.doc_ops = self.doc_ops.split(';')\n\n        self.durability_helper = DurabilityHelper(\n            self.log, len(self.cluster.nodes_in_cluster),\n            self.durability_level)\n\n        # Disable auto-failover to avoid failover of nodes\n        status = RestConnection(self.cluster.master) \\\n            .update_autofailover_settings(False, 120, False)\n        self.assertTrue(status, msg=\"Failure during disabling auto-failover\")\n        self.bucket_helper_obj = BucketHelper(self.cluster.master)\n        self.disk_optimized_thread_settings = self.input.param(\"disk_optimized_thread_settings\", False)\n        if self.disk_optimized_thread_settings:\n            self.set_num_writer_and_reader_threads(num_writer_threads=\"disk_io_optimized\",\n                                                   num_reader_threads=\"disk_io_optimized\")\n\n        try:\n            self.collection_setup()\n        except Java_base_exception as exception:\n            self.handle_setup_exception(exception)\n        except Exception as exception:\n            self.handle_setup_exception(exception)\n        self.supported_d_levels = \\\n            self.bucket_util.get_supported_durability_levels()\n        self.log_setup_status(\"CollectionBase\", \"complete\")\n\n    def tearDown(self):\n        shell = RemoteMachineShellConnection(self.cluster.master)\n        cbstat_obj = Cbstats(shell)\n        for bucket in self.cluster.buckets:\n            if bucket.bucketType != Bucket.Type.MEMCACHED:\n                result = cbstat_obj.all_stats(bucket.name)\n                self.log.info(\"Bucket: %s, Active Resident ratio(DGM): %s%%\"\n                              % (bucket.name,\n                                 result[\"vb_active_perc_mem_resident\"]))\n                self.log.info(\"Bucket: %s, Replica Resident ratio(DGM): %s%%\"\n                              % (bucket.name,\n                                 result[\"vb_replica_perc_mem_resident\"]))\n            if not self.skip_collections_cleanup \\\n                    and bucket.bucketType != Bucket.Type.MEMCACHED:\n                self.bucket_util.remove_scope_collections_for_bucket(\n                    self.cluster, bucket)\n        shell.disconnect()\n        if self.validate_docs_count_during_teardown:\n            self.bucket_util.validate_docs_per_collections_all_buckets(\n                self.cluster)\n        if self.disk_optimized_thread_settings:\n            self.set_num_writer_and_reader_threads(num_writer_threads=\"default\",\n                                                   num_reader_threads=\"default\")\n        super(CollectionBase, self).tearDown()\n\n    @staticmethod\n    def create_sdk_clients(num_threads, master,\n                           buckets, sdk_client_pool, sdk_compression):\n        # Fetch num_collections per bucket. Used for 'req_clients' calc\n        cols_in_bucket = dict()\n        for bucket in buckets:\n            collections_in_bucket = 0\n            for _, scope in bucket.scopes.items():\n                for _, _ in scope.collections.items():\n                    collections_in_bucket += 1\n            cols_in_bucket[bucket.name] = collections_in_bucket\n\n        # Create clients in SDK client pool\n        bucket_count = len(buckets)\n        max_clients = num_threads\n        clients_per_bucket = int(ceil(max_clients / bucket_count))\n        for bucket in buckets:\n            sdk_client_pool.create_clients(\n                bucket=bucket, servers=[master],\n                req_clients=min(cols_in_bucket[bucket.name],\n                                clients_per_bucket),\n                compression_settings=sdk_compression)\n\n    def collection_setup(self):\n        self.bucket_util.add_rbac_user(self.cluster.master)\n\n        # Create bucket(s) and add rbac user\n        if self.bucket_storage == Bucket.StorageBackend.magma:\n            # get the TTL value\n            buckets_spec_from_conf = \\\n                self.bucket_util.get_bucket_template_from_package(\n                    self.spec_name)\n            bucket_ttl = buckets_spec_from_conf.get(Bucket.maxTTL, 0)\n            # Blindly override the bucket spec if the backend storage is magma.\n            # So, Bucket spec in conf file will not take any effect.\n            self.spec_name = \"single_bucket.bucket_for_magma_collections\"\n            magma_bucket_spec = \\\n                self.bucket_util.get_bucket_template_from_package(\n                    self.spec_name)\n            magma_bucket_spec[Bucket.maxTTL] = bucket_ttl\n            buckets_spec = magma_bucket_spec\n        else:\n            buckets_spec = self.bucket_util.get_bucket_template_from_package(\n                self.spec_name)\n        buckets_spec[MetaConstants.USE_SIMPLE_NAMES] = self.use_simple_names\n\n        doc_loading_spec = \\\n            self.bucket_util.get_crud_template_from_package(\n                self.data_spec_name)\n\n        # Process params to over_ride values if required\n        self.over_ride_bucket_template_params(buckets_spec)\n        self.over_ride_doc_loading_template_params(doc_loading_spec)\n        self.set_retry_exceptions_for_initial_data_load(doc_loading_spec)\n        self.bucket_util.create_buckets_using_json_data(self.cluster,\n                                                        buckets_spec)\n        self.bucket_util.wait_for_collection_creation_to_complete(self.cluster)\n\n        # Prints bucket stats before doc_ops\n        self.bucket_util.print_bucket_stats(self.cluster)\n\n        # Change magma quota only for bloom filter testing\n        if self.change_magma_quota:\n            bucket_helper = BucketHelper(self.cluster.master)\n            bucket_helper.set_magma_quota_percentage()\n            self.sleep(30, \"Sleep while magma storage quota setting is taking effect\")\n\n        # Init sdk_client_pool if not initialized before\n        if self.sdk_client_pool is None:\n            self.init_sdk_pool_object()\n\n        self.log.info(\"Creating required SDK clients for client_pool\")\n        self.create_sdk_clients(self.task_manager.number_of_threads,\n                                self.cluster.master,\n                                self.cluster.buckets,\n                                self.sdk_client_pool,\n                                self.sdk_compression)\n\n        doc_loading_task = \\\n            self.bucket_util.run_scenario_from_spec(\n                self.task,\n                self.cluster,\n                self.cluster.buckets,\n                doc_loading_spec,\n                mutation_num=0,\n                batch_size=self.batch_size,\n                process_concurrency=self.process_concurrency)\n        if doc_loading_task.result is False:\n            self.fail(\"Initial doc_loading failed\")\n\n        self.cluster_util.print_cluster_stats(self.cluster)\n\n        ttl_buckets = [\n            \"multi_bucket.buckets_for_rebalance_tests_with_ttl\",\n            \"multi_bucket.buckets_all_membase_for_rebalance_tests_with_ttl\",\n            \"volume_templates.buckets_for_volume_tests_with_ttl\",\n            \"magma_dgm.5_percent_dgm.5_node_2_replica_magma_ttl_256\",\n            \"magma_dgm.5_percent_dgm.5_node_2_replica_magma_ttl_512\",\n            \"magma_dgm.5_percent_dgm.5_node_2_replica_magma_ttl_1024\",\n            \"magma_dgm.5_percent_dgm.5_node_1_replica_magma_ttl_256_single_bucket\",\n            \"magma_dgm.5_percent_dgm.5_node_1_replica_magma_ttl_512_single_bucket\",\n            \"magma_dgm.10_percent_dgm.5_node_2_replica_magma_ttl_256\",\n            \"magma_dgm.10_percent_dgm.5_node_2_replica_magma_ttl_512\",\n            \"magma_dgm.10_percent_dgm.5_node_2_replica_magma_ttl_1024\",\n            \"magma_dgm.20_percent_dgm.5_node_2_replica_magma_ttl_256\",\n            \"magma_dgm.20_percent_dgm.5_node_2_replica_magma_ttl_512\",\n            \"magma_dgm.20_percent_dgm.5_node_2_replica_magma_ttl_1024\",\n            \"magma_dgm.40_percent_dgm.5_node_2_replica_magma_ttl_512\",\n            \"magma_dgm.80_percent_dgm.5_node_2_replica_magma_ttl_512\",\n        ]\n\n        # Verify initial doc load count\n        self.bucket_util._wait_for_stats_all_buckets(self.cluster,\n                                                     self.cluster.buckets,\n                                                     timeout=1200)\n        if self.spec_name not in ttl_buckets:\n            self.bucket_util.validate_docs_per_collections_all_buckets(\n                self.cluster, timeout=2400)\n\n        # Prints bucket stats after doc_ops\n        self.bucket_util.print_bucket_stats(self.cluster)\n\n    def over_ride_bucket_template_params(self, bucket_spec):\n        if self.bucket_storage == Bucket.StorageBackend.magma:\n            # Blindly override the following params\n            bucket_spec[Bucket.evictionPolicy] = \\\n                Bucket.EvictionPolicy.FULL_EVICTION\n        else:\n            for key, val in self.input.test_params.items():\n                if key == \"replicas\":\n                    bucket_spec[Bucket.replicaNumber] = self.num_replicas\n                elif key == \"bucket_size\":\n                    bucket_spec[Bucket.ramQuotaMB] = self.bucket_size\n                elif key == \"num_items\":\n                    bucket_spec[MetaConstants.NUM_ITEMS_PER_COLLECTION] = \\\n                        self.num_items\n                elif key == \"remove_default_collection\":\n                    bucket_spec[MetaConstants.REMOVE_DEFAULT_COLLECTION] = \\\n                        self.input.param(key)\n                elif key == \"bucket_storage\":\n                    bucket_spec[Bucket.storageBackend] = self.bucket_storage\n                elif key == \"compression_mode\":\n                    bucket_spec[Bucket.compressionMode] = self.compression_mode\n                elif key == \"flushEnabled\":\n                    bucket_spec[Bucket.flushEnabled] = int(self.flush_enabled)\n                elif key == \"bucket_type\":\n                    bucket_spec[Bucket.bucketType] = self.bucket_type\n\n    def over_ride_doc_loading_template_params(self, target_spec):\n        for key, value in self.input.test_params.items():\n            if key == \"durability\":\n                target_spec[MetaCrudParams.DURABILITY_LEVEL] = \\\n                    self.durability_level\n            elif key == \"sdk_timeout\":\n                target_spec[MetaCrudParams.SDK_TIMEOUT] = self.sdk_timeout\n            elif key == \"doc_size\":\n                target_spec[\"doc_crud\"][MetaCrudParams.DocCrud.DOC_SIZE] \\\n                    = self.doc_size\n            elif key == \"randomize_value\":\n                target_spec[\"doc_crud\"][MetaCrudParams.DocCrud.RANDOMIZE_VALUE] \\\n                    = self.randomize_value\n\n    def load_data_for_sub_doc_ops(self, verification_dict=None):\n        new_data_load_template = \\\n            self.bucket_util.get_crud_template_from_package(\"initial_load\")\n        new_data_load_template[MetaCrudParams.DURABILITY_LEVEL] = \"\"\n        new_data_load_template[\"doc_crud\"][\n            MetaCrudParams.DocCrud.CREATE_PERCENTAGE_PER_COLLECTION] = 100\n        new_data_load_template[\"subdoc_crud\"][\n            MetaCrudParams.SubDocCrud.INSERT_PER_COLLECTION] = 50\n        doc_loading_task = \\\n            self.bucket_util.run_scenario_from_spec(\n                self.task,\n                self.cluster,\n                self.cluster.buckets,\n                new_data_load_template,\n                mutation_num=0,\n                batch_size=self.batch_size,\n                process_concurrency=self.process_concurrency)\n        if doc_loading_task.result is False:\n            self.fail(\"Extra doc loading task failed\")\n\n        if verification_dict:\n            self.update_verification_dict_from_collection_task(\n                verification_dict,\n                doc_loading_task)\n\n    def update_verification_dict_from_collection_task(self,\n                                                      verification_dict,\n                                                      doc_loading_task):\n        for bucket, s_dict in doc_loading_task.loader_spec.items():\n            for s_name, c_dict in s_dict[\"scopes\"].items():\n                for c_name, _ in c_dict[\"collections\"].items():\n                    c_crud_data = doc_loading_task.loader_spec[\n                        bucket][\"scopes\"][\n                        s_name][\"collections\"][c_name]\n                    for op_type in c_crud_data.keys():\n                        total_mutation = \\\n                            c_crud_data[op_type][\"doc_gen\"].end \\\n                            - c_crud_data[op_type][\"doc_gen\"].start\n                        if op_type in DocLoading.Bucket.DOC_OPS:\n                            verification_dict[\"ops_%s\" % op_type] \\\n                                += total_mutation\n                        elif op_type in DocLoading.Bucket.SUB_DOC_OPS:\n                            verification_dict[\"ops_update\"] \\\n                                += total_mutation\n                        if c_crud_data[op_type][\"durability_level\"] \\\n                                in self.supported_d_levels:\n                            verification_dict[\"sync_write_committed_count\"] \\\n                                += total_mutation\n\n    def validate_cruds_from_collection_mutation(self, doc_loading_task):\n        # Read all the values to validate the CRUDs\n        for bucket, s_dict in doc_loading_task.loader_spec.items():\n            client = self.sdk_client_pool.get_client_for_bucket(bucket)\n            for s_name, c_dict in s_dict[\"scopes\"].items():\n                for c_name, _ in c_dict[\"collections\"].items():\n                    c_crud_data = doc_loading_task.loader_spec[\n                        bucket][\"scopes\"][\n                        s_name][\"collections\"][c_name]\n                    client.select_collection(s_name, c_name)\n                    for op_type in c_crud_data.keys():\n                        doc_gen = c_crud_data[op_type][\"doc_gen\"]\n                        is_sub_doc = False\n                        if op_type in DocLoading.Bucket.SUB_DOC_OPS:\n                            is_sub_doc = True\n                        task = self.task.async_validate_docs(\n                            self.cluster, bucket,\n                            doc_gen, op_type,\n                            scope=s_name, collection=c_name,\n                            batch_size=self.batch_size,\n                            process_concurrency=self.process_concurrency,\n                            sdk_client_pool=self.sdk_client_pool,\n                            is_sub_doc=is_sub_doc)\n                        self.task_manager.get_task_result(task)\n\n    def set_retry_exceptions_for_initial_data_load(self, doc_loading_spec):\n        retry_exceptions = list()\n        retry_exceptions.append(SDKException.AmbiguousTimeoutException)\n        retry_exceptions.append(SDKException.TimeoutException)\n        retry_exceptions.append(SDKException.RequestCanceledException)\n        retry_exceptions.append(SDKException.DocumentNotFoundException)\n        retry_exceptions.append(SDKException.ServerOutOfMemoryException)\n        if self.durability_level:\n            retry_exceptions.append(SDKException.DurabilityAmbiguousException)\n            retry_exceptions.append(SDKException.DurabilityImpossibleException)\n        doc_loading_spec[MetaCrudParams.RETRY_EXCEPTIONS] = retry_exceptions\n\n    def set_num_writer_and_reader_threads(self, num_writer_threads=\"default\", num_reader_threads=\"default\",\n                                          num_storage_threads=\"default\"):\n        bucket_helper = BucketHelper(self.cluster.master)\n        bucket_helper.update_memcached_settings(num_writer_threads=num_writer_threads,\n                                                num_reader_threads=num_reader_threads,\n                                                num_storage_threads=num_storage_threads)\n","sub_path":"pytests/bucket_collections/collections_base.py","file_name":"collections_base.py","file_ext":"py","file_size_in_byte":18085,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"181365617","text":"\"\"\"\nThis code is written by BACHOTTI SAI KRISHNA SHANMUKH EE19B009\n    EE2703 Assignment 5 Solution\n    The Resistor Problem\n\"\"\"\nimport sys\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport mpl_toolkits.mplot3d.axes3d as p3\nimport scipy.linalg as slg\n\n\"\"\"\nThis block of code make to correct input from the command line and raises a customized message in case of error\nIn case no input parameters are passed, the code takes default parameters as shown below\n\"\"\"\n\nif len(sys.argv)==5:\n    try:\n        Nx = int(sys.argv[1])\n        Ny = int(sys.argv[2])\n        radius = float(sys.argv[3])\n        Niter = int(sys.argv[4])\n        \n    except ValueError:\n        print(\"Input arguments must be integers. Radius can be float too\")\n        exit()\n\nelif len(sys.argv)==1:\n    Nx = 25\n    Ny = 25\n    radius = 8\n    Niter = 1500\n    print(\"No input parameters entered. Default parameters are chosen\")\n\nelse:\n    print('\\nExpected Usage: $python3 %s <Nx> <Ny> <Radius> <No. of iterations>' % sys.argv[0])\n    exit()\n\n\"\"\"\nPrint out the values entered by the user/ default values\n\"\"\"\nprint('Nx is',Nx)\nprint('Ny is',Ny)\nprint('radius is',radius)\nprint('No of iterations is',Niter)\n\n\"\"\"\nInitialization\nCreating a 2D array for potential and a meshgrid for visualization purposes\n\"\"\"\nphi = np.zeros((Ny,Nx)) \n#No of columns is Nx and No of rows is Ny\nx = np.arange(Nx) - (Nx-1)/2  \n# x contains the discrete positions along X direction (right)\ny = np.flip(np.arange(Ny) - (Ny-1)/2) \n# y contains the discrete positions along Y direction (top)\nX,Y = np.meshgrid(x,y)\nii = np.where(X*X + Y*Y <= radius**2)  \n# indices /postions of points which lie within the radius\nphi[ii] =1 # These set of points always remain at potential = 1\n\n\"\"\"\nPlot for Initial Potential\n\"\"\"\nfig,ax = plt.subplots(figsize=(6,6),num =0)\nplt.xlabel(r'+ $x\\longrightarrow$')\nplt.ylabel(r'+ $y\\longrightarrow$')\nax.contour(X,Y,phi)\nax.scatter(ii[1]-(Nx-1)/2,ii[0]-(Ny-1)/2,marker = 'o', color ='r')\nplt.title(r'Contour plot of Initial Potential')\n\n\"\"\"\nFinding phi in an iterative method using the Laplace Equation in difference method\nAlso recording absolute error in each iteration\n\"\"\"\nerrors = np.zeros(Niter)\nfor k in range(Niter):\n    oldphi = phi.copy() # saving a copy \n    phi[1:-1,1:-1] = 0.25*(phi[1:-1,0:-2] + phi[1:-1,2:] + phi[0:-2,1:-1] + phi[2:,1:-1]) \n    # estimating phi from laplace equation\n    phi[ii] =1  # restoring the condition of Potential =1 inside circle\n    phi[1:-1,0] = phi[1:-1,1]  # Boundary conditions\n    phi[1:-1,-1] = phi[1:-1,-2] # On hanging\n    phi[0,1:-1] = phi[1,1:-1] #Sides\n    errors[k] = np.max(np.abs(phi-oldphi)) # error\n\n\"\"\"\nUsing Least Square Method (LSTSQ) to get parameters for best fit curve for\ny = A*exp(Bx) or\nlogy = logA + B*x\n\nM = [1 x]\nv = [p1 p2]T\nc = [logy]\n\nHere our parameters p1 and p2 are logA and B respectively \n\"\"\" \ni = np.arange(1,Niter+1) \none_array = np.ones(Niter) # array with all 1s\nM = np.c_[one_array,i]   # for all Niter\nM_500 = np.c_[one_array[500:],i[500:]] # from 500th iter\nv = slg.lstsq(M,np.log(errors))[0]    #Least Squares\nv_500 = slg.lstsq(M_500,np.log(errors[500:]))[0]\n\n\"\"\"\nSemilog Plot of Error\n\"\"\"\nfig,ax = plt.subplots(num =1)\nplt.semilogy(i,errors)\nplt.xlabel(r'No. of iterations') # Labels\nplt.ylabel(r'Error')\nplt.title(r'Error in semilog plot') #Title\nplt.grid() # Grid\n\n\"\"\"\nLog Log plot of Error along with plot of every 50th iter\n\"\"\"\nfig,ax = plt.subplots(num =2)\nax.loglog(i,errors, label='Error')\nax.loglog(i[::50],errors[::50], 'ro',label ='Every 50th iter')\nplt.xlabel(r'No. of iterations') # Labels\nplt.ylabel(r'Error')\nplt.title(r'Error in log-log plot') #Title\nplt.legend() #Legend\nplt.grid() #Grid\n\n\"\"\"\nError using best fit parameters for all iterations\nand Error using best fit parameter from 500th iter\n\"\"\"\nerror_lstsq = np.exp(np.dot(M,v))\nerror500_lstsq = np.exp(np.dot(M_500,v_500))\n\n\"\"\"\nComparing the actual error with lstsq predicted error\n\"\"\"\nfig,ax = plt.subplots(num =3)\nax.loglog(errors, label='Iterative Error (True Error)')\nax.loglog(i[::50],error_lstsq[::50],'ro', label = 'Least Sqaures Fit')\nax.loglog(i[500::50],error500_lstsq[::50],'go', label = 'Least Sqaures Fit from 500th iteration')\nplt.xlabel(r'No. of iterations') #Labels\nplt.ylabel(r'Error')\nplt.title(r'Best fit for Error in log-log plot') #Title\nplt.legend() #Legend\nplt.grid() #Grid\n\n\"\"\"\nv is the solution of lstsq and the parameters are logA and B\n\"\"\"\nA = np.exp(v[0])\nB = v[1]\n\ndef cum_error(x):\n    \"\"\"\n    Input argument x: No. of iterations\n    Output is cumulative max possible error\n    \"\"\"\n    return -A/B*np.exp(B*(x+0.5))\n\n\"\"\"\nLog log plot for cumulative error\n\"\"\"\nfig, ax = plt.subplots(num =4)\nax.loglog(i[100::100],cum_error(i[100::100]),'ro')\nplt.xlabel(r'No. of iterations')\nplt.ylabel(r'Cumulative Error')\nplt.title(r'Cumulative Error in log-log plot (Every 100th iter)')\nplt.grid()\n\n\"\"\"\nContour plot of potential\n\"\"\"\nfig,ax = plt.subplots(figsize=(6,6),num =5)\nplt.xlabel(r'+ $x\\longrightarrow$')\nplt.ylabel(r'+ $y\\longrightarrow$')\nplt.title(r'Contour Plot of Potential $\\phi$')\ncs = ax.contour(X,Y,phi)\nax.scatter(ii[1]-(Nx-1)/2,ii[0]-(Ny-1)/2,marker = 'o', color ='r')\nax.clabel(cs, inline =1, fontsize = 9) \n\n\"\"\"\nSurface plot of potential\n\"\"\"\nfig1=plt.figure(6)     # open a new figure\nax=p3.Axes3D(fig1) # Axes3D is the means to do a surface plot\nplt.title('The 3-D surface plot of the potential')\nplt.xlabel(r'+ $x\\longrightarrow$')\nplt.ylabel(r'+ $y\\longrightarrow$')\nsurf = ax.plot_surface(X, Y, phi, rstride=1, cstride=1, cmap=plt.cm.jet)\n\n\n\"\"\"\nComputing Current density J from the equations\n\"\"\"\nJx = 0.5*(phi[1:-1,:-2]-phi[1:-1,2:]) # 0.5*(phi(x-1,y) - phi(x+1,y)) in cartesian\nJy = 0.5*(phi[2:,1:-1]-phi[:-2,1:-1]) # 0.5*(phi(x, y-1) - phi(x,y+1)) in cartesian\n\n\"\"\"\nPlot for Current flow\n\"\"\"\nfig,ax = plt.subplots(figsize=(6,6),num =7)\nax.quiver(X[1:-1,1:-1],Y[1:-1,1:-1],Jx,Jy)\nax.scatter(ii[1]-(Nx-1)/2,ii[0]-(Ny-1)/2,marker = 'o', color ='r')\nplt.xlabel(r'+ $x\\longrightarrow$')\nplt.ylabel(r'+ $y\\longrightarrow$')\nplt.title('Vector plot of current flow')\nplt.show()\n\n","sub_path":"week5-resistor/EE2703_Assignment5_EE19B009.py","file_name":"EE2703_Assignment5_EE19B009.py","file_ext":"py","file_size_in_byte":6046,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"69061271","text":"from lib.base import AzureBaseADAction\n\n\nclass AzureCreateVMAction(AzureBaseADAction):\n    def run(self, user_object_id, first_name, last_name):\n        if not last_name and not first_name:\n            return {\"updated\": False}\n\n        update = {}\n\n        if first_name:\n            update['givenName'] = first_name\n\n        if last_name:\n            update['surName'] = last_name\n\n        self.graphrbac_client.users.update(user_object_id, update)\n        return {\"updated\": True}\n","sub_path":"actions/update_user_name.py","file_name":"update_user_name.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"77896257","text":"import os\nimport sys\n\nimport re\nimport csv\n\ndef dict2csv(dim_dict, filename):\n\twith open(os.path.join(DATA_PATH, filename), 'w', newline='') as csvfile:\n\t\tfieldnames = [\"room_size\"]\n\t\tproblem_sizes = []\n\t\tfor col_name in dim_dict:\n\t\t\tfieldnames.append(col_name)\n\t\t\tfor ps in dim_dict[col_name]:\n\t\t\t\tif ps not in problem_sizes:\n\t\t\t\t\tproblem_sizes.append(ps)\n\n\t\tproblem_sizes = sorted(problem_sizes)\n\t\t\t\t\n\t\twriter = csv.DictWriter(csvfile, fieldnames=fieldnames)\n\t\twriter.writeheader()\n\n\t\tfor ps in problem_sizes:\n\t\t\trow = {\"room_size\": ps}\n\t\t\tfor col_name in dim_dict:\n\t\t\t\t#print(col_name, dim_dict[col_name])\n\t\t\t\tif dim_dict[col_name] is not None and ps in dim_dict[col_name]:\n\t\t\t\t\trow[col_name] = dim_dict[col_name][ps]\n\t\t\t\telse:\n\t\t\t\t\tdim_dict[col_name] = None\n\t\t\t\n\t\t\twriter.writerow(row)\n\n\ndef find_ints_in_string(string):\n\treturn list(map(int, re.findall(r'\\d+', string)))\n\ndef find_floats_in_string(string):\n\treturn list(map(float, re.findall(r'\\d+\\.\\d+', string)))\n\ndef get_avg_runtime(path, filename):\n\toutput = open(os.path.join(path, filename), \"r\").read()\n\tif not output:\n\t\treturn None\n\n\tstr_runtimes = \" - \".join([s.split(\"seconds\")[0] for s in output.split(\"The process took\") if \"seconds\" in s])\n\truntimes = find_floats_in_string(str_runtimes)\n\tif len(runtimes) == 0:\n\t\tprint(\"missing value for '%s'\"%filename)\n\t\treturn None\n\n\treturn sum(runtimes)/len(runtimes)\n\ndef outputs2csv():\n\tresults = {}\n\tfor filename in os.listdir(OUTPUTS_PATH):\n\t\tif filename.split(\".\")[-1] != \"dat\":\n\t\t\tprint(\"incompatible file: %s\" % filename)\n\t\t\tcontinue\n\n\t\tbasename = filename.split(\".\")[0]\n\t\tif basename.count(\"_\") == 2:\n\t\t\tprogram_type, dim, problem_size = basename.split(\"_\")\n\t\t\tname = program_type\n\t\telif basename.count(\"_\") == 4:\n\t\t\tprogram_type, dim, problem_size, slot_distribute, num_slots = basename.split(\"_\")\n\t\t\tname = \"_\".join([program_type, num_slots, slot_distribute])\n\t\telse:\n\t\t\tprint(\"unsupported_basename: %s\"%basename)\n\n\t\tproblem_size = int(problem_size)\n\t\tif dim not in results:\n\t\t\tresults[dim] = {}\n\t\tif name not in results[dim]:\n\t\t\tresults[dim][name] = {}\n\t\t#print(\"extracting data from %s\" % filename)\n\n\t\tresults[dim][name][problem_size] = get_avg_runtime(OUTPUTS_PATH, filename)\n\n\t#print(results)\n\tfor dim in results:\n\t\tprint(dim)\n\t\tdict2csv(results[dim], dim+\".csv\")\n\t\n\nif __name__ == \"__main__\":\n\tif len(sys.argv) != 3:\n\t\tprint(\"wrong number of arguments: \"+str(sys.argv[1:]))\n\t\tsys.exit(-1)\n\n\tOUTPUTS_PATH = sys.argv[1]\n\tDATA_PATH = sys.argv[2]\n\n\toutputs2csv()\n","sub_path":"proseminar/03/preprocess.py","file_name":"preprocess.py","file_ext":"py","file_size_in_byte":2480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"394614283","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# ## Problem 2: Points to map\n#  \n# In this problem we continue to learn how to turn latitude and longitude coordinates in to geometries.\n# \n\n\nimport pandas as pd\nimport geopandas as gpd\nfrom shapely.geometry import Point\n# YOUR CODE HERE 1 to read data\ndata = pd.read_csv('data/some_posts.csv')\ndata[\"geometry\"]=\"\"\n\nfor i,r in data.iterrows():\n  a =  Point(r[\"lat\"],r[\"lon\"])\n  data.at[i, \"geometry\"] = a\n\n\n# CODE FOR TESTING YOUR SOLUTION\n\n# Check the result\nprint(\"Number of rows:\", len(data))\n\n\n# CODE FOR TESTING YOUR SOLUTION\n\n# Check the result\nprint(data['geometry'].head())\n\n\n# YOUR CODE HERE 2\nimport geopandas as gpd\nfrom pyproj import CRS\ngeo = gpd.GeoDataFrame(data, geometry ='geometry',crs =  CRS.from_epsg(4326).to_wkt())\nfp = 'Kruger_posts.shp'\ngeo.to_file(fp)\n\n# Convert DataFrame into a GeoDataFrame\ngeo=None\n# CODE FOR TESTING YOUR SOLUTION\n\n# Check the geodataframe head\nprint(\"Number of rows:\", len(geo))\nprint(geo.head())\n\n\n# CODE FOR TESTING YOUR SOLUTION\n\n# Check that the output file exists\nimport os\nassert os.path.isfile(fp), \"output shapefile does not exist\"\n\n\n# **Finally:** \n# - **Create a simple map of the points** using the `plot()` -funtion. \n\n# YOUR CODE HERE 3\ngeo.plot()\n# Well done! Now you can move on to Exercise_9_problem_3.\n\ndef func5():\n    return data\n\ndef func6():\n    return geo\n\n\n","sub_path":"Exercise_9_problem_2.py","file_name":"Exercise_9_problem_2.py","file_ext":"py","file_size_in_byte":1367,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"647632785","text":"# Network structure #\nNN_HIDDEN_COUNT = 8\n\n# Training #\nLEARNING_RATE = 1e-4\nTRAIN_ITERATIONS = 5000\nREWARD_HISTORY_DISCOUNT = 0.99\nUPDATE_FREQUENCY = 20\nSTEP_TIMEOUT = .3\n\n# Printing #\nPRINT_TIMEOUT_ITERATIONS = 100\n","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"80364292","text":"import torch\nimport numpy as np\nimport config\nfrom pytorch_pretrained_bert import BertTokenizer, BertModel\n\n\ndef main(corpus):\n\n    type_dict = {}  # {type: index}\n    with open(config.DATA_ROOT + corpus + \"type_set.txt\", \"r\") as f:\n        lines = f.readlines()\n        for line in lines:\n            tokens = line[:-1].split(\" \")\n            type_dict[tokens[1]] = int(tokens[0])\n\n        # mat = get_bert_sent_embedding_by_desc(type_dict, corpus)\n        mat = get_word_embedding_by_type_str(type_dict, corpus)\n    torch.save(mat, config.DATA_ROOT + corpus + config.TYPE_ATTEN_FILE)\n\n\ndef get_bert_sent_embedding_by_desc(type_dict, corpus):\n    with open(config.DATA_ROOT + corpus + \"type_desc.txt\", 'r') as f:\n        matrix = [[]] * len(type_dict)\n        line = f.readline()\n        while line != \"\":\n            no = line[0: line.find(\" \")]\n            pair = line[line.find(\" \") + 1:-1].split(\"\\t\")\n            ttype = pair[0]\n            desc = pair[1]\n\n            print(f\"no: {no}, pair: [{type}, {desc}]\")\n            line = f.readline()\n\n            device = config.CUDA\n            bert = BertModel.from_pretrained(config.BERT_MODEL_PATH)\n            bert.to(device)\n            bert.eval()\n            tokenizer = BertTokenizer.from_pretrained(config.BERT_MODEL_PATH)\n            tokenized_text = tokenizer.tokenize(desc)\n            indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)\n            segments_ids = [0] * len(indexed_tokens)\n            tokens_tensor = torch.tensor([indexed_tokens], device=device)\n            segments_tensors = torch.tensor([segments_ids], device=device)\n            _, sent_embedding = bert(tokens_tensor, segments_tensors)\n\n        matrix[type_dict[ttype]] = sent_embedding\n\n    return torch.stack(matrix)\n\n\ndef get_word_embedding_by_type_str(type_dict, corpus):\n\n    with open(config.DATA_ROOT + corpus + config.TYPE_SET_INDEX_FILE) as f:\n        lines = f.readlines()\n        type_str = {}  # type_word: idx\n        test = {}\n        for line in lines:\n            tokens = line[:-1].split(\" \")\n            idx = int(tokens[0])\n            tstr = tokens[1]\n            words = tstr[tstr.rindex(\"/\") + 1:].split(\"_\")\n            type_str[idx] = words\n\n    glove_dict = {}\n    with open(config.EMBEDDING_ROOT, 'r') as glove:\n        line = glove.readline()\n        while line != \"\":\n            wd = line[:line.index(\" \")]\n            embed = line[line.index(\" \")+1: -1]\n            glove_dict[wd] = embed\n            line = glove.readline()\n\n    matrix = [[] for _ in range(len(type_dict))]\n    result = []\n    for i in range(len(type_dict)):\n        words = type_str[i]\n        for w in words:\n            if glove_dict.get(w) is not None:\n                matrix[i].append([float(i) for i in glove_dict[w].split(\" \")])\n            else:\n                print(f\"Warning: {w} is not in the embedding dict, we will random initialize it values\")\n\n    for i, row in enumerate(matrix):\n\n        r = None\n        if len(row) > 1:\n            r = torch.from_numpy(np.mean(row, axis=0, dtype=np.float32))\n        elif len(row) == 0:\n            r = torch.randn(config.EMBEDDING_DIM, dtype=torch.float)\n        else:\n            r = torch.tensor(row, dtype=torch.float).squeeze()\n\n        result.append(r)\n\n    return torch.stack(result)\n\n\nif __name__ == '__main__':\n    main(config.WIKI)\n","sub_path":"preprocess/type_atten_matrix.py","file_name":"type_atten_matrix.py","file_ext":"py","file_size_in_byte":3344,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"645576484","text":"my_foods = ['pizza', 'falafel', 'carrot cake']\nfriend_foods = my_foods[:]\n\nmy_foods.append('cannoli')\nfriend_foods.append('ice cream')\n\nfor foods in my_foods:\n\tprint(foods)\n\t\nfor foods in friend_foods:\n\tprint(foods)\n","sub_path":"Chapter4/4-12_more_loops.py","file_name":"4-12_more_loops.py","file_ext":"py","file_size_in_byte":216,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"530843651","text":"import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nimport fnmatch\nimport os\nimport time\nstart_time = time.time()\ndirpath1 = \"/home/routray/Desktop/test/slice_output/6/\"\ndirpath = \"/home/routray/Desktop/test/slice_output/7/\"\ncount=len(fnmatch.filter(os.listdir(dirpath), '*.png'))\ncounter=0\n\ndef make_ax(grid=False):\n    fig = plt.figure()\n    ax = fig.gca(projection='3d')\n    ax.set_xlabel(\"x\")\n    ax.set_ylabel(\"y\")\n    ax.set_zlabel(\"z\")\n    ax.grid(grid)\n    return ax\nprint(\"If you want to give colors to voxel press follow the rules below \\n\\r To give single color to voxels press 0 \\n \\r To color by layes press 1\\n\\r To give different colors to voxels press 2.\\n\\r To give different colors to half of structure Press 3. \")\ndif = int(input(\" Please give the input to proceed: \"))\nprint(\"Your decision: \", dif)\n\n# read image into matrix.\n#image =  cv2.imread(\"out_test/output001.png\")\n#m = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n# reading images into matrix.\nprint(\"No.of Slices:\", count)\nif count > 1000:\n\timage =  cv2.imread(\"slice_output/output0001.png\")\n\tm = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\nelif count > 100:\n\timage =  cv2.imread(\"slice_output/output001.png\")\n\tm = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\nelif count > 10:\n\timage =  cv2.imread(\"slice_output/output01.png\")\n\tm = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\nelse:\n\timage =  cv2.imread(\"slice_output/output1.png\")\n\tm = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n# get image properties.\n#h,w,bpp = np.shape(m)\nh,w = np.shape(m)\nprint(np.shape(m))\n#k=[]\n# print pixel value\n#y = 2\n#x = 0\n#print(m[y][x])\nl = np.zeros(shape=(h,w))\n#Iterate all image slides output00.png to output50.png\nfor py in range(0,h):\n    for px in range(0,w):\n        l[py][px]=np.int(m[py][px])\n        #print(m[py][px])\npy=0\npx=0\nn_images=count-1\n#print(m[y])\ng = np.zeros(shape=(h,w))\nk = np.zeros(shape=(count,h,w))\ncolors = np.array([[['#ffffffff']*w]*h]*count)\n\nk1=np.zeros(shape=(w))\nk2=np.zeros(shape=(h,w))\nfor j in range(0,count):\n\t# iterate over the entire image.\n\n\ti=count-j-1\n\tif count >= 100:\n\t\tif i<10:\n\t\t\timage_name = \"output00\"+str(i)+\".png\"\n\t\telif i<100:\n\t\t\timage_name = \"output0\"+str(i)+\".png\"\n\t\telse:\n\t\t\timage_name = \"output\"+str(i)+\".png\"\n\telif count>=10:\n\t\tif i<10:\n\t\t\timage_name = \"output0\"+str(i)+\".png\"\n\t\telse:\n\t\t\timage_name = \"output\"+str(i)+\".png\"\n\telse:\n\t\timage_name = \"output\"+str(i)+\".png\"\n\n\timage =  cv2.imread(\"slice_output/\"+image_name)\n\tprint(image_name)\n\tprint(i)\n\tm = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\t#k2=[]\n\tfor py in range(0,h):\n\t\t#k1=[]\n\t\t#print(k1[0])\n\t\tfor px in range(0,w):\n\t\t\tif (m[py][px]==255):\n\t\t\t\t#x=1,y=1\n\t\t\t\tg[py][px]=g[py][px]+1\n\t\t\t\t#(j*py*px)%2\n\t\t\t\t#counter%2 for voxel\n\t\t\t\t#colors[j][py][]=str('#ff00ffff')\n\t\t\t\tif dif==1:\n\t\t\t\t\tc=j%2\n\t\t\t\telif dif==2:\n\t\t\t\t\tc=counter%2\n\t\t\t\telif dif==3:\n\t\t\t\t\tif j < count/2:\n\t\t\t\t\t\tc=1\n\t\t\t\t\telse:\n\t\t\t\t\t\tc=0\n\t\t\t\telse:\n\t\t\t\t\tc=1\n\n\t\t\t\tif c==1:\n\t\t\t\t\t#if dif==1:\n\t\t\t\t\tcolors[j][py][px]=str('#00ffffff')\n\t\t\t\t\tk[j][py][px]=int(1)\n\t\t\t\telse:\n\t\t\t\t\t#if dif==1:\n\t\t\t\t\tcolors[j][py][px]=str('#ff00ffff')\n\t\t\t\t\t#colors[j][py][px]=str('#ff00ffff')\n\t\t\t\t\tk[j][py][px]=int(2)\n\t\t\t\tcounter=counter+1\n\t\t\telse:\n\t\t\t\tk[j][py][px]=int(0)\t\t\n\t\tprint(k[j])\n\t\t#k2[py]=k1\n\t#k[j]=k2\n\t#filled = np.array(k)\n\t#ax = make_ax(True)\n\t#print(ax)\n\t#ax.voxels(filled,facecolors=colors, edgecolors='gray')\n\t#print(\"k:\\n\")\n\t#print(k)\n\t\n#print(k)\nprint(\"Total no.of Voxels:\")\nprint(count*h*w)\n#print(\"Your decision:\",dif)\n\n#print(m[y][x])\n#print(len(k))\n#print(k)\n#print(len(k[0]))\n#print(len(k))\n#print(g)\n#print(k[1])\n#print(\"Filled Voxel:\")\n#print(counter)\n#colors=explode(colors)\n#print(colors[j])\nfilled = np.array(k)\nprint(\"--- %s seconds ---\" % (time.time() - start_time))\nax = make_ax(True)\nprint(ax)\nax.voxels(filled,facecolors=colors, edgecolors='gray')\n\nplt.show()\n\n\n","sub_path":"voxel_testing.py","file_name":"voxel_testing.py","file_ext":"py","file_size_in_byte":3818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"477113340","text":"from dataset import DataBank\r\nfrom utils_for_wp import file_abs_path, DataPacker, create_mask\r\nfrom eval import update_with_gps\r\nfrom pathlib import Path\r\nfrom create_gt_gps_dist import create_dist\r\nimport argparse\r\n\r\nif __name__ == '__main__':\r\n    # wp_reid_dataset = DataBank(minframes=3)\r\n\r\n    parser = argparse.ArgumentParser(description=\"GPS\")\r\n    parser.add_argument('--exp_name', type=str)\r\n    parser.add_argument('--k', default=9, type=int)\r\n    parser.add_argument('--delta', default=74, type=int)\r\n    parser.add_argument('--iters', default=5, type=int)\r\n    args = parser.parse_args()\r\n\r\n    print('MTMCT')\r\n    exp_name = args.exp_name\r\n    k = args.k\r\n    delta = args.delta\r\n    iters = args.iters\r\n\r\n    root = Path('/data4/shensj/datasets/my_files/gps/GPSReID/MOT/GPSReID/crop_images/')\r\n    file_path = root / exp_name / 'gt_gps_dist.pkl'\r\n    if not file_path.is_file():\r\n        create_dist(exp_name)\r\n    mask_path = root / exp_name / 'mask_for_timestamp.pkl'\r\n    if not mask_path.is_file():\r\n        create_mask(mask_path)\r\n    mask_for_timestamp = DataPacker.load(mask_path)\r\n    distmat = DataPacker.load(root / exp_name / 'feature_origin.pkl')['g2g_distmat']\r\n    # m, n = distmat.shape\r\n    # for i in range(m):\r\n    #     for j in range(n):\r\n    #         if mask_for_timestamp[i][j] == 1 and i != j:\r\n    #             distmat[i][j] = 1e6\r\n    gt_gps_dist = DataPacker.load(root / exp_name / 'gt_gps_dist.pkl')\r\n    update_with_gps(distmat, gt_gps_dist, k=k, delta=delta, iters=iters, root=root / exp_name, mask=mask_for_timestamp)","sub_path":"src/tracker/update_with_gps/WP-ReID/main_debug.py","file_name":"main_debug.py","file_ext":"py","file_size_in_byte":1563,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"210297136","text":"from datetime import datetime\nfrom django.test import TestCase\nfrom django.contrib.auth.models import User\nfrom django.db import IntegrityError\nfrom django.core.exceptions import ValidationError\n\nfrom ..models import Delivery\nfrom ..models import deliverytypes\nfrom ..testhelper import TestHelper\n\nclass TestDelivery(TestCase, TestHelper):\n    def setUp(self):\n        TestHelper.set_memory_deliverystore()\n\n    def _create_testdata(self):\n        self.add(nodes=\"uio:admin(uioadmin).ifi:admin(ifiadmin)\",\n                 subjects=[\"inf1100\"],\n                 periods=[\"period1:admin(teacher1):begins(-5):ends(10)\"],\n                 assignments=[\"assignment1:pub(60)\"],\n                 assignmentgroups=[\"g1:candidate(student1):examiner(examiner1)\",\n                                   \"g2:candidate(student2):examiner(examiner2)\",\n                                   \"g3:candidate(student3,student2):examiner(examiner1,examiner2,examiner3)\",\n                                   \"g4:candidate(student4):examiner(examiner3)\"],\n                 deadlines=['d1:ends(1)'])\n        self.goodFile = {\"good.py\": \"print awesome\"}\n        self.add_delivery(\"inf1100.period1.assignment1.g1\", self.goodFile)\n        self.add_delivery(\"inf1100.period1.assignment1.g2\", self.goodFile)\n        self.add_delivery(\"inf1100.period1.assignment1.g3\", self.goodFile)\n\n    def test_where_is_admin(self):\n        self._create_testdata()\n        teacher1 = User.objects.get(username='teacher1')\n        self.assertEquals(Delivery.where_is_admin(teacher1).count(), 3)\n        delivery0 = self.inf1100_period1_assignment1_g1_d1.deliveries.all()[0]\n        delivery0.successful = False\n        delivery0.save()\n        self.assertEquals(Delivery.where_is_admin(teacher1).count(), 2)\n\n    def test_published_where_is_examiner(self):\n        self._create_testdata()\n        examiner1 = User.objects.get(username='examiner1')\n        deliveries = Delivery.published_where_is_examiner(examiner1)\n        self.assertEquals(deliveries.count(), 2)\n        delivery0 = deliveries.all()[0]\n        delivery0.successful = False\n        delivery0.save()\n        self.assertEquals(Delivery.published_where_is_examiner(examiner1).count(), 1)\n\n    def test_delivery(self):\n        self._create_testdata()\n        assignmentgroup = self.inf1100_period1_assignment1_g3\n        d = self.add_delivery(\"inf1100.period1.assignment1.g3\", self.goodFile,\n                              time_of_delivery=datetime(2005, 1, 1))\n        self.assertEquals(d.deadline.assignment_group, assignmentgroup)\n        self.assertTrue(d.successful)\n        self.assertEquals(d.number, 2)\n        self.assertTrue(d.time_of_delivery, datetime(2005, 1, 1))\n\n        # TODO find a graceful way to handle this error:\n        d.number = 1\n        self.assertRaises(IntegrityError, d.save())\n\n    def test_noalias_missing_feedback(self):\n        self._create_testdata()\n        deadline = self.inf1100_period1_assignment1_g3_d1\n        delivery = deadline.deliveries.create(successful=True,\n            delivery_type=deliverytypes.ALIAS,\n            alias_delivery=None)\n        with self.assertRaises(ValidationError):\n            delivery.clean()\n\n    def test_noalias_with_feedback(self):\n        self._create_testdata()\n        deadline = self.inf1100_period1_assignment1_g3_d1\n        delivery = deadline.deliveries.create(successful=True,\n            delivery_type=deliverytypes.ALIAS,\n            alias_delivery=None)\n        delivery.feedbacks.create(\n            grade = 'A',\n            is_passing_grade = True,\n            points = 100,\n            rendered_view = '',\n            saved_by = self.examiner1\n        )\n        delivery.clean()\n\n    def test_with_alias(self):\n        self._create_testdata()\n        deadline = self.inf1100_period1_assignment1_g3_d1\n        otherdelivery = deadline.deliveries.create(successful=True)\n        delivery = deadline.deliveries.create(successful=True,\n            delivery_type=deliverytypes.ALIAS,\n            alias_delivery=otherdelivery)\n        delivery.clean()\n\n    def test_delete_delivered_by_candidate(self):\n        self._create_testdata()\n        delivery = self.add_delivery(\"inf1100.period1.assignment1.g2\", self.goodFile)\n        delivery = Delivery.objects.get(id=delivery.id) # Re-get from DB just to be doubly sure we are using the same delivery below\n        self.assertEquals(delivery.delivered_by.student, self.student2)\n        group = self.inf1100_period1_assignment1_g2\n        group.candidates.all()[0].delete()\n        delivery = Delivery.objects.get(id=delivery.id) # Re-get from DB\n        self.assertEquals(delivery.delivered_by, None)\n\n    def test_delivery_numbering(self):\n        self._create_testdata()\n        deadline = self.inf1100_period1_assignment1_g1_d1\n        self.assertEquals(deadline.deliveries.count(), 1)\n        self.assertEquals(deadline.deliveries.all()[0].number, 1)\n        d2 = Delivery(deadline=deadline,\n                     delivered_by=deadline.assignment_group.candidates.all()[0])\n        d2.save()\n        d3 = Delivery(deadline=deadline,\n                     delivered_by=deadline.assignment_group.candidates.all()[0])\n        d3.save()\n        self.assertEquals(d2.number, 0)\n        self.assertEquals(d3.number, 0)\n        d3.successful = True\n        d3.save()\n        self.assertEquals(d3.number, 2)\n        d2.successful = True\n        d2.save()\n        self.assertEquals(d2.number, 3)\n\n    def test_published_where_is_candidate(self):\n        self._create_testdata()\n        # Add 2 on g1\n        d = self.add_delivery(\"inf1100.period1.assignment1.g1\", self.goodFile)\n        d = self.add_delivery(\"inf1100.period1.assignment1.g1\", self.goodFile)\n        # Add 3 on g2\n        d = self.add_delivery(\"inf1100.period1.assignment1.g2\", self.goodFile)\n        d = self.add_delivery(\"inf1100.period1.assignment1.g2\", self.goodFile)\n        d = self.add_delivery(\"inf1100.period1.assignment1.g2\", self.goodFile)\n        # Add 2 on g3\n        d = self.add_delivery(\"inf1100.period1.assignment1.g3\", self.goodFile)\n        d = self.add_delivery(\"inf1100.period1.assignment1.g3\", self.goodFile)\n\n        self.assertEquals(Delivery.published_where_is_candidate(self.student1).count(), 3)\n        self.assertEquals(Delivery.published_where_is_candidate(self.student2).count(), 7)\n        self.assertEquals(Delivery.published_where_is_candidate(self.student3).count(), 3)\n        self.assertEquals(Delivery.published_where_is_candidate(self.student4).count(), 0)\n\n        delivery = Delivery.published_where_is_candidate(self.student3)[0]\n        delivery.successful = False\n        delivery.save()\n        self.assertEquals(Delivery.published_where_is_candidate(self.student3).count(), 2)\n\n\n    def test_hard_deadline(self):\n        self._create_testdata()\n        self.add_to_path('uni.ifi;inf1100.period1.assignment0.g1:candidate(student1):examiner(examiner1).d0:ends(1)')\n\n        # Soft deadlines work without any errors\n        deadline = self.inf1100_period1_assignment0_g1_d0\n        self.assertTrue(deadline.deadline < datetime.now())\n        self.add_delivery(\"inf1100.period1.assignment0.g1\", self.goodFile)\n\n        # Hard deadlines\n        assignment = self.inf1100_period1_assignment0\n        assignment.deadline_handling = 1\n        assignment.save()\n        with self.assertRaises(ValidationError):\n            self.add_delivery(\"inf1100.period1.assignment0.g1\", self.goodFile)\n\n\n    def test_override_autoset(self):\n        self.add(nodes=\"uni\",\n                 subjects=[\"sub\"],\n                 periods=[\"p1\"],\n                 assignments=['a1'])\n        self.add_to_path('uni;sub.p1.a1.g1:candidate(student1):examiner(examiner1).d1')\n        d1 = self.sub_p1_a1_g1_d1\n\n        time_of_delivery = datetime(2005, 1, 1, 0, 0, 0)\n        delivery = Delivery(deadline=d1,\n                            number=10,\n                            successful=False,\n                            time_of_delivery=time_of_delivery)\n        delivery.full_clean()\n        delivery.save(autoset_number=False,\n                      autoset_time_of_delivery=False)\n        self.assertEquals(delivery.number, 10)\n        self.assertEquals(delivery.successful, False)\n        self.assertEquals(delivery.time_of_delivery, time_of_delivery)\n\n\n    def _create_copydata(self):\n        self.add(nodes=\"uni\",\n                 subjects=[\"sub\"],\n                 periods=[\"p1\"],\n                 assignments=['a1'])\n        self.add_to_path('uni;sub.p1.a1.g1:candidate(student1):examiner(examiner1).d1')\n\n        # Create delivery for alias_delivery\n        self.add_to_path('uni;sub.p_old.a1.g1:candidate(student1):examiner(examiner1).d1')\n        old_delivery = self.add_delivery(\"sub.p_old.a1.g1\", {\"secondtry.py\": \"print second\"})\n\n        # Make a delivery without any of the default/generated values, so when\n        # we check that they are copied, we get no generate stuff\n        g1 = self.sub_p1_a1_g1\n        d1 = self.sub_p1_a1_g1_d1\n        time_of_delivery = datetime(2005, 1, 1, 0, 0, 0)\n        delivery = Delivery(deadline=d1,\n                            number=10,\n                            successful=False,\n                            delivery_type=1,\n                            delivered_by=g1.candidates.all()[0],\n                            alias_delivery=old_delivery,\n                            time_of_delivery=time_of_delivery)\n        delivery.full_clean()\n        delivery.save(autoset_number=False,\n                      autoset_time_of_delivery=False)\n        delivery.add_file('test.txt', ['Hello', ' world'])\n        return delivery, old_delivery\n\n\n    def test_copy_own_attributes(self):\n        delivery, old_delivery = self._create_copydata()\n        self.add_to_path('uni;sub.p1.a1.g2:candidate(student2).d1')\n        newdeadline = self.sub_p1_a1_g2_d1\n        copy = delivery.copy(newdeadline)\n\n        self.assertEquals(copy.deadline, newdeadline)\n        self.assertEquals(copy.delivery_type, 1)\n        self.assertEquals(copy.number, 10)\n        self.assertEquals(copy.successful, False)\n        self.assertEquals(copy.time_of_delivery, datetime(2005, 1, 1, 0, 0, 0))\n        self.assertEquals(copy.delivered_by.student, self.student1)\n        self.assertEquals(copy.alias_delivery, old_delivery)\n\n        # Check copy_of and the virtual reverse relationship\n        self.assertEquals(copy.copy_of, delivery)\n        self.assertEquals(list(delivery.copies.all()),\n                          [copy])\n\n    def test_copy_filemetas(self):\n        delivery, old_delivery = self._create_copydata()\n        self.add_to_path('uni;sub.p1.a1.g2:candidate(student2).d1')\n        newdeadline = self.sub_p1_a1_g2_d1\n        copy = delivery.copy(newdeadline)\n\n        self.assertEquals(delivery.filemetas.count(), 1)\n        self.assertEquals(copy.filemetas.count(), 1)\n        copied_filemeta = copy.filemetas.all()[0]\n        self.assertEquals(copied_filemeta.get_all_data_as_string(),\n                          'Hello world')\n\n\n    def test_copy_feedbacks(self):\n        delivery, old_delivery = self._create_copydata()\n        self.add_feedback(delivery=delivery,\n                          verdict={'grade': 'F', 'points':10, 'is_passing_grade':False},\n                          rendered_view='This was bad',\n                          timestamp=datetime(2005, 1, 1, 0, 0, 0))\n        self.add_feedback(delivery=delivery,\n                          verdict={'grade': 'C', 'points':40, 'is_passing_grade':True},\n                          rendered_view='Better',\n                          timestamp=datetime(2010, 1, 1, 0, 0, 0))\n\n        self.add_to_path('uni;sub.p1.a1.g2:candidate(student2).d1')\n        newdeadline = self.sub_p1_a1_g2_d1\n        self.assertEquals(delivery.feedbacks.count(), 2)\n        copy = delivery.copy(newdeadline)\n\n        self.assertEquals(delivery.feedbacks.count(), 2)\n        feedbacks = copy.feedbacks.order_by('save_timestamp')\n        self.assertEquals(len(feedbacks), 2)\n        self.assertEquals(feedbacks[0].grade, 'F')\n        self.assertEquals(feedbacks[0].points, 10)\n        self.assertEquals(feedbacks[0].is_passing_grade, False)\n        self.assertEquals(feedbacks[0].save_timestamp, datetime(2005, 1, 1, 0, 0, 0))\n        self.assertEquals(feedbacks[0].rendered_view, 'This was bad')\n\n        self.assertEquals(feedbacks[1].grade, 'C')\n        self.assertEquals(feedbacks[1].points, 40)\n        self.assertEquals(feedbacks[1].is_passing_grade, True)\n        self.assertEquals(feedbacks[1].save_timestamp, datetime(2010, 1, 1, 0, 0, 0))\n        self.assertEquals(feedbacks[1].rendered_view, 'Better')\n","sub_path":"src/devilry/devilry/apps/core/tests/delivery.py","file_name":"delivery.py","file_ext":"py","file_size_in_byte":12602,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"626621870","text":"'''\r\n<단순 변수 사용>\r\n    3. 반복형\r\n    3-5. 인구 계산 프로그램 (2020년 인구 4천 955만, 인구 증가율 - 0.02)\r\n        - 이 때, 인구가 0 밑으로 떨어지는 년도는?\r\n    1626035 이주호\r\n'''\r\n\r\n#변수 초기화\r\npop = 49550000      #인구\r\nratio = -0.02       #인구 증가율\r\nn = 0               #2020년으로부터 지난 해 수\r\n\r\n##계산\r\nwhile(pop > 1):\r\n    pop = pop + (pop * ratio)\r\n    n = n + 1\r\n##계산 종료\r\n\r\n#결과 출력\r\nprint(\"인구가 1 밑으로 떨어지는 년도는 %d년입니다.\" % (2020 + n))\r\n","sub_path":"paper/Paper03-05.py","file_name":"Paper03-05.py","file_ext":"py","file_size_in_byte":572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"27620422","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Aug 21 10:14:11 2018\r\n\r\n@author: NTPU\r\n\"\"\"\r\nimport time\r\nfrom mcpi.minecraft import Minecraft\r\nHank=Minecraft.create()\r\n\r\n\r\n\r\nx,y,z=Hank.player.getTilePos()\r\n\r\nwidth=80\r\nlength=40\r\nheight=8\r\n\r\nblock=5\r\nair=0\r\n\r\nHank.setBlocks(x,y,z,x+width,y+height,z+length,block)\r\nHank.setBlocks(x+1,y+1,z+1,x+width-1,y+height-1,z+length-1,air)\r\n","sub_path":"2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":376,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"262785935","text":"from django.shortcuts import render, redirect\nfrom . import models\nfrom .models import User, Wish\nimport bcrypt\n\n\ndef home_page(request):\n    return render(request, \"index.html\")\n\n\ndef register(request):\n    if request.method == 'POST':\n        name = request.POST['name']\n        username = request.POST['username']\n        errors = models.register_validator(request.POST)\n        if len(errors) > 0:\n            for key, value in errors.items():\n                messages.error(request, value)\n            return redirect('/')\n        user = models.add_user(request.POST)\n        if 'user' not in request.session:\n            request.session['username'] = username\n            request.session['name'] = name\n            request.session['user_id'] = user.id\n        return redirect('/welcome/'+str(user.id))\n\n\ndef login(request):\n    if request.method == 'POST':\n        username = request.POST['username']\n        user = models.get_user(username)\n        print(user)\n        if bcrypt.checkpw(request.POST['password'].encode(), user.password.encode()):\n            if 'user' not in request.session:\n                request.session['username'] = username\n                request.session['name'] = name\n                request.session['user_id'] = user.id\n            return redirect('/welcome/'+str(user.id))\n        errors = {\n            'login': 'Incorrect username or password'\n        }\n        for key, value in errors.items():\n            messages.error(request, value)\n        return redirect('/')\n\n\ndef logout(request):\n    del request.session['username']\n    del request.session['name']\n    del request.session['user_id']\n    return redirect('/')\n\n\ndef welcome(request, id):\n    all_users = User.objects.all()\n    all_wishes = Wish.objects.all()\n    user = User.objects.get(id=id)\n    all_user_wishes = user.wishes.all()\n    context = {\n        \"all_users\": all_users,\n        \"all_wishes\": all_wishes,\n        \"user\": user,\n        \"all_user_wishes\": all_user_wishes\n    }\n    return render(request, 'welcome.html', context)\n\n\ndef new_wish_page(request):\n    return render(request, 'new_wish.html')\n\n\ndef new_wish(request, user_id):\n    if request.method == 'POST':\n        title = request.POST['title']\n        desc = request.POST['desc']\n        errors = models.wish_validator(request.POST)\n        if len(errors) > 0:\n            for key, value in errors.items():\n                messages.error(request, value)\n            return redirect('/new_wish/'+str(user_id))\n        new_wish = models.add_new_wish(user_id, title, desc)\n        return ('/welcome/'+str(user_id))\n\n\ndef add(request, user_id, wish_id):\n    models.add_wish(user_id, wish_id)\n    return redirect('/welcome/'+str(user_id))\n\n\ndef remove(request, user_id, wish_id):\n    models.remove_wish(user_id, wish_id)\n    return redirect('/welcome/'+str(user_id))\n","sub_path":"Django/wishlist/wishlist_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"485278378","text":"import cv2\r\n\r\ndef showbitmap(row,col,bg,h,w):\r\n    for y in range(row,row+h):\r\n        print(str('{:0>2d}') .format(y)+\":\" ,end=\"\")\r\n        for x in range(col,col+w):\r\n            print(bg[y][x],end=\",\")\r\n        print()\r\n    print() \r\n    \r\ndef area(row, col):\r\n    global nn\r\n    if bg[row][col] != 255:\r\n        return\r\n    bg[row][col] = lifearea #記錄生命區的編號    \r\n    if col>1: #左方\r\n        if bg[row][col-1]==255:\r\n            nn +=1\r\n            area(row,col-1)\r\n    if col< w-1: #右方\r\n        if bg[row][col+1]==255:\r\n            nn +=1\r\n            area(row,col+1)             \r\n    if row>1: #上方\r\n        if bg[row-1][col]==255:\r\n            nn+=1            \r\n            area(row-1,col)\r\n    if row<h-1: #下方\r\n        if bg[row+1][col]==255:\r\n            nn+=1            \r\n            area(row+1,col) \r\n\r\nimage = cv2.imread('7.jpg')\r\ngray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)  #灰階\r\n_,thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV) #轉為黑白\r\nh=thresh.shape[0]\r\nw=thresh.shape[1]\r\n# print(\"h,w=\",h,w) # 38,18\r\n\r\nbg=thresh.copy()\r\nshowbitmap(0,0,bg,h,w)\r\n\r\nlifearea=0 # 生命區塊\r\nnn=0       # 每個生命區塊的生命數\r\nlife=[]    # 記錄每個生命區塊的生命數串列\r\n\r\nfor row in range(0,h):\r\n   for col in range(0,w):\r\n      if bg[row][col] == 255:\r\n         nn = 1  #生命起源\r\n         lifearea = lifearea + 1  #有生命區塊數\r\n         area(row,col)  #以生命起源為起點探索每個生命區塊的總生命數\r\n         life.append(nn)\r\n\r\nprint(\"lifearea=\",lifearea) #生命區塊數\r\nprint(life)                 #該生命區塊的總生命數\r\nmaxlife=max(life)           #找到最大的生命數\r\nindexmaxlife=life.index(maxlife) #找到最大的生命數的區塊編號\r\nshowbitmap(0,0,bg,h,w)  #顯示該文字的所有像素\r\n \r\nfor row in range(0,h):\r\n   for col in range(0,w):\r\n      if bg[row][col] == indexmaxlife+1:\r\n          bg[row][col]=255\r\n      else:\r\n          bg[row][col]=0\r\nshowbitmap(0,0,bg,h,w)  \r\n_,bg = cv2.threshold(bg, 127, 255, cv2.THRESH_BINARY_INV)  #轉為黑白        \r\ncv2.imwrite('area.jpg', bg)  #存檔  \r\n\r\ncv2.imshow('Frame', thresh)  #顯示圖形\r\ncv2.moveWindow(\"Frame\",500,450) # move \r\ncv2.imshow('bg', bg)  #顯示圖形\r\ncv2.moveWindow(\"bg\",500,550) # move \r\nkey = cv2.waitKey(0)\r\ncv2.destroyAllWindows()","sub_path":"Life.py","file_name":"Life.py","file_ext":"py","file_size_in_byte":2354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"36350659","text":"import numpy as np\nimport tensorflow as tf\nimport os\nimport matplotlib.pyplot as plt\nimport tensorflow.contrib.slim as slim\n\n\nfilename = \"./faces94/train/DataFace.tfrecords\"\n\n\n# def trans_for_ohe(labels):\n#     \"\"\"Transform a flat list of labels to what one hot encoder needs.\"\"\"\n#     return np.array(labels).reshape(len(labels), -1)\ndef oneHot(labels,numClass):\n\n    oneHot = []\n    for label in labels:\n        vector = np.zeros(numClass,dtype=int);\n        vector[label] = 1;\n        oneHot.append(vector)\n    return oneHot;\n\ndef shuffle(list_image, labels):\n    arr = np.arange(length_img)\n\n    np.random.shuffle(arr)\n\n    Alist_image = []\n\n    Alist_label = []\n\n    for i in range(length_img):\n\n        Alist_image.append(list_image[arr[i]])\n\n        Alist_label.append(labels[arr[i]])\n\n    return Alist_image,Alist_label\n\n# Get label and bytes(image) in TFrecord\ndef read_and_decode_single_example(filename):\n\n    record_iterator = tf.python_io.tf_record_iterator(path=filename)\n    imagesString = []\n    labels = []\n    i=0\n    for string_record in record_iterator:\n        example = tf.train.Example()\n        example.ParseFromString(string_record)\n\n        label = int(example.features.feature['label']\n                     .int64_list\n                     .value[0])\n\n        img_string = (example.features.feature['encoded']\n                      .bytes_list\n                      .value[0])\n\n        height = int(example.features.feature['height']\n                    .int64_list\n                    .value[0])\n        width =  int(example.features.feature['width']\n            .int64_list\n            .value[0])\n\n        imagesString.append(img_string)\n        labels.append(label)\n        #i+=1\n        #if i == 100:\n        #    break\n        # break\n\n    return  labels,imagesString\n\ndef showShape(target,tensor):\n    # print(target)\n    # print(tensor.shape)\n    a = 1\n\nlabels , image_String = read_and_decode_single_example(filename)\n# labels_test , image_String_test = read_and_decode_single_example(filename_test)\n\nlist_image = []\nlist_image_test = []\n\nlength_img = len(image_String)\n# length_img_test = len(image_String_test)\n# print(length_img)\n\nminibatchsize = 32\nheight = 224\nwidth = 224\n\nsess = tf.Session()\n\nfor i in range(0,length_img):\n    _decode_jpeg_data = tf.placeholder(dtype=tf.string)\n    _decode_jpeg = tf.image.decode_jpeg(_decode_jpeg_data, channels=3)\n    # _image_resize = tf.image.resize_images(_decode_jpeg, [height, width])\n    image_resize = sess.run(_decode_jpeg,feed_dict={_decode_jpeg_data : image_String[i]})\n    list_image.append(image_resize)\n    # list_image_test(image_resize)\n    # print(\"anh so :\" + str(labels[i])+\" / \" +str(LableHot[i]))\n    # plt.imshow(image_resize.astype(np.uint8), interpolation='nearest')\n    # plt.show()\n\n\nnumClass = np.amax(labels)\n\nLableHot = oneHot(labels,numClass)\n\nlength_img = len(list_image)\n\nimage_input = tf.placeholder(tf.float32, shape=[None, height , width , 3], name='image_input')\n\nlabel_input = tf.placeholder(tf.float32, shape=[None, numClass], name='label_input')\n\nlabel_argmax = tf.argmax(label_input, dimension=1)\n\n# is_training = False\n#\nconv1_1 = slim.conv2d(inputs=image_input, num_outputs=2, kernel_size=[3, 3], stride=1,\n                             activation_fn=tf.nn.relu, scope='conv1_1')\nshowShape(\"conv1_1 : \",conv1_1)\n\nconv1_2 = slim.conv2d(inputs=conv1_1, num_outputs=2, kernel_size=[3, 3], stride=1,\n                             activation_fn=tf.nn.relu, scope='conv1_2')\nshowShape(\"conv1_2 : \",conv1_2)\npool1 = slim.max_pool2d(inputs=conv1_2, kernel_size=[2, 2], stride=2,scope='pool1')\nshowShape(\"pool1 : \",pool1)\n\nconv2_1 = slim.conv2d(inputs=pool1, num_outputs=4, kernel_size=[3, 3], stride=1,\n                             activation_fn=tf.nn.relu, scope='conv2_1')\nshowShape(\"conv2_1 : \",conv2_1)\n\nconv2_2 = slim.conv2d(inputs=conv2_1, num_outputs=4, kernel_size=[3, 3], stride=1,\n                             activation_fn=tf.nn.relu, scope='conv2_2')\nshowShape(\"conv2_2 : \",conv2_2)\npool2 = slim.max_pool2d(inputs=conv2_2, kernel_size=[2, 2], stride=2,scope='pool1')\nshowShape(\"pool2 : \",pool2)\nconv3_1 = slim.conv2d(inputs=pool2, num_outputs=8, kernel_size=[3, 3], stride=1,\n                             activation_fn=tf.nn.relu, scope='conv3_1')\nshowShape(\"conv3_1 : \",conv3_1)\nconv3_2 = slim.conv2d(inputs=conv3_1, num_outputs=8, kernel_size=[3, 3], stride=1,\n                             activation_fn=tf.nn.relu, scope='conv3_2')\nshowShape(\"conv3_2 : \",conv3_2)\nconv3_3 = slim.conv2d(inputs=conv3_2, num_outputs=8, kernel_size=[3, 3], stride=1,\n                             activation_fn=tf.nn.relu, scope='conv3_3')\nshowShape(\"conv3_3 : \",conv3_3)\npool3 = slim.max_pool2d(inputs=conv3_3, kernel_size=[2, 2], stride=2,scope='pool3')\nshowShape(\"pool3 : \",pool3)\nconv4_1 = slim.conv2d(inputs=pool3, num_outputs=16, kernel_size=[3, 3], stride=1,\n                             activation_fn=tf.nn.relu, scope='conv4_1')\nshowShape(\"conv4_1 : \",conv4_1)\nconv4_2 = slim.conv2d(inputs=conv4_1, num_outputs=16, kernel_size=[3, 3], stride=1,\n                             activation_fn=tf.nn.relu, scope='conv4_2')\nshowShape(\"conv4_2 : \",conv4_2)\nconv4_3 = slim.conv2d(inputs=conv4_2, num_outputs=16, kernel_size=[3, 3], stride=1,\n                             activation_fn=tf.nn.relu, scope='conv4_3')\nshowShape(\"conv4_3 : \",conv4_3)\npool4 = slim.max_pool2d(inputs=conv4_3, kernel_size=[2, 2], stride=2,scope='pool4')\nshowShape(\"pool4 : \",pool4)\nconv5_1 = slim.conv2d(inputs=pool4, num_outputs=16, kernel_size=[3, 3], stride=1,\n                             activation_fn=tf.nn.relu, scope='conv5_1')\nshowShape(\"conv5_1 : \",conv5_1)\nconv5_2 = slim.conv2d(inputs=conv5_1, num_outputs=16, kernel_size=[3, 3], stride=1,\n                             activation_fn=tf.nn.relu, scope='conv5_2')\nshowShape(\"conv5_2 : \",conv5_2)\nconv5_3 = slim.conv2d(inputs=conv5_2, num_outputs=16, kernel_size=[3, 3], stride=1,\n                             activation_fn=tf.nn.relu, scope='conv5_3')\nshowShape(\"conv5_3 : \",conv5_3)\npool5 = slim.max_pool2d(inputs=conv5_3, kernel_size=[2, 2], stride=2,scope='pool5')\nshowShape(\"pool5 : \",pool5)\nflat = tf.reshape(pool5, [-1, 7 * 7 * 16])\nshowShape(\"flat : \",flat)\nfc6 = slim.fully_connected(inputs=flat, num_outputs=32, activation_fn=tf.nn.relu, scope='fc6')\nshowShape(\"fc6 : \",fc6)\ndropout6 = slim.dropout(inputs=fc6, keep_prob=0.5, is_training=False, scope=\"dropout6\")\nshowShape(\"dropout6 : \",dropout6)\nfc7 = slim.fully_connected(inputs=dropout6, num_outputs=32, activation_fn=tf.nn.relu, scope='fc7')\nshowShape(\"fc7 : \",fc7)\ndropout7 = slim.dropout(inputs=fc7, keep_prob=0.5, is_training=False, scope=\"dropout7\")\nshowShape(\"dropout7 : \",dropout7)\nlogits = slim.fully_connected(inputs=dropout7, num_outputs=int(numClass), activation_fn=None, scope='logits')\nshowShape(\"logits : \",logits)\npredictions = tf.nn.softmax(logits=logits, name='predictions')\nshowShape(\"predictions : \",predictions)\nlabel_pred = tf.argmax(predictions, dimension=1)\nshowShape(\"label_pred : \",label_pred)\ncross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits,\n                                                        labels=label_input)\n\ncost = tf.reduce_mean(cross_entropy)\n\noptimizer = tf.train.AdamOptimizer(learning_rate=1e-5).minimize(cost)\n\ncorrect_prediction = tf.equal(label_pred, label_argmax)\n\naccuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32),name='accuracy')\n\nsess = tf.Session()\n\nsess.run(tf.global_variables_initializer())\n\nsaver = tf.train.Saver()\n\nsave_dir = 'savegraphface/'\n\nif not os.path.exists(save_dir):\n    os.makedirs(save_dir)\n\nfor i in range(0,5):\n    print(i)\n    j = 0\n    while(j * minibatchsize < length_img):\n        print(\"........\"+str(j))\n\n        h = (((j+1)*minibatchsize)-1)\n\n        u = length_img - h\n\n        if(u < 0):\n            h = length_img\n\n        list_image_train = list_image[(j*minibatchsize) : h]\n\n        LableHot = oneHot(labels,numClass)\n\n        labels_image_train = LableHot[(j*minibatchsize) : h]\n\n        image_output = sess.run(optimizer,feed_dict={image_input : list_image_train , label_input : labels_image_train })\n\n        j += 1\n\n    accuracy_output_step = sess.run(accuracy,\n                                         feed_dict={image_input: list_image, label_input: LableHot})\n    print(\"accuracy : \"+str(accuracy_output_step))\n\n    list_image, labels = shuffle(list_image, labels)\n\n    tf.train.write_graph(sess.graph.as_graph_def(), save_dir, \"model\"+str(i)+\".pb\", as_text=False)\n\n\nlabel_pred_output = sess.run(label_pred,feed_dict={image_input : list_image})\n\ncorrect_prediction_output = sess.run(correct_prediction,feed_dict={image_input : list_image , label_input : LableHot })\n","sub_path":"DemoTensorFlow/VGGFACE/Train.py","file_name":"Train.py","file_ext":"py","file_size_in_byte":8694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"131896934","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu May 10 22:18:32 2018\n\n@author: SJTU\n\"\"\"\n\nimport pandas as pd\n\nfname = \"E:/KDD/test_data/beijing_17_18_aq.csv\"\nsum_data = pd.read_csv(fname, header = 0)\n\n# get name list\nstationId = []\nstation_col = sum_data['stationId']\nindex_list = station_col.index.tolist()\n\nsta_name = station_col[0]\nstationId.append(sta_name)\nfor i in range(1, len(index_list)):\n    temp = station_col[i]\n    if temp != sta_name:\n        sta_name = temp\n        stationId.append(sta_name)\n\n# open file & get size\ninitial_size_list = []\nsize_list = []\nfor i in range(0, (len(stationId) - 1)):\n    filename = \"E:/KDD/test_data/2018_data/nan_deal_data/df_pre_\" + str(i + 1) + \"_\" + stationId[i] + \".csv\"\n    dataset = pd.read_csv(filename, header = 0, index_col = 2)\n    dataset = dataset.drop([dataset.columns[0]], axis = 1)\n    # get initial size\n    initial_size = dataset.iloc[:, 0].size\n    initial_size_list.append(initial_size)\n    # get drop duplicate size\n    dataset = dataset.drop_duplicates()\n    size = dataset.iloc[:, 0].size\n    size_list.append(size)\n    # print\n    print(stationId[i])\n    print(str(initial_size) + \"\\t\" + str(size) + \"\\n\")\n    # write file\n    outf = \"E:/KDD/test_data/2018_data/drop_duplicate_data/df_dd_\" + str(i + 1) + \"_\" + stationId[i] + \".csv\"\n    dataset.to_csv(outf)\n    \nmin_size = min(size_list)\nmin_size_index = size_list.index(min(size_list))\nprint(min_size)\nprint(min_size_index)\n\nmax_size = max(size_list)\nmax_size_index = size_list.index(max(size_list))\nprint(max_size)\nprint(max_size_index)    \n","sub_path":"KDD/process/air/2018/duplicate.py","file_name":"duplicate.py","file_ext":"py","file_size_in_byte":1557,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"256456566","text":"import argparse\nimport numpy as np\nimport torchvision\nimport torch\nimport os.path as osp\nimport pickle\nimport json\nimport os\nfrom envs.env import Env\nfrom softgym.utils.visualization import save_numpy_as_gif\n# Ravens dataset generation imports\nimport cv2\nfrom PIL import Image\nfrom ravens.dataset import Dataset\nfrom absl import app\nfrom absl import flags\n# Segmentation imports\nimport matplotlib.pyplot as plt\nfrom scipy import ndimage as ndi\nfrom skimage.segmentation import watershed\nfrom skimage.feature import peak_local_max\n\n\ndef segment_depthmap(d_map):\n\t\n\t#https://scikit-image.org/docs/dev/auto_examples/segmentation/plot_watershed.html#sphx-glr-auto-examples-segmentation-plot-watershed-py\n\t\n    image = d_map * 100 # Denormalize\n    img_aux = Image.fromarray(image, 'F') \n    img_aux.show()\n\n    image = image.astype(np.int64)\n    print(\"MAX DEPTH FROM image\", np.amax(image))\n    print(\"MIN DEPTH FROM image\", np.amin(image))\n    distance = ndi.distance_transform_edt(image)\n    coords = peak_local_max(distance, footprint=np.ones((3, 3)), labels=image)\n    mask = np.zeros(distance.shape, dtype=bool)\n    mask[tuple(coords.T)] = True\n    markers, _ = ndi.label(mask)\n    labels = watershed(-distance, markers, mask=image)\n\n    fig, axes = plt.subplots(ncols=3, figsize=(9, 3), sharex=True, sharey=True)\n    ax = axes.ravel()\n\n    ax[0].imshow(image, cmap=plt.cm.gray)\n    ax[0].set_title('Overlapping objects')\n    ax[1].imshow(-distance, cmap=plt.cm.gray)\n    ax[1].set_title('Distances')\n    ax[2].imshow(labels, cmap=plt.cm.nipy_spectral)\n    ax[2].set_title('Separated objects')\n\n    for a in ax:\n        a.set_axis_off()\n\n    fig.tight_layout()\n    plt.show()\n    return\n\ndef cem_make_gif(env, initial_states, action_trajs, configs, save_dir, save_name, img_size=720):\n    all_frames = []\n    for i in range(len(action_trajs)):\n        frames = []\n        env.reset(config=configs[i], initial_state=initial_states[i])\n        frames.append(env.get_image(img_size, img_size))\n        for action in action_trajs[i]:\n            _, reward, _, info = env.step(action, record_continuous_video=True, img_size=img_size)\n            frames.extend(info['flex_env_recorded_frames'])\n        all_frames.append(frames)\n    # Convert to T x index x C x H x W for pytorch\n    all_frames = np.array(all_frames).transpose([1, 0, 4, 2, 3])\n    grid_imgs = [torchvision.utils.make_grid(torch.from_numpy(frame), nrow=5).permute(1, 2, 0).data.cpu().numpy() for frame in all_frames]\n    save_numpy_as_gif(np.array(grid_imgs), osp.join(save_dir, save_name))\n\ndef make_ravens_db(demo_dir, env, initial_states, action_trajs, configs, seed, img_size=128):\n    #C reate ravens Dataset\n    print(\"Save demos to: \", demo_dir)\n    dataset = Dataset(demo_dir)\n    episode= [] # ravens dataset\n\n    for i in range(len(action_trajs)):\n        env.reset(initial_state=initial_states[i])\n        color = env.get_image(img_size, img_size) #TODO Hardcoded ik\n\n        j = 0\n        for action in action_trajs[i]:\n            obs, reward, _, info = env.step(action, img_size=img_size)\n            # ravens action encapsules pick&place -- see ravens/environment.py\n            # softgym even actions are pick, odd are place\n            if j%2==0: \n                action0 = info['picker_pos'][0], [0,0,0,1]\n            else: \n                color = env.get_image(img_size, img_size)                \n                depth = info['depth_map']\n                img = Image.fromarray(depth, 'I') # debug\n\n                obs_dict = {'color': color , 'depth': depth}\n                action1= info['picker_pos'][0], [0,0,0,1] # Dont consider rotations\n                #reward = info[] # get normalized reward from info.... not used\n                action_dict = {'pose0': action0, 'pose1': action1}\n                episode.append((obs_dict, action_dict, reward, info))\n            j+=1\n\n        #segment_depthmap(depth) #debug\n\n        episode.append((obs_dict, None, reward, info))\n        dataset.add(seed, episode)\n\ndef get_env(variant_path):\n    with open(variant_path, 'r') as f:\n        vv = json.load(f)\n\n    env_symbolic = vv['env_kwargs']['observation_mode'] != 'cam_rgb'\n    vv['env_kwargs']['render'] = True\n    env_class = Env\n    env_kwargs = {'env': vv['env_name'],\n                  'symbolic': env_symbolic,\n                  'seed': vv['seed'],\n                  'action_repeat': 1,\n                  'bit_depth': 8,\n                  'image_dim': None,\n                  'env_kwargs': vv['env_kwargs']}\n    env = env_class(**env_kwargs)\n    return env, vv['env_name']\n\n\ndef get_variant_file(traj_path):\n    traj_dir = osp.dirname(traj_path)\n    return osp.join(traj_dir, 'variant.json')\n\n\ndef generate_video(file_paths):\n    envs, env_names = [], []\n    for file_path in file_paths:\n        variant_path = get_variant_file(file_path)\n        env, env_name = get_env(variant_path)\n        envs.append(env)\n        env_names.append(env_name)\n    for env, env_name, file_path in zip(envs, env_names, file_paths):\n        with open(file_path, 'rb') as f:\n            traj_dict = pickle.load(f)\n        initial_states, action_trajs, configs = traj_dict['initial_states'], traj_dict['action_trajs'], traj_dict['configs']\n        cem_make_gif(env, initial_states, action_trajs, configs, save_dir='data/videos/cem', save_name=env_name + '.gif')\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('exp_dir', type=str,\n                        help='path to the snapshot file')\n    args = parser.parse_args()\n    generate_video([osp.join(args.exp_dir, subdir, 'cem_traj.pkl') for subdir in os.listdir(args.exp_dir)])\n    # file_path = osp.join(args.exp_dir, 'cem_traj.pkl')\n    # with open(file_path, 'rb') as f:\n    #     traj_dict = pickle.load(f)\n    # initial_states, action_trajs, configs = traj_dict['initial_states'], traj_dict['action_trajs'], traj_dict['configs']\n    # for action in action_trajs[0]:\n    #     print(action)\n","sub_path":"cem/visualize_cem.py","file_name":"visualize_cem.py","file_ext":"py","file_size_in_byte":5956,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"552012669","text":"import pandas as pd\n\nkmers = pd.read_csv(\"Output_kmers.txt\", header=None)\nkmers.columns = ['kmer']\nkmers['freq'] = kmers.groupby('kmer')['kmer'].transform('count')\n\nmore1 = kmers['freq']!=1\nmore1.head()\nkmer_more1 = kmers[more1]\nmore1List = kmer_more1['kmer']\nmore1List.to_csv(\"seqmore1List.txt\", index = False, header = False)\n","sub_path":"U2.2_Multitons.py","file_name":"U2.2_Multitons.py","file_ext":"py","file_size_in_byte":328,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"376213791","text":"from collections import namedtuple\nimport re\nfrom datetime import datetime\nfrom collections import defaultdict\n\n###helpers functions###\ndef unit(car):\n    '''Replaces , between doublequotes'''\n    if '\"' in car:\n        car = re.sub(r'\"[^\"]*\"', lambda m: m.group(0).replace(',', ''), car)\n        car = car.replace('\"', '')\n    return car\n\n\ndef cast(data_type, value, default):\n    '''Cast data from file'''\n    if data_type == 'summons_number':\n        try:\n            return int(value)\n        except ValueError:\n            return default\n\n    elif data_type == 'issue_date':\n        try:\n            return datetime.strptime(value, '%m/%d/%Y').date()\n        except ValueError:\n            return default\n\n    elif data_type == 'violation_code':\n        try:\n            return int(value)\n        except ValueError:\n            return default\n    \n    elif data_type == 'vehicle_make' and value == '':\n        return default\n\n    else:\n        try:\n            value = value.strip()\n            if not value:\n                return default\n            else:\n                return value\n        except ValueError:\n            return default\n\n\ndef cast_row(data_type, value):\n    return [cast(data_type, value, default='N/A') for data_type, value in zip(data_type, value)]\n#####################\n\n###main\ndef parse_data(): #create header, named tuple and pass data into named tuple\n    with open('nyc_parking_tickets_extract.csv') as f: #\n        header = next(f).replace(' ', '_').replace('\\n', '').lower().split(',') #\n        Parking = namedtuple('Parking', header) #\n        for car in f:\n            car = car.replace('\\n', '')\n            car = unit(car).split(',') # unit() removing commas between doublequotes, spliting to list\n            car = cast_row(header, car) #casting value\n            yield Parking(*car)\n\n\ndef read_data():\n    for row in parse_data():\n        yield row\n\n\n###data processing\ndef number_of_violations():\n    number = defaultdict(int)   \n    for vehicle in parse_data():\n        if vehicle.vehicle_make != 'N/A':\n            number[vehicle.vehicle_make] += 1\n            \n    return  {make: count for make, count in sorted(number.items(), key=lambda t: t[1], reverse=True)}\n\nprint(number_of_violations())\n\n\n","sub_path":"2_iter_gen/project3/project3edit.py","file_name":"project3edit.py","file_ext":"py","file_size_in_byte":2243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"612780926","text":"import math\r\ndef y_m(x):\r\n    \"\"\"Вычисляем функцию по формуле x^4+4^x\r\n    >>> y_m(1)\r\n    7\r\n    \"\"\"\r\n    return (x**4+4**x)\r\nx=int(input('Введите x = '))\r\nprint('Функция = ', y_m(x))\r\nif __name__==\"__main__\":\r\n    import doctest\r\n    doctest.testmod()\r\n","sub_path":"doctest_7.5.py","file_name":"doctest_7.5.py","file_ext":"py","file_size_in_byte":294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"276144612","text":"\nimport sqlite3\nimport numpy as np\n\n# initialize the SQLite database connection, get list of existing tables\ncon = sqlite3.connect('db_wiki.sqlite')\ncursor = con.cursor()\ntitles = [x[0] for x in cursor.execute(\"\"\"SELECT title FROM wiki_attrs\"\"\")]\ncon.close()\nprint('{} pages in the database'.format(len(titles)))","sub_path":"data-incubator 2016/wikipedia/db_count.py","file_name":"db_count.py","file_ext":"py","file_size_in_byte":312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"371353693","text":"from keras.datasets import mnist\nfrom keras.models import Sequential, Model\nfrom keras.layers import Input, Dense, Activation\nfrom keras.layers.advanced_activations import LeakyReLU\nfrom keras.optimizers import Adam\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom keras.layers.core import Reshape, Dense, Dropout, Flatten\nfrom keras import initializers\n\n# exp5: batch size 128, add two layers to generator\n\n# Load data\n(X_train, _), (_, _) = mnist.load_data()\n\n# Preprocessing\nX_train = X_train.reshape(-1, 784)\nX_train = X_train.astype('float32')/255\n\n# Set the dimensions of the noise\nz_dim = 100\n\n# Optimizer\nadam = Adam(lr=0.0002, beta_1=0.5)\n\n# #demo G\n# g = Sequential()\n# g.add(Dense(256, input_dim=z_dim, kernel_initializer=initializers.RandomNormal(stddev=0.02)))\n# g.add(LeakyReLU(0.2))\n# g.add(Dense(512))\n# g.add(LeakyReLU(0.2))\n# g.add(Dense(1024))\n# g.add(LeakyReLU(0.2))\n# g.add(Dense(784, activation='tanh'))\n# g.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])\n\n# Generator\ng = Sequential()\ng.add(Dense(units=256,input_dim = z_dim))\ng.add(LeakyReLU(alpha=0.2))\ng.add(Dense(units=512))\ng.add(LeakyReLU(alpha=0.2))\ng.add(Dense(units=1024))\ng.add(LeakyReLU(alpha=0.2))\ng.add(Dense(784, activation='tanh'))\ng.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])\n\n\n\n# Discrinimator\nd = Sequential()\nd.add(Dense(1024, input_dim=784, kernel_initializer=initializers.RandomNormal(stddev=0.02)))\nd.add(LeakyReLU(0.2))\nd.add(Dropout(0.3))\nd.add(Dense(512))\nd.add(LeakyReLU(0.2))\nd.add(Dropout(0.3))\nd.add(Dense(256))\nd.add(LeakyReLU(0.2))\nd.add(Dropout(0.3))\nd.add(Dense(1, activation='sigmoid'))\nd.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])\n\n# GAN\nd.trainable = False\ninputs = Input(shape=(z_dim, ))\nhidden = g(inputs)\noutput = d(hidden)\ngan = Model(inputs, output)\ngan.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])\n\n# Training\ndef train(epochs=1, plt_frq=1, BATCH_SIZE=128):\n    batchCount = int(X_train.shape[0] / BATCH_SIZE)\n    print('Epochs:', epochs)\n    print('Batch size:', BATCH_SIZE)\n    print('Batches per epoch:', batchCount)\n\n\n    for i in range(batchCount):\n        # Create a batch by drawing random index numbers from the training set\n        image_batch = X_train[np.random.randint(0, X_train.shape[0], size=BATCH_SIZE)]\n        # Create noise vectors for the generator\n        noise = np.random.normal(0, 1, size=(BATCH_SIZE, z_dim))\n\n        # Generate the images from the noise\n        generated_images = g.predict(noise)\n        X = np.concatenate((image_batch, generated_images))\n\n        # Create labels\n        y = np.zeros(2 * BATCH_SIZE)\n        y[:BATCH_SIZE] = 1\n\n        # Train discriminator on generated images\n        d.trainable = True\n        d_loss = d.train_on_batch(X, y)\n\n\n        # Train generator\n        noise = np.random.normal(0, 1, size=(BATCH_SIZE, z_dim))\n        y2 = np.ones(BATCH_SIZE)\n        d.trainable = False\n        g_loss = gan.train_on_batch(noise, y2)\n\n        if i == batchCount-1:\n            return [g_loss[0],d_loss[0]]\n\n\nepochCount = 200\ng_loss_arr = []\nd_loss_arr = []\n\nfor i in range(epochCount):\n    loss_arr = train(i)\n    g_loss_arr.append(loss_arr[0])\n    d_loss_arr.append(loss_arr[1])\n\n# plot loss\nLossFigure = plt.figure()\nplt.xlabel(u'Epochs')\n\nplt.ylabel(u'Loss')\n\nmy_x_ticks = np.arange(0,epochCount,20)\nplt.xticks(my_x_ticks)\n\nplt.plot(np.arange(0,epochCount),g_loss_arr)\nplt.plot(np.arange(0,epochCount),d_loss_arr)\nplt.legend([\"g__training_loss\",\"d_training_loss\"],loc=2)\nLossFigure.savefig(\"LossFigures/exp5_loss.png\")\nplt.show()\n\n#================================================\n# 测试阶段\n#================================================\n# Generate images\nnp.random.seed(504)\nh = w = 28\nnum_gen = 25\n\nz = np.random.normal(size=[num_gen, z_dim])\ngenerated_images = g.predict(z)\n\n# plot of generation\nn = np.sqrt(num_gen).astype(np.int32)\nI_generated = np.empty((h*n, w*n))\nfor i in range(n):\n    for j in range(n):\n        I_generated[i*h:(i+1)*h, j*w:(j+1)*w] = generated_images[i*n+j, :].reshape(28, 28)\n\nGeneratedImages = plt.figure(figsize=(4, 4))\nplt.axis(\"off\")\nplt.imshow(I_generated, cmap='gray')\nGeneratedImages.savefig(\"GeneratedImages/exp5_res.png\")\nplt.show()\n\n\n# # serialize model to JSON\n# model_json = g.to_json()\n# with open(\"generator.json\", \"w\") as json_file:\n#     json_file.write(model_json)\n# # serialize weights to HDF5\n# g.save_weights(\"generator.h5\")\n# print(\"Saved model to disk\")\n# for i in range(1,20):\n#     train(epochs=i)","sub_path":"exp5.py","file_name":"exp5.py","file_ext":"py","file_size_in_byte":4558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"330962825","text":"\"\"\"\nspark.py\n~~~~~~~~\nModule containing helper function for use with Apache Spark\n\"\"\"\n\nfrom pyspark.sql import SparkSession\nfrom utils import logging\nfrom os import listdir, path\nimport json\nfrom pyspark import SparkFiles\n\n\ndef start_spark(app_name='etl_app', master='local[*]',\n                files=[], spark_config={}):\n\n    spark_builder = (\n        SparkSession\n            .builder\n            .master(\"local\")\n            .appName(app_name))\n\n    spark_builder.config('spark.files', files)\n    spark_builder.config(\"spark.sql.autoBroadcastJoinThreshold\", -1)\n\n    for key, val in spark_config.items():\n        spark_builder.config(key, val)\n\n    # create session and retrieve Spark logger object\n    spark_sess = spark_builder.getOrCreate()\n    spark_logger = logging.Log4j(spark_sess)\n\n    # get config file if sent to cluster with --files\n    spark_files_dir = SparkFiles.getRootDirectory()\n    config_files = [filename\n                    for filename in listdir(spark_files_dir)\n                    if filename.endswith('config.json')]\n\n    try:\n        path_to_config_file = path.join(spark_files_dir, config_files[0])\n        with open(path_to_config_file, 'r') as config_file:\n            config_dict = json.load(config_file)\n    except Exception as e:\n        spark_logger.warn(\"no config file found\" + str(e))\n\n    return spark_sess, spark_logger, config_dict","sub_path":"utils/spark.py","file_name":"spark.py","file_ext":"py","file_size_in_byte":1375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"334287963","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 27 19:05:38 2018\n@author: xiaoqian\n\"\"\"\nimport os\nimport os.path as op\nfrom glob import glob\nimport re\nimport pandas as pd\nimport numpy as np\nimport nibabel as ni\nimport nilearn\nimport subprocess\nfrom sklearn.covariance import GraphLassoCV\nimport shutil\nfrom scipy import stats\nfrom scipy.special import comb\nfrom scipy.stats import pearsonr\n\nProjDir = '/Users/xiaoqian/Projects/aTBS'\ndataDir = ProjDir\nbehavDir = os.path.join(dataDir, 'source/behavioral')\nresultDir = os.path.join(dataDir, 'cor_addROI')\nsub_list = np.array([1,2,3,4,11,14,16,18,19,21,24,26,28,29])\n#FC\n#mat_baseline\n#mat_1w\n#disMat_1wBaselie\n#beahv\n#HAMD\n#MADRS\n\n#load behavioral data\nbaseline_file = os.path.join(behavDir, 'Behavior_baseline.csv')\nb_baseline = pd.read_csv(baseline_file)\nb1w_file = os.path.join(behavDir, 'Behavior_1w.csv')\nb_1w = pd.read_csv(b1w_file)\nbase_hamd = b_baseline.loc[:,\"hamd21_total\"]\nw1_hamd = b_1w.loc[:,\"hamd21_total\"]\nbase_madrs = b_baseline.loc[:,\"hamd21_total\"]\nw1_madrs = b_1w.loc[:,\"madrs_score\"]\ndiff_hamd = b_baseline.loc[:,\"hamd21_total\"] - b_1w.loc[:,\"hamd21_total\"]\ndiff_madrs = b_baseline.loc[:,\"madrs_score\"] - b_1w.loc[:,\"madrs_score\"]\n\nbaseFC_baseH = pd.DataFrame()\nbaseFC_baseM = pd.DataFrame()\nbaseFC_1wH = pd.DataFrame()\nbaseFC_1wM = pd.DataFrame()\nbaseFC_diffH = pd.DataFrame()\nbaseFC_diffM = pd.DataFrame()\nw1FC_baseH = pd.DataFrame()\nw1FC_baseM = pd.DataFrame()\nw1FC_1wH = pd.DataFrame()\nw1FC_1wM = pd.DataFrame()\nw1FC_diffH = pd.DataFrame()\nw1FC_diffM = pd.DataFrame()\ndiffFC_baseH = pd.DataFrame()\ndiffFC_baseM = pd.DataFrame()\ndiffFC_1wH = pd.DataFrame()\ndiffFC_1wM = pd.DataFrame()\ndiffFC_diffH = pd.DataFrame()\ndiffFC_diffM = pd.DataFrame()\nfor i in range(mat_1w.shape[0]):\n    base_FC = pd.DataFrame(np.transpose(mat_baseline[i,:,:]))\n    w1_FC = pd.DataFrame(np.transpose(mat_1w[i,:,:]))\n    diff_FC = w1_FC - base_FC   \n    baseFC_baseH[i] = base_FC.corrwith(base_hamd)\n    baseFC_baseM[i] = base_FC.corrwith(base_madrs)\n    baseFC_1wH[i] = base_FC.corrwith(w1_hamd)\n    baseFC_1wM[i] = base_FC.corrwith(w1_madrs)\n    baseFC_diffH[i] = base_FC.corrwith(diff_hamd)\n    baseFC_diffM[i] = base_FC.corrwith(diff_madrs)\n    w1FC_baseH[i] = w1_FC.corrwith(base_hamd)\n    w1FC_baseM[i] = w1_FC.corrwith(base_madrs)\n    w1FC_1wH[i] = w1_FC.corrwith(w1_hamd)\n    w1FC_1wM[i] = w1_FC.corrwith(w1_madrs)\n    w1FC_diffH[i] = w1_FC.corrwith(diff_hamd)\n    w1FC_diffM[i] = w1_FC.corrwith(diff_madrs)    \n    diffFC_baseH[i] = diff_FC.corrwith(base_hamd)\n    diffFC_baseM[i] = diff_FC.corrwith(base_madrs)\n    diffFC_1wH[i] = diff_FC.corrwith(w1_hamd)\n    diffFC_1wM[i] = diff_FC.corrwith(w1_madrs)\n    diffFC_diffH[i] = diff_FC.corrwith(diff_hamd)\n    diffFC_diffM[i] = diff_FC.corrwith(diff_madrs)\n\nzbaseFC_baseH = 0.5*(np.log(1+baseFC_baseH)-np.log(1-baseFC_baseH))\nzbaseFC_baseM = 0.5*(np.log(1+baseFC_baseM)-np.log(1-baseFC_baseM))\nzbaseFC_1wH = 0.5*(np.log(1+baseFC_1wH)-np.log(1-baseFC_1wH))\nzbaseFC_1wM = 0.5*(np.log(1+baseFC_1wM)-np.log(1-baseFC_1wM))\nzbaseFC_diffH = 0.5*(np.log(1+baseFC_diffH)-np.log(1-baseFC_diffH))\nzbaseFC_diffM = 0.5*(np.log(1+baseFC_diffM)-np.log(1-baseFC_diffM))\nzw1FC_baseH = 0.5*(np.log(1+w1FC_baseH)-np.log(1-w1FC_baseH))\nzw1FC_baseM = 0.5*(np.log(1+w1FC_baseM)-np.log(1-w1FC_baseM))\nzw1FC_1wH = 0.5*(np.log(1+w1FC_1wH)-np.log(1-w1FC_1wH))\nzw1FC_1wM = 0.5*(np.log(1+w1FC_1wM)-np.log(1-w1FC_1wM))\nzw1FC_diffH = 0.5*(np.log(1+w1FC_diffH)-np.log(1-w1FC_diffH))\nzw1FC_diffM = 0.5*(np.log(1+w1FC_diffM)-np.log(1-w1FC_diffM))\nzdiffFC_baseH = 0.5*(np.log(1+diffFC_baseH)-np.log(1-diffFC_baseH))\nzdiffFC_baseM = 0.5*(np.log(1+diffFC_baseM)-np.log(1-diffFC_baseM))\nzdiffFC_1wH = 0.5*(np.log(1+diffFC_1wH)-np.log(1-diffFC_1wH))\nzdiffFC_1wM = 0.5*(np.log(1+diffFC_1wM)-np.log(1-diffFC_1wM))\nzdiffFC_diffH = 0.5*(np.log(1+diffFC_diffH)-np.log(1-diffFC_diffH))\nzdiffFC_diffM = 0.5*(np.log(1+diffFC_diffM)-np.log(1-diffFC_diffM))\n\nnp.where(zbaseFC_baseH>=0.6)\n","sub_path":"FC/AromaO/FC_behave.py","file_name":"FC_behave.py","file_ext":"py","file_size_in_byte":3967,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"333079569","text":"\nimport os\nimport sys\nimport numpy as np\nimport math\n\nfrom shapely.geometry import mapping, shape\nimport rasterio\nfrom rasterio.mask import mask\nfrom pygeoprocessing import routing\nimport pandas as pd\nimport geopandas as gpd\nfrom scipy.signal import medfilt\nimport pickle\n\nfrom skimage import img_as_float\nfrom skimage.feature import peak_local_max\nfrom shapely.geometry import Point, Polygon, MultiPolygon\nfrom shapely.ops import cascaded_union\n\n\ndef _raster_mask(input_dem, polygon, out_name):\n    \"\"\" mask a raster file along the polygon and saves the new raster\n\n    Parameters\n    ----------\n    input_dem   : str\n        path to the raster file\n    polygon     : shapely.geometry.Polygon instance\n        outline polygon\n    out_name    : str\n        name of the output raster\n\n    Returns\n    -------\n    path to the output raster\n    \"\"\"\n\n    output_dem = os.path.join(os.path.dirname(input_dem), out_name+'.tif')\n\n    geoms = [mapping(polygon)]\n\n    with rasterio.open(input_dem) as src:\n        out_image, out_transform = mask(src, geoms, nodata=np.nan, crop=False)\n        out_meta = src.meta.copy()\n\n    out_meta.update({\"driver\": \"GTiff\",\n                     \"height\": out_image.shape[1],\n                     \"width\": out_image.shape[2],\n                     \"nodata\": np.nan,\n                     \"transform\": out_transform})\n    with rasterio.open(output_dem, \"w\", **out_meta) as dest:\n        dest.write(out_image)\n\n    return output_dem\n\n\ndef _convert_to_polygon(gpd_obj):\n    \"\"\"convert geopandas object, which could include Multipolygons to a new\n    geopandas object containing only Polygons\n\n    Parameters\n    ----------\n    gpd_obj :   gpd.GeoDataFrame\n\n    Returns\n    -------\n    gpd.GeoDataFrame\n    \"\"\"\n    for i in gpd_obj.index:\n        if gpd_obj.loc[i, 'geometry'].type is not 'Polygon':\n            geom = gpd_obj.loc[i, 'geometry']\n            area = [glac.area for glac in geom]\n            gpd_obj.set_value(i, 'geometry', geom[np.argmax(area)])\n            for j in range(len(geom)):\n                if not j == np.argmax(area) and geom[j].area > 1:\n                    gpd_obj, bool = _merge_sliver(gpd_obj, geom[j])\n    return gpd_obj\n\n\ndef _compactness(polygon):\n    \"\"\" check if polygon satisfy glacier compactness (Allen,1998)\n\n    Parameters\n    ----------\n    polygon: shapely.geometry.Polygon instance\n\n    Returns\n    -------\n    bool\n    \"\"\"\n    coord = np.array(polygon.exterior.coords)\n\n    y_min = Point(coord[np.argmin(coord[:, 1])])\n    y_max = Point(coord[np.argmax(coord[:, 1])])\n    x_min = Point(coord[np.argmin(coord[:, 0])])\n    x_max = Point(coord[np.argmax(coord[:, 0])])\n    # calculate max distance(perimeter)\n    max_dist = y_min.distance(y_max)\n    x_dist = x_min.distance(x_max)\n    if x_dist > max_dist:\n        max_dist = x_dist\n    if max_dist*math.pi/polygon.boundary.length > 0.5:\n        return True\n    else:\n        return False\n\n\ndef _compute_altitude(dem, polygon):\n    \"\"\" compute altitude range for a polygon\n\n    Parameters\n    ----------\n    dem     :   str\n        path to a DEM file\n    polygon :   shapely.geometry.Polygon\n        polygon of the glacier divide\n    Returns\n    -------\n    float   : altitude range\n    \"\"\"\n    geoms = [mapping(polygon)]\n    with rasterio.open(dem) as src:\n        out_image, out_transform = mask(src, geoms, nodata=np.nan, crop=False)\n    altitude = np.nanmax(out_image)-np.nanmin(out_image)\n    return altitude\n\n\ndef _filter_divides(gpd_obj, filter_area, filter_alt_range,\n                    filter_perc_alt_range):\n    \"\"\" filter divides\n\n    Parameters\n    ----------\n    gpd_obj                 : gpd.GeoDataFrame\n    filter_area             : bool\n                              (True: keep a divide only if it's area is not\n                               smaller than 2% of the largest divide)\n    filter_alt_range        : bool\n                              (True: keep a divide only if the absolute\n                               altitude range of the divide is larger than 100m\n    filter_perc_alt_range   : bool\n                              (True: keep a divide only if the altitude range\n                               of the divide is larger than 10% of the glaciers\n                               total altitude range\n\n    Returns\n    -------\n    filtered gpd.GeoDataFrame object\n    \"\"\"\n\n    # initialise nokeep\n    nokeep = pd.Series(np.zeros(len(gpd_obj), dtype=np.bool))\n    if filter_area is True:\n        nokeep = nokeep | (gpd_obj['Perc_Area'] < 0.02)\n    if filter_alt_range is True:\n        nokeep = nokeep | (gpd_obj['Alt_Range'] < 100)\n    if filter_perc_alt_range is True:\n        nokeep = nokeep | (gpd_obj['Perc_Alt_Range'] < 0.1)\n\n    gpd_obj['keep'] = ~nokeep\n\n    if np.sum(gpd_obj['keep']) in [0, 1]:\n        # Nothing to do! The divide should be ignored\n        return gpd_obj, False\n\n    while not gpd_obj['keep'].all():\n        geom = gpd_obj.loc[~gpd_obj['keep']].iloc[0]\n        gpd_obj = gpd_obj.drop(geom.name)\n        gpd_obj, bool = _merge_sliver(gpd_obj, geom.geometry)\n    return gpd_obj, True\n\n\ndef _check_contain_divides(glacier_poly, id):\n    \"\"\"\n    check if any object from glacier_poly contains glacier_poly.loc[id,\n    'geometry'] and correct it\n\n    Parameters\n    ----------\n    glacier_poly    : gpd.GeoDataFrame\n    id              : int\n\n    Returns\n    -------\n    gpd.GeoDataFrame\n    \"\"\"\n    exterior = glacier_poly.copy()\n    for i in exterior.index:\n        coord = exterior.loc[i, 'geometry'].exterior\n        exterior.loc[i, 'geometry'] = Polygon(coord)\n    contain = glacier_poly[exterior.contains(glacier_poly.loc[id, 'geometry'])]\n    glacier_fid = [(glacier_poly.loc[j].FID) for j in glacier_poly.index]\n    for i in contain.index.drop(id):\n        if contain.loc[i].FID in glacier_fid:\n            to_merge = glacier_poly.loc[id, 'geometry']\n\n            glacier_poly = glacier_poly.loc[glacier_poly.index.drop(id), :]\n            glacier_poly, bool = _merge_sliver(glacier_poly, to_merge)\n    return glacier_poly\n\n\ndef _fill_pits_with_saga(dem, saga_cmd=None):\n    \"\"\" fill pits with SAGA GIS\n\n    Parameters\n    ----------\n    dem     : str\n        path to the raster file\n    saga_cmd:   str\n        path to saga_cmd.exe (only needed on windows system)\n\n    Returns\n    -------\n    path to the new raster file\n    \"\"\"\n    saga_dir = os.path.join(os.path.dirname(dem), 'saga')\n    if not os.path.exists(saga_dir):\n        # create directory for saga_output\n        os.makedirs(saga_dir)\n    saga_filled = os.path.join(saga_dir, 'filled.sdat')\n    filled_dem = os.path.join(os.path.dirname(dem), 'filled_dem.tif')\n    if sys.platform.startswith('linux'):\n        os.system('saga_cmd ta_preprocessor 4 -ELEV:' + dem + ' -FILLED:'\n                  + saga_filled + ' > /dev/null')\n        os.system('gdal_translate ' + saga_filled + ' ' + filled_dem\n                  + ' > /dev/null')\n    elif sys.platform.startswith('win'):\n        os.system('\"'+saga_cmd+' ta_preprocessor 4 -ELEV:'+dem+' -FILLED:'\n                  + saga_filled+' \"')\n        os.system('\" gdal_translate '+saga_filled+' '+filled_dem\n                  + ' \"')\n    return filled_dem\n\n\ndef _flowacc(input_dem):\n    \"\"\"create a raster which only contains flowaccumulation at the gutter,\n    used for pour_point identification\n\n    Parameters\n    ----------\n    input_dem   : str\n        path to a raster file\n\n    Returns\n    -------\n    path to raster file with the flow accumulation gutter\n    \"\"\"\n    flow_direction = os.path.join(os.path.dirname(input_dem), 'flow_dir.tif')\n    flow_accumulation = os.path.join(os.path.dirname(input_dem),\n                                     'flow_accumulation.tif')\n    # calculate flow direction\n    routing.flow_direction_d_inf(input_dem, flow_direction)\n    # calculate flow_accumulation\n    routing.flow_accumulation(flow_direction, input_dem, flow_accumulation)\n    # mask along gutter\n    gutter_shp = os.path.join(os.path.dirname(input_dem), 'gutter.shp')\n    gutter = gpd.read_file(gutter_shp)['geometry'][0]\n    _raster_mask(flow_accumulation, gutter, 'flow_gutter')\n\n    return os.path.join(os.path.dirname(input_dem), 'flow_gutter.tif')\n\n\ndef flowshed_calculation(dem, shp):\n    \"\"\" calculate flowsheds\n\n    Parameters\n    ----------\n    dem : str\n        path to a raster file\n    shp : str\n        path to a shape file\n\n    Returns\n    -------\n    path to the shape file containing all the flowsheds\n    (shapely.geometry.Polygon)\n    \"\"\"\n    dir = os.path.dirname(dem)\n    watershed_dir = os.path.join(dir, 'all_watersheds.shp')\n    flowshed_dir = os.path.join(dir, 'flowshed.shp')\n    # calculate watersheds\n    routing.delineate_watershed(dem, shp, 1, 100, watershed_dir,\n                                os.path.join(dir, 'snapped_outlet_points.shp'),\n                                os.path.join(dir, 'stream_out_uri.tif'))\n    watersheds = gpd.read_file(watershed_dir).buffer(0)\n    pour_points = gpd.read_file(dir + '/pour_points.shp')\n    flowsheds = gpd.GeoSeries(watersheds.intersection(co), crs=crs)\n    # remove empty objects\n\n    flowsheds = flowsheds[(~flowsheds.is_empty) & (flowsheds.type != 'Point')\n                          & (flowsheds.type != 'MultiPoint')\n                          & (flowsheds.type != 'LineString')\n                          & (flowsheds.type != 'MultiLineString')]\n    collections = flowsheds[flowsheds.type == 'GeometryCollection']\n    for index in collections.index:\n        multi = MultiPolygon()\n        for geo in collections.loc[index]:\n            if geo.type is 'Polygon':\n                multi = multi.union(geo)\n        flowsheds.loc[index] = multi\n    # if object is Multipolygon split it\n    for i, shed in enumerate(flowsheds):\n        if shed.type is 'MultiPolygon':\n            # find polygon with minimal distance to pour point\n            dist = []\n            for s0 in shed:\n                dist.append(s0.distance(pour_points.loc[i, 'geometry']))\n            # add each polygon to all_watershed.shp\n            for j, s1 in enumerate(shed):\n                # polygon nearest to PP get current id\n                if j == np.argmin(dist):\n                    flowsheds.loc[i] = shape(s1)\n                # all other poylgons were added at the end\n                else:\n                    s3 = gpd.GeoSeries(s1)\n                    flowsheds = flowsheds.append(s3, ignore_index=True)\n    result = gpd.GeoDataFrame(geometry=flowsheds)\n    result.to_file(flowshed_dir)\n    return flowshed_dir\n\n\ndef _gutter(masked_dem, depth):\n    \"\"\" create a new raster, with lowered values at the gutter (beyond\n        the outlines\n    Parameters\n    ----------\n    masked_dem  : str\n        path to a raster file\n    depth       : int\n        raster will be lowered by dept along gutter\n\n    Returns\n    -------\n    path to the output raster\n\n    \"\"\"\n    # create gutter shp\n    gutter_shp = os.path.join(os.path.dirname(masked_dem), 'gutter.shp')\n    outline_exterior = Polygon(co.exterior)\n    gutter_shape = outline_exterior.buffer(pixelsize * 2).difference(\n        outline_exterior.buffer(pixelsize))\n    gpd.GeoSeries(gutter_shape, crs=crs).to_file(gutter_shp)\n\n    # lower dem along gutter\n    gutter_dem = _raster_mask(masked_dem, gutter_shape, 'gutter')\n    gutter2_dem = os.path.join(os.path.dirname(gutter_dem), 'gutter2.tif')\n    with rasterio.open(masked_dem) as src1:\n        mask_band = np.array(src1.read(1))\n        with rasterio.open(gutter_dem) as src:\n            mask_band = np.float32(mask_band - depth * (~np.isnan(np.array(\n                src.read(1)))))\n        with rasterio.open(gutter2_dem, \"w\", **src.meta.copy()) as dest:\n            dest.write_band(1, mask_band)\n\n    return gutter2_dem\n\n\ndef identify_pour_points(dem):\n    \"\"\" create flow accumulation gutter and identify pour points\n\n    Parameters\n    ----------\n    dem :   str\n        path to the raster file\n\n    Returns\n    -------\n    path to a shape file containing all pour points\n    \"\"\"\n    # calculation of flow accumulation and flow direction\n    flow_gutter = _flowacc(dem)\n\n    # identify Pour Points\n    pour_points_shp = _pour_points(flow_gutter)\n\n    return pour_points_shp\n\n\ndef _intersection_of_glaciers(gpd_obj, index):\n    \"\"\" create a GeoDataFrame object including all intersection areas between\n    gpd_obj and gpd_obj[index]\n\n    Parameters\n    ----------\n    gpd_obj :   gpd.GeoDataFrame object\n        for that intersection should be identified\n    index   :   int\n        index of one element in gpd_obj\n\n    Returns\n    -------\n     gpd.GeoDataFrame object with intersection areas\n    \"\"\"\n    gpd_obj = gpd_obj.intersection(gpd_obj.loc[index, 'geometry'])\n    gpd_obj = gpd.GeoDataFrame(geometry=gpd_obj[gpd_obj.index != index],\n                               crs=crs)\n    if not gpd_obj.empty:\n        gpd_obj = _make_polygon(gpd_obj)\n    return gpd_obj\n\n\ndef _create_p_glac(shp):\n    \"\"\"\n\n    Parameters\n    ----------\n    shp :  str\n        path to a shape file containing pour points\n\n    Returns\n    -------\n    gpd.GeoDataFrame with the P_glac areas\n    \"\"\"\n    a = 14.3\n    b = 0.5,\n    c = 3500\n    pp = gpd.read_file(shp)\n    geoms = [co.intersection(pp.loc[i, 'geometry'].buffer(\n        _p_glac_radius(a, b, c, pp.loc[i, 'flowacc']))) for i in pp.index]\n    p_glac = gpd.GeoDataFrame(geometry=geoms, crs=crs)\n\n    # delete empty objects\n    p_glac = p_glac[~p_glac.is_empty]\n\n    # if p_glac is Multipolygon choose only nearest polygon\n    for i in p_glac.index:\n        if p_glac.loc[i, 'geometry'].type is 'MultiPolygon':\n            point = pp.loc[i, 'geometry']\n            dist = [j.distance(point) for j in p_glac.loc[i, 'geometry']]\n            min_dist = np.argmin(dist)\n            p_glac.loc[i, 'geometry'] = p_glac.loc[i, 'geometry'][min_dist]\n    return p_glac\n\n\ndef merge_flows(shed_shp, pour_point_shp):\n    \"\"\"merge the flowsheds together. First, P_glac(circle which radius depends\n    on the flowaccumulation) is calculated for each pour point. If one or more\n    fowsheds overlaie by the area of this circle, they are merged together.\n    Sliver polygons are merged to the polygon with the longest shared boundary.\n    Finally, overlaps are corrected and the resulting divides will be saved in\n    separated folders.\n\n    Parameters\n    ----------\n    shed_shp        :   str\n        path to the shape file containing the flowsheds, that should be merged\n    pour_point_shp  :   str\n        path to the shape file containing the pour points\n\n    Returns\n    number of glaciers\n    -------\n\n    \"\"\"\n    import time\n    start = time.time()\n    p_glac_dir = os.path.join(os.path.dirname(pour_point_shp), 'p_glac.shp')\n    p_glac = _create_p_glac(pour_point_shp)\n    p_glac.to_file(p_glac_dir)\n\n    flows = gpd.read_file(shed_shp)\n    # merge overlaps (p_glac, flowsheds)\n    for j in p_glac.index:\n        overlaps = flows[flows.intersects(p_glac.loc[j, 'geometry'])]\n        if len(overlaps) > 1:\n            union = cascaded_union(overlaps.loc[:, 'geometry'])\n            flows.set_value(overlaps.index[0], 'geometry', union)\n            del_ids = overlaps.index.drop(overlaps.index[0])\n            flows = flows.loc[flows.index.difference(del_ids)]\n    # add gaps\n    all_flows = cascaded_union(flows.geometry)\n    if all_flows.type is Polygon:\n        difference = co.difference(all_flows)\n    else:\n        difference = co.difference(all_flows.simplify(0.00001))\n\n    # gpd.GeoDataFrame(geometry=[difference], crs=crs).plot()\n    if (difference.type is 'Polygon') and (difference.area > 0.1):\n        glaciers, done = _merge_sliver(flows, difference)\n    last_sliver = []\n\n    if difference.type is 'MultiPolygon':\n        for polygon in difference:\n            if polygon.area > 0.1:\n                glaciers, done = _merge_sliver(flows, polygon)\n                if not done:\n                    last_sliver.append(polygon)\n    slivers = flows[flows.geometry.apply(_is_sliver)]\n    glaciers = flows.loc[flows.index.difference(slivers.index)]\n    if len(glaciers) <= 1:\n        return 1\n    glaciers.to_file(os.path.join(os.path.dirname(pour_point_shp),\n                                  'glaciers.shp'))\n    if not slivers.empty:\n        slivers.to_file(os.path.join(os.path.dirname(pour_point_shp),\n                                     'slivers.shp'))\n    # merge slivers to glaciers\n    for k in slivers.index:\n        sliver = slivers.loc[k, 'geometry']\n        glaciers, done = _merge_sliver(glaciers, sliver)\n        if not done:\n            last_sliver.append(sliver)\n\n    for polygon in last_sliver:\n        glaciers, done = _merge_sliver(glaciers, polygon)\n    # correct overlapping of glaciers\n    for id in glaciers.index:\n        glaciers = _merge_overlaps(glaciers, id)\n\n    for id in glaciers.index:\n        if id in glaciers.index:\n            glaciers = _split_overlaps(glaciers, id)\n\n    glaciers = _convert_to_polygon(glaciers)\n\n    # check if divide is inside another divide\n    for id in glaciers.index:\n        if id in glaciers.index:\n            glaciers = _check_contain_divides(glaciers, id)\n            # compute altitude range\n        if id in glaciers.index:\n            poly = glaciers.loc[id, 'geometry']\n            glaciers.loc[id, 'Alt_Range'] = _compute_altitude(filled_dem, poly)\n        # compute percentual altitude range\n        max_alt = np.max(glaciers.loc[:, 'Alt_Range'])\n        per_alt_range = glaciers.loc[:, 'Alt_Range']/max_alt\n        glaciers.loc[:, 'Perc_Alt_Range'] = per_alt_range\n\n    glaciers.loc[:, 'Area'] = glaciers.geometry.area/10**6\n    glaciers = glaciers.sort_values('Area', ascending=False)\n    glaciers = glaciers.reset_index()\n\n    glaciers['Perc_Area'] = glaciers.Area / glaciers.loc[0].Area\n\n    # save glaciers\n    glaciers.to_file(os.path.join(os.path.dirname(pour_point_shp),\n                                  'divides.shp'))\n    return len(glaciers)\n\n\ndef merge_sliver_poly(glacier_poly, polygon):\n    \"\"\"Sliver polygon will be merged to the polygon of glacier_poly with the\n    longest shared boundary.\n\n    Parameters\n    ----------\n    glacier_poly: gpd.GeoDataFrame\n        contains all glacier geometries\n    polygon     : shapely.geometry.Polygon instance\n        geometry of the sliver polygon\n\n    Returns\n    -------\n    new gpd.GeoDataFrame, where sliver is merged\n    \"\"\"\n    max_b = 0\n    max_b_id = -1\n    for i, glac in glacier_poly.iteritems():\n        if polygon.boundary.intersection(glac).length > max_b:\n            max_b_id = i\n            max_b = polygon.boundary.intersection(glac).length\n    if not max_b_id == -1:\n        glacier_poly[max_b_id] = glacier_poly[max_b_id].union(shape(polygon))\n    return glacier_poly\n\n\ndef _make_polygon(gpd_obj):\n    \"\"\"select geometry which are from the type Polygon, MultiPolygon or\n     GeometryCollection. The last one is converted to a Polygon/Multipolygon\n     (Line Strings/MultiLineStrings are removed)\n\n    Parameters\n    ----------\n    gpd_obj :   gpd.GeoDataFrame\n\n    Returns\n    -------\n    gpd.GeoDataFrame, that only contains Polygons or MultiPolygons\n    \"\"\"\n\n    gpd_obj = gpd_obj[(gpd_obj.type == 'GeometryCollection') |\n                      (gpd_obj.type == 'Polygon') |\n                      (gpd_obj.type == 'MultiPolygon')]\n    if not gpd_obj.empty:\n        collection = gpd_obj[(~gpd_obj.is_empty) &\n                             (gpd_obj.type == 'GeometryCollection')]\n        # choose only polygons or multipolygons\n        for c in collection.index:\n            geo = collection.loc[c, 'geometry']\n            new = MultiPolygon()\n            for obj in geo:\n                if obj.type in ['Polygon', 'MultiPolygon']:\n                    new = new.union(obj)\n            gpd_obj = gpd_obj.copy()\n            gpd_obj.loc[c, 'geometry'] = new\n    return gpd_obj\n\n\ndef _p_glac_radius(a, b, c, f):\n    \"\"\"calculate the radius of P_glac\n\n    Parameters\n    ----------\n    a   : float (14.3)\n    b   : float (0.5)\n    c   : float (3500m)\n      constrain the radius\n    f   : int\n        flowaccumulation value\n\n    Returns\n    -------\n\n    \"\"\"\n    if a * (f ** b) < c:\n        return a * (f ** b)\n    else:\n        return c\n\n\ndef _is_sliver(polygon):\n    \"\"\"check if polygon is a sliver polygon\n\n    Parameters\n    ----------\n    polygon : shapely.geometry.Polygon instance\n\n    Returns\n    -------\n    bool\n    \"\"\"\n    if polygon.type is not 'Polygon':\n        max_poly = np.argmax(poly.area for poly in polygon)\n        polygon = polygon[max_poly]\n\n    if polygon.area < 100000 or (polygon.area < 200000 and _compactness(\n            polygon)):\n        return True\n    else:\n        return False\n\n\ndef _merge_sliver(gpd_obj, polygon):\n    \"\"\"merge sliver polygon to the glacier with the longest shared boundary.\n    If polygon does not touch the glaciers, False will be returned.\n\n    Parameters\n    ----------\n    gpd_obj : gpd.GeoDataFrame\n        contains the geometry of each glacier\n    polygon : shapely.geometry.Polygon instance\n        sliver polygon, which should be merged\n\n    Returns\n    -------\n    new gpd.GeoDataFrame,\n    bool\n    \"\"\"\n\n    intersection_array = gpd_obj.intersection(polygon.boundary).length\n\n    if np.max(intersection_array) != 0:\n        max_b = np.argmax(intersection_array)\n        poly = gpd_obj.loc[max_b, 'geometry'].simplify(0.01).buffer(0)\n        geom = poly.union(polygon.buffer(0)).buffer(0)\n        if geom.type is not 'Polygon':\n            geom = geom.buffer(-0.01).buffer(0.01)\n        gpd_obj.set_value(max_b, 'geometry', geom)\n        merged = True\n    # sliver does not touch glacier at the moment. Try again in the end\n    else:\n        merged = False\n    return [gpd_obj, merged]\n\n\ndef _merge_overlaps(gpd_obj, l):\n    \"\"\"correct glacier overlaps from gpd_obj.loc[l, 'geometry]\n\n    Parameters\n    ----------\n    gpd_obj : gpd.GeoDataFrame\n    l       : int\n\n    Returns\n    -------\n\n    \"\"\"\n    if l in gpd_obj.index:\n        inter = _intersection_of_glaciers(gpd_obj, l)\n        # if intersection area  > 50%\n        merge = inter[inter.area / gpd_obj.loc[l, 'geometry'].area > 0.5]\n        while not merge.empty:\n            if len(merge.index) == 1:\n                poly = gpd_obj.loc[l, 'geometry'].union(\n                    gpd_obj.loc[merge.index[0], 'geometry'])\n                gpd_obj.set_value(l, 'geometry', poly)\n                gpd_obj = gpd_obj.loc[gpd_obj.index.difference(merge.index)]\n                inter = _intersection_of_glaciers(gpd_obj, l)\n                to_merge = inter.area / gpd_obj.loc[l, 'geometry'].area > 0.5\n                merge = inter[to_merge]\n\n            if len(merge.index) > 1:\n                cascaded = cascaded_union(gpd_obj.loc[merge.index, 'geometry'])\n                gpd_obj.set_value(l, 'geometry', gpd_obj.loc[l, 'geometry'].\n                                  union(cascaded))\n                gpd_obj = gpd_obj.loc[gpd_obj.index.difference(merge.index)]\n                inter = _intersection_of_glaciers(gpd_obj, l)\n                to_merge = inter.area / gpd_obj.loc[l, 'geometry'].area > 0.5\n                merge = inter[to_merge]\n    return gpd_obj\n\n\ndef _split_overlaps(gpd_obj, l):\n    \"\"\"\n    if glaciers overlaps just a little bit (not more than 50 % of one of them),\n    the glacier will be split. The overlapping are will be related to the\n    bigger one.\n\n    Parameters\n    ----------\n    gpd_obj : gpd.GeoDataFrame object\n    l       : int\n\n    Returns\n    -------\n    gpd.GeoDataFrame object\n    \"\"\"\n    split = _intersection_of_glaciers(gpd_obj, l)\n    for k in split.index:\n        if l in gpd_obj.index and k in gpd_obj.index:\n            if gpd_obj.loc[l, 'geometry'].area > gpd_obj.loc[k,\n                                                             'geometry'].area:\n                gpd_obj = _split_glacier(gpd_obj, k, split.loc[k, 'geometry'])\n            else:\n                gpd_obj = _split_glacier(gpd_obj, l, split.loc[k, 'geometry'])\n    return gpd_obj\n\n\ndef _split_glacier(gpd_obj, index, polygon):\n    \"\"\"\n    split the object\n\n    Parameters\n    ----------\n    gpd_obj : gpd.GeoDataFrame\n    index   : int\n    polygon : shapely.geometry object\n\n    Returns\n    -------\n    gpd.GeoDataFrame object\n    \"\"\"\n    diff = gpd_obj.loc[index, 'geometry'].difference(polygon)\n\n    if diff.type is 'Polygon':\n        if not _is_sliver(diff):\n            gpd_obj.loc[index, 'geometry'] = diff\n        else:\n            gpd_obj = gpd_obj[gpd_obj.index != index]\n            gpd_obj, done = _merge_sliver(gpd_obj, diff)\n\n    else:\n        # choose largest polygon\n        max_poly = np.argmax([obj.area for obj in diff])\n        if not _is_sliver(diff[max_poly]):\n            gpd_obj.loc[index, 'geometry'] = diff[max_poly]\n        else:\n            gpd_obj = gpd_obj[gpd_obj.index != index]\n            gpd_obj, done = _merge_sliver(gpd_obj, diff[max_poly])\n        # rest merged as sliver polygon\n        rest = diff.difference(diff[max_poly])\n        gpd_obj, done = _merge_sliver(gpd_obj, rest)\n    return gpd_obj\n\n\ndef _smooth_dem(dem):\n    \"\"\"\n    smooth the dem file (5x5 median filter is applied)\n\n    Parameters\n    ----------\n    dem :   str\n        path to the dem file\n\n    Returns\n    -------\n    str to the smoothed dem file\n    \"\"\"\n    smoothed_dem = os.path.join(os.path.dirname(dem), 'smoothed.tif')\n    with rasterio.open(dem) as src:\n        array = src.read()\n        profile = src.profile\n    # apply a 5x5 median filter to each band\n    filtered = medfilt(array, (1, 5, 5)).astype('int16')\n    with rasterio.open(smoothed_dem, 'w', **profile) as dst:\n        dst.write(filtered)\n    return smoothed_dem\n\n\ndef _transform_coord(tupel, transform):\n    \"\"\"\n    transform pixel numbers to coordinates\n    Parameters\n    ----------\n    tupel       : [int,int]\n    transform   : transform object from rasterio\n\n    Returns\n    -------\n    shapely.geometry.Point object\n    \"\"\"\n    new_x = transform[0]+(tupel[1]+1)*transform[1]-transform[1]/2\n    new_y = transform[3]+tupel[0]*transform[-1]-transform[1]/2\n\n    return Point(new_x, new_y)\n\n\ndef _pour_points(dem):\n    \"\"\"\n\n    Parameters\n    ----------\n    dem : str\n        path to a dem file\n\n    Returns\n    -------\n    path to a shapefile containing all pour points\n    \"\"\"\n    # open gutter with flow accumulation\n    with rasterio.open(dem) as src:\n        # TODO: new rasterio version will return affine.Affine()\n        # --> order will change\n        transform = src.transform\n        band = np.array(src.read(1))\n    im = img_as_float(band)\n    nan = np.where(np.isnan(im))\n    # set nan to zero\n    im[nan] = 0\n    # calculate maxima\n    coordinates = peak_local_max(im, min_distance=1)\n    # transform maxima to (flowaccumulation,coordinates)\n    new_coord = []\n    new = []\n    dtype = [('flowaccumulation', float), ('coordinates', object)]\n    # transform coordinates\n    for x, y in coordinates:\n        new_coord.append((im[x][y], _transform_coord([x, y], transform)))\n        new.append(Point(_transform_coord([x, y], transform)))\n    new_coord = np.array(new_coord, dtype=dtype)\n    # sort array  by flowaccumulation\n    new_coord = np.sort(new_coord, order='flowaccumulation')\n    # reverse array\n    new_coord = new_coord[::-1]\n    pp = gpd.GeoDataFrame({'flowacc': new_coord['flowaccumulation']},\n                          geometry=new_coord['coordinates'],  crs=crs)\n    pp_shp = os.path.join(os.path.dirname(dem), 'pour_points.shp')\n    pp.to_file(pp_shp)\n    return pp_shp\n\n\ndef preprocessing(dem, shp, saga_cmd=None):\n\n    \"\"\" Run all preprocessing tasks:\n\n        fill pits from DEM,\n        mask DEM along buffer1,\n        lower it by 100 m along buffer2\n\n    Parameters\n    ----------\n    dem     : str\n        path to the DEM file\n    shp     : str\n        path to the shape file (outlines.shp)\n    saga_cmd: str\n        path to the SAGA GIS executable file (needed on win system)\n\n    Returns\n    -------\n    path to the output raster\n    \"\"\"\n\n    # global outlines\n    global crs\n    global schema\n    global pixelsize\n    global co\n    global out1\n    global filled_dem\n    pixelsize = 40\n\n    smoothed_dem = _smooth_dem(dem)\n    # fill pits\n    filled_dem = _fill_pits_with_saga(smoothed_dem, saga_cmd=saga_cmd)\n\n    # read outlines with gdal\n    out1 = gpd.read_file(shp)\n    crs = out1.crs\n    co = out1.loc[0, 'geometry'].buffer(0)\n\n    # mask dem along buffer1\n    masked_dem = _raster_mask(filled_dem, co.buffer(4*pixelsize), 'masked')\n\n    # lower dem by l_gutter along gutter\n    gutter_dem = _gutter(masked_dem, 100)\n\n    return gutter_dem\n\n\ndef dividing_glaciers(input_dem, input_shp, saga_cmd=None):\n    \"\"\" This is the main structure of the algorithm\n\n    Parameters\n    ----------\n    input_dem : str\n        path to the raster file(.tif) of the glacier, resolution has to be 40m!\n    input_shp : str\n        path to the shape file of the outline of the glacier\n\n    Returns\n    -------\n    number of divides (int)\n    \"\"\"\n    if sys.platform.startswith('win'):\n        saga_cmd = 'C:\\\\\"Program Files\"\\\\SAGA-GIS\\\\saga_cmd.exe'\n        gutter_dem = preprocessing(input_dem, input_shp, saga_cmd=saga_cmd)\n    else:\n        gutter_dem = preprocessing(input_dem, input_shp)\n    pour_points_dir = identify_pour_points(gutter_dem)\n    flowsheds_dir = flowshed_calculation(gutter_dem, pour_points_dir)\n    no_glaciers = merge_flows(flowsheds_dir, pour_points_dir)\n\n    # merge_flowsheds(p_glac, flowsheds_dir)\n\n    # Allocation of flowsheds to individual glaciers\n    # & Identification of sliver polygons\n    # no_glaciers, all_polygon = merge_flowsheds(p_glac, watersheds)\n\n    # delete files which are not needed anymore\n    '''\n    for file in os.listdir(os.path.dirname(input_shp)):\n        for word in ['P_glac', 'all', 'flow', 'glaciers', 'gutter', 'p_glac',\n                     'pour', 'smoothed', 'snapped', 'stream', 'masked',\n                     'slivers', 'filled']:\n            if file.startswith(word):\n                os.remove(os.path.join(os.path.dirname(input_shp), file))\n    '''\n    return no_glaciers\n","sub_path":"partitioning/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":29890,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"173462063","text":"from odoo import models, fields, api\nfrom odoo.exceptions import except_orm, Warning, RedirectWarning\nfrom datetime import date, datetime, timedelta\nimport calendar\nimport time\n\nclass report_ar_mutation_pdf(models.AbstractModel):\n\n    _name = 'report.kg_account.report_ar_mutation_pdf'\n\n    @api.model\n    def get_report_values(self, docids, data=None):    \n        # month_range = calendar.monthrange(int(data['year']),data['period'])\n        # start_date = str(data['year']) + '-' + str(data['period']).rjust(2,'0') + '-' + str(month_range[0]).rjust(2,'0')\n        # end_date = str(data['year']) + '-' + str(data['period']).rjust(2,'0') + '-' + str(month_range[1]).rjust(2,'0')\n        \n        start_date = data['start_date']\n        end_date = data['end_date']\n        company_id = data['company_id']\n        partner_list = []\n\n        company_ids = self.env['res.company'].browse(int(data['company_id']))\n\n        # search begining folio\n        bg_folio_ids = self.env['account.invoice'].search([\n            ('type', '=', 'out_invoice'),\n            ('state', 'in', ['open', 'paid']),\n            ('date_invoice', '<', start_date),\n            ('company_id', '=', company_id),\n            ])\n\n        for inv in bg_folio_ids:\n            if inv.partner_id.id not in partner_list:\n                partner_list.append(inv.partner_id.id)\n\n        # search beginning credit note\n        bg_creditnote_ids = self.env['account.invoice'].search([\n            ('type', '=', 'out_refund'),\n            ('state', 'in', ['open', 'paid']),\n            ('date_invoice', '<', start_date),\n            ('company_id', '=', company_id),\n            ])\n\n        for cn in bg_creditnote_ids:\n            if cn.partner_id.id not in partner_list:\n                partner_list.append(cn.partner_id.id)\n\n        # search begining adjustment/write off\n        bg_adjustment_ids = self.env['account.payment'].search([\n            # ('payment_type', '=', 'inbound'),\n            ('partner_type', '=', 'customer'),\n            ('writeoff_account_id', '<>', False),\n            ('state', 'in', ['posted', 'sent', 'reconciled']),\n            ('payment_date', '<', start_date),\n            ('company_id', '=', company_id),\n            ])\n\n        bg_adjustment_ids = bg_adjustment_ids.filtered(lambda payment: ('POS' or 'pos') not in payment.name)\n\n        for adj in bg_adjustment_ids:\n            if adj.partner_id.id not in partner_list:\n                partner_list.append(adj.partner_id.id)\n\n        # search begining payment\n        bg_payment_ids = self.env['account.payment'].search([\n            # ('payment_type', '=', 'inbound'),\n            ('partner_type', '=', 'customer'),\n            ('state', 'in', ['posted', 'sent', 'reconciled']),\n            ('payment_date', '<', start_date),\n            ('company_id', '=', company_id),\n            ('is_advance_payment', '=', False),\n            ])\n\n        bg_payment_ids = bg_payment_ids.filtered(lambda payment: ('POS' or 'pos') not in payment.name)\n\n        for inv in bg_payment_ids:\n            if inv.partner_id.id not in partner_list:\n                partner_list.append(inv.partner_id.id)\n\n        # search new invoice (current period)\n        folio_ids = self.env['account.invoice'].search([\n            ('type', '=', 'out_invoice'),\n            ('state', 'in', ['open', 'paid']),\n            ('date_invoice', '>=', start_date),\n            ('date_invoice', '<=', end_date),\n            ('company_id', '=', company_id),\n            ])\n\n        for inv in folio_ids:\n            if inv.partner_id.id not in partner_list:\n                partner_list.append(inv.partner_id.id)\n\n        # search adjustment/write off (current period)\n        adjustment_ids = self.env['account.payment'].search([\n            # ('payment_type', '=', 'inbound'),\n            ('partner_type', '=', 'customer'),\n            ('writeoff_account_id', '<>', False),\n            ('state', 'in', ['posted', 'sent', 'reconciled']),\n            ('payment_date', '>=', start_date),\n            ('payment_date', '<=', end_date),\n            ('company_id', '=', company_id),\n            ])\n\n        adjustment_ids = adjustment_ids.filtered(lambda payment: ('POS' or 'pos') not in payment.name)\n\n        for adj in adjustment_ids:\n            if adj.partner_id.id not in partner_list:\n                partner_list.append(adj.partner_id.id)\n\n        # search payment (current period)\n        payment_ids = self.env['account.payment'].search([\n            # ('payment_type', '=', 'inbound'),\n            ('partner_type', '=', 'customer'),\n            ('state', 'in', ['posted', 'sent', 'reconciled']),\n            ('payment_date', '>=', start_date),\n            ('payment_date', '<=', end_date),\n            ('company_id', '=', company_id),\n            ('is_advance_payment', '=', False),\n            ])\n\n        payment_ids = payment_ids.filtered(lambda payment: ('POS' or 'pos') not in payment.name)\n\n        for p in payment_ids:\n            if p.partner_id.id not in partner_list:\n                partner_list.append(p.partner_id.id)\n\n        # search for credit note\n        creditnote_ids = self.env['account.invoice'].search([\n            ('type', '=', 'out_refund'),\n            ('state', 'in', ['open', 'paid']),\n            ('date_invoice', '>=', start_date),\n            ('date_invoice', '<=', end_date),\n            ('company_id', '=', company_id),\n            ])\n\n        for cn in creditnote_ids:\n            if cn.partner_id.id not in partner_list:\n                partner_list.append(cn.partner_id.id)\n\n\n        #START LOOPING\n        total_bg_folio = 0\n        total_bg_payment = 0\n        total_bg_creditnote = 0\n        total_bg_adjustment = 0\n        total_bg_balance = 0\n        total_folio = 0\n        total_payment = 0\n        total_creditnote = 0\n        total_adjustment = 0\n        total_all = 0\n\n\n        data_report = []\n\n        for p in partner_list:\n            partner_code = '-'\n            partner_name = 'NO NAME'\n            partner_ids = self.env['res.partner'].browse(p)\n            if partner_ids:\n                partner_name = partner_ids.name\n                partner_code = partner_ids.id\n\n            #BEGINNING BALANCE\n            lines = bg_folio_ids.filtered(lambda r: r.partner_id.id == p)\n            amount_bg_folio = sum(l.amount_total for l in lines)\n\n            bg_cn = bg_creditnote_ids.filtered(lambda r: r.partner_id.id == p)\n            amount_bg_creditnote = sum(l.amount_total for l in bg_cn)\n\n            bg_adj_in = bg_adjustment_ids.filtered(lambda r: r.partner_id.id == p and r.payment_type == 'inbound')\n            amount_bg_adjustment_in = sum(p.writeoff_amount for p in bg_adj_in)\n\n            bg_adj_out = bg_adjustment_ids.filtered(lambda r: r.partner_id.id == p and r.payment_type == 'outbound')\n            amount_bg_adjustment_out = sum(p.writeoff_amount for p in bg_adj_out)\n\n            bg_payment_in = bg_payment_ids.filtered(lambda r: r.partner_id.id == p and r.payment_type == 'inbound')\n            amount_bg_payment_in = sum(p.amount for p in bg_payment_in)\n\n            bg_payment_out = bg_payment_ids.filtered(lambda r: r.partner_id.id == p and r.payment_type == 'outbound')\n            amount_bg_payment_out = sum(p.amount for p in bg_payment_out)\n\n            amount_bg_balance = amount_bg_folio - amount_bg_creditnote - amount_bg_payment_in + amount_bg_payment_out - amount_bg_adjustment_in + amount_bg_adjustment_out\n\n            #AMOUNT BALANCE\n            # custom code by andi\n            invoices = folio_ids.filtered(lambda r: r.partner_id.id == p)\n            amount_folio = sum(l.amount_total for l in invoices)\n            # end of custom code\n\n            #PAYMENT BALANCE\n            # custom code by andi\n            # amount_adv_payments = 0.0\n            # amount_total_invoices = sum(invoice.amount_total for invoice in invoices) or 0.0\n            # amount_residual_invoices = sum(invoice.residual for invoice in invoices) or 0.0\n\n            # code for compute the amount of adv payment used for the invoice if someday needed\n            # for invoice in invoices:\n            #     adv_payment_amount_used = 0.0\n            #     for adv_payment in invoice.advance_payment_ids:\n            #         adv_payment_amount_used = adv_payment.amount - adv_payment.residual_temp\n            #         amount_adv_payments +=  adv_payment_amount_used\n            # end of code\n\n            # amount_payment = amount_total_invoices - amount_residual_invoices\n            payments_in = payment_ids.filtered(lambda r: r.partner_id.id == p and r.payment_type == 'inbound')\n            amount_payment_in = sum(payment.amount for payment in payments_in) or 0.0\n\n            payments_out = payment_ids.filtered(lambda r: r.partner_id.id == p and r.payment_type == 'outbound')\n            amount_payment_out = sum(payment.amount for payment in payments_out) or 0.0\n\n            amount_payment = amount_payment_in - amount_payment_out\n\n            # end of custom code\n\n            # origin code by mas mario\n            # lines = payment_ids.filtered(lambda r: r.partner_id.id == p)\n            # amount_payment = sum(l.amount for l in lines)\n            # end of origin code\n            \n            # CreditNote\n            lines = creditnote_ids.filtered(lambda r: r.partner_id.id == p)\n            amount_creditnote = sum(l.amount_total for l in lines)\n\n            # Adjustment\n            adj_in = adjustment_ids.filtered(lambda r: r.partner_id.id == p and r.payment_type == 'inbound')\n            amount_adjustment_in = sum(p.writeoff_amount for p in adj_in) or 0.0\n\n            adj_out = adjustment_ids.filtered(lambda r: r.partner_id.id == p and r.payment_type == 'outbound')\n            amount_adjustment_out = sum(p.writeoff_amount for p in adj_out) or 0.0\n\n            amount_adjustment = amount_adjustment_in - amount_adjustment_out\n            \n            data_report.append({\n                'partner_code'          : partner_code,\n                'partner_name'          : partner_name,\n                'amount_bg_balance'     : amount_bg_balance,\n                'amount_folio'          : amount_folio,\n                'amount_payment'        : amount_payment,\n                'amount_creditnote'     : amount_creditnote,\n                'amount_adjustment'     : amount_adjustment,\n                'amount_total'          : amount_bg_balance + amount_folio - amount_creditnote - amount_adjustment - amount_payment,\n                })\n\n            total_bg_balance    += amount_bg_balance\n            total_folio         += amount_folio\n            total_payment       += amount_payment\n            total_creditnote    += amount_creditnote\n            total_adjustment    += amount_adjustment\n            total_all = total_bg_balance + total_folio - total_creditnote - total_payment - total_adjustment\n\n        data_total = []\n        data_total.append(total_bg_balance)\n        data_total.append(total_folio)\n        data_total.append(total_creditnote)\n        data_total.append(total_payment)\n        data_total.append(total_adjustment)\n        data_total.append(total_all)\n\n        # custom code by andi to sort data_report by its partner_name\n        if data_report:\n            sorted_data_report = sorted(data_report, key=lambda data: (data.get('partner_name') or ''))\n            data_report = sorted_data_report\n        # end of custom code\n\n        return {\n            'docs'      : data_report,\n            'data_total': data_total,\n            'start_date': start_date,\n            'end_date'  : end_date,\n            'company_id': company_ids.name,\n            'printed_by': self.env.user.name,\n            'printed_on': (datetime.now() + timedelta(hours=7)).strftime(\"%Y-%m-%d %H:%M:%S\"),\n        }\n\n","sub_path":"local/kg_account/reports/report_ar_mutation_pdf.py","file_name":"report_ar_mutation_pdf.py","file_ext":"py","file_size_in_byte":11724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"252687188","text":"# -*- coding: utf-8 -*-\n\"\"\"\n/dms/edufileitem/utils.py\n\n.. enthaelt Hilfefunktionen fuer Dateien in Lernarchiven\n         Django content Management System\n\nHans Rauch\nhans.rauch@gmx.net\n\nDie Programme des dms-Systems koennen frei genutzt und den spezifischen\nBeduerfnissen entsprechend angepasst werden.\n\n0.01  11.09.2007  Beginn der Arbeit\n\"\"\"\n\nfrom django.utils.translation import ugettext as _\n\nfrom dms.settings       import DOWNLOAD_URL\n\nfrom dms.queries        import get_item_container_data_object_by_id\n\nfrom dms_ext.extension    import * # dms-Funktionen ueberschreiben\n\n# -----------------------------------------------------\ndef get_edu_file_url(item_container):\n  \"\"\" liefert die URL der eigentlichen Datei \"\"\"\n  # --- PROBLEM Einblendung\n  if item_container.is_data_object:\n    file_url = DOWNLOAD_URL + item_container.container.path\n  else:\n    real_item_container = get_item_container_data_object_by_id(item_container.item.id)\n    if real_item_container != None:\n      file_url = DOWNLOAD_URL + real_item_container[0].container.path\n    else:\n      file_url = ''\n  return file_url + item_container.item.name\n  ## --- ..[:-5] entfernt '.html' von <name>\n  #return file_url + item_container.item.name[:-5]\n","sub_path":"edufileitem/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1218,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"127819412","text":"from django.conf.urls import url\nfrom . import views\n\napp_name = 'index'\n\nurlpatterns = [\n    url(r'^$', views.home, name='home'),\n    url(r'^(?P<game_id>\\d+)/$', views.detail_game, name='detail_game'),\n    url(r'^(?P<game_id>\\d+)/delete/$', views.delete, name='delete'),\n    url(r'^(?P<game_id>\\d+)/NewOrder/$', views.order_create, name='order_create'),\n\n]\n","sub_path":"index/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":358,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"204131163","text":"import click\nfrom os import walk\nfrom pathlib import Path, PurePosixPath\nfrom dropbox import Dropbox\nfrom dropbox.exceptions import AuthError, BadInputError\n\n\ndef validate_dropbox_path(ctx, dropbox_path, scope_path):\n    dropbox_path = Path(dropbox_path)\n    if not dropbox_path.is_dir():\n        ctx.fail(\"The dropbox path must be a valid directory.\")\n\n    return dropbox_path\n\n\ndef validate_scope_path(ctx, dropbox_path, scope_path):\n    scope_path = PurePosixPath(scope_path)\n    if not scope_path.is_absolute():\n        scope_path = \"/\" / scope_path\n\n    target_path = dropbox_path / scope_path.relative_to(\"/\")\n    if not target_path.is_dir():\n        ctx.fail(\"The scope must be a valid directory inside the dropbox directory.\")\n\n    return scope_path, target_path\n\n\ndef validate_access_token(ctx, access_token):\n    try:\n        db = Dropbox(access_token)\n        db.users_get_current_account()\n    except AuthError:\n        ctx.fail(\"The OAuth2 access token is invalid.\")\n    except BadInputError:\n        ctx.fail(\"The OAuth2 access token token is malformed.\")\n\n    return db\n\n\ndef os_files_and_folders(dropbox_path, target_path):\n    for path, folders, files in walk(target_path):\n        entries = folders + files\n        for entry in entries:\n            yield Path(path, entry).relative_to(dropbox_path)\n\n\n# TODO: Handle scope_path doesn't exist and other errors.\n# TODO: Could show the progress of how many pages have been retrieved.\ndef db_files_and_folders(db, scope_path):\n    if scope_path == \"/\":\n        scope_path = \"\"\n\n    entries = []\n\n    result = db.files_list_folder(scope_path, recursive=True)\n    entries.extend(result.entries)\n\n    while result.has_more:\n        result = db.files_list_folder_continue(result.cursor)\n        entries.extend(result.entries)\n\n    entries = list(map(lambda entry: PurePosixPath(entry.path_display), entries))\n\n    return entries\n\n\n# TODO: Handle collision and other errors.\ndef db_rename(db, old_path, new_path, temporary_suffix=\"_\"):\n    intermediate_path = old_path + temporary_suffix\n    db.files_move(old_path, intermediate_path)\n    db.files_move(intermediate_path, new_path)\n\n\ndef create_path_lookup(paths):\n    lookup = {}\n\n    for path in paths:\n        lookup[path.as_posix().lower()] = path\n\n    return lookup\n\n\n# TODO: Could create a progress bar.\n# TODO: Could rename using the batch API call. Make the script faster.\ndef run(ctx, access_token, dropbox_path, scope_path, mode, dry_run):\n    dropbox_path = validate_dropbox_path(ctx, dropbox_path, scope_path)\n    scope_path, target_path = validate_scope_path(ctx, dropbox_path, scope_path)\n    db = validate_access_token(ctx, access_token)\n\n    db_paths = db_files_and_folders(db, scope_path.as_posix())\n    db_lookup = create_path_lookup(db_paths)\n\n    for os_path in os_files_and_folders(dropbox_path, target_path):\n\n        lookup_path = (\"/\" / os_path).as_posix().lower()\n        if lookup_path in db_lookup:\n            db_path = db_lookup[lookup_path]\n\n            if os_path.name != db_path.name:\n                if mode == \"push\":\n                    new_db_path = \"/\" / os_path\n\n                    click.echo(\"PUSH: \" + str(db_path) + \" -> \" + os_path.name)\n\n                    if not dry_run:\n                        db_rename(db, db_path.as_posix(), new_db_path.as_posix())\n\n                if mode == \"pull\":\n\n                    click.echo(\"PULL: \" + str(os_path) + \" -> \" + db_path.name)\n\n                    if not dry_run:\n                        old_os_path = dropbox_path / os_path\n                        new_os_path = old_os_path.with_name(db_path.name)\n                        old_os_path.rename(new_os_path)\n","sub_path":"core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":3653,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"213837126","text":"from appium import webdriver\n\nfrom test_appium.page.basepage import BasePage\nfrom test_frame.page.main_page import MainPage\n\n\nclass App(BasePage):\n    def start(self): #启动APP\n        if self.driver == None:\n            desired_caps = {}\n            desired_caps['platformName'] = 'Android'\n            desired_caps['platformVersion'] = '6.0'\n            desired_caps['deviceName'] = '127.0.0.1:7555'\n            desired_caps['appPackage'] = 'com.tencent.wework'\n            desired_caps['appActivity'] = '.launch.WwMainActivity'\n            desired_caps['autoGrantPermissions'] = True  # 自动点掉弹框\n            desired_caps['dontStopAppOnReset'] = True  #不停止App,继续运行\n            # \"appActivity\": \".view.WelcomeActivityAlias\",\n            desired_caps['noReset'] = 'true'  # 此项的意思为，1,当界面有弹框，关闭界面，当再次打开应用时，弹框会消失\n                                              # 2,是否在测试前后重置相关环境（例如首次打开弹框，参考第一条注释，或者是登录信息）\n            # desired_caps['dontStopAppOnReset'] = \"true\"  # 首次启动App的时候，不停止App(可以调试或者运行的时候提升运行速度)\n            desired_caps['skipDeviceInitialization'] = \"true\"  # 跳过安装，权限设置等操作(可以调试或者运行的时候提升运行速度)\n            desired_caps['unicodeKeyBoard'] = 'true'  # 设置输入框可以进行中文输入\n            desired_caps['resetKeyBoard'] = 'true'\n            desired_caps['newCommandTimeout'] = 300\n            self.driver = webdriver.Remote(\"http://127.0.0.1:4723/wd/hub\", desired_caps)\n            self.driver.implicitly_wait(80)\n        else:\n            self.driver.launch_app()\n        return self\n\n    def restart(self): #重启App\n        self.driver.quit()\n        self.driver.launch_app()\n        return self\n\n    def stop(self):  #关闭App\n        self.driver.quit()\n        return self\n\n    def goto_main(self)->MainPage: #定义进入页面\n        return MainPage(self.driver)  #跳转到主页面\n\n\n","sub_path":"test_frame/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2093,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"265075765","text":"#!/usr/bin/env python\n# Connection.py\n# Importing the Required Libraries.\nimport time\nimport datetime\nimport pymongo\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom pymongo import MongoClient\nfrom sklearn.cross_validation import train_test_split\n# Connecting with Local MongoDB Database.\nMONGO_HOST = \"139.59.66.172\"\nMONGO_PORT = 27017\nMONGO_DB = \"sodelhi\"\n# MONGO_USER = \"Username\"\n# MONGO_PASS = \"password\"\nconnection = MongoClient(MONGO_HOST, MONGO_PORT)\ndb = connection[MONGO_DB]\n# Data_Retrival.py\n# Retriving Data from users collection of the MongoDb Database.\nfinal_data=[]\nfor obj in db.users.find({\"feed._id\":{'$exists':True},\"feed.data.like.status\":{'$exists':True},\"interests\":{'$exists':True},\"feed.data.share.status\":{'$exists':True},\"feed.data.bookmark.status\":{'$exists':True}},{\"_id\":1,\"interests\":1,\"username\":1,\"feed.unique_id\":1,\"feed.data.read.duration\":1,\"feed.data.share.status\":1,\"feed.data.like.status\":1,\"feed.data.bookmark.status\":1}):#.skip(3741 - 500):\n    for feed_id in obj['feed']:\n            final_obj = {}\n            final_obj['_id']=obj['_id']\n            final_obj['users_name'] = obj['username']\n            final_obj['interests']=obj['interests']\n######## test on original dataset \n#             if 'read' in feed_id['data'] and  len(feed_id['data']['read'])>0 and 'duration' in feed_id['data']['read'][0]:\n#                 final_obj['timestamp']= feed_id['data']['read'][0]['duration']\n            if 'bookmark' in feed_id['data'] and 'status' in feed_id['data']['bookmark']:\n                final_obj['bookmark']= feed_id['data']['bookmark']['status']\n            if 'share' in feed_id['data'] and  len(feed_id['data']['share'])>0 and 'status' in feed_id['data']['share'][0]:\n                final_obj['share']= feed_id['data']['share'][0]['status']\n            if 'like' in feed_id['data'] and 'status' in feed_id['data']['like']:\n                final_obj['like']= feed_id['data']['like']['status']\n            if 'unique_id' in feed_id:\n                final_obj['article_id'] = feed_id['unique_id']\n            final_data.append(final_obj)\n# print final_data \nDataset=pd.DataFrame(final_data)\nDataset.set_index('_id', inplace=True)\n# Filtering out the a_users_name,unique_id from the Dataset.\nFinal_Dataset=Dataset.filter(['_id','users_name','article_id','interests'])\n#Convert the millisecond to date and time.\n# Final_Dataset[''] = pd.to_datetime(Final_Dataset[''], unit='ms')\n#Getting the  date of the user\n# Final_Dataset[''] = [d.date() for d in Final_Dataset['']]\n# Filtering out the timestamp,bookmark_status,like_status,share_status from the Dataset.\nrating=Dataset.filter(['bookmark','like','share'])#Add Timestamp\n# Repalcing the NaN value with zero's.\nrating=rating.fillna(0)\n# Asigning Weight to the Timestamp,Like,Share,Bookmark.\n# like=0.75\n# share=3\n# bookmarks=1.25\n#Timestamp=?\nWeight=(1.25,0.75,3)\n# Finding the weighted Ratings.\nweighted_ratings=rating * Weight\n# weighted_ratings.\n# weighted_ratings.head()\n# Adding Ratings Field in the Final_Dataset.\nFinal_Dataset['rating']=weighted_ratings.sum(1)\n# Final_Dataset.\n#Getting Feed_id from the Unique_id\nFinal_Dataset[\"article_id\"] = Final_Dataset[\"article_id\"].map(lambda d: d.split(\"-\",2)[2:][0])\n# Final_Dataset['article_id'] = Final_Dataset['article_id'].str.extract('(......-\\d\\d\\d\\d)', expand=True)\nFinal_Dataset['interests']=Final_Dataset.interests.apply(' | '.join)\nusers = Final_Dataset['users_name'].unique()\ntrain_data, test_data = train_test_split(Final_Dataset, test_size = 0.20, random_state=0)\nFinal_Dataset.to_csv('/home/shaktisocity/Desktop/khwahish2/data.csv',encoding='utf-8')\n#test_data.to_csv('/home/shaktisocity/Desktop/khwahish2/test_rating_data.csv',encoding='utf-8')\n#train_data.to_csv('/home/shaktisocity/Desktop/khwahish2/train_rating_data.csv',encoding='utf-8')\n\n","sub_path":"data_c_c.py","file_name":"data_c_c.py","file_ext":"py","file_size_in_byte":3834,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"56356266","text":"from typing import List, Optional\nfrom uuid import uuid4\n\nfrom lnbits.db import open_db\nfrom lnbits.settings import DEFAULT_WALLET_NAME\n\nfrom .models import User, Wallet, Payment\n\n\n# accounts\n# --------\n\n\ndef create_account() -> User:\n    with open_db() as db:\n        user_id = uuid4().hex\n        db.execute(\"INSERT INTO accounts (id) VALUES (?)\", (user_id,))\n\n    new_account = get_account(user_id=user_id)\n    assert new_account, \"Newly created account couldn't be retrieved\"\n\n    return new_account\n\n\ndef get_account(user_id: str) -> Optional[User]:\n    with open_db() as db:\n        row = db.fetchone(\"SELECT id, email, pass as password FROM accounts WHERE id = ?\", (user_id,))\n\n    return User(**row) if row else None\n\n\ndef get_user(user_id: str) -> Optional[User]:\n    with open_db() as db:\n        user = db.fetchone(\"SELECT id, email FROM accounts WHERE id = ?\", (user_id,))\n\n        if user:\n            extensions = db.fetchall(\"SELECT extension FROM extensions WHERE user = ? AND active = 1\", (user_id,))\n            wallets = db.fetchall(\n                \"\"\"\n                SELECT *, COALESCE((SELECT balance FROM balances WHERE wallet = wallets.id), 0) AS balance_msat\n                FROM wallets\n                WHERE user = ?\n                \"\"\",\n                (user_id,),\n            )\n\n    return (\n        User(**{**user, **{\"extensions\": [e[0] for e in extensions], \"wallets\": [Wallet(**w) for w in wallets]}})\n        if user\n        else None\n    )\n\n\ndef update_user_extension(*, user_id: str, extension: str, active: int) -> None:\n    with open_db() as db:\n        db.execute(\n            \"\"\"\n            INSERT OR REPLACE INTO extensions (user, extension, active)\n            VALUES (?, ?, ?)\n            \"\"\",\n            (user_id, extension, active),\n        )\n\n\n# wallets\n# -------\n\n\ndef create_wallet(*, user_id: str, wallet_name: Optional[str] = None) -> Wallet:\n    with open_db() as db:\n        wallet_id = uuid4().hex\n        db.execute(\n            \"\"\"\n            INSERT INTO wallets (id, name, user, adminkey, inkey)\n            VALUES (?, ?, ?, ?, ?)\n            \"\"\",\n            (wallet_id, wallet_name or DEFAULT_WALLET_NAME, user_id, uuid4().hex, uuid4().hex),\n        )\n\n    new_wallet = get_wallet(wallet_id=wallet_id)\n    assert new_wallet, \"Newly created wallet couldn't be retrieved\"\n\n    return new_wallet\n\n\ndef delete_wallet(*, user_id: str, wallet_id: str) -> None:\n    with open_db() as db:\n        db.execute(\n            \"\"\"\n            UPDATE wallets AS w\n            SET\n                user = 'del:' || w.user,\n                adminkey = 'del:' || w.adminkey,\n                inkey = 'del:' || w.inkey\n            WHERE id = ? AND user = ?\n            \"\"\",\n            (wallet_id, user_id),\n        )\n\n\ndef get_wallet(wallet_id: str) -> Optional[Wallet]:\n    with open_db() as db:\n        row = db.fetchone(\n            \"\"\"\n            SELECT *, COALESCE((SELECT balance FROM balances WHERE wallet = wallets.id), 0) AS balance_msat\n            FROM wallets\n            WHERE id = ?\n            \"\"\",\n            (wallet_id,),\n        )\n\n    return Wallet(**row) if row else None\n\n\ndef get_wallet_for_key(key: str, key_type: str = \"invoice\") -> Optional[Wallet]:\n    with open_db() as db:\n        row = db.fetchone(\n            \"\"\"\n            SELECT *, COALESCE((SELECT balance FROM balances WHERE wallet = wallets.id), 0) AS balance_msat\n            FROM wallets\n            WHERE adminkey = ? OR inkey = ?\n            \"\"\",\n            (key, key),\n        )\n\n        if not row:\n            return None\n\n        if key_type == \"admin\" and row[\"adminkey\"] != key:\n            return None\n\n        return Wallet(**row)\n\n\n# wallet payments\n# ---------------\n\n\ndef get_wallet_payment(wallet_id: str, checking_id: str) -> Optional[Payment]:\n    with open_db() as db:\n        row = db.fetchone(\n            \"\"\"\n            SELECT payhash as checking_id, amount, fee, pending, memo, time\n            FROM apipayments\n            WHERE wallet = ? AND payhash = ?\n            \"\"\",\n            (wallet_id, checking_id),\n        )\n\n    return Payment(**row) if row else None\n\n\ndef get_wallet_payments(\n    wallet_id: str, *, complete: bool = False, pending: bool = False, outgoing: bool = False, incoming: bool = False\n) -> List[Payment]:\n    \"\"\"\n    Filters payments to be returned by complete | pending | outgoing | incoming.\n    \"\"\"\n\n    clause = \"\"\n    if complete and pending:\n        clause += \"\"\n    elif complete:\n        clause += \"AND ((amount > 0 AND pending = 0) OR amount < 0)\"\n    elif pending:\n        clause += \"AND pending = 1\"\n    else:\n        raise TypeError(\"at least one of [complete, pending] must be True.\")\n\n    if outgoing and incoming:\n        clause += \"\"\n    elif outgoing:\n        clause += \"AND amount < 0\"\n    elif incoming:\n        clause += \"AND amount > 0\"\n    else:\n        raise TypeError(\"at least one of [outgoing, incoming] must be True.\")\n\n    with open_db() as db:\n        rows = db.fetchall(\n            f\"\"\"\n            SELECT payhash as checking_id, amount, fee, pending, memo, time\n            FROM apipayments\n            WHERE wallet = ? {clause}\n            ORDER BY time DESC\n            \"\"\",\n            (wallet_id,),\n        )\n\n    return [Payment(**row) for row in rows]\n\n\ndef delete_wallet_payments_expired(wallet_id: str, *, seconds: int = 86400) -> None:\n    with open_db() as db:\n        db.execute(\n            \"\"\"\n            DELETE\n            FROM apipayments WHERE wallet = ? AND pending = 1 AND time < strftime('%s', 'now') - ?\n            \"\"\",\n            (wallet_id, seconds),\n        )\n\n\n# payments\n# --------\n\n\ndef create_payment(\n    *, wallet_id: str, checking_id: str, amount: int, memo: str, fee: int = 0, pending: bool = True\n) -> Payment:\n    with open_db() as db:\n        db.execute(\n            \"\"\"\n            INSERT INTO apipayments (wallet, payhash, amount, pending, memo, fee)\n            VALUES (?, ?, ?, ?, ?, ?)\n            \"\"\",\n            (wallet_id, checking_id, amount, int(pending), memo, fee),\n        )\n\n    new_payment = get_wallet_payment(wallet_id, checking_id)\n    assert new_payment, \"Newly created payment couldn't be retrieved\"\n\n    return new_payment\n\n\ndef update_payment_status(checking_id: str, pending: bool) -> None:\n    with open_db() as db:\n        db.execute(\"UPDATE apipayments SET pending = ? WHERE payhash = ?\", (int(pending), checking_id,))\n\n\ndef delete_payment(checking_id: str) -> None:\n    with open_db() as db:\n        db.execute(\"DELETE FROM apipayments WHERE payhash = ?\", (checking_id,))\n","sub_path":"lnbits/core/crud.py","file_name":"crud.py","file_ext":"py","file_size_in_byte":6549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"346044491","text":"\"\"\"\nTop-level classes and functions to help running MomentsML on GREAT3.\n\"\"\"\n\nimport logging\nlogger = logging.getLogger(__name__)\n\nfrom astropy.table import Table\nimport os\n\nimport utils\nfrom momentsml import tools\n\n\n\nclass GREAT3Run(utils.Branch):\n\t\"\"\"\n\tThis is a simple class to group frequently used variables, on top of the Branch class.\n\tUnlike Branch, it does specify paths to MomentsML-internal temporary files and directories, and handles a workdir.\n\t\"\"\"\n\t\n\tdef __init__(self, experiment, obstype, sheartype, datadir, truthdir, workdir, g3publicdir, subfields=None, ncpu=None, skipdone=False):\n\t\t\n\t\tutils.Branch.__init__(self, experiment, obstype, sheartype, datadir, truthdir)\n\t\tlogger.info(\"Getting ready to work on branch %s-%s-%s\" % (experiment, obstype, sheartype))\n\n\t\tself.workdir=workdir\n\t\tif self.workdir == None:\n\t\t\tlogger.warning(\"Better specify a workdir, I think.\")\n\t\t\tself.workdir = \"./%s\" % (self.get_branchacronym())\n\t\tself.mkdirs()\n\t\t\n\t\tself.g3publicdir = g3publicdir\n\t\t\n\t\tself.subfields=subfields\n\t\tif self.subfields is None:\n\t\t\tself.subfields=range(200)\n\t\t\t\n\t\tself.ncpu = ncpu\n\t\tif ncpu is None:\n\t\t\tself.ncpu = 1\n\t\t\t\n\t\tself.skipdone = skipdone\n\n\t\t# Those, and further variables, can be wildly added later:\n\t\tself.simparams_name = None\n\t\tself.trainparams_name = None\n\t\t\n\n\t\n\tdef __str__(self):\n\t\t\"\"\"\n\t\tA tiny self-description, for logging\n\t\t\"\"\"\n\t\treturn \"GREAT3Run on branch %s in workdir '%s'\" % (self.get_branchacronym(), self.workdir)\n\n\t\t\n\tdef mkdirs(self, subfield=None):\n\t\t\"\"\"\n\t\tCreates the working directories. \n\t\t\"\"\"\n\n\t\tif not os.path.isdir(self.workdir):\n\t\t\tos.makedirs(self.workdir)\n\t\n\t\tif subfield is not None:\n\t\t\tdirpath = self.subpath(subfield)\n\t\t\tif not os.path.isdir(dirpath):\n\t\t\t\tos.makedirs(dirpath)\n\t\t\t\t\n\t\t\t# Now must create the sub-directories:\n\t\t\tfor subfolder in [\"obs\",\"sim\",\"ml\",\"pred\",\"out\",\"val\"]:\n\t\t\t\tdirpath = self.subpath(subfield, subfolder)\n\t\t\t\tif not os.path.isdir(dirpath):\n\t\t\t\t\tos.makedirs(dirpath)\n\n\n\tdef path(self,*args):\n\t\t\"\"\"\n\t\tA helper function that returns a filepath within the working directory.\n\t\t\n\t\t:param args: strings, must be in order of the filepath, similar to os.path.join()\n\t\t\n\t\tExample usage::\n\t\t\n\t\t\t>>> self.path(\"obs\",\"catalogue_000.fits\")\n\t\t\t\n\t\twill return the filepath: self.workdir/obs/catalogue_000.fits\n\t\t\"\"\"\n\t\treturn os.path.join(self.workdir,\"/\".join(args))\n\t\n\t\n\tdef subpath(self, subfield, *args):\n\t\t\"\"\"\n\t\tSimilar, but first argument is a subfield number\n\t\t\"\"\"\n\t\t\n\t\t\n\t\t\n\t\treturn os.path.join(self.workdir, \"%03i\" % subfield, \"/\".join(args))\n\t\n\n","sub_path":"wrappers/momentsmlgreat3/great3.py","file_name":"great3.py","file_ext":"py","file_size_in_byte":2528,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"532149498","text":"#EJEMPLO 13\r\n#Nombre: Carlos Homero Vacacela Velez\r\n#Aula: Software A1\r\n\r\n#Ejercicio 6: Dado el sueldo de un empleado, encontrar el nuevo sueldo si obtiene \r\n#un aumento del 10% si su sueldo es inferior a $600, en caso contrario no tendrá aumento.\r\n\r\nclass Aumento:\r\n    def Dinero(self):\r\n        SUELDO=float(input(\"Ingrese el sueldo del empleado:\"))\r\n        if SUELDO < 600:\r\n            NS=SUELDO + SUELDO*0.1\r\n        else:\r\n            NS=SUELDO    \r\n        print(\"Sueldo a recibir:\",NS)\r\n        print(\"\\n\")\r\n        print(\"**FIN DE LA EJECUCIÓN**\")\r\n\r\naumento= Aumento()\r\naumento.Dinero()","sub_path":"13.py","file_name":"13.py","file_ext":"py","file_size_in_byte":600,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"403591894","text":"import os\r\nimport csv\r\n\r\nbank_csv = os.path.join('..', 'Resources', 'PyBank_File.csv')\r\n\r\nProfitLoss = []\r\n\r\nAverage = 0\r\n\r\nLines = 0\r\n\r\nLastmonth = []\r\n\r\ndate= []\r\n\r\nwith open(bank_csv, 'r') as csvfile:\r\n\r\n    csvreader = csv.reader(csvfile, delimiter=',')\r\n\r\n    header = next(csvreader)\r\n\r\n    for row in csvreader:\r\n        ProfitLoss.append(float(row[1]))\r\n        Lines += 1\r\n        date.append(row[0])\r\n       \r\n    for i in range(1,Lines):\r\n        Lastmonth.append(ProfitLoss[i] - ProfitLoss[i-1])   \r\n        Average = round(sum(Lastmonth)/(Lines-1),2)\r\n\r\n        max_date = str(date[Lastmonth.index(max(Lastmonth))])\r\n        min_date = str(date[Lastmonth.index(min(Lastmonth))])\r\n\r\n\r\nprint(\"Financial Analysis\")\r\nprint(\"----------------------------\")\r\nprint(f'Total Months: {str(Lines)}')\r\nprint(f'Total: ${sum(ProfitLoss)}')\r\nprint(f'Average Change: ${str(Average)}')\r\nprint(f'Greatest increase in profits: {max_date} (${max(Lastmonth)})')\r\nprint(f'Greatest decrease in profits: {min_date} (${min(Lastmonth)})')\r\n\r\noutput_file = os.path.join(\"analysis_results.txt\")\r\nwith open(output_file, \"w\", newline=\"\") as datafile:\r\n    datafile.write('Financial Analysis\\n')\r\n    datafile.write(f'Total Months: {str(Lines)}\\n')\r\n    datafile.write(f'Total: ${sum(ProfitLoss)}\\n')\r\n    datafile.write(f'Average Change: ${str(Average)}\\n')\r\n    datafile.write(f'Greatest increase in profits: {max_date} (${max(Lastmonth)})\\n')\r\n    datafile.write(f'Greatest decrease in profits: {min_date} (${min(Lastmonth)})\\n')\r\n\r\n\r\n\r\n","sub_path":"python-challenge/PyBank/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1522,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"185013290","text":"import os,shutil,logging,sys,pathlib\nlogging.basicConfig(level=logging.INFO,format=' %(asctime)s - %(levelname)s - %(message)s')\n\nclass MyCopyFile:\n    def __init__(self, src,dst):\n        self.src = src\n        if not os.path.exists(self.src):\n            error_message = \"源文件或文件夹不存在——\" + src\n            logging.error(error_message)\n            sys.exit()\n        self.dst = dst\n        self.dst_type = self.judge_file_dir( self.dst )\n\n    def copy(self):\n        if os.path.isdir(self.src):\n            if self.judge_file_dir(self.dst) == \"dir\":\n                logging.debug(\"拷贝文件夹至文件夹\" + \"Source: \" + self.src + \". Destination: \" + self.dst)\n                self.copy_dir_to_dir(self.src, self.dst)\n                self.status = True\n            elif self.judge_file_dir(self.dst) == \"file\":\n                error_message = \"不能将一个文件夹拷贝至文件！\" + '\\t' + \"源文件: \" + self.src + '\\t' + \"目的文件:\" + self.dst\n                logging.error(error_message)\n                self.status = False\n            else:\n                error_message = \"目的路径非文件夹或文件！\"\n                logging.error(error_message)\n                self.status = False\n        elif os.path.isfile(self.src):\n            if self.judge_file_dir(self.dst) == \"dir\":\n                logging.debug(\"拷贝文件至文件夹——\" + \"Source: \" + self.src + \". Destination: \" + self.dst)\n                self.copy_file_to_dir(self.src, self.dst)\n                self.dst_file = os.path.join(self.dst,os.path.basename(self.src))\n                self.status = True\n            elif self.judge_file_dir(self.dst) == \"file\":\n                dirpath = os.path.dirname(self.dst)\n                logging.info(dirpath)\n                if not os.path.exists(dirpath):\n                    os.makedirs(dirpath)\n                logging.debug(\"拷贝文件——\" + \"Source: \" + self.src + \". Destination: \" + self.dst)\n                shutil.copyfile(self.src, self.dst)\n                self.dst_file = self.dst\n                self.status = True\n            else:\n                error_message = \"请检查目的地址是否存在\"\n                logging.error(error_message)\n                self.status = False\n        else:\n            error_message = \"源文件或文件夹不正确——\" + src\n            logging.error(error_message)\n            self.status = False\n\n    def judge_file_dir(self,path):\n        if type(path) != str:\n            error_message = \"路径非字符串形式，请检查。\"\n            logging.error(error_message)\n            sys.exit()\n        paths = path.split(\"\\\\\")\n        lastelement = paths[len(paths) - 1]\n        logging.debug(lastelement)\n        if len(lastelement.split(\".\")) > 1:\n            return \"file\"\n        else:\n            return \"dir\"\n\n    def copy_dir_to_dir(self,dst):\n        base = os.path.basename(self.src)\n        dst = os.path.join(dst, base)\n        names = os.listdir(self.src)\n        if not os.path.exists(dst):\n            os.makedirs(dst)\n        for name in names:\n            srcname = os.path.join(self.src, name)\n            if os.path.isdir(srcname):\n                self.copy_dir_to_dir(srcname, dst)\n            else:\n                shutil.copy2(srcname, dst)\n\n    def copy_file_to_dir(self,src, dst):\n        base = os.path.basename(src)\n        if not os.path.exists(dst):\n            os.makedirs(dst)\n        dst = os.path.join(dst, base)\n        shutil.copyfile(src, dst)\n\n    def list_all_member(self):\n        for name,value in vars(self).items():\n          print('%s=%s'%(name,value))\n\n","sub_path":"Work/CopyFile/MyCopyFile.py","file_name":"MyCopyFile.py","file_ext":"py","file_size_in_byte":3625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"309497953","text":"#Input Script template \n#Used mainly on Hackerrank.com\n#Author: pr0x1ma\ndef inputScript():\n\tA = []\n\t#Gets string of numbers\n\tstrng = input()\n\n\t#trims whitespace\n\ttrimmed = strng.replace(\" \", \"\")\n\n\t#converts to integers then to a list of integers \n\t#to be processed\n\tind = list(map(int, trimmed))\n\tprint(ind)\n\n\ninputScript()","sub_path":"inputScript.py","file_name":"inputScript.py","file_ext":"py","file_size_in_byte":323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"230770251","text":"#!/usr/bin/env python\r\n\r\nimport LFPy\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\n# Define cell parameters\r\ncell_parameters = {          # various cell parameters,\r\n    'morphology' : 'stick.hoc',\r\n    'cm' : 1.0,         # membrane capacitance\r\n    'Ra' : 150,        # axial resistance\r\n    'passive_parameters':dict(g_pas=1/30000., e_pas=-65),\r\n    'v_init' : -65.,    # initial crossmembrane potential\r\n    'passive' : True,   # switch on passive mechs\r\n    'nsegs_method' : 'lambda_f',\r\n    'lambda_f' : 500.,\r\n    'dt' : 2.**-7,   # [ms] dt's should be in powers of 2 for both,\r\n    'tstart' : 0.,    # start time of simulation, recorders start at t=0\r\n    'tstop' : 3.,   # stop simulation at 200 ms. These can be overridden\r\n                        # by setting these arguments i cell.simulation()\r\n    \"extracellular\": True,\r\n}\r\n\r\ncell = LFPy.Cell(**cell_parameters)\r\ncell.set_pos(z=-500)\r\nn_tsteps = int(cell.tstop / cell.dt + 1)\r\nt = np.arange(n_tsteps) * cell.dt\r\n\r\n# Make a linear external field\r\next_field = np.vectorize(lambda z: 0. + z/np.max(cell.zmid) * 10.)\r\n\r\npulse = np.zeros(n_tsteps)\r\npulse[100:] = 1.\r\n\r\n# Calculate time dependent field for each cell compartment\r\nv_cell_ext = np.zeros((cell.totnsegs, n_tsteps))\r\nv_cell_ext[:, :] = ext_field(cell.zmid).reshape(cell.totnsegs, 1) * pulse.reshape(1, n_tsteps)\r\ncell.insert_v_ext(v_cell_ext, t)\r\n\r\nzs = np.linspace(np.min(cell.zmid), np.max(cell.zmid), 4)\r\ncell_plot_idxs = [cell.get_closest_idx(0, 0, z) for z in zs]\r\n\r\n# This function is used to color code compartments for plotting\r\ncell_pos_clr = lambda z: plt.cm.jet(1.0 * (z - np.min(zs)) / (np.max(zs) - np.min(zs)))\r\n\r\n\r\n# Run simulation, electrode object argument in cell.simulate\r\nprint(\"running simulation...\")\r\ncell.simulate(rec_imem=True, rec_vmem=True)\r\n\r\n# Plot results\r\nplt.close('all')\r\nfig = plt.figure(figsize=[12, 5])\r\nfig.subplots_adjust(wspace=0.6, top=0.83)\r\nfig.suptitle(\"Stick cell in linear external potential\")\r\n\r\nax1 = plt.subplot(151, title=\"Stick cell\", aspect=1, frameon=False, xlim=[-100, 100], xlabel=\"x [$\\mu$m]\", ylabel=\"y [$\\mu$m]\")\r\nax2 = plt.subplot(152, title=\"External potential\\n(Ue)\", xlim=[0, cell.tstop], xlabel=\"Time [ms]\", ylabel=\"mV\")\r\nax3 = plt.subplot(153, title=\"Membrane potential\\n(Vm)\", sharex=ax2, xlabel=\"Time [ms]\", ylabel=\"[mV]\")\r\nax4 = plt.subplot(154, title=\"Ui = Ue + Vm\", sharex=ax3, xlabel=\"Time [ms]\", ylabel=\"[mV]\")\r\nax5 = plt.subplot(155, title=\"Transmembrane currents\", sharex=ax3, xlabel=\"Time [ms]\", ylabel=\"nA\")\r\n\r\n[ax1.plot([cell.xstart[idx], cell.xend[idx]],\r\n          [cell.zstart[idx], cell.zend[idx]], '-',\r\n          c='k', clip_on=False) for idx in range(cell.totnsegs)]\r\n\r\nfor num, idx in enumerate(cell_plot_idxs):\r\n    ax1.plot(cell.xmid[idx], cell.zmid[idx], 'D', c=cell_pos_clr(cell.zmid[idx]))\r\n    ax2.plot(cell.tvec, v_cell_ext[idx], c=cell_pos_clr(cell.zmid[idx]))\r\n    ax3.plot(cell.tvec, cell.vmem[idx, :], c=cell_pos_clr(cell.zmid[idx]))\r\n    ax4.plot(cell.tvec, v_cell_ext[idx] + cell.vmem[idx, :], c=cell_pos_clr(cell.zmid[idx]))\r\n    ax5.plot(cell.tvec, cell.imem[idx, :], c=cell_pos_clr(cell.zmid[idx]))\r\n\r\nplt.savefig('example_stick.png')\r\n","sub_path":"ephaptic_example_stick.py","file_name":"ephaptic_example_stick.py","file_ext":"py","file_size_in_byte":3167,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"160534142","text":"import time\n\nimport phonenumbers\nimport random\nimport timeit\nimport math\nimport os\n\nif __name__ == \"__main__\":\n    print(\"executed as main\")\nelse:\n\n    #sum\n    def sum(number1, number2):\n        sum = number1 + number2\n        return sum\n\n    #rest\n    def remainder(number1, number2):\n        try:\n            if number2 == 0:\n                throwing_error()\n            else:\n                return int(number1 % number2)\n        except ZeroDivisionError:\n            return \"divisor is less than 1\"\n\n\n    #division\n    def division(number1, number2):\n        try:\n            if number2 == 0:\n                throwing_error()\n            else:\n                return int(number1 / number2)\n        except ZeroDivisionError:\n            return \"divisor is less than 1\"\n\n\n    #difference\n    def difference(number1, number2):\n        diff = number1 - number2\n        return diff\n\n    #raise Exception for asserRaises tests\n    def throwing_error():\n        raise Exception\n\n    #power\n    def power(b, p):\n        try:\n            if b == p == 0 or b < 0 or p < 0:\n                throwing_error()\n            else:\n                return int(math.pow(int(b), int(p)))\n        except OverflowError:\n            throwing_error()\n            return \"too big number\"     #I guess this is not reachable\n\n\n\n    # write to file 2 numbers each on a different line\n    def writing_to_file(path, b, p):\n        f = open(path, \"w\")\n        f.write(str(b) + '\\n')\n        f.write(str(p) + '\\n')\n        f.close()\n        pass\n\n        # try:\n        #     return math.pow(b, p)\n        # except OverflowError:\n        #     return \"too big number\"\n\n    #read from a file\n    def power_from_file(path):\n        try:\n            if os.path.isfile(path):\n                f = open(path, \"r\")\n                numbers = verify_file(path)\n                base = int(numbers[0])          #\"integarize\" the elements, otherwise they are str type\n                power = int(numbers[1])\n                if base == power == 0 or base < 0 or power < 0:     #check for corner cases\n                    throwing_error()\n                else:\n                    return int(math.pow(base, power))\n            else:\n                throwing_error()\n\n        except OverflowError:\n            throwing_error()\n            return \"too big number\"\n        finally:\n            f.close()\n\n    #verify if there are only digits on the file + stripping the \\n character\n    def verify_file(path):\n        try:\n            f = open(path, \"r\")\n            numbers = []\n            for number in f:\n                number = number.rstrip()   #strip \\n\n                numbers.append(number)     #form the list\n            for number in numbers:\n                if number.isdigit() == True:\n                    pass\n                else:\n                    throwing_error()\n            return numbers\n        finally:\n            f.close()\n\n\n\n\n\n    # def power_from_file():\n    #     f = open(\"file.txt\", \"r\")\n    #     for line in f:\n    #         line = line.rstrip()\n    #     return line\n\n    def raise_number_parse_exception():\n        raise phonenumbers.NumberParseException\n\n\n    #verify a string\n    def phoneValidator(phone_number):\n        phone_number_parsed = phonenumbers.parse(phone_number)\n        if phonenumbers.is_possible_number(phone_number_parsed) == True:\n            return True\n        else:\n            raise Exception\n\n\n    #working with values from a defined range --- build an increasing list of a fixed length\n    def rangedListFabric(number):\n        list = [*range(int(number))]\n        for i in list:\n            list[i] = list[i]+10\n        return list\n\n\n    #poor sorting alghortim ----> selection sort\n    def random_generator_and_selection_sort(values):\n        argument = random_list_generator(values)\n        for i in range(len(argument)):\n            min_idx = i\n            for j in range(i + 1, len(argument)):\n                if argument[min_idx] > argument[j]:\n                    min_idx = j\n            argument[i], argument[min_idx] = argument[min_idx], argument[i]\n        return argument\n\n    #poor sorting alghortim ----> selection sort -----> passing a list instead of value\n    def selection_sort_list(list):\n        argument = list\n        for i in range(len(argument)):\n            min_idx = i\n            for j in range(i + 1, len(argument)):\n                if argument[min_idx] > argument[j]:\n                    min_idx = j\n            argument[i], argument[min_idx] = argument[min_idx], argument[i]\n        return argument\n\n\n    def partition(start, end, array):\n        # Initializing pivot's index to start\n        pivot_index = start\n        pivot = array[pivot_index]\n\n        while start < end:\n            while start < len(array) and array[start] <= pivot:\n                start += 1\n            while array[end] > pivot:\n                end -= 1\n            if (start < end):\n                array[start], array[end] = array[end], array[start]\n        array[end], array[pivot_index] = array[pivot_index], array[end]\n        return end\n\n\n    def quick_sort(start, end, array):\n        if (start < end):\n            p = partition(start, end, array)\n            quick_sort(start, p - 1, array)\n            quick_sort(p + 1, end, array)\n        return array\n\n\n    #generate random list and sort it with quicksort - no shit sherlock\n    def random_generator_and_quicksort(argument):\n        list = random_list_generator(argument)\n        return quick_sort(0, len(list)-1, list)\n\n    def random_list_generator(argument):\n        list = random.sample(range(10, 100), argument)\n        return list\n\n\n    #measure time elapsed when sorting a list\n    def timer_function_quicksort(argument):\n        start = timeit.timeit()\n        random_generator_and_quicksort(argument)\n        end = timeit.timeit()\n        dif = start - end\n        if dif < 0:\n            dif = dif * (-1)\n        return dif\n\n    def timer_function_selection_sort(argument):\n        start = timeit.timeit()\n        random_generator_and_selection_sort(argument)\n        end = timeit.timeit()\n        dif = start - end\n        if dif < 0:\n            dif = dif * (-1)\n        return dif\n\n    #pass a list as argument and sort with quicksort ---- maybe for future purposes\n    def quick_sort_list(list):\n        return quick_sort(0, len(list)-1, list)\n","sub_path":"Methods.py","file_name":"Methods.py","file_ext":"py","file_size_in_byte":6342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"64537873","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nclass ConvNorm(torch.nn.Module):\n    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,\n                 padding=None, dilation=1, bias=True, w_init_gain='linear'):\n        super(ConvNorm, self).__init__()\n        if padding is None:\n            assert(kernel_size % 2 == 1)\n            padding = int(dilation * (kernel_size - 1) / 2)\n\n        self.conv = torch.nn.Conv1d(in_channels, out_channels,\n                                    kernel_size=kernel_size, stride=stride,\n                                    padding=padding, dilation=dilation,\n                                    bias=bias)\n\n        torch.nn.init.xavier_uniform_(\n            self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain))\n\n    def forward(self, signal):\n        conv_signal = self.conv(signal)\n        return conv_signal\n    \nclass Discriminator(nn.Module):\n    \"\"\"discriminator model\"\"\"\n    def __init__(self, dim_deck=80, dim_pre=128):\n        super(Discriminator, self).__init__()\n        convolutions = []\n        for i in range(3):\n            conv_layer = nn.Sequential(\n                ConvNorm(dim_deck if i==0 else dim_pre,\n                         dim_pre,\n                         kernel_size=5, stride=1,\n                         padding=2,\n                         dilation=1, w_init_gain='relu'),\n                nn.InstanceNorm1d(dim_deck) if i==0 else nn.InstanceNorm1d(dim_pre))\n            convolutions.append(conv_layer)\n        self.convolutions = nn.ModuleList(convolutions)\n        self.dense = nn.Linear(dim_pre,1)\n    def forward(self, x):\n        for conv in self.convolutions:\n            x = F.relu(conv(x))\n        patch_size = 32\n        x_sample = torch.randint(low = 0, high = x.size(2) - patch_size, size = (1,))\n        \n        x = x[:,:,x_sample:x_sample+patch_size]\n        x = torch.mean(x,dim=2)\n        mean_val = self.dense(x)\n        #mean_val = torch.clamp(mean_val, 0, 1, out=None) \n        return mean_val\n\n    \n\n    \nclass LatentClassifier(nn.Module):\n    \"\"\"discriminator model\"\"\"\n    def __init__(self, nc = 376, ns=0.2, dim_pre=128, dim_deck=64):\n        super(LatentClassifier, self).__init__()\n        self.ns = ns\n        self.nc = nc\n        \n        self.lstm1 = nn.LSTM(dim_deck, dim_pre, 2, batch_first=True)\n        convolutions = []\n        for i in range(3):\n            conv_layer = nn.Sequential(\n                ConvNorm(dim_deck if i==0 else dim_pre,\n                         dim_pre,\n                         kernel_size=3, stride=1,\n                         padding=2,\n                         dilation=1, w_init_gain='relu'))\n            convolutions.append(conv_layer)\n        self.convolutions = nn.ModuleList(convolutions)\n        #self.lstm2 = nn.LSTM(dim_pre, 32, 2, batch_first=True)\n        self.softmax = nn.LogSoftmax(dim=1)\n        self.dense = nn.Linear(dim_pre,nc)\n    def forward(self, x):\n        #x,_ = self.lstm1(x)\n        #x = x.transpose(1,2)\n        for conv in self.convolutions:\n            x = F.relu(conv(x))\n        patch_size = 32\n        x_sample = torch.randint(low = 0, high = x.size(2) - patch_size, size = (1,))\n        \n        x = x[:,:,x_sample:x_sample+patch_size]\n        \n        x = torch.mean(x,dim=2)\n        x = self.dense(x)\n        x = self.softmax(x)\n        \n        return x\nclass Speakernet(nn.Module):\n    def __init__(self, dim_pre=128, dim_deck=64):\n        super(Speakernet, self).__init__()\n        \n        self.lstm1 = nn.LSTM(dim_deck, dim_pre, 2, batch_first=True)\n        convolutions = []\n        for i in range(3):\n            conv_layer = nn.Sequential(\n                ConvNorm(dim_pre,\n                         dim_pre,\n                         kernel_size=3, stride=1,\n                         padding=1,\n                         dilation=1, w_init_gain='relu'))\n            convolutions.append(conv_layer)\n        self.convolutions = nn.ModuleList(convolutions)\n        \n    def forward(self, x):\n        x = x.transpose(1,2)\n        x,_ = self.lstm1(x)\n        x = x.transpose(1,2)\n        for conv in self.convolutions:\n            x = F.relu(conv(x))\n        \n        return x\n\nclass Postnet(nn.Module):\n    \"\"\"Postnet\n        - Five 1-d convolution with 512 channels and kernel size 5\n    \"\"\"\n\n    def __init__(self):\n        super(Postnet, self).__init__()\n        self.convolutions = nn.ModuleList()\n\n        self.convolutions.append(\n            nn.Sequential(\n                ConvNorm(80, 512,\n                         kernel_size=5, stride=1,\n                         padding=2,\n                         dilation=1, w_init_gain='tanh'),\n                nn.BatchNorm1d(512))\n        )\n\n        for i in range(1, 5 - 1):\n            self.convolutions.append(\n                nn.Sequential(\n                    ConvNorm(512,\n                             512,\n                             kernel_size=5, stride=1,\n                             padding=2,\n                             dilation=1, w_init_gain='tanh'),\n                    nn.BatchNorm1d(512))\n            )\n\n        self.convolutions.append(\n            nn.Sequential(\n                ConvNorm(512, 80,\n                         kernel_size=5, stride=1,\n                         padding=2,\n                         dilation=1, w_init_gain='linear'),\n                nn.BatchNorm1d(80))\n            )\n\n    def forward(self, x):\n        for i in range(len(self.convolutions) - 1):\n            x = torch.tanh(self.convolutions[i](x))\n\n        x = self.convolutions[-1](x)\n\n        return x    ","sub_path":"model/tmp.py","file_name":"tmp.py","file_ext":"py","file_size_in_byte":5564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"562673061","text":"__author__ = \"Cyril Jaquier\"\n__version__ = \"$Revision: 1.1 $\"\n__date__ = \"$Date: 2010-07-25 12:46:56 $\"\n__copyright__ = \"Copyright (c) 2004 Cyril Jaquier\"\n__license__ = \"GPL\"\nimport unittest\nfrom client.jailreader import JailReader\nclass JailReaderTest(unittest.TestCase):\n\tdef setUp(self):\n\t\t\"\"\"Call before every test case.\"\"\"\n\tdef tearDown(self):\n\t\t\"\"\"Call after every test case.\"\"\"\n\tdef testSplitAction(self):\n\t\taction = \"mail-whois[name=SSH]\"\n\t\texpected = ['mail-whois', {'name': 'SSH'}]\n\t\tresult = JailReader.splitAction(action)\n\t\tself.assertEquals(expected, result)\n\t\t\n","sub_path":"fstmerge/examples/Fail2ban/rev579-732/right-branch-732/testcases/clientreadertestcase.py","file_name":"clientreadertestcase.py","file_ext":"py","file_size_in_byte":575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"321475405","text":"from stable_baselines3.common.env_checker import check_env\nfrom stable_baselines3 import SAC, PPO\nfrom stable_baselines3.common.vec_env import VecNormalize, VecFrameStack, VecTransposeImage\nimport os.path as osp\nfrom stable_baselines3.common.env_util import make_vec_env\nfrom stable_baselines3.common.callbacks import CallbackList, CheckpointCallback, EvalCallback, EventCallback, BaseCallback\nfrom sb_code.Logger import Logger\nfrom sys import platform\nimport torch\nimport gym\nfrom stable_baselines3.common.evaluation import evaluate_policy\n# Below is training on linux and if GPU is available\nfrom stable_baselines3.sac.policies import MlpPolicy\n\nPROJECT_PATH = osp.abspath(osp.dirname(osp.dirname(__file__)))\n\n# Step 1. Initialize the environment\nenv = gym.make('CartPole-v1')\n\n# Step 2. Check the custom environment. Must do it before any wrappers\ncheck_env(env)\n\n\n#eval_env = env # Make a seperate evaluation environment without vectorized version as in train env\n\n# Step 3.b. To make Vectorized Environment to be able to use Normalize or FramStack (Optional)\nenv = make_vec_env(lambda: env, n_envs=1)\n# Step 3.b Passing through Normalization and stack frame (Optional)\n\nenv = VecFrameStack(env, n_stack=1) # Use 1 for now because we use image\n#env = VecNormalize(env, norm_obs=True, norm_reward=True, clip_obs=10.) # If using normalize, must save\n\n# Step 4. Make Logger corrsponding to the name of algorithm\nlogger = Logger(\"ppo\")\n\n# Step 5. Creating callbacks\nclass SaveNormalization(BaseCallback):\n    \"\"\"\n    Base class for triggering callback on event.\n\n    :param callback: (Optional[BaseCallback]) Callback that will be called\n        when an event is triggered.\n    :param verbose: (int)\n    \"\"\"\n    def __init__(self, env = None, save_path=None):\n        super(SaveNormalization, self).__init__()\n        self.save_path = save_path\n\n    def _on_step(self) -> bool:\n        if self.model.get_vec_normalize_env() is not None:\n            self.model.get_vec_normalize_env().save(self.save_path)\n            if self.verbose > 1:\n                print(f\"Saving VecNormalize to {self.save_path}\")\n        return True\n\ncheckpoint_callback = CheckpointCallback(save_freq=30000, save_path=logger.output_dir,\n                                         name_prefix='rl_model')\n\nsavestats_callback = SaveNormalization(save_path=osp.join(logger.output_dir, \"vec_normalization.pkl\")) # If using normalize, must create this callback\n\neval_callback = EvalCallback(eval_env = env, n_eval_episodes=5, callback_on_new_best=savestats_callback,\n                             eval_freq=1000,\n                             best_model_save_path=osp.join(logger.output_dir, \"best_model\"),\n                             log_path=osp.join(logger.output_dir, \"results\"))\n\ncallback = CallbackList([checkpoint_callback, eval_callback])\n\nmodel = PPO('MlpPolicy', env=env, verbose=1)\n\nmean_reward, std_reward = evaluate_policy(model, env, n_eval_episodes=100)\n\nprint(f\"Before mean_reward:{mean_reward:.2f} +/- {std_reward:.2f}\")\n\n\nmodel.learn(total_timesteps=150000, log_interval=5, callback=callback) # Log_interval = number of episodes\n","sub_path":"sb_code/check_my_impl.py","file_name":"check_my_impl.py","file_ext":"py","file_size_in_byte":3114,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"196841669","text":"import numpy as np\nimport torch\nimport os\n\nclass AttrPredictor(object):\n\n    def __init__(self, cfg, tops_type=[3, 5, 10]):\n        \"\"\"Create the empty array to count true positive(tp),\n            true negative(tn), false positive(fp) and false negative(fn).\n\n        Args:\n            class_num : number of classes in the dataset\n            tops_type : default calculate top3, top5 and top10\n        \"\"\"\n\n        attr_cloth_file = open(cfg.attr_cloth_file).readlines()\n        self.attr_idx2name = {}\n        for i, line in enumerate(attr_cloth_file[2:]):\n            self.attr_idx2name[i] = line.strip('\\n').split()[0]\n\n    def print_attr_name(self, pred_idx):\n        for idx in pred_idx:            \n            #print(self.attr_idx2name[idx],idx)\n            with open('results.txt','a') as f:\n              f.write(str(idx)+',')\n        with open('results.txt','a') as f:\n            f.write('\\n')\n        for idx in pred_idx:\n            with open('results.txt','a') as f:\n              f.write(str(self.attr_idx2name[idx])+',')\n              \n    def show_prediction(self, pred,filename):\n        if isinstance(pred, torch.Tensor):\n            data = pred.data.cpu().numpy()\n        elif isinstance(pred, np.ndarray):\n            data = pred\n        else:\n            raise TypeError('type {} cannot be calculated.'.format(type(pred)))\n        with open('results.txt','a') as f:\n              f.write('\\n'+str(filename)+'\\n')\n        for i in range(pred.size(0)):\n            indexes = np.argsort(data[i])[::-1]\n            idx5= indexes\n            \n            #print('[ Top5 Prediction ]')\n            self.print_attr_name(idx5)\n","sub_path":"mmfashion/core/evaluation/attr_predict_demo.py","file_name":"attr_predict_demo.py","file_ext":"py","file_size_in_byte":1642,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"548246229","text":"import numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim\nimport torch.nn.functional as F\nimport os\n\nimport models\nfrom datasets import ISIC_few_shot, EuroSAT_few_shot, CropDisease_few_shot, Chest_few_shot, miniImageNet_few_shot, tiered_ImageNet_few_shot\n\nfrom tqdm import tqdm\nimport pandas as pd\nimport argparse\nimport random\nimport copy\nimport warnings\nfrom utils import to_one_hot,  AverageMeter,  loss_calc\nimport utils\nfrom methods.baselinetrain import BaselineTrain\n\n\n\n\ndef evaluate(dataloader, params): \n    print(\"Loading Model: \", params.embedding_load_path)\n    if params.embedding_load_path_version == 0:\n        state = torch.load(params.embedding_load_path)['state']\n        state_keys = list(state.keys())\n        #print(state_keys)\n        for _, key in enumerate(state_keys):\n            if \"feature.\" in key:\n                # an architecture model has attribute 'feature', load architecture feature to backbone by casting name from 'feature.trunk.xx' to 'trunk.xx'\n                newkey = key.replace(\"feature.\", \"\")\n                state[newkey] = state.pop(key)\n            else:\n                state.pop(key)\n            \n        sd = state\n    elif params.embedding_load_path_version == 1:\n        sd = torch.load(params.embedding_load_path)\n\n        if 'epoch' in sd:\n            print(\"Model checkpointed at epoch: \", sd['epoch'])\n        sd = sd['model']\n    # elif params.embedding_load_path_version == 3:\n    #     state = torch.load(params.embedding_load_path)\n    #     print(\"Model checkpointed at epoch: \", state['epoch'])\n    #     state = state['model']\n    #     state_keys = list(state.keys())\n    #     for _, key in enumerate(state_keys):\n    #         if \"module.\" in key:\n    #             # an architecture model has attribute 'feature', load architecture feature to backbone by casting name from 'feature.trunk.xx' to 'trunk.xx'\n    #             newkey = key.replace(\"module.\", \"\")\n    #             state[newkey] = state.pop(key)\n    #         else:\n    #             state.pop(key)\n    #     sd = state\n    else:\n        raise ValueError(\"Invalid load path version!\")\n\n    if params.model == 'resnet10':\n        pretrained_model = models.ResNet10()\n        feature_dim = pretrained_model.final_feat_dim\n    elif params.model == 'resnet12':\n        pretrained_model = models.Resnet12(width=1, dropout=0.1)\n        feature_dim = pretrained_model.output_size\n    elif params.model == 'resnet18':\n        pretrained_model = models.resnet18(remove_last_relu=False, \n                                        input_high_res=True)\n        feature_dim = 512\n    elif params.model == 'vgg11':\n        pretrained_model = models.vgg11_bn()\n        pretrained_model.final_feat_dim = 512\n        feature_dim = 512\n    else:\n        raise ValueError(\"Invalid model!\")\n\n\n    pretrained_model.load_state_dict(sd)\n\n    model = BaselineTrain(pretrained_model, 64)\n    model.load_state_dict(torch.load(params.embedding_load_path)['state'])\n    pretrained_model = model\n\n\n    acc_all = []\n\n    pretrained_model.cuda()\n    total = 0\n    correct = 0.0\n\n    meters = utils.AverageMeterSet()\n    for i, (x, y) in tqdm(enumerate(dataloader)):\n        x = x.cuda()\n        y = y.cuda()\n\n        pretrained_model.eval()\n        scores = pretrained_model(x)\n        \n        \n        perf = utils.accuracy(scores.data,\n                              y.data, topk=(1, 5))\n        meters.update('top1', perf['average'][0].item(), len(x))\n        meters.update('top5', perf['average'][1].item(), len(x))\n\n        #_, pred = torch.max(scores.data, 1)\n        #total += y.size(0)\n        #correct += (pred==y).sum().item()\n       \n        ###############################################################################################\n   # print('Test Acc = %d %%' %\n    #            (100 * correct/total))\n    #print(correct, total)\n\n    print(\"Top1 Avg {:f}\".format(meters.__getitem__('top1')))\n\ndef main(params):\n\n    #if params.target_dataset == 'ISIC':\n    #    datamgr = ISIC_few_shot\n    #elif params.target_dataset == 'EuroSAT':\n    #    datamgr = EuroSAT_few_shot\n    #elif params.target_dataset == 'CropDisease':\n    #    datamgr = CropDisease_few_shot\n    #elif params.target_dataset == 'ChestX':\n    #    datamgr = Chest_few_shot\n    #elif params.target_dataset == 'miniImageNet_train':\n    #    datamgr = miniImageNet_few_shot\n    #elif params.target_dataset == 'miniImageNet_val':\n    #    datamgr = miniImageNet_few_shot\n    #else:\n    #    print(params.target_dataset)\n    #    raise ValueError(\"Invalid Dataset!\")\n\n    for i in ['miniImageNet_val', 'miniImageNet_train' ]:\n        params.target_dataset = i\n        datamgr = miniImageNet_few_shot\n    \n        results = {}\n        shot_done = []\n        print(params.target_dataset)\n        torch.backends.cudnn.deterministic = True\n        torch.backends.cudnn.benchmark = False\n        np.random.seed(params.seed)\n        torch.random.manual_seed(params.seed)\n        torch.cuda.manual_seed(params.seed)\n        random.seed(params.seed)\n        dataloader = datamgr.SimpleDataManager(params.image_size, params.batch_size).get_data_loader(aug=False, train_or_val=params.target_dataset=='miniImageNet_train')\n        evaluate(dataloader, params)\n\n\nif __name__=='__main__':\n    parser = argparse.ArgumentParser(\n        description='Evaluation script')\n    parser.add_argument('--target_dataset', default='miniImagenet',\n                        help='test target dataset')\n    parser.add_argument('--batch_size', type=int, default=128,\n                        help='Size of batch')\n    parser.add_argument('--image_size', type=int, default=224,\n                        help='Resolution of the input image')\n    parser.add_argument('--train_aug', action='store_true',\n                        help='perform data augmentation or not during training ')\n    parser.add_argument('--model', default='resnet10',\n                        help='backbone architecture')\n    parser.add_argument('--seed', default=1, type=int, help='random seed')\n    parser.add_argument('--embedding_load_path', type=str,\n                        help='path to load embedding')\n    parser.add_argument('--embedding_load_path_version', type=int, default=1, \n                        help='how to load the embedding')\n\n    \n    params = parser.parse_args()\n    main(params)\n   \n","sub_path":"evaluation_norm/finetune.py","file_name":"finetune.py","file_ext":"py","file_size_in_byte":6336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"3007460","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom functools import partial\n\nimport tf_euler\n\nimport tensorflow as tf\nfrom tensorflow.python.util import nest\nimport tf_context as ctx\nimport base_runner\nimport graph_tag\nimport numpy as np\n\nfrom pareto_mtl import ParetoMTL\nimport tf_sort\n\n \n\nnation_sp = ctx.get_config(\"nation\")\n \n\nprint('nation',nation_sp)\n# print('flag_exp_loss',flag_exp_loss)\ngm_loss_weight = 36.\nflag_dist = 1.\nflag_RI = 0\nvar_weight=1e-5\ngate_weight = 1.\ngate_th=0.8\nflag_gate_above=True\nsame_nation_weight=0.0\nflag_uniform_nation_weight=False\ncate_align_weight=0.1\n \nprint('gm_loss_weight',gm_loss_weight)\nprint('flag_dist',flag_dist)\nprint('flag_RI',flag_RI)\nprint('var_weight',var_weight)\nprint('gate_weight',gate_weight)\nprint('gate_th',gate_th)\nprint('flag_gate_above',flag_gate_above)\nprint('same_nation_weight',same_nation_weight)\nprint('flag_uniform_nation_weight',flag_uniform_nation_weight)\nprint('cate_align_weight',cate_align_weight)\n \nif flag_RI:\n    from util_pretrain_RI import *\nelse:\n    from util_pretrain import *\n\nif flag_dist:\n    from graph_matching_pretrain_dist import *\nelse:\n    from graph_matching_pretrain_prod import *\n \n\n\ndef greater_than_zero(x):\n    x=tf.maximum(0.0*x,x)\n    x = tf.sign(x)\n    return x\n\n\n \n \n\ndef random_randint(limit,n_row):\n    a = tf.random_uniform((n_row,limit))\n    a = tf.argmax(a,axis=1)\n    return a\n\n \n\n\ndef get_grad_norm(grad_r):\n    grad_norm = 0.0\n    for i_grad_r,e_grad_r in enumerate(grad_r):\n        if e_grad_r is None:\n            break\n        else:\n            grad_norm += tf.reduce_sum(tf.square(e_grad_r))\n    grad_norm = tf.maximum(0.0*grad_norm + 1e-7,grad_norm)\n    grad_norm = tf.sqrt(grad_norm)\n    return grad_norm\n\ndef get_grad_prod(grad_1,grad_2):\n    grad_prod = 0.0\n    for i in range(len(grad_1)):\n        if grad_1[i] is None or grad_2[i] is None :\n            break\n        else:\n            grad_prod += tf.reduce_sum(grad_1[i]*grad_2[i])\n    \n    return grad_prod\n \n\ndef stop_grad_list(x_list):\n  \n    return [tf.stop_gradient(x) for x in x_list]\n\ndef gen_paired_meta_loss(main_loss,aux_loss, weights, flag_stop_gradient):\n \n    lm,ls = main_loss,aux_loss \n   \n    grad_m = tf.gradients(lm, weights)\n    grad_norm_m = get_grad_norm(grad_m)\n    grad_s = tf.gradients(ls, weights)\n    grad_norm_s = get_grad_norm(grad_s)\n     \n    if flag_stop_gradient:\n        grad_m = stop_grad_list(grad_m)\n        grad_norm_m = tf.stop_gradient(grad_norm_m)\n  \n    loss_grad_match = 0.\n  \n    grad_prod = get_grad_prod(grad_m, grad_s)\n    loss_grad_match += - grad_prod / (grad_norm_m * grad_norm_s)\n      \n    return tf.reduce_mean(loss_grad_match)\n\ndef get_variable_list(regex_pattern):\n        import re\n#         regex_pattern = 'hidden[12]'\n        train_vars = []\n        var_names = []\n        size_list = []\n        for var in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES):\n            if re.search(regex_pattern, var.op.name):\n                train_vars.append(var)\n                var_names.append(var.op.name)\n                size_list.append(tf.shape(var))\n        print('----------------------------- Begin: var_names of %s -----------------------------'%regex_pattern)\n        print(var_names)\n        print('----------------------------- End: var_names of %s -----------------------------'%regex_pattern)\n        \n        return train_vars,size_list\n\ndef grad_flatten(grads,size_list):\n    grad_list = []\n    for i,e in enumerate(grads):\n        if e is not None:\n            x =e \n        else:\n            x = tf.zeros(size_list[i])\n        grad_list.append(tf.reshape(x,[-1]))\n    return tf.concat(grad_list,0)\n\n\n# from epo_lp import EPO_LP\n\ndef get_high64bit(tensor):\n    # 1-D tensor\n    # input: [1, 2, 3, 4, 5, 6], return: [1, 3, 5]\n    tensor = tf.reshape(tensor, [-1, 2])\n    return tensor[:, 0]\n\n\ndef tohash128(sp_tensors):\n    for i in range(len(sp_tensors)):\n        sp = sp_tensors[i]\n\n        indices = sp.indices\n        n = tf.shape(indices)[0]\n        dim = tf.shape(indices)[1]\n        indices = tf.multiply(indices, [1, 2])\n        zero_indices = tf.add(indices, [0, 1])\n        new_indices = tf.stack([indices, zero_indices], 0)\n        new_indices = tf.transpose(new_indices, perm=[1, 0, 2])\n        new_indices = tf.reshape(new_indices, [2 * n, dim])\n\n        values = sp.values\n        zero = tf.zeros_like(values)\n        new_values = tf.stack([values, zero])\n        new_values = tf.transpose(new_values)\n        new_values = tf.reshape(new_values, [-1])\n\n        shape = sp.dense_shape\n        new_shape = tf.multiply(shape, [1, 2])\n\n        sp_tensors[i] = tf.SparseTensor(indices=new_indices, values=new_values, dense_shape=new_shape)\n\n\ndef sparse_embedding(sp_tensors, input_dimensions, embedding_dimension, ps_num, init_val, stddev, names):\n    l = []\n    print('len(sp_tensors)',len(sp_tensors))\n    print('len(names)',len(names))\n    for i in range(len(sp_tensors)):\n        with tf.variable_scope(names[i]):\n            print(\"sparse_embedding name:\" + names[i] + \" dim:\" + str(input_dimensions[i]))\n            rst = full_connect_sparse(sp_tensors[i], [input_dimensions[i], embedding_dimension[i]], None, True, ps_num,\n                                      None, init_val, stddev)\n            l.append(rst)\n    return l\n\ndef init_graph():\n    zk_addr = ctx.get_config('euler', 'zk_addr')\n    zk_path = '/euler/{}'.format(ctx.get_app_id())\n    shard_num = ctx.get_config('euler', 'shard_num')\n    tf_euler.initialize_graph({\n          'mode': 'remote',\n          'zk_server': zk_addr,\n          'zk_path': zk_path,\n          'shard_num': shard_num,\n          'num_retries':10\n      })\n\ndef sample_by_walk(src, edge_type, walk_len=1, walk_p=1, walk_q=1):\n    walk_path = [edge_type]\n    walk_path.extend([['1'] for i in range(walk_len - 1)])\n    path = tf_euler.random_walk(\n        src, walk_path,\n        p=walk_p,\n        q=walk_q)\n\n    path_s = tf.slice(path, [0,1], [-1, walk_len])\n    pos = tf.reshape(path_s, [-1])\n    negs_copy = tf_euler.sample_node_with_src(pos, 6 * walk_len)\n\n    src_s = tf.reshape(src, [-1, 1])\n    src_copy = tf.concat([src_s for i in range(walk_len)], 1)\n    #have some problems\n    neg_nodes = tf.reshape(negs_copy, [-1])\n    src_nodes = tf.reshape(src_copy, [-1])\n    return src_nodes, pos, neg_nodes\n\n\ndef get_neighbors(src, pos, negs,q_part_features,i_part_features, i_nei_cnt=5, q_nei_cnt=5):\n    # q_part_features = ['query_norm']\n    # q_features = \"query_sorted_norm_keyword\"\n\n    # i_part_features = ['item_id']\n\n    src_i_nodes, _, _ = tf_euler.sample_neighbor(src, edge_types=['1', '3'], count=i_nei_cnt)\n    print(\"======== neighbor count: {}\".format(i_nei_cnt))\n    src_i_nodes_filled = tf_euler.get_sparse_feature(src_i_nodes, i_part_features)\n    src_q_nodes, _, _ = tf_euler.sample_neighbor(src, edge_types=['2'], count=q_nei_cnt)\n    src_q_nodes_filled = tf_euler.get_sparse_feature(src_q_nodes, q_part_features)\n\n    pos_i_nodes, _, _ = tf_euler.sample_neighbor(pos, edge_types=['1', '3'], count=i_nei_cnt)\n    pos_i_nodes_filled = tf_euler.get_sparse_feature(pos_i_nodes, i_part_features)\n    pos_q_nodes, _, _ = tf_euler.sample_neighbor(pos, edge_types=['2'], count=q_nei_cnt)\n    pos_q_nodes_filled = tf_euler.get_sparse_feature(pos_q_nodes, q_part_features)\n\n    neg_i_nodes, _, _ = tf_euler.sample_neighbor(negs, edge_types=['1', '3'], count=i_nei_cnt)\n    neg_i_nodes_filled = tf_euler.get_sparse_feature(neg_i_nodes, i_part_features)\n    neg_q_nodes, _, _ = tf_euler.sample_neighbor(negs, edge_types=['2'], count=q_nei_cnt)\n    neg_q_nodes_filled = tf_euler.get_sparse_feature(neg_q_nodes, q_part_features)\n\n    tohash128(src_i_nodes_filled)\n    tohash128(src_q_nodes_filled)\n    tohash128(pos_i_nodes_filled)\n    tohash128(pos_q_nodes_filled)\n    tohash128(neg_i_nodes_filled)\n    tohash128(neg_q_nodes_filled)\n\n    return src_i_nodes_filled, src_q_nodes_filled, pos_i_nodes_filled, \\\n           pos_q_nodes_filled, neg_i_nodes_filled, neg_q_nodes_filled\n\ndef get_feature(src,full_features):\n    src_filled = tf_euler.get_sparse_feature(src, full_features)\n    tohash128(src_filled)\n    return src_filled\n\ndef get_neighbor(x,q_part_features,i_part_features,c_part_features, q_nei_cnt=5,i_nei_cnt=5,c_nei_cnt=5 ):\n \n\n    q_nodes, _, _ = tf_euler.sample_neighbor(x, edge_types=['2','5'], count=i_nei_cnt)\n    i_nodes, _, _ = tf_euler.sample_neighbor(x, edge_types=['0','1', '3','7'], count=i_nei_cnt)\n    c_nodes, _, _ = tf_euler.sample_neighbor(x, edge_types=['46','66', '9'], count=i_nei_cnt)\n    \n#     q_nodes, _, _ = tf_euler.sample_neighbor(x, edge_types=['2' ], count=i_nei_cnt)\n#     i_nodes, _, _ = tf_euler.sample_neighbor(x, edge_types=['1', '3' ], count=i_nei_cnt)\n#     c_nodes, _, _ = tf_euler.sample_neighbor(x, edge_types=['46','66', '9'], count=i_nei_cnt)\n    \n     \n    q_f = get_feature(q_nodes,q_part_features)\n    i_f = get_feature(i_nodes,i_part_features)\n    c_f = get_feature(c_nodes,c_part_features)\n    \n    return q_f,i_f,c_f\n     \ndef sample_pos_n_feature(source,full_features,q_part_features,i_part_features,c_part_features):\n    node_list = []\n    src, pos, neg = sample_by_walk(source, ['0','1', '3'])\n    node_list.append({'src':src,'pos':pos,'neg':neg})\n    \n    for e_level in ['6','5','4','3','2','1']:\n        _, pos, neg = sample_by_walk(source, ['4'+e_level, '6'+e_level])\n        node_list.append({'src':src,'pos':pos,'neg':neg})\n        \n    feature_dic = {'self':[],'q':[],'i':[],'c':[]}\n    for e in node_list:\n        dic_self,dic_q,dic_i,dic_c ={},{},{},{}\n        for e_type in e:\n            self_f = get_feature(e[e_type],full_features)\n            q_f,i_f,c_f = get_neighbor(e[e_type],q_part_features,i_part_features,c_part_features)\n            dic_self[e_type],dic_q[e_type],dic_i[e_type],dic_c[e_type]=self_f,q_f,i_f,c_f\n        feature_dic['self'].append(dic_self)\n        feature_dic['q'].append(dic_q)\n        feature_dic['i'].append(dic_i)\n        feature_dic['c'].append(dic_c)\n            \n         \n    return feature_dic\n\n\ndef fake_convolution(node, q_node_nei,i_node_nei,c_node_nei, q_num=5, i_num=5,c_num=5,   q_dim=8,i_dim=8,c_dim=8):\n    q_node_nei_s = tf.reshape(q_node_nei, [-1, q_num, q_dim])\n    q_nei = tf.reduce_mean(q_node_nei_s, 1)\n    i_node_nei_s = tf.reshape(i_node_nei, [-1, i_num, i_dim])\n    i_nei = tf.reduce_mean(i_node_nei_s, 1)\n    c_node_nei_s = tf.reshape(c_node_nei, [-1, c_num, c_dim])\n    c_nei = tf.reduce_mean(c_node_nei_s, 1)\n    \n\n    res = tf.concat([node, q_nei, i_nei, c_nei], 1)\n    return res\n\n\ndef inference(tensors, keep_prob=1.0, ps_num=1):\n    id_embedding_dim = 24\n\n    embedding_ini_val = 1\n    embedding_stddev = 0.0002\n\n    dense_ini_val = 2\n    dense_stddev = 0.36\n                   \n      \n     \n    nation_list=['TH','VN','SG','MY','ID','PH']\n    feature_list=[\n        {\n            'feature_name':'query_feature1',\n            'feature_dim':int(182309924*1.1),\n            'emb_dim':8\n        },\n        {\n            'feature_name':'query_feature2',\n            'feature_dim':int(13286936*1.1),\n            'emb_dim':8\n        },\n        {\n            'feature_name':'item_feature1',\n            'feature_dim':int(18357245*1.1),\n            'emb_dim':8\n        },\n        {\n            'feature_name':'item_id',\n            'feature_dim':int(405929642*1.1),\n            'emb_dim':8\n        },\n        {\n            'feature_name':'global_category_id',\n            'feature_dim':int(4273*1.1),\n            'emb_dim':8\n        },\n        {\n            'feature_name':'item_feature9',\n            'feature_dim':int(175863*1.1),\n            'emb_dim':8\n        },\n        {\n            'feature_name':'item_feature10',\n            'feature_dim':int(1025024*1.1),\n            'emb_dim':8\n        },\n        {\n            'feature_name':'item_feature11',\n            'feature_dim':int(461654*1.1),\n            'emb_dim':8\n        },\n        {\n            'feature_name':'item_feature12',\n            'feature_dim':int(1030492*1.1),\n            'emb_dim':8\n        },\n        {\n            'feature_name':'item_feature13',\n            'feature_dim':3,\n            'emb_dim':8\n        },\n        {\n            'feature_name':'item_feature14',\n            'feature_dim':9,\n            'emb_dim':8\n        },\n        {\n            'feature_name':'item_feature15',\n            'feature_dim':4,\n            'emb_dim':8\n        },\n        {\n            'feature_name':'item_feature16',\n            'feature_dim':9,\n            'emb_dim':8\n        },\n        {\n            'feature_name':'item_feature17',\n            'feature_dim':7,\n            'emb_dim':8\n        },\n        {\n            'feature_name':'item_feature18',\n            'feature_dim':3,\n            'emb_dim':8\n        },\n        {\n            'feature_name':'item_feature2',\n            'feature_dim':int(10683948*1.1),\n            'emb_dim':8\n        },\n        {\n            'feature_name':'item_feature3',\n            'feature_dim':int(21677772*1.1),\n            'emb_dim':8\n        },\n        {\n            'feature_name':'item_feature5',\n            'feature_dim':int(117184*1.1),\n            'emb_dim':8\n        },\n        {\n            'feature_name':'item_feature6',\n            'feature_dim':int(687583*1.1),\n            'emb_dim':8\n        },\n        {\n            'feature_name':'item_feature7',\n            'feature_dim':int(339428*1.1),\n            'emb_dim':8\n        },\n        {\n            'feature_name':'category_feature2',\n            'feature_dim':int(3425*1.1),\n            'emb_dim':8\n        },\n        {\n            'feature_name':'query_feature3',\n            'feature_dim':int(14318897*1.1),\n            'emb_dim':8\n        },\n        {\n            'feature_name':'item_feature19',\n            'feature_dim':2,\n            'emb_dim':8\n        },\n        {\n            'feature_name':'item_feature10',\n            'feature_dim':11,\n            'emb_dim':8\n        },\n        {\n            'feature_name':'item_feature11',\n            'feature_dim':4,\n            'emb_dim':8\n        },\n        {\n            'feature_name':'item_feature12',\n            'feature_dim':4,\n            'emb_dim':8\n        },\n        {\n            'feature_name':'item_feature13',\n            'feature_dim':2,\n            'emb_dim':8\n        },\n        {\n            'feature_name':'level',\n            'feature_dim':7,\n            'emb_dim':8\n        },\n        {\n            'feature_name':'is_leaf',\n            'feature_dim':2,\n            'emb_dim':8\n        },\n        {\n            'feature_name':'nation',\n            'feature_dim':6,\n            'emb_dim':8\n        }\n    ]\n\n \n    # dic = {\n    #     'ID':\t6191,\n    #     'MY':\t4632,\n    #     'PH':\t6036,\n    #     'SG':\t4224,\n    #     'TH':\t7153,\n    #     'VN':\t4115\n    # }\n    # for e_nation in nation_list:\n    #     feature_list.append(\n    #         {\n    #             'feature_name':'%s_venture_category_name_l10n_seg' % e_nation,\n    #             'feature_dim':int(dic[e_nation]*1.1),\n    #             'emb_dim':8\n    #         }\n    #     )\n \n    dic = {\n        '1':\t53,\n        '2':\t361,\n        '3':\t1614,\n        '4':\t2159,\n        '5':\t7033,\n        '6':\t4380\n    }\n    for e_level in ['1','2','3','4','5','6']:\n        feature_list.append(\n            {\n                'feature_name':'global_category_id_level%s' % e_level,\n                'feature_dim':int(dic[e_level]*1.1),\n                'emb_dim':8\n            }\n        )\n\n    dic = {\n        '6':\t276,\n        '7':\t2708,\n        '8':\t4791,\n        '9':\t5432,\n        '10':\t3903,\n        '11':\t2534\n    }\n    \n    for e_level in ['6','7','8','9','10','11']:\n        feature_list.append(\n            {\n                'feature_name':'category_feature%s' % e_level,\n                'feature_dim':int(dic[e_level]*1.1),\n                'emb_dim':8\n            }\n        )\n\n    dic = {\n        '12':\t569,\n        '13':\t3985,\n        '14':\t9136,\n        '15':\t11316,\n        '16':\t8008,\n        '17':\t4697\n    }\n    for e_level in ['12','13','14','15','16','17']:\n        feature_list.append(\n            {\n                'feature_name':'category_feature%s' % e_level,\n                'feature_dim':int(dic[e_level]*1.1),\n                'emb_dim':8\n            }\n        )\n                   \n      \n \n    # dic = {\n    #     'ID':\t6192,\n    #     'MY':\t4632,\n    #     'PH':\t6036,\n    #     'SG':\t4224,\n    #     'TH':\t7153,\n    #     'VN':\t4115\n    # }\n    # for e_nation in nation_list:\n    #     feature_list.append(\n    #         {\n    #             'feature_name':'%s_venture_category_name_l10n_seg_alllevel' % e_nation,\n    #             'feature_dim':int(dic[e_nation]*1.1),\n    #             'emb_dim':8\n    #         }\n    #     )\n   \n    full_feature_dims = []\n    full_feature_names = []\n    full_emb_dim = []\n\n    for e in feature_list:\n        full_feature_names.append(e['feature_name'])\n        full_feature_dims.append(e['feature_dim'])\n        full_emb_dim.append(e['emb_dim'])\n\n    full_feature_names = (full_feature_names)\n \n    part_q_feature_names = ['query_norm' ]\n    part_q_feature_dims = [ int(182309924*1.1) ]\n    part_q_emb_dim = [8 ]\n\n    part_i_feature_names = ['item_id' ]\n    part_i_feature_dims = [int(405929642*1.1) ]\n    part_i_emb_dim = [8 ]\n    \n    part_c_feature_names = ['venture_category_name_en' ]\n    part_c_feature_dims = [int(4273*1.1) ]\n    part_c_emb_dim = [8 ]\n     \n    full_emb_dim_sum=np.sum(full_emb_dim)+np.sum(part_q_emb_dim)+np.sum(part_i_emb_dim)+np.sum(part_c_emb_dim)\n    part_q_emb_dim_sum=np.sum(part_q_emb_dim)\n    part_i_emb_dim_sum=np.sum(part_i_emb_dim)\n    part_c_emb_dim_sum=np.sum(part_c_emb_dim)\n    \n    dic_info={\n        'self':\n        {\n            'feature_names':full_feature_names,\n            'feature_dims':full_feature_dims,\n            'emb_dim':full_emb_dim\n        },\n        'q':\n        {\n            'feature_names':part_q_feature_names,\n            'feature_dims':part_q_feature_dims,\n            'emb_dim':part_q_emb_dim\n        },\n        'i':\n        {\n            'feature_names':part_i_feature_names,\n            'feature_dims':part_i_feature_dims,\n            'emb_dim':part_i_emb_dim\n        },\n        'c':\n        {\n            'feature_names':part_c_feature_names,\n            'feature_dims':part_c_feature_dims,\n            'emb_dim':part_c_emb_dim\n        }\n    }\n \n    \n    neg_num = 6\n    walk_len=1\n\n    type_cnt = 2\n\n    swd_dim = 128\n    swd_dim_gwd = 8\n    \n    swd_limit = 512\n    swd_limit_gwd = 128\n\n    # model_config = ctx.get_config('reader')\n    batch_size = ctx.get_config('batch_size')\n\n    init_graph()\n\n    source=tensors.data[0][0].values\n    print('len(tensors.data[0])',len(tensors.data[0]))\n    print('len(tensors.data)',len(tensors.data))\n    # print('source[:5]',source[:5])\n    print('tensors.data[1]',tensors.data[1])\n    print('tensors.data[2]',tensors.data[2])\n    print('tensors.data[3]',tensors.data[3])\n    print('tensors.data[4]',tensors.data[4])\n    # print('tensors.data[0][1].values[:5]',tensors.data[0][1].values[:5])\n    # print('tensors.data[0][2].values[:5]',tensors.data[0][2].values[:5])\n    # negs = tensors.data[1][0].values\n\n    \n    \n    print('tf.__version__', tf.__version__)\n\n    import sys\n    print('sys.version',sys.version)\n\n    dic_nation2int={'TH':1,'VN':2,'SG':3,'MY':4,'ID':5,'PH':6}\n    nation_vec = tensors.data[1]\n    nation_onehot = []\n    nation_idx_where = []\n  \n    for e_nation in nation_list:\n        eq_nation = tf.to_float(tf.equal(tf.cast(nation_vec,tf.int32), tf.cast(dic_nation2int[e_nation],tf.int32)))\n        nation_onehot.append(eq_nation)\n        idx_where = tf.cast(tf.where(tf.equal(eq_nation,1))[:,0],dtype=tf.int32)\n        idx_where = tf.concat([idx_where,[tf.shape(eq_nation)[0],tf.shape(eq_nation)[0]+1]],0)\n        nation_idx_where.append(idx_where)\n    \n    nation_onehot = tf.concat(nation_onehot,1)\n    nation_exist = tf.to_float(tf.greater(tf.reduce_sum(nation_onehot,0),0)) \n    \n    wd_idx = tf.range(swd_limit)\n    wd_idx_norm = tf.to_float(wd_idx)/tf.to_float(swd_limit)\n    \n    gwd_idx_left = random_randint(swd_limit_gwd,swd_limit_gwd)\n    gwd_idx_right = random_randint(swd_limit_gwd,swd_limit_gwd)\n    gwd_idx = [gwd_idx_left,gwd_idx_right]\n    gwd_idx_norm = [tf.to_float(gwd_idx_left)/tf.to_float(swd_limit),\n                    tf.to_float(gwd_idx_right)/tf.to_float(swd_limit)]\n    \n    cgwd_idx_left = random_randint(swd_limit_gwd,swd_limit_gwd*(walk_len+neg_num))\n    cgwd_idx_right = random_randint(swd_limit_gwd*(walk_len+neg_num),swd_limit_gwd*(walk_len+neg_num))\n    cgwd_idx = [cgwd_idx_left,cgwd_idx_right]\n    cgwd_idx_norm = [tf.to_float(cgwd_idx_left)/tf.to_float(swd_limit),\n                    tf.to_float(cgwd_idx_right)/tf.to_float(swd_limit*(walk_len+neg_num))]\n    \n\n    Theta=tf.random_normal((swd_dim,32))\n    Theta=tf.nn.l2_normalize(Theta,axis=1)\n    \n    Theta_gwd=tf.random_normal((swd_dim_gwd,32))\n    Theta_gwd=tf.nn.l2_normalize(Theta_gwd,axis=1)\n    \n\n    source = get_high64bit(source)\n    # negs = get_high64bit(negs)\n\n    \n\n    feature_dic = sample_pos_n_feature(source,full_feature_names,part_q_feature_names,part_i_feature_names,part_c_feature_names)\n\n    # contain_same_cate ------------\n    cate_vec = tensors.data[4]\n    cate_vec = tf.to_float(cate_vec)\n    cate_vec = tf.reshape(cate_vec,[-1,1])\n    n_nation=len(nation_list)\n    cate_mat = nation_onehot * cate_vec\n    contain_same_cate = []\n    for i_nation in range(n_nation):\n        i_cate = tf.reshape(cate_mat[:,i_nation],[-1,1])\n        i_mask = tf.reshape(nation_onehot[:,i_nation],[-1,1])\n        for j_nation in range(n_nation):\n            j_cate = tf.reshape(cate_mat[:,j_nation],[-1,1])\n            j_mask = tf.reshape(nation_onehot[:,j_nation],[-1,1])\n            ij_mask = tf.matmul(i_mask,j_mask,transpose_b=True)\n            cate_join = tf.to_float(tf.equal(i_cate, tf.transpose(j_cate))) * ij_mask\n            cate_join = tf.reduce_sum(cate_join,keepdims=True)\n            contain_same_cate.append(cate_join)\n    contain_same_cate = tf.concat(contain_same_cate,0)\n    contain_same_cate = tf.reshape(contain_same_cate,[n_nation,n_nation])\n    contain_same_cate = greater_than_zero(contain_same_cate)\n    #--------------------------------\n\n    with tf.variable_scope(\"embedding\", reuse=tf.AUTO_REUSE) as scope:\n        embedding_dic={'self':[],'q':[],'i':[],'c':[]}\n        for e_part in feature_dic:\n            e_list = feature_dic[e_part]\n            for e in e_list:\n                dic={}\n                for e_type in e:\n                    node_embed = sparse_embedding(e[e_type], dic_info[e_part]['feature_dims'],\n                                               dic_info[e_part]['emb_dim'],\n                                               ps_num,\n                                               init_val=embedding_ini_val,\n                                               stddev=embedding_stddev,\n                                               names=dic_info[e_part]['feature_names'])\n                    node_embed_c = tf.concat(node_embed, 1)\n                    dic[e_type]=node_embed_c\n                embedding_dic[e_part].append(dic)\n    \n    input_list = []        \n    n_level = len(embedding_dic['self'])\n    for i_level in range(n_level):\n        dic={}\n        for e_type in embedding_dic['self'][i_level]:\n            e_input = fake_convolution(embedding_dic['self'][i_level][e_type],\n                                       embedding_dic['q'][i_level][e_type],\n                                       embedding_dic['i'][i_level][e_type],\n                                       embedding_dic['c'][i_level][e_type],\n                    q_dim=part_q_emb_dim_sum,i_dim=part_i_emb_dim_sum,c_dim=part_c_emb_dim_sum)\n            dic[e_type]=e_input\n        input_list.append(dic)\n\n     \n        \n    with tf.variable_scope(\"gate_current_dnn\", reuse=tf.AUTO_REUSE):\n        gate_current_id_deep_list = []\n        gate_current_id_deep_list_swd=[]\n        current_id_ays_list_swd=[]\n        current_id_ays_list_sgwd=[]\n        for i_dic,e_dic in enumerate(input_list):\n            current_id_deep = gate_feature_net(e_dic['src'],\n                                         full_emb_dim_sum, ps_num,\n                                         dense_ini_val,\n                                         dense_stddev)\n            current_id_deep_gate = gate_wd_net(current_id_deep, ps_num, dense_ini_val, dense_stddev)\n            gate_current_id_deep_list.append(current_id_deep)\n            \n            if flag_RI:\n                current_id_deep_for_sort = mapping_fun(current_id_deep, ps_num, dense_ini_val, dense_stddev,nation_list)\n            else:\n                current_id_deep_for_sort = current_id_deep\n            x_list_wd,idx_list_wd=swd_proj_src(current_id_deep_for_sort, Theta,nation_idx_where)\n            x_list_gwd,idx_list_gwd=swd_proj_src(current_id_deep_for_sort, Theta_gwd,nation_idx_where)\n             \n            gate_feature_list=swd_proj_src_with_idx(current_id_deep, idx_list_gwd,nation_idx_where)\n            gate_list=swd_proj_src_with_idx(current_id_deep_gate, idx_list_wd,nation_idx_where)\n            gate_current_id_deep_list_swd.append([gate_feature_list,gate_list])\n            \n            current_id_ays_list_swd.append([x_list_wd,idx_list_wd])\n            current_id_ays_list_sgwd.append([x_list_gwd,idx_list_gwd])\n             \n        \n    with tf.variable_scope(\"gate_node_dnn\", reuse=tf.AUTO_REUSE) as scope:\n        gate_pos_id_deep_list,gate_node_neg_id_deep_list = [],[]\n        gate_node_id_ays_list_swd=[]\n        node_id_ays_list_swd = []\n        for i_dic,e_dic in enumerate(input_list):\n            pos_id_deep = gate_feature_net(e_dic['pos'], full_emb_dim_sum, ps_num,\n                                     dense_ini_val,\n                                     dense_stddev)\n            node_neg_id_deep = gate_feature_net(e_dic['neg'], full_emb_dim_sum, ps_num,\n                                          dense_ini_val,\n                                          dense_stddev)\n            gate_pos_id_deep_list.append(pos_id_deep)\n            gate_node_neg_id_deep_list.append(node_neg_id_deep)\n            \n            pos_id_deep_gate = gate_wd_net(pos_id_deep, ps_num, dense_ini_val, dense_stddev)\n            node_neg_id_deep_gate = gate_wd_net(node_neg_id_deep, ps_num, dense_ini_val, dense_stddev)\n            \n            if flag_RI:\n                pos_id_deep_for_sort = mapping_fun(pos_id_deep, ps_num, dense_ini_val, dense_stddev,nation_list)\n                node_neg_id_deep_for_sort = mapping_fun(node_neg_id_deep, ps_num, dense_ini_val, dense_stddev,nation_list)\n            else:\n                pos_id_deep_for_sort = pos_id_deep\n                node_neg_id_deep_for_sort = node_neg_id_deep\n\n            \n            x_list_gwd,idx_list_gwd=swd_proj_pos_neg(pos_id_deep_for_sort,node_neg_id_deep_for_sort, Theta_gwd, nation_idx_where,neg_num)\n  \n            gate_feature_list=swd_proj_pos_neg_with_idx(pos_id_deep,node_neg_id_deep, idx_list_gwd, nation_idx_where,neg_num)\n            gate_list=swd_proj_pos_neg_with_idx(pos_id_deep_gate,node_neg_id_deep_gate, idx_list_gwd, nation_idx_where,neg_num)\n            gate_node_id_ays_list_swd.append([gate_feature_list,gate_list])\n            \n            node_id_ays_list_swd.append([x_list_gwd,idx_list_gwd])\n             \n        \n        #for inference\n        gate_node_src_context_ays_swd = []\n        node_src_context_ays_swd = []\n        node_src_context_ays_sgwd = []\n        gate_node_src_context_deep = gate_feature_net(input_list[0]['src'], full_emb_dim_sum, ps_num,\n                                      dense_ini_val,\n                                      dense_stddev)\n        gate_node_src_context_deep_gate = gate_wd_net(gate_node_src_context_deep, ps_num, dense_ini_val, dense_stddev)\n        \n        if flag_RI:\n            gate_node_src_context_deep_for_sort = mapping_fun(gate_node_src_context_deep, ps_num, dense_ini_val, dense_stddev,nation_list)\n        else:\n            gate_node_src_context_deep_for_sort = gate_node_src_context_deep\n\n        x_list_wd,idx_list_wd=swd_proj_src(gate_node_src_context_deep_for_sort, Theta,nation_idx_where)\n        x_list_gwd,idx_list_gwd=swd_proj_src(gate_node_src_context_deep_for_sort, Theta_gwd,nation_idx_where)\n \n        gate_feature_list=swd_proj_src_with_idx(gate_node_src_context_deep, idx_list_gwd,nation_idx_where)\n        gate_list=swd_proj_src_with_idx(gate_node_src_context_deep_gate, idx_list_wd,nation_idx_where)\n        gate_node_src_context_ays_swd.append([gate_feature_list,gate_list])\n        \n        node_src_context_ays_swd.append([x_list_wd,idx_list_wd])\n        node_src_context_ays_sgwd.append([x_list_gwd,idx_list_gwd])\n        \n          \n    with tf.variable_scope(\"gate_att_sim\", reuse=tf.AUTO_REUSE) as scope:\n        n_level = len(gate_current_id_deep_list)\n        gate_att_sim_list = []\n        for i_level in range(n_level):\n            current_id_ays=gate_current_id_deep_list[i_level]\n            pos_id_ays = gate_pos_id_deep_list[i_level]\n            node_neg_id_ays = gate_node_neg_id_deep_list[i_level]\n            \n            current_id_ays = tf.reshape(current_id_ays, [-1, 1, 32])\n            pos_id_ays = tf.reshape(pos_id_ays, [-1, walk_len, 32])\n            node_neg_id_ays_re = tf.reshape(node_neg_id_ays, [-1, neg_num, 32])\n            node_id_ays = tf.concat([pos_id_ays, node_neg_id_ays_re], 1)\n            att_sim_con = batch_cosine_fun(current_id_ays, node_id_ays)\n            gate_att_sim_list.append(att_sim_con)\n        \n        att_sim_list = gate_att_sim_list\n\n    print('tensors.data[1]',tensors.data[1])\n    print('tensors.data[2]',tensors.data[2])\n    print('tensors.data[3]',tensors.data[3])\n    print('tensors.data[4]',tensors.data[4])\n    print('tf.__version__', tf.__version__)\n    import sys\n    print('sys.version',sys.version)\n    \n    # n_tasks,n_params,preference = n_level,1,np.array([1,0,0,0,0,0,0])\n    # epo_lp = EPO_LP(m=n_tasks, n=n_params, r=preference)\n\n    align_info = [current_id_ays_list_swd,node_id_ays_list_swd,node_src_context_ays_swd,\\\n                  current_id_ays_list_sgwd,node_src_context_ays_sgwd,\\\n                      wd_idx,wd_idx_norm,gwd_idx,gwd_idx_norm,cgwd_idx,cgwd_idx_norm,nation_exist,\\\n                  gate_current_id_deep_list_swd,gate_node_id_ays_list_swd,gate_node_src_context_ays_swd,\\\n                 ps_num, dense_ini_val, dense_stddev,\\\n                 gate_att_sim_list,contain_same_cate]\n\n    \n    return att_sim_list,nation_onehot,align_info\n\n\ndef id_dnn_net(id_embedding, input_dim, ps_num, init_val, stddev):\n    with tf.variable_scope(\"layer1\"):\n        layer1_output0 = full_connect(id_embedding, [input_dim, 256], [256], False, ps_num, init_val, stddev)\n        layer1_output = tf.nn.elu(layer1_output0)\n\n    with tf.variable_scope(\"layer2\"):\n        layer2_output0 = full_connect(layer1_output, [256, 256], [256], False, ps_num, init_val, stddev)\n        layer2_output = tf.nn.elu(layer2_output0)\n\n    return layer2_output\n\n\ndef id_ays_net(id_deep, ps_num, init_val, stddev):\n    with tf.variable_scope(\"layer1\"):\n        x = full_connect(id_deep, [256, 32], [32], False, ps_num, init_val, stddev)\n        x = tf.nn.elu(x)\n        x=tf.nn.l2_normalize(x,axis=1)\n    return x\n\ndef gate_feature_net(id_embedding, input_dim, ps_num, init_val, stddev):\n    with tf.variable_scope(\"gate_feature_layer1\"):\n        x = full_connect(id_embedding, [input_dim, 256], [256], False, ps_num, init_val, stddev)\n        x = tf.nn.elu(x)\n\n    with tf.variable_scope(\"gate_feature_layer2\"):\n        x = full_connect(x, [256, 256], [256], False, ps_num, init_val, stddev)\n        x = tf.nn.elu(x)\n        \n    with tf.variable_scope(\"gate_feature_layer3\"):\n        x = full_connect(x, [256, 32], [32], False, ps_num, init_val, stddev)\n        x = tf.nn.elu(x)\n        x=tf.nn.l2_normalize(x,axis=1)\n    return x\n\ndef gate_wd_net(id_deep, ps_num, init_val, stddev):\n    with tf.variable_scope(\"wd_layer1\"):\n        x = full_connect(tf.stop_gradient(id_deep), [32, 6], [6], False, ps_num, init_val, stddev)\n        x = tf.sigmoid(x)\n    return x\n\ndef gate_gwd_net(id_deep, ps_num, init_val, stddev, sp_name):\n    with tf.variable_scope(\"gwd_layer1_%s\"%sp_name):\n        x = full_connect(tf.stop_gradient(id_deep), [96, 32], [32], False, ps_num, init_val, stddev)\n        x = tf.nn.elu(x)\n        \n    with tf.variable_scope(\"gwd_layer2_%s\"%sp_name):\n        x = full_connect(x, [32, 6], [6], False, ps_num, init_val, stddev)\n        x = tf.sigmoid(x)\n         \n    return x\n\ndef gate_cgwd_net(id_deep, ps_num, init_val, stddev, sp_name):\n    with tf.variable_scope(\"cgwd_layer1_%s\"%sp_name):\n        x = full_connect(tf.stop_gradient(id_deep), [96, 32], [32], False, ps_num, init_val, stddev)\n        x = tf.nn.elu(x)\n        \n    with tf.variable_scope(\"cgwd_layer2_%s\"%sp_name):\n        x = full_connect(x, [32, 6], [6], False, ps_num, init_val, stddev)\n        x = tf.sigmoid(x)\n         \n    return x\n\ndef batch_cosine_fun(ays_src, ays_dst):\n    # ays_src [batch, 1, dim]\n    # ays_dst [batch, num, dim]\n    src_norm = tf.sqrt(tf.reduce_sum(tf.square(ays_src), 2, True)) # [batch, 1, 1]\n    src_norm = tf.squeeze(src_norm, -1) # [batch, 1]\n    dst_norm = tf.sqrt(tf.reduce_sum(tf.square(ays_dst), 2, True)) # [batch, num, 1]\n    dst_norm = tf.squeeze(dst_norm, -1) # [batch, num]\n    \n    prod = tf.matmul(ays_src, ays_dst, transpose_b=True) # [batch, 1, num]\n    prod = tf.squeeze(prod, 1) # [batch, num]\n    norm_prod = src_norm * dst_norm # [batch, num]\n    cosine = tf.truediv(prod, norm_prod)\n    return cosine\n\ndef cosine_fun(ays_src, ays_dst):\n    src_norm = tf.sqrt(tf.reduce_sum(tf.square(ays_src), 1, True))\n    dst_norm = tf.sqrt(tf.reduce_sum(tf.square(ays_dst), 1, True))\n\n    prod = tf.reduce_sum(tf.multiply(ays_src, ays_dst), 1, True)\n    norm_prod = tf.multiply(src_norm, dst_norm)\n\n    cosine = tf.truediv(prod, norm_prod)\n    return cosine\n\ndef get_initializer(init_val=1, dtype=tf.float32, stddev=0.1, value=0.0):\n    if init_val == 0:\n        return tf.constant_initializer(value=value, dtype=dtype)\n    elif init_val == 1:\n        return tf.truncated_normal_initializer(dtype=dtype, stddev=stddev)\n    elif init_val == 2:\n        # factor*[-sqrt(3) / sqrt(dim), sqrt(3) / sqrt(dim)]\n        # stddev=factor/sqrt(N)\n        # where factor=input stddev!!!!!!!!!!!!!!!\n        return tf.uniform_unit_scaling_initializer(factor=stddev, seed=10, dtype=tf.float32)\n    else:\n        return None\n\ndef full_connect_sparse(train_inputs, weights_shape, biases_shape, no_biases, ps_num, sp_weights, init_val, stddev):\n    # weights\n    from tf_ps.ps_context import variable_info\n    with variable_info(batch_read=3000, var_type='hash'):\n        if ps_num > 1:\n            weights = tf.get_variable(\"weights\",\n                                      weights_shape,\n                                      initializer=get_initializer(init_val=init_val, stddev=stddev),\n                                      partitioner=tf.min_max_variable_partitioner(max_partitions=ps_num))\n        else:\n            weights = tf.get_variable(\"weights\",\n                                      weights_shape,\n                                      initializer=get_initializer(init_val=init_val, stddev=stddev))\n\n    train = tf.nn.embedding_lookup_sparse(weights, sp_ids=train_inputs, sp_weights=sp_weights, combiner=\"sum\") #\"mean\", \"sqrtn\" and \"sum\"\n    if not no_biases:\n        # biases\n        biases = tf.get_variable(\"biases\",\n                                 biases_shape,\n                                 initializer=get_initializer(init_val=init_val, stddev=stddev))\n        train = train + biases\n    return train\n\n\ndef full_connect(train_inputs, weights_shape, biases_shape, no_biases, ps_num, init_val, stddev):\n    # weights\n    if ps_num > 1:\n        weights = tf.get_variable(\"weights\",\n                                  weights_shape,\n                                  initializer=get_initializer(init_val=init_val, stddev=stddev),\n                                  regularizer=tf.nn.l2_loss,\n                                  partitioner=tf.min_max_variable_partitioner(max_partitions=ps_num))\n    else:\n        weights = tf.get_variable(\"weights\",\n                                  weights_shape,\n                                  regularizer=tf.nn.l2_loss,\n                                  initializer=get_initializer(init_val=init_val, stddev=stddev))\n\n    if no_biases:\n        # matmul\n        train = tf.matmul(train_inputs, weights)\n        return train\n    else:\n        # biases\n        biases = tf.get_variable(\"biases\",\n                                 biases_shape,\n                                 initializer=get_initializer(init_val=0, value=0.0002))\n        # matmul\n        train = tf.matmul(train_inputs, weights) + biases\n        return train\n\n\ndef sigmoid_loss(input_list, weight_decay=0.0001, gama=5.0):\n#     sim_list,epo_lp = input_list\n#     m = len(sim_list)\n    \n    sim_list,nation_onehot,align_info  = input_list\n    \n    current_id_ays_list_swd,node_id_ays_list_swd,node_src_context_ays_swd,\\\n    current_id_ays_list_sgwd,node_src_context_ays_sgwd,\\\n    wd_idx,wd_idx_norm,gwd_idx,gwd_idx_norm,cgwd_idx,cgwd_idx_norm,nation_exist,\\\n    gate_current_id_deep_list_swd,gate_node_id_ays_list_swd,gate_node_src_context_ays_swd,\\\n    ps_num, init_val, stddev,\\\n    gate_att_sim_list,contain_same_cate= align_info\n    \n      \n    \n    \n\n    \n    nation_mean = nation_onehot/(tf.reduce_sum(nation_onehot, 0, keep_dims=True)+1e-6)\n    \n    solver=ParetoMTL()\n\n    \n     \n    loss_sum = 0.0\n    cont_loss = 0.0\n    for i,sim in enumerate(sim_list):\n        if i > 1 and i < len(sim_list)-1:\n            continue\n        one_labels = tf.ones([tf.shape(sim)[0], 1], dtype=tf.float32)\n        zero_labels = tf.zeros([tf.shape(sim)[0], 6], dtype=tf.float32)\n        label = tf.concat([one_labels, zero_labels], -1)\n\n        if i==0:\n            loss = tf.nn.weighted_cross_entropy_with_logits(targets=label, logits=gama*sim, pos_weight=2.0)\n        # loss = tf.nn.softmax_cross_entropy_with_logits_v2(logits=gama*sim, labels=label)\n        else:\n            # loss = tf.nn.softmax_cross_entropy_with_logits_v2(logits=gama*sim, labels=label)\n            prob = tf.nn.softmax(gama * sim)\n            loss_pos = -label*tf.log(prob+1e-6)\n            # loss_neg = label*tf.log(1.-prob+1e-6)\n            loss = loss_pos \n        loss = tf.reduce_sum(loss, 1, keep_dims=True)\n        loss = nation_mean*loss\n        # loss = nation_onehot*loss\n\n \n        \n        loss = tf.reduce_sum(loss, 0) \n        # loss = tf.reduce_mean(loss, 0) \n        cont_loss += 1.0\n        \n        loss_each = loss\n        \n        if i == 0:\n            loss = loss_each\n        \n            nation_sp = ctx.get_config(\"nation\")\n            dic_nation2exp = {'TH':2,'VN':3,'SG':1,'MY':3,'ID':4,'PH':4}\n            flag_exp = dic_nation2exp[nation_sp]\n\n            flag_exp=3\n\n            if flag_exp == 1 or flag_exp == 2:\n                if flag_exp == 1:\n                    pref_vecs = tf.eye(6)\n                    pref_vecs = tf.concat([tf.random_uniform((1,6)),tf.ones((1,6))/6.],0)\n                    pref_vecs = pref_vecs/(tf.reduce_sum(pref_vecs,1,keep_dims=True)+1e-6)\n                if flag_exp == 2:\n                    pref_vecs = tf.eye(6)\n                    pref_vecs = tf.concat([tf.ones((1,6))/6.,tf.ones((1,6))/6.],0)\n                    pref_vecs = pref_vecs/(tf.reduce_sum(pref_vecs,1,keep_dims=True)+1e-6)\n\n                weights,size_list=get_variable_list('gate_feature_layer')\n                grad_list = []\n                for j in range(6):\n                    grad = tf.gradients(loss[j], weights)\n                    grad=grad_flatten(grad,size_list)\n                    grad=tf.stop_gradient(grad)\n                    grad_list.append(grad)\n                    print('grad.get_shape()',grad.get_shape())\n                grads = tf.stack(grad_list)\n           \n                loss_value = tf.stop_gradient(loss)\n\n                i_task = 0\n\n                # flag, weight_init=solver.get_d_paretomtl_init(grads,loss_value,pref_vecs,i_task)\n                weight=solver.get_d_paretomtl(grads,loss_value,pref_vecs,i_task)\n\n                # flag = tf.cast(flag,dtype=weight_init.dtype)\n                # weight = flag * weight + (1.-flag)*weight_init\n\n            if flag_exp == 3:\n                weight = tf.random_uniform((1,6))\n                weight = weight/(tf.reduce_sum(weight)+1e-6)\n                \n            if flag_exp == 4:\n                weight = tf.ones((1,6))/6.\n\n            print('flag_exp',flag_exp)\n                \n            \n            weight = tf.stop_gradient(tf.reshape(weight,[-1]))\n\n            loss_vec = loss\n            \n            loss =  tf.reduce_sum(loss * weight) \n            reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)\n            loss_param_regu = cont_loss * weight_decay * tf.add_n(reg_losses)\n            # loss_sum += loss + loss_param_regu\n            loss_sum += loss  \n        elif i == 1:\n            loss = loss_each\n \n            loss_vec2 = loss\n            loss =  tf.reduce_mean(loss  )\n            loss_param_regu = cont_loss* weight_decay * tf.add_n(reg_losses)\n            loss_sum += cate_align_weight * loss + loss_param_regu\n            break\n#         break\n\n    # graph matching loss -------------------------------------\n \n    loss_swd,show_list_swd = swd_loss(current_id_ays_list_swd,node_src_context_ays_swd,nation_exist,wd_idx,wd_idx_norm,\n                       gate_current_id_deep_list_swd,gate_node_src_context_ays_swd,nation_weights=weight,gm_loss_weight=gm_loss_weight,\n                       flag_dist=flag_dist,var_weight=var_weight,gate_weight=gate_weight,gate_th=gate_th,flag_gate_above=flag_gate_above,\n                       same_nation_weight=same_nation_weight,flag_uniform_nation_weight=flag_uniform_nation_weight,\n                       contain_same_cate=contain_same_cate)\n \n    with tf.variable_scope(\"gate_pairwise_dnn\", reuse=tf.AUTO_REUSE) as scope:\n        loss_sgwd,show_list_sgwd = sgwd_loss(current_id_ays_list_sgwd,node_src_context_ays_sgwd,nation_exist,gwd_idx,gwd_idx_norm,\n                            gate_current_id_deep_list_swd,gate_node_src_context_ays_swd,\n                            ps_num, init_val, stddev,\n                            mode='gwd',nation_weights=weight,gm_loss_weight=gm_loss_weight,flag_dist=flag_dist,var_weight=var_weight,\n                            gate_weight=gate_weight,gate_th=gate_th,flag_gate_above=flag_gate_above,\n                            same_nation_weight=same_nation_weight,flag_uniform_nation_weight=flag_uniform_nation_weight,\n                            contain_same_cate=contain_same_cate)\n     \n        loss_scgwd,show_list_scgwd = sgwd_loss(current_id_ays_list_sgwd,node_id_ays_list_swd,nation_exist,cgwd_idx,cgwd_idx_norm,\n                            gate_current_id_deep_list_swd,gate_node_id_ays_list_swd,\n                            ps_num, init_val, stddev,\n                            mode='cgwd',nation_weights=weight,gm_loss_weight=gm_loss_weight,flag_dist=flag_dist,var_weight=var_weight,\n                            gate_weight=gate_weight,gate_th=gate_th,flag_gate_above=flag_gate_above,\n                            same_nation_weight=same_nation_weight,flag_uniform_nation_weight=flag_uniform_nation_weight,\n                            contain_same_cate=contain_same_cate)\n    \n \n    loss_regu = loss_swd + loss_sgwd + loss_scgwd\n\n    #-----------------------------------------------------------\n \n    global_step = tf.contrib.framework.get_or_create_global_step()\n    global_step = tf.to_float(global_step)\n    flag_global_step = greater_than_zero(global_step-500000.)\n\n    loss_sum = loss_sum + 0.1 * loss_regu\n         \n    return loss_sum, [loss,loss_param_regu,loss_regu,loss_vec,weight,loss_vec2,\n                     show_list_swd,\n                     show_list_sgwd,\n                     show_list_scgwd,flag_global_step]\n\ndef auc(input_list, num_thresholds=200, decay_rate=1):\n    sim_list,nation_onehot,align_info  = input_list\n\n    sim=sim_list[0]\n    neg_num = tf.shape(sim)[1] - 1\n    sample_num = tf.shape(sim)[0]\n    labels_matrix = tf.concat([tf.ones([sample_num, 1], tf.int32), tf.zeros([sample_num, neg_num], tf.int32)], axis=1)\n    labels = tf.reshape(labels_matrix, [-1, 1])\n\n    predictions = tf.reshape(tf.nn.sigmoid(sim, name='sigmoid_auc'), [-1, 1])\n    _, auc_op = tf.contrib.metrics.streaming_auc(predictions, labels, num_thresholds=num_thresholds,\n                                                     decay_rate=decay_rate)\n    return auc_op","sub_path":"International_Entity_Graphs/model_train.py","file_name":"model_train.py","file_ext":"py","file_size_in_byte":44631,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"176624090","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Aug 30 00:43:06 2017\n\n@author: paulo\n\"\"\"\n\nimport tkinter as tk\nimport ttk\nimport tkMessageBox\nfrom MN.NewtonRapson import NewtonRapson\nimport matplotlib\nmatplotlib.use(\"TkAgg\")\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg\nfrom matplotlib.figure import Figure\nimport sympy as sp\nimport cmath as cm\n\nclass GUI(tk.Frame):\n    \"\"\" Interfaz grafica de usuario \"\"\"\n\n    def __init__(self, parent=None):\n        tk.Frame.__init__(self, parent)\n        self.parent = parent\n        self.graph = tk.Toplevel(self.parent)\n        self.initComponent()\n        self.initGraph()\n        self.graph.withdraw()\n        self.parent.mainloop()\n\n    def initComponent(self):\n        \"\"\" Componentes de la aplicación \"\"\"\n        self.parent.title(\"Método de Newton-Rapson (raices imaginarias)\")\n        self.parent.resizable(0, 0)\n        self.parent.config(bg=\"white\")\n        self.renderingWindow(self.parent)\n        \n        # Opciones\n        font = ('Verdana', 14)\n        bg = \"white\"\n        vcmd = (self.parent.register(self.validate),\n                '%d', '%i', '%P', '%s', '%S', '%v', '%V', '%W')\n        \n        # Objetos\n        lblEcuacion = tk.Label(self.parent, text=\"Ingrese la ecuación:\",\n                               anchor=tk.W, justify=tk.LEFT)\n        lblEcuacion.place(x=10, y=10, width=300, height=20)\n        lblEcuacion.config(font=font, bg=bg)\n        \n        txtInstruccion = \"Ingrese un valor inicial para calcular la raiz:\"\n        lblInstruccion = tk.Label(self.parent, text=txtInstruccion,\n                                  anchor=tk.W, justify=tk.LEFT)\n        lblInstruccion.place(x=10, y=50, width=700, height=20)\n        lblInstruccion.config(font=font, bg=bg)\n        \n        lblInferior = tk.Label(self.parent, text=\"V. Inicial:\",\n                                  anchor=tk.W, justify=tk.LEFT)\n        lblInferior.place(x=10, y=90, width=140, height=20)\n        lblInferior.config(font=font, bg=bg)\n        \n        lblError = tk.Label(self.parent, text=\"Error:\",\n                                  anchor=tk.W, justify=tk.LEFT)\n        lblError.place(x=540, y=90, width=110, height=20)\n        lblError.config(font=font, bg=bg)\n        \n        lblRes = tk.Label(self.parent, text=\"Resultados:\",\n                                  anchor=tk.W, justify=tk.LEFT)\n        lblRes.place(x=10, y=170, width=700, height=20)\n        lblRes.config(font=font, bg=bg)\n        \n        self.txtEcuacion = tk.Entry(self.parent)\n        self.txtEcuacion.place(x=320, y=10, width=200, height=20)\n        self.txtEcuacion.focus()\n        \n        v = tk.StringVar(self.parent, value='0')\n        self.txtInferior = tk.Entry(self.parent, state='disabled',\n                                    textvariable=v)\n        self.txtInferior.place(x=160, y=90, width=130, height=20)\n        \n        self.txtError = tk.Entry(self.parent, validate = 'key',\n                                 validatecommand = vcmd)\n        self.txtError.place(x=660, y=90, width=130, height=20)\n        \n        btnGrafica = tk.Button(self.parent, text=\"Graficar\",\n                               command=lambda: self.graficar())\n        btnGrafica.place(x=670, y=10, width=120, height=20)\n        btnGrafica.config(font=font)\n        \n        btnRaiz = tk.Button(self.parent, text=\"Obtener raices\",\n                            command=lambda: self.raiz())\n        btnRaiz.place(x=10, y=130, width=200, height=20)\n        btnRaiz.config(font=font)\n        \n        self.tree = ttk.Treeview(self.parent)\n        vsb = ttk.Scrollbar(self.parent, orient=\"vertical\",\n                            command=self.tree.yview)\n        vsb.place(x=775, y=210, height=380)\n        self.tree.configure(yscrollcommand=vsb.set)\n        self.tree[\"columns\"]=(\"Xi\", \"f(Xi)\", \"f'(Xi)\", \"g(x)\")\n        self.tree.column(\"#0\", width=50)\n        self.tree.column(\"Xi\", width=170)\n        self.tree.column(\"f(Xi)\", width=180)\n        self.tree.column(\"f'(Xi)\", width=180)\n        self.tree.column(\"g(x)\", width=185)\n        self.tree.heading(\"Xi\", text=\"Xi\")\n        self.tree.heading(\"f(Xi)\", text=\"f(Xi)\")\n        self.tree.heading(\"f'(Xi)\", text=\"F'(Xi)\")\n        self.tree.heading(\"g(x)\", text=\"g(x)\")\n        self.tree.place(x=10, y=210, width=765, height=380)\n        self.tree.insert(\"\" , 0, text=\"1\", values=(\"\",\"\",\"\",\"\"))\n        \n    def initGraph(self):\n        \"\"\" Componentes de la aplicación \"\"\"\n        self.graph.title(\"Grafica\")\n        self.graph.resizable(0, 0)\n        self.graph.config(bg=\"white\")\n        self.renderingWindow(self.graph)\n        #self.graph.focus_set() # Focus\n        self.graph.protocol(\"WM_DELETE_WINDOW\", \"onexit\") # quitamos cerrar\n        \n    def renderingWindow(self, frame):\n        \"\"\" Le da tamaño a la ventana y la centra en pantall \"\"\"\n        w = 800\n        h = 600\n        ws = self.parent.winfo_screenwidth()\n        hs = self.parent.winfo_screenheight()\n        x = (ws/2) - (w/2)\n        y = (hs/2) - (h/2)\n        frame.geometry('%dx%d+%d+%d' % (w, h, x, y))\n        \n    def raiz(self):\n        # Obtenemos los valores\n        ec = self.txtEcuacion.get()\n        inicial = cm.sqrt(-1)\n        err = self.txtError.get()\n        text = \"f(x) = \"+str(ec)+\"\\n\\n\"\n        \n        # verificamos que todos los campos esten llenos\n        if len(ec)!=0 and len(err)!=0 :\n            # Limpiamos el treeView\n            self.tree.delete(*self.tree.get_children())\n            \n            # Creamos un objeto de la clase Regla Falsa\n            nr = NewtonRapson(inicial, ec, err, self.tree)\n            \n            # Obtenemos el total de raices\n            nr.roots()\n            \n            # comprobamos el numero de raices\n            if (nr.root + len(nr.complex)) == 0 :\n                # Mensaje a mostrar\n                text = \"La función dada no tiene raices\"\n                \n                # Mostramos el mensaje\n                tkMessageBox.showerror(title=\"Error\", message=text)\n            else :\n                textTemp = \"f(x) = \"+str(ec)+\"\\n\\n\"\n                textTemp += \"Tiene \"+str(len(nr.complex))+\" raices imaginarias\\n\"\n                textTemp += \"El valor inicial se calculara automaticamente\"\n                tkMessageBox.showwarning(title=\"Atención\", message=textTemp)\n                # Contador de raices\n                count = 1\n                \n                # verificamos si tiene raices complejas\n                if len(nr.complex) > 0:\n                    nr.inicial = (nr.complex[0] - 1j)\n                    raiz = nr.estimate(1)\n                    \n                    control = 2\n                    \n                    while len(nr.complex) >= control:\n                        nr.inicial = (nr.complex[control-1] - 1j)\n                        raiz = nr.estimate(1)\n                    \n                        control += 1\n                \n                # Obtenemos las raices reales\n                for r in nr.real:\n                    text += \"Raiz \"+str(count)+\": \"+str(r)+\"\\n\"\n                    count += 1\n                \n                # Obtenemos las raices imaginarias\n                for r in nr.complex:\n                    text += \"Raiz \"+str(count)+\": \"+str(r)+\"\\n\"\n                    count += 1\n                    \n                # Mostramos el mensaje\n                tkMessageBox.showinfo(title=\"Resultados\", message=text)\n        else :\n            # Mensaje de error\n            text = \"Por favor llene todos los campos\"\n            # Mostramos el mensaje\n            tkMessageBox.showerror(title=\"Error\", message=text)\n            \n    def validate(self, action, index, value_if_allowed,\n                       prior_value, text, validation_type, trigger_type, widget_name):\n        if action == \"1\":\n            if text in '0123456789.\\-\\+':\n                try:\n                    float(value_if_allowed)\n                    return True\n                except ValueError:\n                    return False\n            else:\n                return False\n        else:\n            return True\n        \n    def graficar(self):\n        \"\"\"\n        Graficamos la funcion con ayuda de matplotlib\n        \"\"\"\n        \n        font = ('Verdana', 14)\n        ec = self.txtEcuacion.get().lower()\n        \n        # verificamos que todos los campos esten llenos\n        if len(ec) != 0 :\n            # Limpiamos la ventana\n            for widget in self.graph.winfo_children():\n                widget.destroy()\n    \n            self.graph.deiconify()\n            nr = NewtonRapson(0,ec)\n            \n            f = Figure(figsize=(5,5), dpi=100)\n            a = f.add_subplot(111)\n            # Valores del eje X que toma el gráfico.\n            _x = range(-10, 15)\n            # Graficar ambas funciones.\n            a.plot(_x, [nr.f(i) for i in _x])\n            # Establecer el color de los ejes.\n            a.axhline(0, color=\"black\")\n            a.axvline(0, color=\"black\")\n            # Limitar los valores de los ejes.\n            a.set_xlim(-10, 10)\n            a.set_ylim(-50, 50)\n            # Etiquetas de los ejes\n            a.set_xlabel('X')\n            a.set_ylabel('Y')\n            a.set_title('f(x) = '+str(ec))\n            a.grid(True)\n    \n            canvas = FigureCanvasTkAgg(f, self.graph)\n            canvas.show()\n            canvas.get_tk_widget().pack(side=tk.TOP, fill=tk.X,\n                                expand=True)\n    \n            btnClose = tk.Button(self.graph, text=\"Cerrar\",\n                            command=lambda: self.close())\n            btnClose.config(font=font)\n            btnClose.pack(side=tk.BOTTOM, fill=tk.X)\n    \n            toolbar = NavigationToolbar2TkAgg(canvas, self.graph)\n            toolbar.update()\n            canvas._tkcanvas.pack(side=tk.TOP)\n        else:\n            # Mensaje de error\n            text = \"Por favor ingrese la ecuacion\"\n            tkMessageBox.showerror(title=\"Error\", message=text)\n            \n    def close(self):\n        self.graph.withdraw();","sub_path":"Metodos numericos/Método Newton-Rapson (imaginarios)/principal/MN/GUI.py","file_name":"GUI.py","file_ext":"py","file_size_in_byte":10029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"620237810","text":"#Calcular o juros de depósitos ao longo de 24 meses.\n\ntaxa_de_juros = float(input(\"Digite o valor da taxa de juros dos 24 meses: \"))\nmeses = 1\ntotal = 0\ncontrole = 0\n\nwhile meses <= 24:\n    depósito = float(input(\"Digite o depósito do %d mês: \" % meses))\n    controle += depósito\n    total += depósito * taxa_de_juros / 100\n    print(\"%s° mês foi depositado R$%5.2f e já foi cobrado R$%d em juros no total.\" % (meses,depósito,total))\n    meses += 1\nprint(\"O total depositado nesses 24 meses foi R$%5.2f e o total de juros a ser pago é: R$%5.2f\" % (controle, total))","sub_path":"Capítulo 5 - Repetições/exe5-12.py","file_name":"exe5-12.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"201737311","text":"from discord.ext import commands\n\nfrom datetime import date\nimport config\n\ncogs = [ \"minigames\" ]\n\nclass testbot(commands.Bot):\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        for package in cogs:\n            self.load_extension(package)\n\n    async def on_ready(self):\n        channel = self.get_channel(config.test_channel())\n        await channel.send(f\"logged in on {date.today():%B} {date.today().day} {date.today().year}\")\n\n    async def on_command_error(self, ctx, error):\n        ignored = (commands.CommandNotFound, commands.CheckFailure)\n        error = getattr(error, 'original', error)\n\n        if isinstance(error, ignored):\n            return\n        elif isinstance(error, commands.MissingRequiredArgument):\n            return await ctx.send(\"Missing required argument: \" + error.param.name)\n\n        return await ctx.send(\"An exception has occured: `{}`\".format(error.__class__.__name__))\n\nbot = testbot(command_prefix = commands.when_mentioned_or('.'))\ntoken = config.discord_token()\nbot.run(token)\n","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":1058,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"565295264","text":"# rename_file.py : 폴더 내의 파일을 불러와 이름 변경\n# original 폴더 > (hood_T/long_T/...) 폴더 > hood_T_1.jpg 파일 있음.\n# hood_T_1 (2).jpg -> hood_T_2.jpg와 같이 이름을 순서대로 변경\nimport os\n\nfolder_path = \"D:\\\\Vehicle\"  # 폴더 이름에 따라 파일이름을 바꿀 것이므로 그 상위 폴더인 original을 path로 설정\n\nfolder_list = os.listdir(folder_path)  # 각 폴더 이름 (hood_T/hong_T/...)\n# folder_list = [\"hood_T\", \"long_T\"]\ncount = 1\nfor folder_name in folder_list:  # 각 폴더 이름(hood_T)의 파일이름 얻기\n    file_path = folder_path + \"\\\\\" + folder_name  # file_path : D:\\\\python_D\\\\fashion_data\\\\original\\\\hood_T\n\n    file_list = os.listdir(file_path)  # file_list[0] : D:\\\\python_D\\\\fashion_data\\\\original\\\\hood_T\\\\hood_T_1.jpg\n    # file_list.sort()\n\n    print(\"-------------------------------------------------\")\n    print(folder_name + \" have \" + str(len(file_list)) + \" files.\")\n    print(\"file_path : \" + file_path)\n\n    # (hood_T_1(2).jpg -> 1.jpg) 이미 이름이 있는 파일이면 오류가 나기에 먼저 숫자로된 아무 이름으로 만들고, 다시 이름을 지정합니다.\n    for file_name in file_list:\n        old_name = file_path + \"\\\\\" + file_name  # old_name = D:\\\\python_D\\\\fashion_data\\\\original\\\\hood_T\\\\hood_T_1 (2).jpg\n        # new_name = file_path + \"\\\\\" + str(\n        new_name = file_path + \"\\\\\" + str(\n            count) + \".jpg\"  # new_name = D:\\\\python_D\\\\fashion_data\\\\original\\\\hood_T\\\\1.jpg\n\n        try:\n            os.rename(old_name, new_name)\n            print(\"success : \" + file_name + \" -> \" + str(count) + \".jpg\")\n        except:\n            print(\"fail : \" + file_name + \" -> \" + str(count) + \".jpg\")\n            print(\"=========files already renamed original(abcde.jpg) to number(1.jpg)===========\")\n\n        count = count + 1\n\n    # (1.jpg -> hood_T_1.jpg) : count 값을 다시 지정해 이름을 지정함.\n    file_list = os.listdir(file_path)  # 1.jpg 로 바뀐 이름을 가져와야 되기 때문에 다시 로드\n    count = 1\n    for file_name in file_list:\n        countstr = str(count).zfill(5)  # 숫자 5자리로 맞추려고 추가\n        old_name = file_path + \"\\\\\" + file_name  # old_name = 1.jpg\n        new_name = file_path + \"\\\\\" + folder_name + countstr + \".jpg\"  # new_name = hood_T_1.jpg\n\n        try:\n            os.rename(old_name, new_name)\n            print(\"success : \" + file_name + \" -> \" + folder_name + \"000\" + str(count) + \".jpg\")\n        except:\n            print(\"fail : \" + file_name + \" -> \" + folder_name + \"000\" + str(count) + \".jpg\")\n            print(\"=========files already renamed number(1.jpg) to new_name(hood_T_1.jpg)===========\")\n            break\n        count = count + 1","sub_path":"ModifyData/modify_filename.py","file_name":"modify_filename.py","file_ext":"py","file_size_in_byte":2755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"428251032","text":"import pandas as pd\nimport datetime as dt\n\n#sample both files\n#file1 = pd.read_csv('profileIDs_file1.csv', nrows=200000) \n#file2 = pd.read_csv('profileIDs_file2.csv', names = [\"Profile ID\", \"Last Updated\"], nrows=300000)\n\n#read entire files\nfile1 = pd.read_csv('profileIDs_file1.csv')\nfile2 = pd.read_csv('profileIDs_file2.csv', names = [\"Profile ID\", \"Last Updated\"])\n\nprint(\"---------------\")\n#print(\"Raw file\")\n#print(file)\n\ndf1 = pd.DataFrame(file1)\ndf2 = pd.DataFrame(file2)\ndf3 = pd.concat([df1, df2])\n#print(df)\n\n#print(\"---------------\")\n#print(\"groupby month and year\")\n#df1['Last Updated'] = pd.to_datetime(df1['Last Updated'])\n#df2['Last Updated'] = pd.to_datetime(df2['Last Updated'])\n\n#print(\"-------- File 1 --------\")\n#print(df1.groupby([df1['Last Updated'].dt.year.rename('year'), df1['Last Updated'].dt.month.rename('month')]).agg({'count'}))\n#print(\"-------- File 2 --------\")\n#print(df2.groupby([df2['Last Updated'].dt.year.rename('year'), df2['Last Updated'].dt.month.rename('month')]).agg({'count'}))\n\nprint(\"------ whole file ------ \")\ndf3['Last Updated'] = pd.to_datetime(df3['Last Updated'])\n#print(df3)\nprint(df3.groupby([df3['Last Updated'].dt.year.rename('year'), df3['Last Updated'].dt.month.rename('month')]).agg({'count'}))\n","sub_path":"profilereport.py","file_name":"profilereport.py","file_ext":"py","file_size_in_byte":1254,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"502926439","text":"# copy the code from sum1.py into this file, THEN:\n# change your program so it keeps reading numbers until it gets a -1, then prints the sum of all numbers read\nsum = []\nsum = int()\n\n\nNotDoneNumber = True\nwhile NotDoneNumber:\n    response = input(\"Please type a number\")\n    if \"-1\" in response:\n        print(sum)\n        NotDoneNumber = False\n\n    else:\n        sum = int(response) + sum","sub_path":"tests/t1/sum2.py","file_name":"sum2.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"519201153","text":"import pandas as pd\nimport numpy as np\nfrom sklearn.metrics import confusion_matrix, f1_score\nfrom sklearn.base import BaseEstimator\nfrom typing import Text, Dict\n\n\ndef evaluate(df: pd.DataFrame, target_column: Text, clf: BaseEstimator) -> Dict:\n    \"\"\"Evaluate classifier on a dataset\n\n    Args:\n        df {pandas.DataFrame}: dataset\n        target_column {Text}: target column name\n        clf {sklearn.base.BaseEstimator}: classifier (trained model)\n\n    Returns:\n        Dict: Dict of reported metrics\n            'f1' - F1 score\n            'cm' - Confusion Matrix\n            'actual' - true values for test data\n            'predicted' - predicted values for test data\n    \"\"\"\n\n    # Get X and Y\n    y_test = df.loc[:, target_column].values.astype('int32')\n    X_test = df.drop(target_column, axis=1).values.astype('float32')\n\n    prediction = clf.predict(X_test)\n    f1 = f1_score(y_true=y_test, y_pred=prediction, average='macro')\n    cm = confusion_matrix(prediction, y_test)\n\n    return {\n        'f1': f1,\n        'cm': cm,\n        'actual': y_test,\n        'predicted': prediction\n    }\n","sub_path":"src/evaluate/evaluate.py","file_name":"evaluate.py","file_ext":"py","file_size_in_byte":1101,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"495957241","text":"\nimport logging\nimport sys\nimport pandas as pd\n\n\nfrom go_overlap.go.terms_to_annotation import get_and_insert_annotations\nfrom go_overlap.compute_set_overlaps.set_overlaps import \\\n    compute_go_overlap_matrix, compute_non_go_overlaps\nfrom go_overlap.create_term_subtree_gene_matrix.term_subtree_gene_matrix import \\\n    create_gene_subtree_matrix, create_gene_list_bool_array\nfrom go_overlap.prepare_input_lists.lists_to_bool_vectors import \\\n    prepare_gene_vectors_for_computation\n\nfrom biomartian.bm_queries.bm_query import get_attributes, get_datasets, get_marts, get_bm\n\nfrom go_overlap.statistics.stats import compute_statistics, \\\nfind_appropriate_statistical_test\n\n\ndef get_all_genes(dataset, experiment_genes):\n\n    \"\"\"Return all gene names from biomart.\"\"\"\n\n    logging.info(\"Getting all genes from biomart for \" + dataset)\n    df = get_bm(\"external_gene_name\", \"go_id\", dataset, \"ensembl\")\n    df = df[\"external_gene_name\"].drop_duplicates()\n\n    # this is only true if experiment genes was given\n    if isinstance(experiment_genes, pd.Series):\n        df = df[df.str.upper().isin(experiment_genes.str.upper())]\n\n    return df\n\n\ndef reorder_df(df):\n\n    \"\"\"Reorder the columns for final output.\n\n    The cols in the df are different depending on which stats method was used\n    to compare the difference between input lists.\n    \"\"\"\n\n    if \"chi_sq\" in df:\n        reordered_columns = [\"go_root\", \"ontology\", \"chi_sq_fdr\", \"a_hpgm_go_fdr\", \"a_odds\", \"b_hpgm_go_fdr\",\n                             \"b_odds\", \"a_I_b_I_go\", \"a_M_b_I_go\", \"b_M_a_I_go\", \"go_subtree\", \"U\", \"a_I_b\",\n                             \"a_M_b\", \"b_M_a\", \"chi_sq\", \"test_obs\", \"b_hpgm_go\", \"a_hpgm_go\", \"go_annotation\"]\n        df = df.sort(\"chi_sq_fdr\")\n\n    elif \"fisher\" in df:\n        reordered_columns = [\"go_root\", \"ontology\", \"fisher_fdr\", \"a_hpgm_go_fdr\", \"a_odds\", \"b_hpgm_go_fdr\",\n                             \"b_odds\",  \"a_I_b_I_go\", \"a_M_b_I_go\", \"b_M_a_I_go\", \"go_subtree\", \"U\", \"a_I_b\",\n                             \"a_M_b\", \"b_M_a\", \"fisher\", \"b_hpgm_go\", \"a_hpgm_go\", \"go_annotation\"]\n        df = df.sort(\"fisher_fdr\")\n\n    else:\n        reordered_columns = [\"go_root\", \"ontology\", \"a_hpgm_go_fdr\", \"a_odds\", \"a\", \"a_I_go\", \"go_subtree\",\n                             \"U\", \"a_hpgm_go\", \"go_annotation\"]\n        df = df.sort(\"a_hpgm_go_fdr\")\n\n    df = df[reordered_columns]\n\n    return df\n\ndef _remove_go_terms_with_too_many_genes(df, max_genes_prct_limit):\n\n    if max_genes_prct_limit == 0:\n        return df\n\n    go_genes_prct_of_u = df.go_subtree / df.U\n    go_terms_with_too_many_genes = go_genes_prct_of_u > max_genes_prct_limit\n\n    df = df.drop(df[go_terms_with_too_many_genes].index)\n\n    return df\n\n\n\ndef main(ontologies, dataset, gene_vectors, experiment_genes, nb_cpus, max_genes_prct_limit):\n\n    # writes command run as first line to stdout\n    print(\"# \" + \" \".join(sys.argv))\n\n    all_genes_vector = get_all_genes(dataset, experiment_genes)\n\n    bool_vectors = prepare_gene_vectors_for_computation(gene_vectors,\n                                                        all_genes_vector)\n\n    gene_subtree_df = create_gene_subtree_matrix(ontologies, dataset,\n                                                 all_genes_vector, nb_cpus)\n\n\n    go_overlap_matrix = compute_go_overlap_matrix(gene_subtree_df, bool_vectors,\n                                                  nb_cpus)\n\n    logging.debug(\"The overlap data looks like:\\n {}\".format(go_overlap_matrix))\n\n    go_overlap_matrix = _remove_go_terms_with_too_many_genes(go_overlap_matrix, max_genes_prct_limit)\n\n    df = compute_statistics(go_overlap_matrix, gene_vectors)\n\n    logging.debug(\"Df with stats:\\n {}\".format(df))\n\n    df = get_and_insert_annotations(df, dataset, nb_cpus)\n\n    logging.debug(\"Df with annotations:\\n {}\".format(df))\n\n    df = reorder_df(df)\n\n    logging.debug(\"Reordered df:\\n {}\".format(df))\n\n    df.to_csv(sys.stdout, index=False, header=True, sep=\"\\t\", quotechar=\"'\")\n","sub_path":"go_overlap/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"60412960","text":"from flask_sqlalchemy import SQLAlchemy\nfrom flask import current_app\n\ndb = SQLAlchemy(session_options=dict(expire_on_commit=False))\n\n\ndef persistence(objs):\n    try:\n        if isinstance(objs, dict):\n            db.session.add_all(objs)\n        else:\n            db.session.add(objs)\n        db.session.commit()\n        return True\n    except Exception as err:\n        current_app.logger.error(err)\n        db.session.rollback()\n        db.session.flush()\n        return False\n\n\ndef remove(objs):\n    try:\n        if isinstance(objs, dict):\n            db.session.delete_all(objs)\n        else:\n            db.session.delete(objs)\n        db.session.commit()\n        return True\n    except Exception as err:\n        current_app.logger.error(err)\n        db.session.rollback()\n        db.session.flush()\n        return False\n","sub_path":"src/database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":826,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"462596313","text":"from __future__ import unicode_literals\n\nfrom copy import deepcopy\n\nfrom .util import LocalParams, X, make_fq, _pop_from_kwargs\nfrom .types import instantiate, get_to_python\nfrom .pysolr import Solr\n\n\ndef zip_counts(counts, n, over=0):\n    acc = ()\n    for v in counts:\n        acc = acc + (v,)\n        if len(acc) % (n + over) == 0:\n            yield acc\n            acc = acc[n:]\n    if len(acc) == n:\n        yield acc + (None,) * over\n\n\nclass FacetField(object):\n    def __init__(self, field, local_params=None, instance_mapper=None,\n                 type=None, **facet_params):\n        self.field = field\n        self.local_params = LocalParams(local_params)\n        self.key = self.local_params.get('key', self.field)\n        self.type = instantiate(type)\n        self.to_python = get_to_python(self.type)\n        self.facet_params = facet_params\n        self.values = []\n        self._instance_mapper = instance_mapper\n\n    def __deepcopy__(self, memodict):\n        # Fix for http://bugs.python.org/issue1515\n        # self._instance_mapper can be instance method\n        obj = type(self)(self.field, local_params=self.local_params,\n                         instance_mapper=self._instance_mapper,\n                         type=self.type, **self.facet_params)\n        return obj\n\n    def instance_mapper(self, ids):\n        if self._instance_mapper:\n            return self._instance_mapper(ids)\n        return {}\n        \n    def get_params(self):\n        params = {}\n        params['facet'] = True\n        params['facet.field'] = [make_fq(X(self.field), self.local_params)]\n        for p, v in self.facet_params.items():\n            params['f.{}.facet.{}'.format(self.field, p)] = v\n        return params\n\n    def process_data(self, results):\n        self.values = []\n        raw_facet_fields = results.raw_results.facets['facet_fields']\n        facet_data = raw_facet_fields[self.key]\n        for val, count in zip_counts(facet_data, 2):\n            self.values.append(\n                FacetValue(self.to_python(val), count, facet=self))\n\n    def _populate_instances(self):\n        values = [fv.value for fv in self.values]\n        instances_map = self.instance_mapper(values)\n        for fv in self.values:\n            fv._instance = instances_map.get(fv.value)\n\n\nclass FacetValue(object):\n    def __init__(self, value, count, facet=None):\n        self.value = value\n        self.count = count\n        self.facet = facet\n        self.pivot = None\n\n    @property\n    def instance(self):\n        if not hasattr(self, '_instance'):\n            if self.facet:\n                self.facet._populate_instances()\n            else:\n                self._instance = None\n        return self._instance\n\n\nclass FacetRange(object):\n    def __init__(self, field, start, end, gap,\n                 local_params=None, type=None, **facet_params):\n        self.field = field\n        self.orig_start = self.start = start\n        self.orig_end = self.end = end\n        self.orig_gap = self.gap = gap\n        self.local_params = LocalParams(local_params)\n        self.key = self.local_params.get('key', self.field)\n        self.type = instantiate(type)\n        self.to_python = get_to_python(self.type)\n        self.facet_params = facet_params\n        self.values = []\n\n    def get_params(self):\n        params = {}\n        params['facet'] = True\n        params['facet.range'] = [make_fq(X(self.field), self.local_params)]\n        params['f.{}.facet.range.start'.format(self.field)] = self.orig_start\n        params['f.{}.facet.range.end'.format(self.field)] = self.orig_end\n        gap = self.orig_gap\n        if isinstance(gap, (list, tuple)):\n            gap = ','.join(gap)\n        params['f.{}.facet.range.gap'.format(self.field)] = gap\n        for p, v in self.facet_params.items():\n            params['f.{}.facet.range.{}'.format(self.field, p)] = v\n        return params\n\n    def process_data(self, results):\n        raw_facet_data = results.raw_results.facets \\\n                                            .get('facet_ranges', {}) \\\n                                            .get(self.key, {})\n        self.start = self.to_python(raw_facet_data.get('start', self.start))\n        self.end = self.to_python(raw_facet_data.get('end', self.end))\n        self.gap = raw_facet_data.get('gap', self.gap)\n        facet_counts = raw_facet_data.get('counts', [])\n        for start, count, end in zip_counts(facet_counts, 2, 1):\n            start = self.to_python(start)\n            if end is None:\n                end = self.end\n            else:\n                end = self.to_python(end)\n            self.values.append(\n                FacetRangeValue(start, end, count, facet=self))\n\n\nclass FacetRangeValue(object):\n    def __init__(self, start, end, count, facet=None):\n        self.count = count\n        self.start = start\n        self.end = end\n        self.facet = facet\n\n        \nclass FacetQuery(object):\n    def __init__(self, fq, local_params=None):\n        self.fq = fq\n        self.local_params = LocalParams(local_params)\n        self.key = self.local_params.get('key',\n                                         make_fq(self.fq, self.local_params))\n        self.count = None\n\n    def get_params(self):\n        params = {}\n        params['facet'] = True\n        params['facet.query'] = [make_fq(self.fq, self.local_params)]\n        return params\n        \n    def process_data(self, results):\n        raw_facet_queries = results.raw_results.facets['facet_queries']\n        self.count = raw_facet_queries[self.key]\n\n\nclass FacetPivot(object):\n    def __init__(self, *fields, **kwargs):\n        self.fields = []\n        self.instance_mappers = {}\n        self.types = {}\n        self.to_pythons = {}\n        self.facet_params = {}\n        for field in fields:\n            kw = {}\n            if isinstance(field, (list, tuple)):\n                if len(field) == 1:\n                    field = field[0]\n                elif len(field) == 2:\n                    field, kw = field\n            self.instance_mappers[field] = _pop_from_kwargs(kw, 'instance_mapper')\n            self.types[field] = _pop_from_kwargs(kw, 'type')\n            self.to_pythons[field] = get_to_python(self.types[field])\n            self.facet_params[field] = kw\n            self.fields.append(field)\n        self.field = self.fields[0]\n        self.name = ','.join(self.fields)\n        self.local_params = LocalParams(\n            _pop_from_kwargs(kwargs, 'local_params'))\n        self.key = self.local_params.get('key', self.name)\n        self.values = []\n\n    def get_params(self):\n        params = {}\n        params['facet'] = True\n        params['facet.pivot'] = [make_fq(X(self.name), self.local_params)]\n        for field, facet_params in self.facet_params.items():\n            for p, v in facet_params.items():\n                params['f.{}.facet.{}'.format(field, p)] = v\n        return params\n\n    def get_value(self, value):\n        for fv in self.values:\n            if fv.value == value:\n                return fv\n    \n    def process_data(self, results):\n        self.values = []\n        raw_pivot = results.raw_results.facets.get('facet_pivot', {}).get(self.key, {})\n        self.process_pivot(raw_pivot, self)\n\n    def process_pivot(self, raw_pivot, root_pivot):\n        self.root_pivot = root_pivot\n        for facet_data in raw_pivot:\n            to_python = root_pivot.to_pythons[self.fields[0]]\n            fv = FacetValue(\n                to_python(facet_data['value']), facet_data['count'], facet=self)\n            if 'pivot' in facet_data:\n                fv.pivot = FacetPivot(*self.fields[1:])\n                fv.pivot.process_pivot(facet_data['pivot'], root_pivot)\n            self.values.append(fv)\n        \n    def _populate_instances(self, field=None):\n        if field is None:\n            return self.root_pivot._populate_instances(field=self.field)\n        \n        facet_values = []\n        pivots = [self]\n        while pivots:\n            next_pivots = []\n            for cur_pivot in pivots:\n                for fv in cur_pivot.values:\n                    if fv.pivot:\n                        next_pivots.append(fv.pivot)\n                    if cur_pivot.field == field:\n                        facet_values.append(fv)\n            pivots = next_pivots\n\n        values = set([fv.value for fv in facet_values])\n        instance_mapper = self.instance_mappers.get(field)\n        instances_map = instance_mapper(values) if instance_mapper else {}\n        for fv in facet_values:\n            fv._instance = instances_map.get(fv.value)\n","sub_path":"solar/facets.py","file_name":"facets.py","file_ext":"py","file_size_in_byte":8534,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"379176633","text":"import torch\nfrom torch import nn\nfrom utils.vgg import vgg16\nimport numpy as np\nfrom my_packages.VOSProjection import VOSProjectionModule\n\nclass _SR_loss(nn.Module):\n    def __init__(self):\n        super(_SR_loss, self).__init__()\n        vgg = vgg16()\n        loss_network = nn.Sequential(*list(vgg.features)[:31]).eval()\n        for param in loss_network.parameters():\n            param.requires_grad = False\n        self.loss_network = loss_network\n        self.mse_loss = nn.MSELoss()\n        self.tv_loss = TVLoss()\n\n    def forward(self, output, target):\n        perception_loss = self.mse_loss(self.loss_network(output), self.loss_network(target))\n        image_loss = self.mse_loss(output, target)\n        tv_loss = self.tv_loss(output)\n        return image_loss + 0.006 * perception_loss + 2e-8 * tv_loss\n\n\nclass TVLoss(nn.Module):\n    def __init__(self, tv_loss_weight=1):\n        super(TVLoss, self).__init__()\n        self.tv_loss_weight = tv_loss_weight\n\n    def forward(self, x):\n        batch_size = x.size()[0]\n        h_x = x.size()[2]\n        w_x = x.size()[3]\n        count_h = self.tensor_size(x[:, :, 1:, :])\n        count_w = self.tensor_size(x[:, :, :, 1:])\n        h_tv = torch.pow((x[:, :, 1:, :] - x[:, :, :h_x - 1, :]), 2).sum()\n        w_tv = torch.pow((x[:, :, :, 1:] - x[:, :, :, :w_x - 1]), 2).sum()\n        return self.tv_loss_weight * 2 * (h_tv / count_h + w_tv / count_w) / batch_size\n\n    @staticmethod\n    def tensor_size(t):\n        return t.size()[1] * t.size()[2] * t.size()[3]\n\nclass _Flow_loss(nn.Module):\n    def __init__(self):\n        super(_Flow_loss, self).__init__()\n        self.mse_loss = nn.MSELoss()\n        self.SR_loss = _SR_loss()\n\n    def forward(self, outputs):\n        flow_loss = []\n        for i in range(len(outputs) - 1):\n            '''need to test which one is better'''\n            flow_loss.append(self.SR_loss(outputs[i], outputs[i + 1]))\n            # flow_loss.append(self.mse_loss(outputs[i], outputs[i + 1]))\n        return 0.005 * np.mean(outputs)\n\nclass _loss4object(nn.Module):\n    def __iter__(self):\n        super(_loss4object, self).__init__()\n        self.VOS = VOSProjectionModule()\n\n    def forward(self, frames, target=None):\n        if target != None:\n            # for SRloss\n            obj_segmentation = self.VOS(frames[0], frames[1])\n            num_objects = len(np.unique(obj_segmentation.flatten()))\n            masks = [obj_segmentation==i for i in range(num_objects)]\n            masked = [(np.bitwise_and(frames[1], mask), np.bitwise_and(target, mask)) for mask in masks]\n            return masked\n\n        else:\n            # for Flowloss\n            obj_segmentation = self.VOS(frames[0], frames[1])\n            num_objects = len(np.unique(obj_segmentation.flatten()))\n            masks = [obj_segmentation==i for i in range(num_objects)]\n            masked_outputs = [[np.bitwise_and(output, mask) for output in frames] for mask in masks]\n            return masked_outputs\n","sub_path":"loss_function.py","file_name":"loss_function.py","file_ext":"py","file_size_in_byte":2969,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"108446138","text":"#!/bin/python3\n\nfrom os import environ\nfrom itertools import combinations\nfrom functools import reduce\nfrom operator import mul\n\ndef prod (iterable):\n    #res = 1\n    #for v in iterable:\n    #    res *= v\n    #return res\n    return reduce (mul, iterable, 1)\n\n# Complete the solve function below.\ndef solve (a_, days):\n    a_.sort () # stay only with smallest values - remove their multiples\n    a = []\n    for e in a_:\n        if not any (e % x == 0 for x in a):\n            a.append (e)\n    result = []\n    for l, r in days:\n        # get unique count of multiples of coins for right margin ucr\n        n = len (a)\n        l -= 1  # subtract all sets up to l - 1\n        ucr = 0\n        for c in a: ucr += r // c - l // c\n        for i in range (2, n + 1):\n            for cmbprd in map (prod, combinations (a, i)):\n                ucr += (-1) ** (i - 1) * (r // cmbprd - l // cmbprd)\n        \"\"\"\n        # get unique count of multiples of coins for left margin ucl\n        ucl = 0\n        for c in a: ucl += l // c\n        for i in range (2, n + 1):\n            for cmbprd in map (prod, combinations (a, i)):\n                ucl += (-1) ** (i - 1) * (l // cmbprd)\n        \"\"\"\n        result.append (ucr)\n    return result\n\nif __name__ == '__main__':\n    fptr = open (environ ['OUTPUT_PATH'], 'w')\n    a_count = int (input ())\n    a = []\n    for _ in range(a_count): a.append (int (input ()))\n    d = int (input ())\n    days = []\n    for _ in range (d):\n        days.append (list (map (int, input ().rstrip ().split ())))\n    result = solve (a, days)\n    fptr.write ('\\n'.join (map (str, result)))\n    fptr.write ('\\n')\n    fptr.close ()\n","sub_path":"_metha_typ_supermarket.py","file_name":"_metha_typ_supermarket.py","file_ext":"py","file_size_in_byte":1639,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"57842025","text":"import time\n\ncnt = 0\n\n# 在文件末尾追加内容,用以测试tail文件的正确性\nwhile(True):\n    cnt += 1\n    with open(\"./file_model/Myfun/test.txt\",mode=\"at\",encoding=\"utf-8\") as f:\n        f.write(f\"{cnt} {cnt} {cnt}\\n\")\n        time.sleep(1)\n        print(\"已成功写入\")\n","sub_path":"file_learn/Myfun/tail_test.py","file_name":"tail_test.py","file_ext":"py","file_size_in_byte":286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"494978391","text":"# depyct/color/parser.py\n# Copyright (c) 2012-2017 the Depyct authors and contributors <see AUTHORS>\n#\n# This module is part of Depyct and is released under the MIT License:\n# http://www.opensource.org/licenses/mit-license.php\n\"\"\"\nFormats:\n\n    l(1)\n    la(1, 2)\n    rgb(1, 2, 3)\n    hsv(1, 2, 3)\n    hsl(1, 2, 3)\n    rgba(1, 2, 3, 4)\n    cmyk(1, 2, 3, 4)\n    #aaa\n    #aaaaaa\n\n\"\"\"\nimport re\n\nl_re = re.compile(r\"^l\\((\\d+)\\)$\")\nla_re = re.compile(r\"^la\\((\\d+),(\\d+)\\)$\")\nrgb_re = re.compile(r\"^rgb\\((\\d+),(\\d+),(\\d+)\\)$\")\nhsv_re = re.compile(r\"^hsv\\((\\d+),(\\d+),(\\d+)\\)$\")\nhsl_re = re.compile(r\"^hsl\\((\\d+),(\\d+),(\\d+)\\)$\")\nrgba_re = re.compile(r\"^rgba\\((\\d+),(\\d+),(\\d+),(\\d+)\\)$\")\ncmyk_re = re.compile(r\"^cmyk\\((\\d+),(\\d+),(\\d+),(\\d+)\\)$\")\nweb_short_re = re.compile(r\"^#([\\da-fA-F])([\\da-fA-F])([\\da-fA-F])$\")\nweb_long_re = re.compile(r\"^#([\\da-fA-F]{2})([\\da-fA-F]{2})([\\da-fA-F]{2})$\")\n\ncolor_regexes = {\n        \"l\": l_re,\n        \"la\": la_re,\n        \"rgb\": rgb_re,\n        \"hsv\": hsv_re,\n        \"hsl\": hsl_re,\n        \"rgba\": rgba_re,\n        \"cmyk\": cmyk_re\n    }\n\ntest_values = [\n    (l_re, \"l(10)\"),\n    (la_re, \"la(10,100)\"),\n    (rgb_re, \"rgb(10,20,30)\"),\n    (hsv_re, \"hsv(10,20,30)\"),\n    (hsl_re, \"hsl(10,20,30)\"),\n    (rgba_re, \"rgba(10,20,30,40)\"),\n    (cmyk_re, \"cmyk(10,20,30,40)\"),\n    (web_short_re, \"#341\"),\n    (web_long_re, \"#a7882d\"),\n    ]\n\n\ndef parse_color_string(color):\n    try:\n        if color.startswith(\"#\"):\n            if len(color) == 4:\n                m = web_short_re.match(color)\n                res = tuple(int(c*2, 16) for c in m.groups())\n            else:\n                m = web_long_re.match(color)\n                res = tuple(int(c, 16) for c in m.groups())\n            return \"web\", res\n        else:\n            prefix = color.split(\"(\")[0]\n            regex = color_regexes[prefix]\n            m = regex.match(color)\n            return prefix, tuple(int(c) for c in m.groups())\n    except:\n        raise ValueError(\"Not a valid color\")\n\n\nif __name__ == \"__main__\":\n    for _, color in test_values:\n        try:\n            print(parse_color_string(color))\n        except ValueError:\n            print(\"{} didn't parse\".format(color))\n","sub_path":"lib/depyct/color/parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":2185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"234247037","text":"\"\"\"\n163\nmissing ranges\neasy\n\"\"\"\n\nfrom typing import List\n\nclass Solution:\n    def findMissingRanges(self, nums: List[int], lower: int, upper: int) -> List[str]:\n\n\n        res = []\n\n        def add_range(left, right):\n            if left == right:\n                res.append(str(left))\n            elif left < right:\n                res.append(str(left) + \"->\" + str(right))\n\n        if not nums:\n            add_range(lower, upper)\n            return res\n\n        for i in range(len(nums)):\n            if i == 0:\n                left, right = lower, nums[i]-1\n            else:\n                left, right = nums[i-1]+1, nums[i]-1\n\n            add_range(left, right)\n\n        add_range(nums[-1]+1, upper)\n\n        return res\n\n\nsol = Solution()\nnums = [0,1,3,50,75]\nlower = 0\nupper = 99\nprint(sol.findMissingRanges(nums, lower, upper))","sub_path":"Q163.py","file_name":"Q163.py","file_ext":"py","file_size_in_byte":835,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"240077343","text":"from datetime import datetime\r\nimport io\r\nimport requests\r\nfrom sqlalchemy.engine import create_engine\r\n\r\nimport pandas as pd\r\nimport psycopg2 as pg\r\nfrom DBUtil.option_data import get_list\r\n\r\n\r\n\n\r\ndef get_date():\r\n    return str(datetime.today().strftime('%d%m%Y'))\r\n\r\n\r\n      \r\n        \r\n    \r\ndef extract_volatility():\r\n    conn = pg.connect(database=\"optiondata\", user = \"postgres\", password = \"superuser\", host = \"127.0.0.1\", port = \"5432\")\r\n    df1=pd.DataFrame()\r\n    engine = create_engine('postgresql://postgres:superuser@localhost:5432/optiondata')\r\n    csv_url = 'https://www.nseindia.com/archives/nsccl/volt/CMVOLT_'+str(datetime.today().strftime('%d%m%Y'))+'.CSV'\r\n    req = requests.get(csv_url).content\r\n    df= pd.read_csv(io.StringIO(req.decode(encoding = 'UTF-8',errors = 'strict')))\r\n    df1['Date']=df['Date']\r\n    df1['symbol']=df['Symbol']\r\n    df1['Previous Volatility']=df['Previous Day Underlying Volatility (D)']\r\n    df1['Daily Volatility']=df['Current Day Underlying Daily Volatility (E) = Sqrt(0.94*D*D + 0.06*C*C)']\r\n    df1['Annualised Volatility']=df['Underlying Annualised Volatility (F) = E*Sqrt(365)']    \r\n    df1.to_sql(\"volatility_data\", engine,if_exists='append')\r\n    conn.cursor().execute(\"delete from volatility_data  where volatility_data.symbol not in (\"+str(get_list()).strip(\"[\").strip(\"]\")+\")\")\r\n    conn.commit()\r\n\r\n","sub_path":"DataExtract/original/volatility.py","file_name":"volatility.py","file_ext":"py","file_size_in_byte":1364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"149916423","text":"#!/usr/bin/python3\n\nimport os\nimport subprocess\nimport config\nimport threading\nimport time\nimport random\nimport work\n\nclass SSHClient:\n  def __init__(self, user, host):\n    self.ssh_cmd_base = 'ssh -q -o StrictHostKeyChecking=no -o PasswordAuthentication=no'\n    self.user = user\n    self.host = host\n    self.ssh_cmd = '{0} {1}@{2}'.format(self.ssh_cmd_base, self.user, self.host)\n\n  def reachable(self):\n    reachable_cmd = '{0} -n'.format(self.ssh_cmd)\n    with open(os.devnull, 'w') as devnull:\n      return_code = subprocess.call(reachable_cmd.split(' '), stdout = devnull, stderr = devnull)\n    return return_code == 0\n\n  def run_script(self, script_code):\n    remote_python_ssh_cmd = '{0} -t python'.format(self.ssh_cmd)\n    remote_python_process = subprocess.Popen(remote_python_ssh_cmd.split(' '), stdin=subprocess.PIPE)\n    remote_python_process.communicate(input=bytes(script_code, 'UTF-8'))\n    remote_python_process.wait()\n    return remote_python_process.returncode == 0\n\nclass Worker(threading.Thread):\n  def __init__(self, user, host):\n    threading.Thread.__init__(self)\n    self.user = user\n    self.host = host\n    self.task = None\n\n  def assign_task(self, task):\n    self.task = task\n\n  def set_pools(self, worker_pool, task_pool):\n    self.worker_pool = worker_pool\n    self.task_pool = task_pool\n\n  def run(self):\n    success = SSHClient(self.user, self.host).run_script(\\\n        'import subprocess; import sys; sys.exit(subprocess.call({0}))'.format(self.task))\n    if success:\n      self.task_pool.completed(self.task)\n    else:\n      self.task_pool.put_back(self.task)\n    self.worker_pool.put(self)\n\nclass WorkerPool:\n  def __init__(self, config_stations):\n    self.stations = {}\n    self.global_lock = threading.Lock()\n    self._build_station_dict(config_stations)\n    self._filter_available_stations()\n    self.free_workers_sem_max = sum(self.stations.values())\n    self.free_workers_sem = threading.Semaphore(self.free_workers_sem_max)\n    print('Station count: {0}'.format(len(self.stations)))\n    print('Thread count: {0}'.format(sum(self.stations.values())))\n\n  def _build_station_dict(self, config_stations):\n    for user, host, max_threads in config_stations:\n      self.stations[user, host] = max_threads\n\n  def _filter_available_stations(self):\n    for user, host in list(self.stations.keys()):\n      if not SSHClient(user, host).reachable():\n        print(user + '@' + host + ' *OFFLINE*')\n        del self.stations[user, host]\n      else:\n        print(user + '@' + host + ' *ONLINE*')\n\n  def get(self):\n    self.free_workers_sem.acquire()\n    self.global_lock.acquire()\n    worker = None\n    for (user_host, available_threads) in self.stations.items():\n      if available_threads > 0:\n        self.stations[user_host] -= 1\n        (user, host) = user_host\n        worker = Worker(user, host)\n        break\n    self.global_lock.release()\n    return worker\n\n  def put(self, worker):\n    self.global_lock.acquire()\n    self.stations[worker.user, worker.host] += 1\n    self.free_workers_sem.release()\n    self.global_lock.release()\n\n  def wait_all(self):\n    for i in range(0, self.free_workers_sem_max):\n      self.free_workers_sem.acquire()\n    print('WorkerPool: waited all workers, semaphore is now on zero.')\n\nclass UnorderedTaskPool:\n  def __init__(self, work_todo):\n    self.count_to_work = len(work_todo)\n    self.count_done = 0\n    self.todo_list = work_todo\n    self.global_lock = threading.Lock()\n\n  def done(self):\n    self.global_lock.acquire()\n    remaining = self.count_to_work - self.count_done\n    self.global_lock.release()\n    return remaining == 0\n\n  def get(self):\n    task = None\n    self.global_lock.acquire()\n    if self.todo_list:\n      task = self.todo_list.pop()\n    self.global_lock.release()\n    return task\n\n  def put_back(self, task):\n    self.global_lock.acquire()\n    self.todo_list.append(task)\n    self.global_lock.release()\n\n  def completed(self, task):\n    self.global_lock.acquire()\n    self.count_done += 1\n    self.global_lock.release()\n\n  def print_stats(self):\n    done = self.count_done\n    total = self.count_to_work\n    percent = 100.0 * done / total\n    print('UnorderedTaskPool: progress {0:.2f}% ({1}/{2})'.format(percent, done, total))\n\ndef wait_a_bit():\n  time.sleep(1)\n\ndef do_work(workers, tasks):\n  while not tasks.done():\n    task = tasks.get()\n    if task is not None:\n      worker = workers.get()\n      worker.assign_task(task)\n      worker.set_pools(workers, tasks)\n      worker.start()\n    else:\n      wait_a_bit()\n    tasks.print_stats()\n  print()\n  workers.wait_all()\n\ndef main():\n  workers = WorkerPool(config.stations)\n  tasks = UnorderedTaskPool(work.todo)\n  do_work(workers, tasks)\n\nif __name__ == '__main__':\n  main()\n","sub_path":"osm/processing/ssh-cluster/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"284858971","text":"# The purpose of this code is to break the components of the atmospheric mass loss calculation into functions\nimport numpy as np\nimport matplotlib.pyplot as pp\nfrom astropy.io import ascii\nfrom astropy.table import Table\n\n\n\n\n# Global parameters\n_bol_luminosity = 1e46\n_potential_r_0 = 3000 # in pc\n_potential_z_0 = 300 # in pc\n_potential_v_0 = 3e-4 # in pc/yr\n\n# The first function we will define is the emission spectrum of the AGN\ndef emission_spec():\n    richards = ascii.read('/Users/Jenna/Documents/Matt Mechtley Quasar Research/Richards_Mean_Quasar_Data.txt')\n    wavelength = 3e14 / 10**richards['LogF']\n    flux = richards['All']\n    flux = 10**flux\n    flux = flux / (10**richards['LogF'])\n    my_table = Table([wavelength, flux], names=['wavelength', 'flux']) \n    my_table['wavelength'].unit = 'microns'\n    my_table['flux'].unit = 'erg / s / Hz'\n    print(my_table)\n    return my_table\n    # Need wavelength and flux columns --> wavelength v. flux before dust & wavelength v. flux after dust\n\n# Calculate flux at planet using luminosity\ndef spec_in_flux(spec_in_luminosity, distance):\n    distance_cm = distance * 3.086e18\n    spec_in_luminosity['flux'] = spec_in_luminosity['flux'] / (4*np.pi*distance_cm**2)\n    return spec_in_luminosity\n\n# The next function we will define is the luminosity (the normalization of the brightness of the spectrum)\ndef luminosity():\n    # Essentially a constant value that we can plug in\n    # Multiply table column by constant\n    # Bolometric correction at 5100 angstroms = 12.17\n\n    return\n\n# The third function we will define is the distance from the quasar as a function of the orbital parameters\ndef r_phi_z_position(current_position, current_velocity, ang_momentum, q, delta_time):\n    # Parameters will include eccentricity of orbit and angular momentum\n    radius, phi, height = current_position # Vector unpacking\n    phi_dot = ang_momentum / radius ** 2\n    vel_r_dot = ang_momentum ** 2 / radius ** 3 \\\n               - radius / (radius ** 2 + height ** 2)\n    vel_z_dot = -height / q / (radius ** 2 + height ** 2)\n\n    # Calculate all new positions from old velocity, then calculate new velocity\n    r_phi_z_dot = np.array((current_velocity[0], phi_dot, current_velocity[1] / q))\n    new_position = current_position + r_phi_z_dot * delta_time\n    # Calculates the change in the position with the time step\n    new_velocity = current_velocity + np.array((vel_r_dot, vel_z_dot)) * delta_time\n\n    return new_position, new_velocity\n\n# The fourth function we will define is the metallicity of the host star system\ndef metallicity_host():\n    # Z component of the original calculation - do we need to manipulate the original equation to get this?\n    # We need to talk to Patrick Young on this one\n    # This will consider photochemistry as well - talk to Ariel Anbar \n\n    return \n\ndef torus_obscuration(position, luminosity, r_in=20, r_out=600, tau_e=10, beta=1.1, gamma=1):\n    # Units for radius: AU; min. 20, max. 600\n    theta_h = np.arccos((1 + 3*luminosity / 10**42.65)**(1 - 2*0.44))\n    # Values for theta_h come from Lusso et al paper and generate the dashed line from figure 19 right panel\n    equatorial_density = r_out**(1-beta) / (1-beta) - r_in**(1-beta) / (1-beta)\n    density_constant = tau_e / equatorial_density\n    x_y_z_position = np.array((position[0]*np.cos(position[1]), position[0]*np.sin(position[1]), position[2]))\n    polar_angle = np.dot(x_y_z_position, (0,0,1)) / np.sqrt(np.dot(x_y_z_position, x_y_z_position))\n\n    if np.arccos(np.abs(polar_angle)) < theta_h:\n        return 0\n    else:\n        return 0.61*density_constant*equatorial_density*np.exp(-gamma*np.abs(polar_angle))\n\n# The last function we will define is the dust obscurity as a function of where the planet lies in the taurus\ndef dust_obscurity(spectrum, a_v):\n    # dust_obscurity will essentially calculate the angle between the planet and the axis of the quasar\n    # k_lam is only valid for UV wavelengths (0.12 - 0.63 microns)\n    spectrum = spectrum.copy()\n    lam = spectrum['wavelength']\n    k_lam = np.zeros_like(lam)\n    uv_opt = (lam < 0.63) & (lam > 0.12)\n    k_lam[uv_opt] = 2.569*(-2.156+(1.509/lam[uv_opt])-(0.198/lam[uv_opt]**2)+(0.011/lam[uv_opt]**3))+4.05\n    opt_nir = (lam >= 0.63) & (lam < 2.2)\n    k_lam[opt_nir] = 2.659*(-1.857+(1.040/lam[opt_nir]))+4.05\n    # X-ray UV eventually\n    # 4.05 is the Calzetti R_V value\n    a_lam = k_lam * a_v / 4.05\n    # Converting attentuation in magnitudes to attenuation in flux\n    spectrum['flux'] = spectrum['flux'] * 10**(-0.4 * a_lam)\n    return spectrum\n\ndef main_loop(): # Change function name? Play around with initial conditions\n    step_size = 1e-3\n    steps = int(30 / step_size)\n    # Initial conditions\n    r_phi_z_0 = np.array((2.67, 0.0, 0.17))\n    vel_0 = np.array((0.0, 0.0))\n    q = _potential_z_0 / _potential_r_0\n    # Angular momentum of the orbit\n    Lz = 2.5\n\n    spectrum = emission_spec()\n\n    # Calculate velocities at t=1/2 (leapfrog integration)\n    pos_half, vel_half = r_phi_z_position(r_phi_z_0, vel_0, Lz, q, step_size / 2.0)\n    # 2.0 rather than 2 because we want a floating point value rather than an integer\n\n    current_position = r_phi_z_0\n    current_velocity = vel_half\n\n    all_positions = np.zeros((steps, 3))\n    all_avs = np.zeros(steps)\n\n    for step in range(steps):\n        current_position, current_velocity = r_phi_z_position(current_position, current_velocity, Lz, q, step_size)\n        current_position_phys = current_position * (_potential_r_0, 1, _potential_z_0) # Redimensionalizing units\n        all_positions[step, :] = current_position_phys\n        all_avs[step] = torus_obscuration(current_position_phys, _bol_luminosity)\n        red_spec = dust_obscurity(spectrum, all_avs[step])\n        flux_spec = spec_in_flux(red_spec, np.sqrt(current_position_phys[0]**2 + current_position_phys[2]**2))\n        # Flux_spec is the thing that actually interacts with the atmosphere\n\n\n    pp.plot(all_positions[:, 0], all_positions[:, 2])\n    pp.xlabel('Radius')\n    pp.ylabel('Disk Height')\n    # No title on plots if you're putting them in a journal\n    pp.show()\n\n# Plot y vs. x\n    x = all_positions[:, 0]*np.cos(all_positions[:, 1])\n    y = all_positions[:, 0]*np.sin(all_positions[:, 1])\n    pp.plot(x, y, label='$\\~L_z = %0.2g$, $q = %0.2g$' % (Lz, q))\n    pp.xlabel('$x$')\n    pp.ylabel('$y$')\n    pp.legend(loc='upper right')\n    pp.title('X/Y Position Plot')\n    pp.show()\n\n# Plot av vs. time\n    steps_myr = np.arange(steps) * step_size * _potential_r_0/_potential_v_0 / 1e6\n    pp.plot(steps_myr, all_avs)\n    pp.xlabel('Time (Myr)')\n    pp.ylabel('$A_V$')\n    pp.show()\n\n# Find zero crossings (in terms of radius and radial velocity)\n# Zero crossings are where the star crosses the zero point in the z-plane (when it moves down); tells us if the star is\n# moving toward or away from the center\n#up_zc_mask = np.diff(np.sign(heights), 1) > 0\n\n# START EDITING HERE\n#plot_title = r'$\\~v_{r,\\tau=0} = %0.2g$, $\\~v_{z,\\tau=0} = %0.2g$,' \\\n             #r'$\\~r_{\\tau=0} = %0.2g$, $\\~z_{\\tau=0} = %0.2g$' % \\\n             #(vel_r0, vel_z0, rad0, height0) #r_phi_z_0, vel_half\n\n# Plot upward zero crossings, r vs. v_r\n#ax.plot(radii[1:][up_zc_mask], vel_rs[1:][up_zc_mask], 'o',\n        #label='$\\~L_z = %0.2g$, $q = %0.2g$' % (Lz, q)) # Might need to fix this too... Check once other calculations\n# are complete\n#ax.set_xlabel('$\\~r$')\n#ax.set_ylabel('$\\~v_r$')\n#ax.legend(loc='lower right')\n#ax.set_title(plot_title)\n#pp.show()\n\n\nif __name__ == \"__main__\":\n    main_loop()\n    # spectrum = emission_spec()\n    # pp.loglog(spectrum['wavelength'], spectrum['flux'], color=\"blue\")\n    # pp.xlabel('Wavelength ($\\mu$m)')\n    # pp.ylabel('Flux (erg / s / Hz)')\n    # spectrum = dust_obscurity(spectrum, 2.0) # a_v here is a free variable\n    # pp.loglog(spectrum['wavelength'], spectrum['flux'], color=\"red\")\n    # pp.xlabel('Wavelength ($\\mu$m)')\n    # pp.ylabel('Flux (erg / s / Hz)')\n    # spectrum = spec_in_flux(spectrum, distance=10) # distance here is also a free variable\n    # pp.loglog(spectrum['wavelength'], spectrum['flux'], color=\"green\")\n    # pp.xlabel('Wavelength ($\\mu$m)')\n    # pp.ylabel(r'Flux ($f_\\nu$, arbitrary)')\n    # pp.show() ","sub_path":"Atmospheric Mass Loss Functions.py","file_name":"Atmospheric Mass Loss Functions.py","file_ext":"py","file_size_in_byte":8210,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"197213541","text":"import logging\nfrom typing import Optional\n\nimport dpkt\nfrom dpkt.dns import DNS\nfrom dpkt.ip import IP\nfrom dpkt.udp import UDP\nfrom munch import Munch\n\nfrom core.configuration.data import ConfigurationData\nfrom core.packet_parsers.base import PacketParserInterface\nfrom core.lib.ip_utils import IpAddrUtils\n\n\nclass DnsPacketParser(PacketParserInterface):\n    def __init__(self, config: ConfigurationData):\n        self.config = config\n        self.ip_utils = IpAddrUtils()\n\n    @staticmethod\n    def load_dns_packet_from_ip_packet(ip_packet: IP) -> Optional[DNS]:\n        try:\n            udp_packet = UDP(ip_packet.data)\n            return DnsPacketParser.load_dns_packet_from_udp_packet(udp_packet)\n\n        except BaseException as ex:\n            logging.warning('Can not extract DNS packet from UDP packet. Error: `%s`', ex)\n            raise ex\n\n    @staticmethod\n    def load_dns_packet_from_udp_packet(udp_packet: UDP) -> Optional[DNS]:\n        try:\n            return DNS(udp_packet.data)\n\n        except Exception as ex:\n            logging.warning('Can not extract DNS packet from UDP packet. Error: `%s`', ex)\n            raise ex\n\n    def extract_data(self, packet: DNS) -> Munch:\n        data = Munch()\n        try:\n            data.dns_type = packet.qr\n            data.dns_op = packet.op\n            data.dns_rcode = packet.rcode\n\n            if data.dns_type == dpkt.dns.DNS_Q:\n                # This is a DNS query\n                data.update(self.extract_data_from_dns_query(packet))\n\n            elif data.dns_type == dpkt.dns.DNS_R:\n                # This is a DNS response\n                data.update(self.extract_data_from_dns_response(packet))\n\n        except BaseException as ex:\n            logging.warning('Unable to extract DNS from `%s`. Error: `%s`', type(packet), ex)\n            raise ex\n\n        return data\n\n    def extract_data_from_dns_query(self, dns_packet: DNS) -> Munch:\n        data = Munch()\n        try:\n            if len(dns_packet.qd) > 1:\n                if self.config.use_numeric_values is True:\n                    data.dns.query_multiple_domains = 1\n                else:\n                    data.dns_query_multiple_domains = True\n\n            data.dns_query_domain = self.config.FieldDelimiter.join([q.name for q in dns_packet.qd])\n            data.dns_query_type = self.config.FieldDelimiter.join([str(q.type) for q in dns_packet.qd])\n            data.dns_query_cls = self.config.FieldDelimiter.join([str(q.cls) for q in dns_packet.qd])\n\n        except BaseException as ex:\n            logging.warning('Unable to extract DNS from `%s`. Error: `%s`', type(dns_packet), ex)\n            raise ex\n\n        return data\n\n    def extract_data_from_dns_response(self, dns_packet: DNS) -> Munch:\n        data = Munch()\n        # Process and get responses based on record types listed in\n        # http://en.wikipedia.org/wiki/List_of_DNS_record_types\n        dns_ans_ip_list = []\n        dns_ans_name_list = []\n        dns_ans_ttl = []\n\n        try:\n            for answer in dns_packet.an:\n                data.dns_ans_type = answer.type\n                if answer.type == dpkt.dns.DNS_CNAME:\n                    data.dns_ans_cname = answer.name\n                    data.dns_ans_cname_ttl = answer.ttl\n\n                elif answer.type == dpkt.dns.DNS_A or answer.type == dpkt.dns.DNS_AAAA:\n                    if hasattr(answer, 'ip'):\n                        dns_ans_ip_list.append(self.ip_utils.inet_to_str(answer.ip))\n                    dns_ans_name_list.append(answer.name)\n                    dns_ans_ttl.append(answer.ttl)\n                # TODO: Handle other types of dns answers:\n                # Ref: https://engineering-notebook.readthedocs.io/en/latest/engineering/dpkt.html#dns-answer\n\n            data.dns_ans_name = self.config.FieldDelimiter.join(dns_ans_name_list)\n            # We are using only max value because in experience ttl is same even if there is separate ttl for each IP\n            # address in DNS response\n            if dns_ans_ttl:\n                data.dns_ans_ttl = max(dns_ans_ttl)\n            else:\n                data.dns_ans_ttl = None\n\n            if self.config.use_numeric_values is True:\n                dns_ans_ip_list = map(self.ip_utils.ip_to_int, dns_ans_ip_list)\n            data.dns_ans_ip = self.config.FieldDelimiter.join([str(ip) for ip in dns_ans_ip_list])\n\n        except Exception as ex:\n            logging.error('Unable to process dns answers packet. Error: `%s`', ex)\n            raise ex\n\n        return data\n","sub_path":"core/packet_parsers/dns_parser.py","file_name":"dns_parser.py","file_ext":"py","file_size_in_byte":4516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"225106059","text":"#!/usr/bin/env python3\n\nimport sys\nimport socket\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport xml.etree.ElementTree as ET\nimport matplotlib as mpl\nfrom geolite2 import geolite2\nfrom mpl_toolkits.basemap import Basemap\nimport matplotlib.pyplot as plt\nimport networkx as nx\n\nclass GUI():\n\tdef __init__(self, data):\n\t\tmpl.rcParams['toolbar'] = 'None'\n\t\tmpl.rcParams.update({'font.size': 14})\n\t\tself.rows = [\"Dns\", \"Minimum latency\", \"Average latency\",\"Maximum latency\", \"errors\"]\n\t\tself.data = data\n\t\tself.cellsText = []\n\t\tself.columns = ()\n\t\tself.corners = (data[\"header\"][\"lowCorner\"], data[\"header\"][\"highCorner\"])\n\n\tdef addGraph(self):\n\t\ta = plt.figure(num=\"QuarksrouteGraph\")\n\t\tself.columns = tuple([(hop[\"responses\"][0][\"ip\"]) for hop in self.data[\"hops\"] if \"ip\" in hop[\"responses\"][0]])\n\t\tminimumLat = [hop[\"responses\"][0][\"min\"] for hop in self.data[\"hops\"] if \"min\" in hop[\"responses\"][0]]\n\t\tmaximumLat = [hop[\"responses\"][0][\"max\"]  for hop in self.data[\"hops\"] if \"max\" in hop[\"responses\"][0]]\n\t\tdiff = [hop[\"responses\"][0][\"max\"] - hop[\"responses\"][0][\"min\"] + (max(maximumLat) / 20) for hop in self.data[\"hops\"] if \"max\" in hop[\"responses\"][0] and \"min\" in hop[\"responses\"][0]]\n\t\tb_color  = ['b'] * len(self.rows)\n\t\tindex = np.arange(len(self.columns))\n\t\tplt.bar(index, diff,  0.3, bottom=minimumLat, color=b_color)\n\t\tplt.subplots_adjust(left=0.09, bottom=0.57, right=0.99, top=0.93)\n\n\tdef addTable(self):\n\t\ta = plt.figure(num=\"QuarksrouteGraph\")\n\t\tif self.columns:\n\t\t\tplt.title('Results for: {}.\\n{} device(s) did not responded'.format(self.data[\"header\"][\"target\"], self.data[\"header\"][\"timeouts\"]))\n\t\t\tself.cellsText.append([hop[\"responses\"][0][\"dns\"][:16] for hop in self.data[\"hops\"]])\n\t\t\tself.cellsText.append([\"{:.2f}\".format(float(hop[\"responses\"][0][\"min\"])) for hop in self.data[\"hops\"]])\n\t\t\tself.cellsText.append([\"{:.2f}\".format(float(hop[\"responses\"][0][\"avg\"])) for hop in self.data[\"hops\"]])\n\t\t\tself.cellsText.append([\"{:.2f}\".format(float(hop[\"responses\"][0][\"max\"])) for hop in self.data[\"hops\"]])\n\t\t\tself.cellsText.append([\"{}\".format(hop[\"responses\"][0][\"errors\"]) for hop in self.data[\"hops\"]])\n\t\t\tc_color  = ['c'] * len(self.rows)\n\t\t\ttable = plt.table(cellText=self.cellsText,\n\t\t\t                      rowLabels=self.rows,\n\t\t\t                      rowColours=c_color,\n\t\t\t                      colLabels=self.columns,\n\t\t\t                      cellLoc='center',\n\t\t\t                      bbox=[0, -1.50, 1., 1.5],\n\t\t\t                      loc='bottom')\n\t\t\tfor cell in table._cells:\n\t\t\t\tif cell[1] != -1:\n\t\t\t\t\ttable._cells[cell].get_text().set_rotation(50)\n\t\t\t\t\ttable._cells[cell].get_text().set_wrap(True)\n\t\t\ttable.auto_set_font_size(False)\n\t\t\ttable.set_fontsize(9)\n\t\t\tmaximumLat = [res[\"max\"]  for hop in self.data[\"hops\"] for hop in self.data[\"hops\"] for res in hop[\"responses\"]]\t\t\n\t\t\tplt.ylabel(\"Latency\")\n\t\t\tplt.yticks(np.arange(0, max(maximumLat) * 1.1, step=max(maximumLat) / 10))\n\t\t\tplt.xticks([])\t\t\n\t\telse:\n\t\t\tplt.title('No results found for: {}'.format(self.data[\"header\"][\"target\"]))\n\n\tdef addMap(self):\n\t\tplt.figure(num=\"QuarksrouteMap\")\n\t\tm=Basemap(llcrnrlon=self.corners[0][0], llcrnrlat=self.corners[0][1],urcrnrlon=self.corners[1][0],urcrnrlat=self.corners[1][1])\n\t\tm.drawmapboundary(fill_color='#A6CAE0', linewidth=0)\n\t\tm.fillcontinents(color='grey', alpha=0.6, lake_color='blue')\n\t\tm.drawcountries(color=\"white\")\n\t\tm.drawcoastlines(linewidth=0.1, color=\"white\")\n\t\thead = ()\n\t\tindex = 0\n\t\tfor hop in self.data[\"hops\"]:\n\t\t\tif hop[\"geo\"]:\n\t\t\t\tif head is not ():\n\t\t\t\t\tm.drawgreatcircle(head[0], head[1], hop[\"geo\"][\"location\"][\"longitude\"], hop[\"geo\"][\"location\"][\"latitude\"], linewidth=2, color='blue')\n\t\t\t\thead = (hop[\"geo\"][\"location\"][\"longitude\"], hop[\"geo\"][\"location\"][\"latitude\"])\n\t\t\t\tm.plot(head[0], head[1], linestyle='none', marker=\"o\", markersize=8, alpha=0.6, c=\"cyan\", markeredgecolor=\"black\", markeredgewidth=1)\n\t\t\t\tplt.annotate(index,  xy=(head[0], head[1] + 2))\n\t\t\t\tindex=index+1\n\n\tdef addTree(self):\n\t\tplt.figure(num=\"QuarksrouteTree\")\n\t\tG = nx.Graph()\n\t\tlightData = []\n\t\tfor hops in self.data[\"hops\"]:\n\t\t\thop = []\n\t\t\tfor resp in hops[\"responses\"]:\n\t\t\t\tif resp[\"ip\"] and not [True for h in hop if h[\"ip\"] == resp[\"ip\"]]:\n\t\t\t\t\thop.append(resp)\n\t\t\tif hop: lightData.append(hop)\n\t\tcpt = 0\n\t\ttmp = {}\n\t\tedgeLabels = {}\n\t\tfor hop in lightData:\n\t\t\tfirst = True\n\t\t\tfor resp in hop:\n\t\t\t\tif not tmp:\n\t\t\t\t\ttmp = resp\n\t\t\t\tif first:\n\t\t\t\t\tif cpt == 0 or cpt == len(lightData) - 1:\n\t\t\t\t\t\tG.add_node(resp[\"ip\"], ends=False, start=True)\n\t\t\t\t\telse:\n\t\t\t\t\t\tG.add_node(resp[\"ip\"], ends=True, start=False,)\n\t\t\t\t\tfirst = False\n\t\t\t\t\tif tmp:\n\t\t\t\t\t\tG.add_edge(resp[\"ip\"], tmp[\"ip\"], path=True, label=\"toto\")\n\t\t\t\t\t\tedgeLabels[(tmp[\"ip\"],resp[\"ip\"])] = resp[\"avg\"]\n\t\t\t\t\t\ttmp = resp\n\t\t\t\telse:\n\t\t\t\t\tG.add_node(resp[\"ip\"], ends=False, start=False,)\n\t\t\t\t\tif tmp:\n\t\t\t\t\t\tG.add_edge(tmp[\"ip\"], resp[\"ip\"], path=False, label=\"toto\")\n\t\t\t\t\t\tedgeLabels[(tmp[\"ip\"],resp[\"ip\"])] = resp[\"avg\"]\n\t\t\tcpt = cpt + 1\n\t\tpos = nx.spring_layout(G,k=0.2)\n\t\tstartNodes = [node[0] for node in G.nodes(data=True) if node[1][\"start\"]]\n\t\tendsNodes = [node[0] for node in G.nodes(data=True) if node[1][\"ends\"] and not node[1][\"start\"]]\n\t\totherNodes = [node[0] for node in G.nodes(data=True) if not node[1][\"ends\"] and not node[1][\"start\"]]\n\t\tpathEdges = [(u, v) for (u, v, d) in G.edges(data=True) if d['path']]\n\t\totherEdges = [(u, v) for (u, v, d) in G.edges(data=True) if not d['path']]\n\t\tnx.draw_networkx_nodes(G, pos, nodelist=endsNodes, node_color='g',node_shape='.')\n\t\tnx.draw_networkx_nodes(G, pos, nodelist=otherNodes, node_color='r', node_shape='.')\n\t\tnx.draw_networkx_nodes(G, pos, nodelist=startNodes, node_color='g', node_shape='s')\n\t\tnx.draw_networkx_edges(G, pos, edgelist=pathEdges, width=1)\n\t\tnx.draw_networkx_edges(G, pos, edgelist=otherEdges, alpha=0.5, edge_color='b', style='dashed')\n\n\t\tpos_higher = {}\n\t\ty_off = 0.06\n\t\tfor k, v in pos.items():\n\t\t\tpos_higher[k] = (v[0], v[1]+y_off)\n\t\tplt.axis('off')\n\t\tnx.draw_networkx_labels(G, pos_higher, font_size=10, font_family='sans-serif')\n\n\n\tdef render(self):\n\t\tself.addMap()\n\t\tself.addTree()\n\t\tself.addGraph()\n\t\tself.addTable()\n\t\tplt.show()\n\ndef parseFile(filename):\n\ttry:\n\t\twith open(filename, \"r\") as file: \n\t\t\troot = ET.parse(file).getroot()\n\texcept IOError as e:\n\t\tprint(e)\n\texcept:\n\t\tprint(\"Your file is probably not a correct XML file\")\n\telse:\n\t\tdata = {\"header\":{}, \"hops\":[]}\n\t\theader = root.find('header')\n\t\tdata[\"header\"][\"numQueries\"] = header.get('numQueries')\n\t\tdata[\"header\"][\"target\"] = header.get('target')\n\t\tfor entry in root.findall('hops/entry'):\n\t\t\thop = {\"responses\":[], \"id\": entry.get(\"id\")}\n\t\t\tresponseData = {\"queries\": []}\n\t\t\tfor response in entry.findall('response'):\n\t\t\t\tresponseData = {\"queries\": []}\n\t\t\t\tresponseData[\"ip\"] = response.get(\"ip\") if response.get(\"ip\") else \"\"\n\t\t\t\tresponseData[\"dns\"] = response.get(\"dns\") if response.get(\"dns\") else \"\"\n\t\t\t\tresponseData[\"errors\"] = response.get(\"errors\") if response.get(\"errors\") else \"\"\n\t\t\t\tqueries = []\n\t\t\t\tfor query in response.findall('queries/query'):\n\t\t\t\t\tqueryData = {}\n\t\t\t\t\tqueryData[\"value\"] = query.get(\"value\")\n\t\t\t\t\tqueryData[\"unit\"] = query.get(\"unit\")\n\t\t\t\t\tqueries.append(queryData)\n\t\t\t\tresponseData[\"queries\"] = queries\n\t\t\t\thop[\"responses\"].append(responseData)\n\t\t\tdata[\"hops\"].append(hop)\n\t\treturn data\n\treturn None\n\ndef getHostAddress():\n\ttarget_host = \"api.ipify.org\"\n\trequest = \"GET / HTTP/1.1\\r\\nHost:%s\\r\\n\\r\\n\" % target_host\n\ttarget_port = 80\n\tclient = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\tclient.settimeout(3.0) \n\ttry:\n\t\tclient.connect((target_host,target_port))  \n\t\tclient.send(request.encode())  \n\t\tresponse = client.recv(4096)\n\texcept:\n\t\treturn None\n\tfinally:\n\t\tclient.close()\n\treturn response.decode().split(\"\\r\\n\\r\\n\")[1]\n\ndef calcGeopos(data):\n\tlowCorner = highCorner = (None, None)\n\tfor hop in data[\"hops\"]:\n\t\tif hop[\"geo\"] is not None:\n\t\t\tlatitude = hop[\"geo\"][\"location\"][\"latitude\"]\n\t\t\tlongitude = hop[\"geo\"][\"location\"][\"longitude\"]\n\t\t\tif lowCorner[0] is None or longitude < lowCorner[0]:\n\t\t\t\tlowCorner = (longitude, lowCorner[1])\n\t\t\tif lowCorner[1] is None or latitude < lowCorner[1]:\n\t\t\t\tlowCorner = (lowCorner[0], latitude)\n\t\t\tif highCorner[0] is None or longitude > highCorner[0]:\n\t\t\t\thighCorner = (longitude, highCorner[1])\n\t\t\tif highCorner[1] is None or latitude > highCorner[1]:\n\t\t\t\thighCorner = (highCorner[0], latitude)\n\tdata[\"header\"][\"lowCorner\"] = (lowCorner[0] + ((-180 + lowCorner[0]) / 5), lowCorner[1] + ((-85 + lowCorner[1]) / 1.5) )\n\tdata[\"header\"][\"highCorner\"] = (highCorner[0] + ((180 - highCorner[0]) / 5),  highCorner[1] + ((85 - highCorner[1]) / 1.5)) \n\treturn data\n\ndef findTimeouts(data):\n\tcpt = 0\n\tfor hop in data[\"hops\"]:\n\t\tfor response in hop[\"responses\"]:\n\t\t\tif not response[\"ip\"] and not response[\"dns\"] and not response[\"queries\"]: cpt = cpt + 1\n\tdata[\"header\"][\"timeouts\"] = str(cpt)\n\treturn data\n\ndef calcStats(data):\n\tdata = findTimeouts(data)\n\t#data[\"hops\"] = [hop for hop in data[\"hops\"] if hop[\"ip\"]]\n\treader = geolite2.reader()\n\tfor hop in data[\"hops\"]:\n\t\thop[\"geo\"] = None\n\t\tif hop[\"id\"] == \"1\":\n\t\t\thop[\"geo\"] = reader.get(getHostAddress()) \n\t\telif hop[\"responses\"][0][\"ip\"]:\n\t\t\thop[\"geo\"] = reader.get(hop[\"responses\"][0][\"ip\"])\n\t\tfor response in hop[\"responses\"]:\n\t\t\tresponse[\"min\"] = response[\"delta\"] = response[\"max\"] = response[\"avg\"] = response[\"unit\"] = sum = 0.\n\t\t\tfor query in response[\"queries\"]:\n\t\t\t\tif response[\"max\"] == 0 or float(query[\"value\"]) > response[\"max\"]: response[\"max\"] = float(query[\"value\"])\n\t\t\t\tif response[\"min\"] == 0 or float(query[\"value\"]) < response[\"min\"]: response[\"min\"] = float(query[\"value\"])\n\t\t\t\tsum = sum + float(query[\"value\"])\n\t\t\t\tresponse[\"unit\"] = query[\"unit\"]\n\t\t\tif len(response[\"queries\"]) > 0: response[\"avg\"] = \"{0:.2f}\".format(sum / len(response[\"queries\"]))\n\t\t\tresponse[\"delta\"] = (response[\"max\"] - response[\"min\"])  / 2\n\treturn(calcGeopos(data))\n\ndef main(filename):\n\tdata = parseFile(filename)\n\tif data is not None:\n\t\tdata = calcStats(data)\t\n\t\tgui = GUI(data)\n\t\tgui.render()\n\nif __name__ == \"__main__\":\n\tif len(sys.argv) < 2:\n\t\tprint(\"Usage: {} ./results.xml\".format(sys.argv[0]))\n\telse:\n\t\tmain(sys.argv[1])\n","sub_path":"analyse.py","file_name":"analyse.py","file_ext":"py","file_size_in_byte":10109,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"52825481","text":"from Model import Model\r\nfrom scipy.integrate import solve_ivp\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom inspect import isfunction\r\n\r\nclass GeneralModel(Model):\r\n    def __init__(self, times, B, A, K, iv_list, input_fluxes, xi=1):\r\n        self.times = times\r\n        self.B = B\r\n        self.A = A\r\n        self.K = K\r\n        self.iv_list = iv_list\r\n        self.input_fluxes = input_fluxes\r\n        self.xi = xi\r\n        self.pool_n = self.get_pool_n()\r\n\r\n        self.Y = self.ode_solver()\r\n        self.create_headers()\r\n        self.df = self.get_df()\r\n        self.get_c_input()\r\n        self.get_diagnostic_variables()\r\n\r\n    def ode_solver(self):\r\n        t = [self.times[0], self.times[-1]]\r\n        y = solve_ivp(self.right_hand_equation, t, self.iv_list, t_eval = self.times, vectorized = True)\r\n        return y\r\n\r\n    def get_input(self, t, y):\r\n        idx = (np.abs(self.times - t)).argmin()\r\n        if self.times[idx] > t:\r\n            idx = idx - 1\r\n\r\n        if isfunction(self.input_fluxes):\r\n            self.tmp_input_fluxes = self.input_fluxes(t, y)\r\n        else:\r\n            if type(self.input_fluxes) == np.ndarray:\r\n                self.tmp_input_fluxes = self.input_fluxes[idx]\r\n            else:\r\n                self.tmp_input_fluxes = self.input_fluxes\r\n\r\n        if isfunction(self.B):\r\n            self.tmp_B = self.B(t, y)\r\n        else:\r\n            if type(self.B) == np.ndarray:\r\n                if self.B.shape[1] != 1 and self.B.shape[2] != 1:\r\n                    self.tmp_B = self.B[idx].reshape(self.pool_n, 1)\r\n                else:\r\n                    self.tmp_B = self.B\r\n\r\n        if isfunction(self.A):\r\n            self.tmp_A = self.A(t, y)\r\n        else:\r\n            self.tmp_A = self.A\r\n\r\n        if isfunction(self.K):\r\n            self.tmp_K = self.K(t, y)\r\n        else:\r\n            self.tmp_K = self.K\r\n\r\n        if isfunction(self.xi):\r\n            self.tmp_xi = self.xi(t, y)\r\n        else:\r\n            if type(self.xi) == np.ndarray:\r\n                self.tmp_xi = self.xi[idx]\r\n            else:\r\n                self.tmp_xi = self.xi\r\n\r\n    def right_hand_equation(self, t, y):\r\n        self.get_input(t, y)\r\n        dydt = np.multiply(self.tmp_B, self.tmp_input_fluxes) + np.matmul(np.matmul(self.tmp_xi * self.tmp_A, self.tmp_K), y)\r\n        return dydt\r\n\r\n    def get_x(self):\r\n        return self.Y.y\r\n\r\n    def get_df(self):\r\n        res = self.Y.y.T\r\n        df = pd.DataFrame(res)\r\n        df.columns = self.create_headers()\r\n        return df\r\n\r\n    def write_output(self, filename):\r\n        self.df.to_csv(filename, index=False)\r\n\r\n    def get_diagnostic_variables(self):\r\n\r\n        carbon_storage_capacity = []\r\n        carbon_storage_potential = []\r\n        residence_time = []\r\n        carbon_storage = []\r\n        baseline_residence_time = []\r\n\r\n        dimensions = self.Y.y.T.shape\r\n        X = np.zeros(dimensions)\r\n        Xc = np.zeros(dimensions)\r\n        Xp = np.zeros(dimensions)\r\n        \r\n        for i in range(0, len(self.times)):\r\n\r\n            t = self.times[i]\r\n            Y = self.Y.y\r\n            y = Y[:, i]\r\n            y = y.reshape([y.shape[0], 1])\r\n\r\n            dydt = self.right_hand_equation(t, y)\r\n            matrix_AK = np.matmul(self.tmp_xi * self.tmp_A, self.tmp_K)\r\n            inverse_AK = np.linalg.inv(matrix_AK)\r\n            matrix_Residence = np.matmul(-inverse_AK, self.tmp_B)\r\n            Residence_time = np.sum(matrix_Residence)\r\n\r\n            baseline_matrix_AK = np.matmul(self.tmp_A, self.tmp_K)\r\n            baseline_inverse_AK = np.linalg.inv(baseline_matrix_AK)\r\n            matrix_baseline_Residence = np.matmul(-baseline_inverse_AK, self.tmp_B)\r\n            baseline_Residence_time = np.sum(matrix_baseline_Residence)\r\n\r\n            Xc[i] = np.multiply(matrix_Residence, self.tmp_input_fluxes).reshape(dimensions[1])\r\n            X[i] = y.reshape(dimensions[1])\r\n            Xp[i] = -np.matmul(inverse_AK, dydt).reshape(dimensions[1])\r\n\r\n            carbon_storage_capacity.append(np.sum(np.multiply(matrix_Residence, self.tmp_input_fluxes)))\r\n            carbon_storage_potential.append(-np.sum(np.matmul(inverse_AK, dydt)))\r\n            residence_time.append(Residence_time)\r\n            carbon_storage.append(np.sum(y))\r\n            baseline_residence_time.append(np.sum(baseline_Residence_time))\r\n\r\n        self.df[\"Tres\"] = residence_time\r\n        self.df[\"X\"] = carbon_storage\r\n        self.df[\"Xc\"] = carbon_storage_capacity\r\n        self.df[\"Xp\"] = carbon_storage_potential\r\n        self.df[\"Tbres\"] = baseline_residence_time\r\n        self.df[\"xi\"] = self.xi\r\n\r\n        self.Xc = pd.DataFrame(Xc)\r\n        self.X = pd.DataFrame(X)\r\n        self.Xp = pd.DataFrame(Xp)\r\n\r\n    def sasu_spinup(self):\r\n        #Xss = -(AK)^(-1)BU\r\n        dimensions = self.Y.y.T.shape\r\n        X = np.zeros(dimensions)\r\n\r\n        for i in range(0, len(self.times)):\r\n            t = self.times[i]\r\n            Y = self.Y.y\r\n            y = Y[:, i]\r\n            y = y.reshape([y.shape[0], 1])\r\n            self.get_input(t, y)\r\n            dydt = 0\r\n            matrix_AK = np.matmul(self.tmp_xi * self.tmp_A, self.tmp_K)\r\n            inverse_AK = np.linalg.inv(matrix_AK)\r\n            matrix_BU = np.multiply(self.tmp_B, self.tmp_input_fluxes)\r\n            X[i] = np.matmul(-inverse_AK, matrix_BU).reshape(dimensions[1])\r\n\r\n        col_headers = self.create_headers()\r\n        for i in range(0, dimensions[1]):\r\n            self.df[col_headers[i]] = X[:, i]\r\n\r\n    def create_headers(self):\r\n        col_headers = []\r\n        rows = self.Y.y.shape[0]\r\n        for i in range(0, rows):\r\n            col_headers.append(\"X\" + str(i+1))\r\n        return col_headers\r\n\r\n    def get_x_df(self):\r\n        return self.df\r\n\r\n    def get_pool_n(self):\r\n        return len(self.iv_list)\r\n\r\n    def get_c_input(self):\r\n        c_input = []\r\n        if type(self.input_fluxes) == np.ndarray or type(self.input_fluxes) == list:\r\n            c_input = self.input_fluxes\r\n        if isfunction(self.input_fluxes):\r\n            for i in range(0, len(self.times)):\r\n                t = self.times[i]\r\n                Y = self.Y.y\r\n                y = Y[:, i]\r\n                y = y.reshape([y.shape[0], 1])\r\n                c_input.append(self.input_fluxes(t, y))\r\n        else:\r\n            c_input = self.input_fluxes\r\n        self.df[\"C_input\"] = c_input\r\n        headers = [\"C_input\"] + self.create_headers()\r\n        self.df = self.df[headers]","sub_path":"Source_code/unit_2/GeneralModel.py","file_name":"GeneralModel.py","file_ext":"py","file_size_in_byte":6450,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"561294168","text":"import warnings\nwarnings.filterwarnings(\"ignore\")\nimport pandas as pd\nimport numpy as np\nimport awswrangler as wr\nfrom flask import Flask, render_template, request, session, url_for, redirect, jsonify\nimport os\n\napp = Flask(__name__)\nBUCKET_NAME = os.getenv(\"BUCKET_NAME\", \"covid19-analysis-test\")\nDATABASE_NAME = os.getenv(\"DATABASE_NAME\", \"covid19-database\")\n\n@app.route(\"/process\", methods=[\"GET\", \"POST\"])\ndef process():\n    if request.method == 'GET':\n        return jsonify({\"message\": \"POST request expected\"})\n    json_data = request.json\n\n    file_name = json_data[\"file_name\"]\n    # Change for read data from S3 using aws wrangler\n    try:\n        print(\"[INFO] Reading Data from S3\")\n        path1 = f\"s3://{BUCKET_NAME}/to_process/{file_name}\"\n        df_covid = wr.s3.read_csv([path1])\n        # data_path = f\"./{file_name}\"\n        # df_covid = pd.read_csv(data_path, sep=',')\n    except Exception as e:\n        print(f\"[ERROR] {e}\")\n        raise e\n    \n    print(\"[INFO] Building new Dataset\")\n    # Get only the lastest cases of Covid from each State\n    df_recent_cases = df_covid.loc[df_covid['is_last'] == True]\n    df_state_dataset_last = df_recent_cases.loc[df_recent_cases[\"place_type\"] == \"state\"]\n    state_cases_death = df_state_dataset_last[['state', 'confirmed', 'deaths']]\n\n    # Creating Dataset from dataframe cases\n    df_datetime_cases = df_covid.loc[df_covid['is_last'] == False]\n    df_datetime_cases['date'] = pd.to_datetime(df_datetime_cases['date'], format='%Y-%m-%d')\n    df_datetime_cases_state = df_datetime_cases.loc[df_datetime_cases[\"place_type\"] == \"state\"]\n    df_datetime_cases_state = df_datetime_cases_state[['date','state', 'confirmed', 'deaths']]\n\n\n    # Upload to Amazon S3 and create Athena Table with AWS Wrangler\n    # Storing data on Data Lake\n\n    if DATABASE_NAME not in wr.catalog.databases().values:\n        wr.catalog.create_database(DATABASE_NAME)\n    \n    print(\"[INFO] Writing data in S3\")\n    wr.s3.to_parquet(\n        df=state_cases_death,\n        path=f\"s3://{BUCKET_NAME}/processed/agreggated-table\",\n        dataset=True,\n        database=DATABASE_NAME,\n        table=\"covid-brazil-state\",\n        mode=\"overwrite\"\n    )\n\n    wr.s3.to_parquet(\n        df=df_datetime_cases_state,\n        path=f\"s3://{BUCKET_NAME}/processed/dataframe-table\",\n        dataset=True,\n        database=DATABASE_NAME,\n        table=\"covid-brazil-datetime\",\n        mode=\"overwrite\"\n    )\n\n    return jsonify({\"message\" : f\"File {file_name} processed\"})\n\nif __name__ == \"__main__\":\n    app.run(debug=True, host='0.0.0.0')","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"93460011","text":"import ontology_wrapper\nimport file_interface\nimport time\nimport threading\nimport sys\n\n# The executable file needs to have extra quotationmarks to be able to be executed by the os.system command\nGRAPH_EXECUTABLE=r'\"C:\\Users\\Dan\\AppData\\Local\\GraphDB Free\\GraphDB Free.exe\"'\nGRAPH_URL=\"http://192.168.0.19:7200/repositories/company_ownership_ontology\"\n\n# This is the utility interface for access to the ontology outside of the searches\ngraphInterface=ontology_wrapper.Interface(GRAPH_URL)\ngraphInterface.connectToGraph(graphExecutable=GRAPH_EXECUTABLE, graphURL=GRAPH_URL)\n\n# The fringe is a nested dictionary of expanded and unexpanded nodes, it is implemented as a dictionary of\n# string keys and another dictionary stating the parent and whether it has been explored\nnode = {\"parentName\":\"\",\n        \"companyID\" : \"\"}\nfringe={\"nodeName\":node}\n\n# The path is the list of people and companies used to get from the starting node to the goal node\n# The path will be ordered such that it is a list of three strings, as shown below:\n# Index     DirectorName        Company     Child's Company\n# 0         SULLIVAN DANIEL J   CTG         SNDR\n# 1         SULLIVAN DANIEL J   SNDR        \n# 1         ....                ...         ...\npossiblePath=[]\npaths=[]\n\nqueriesMade = 0\n\n# Create a threading lock for use when the search needs to query the ontology\nqueryLock = threading.Lock()\nkillLock = threading.Lock()\nkillRequest = 0\n\ndef constructPath(halfwayNode):\n    parent = fringe[halfwayNode[\"name\"]][\"parentName\"]\n    while parent in fringe.keys():\n        possiblePath.append([ parent, possiblePath[-1][2], fringe[parent][\"parentCompany\"] ])\n        parent = fringe[parent][\"parentName\"]\n    possiblePath[-1][1] = possiblePath[-2][2]\n\ndef breadthFirstSearch(ontologyInterface, currentNode={\"name\":\"N/A\",\"compnayID\":\"N/A\"}, goalNode=\"N/A\", manageFringe=False):\n    \"\"\"\n    This function implements a breadth first search on the ontology\n    \n    Keyword Arguments:\n        currentNode {dict} -- the name of the starting node (default: {{str:str,str:str,str:str}})\n        goalNode {str} -- the name of the goal node (default: {str})\n        manageFringe {bool} -- parameter used to determine if this search will add the unexpanded nodes to the fringe used in the bidierctional search (default: {bool})\n\n    Returns:\n        results {bool} -- returns True if solution found, returns False if there is no bath between the nodes\n    \"\"\"\n    global killRequest\n    global possiblePath\n    global paths\n    global queriesMade\n    # Add the starting Node to the fringe\n    if manageFringe:\n        fringe[currentNode[\"name\"]] = {\"parentName\": \"\",\n                            \"parentCompany\": \"N/A\"}\n\n    # Check if the starting node is the goal node\n    if currentNode[\"name\"] == goalNode:\n        return True\n    frontier=[currentNode]\n    explored = {}\n\n    while True:\n        # Check if the other tread has finished the search\n        if killRequest == 1 or killLock.locked():\n            sys.exit()\n\n        # Check if we have explored all available nodes\n        if frontier==[]:\n            return False\n        currentNode=frontier.pop()\n        explored[currentNode[\"name\"]] = currentNode\n        \n        # Acquire the ontology lock and return all the directors connected to the current node by one intermediate company\n        queryLock.acquire()\n        children=ontologyInterface.queryOntology(currentNode)\n        queriesMade+=2\n        queryLock.release()\n\n        # Add the children to the fringe as they are discovered\n        if manageFringe:\n            for child in children:\n                fringe[child[\"name\"]] = {\"parentName\":currentNode[\"name\"],\n                                        \"parentCompany\": child[\"companyID\"]}\n        \n\n        # For all of the children of the currently selected node, check if it is the goal node\n        for child in children:\n            child[\"parent\"] = currentNode[\"name\"]\n            # Only check the child nodes if we have not previously explored them\n            if not(child[\"name\"] in frontier or child[\"name\"] in explored.keys()):\n                if child[\"name\"] == goalNode:\n                    # The goal node has been found, create the path from the goal node back to the start node\n                    possiblePath.insert(0, [child[\"name\"], child[\"companyID\"], \"N/A\"])\n                    parent = explored[child[\"parent\"]]\n                    while parent != None:\n                        possiblePath.insert(0, [parent[\"name\"], parent[\"companyID\"], possiblePath[0][1]])\n                        if \"parent\" in (explored[parent[\"name\"]].keys()):\n                            parent = explored[parent[\"parent\"]]\n                        else:\n                            parent=None\n                    return True\n\n                # Check if the current node is in the fringe, if true then construct the path from the halfway point and return true\n                elif child[\"name\"] in fringe.keys() and manageFringe == False:\n                    # Kill the thread if we have already found a path\n                    if len(paths) > 1:\n                        # Flag the kill request for the other thread\n                        killLock.acquire()\n                        killRequest = 1\n                        killLock.release()\n\n                    # Construct the end of the path from the halfway node\n                    possiblePath.insert(0, [currentNode[\"name\"], currentNode[\"companyID\"], fringe[child[\"name\"]][\"parentCompany\"] ] )\n                    constructPath(child)\n                    possiblePath[0][2] = possiblePath[1][1] \n\n                    # Construct the path from the halfway point back to the start node\n                    parent = explored[child[\"parent\"]]\n                    while parent != None:\n                        possiblePath.insert(0, [parent[\"name\"], parent[\"companyID\"], possiblePath[0][1]])\n                        if \"parent\" in (explored[parent[\"name\"]].keys()):\n                            parent = explored[parent[\"parent\"]]\n                        else:\n                            parent=None\n                    paths.append(possiblePath)\n                    possiblePath = []\n                \n                # If not the goal node, add the child to the front of the frontier\n                frontier.insert(0, child)\n\ndef recursiveDLS(ontologyInterface, currentNode={\"name\":\"N/A\",\"compnayID\":\"N/A\"}, goalNode=\"N/A\", limit=0, manageFringe=False):\n    \"\"\"\n    Implement the recursive function of a deepening search\n    \n    Arguments:\n        currentNode {dict} -- Denotes the name of the current node selected for expansion in the search (default: {{str:str, str:str}})\n        goalNode {str}    -- Denotes the name of the goal node of the search (default: {str})\n        limit {int}       -- The depth limit (how many layers down of expansion from this node left) (default: {int})\n        search {bool}     -- Denotes which of the two searches being implemented we are in, used to know\n                             whether to add to the fringe or not (default: {bool})\n    \n    Returns:\n        results {bool/string} -- returns whether the search was successful, the search failed or reached a cutoff point\n    \"\"\"\n    # Check if the other tread has finished the search\n    global killRequest\n    global possiblePath\n    global paths\n    global queriesMade\n    if killRequest == 1 or killLock.locked():\n        sys.exit()\n\n    # Check if the current node is the goal node\n    if currentNode[\"name\"] == goalNode:\n        possiblePath.insert(0, [currentNode[\"name\"], currentNode[\"companyID\"],\"N/A\"])\n        return True\n\n    # Check if the current node is in the fringe, if true then construct the path from the halfway point and return true\n    elif currentNode[\"name\"] in fringe.keys() and manageFringe == False:\n        if len(paths) > 1:\n            # Flag the kill request for the other thread\n            killLock.acquire()\n            killRequest = 1\n            killLock.release()\n\n        # Construct the end of the path from the other search\n        possiblePath.insert(0, [currentNode[\"name\"], currentNode[\"companyID\"], fringe[currentNode[\"name\"]][\"parentCompany\"] ] )\n        constructPath(currentNode)\n        possiblePath[0][2] = possiblePath[1][1]\n        paths.append(possiblePath)\n        possiblePath=[]\n\n    # Check if we are at the cutoff\n    elif limit==0:\n        return \"cutoff\"\n    else:\n        cuttoffOccurred=False\n        # The goal node has not yet been found and we are within the current set limit so we\n        # iterate deeper, if there are no children then cutoff never occurred --> failure is returned\n        queryLock.acquire()\n        children=ontologyInterface.queryOntology(currentNode)\n        queriesMade+=2\n        queryLock.release()\n\n        # Add the children to the fringe as they are discovered\n        if manageFringe:\n            for child in children:\n                fringe[child[\"name\"]] = {\"parentName\":currentNode[\"name\"],\n                                        \"parentCompany\": child[\"companyID\"]}\n\n        # Process the discovered children\n        for child in children:\n            result=recursiveDLS(ontologyInterface, child, goalNode, limit-1, manageFringe)\n            # If the child is beyond the depth limit\n            if result==\"cutoff\":\n                cuttoffOccurred=True\n\n            # If the child is the goal node, as the recursive function propagates up we add the current node to the front of the path\n            elif result==True:\n                possiblePath.insert(0, [currentNode[\"name\"], currentNode[\"companyID\"], possiblePath[0][1]])\n                return result\n\n        # If the goal node has not been found within the depth limit\n        if cuttoffOccurred:\n            return \"cutoff\"\n\n        # If the goal has not been found and we are within the depth limit\n        else:\n            return False\n\ndef iterativeDeepening(ontologyInterface, startNode=\"N/A\", goalNode=\"N/A\", maxDepth=15, manageFringe=False):\n    \"\"\"\n    This function is used to implement a iterative deepening search on the ontology, \n    up to the maximum depth of the ontology.\n    \n    Arguments:\n        startNode {str} -- Denotes the name of the starting node of the search (default: {str})\n        goalNode {str}  -- Denotes the name of the goal node of the search (default: {str})\n        maxDepth {int}  --  The maximum depth of the ontology, retrieved from the ontology connections file (default: {int})\n        manageFringe {bool}  --  Boolean variable used to indicate which of the two searches is currently being run (default: {bool})\n    \n    Returns:\n        result {boolean} -- Returns whether or not the search was successful, if it was then the Path list will contain the optimal path\n    \"\"\"\n    # If this search is managing the fringe, add the starting node to the fringe\n    if manageFringe:\n        fringe[startNode] = {\"parentName\": \"\",\n                            \"parentCompany\": \"N/A\"}\n\n    startNode={\"name\":startNode, \"companyID\": \"N/A\"}\n    for depth in range(1, maxDepth):\n        result = recursiveDLS(ontologyInterface, startNode, goalNode, depth, manageFringe)\n        if result != \"cutoff\":\n            return result\n        # Need to reset the expanded companies at each iteration\n        ontologyInterface.resetExpandedCompanies()\n\ndef calculateCost(node={\"name\":\"N/A\",\"compnayID\":\"N/A\"}, depth=0):\n    \"\"\"\n    This function calculates the estimated cost of the node that it is given, based on \n    the number of intermediaries between it and the starting node and the connectivity \n    of the node. This function uses the utility graph interface, so as not to interfere\n    with the other searches expanded companies and filtering in place\n    \n    Keyword Arguments:\n        node {dict} --  the name and company ID of the current node selected for cost \n                        estimation (default: {{str:str, str:str}})\n        depth {int} --  The current depth of the search (default: {int})\n    \n    Returns:\n        int -- The estimated cost of the node\n    \"\"\"\n    global queriesMade\n    # Acquire the ontology lock and get the connectivity of the next node\n    queryLock.acquire()\n    graphInterface.resetExpandedCompanies()\n    connections=len(graphInterface.queryOntology(node))\n    queriesMade+=2\n    queryLock.release()\n    \n    depthFactor = (1 - depth/6) if depth < 6 else 1\n    connectionFactor = connections/50 \n    cost = 1 - 0.6*connectionFactor + 0.4*depthFactor\n    return cost\n\ndef RBFS(ontologyInterface, currentNode={\"name\":\"N/A\",\"companyID\":\"N/A\"}, goalNode=\"N/A\", fLimit=15, depth=0, parentCompanies=[], manageFringe=False):\n    \"\"\"\n    Implement the recursive function of the best first search\n    \n    Keyword Arguments:\n        currentNode {dict}  --  Denotes the name of the current node selected for \n                                expansion in the search (default: {{str:str, str:str}})\n        goalNode {str}      --  Denotes the name of the goal node of the search (default: {str})\n        fLimit {int}        --  The maximum cost of the node we are willing to expand (default: {int})\n        manageFringe {bool} --  Boolean variable used to indicate which of the two searches is currently being run (default: {bool})\n\n    Returns:\n        result {boolean} -- Returns whether or not the search was successful, if it was \n                            then the Path list will contain the optimal path between the nodes\n    \"\"\"\n    # Check if the other tread has finished the search\n    global killRequest\n    global possiblePath\n    global paths\n    global queriesMade\n    if killRequest == 1 or killLock.locked():\n        sys.exit()\n\n    # Check if the currentNode is the goal node\n    if currentNode[\"name\"] == goalNode:\n        possiblePath.insert(0, [currentNode[\"name\"], currentNode[\"companyID\"], \"N/A\"] )\n        return True\n    # Check if the current node is in the fringe, if true then construct the path \n    # from the halfway point and return true\n    elif currentNode[\"name\"] in fringe.keys() and manageFringe == False:\n        # Flag the kill request for the other thread\n        killLock.acquire()\n        killRequest = 1\n        killLock.release()\n\n        # Construct the end of the path from the other search\n        possiblePath.insert(0, [currentNode[\"name\"], currentNode[\"companyID\"], fringe[currentNode[\"name\"]][\"parentCompany\"] ] )\n        constructPath(currentNode)\n        possiblePath[0][2] = possiblePath[1][1]\n        paths.append(possiblePath)\n        possiblePath=[]\n    \n    newList = parentCompanies[:]\n    newList.append(currentNode[\"companyID\"])\n    ontologyInterface.setExpandedCompanies(newList)\n    # Acquire the ontology lock and return all the people connected to the current \n    # node by one intermidiate company\n    queryLock.acquire()\n    successors=ontologyInterface.queryOntology(currentNode)\n    queriesMade+=2\n    queryLock.release()\n    depth += 1\n    if successors == []:\n        return False, 1000000\n    \n    # Set the cost of the successors to the maximum between the cost of the current \n    # node and the sum of the cost so far (i.e. the depth) and their estimated cost\n    for s in successors:\n        s[\"cost\"] = max(currentNode[\"cost\"], depth+calculateCost(s, depth))\n        # If this search is required to manage the fringe, add all of the successors \n        # to the fringe as they are generated\n        if manageFringe:\n            fringe[s[\"name\"]] = {\"parentName\":currentNode[\"name\"],\n                                \"parentCompany\": s[\"companyID\"]}\n\n    # Sort the successors into cost order\n    successors=sorted(successors, key=lambda x: (x[\"cost\"]))\n\n    # Check if cost of all children is greater than the fLimit\n    if successors[0][\"cost\"] > fLimit:\n        return False, successors[0][\"cost\"]\n\n    best=successors.pop(0)\n    alternative=successors[0]\n    while True:\n        # Recursively explore the graph\n        result = RBFS(ontologyInterface, best, goalNode, min(fLimit, alternative[\"cost\"]), depth, newList, manageFringe)\n        # If the goal node has been found, generate the path as the search ascends\n        #  the recursion calls\n        if result == True:\n            possiblePath.insert(0, [currentNode[\"name\"], currentNode[\"companyID\"], possiblePath[0][1]] )\n            return True\n        elif result[1] != 1000000:\n            # Set best node cost to the cost of their children\n            best[\"cost\"] = result[1]\n            successors.append(best)\n            successors=sorted(successors, key=lambda x: (x[\"cost\"]))\n        best = successors.pop(0)\n        alternative=successors[0]\n\ndef recursiveBestFirstSearch(ontologyInterface, startName=\"N/A\", goalName=\"N/A\", fLimit=100, manageFringe=False):\n    \"\"\"\n    This function implements a version of the best first search algorithm, the base cost is 1 as there is no actual distance between any node and the goal node\n    \n    Keyword Arguments:\n        startNode {str} -- Denotes the name of the starting node of the search (default: {str})\n        goalNode {str} -- Denotes the name of the goal node of the search (default: {str})\n        fLimit {int} -- The Maximum cost of a node that we are willing to expand (default: {int})\n        manageFringe {bool} -- Boolean variable used to indicate if this search should be adding unexplored nodes to the fringe (default: {bool})\n\n    Returns:\n        result {boolean} -- Returns whether or not the search was successful, if it was then the Path list will contain the optimal path\n    \"\"\"\n    # If this search is managing the fringe, add the starting node to the fringe\n    if manageFringe:\n        fringe[startName] = {\"parentName\": \"\",\n                            \"parentCompany\": \"N/A\"}\n    initialNode = {\"name\":startName, \"companyID\":\"N/A\"}\n    initialNode[\"cost\"] = 0\n    return RBFS(ontologyInterface, initialNode, goalName, fLimit=fLimit, depth=0, manageFringe=manageFringe)\n\ndef bidirectionalSearch():\n    \"\"\"\n    The purpose of this function is to run two searches concurrently, one starting from the start node, the other from\n    the goal node. With an aim to meet in the middle, in order to reduce the execution time of the search.\n\n    At least one search must manage a fringe.\n    \"\"\"\n    global possiblePath\n    global paths\n    global killRequest\n    global fringe\n    global queriesMade\n    ontology_1=ontology_wrapper.Interface(GRAPH_URL)\n    ontology_2=ontology_wrapper.Interface(GRAPH_URL)\n    totalTime = 0\n\n    startName = \"Pyle Robert D\"\n    goalNames = [\"Wallace Mark E\",\n            \"WOLFE ROBERT H\",\n            \"WOOD PHOEBE A\",\n            \"WHITE MILES D\",\n            \"HERNANDEZ ENRIQUE JR\",\n            \"FABRIKANT CHARLES\",\n            \"WEBSTER STEVEN A\"]\n    startNode={\"name\" : startName,\"companyID\" : \"N/A\"}\n    for i in range(0, len(goalNames)):\n        goalNode={\"name\" : goalNames[i],\"companyID\" : \"N/A\"}\n        # Start running two searches concurrently, with each search starting from the opposite end of the relationship\n        # Dual Iterative deepening searches\n        # searchA = \"IDS\"\n        # searchB = \"IDS\"\n        # t1 = threading.Thread(target=iterativeDeepening, args=[ontology_1, startName, goalNames[i], 10, False])\n        # t2 = threading.Thread(target=iterativeDeepening, args=[ontology_2, goalNames[i], startName, 10, True])\n        # Dual breadth first searches\n        # searchA = \"BFS\"\n        # searchB = \"BFS\"\n        # t1 = threading.Thread(target=breadthFirstSearch, args=[ontology_1, startNode, goalNames[i], False])\n        # t2 = threading.Thread(target=breadthFirstSearch, args=[ontology_2, goalNode, startName, True])\n        # Dual best first searches\n        # searchA = \"RBFS\"\n        # searchB = \"RBFS\"\n        # t1 = threading.Thread(target=recursiveBestFirstSearch, args=[ontology_1, startName, goalNames[i], 10, False])\n        # t2 = threading.Thread(target=recursiveBestFirstSearch, args=[ontology_2, goalNames[i], startName, 10, True])\n        # 1 IDS 1 BFS\n        # searchA = \"BFS\"\n        # searchB = \"IDS\"\n        # t1 = threading.Thread(target=iterativeDeepening, args=[ontology_1, startName, goalNames[i], 10, False])\n        # t2 = threading.Thread(target=breadthFirstSearch, args=[ontology_2, goalNode, startName, True])\n        # searchA = \"IDS\"\n        # searchB = \"BFS\"\n        # t1 = threading.Thread(target=iterativeDeepening, args=[ontology_1, startName, goalNames[i], 10, True])\n        # t2 = threading.Thread(target=breadthFirstSearch, args=[ontology_2, goalNode, startName, False])\n        # 1 IDS 1 RBFS\n        # searchA = \"RBFS\"\n        # searchB = \"IDS\"\n        # t1 = threading.Thread(target=iterativeDeepening, args=[ontology_1, startName, goalNames[i], 10, False])\n        # t2 = threading.Thread(target=recursiveBestFirstSearch, args=[ontology_2, goalNames[i], startName, 10, True])\n        # t1 = threading.Thread(target=iterativeDeepening, args=[ontology_1, startName, goalNames[i], 10, True])\n        # t2 = threading.Thread(target=recursiveBestFirstSearch, args=[ontology_2, goalNames[i], startName, 10, False])\n        # # 1 BFS 1 RBFS\n        # searchA = \"RBFS\"\n        # searchB = \"BFS\"\n        # t2 = threading.Thread(target=breadthFirstSearch, args=[ontology_2, goalNode, startName, True])\n        t1 = threading.Thread(target=recursiveBestFirstSearch, args=[ontology_1, startName, goalNames[i], 10, True])\n        t2 = threading.Thread(target=breadthFirstSearch, args=[ontology_2, goalNode, startName, False])\n        startTime = time.time()\n        t1.start()\n        t2.start()\n        t1.join()\n        t2.join()\n        totalTime += (time.time() - startTime)\n        paths.append(possiblePath)\n        print(queriesMade)\n        queriesMade=0\n        ontology_1.resetExpandedCompanies()\n        ontology_2.resetExpandedCompanies()\n        possiblePath=[]\n        paths=[]\n        fringe={}\n        killRequest=0\n        # time.sleep(2)\n\n        # averageTime = totalTime/25\n        # file_interface.writeSearchTimes(searchA, searchB, averageTime)\n        time.sleep(10)\n\ndef singleSearch():\n    global paths\n    global possiblePath\n    global fringe\n    global queriesMade\n    ontology_1=ontology_wrapper.Interface(GRAPH_URL)\n    totalTime = 0\n    search = \"\"\n    startName = \"Pyle Robert D\"\n    goalNames = [\"Wallace Mark E\",\n                \"WOLFE ROBERT H\",\n                \"WOOD PHOEBE A\",\n                \"WHITE MILES D\",\n                \"HERNANDEZ ENRIQUE JR\"]\n    startNode={\"name\" : startName,\"companyID\" : \"N/A\"}\n    goalNode={\"name\" : goalNames[0],\"companyID\" : \"N/A\"}\n    for i in range(0,len(goalNames)):\n        startTime = time.time()\n        # # Iterarative Deepening Search\n        # iterativeDeepening(ontology_1, startName, goalNames[i], 10, False)\n        # search = \"IDS\"\n        # Breadth First Search\n        # breadthFirstSearch(ontology_1, startNode, goalNames[i], False)\n        # search = \"BFS\"\n        # Best First Search\n        recursiveBestFirstSearch(ontology_1, startName, goalNames[i], 10, False)\n        search = \"RBFS\"\n        totalTime += (time.time() - startTime)\n        paths.append(possiblePath)\n        print(queriesMade)\n        queriesMade=0\n        ontology_1.resetExpandedCompanies()\n        possiblePath=[]\n        paths = []\n        # averageTime = totalTime/25\n        # file_interface.writeSearchTimes(search, i+2, averageTime)\n        time.sleep(30)\n\nif __name__==\"__main__\":\n    bidirectionalSearch()\n    # singleSearch()","sub_path":"Software/search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":23417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"211331615","text":"# TO-DO: Complete the selection_sort() function below \ndef selection_sort( arr ):\n    # loop through n-1 elements\n    for i in range(0, len(arr) - 1):\n        cur_index = i\n        smallest_index = cur_index\n        # TO-DO: find next smallest element\n        # (hint, can do in 3 loc) \n        for x in range(i,len(arr)):\n            if(arr[x] < arr[smallest_index]):\n                smallest_index = x\n    \n        # TO-DO: swap\n        t = arr[cur_index]\n        arr[cur_index] = arr[smallest_index]\n        arr[smallest_index] = t\n\n    return arr\n\n\n# TO-DO:  implement the Bubble Sort function below\ndef bubble_sort( arr ):\n    for i in range(0, len( arr )-1):\n        for x in range(0, len( arr )-1):\n            if(arr[x] > arr[x+1]):\n                t = arr[x]\n                arr[x] = arr[x+1]\n                arr[x+1] = t\n\n    return arr\n\n\n# STRETCH: implement the Count Sort function below\ndef count_sort( arr, maximum=-1 ):\n    if(len(arr) == 0):\n        return arr\n    elif(any([True if x < 0 else False for x in arr])):\n        return \"Error, negative numbers not allowed in Count Sort\"\n    \n    # Make counting list of length = largest integer in arr\n    count_arr = [0]*(max(arr)+1)\n    out_arr = [0]*(len(arr))\n\n    # Count instances of each numberin the counting list\n    for x in arr:\n        count_arr[x] = count_arr[x] + 1\n\n    for x in range(0,len(count_arr)-1):\n        count_arr[x+1] = count_arr[x+1] + count_arr[x]\n\n    for x in arr[::-1]:\n        index = count_arr[x] - 1\n        out_arr[index] = x\n\n    arr = out_arr\n\n    return arr","sub_path":"src/iterative_sorting/iterative_sorting.py","file_name":"iterative_sorting.py","file_ext":"py","file_size_in_byte":1558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"397687748","text":"###################################################################\n# Author    :   Sivaprakash.B                                     #\n# Email     :   sivaprakash674@gmail.com                          #\n# Purpose   :   Android or ADB log analysing tool on python       #\n###################################################################\n\n\n# Validating the import as python versions < 3 supports Tkinter ( T - Upper Case )\n# And python versions >3 supports tkinter ( T - Lower Case )\n\ntry:\n    import Tkinter as tkin         # This is for python2\n    import tkFileDialog            # For providing dialog box option for selecting files\n    import tkMessageBox            # For displaying message box alerts\n    import webbrowser              # For converting text into clickable links\nexcept:\n    import tkinter as tkin         # This is for python3\n    tkFileDialog=tkin.filedialog   # For providing dialog box option for selecting files\n    tkMessageBox=tkin.messagebox   # For displaying message box alerts\n    import webbrowser              # For converting text into clickable links\n\nimport subprocess                  # For validating the output on the search query\n\n\n# Declaring variables required for the code \n\nm=tkin.Tk()                        # Creating root object ( m )  of the Tkinter ot tkinter \nm.title('LogPad')                  # Adding title to the root object or the GUI Window.\nm.resizable(0,0)                   # Setting the window not to be resized due to python limitations\n\nfilename =\"\"                       # Variable to hold the file path of the file to be analysied \nbgcolor='black'                    # Variable to hold Background color for the theme\nfgcolor='green'                    # Variable to hold foreground or text color for the theme\n\n# Variable to hold the color code for the logs based on the type log filter. \n\nlogscolorcode = {'E':'red', 'W':'yellow','D':'green', 'I':'white','V':'blue','all':'white'} \n\n# Variable to hold the text with link to reach the gitHub page.\n\ntextforrating = \"If you like the project mark the star at https://github.com/sivapraksh674/logpad \\n\"\n\n# Top frame holds the file selection and file path display section \n\ntopframe = tkin.Frame(m)\ntopframe.configure(bg=bgcolor)\ntopframe.pack( side = tkin.TOP, expand=tkin.TRUE)\n\n# Search frame is used to set the search filters and have search box enter the text for search\n\nsearchframe = tkin.Frame(m)\nsearchframe.configure(bg=bgcolor)\nsearchframe.pack( side = tkin.TOP,expand=tkin.TRUE )\n\n# Bottom frame holds the Log Area to display the logs.\n\nbottomframe = tkin.Frame(m)\nbottomframe.configure(bg=bgcolor)\nbottomframe.pack( side = tkin.TOP, expand=tkin.TRUE )\n\n\n# Function to select the file to parse the logs.\n\ndef SelectFile():\n     global filename\n     print (textforrating)\n     filename = tkFileDialog.askopenfilename(initialdir = \".\",title = \"Select file\",filetypes = ((\"text files\",\"*.txt\"),(\"all files\",\"*.*\")))\n        \n     # Validation to check if a file is selected or not \n    \n     if len(filename) == 0 :\n         return\n    \n     filepatharea.delete('1.0', tkin.END)\n     filepatharea.insert(tkin.INSERT,filename)\n     searchquery = \"awk '{ print;}' \"+ filename\n     logarea.config(fg=logscolorcode[logtype.get()]) \n     logarea.insert(tkin.INSERT,subprocess.check_output(searchquery,shell=True))\n\n# Search function to filter the logs based on selection \n\ndef SearchFunction():\n     global filename,logtype,cataegory\n     print (textforrating)\n\n     # Validation to check if a file is selected or not \n     if len(filename) == 0 :\n         tkMessageBox.showerror(\"Error\", \"Please select the log file to perform search\")\n         return\n    \n     # Clearing Log Area for new search results\n        \n     logarea.delete('1.0', tkin.END)\n\n     # Validation to if search should be performed only on classname or not.\n\n     if cataegory.get() :\n         if logtype.get() == \"all\" :\n            searchquery = \"awk '{ print $6;}' \"+ filename +\" | sort -u \"\n         else :\n             searchquery = \"awk '{ if( $5 == \\\"\"+logtype.get()+\"\\\" ) print $6;}' \" + filename + \" | sort -u \"\n     else :\n         if logtype.get() == \"all\" :\n            searchquery = \"awk '{ print;}' \"+ filename\n         else :\n             searchquery = \"awk '{ if( $5 == \\\"\"+logtype.get()+\"\\\" ) print;}' \"+ filename \n     if searchbox.get(\"1.0\",'end-1c') :\n         searchquery = searchquery+\" | grep \\\"\"+ searchbox.get(\"1.0\",'end-1c') + \"\\\" \"\n     print ( \"\\n\" + searchquery )  \n    \n     logarea.config(fg=logscolorcode[logtype.get()]) \n     logarea.insert(tkin.INSERT,subprocess.check_output(searchquery,shell=True))\n\n# Event Call Back for Key Down Events\n\ndef HandleKeyRelease (e) :\n    print ( \"Keypressed\" , e.char )\n    SearchFunction()\n\n# Callback function to call a web URL\n\ndef callback(url):\n    webbrowser.open_new(url)\n\n# File Path label creation and packing to the view area\n\nfilepathlabel = tkin.Label(topframe,justify = tkin.LEFT)\nfilepathlabel.config(text = \"File Path :\",bg=bgcolor,fg=fgcolor)\nfilepathlabel.pack(anchor=tkin.W)\n\n# File Path creation and packing to the view area\n\nfilepatharea = tkin.Text(topframe,wrap=tkin.WORD,width=200, height= 2)\nfilepatharea.configure(bg=bgcolor,fg=fgcolor,highlightbackground=bgcolor)\nfilepatharea.pack(fill=\"none\", expand=tkin.TRUE)\n\n# Select File action button creation and packing to the view area\n\nselectfilebutton = tkin.Button(topframe, text='SELECT FILE',command=SelectFile)\nselectfilebutton.pack(side=tkin.BOTTOM)\n\n# ( Deprecated ) Get Logs action button creation and packing to the view area\n\n#searchbutton = tkin.Button(topframe, text='Search', bg='green',fg='white', command=SearchFunction)\n#searchbutton.pack(fill=\"none\",side=tkin.RIGHT)\n\n# Search box label creation and packing to the view area\n\nsearchboxlabel = tkin.Label(searchframe,justify = tkin.LEFT)\nsearchboxlabel.config(text = \"Search Area\",bg=bgcolor,fg=fgcolor)\nsearchboxlabel.pack(side=tkin.LEFT)\n \n# Search box text area creation and packing to the view area\n\nsearchbox=tkin.Text(searchframe, height=2, width=50, borderwidth=2, relief=tkin.GROOVE)\nsearchbox.configure(highlightbackground=bgcolor)\nsearchbox.bind('<KeyRelease>',HandleKeyRelease)\nsearchbox.pack(side=tkin.LEFT)\n\n\n# Class name Category selection Check Box creation\n\ncataegory = tkin.IntVar()\nclassnamecheckbox = tkin.Checkbutton(searchframe,variable=cataegory,command=SearchFunction)\nclassnamecheckbox.config(bg=bgcolor)\nclassnamecheckbox.pack(side=tkin.LEFT)\n\n# Search box label creation and packing to the view area\n\nclassnamecheckboxlabel = tkin.Label(searchframe,justify = tkin.LEFT)\nclassnamecheckboxlabel.config(text = \"Search Class Name Alone\",bg=bgcolor,fg=fgcolor)\nclassnamecheckboxlabel.pack(side=tkin.LEFT)\n\n\n# Logs Type ( E - Error, W - Warning, D - Debug, I - Information, V - Verbose , ALL ) Category selection \n# Radio box creation, packing and default option selection.\n\nlogtype= tkin.StringVar()\ntkin.Radiobutton(searchframe, \n              value='E',     # E - Error Category Radio Button  \n              variable=logtype, \n              command=SearchFunction,\n              bg=bgcolor,fg=fgcolor).pack(side=tkin.LEFT)\ntkin.Label(searchframe,justify = tkin.LEFT, text = \"E\",bg=bgcolor,fg=logscolorcode['E'], padx = 20).pack(side=tkin.LEFT)\n\ntkin.Radiobutton(searchframe, \n              value='W',     # W - Warning Category Radio Button  \n              variable=logtype, \n              command=SearchFunction,\n              bg=bgcolor,fg=fgcolor).pack(side=tkin.LEFT)\ntkin.Label(searchframe,justify = tkin.LEFT, text = \"W\",bg=bgcolor,fg=logscolorcode['W'], padx = 20).pack(side=tkin.LEFT)\n\ntkin.Radiobutton(searchframe, \n              value='D',     # D - Debug Category Radio Button  \n              variable=logtype, \n              command=SearchFunction,\n              bg=bgcolor,fg=fgcolor).pack(side=tkin.LEFT)\ntkin.Label(searchframe,justify = tkin.LEFT, text = \"D\",bg=bgcolor,fg=logscolorcode['D'], padx = 20).pack(side=tkin.LEFT)\n\ntkin.Radiobutton(searchframe, \n              value='I',     # I - Info Category Radio Button  \n              variable=logtype,\n              command=SearchFunction, \n              bg=bgcolor,fg=fgcolor).pack(side=tkin.LEFT)\ntkin.Label(searchframe,justify = tkin.LEFT, text = \"I\",bg=bgcolor,fg=logscolorcode['I'], padx = 20).pack(side=tkin.LEFT)\n\ntkin.Radiobutton(searchframe, \n              value='V',     # V - Verbose Category Radio Button  \n              variable=logtype, \n              command=SearchFunction,\n              bg=bgcolor,fg=fgcolor).pack(side=tkin.LEFT)\ntkin.Label(searchframe,justify = tkin.LEFT, text = \"V\",bg=bgcolor,fg=logscolorcode['V'], padx = 20).pack(side=tkin.LEFT)\n\nR1=tkin.Radiobutton(searchframe, \n              value='all',    # ALL Category Radio Button \n              variable=logtype, \n              command=SearchFunction,\n              bg=bgcolor,fg=fgcolor)\nR1.pack(side=tkin.LEFT)\nR1.select()\ntkin.Label(searchframe,justify = tkin.LEFT, text = \"ALL\",bg=bgcolor,fg=logscolorcode['all'], padx = 20).pack(side=tkin.LEFT)\n\n\n# Log Area Label creation and packing to the view area\n\nlogarealabel = tkin.Label(bottomframe,justify = tkin.LEFT)\nlogarealabel.config(text = \"Log Area\", bg=bgcolor,fg=fgcolor)\nlogarealabel.pack(anchor=tkin.W)\n\n# Log Area creation and packing to the view area\n\nlogarea = tkin.Text(bottomframe,wrap=tkin.WORD,width=200, height= 30, borderwidth=2, relief=tkin.GROOVE, bg=bgcolor, fg=fgcolor)\nlogarea.pack(fill=\"none\", expand=tkin.TRUE)\n\n# Added hyperlink to the Github Repository \n\ngithublink = tkin.Label(m, text=\"If you like the project click here to mark the star at github \\n https://github.com/sivapraksh674/logpad\", bg=bgcolor, fg=fgcolor, cursor=\"hand2\")\ngithublink.pack()\ngithublink.bind(\"<Button-1>\", lambda e: callback(\"https://github.com/sivapraksh674/logpad\"))\n\n# Adding config to the Root Element ( m )\n\nm.config(bg=bgcolor)\nm.mainloop()\n","sub_path":"logpad.py","file_name":"logpad.py","file_ext":"py","file_size_in_byte":9868,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"17687995","text":"import numpy as np\nimport random\n\n\ndef gaussX(N, variance=1):\n    \"\"\"\n    a) Write a function named “gaussX” that returns samples drawn from the\n    distribution along with the corresponding class labels.\n\n    Inputs:\n               N - desired number of samples\n        variance - variance of desired circular symmetric Gaussian\n    \"\"\"\n    # split N approximately equally\n    first_half = int(N/2)\n    second_half = N - first_half\n\n    # identify labels for the classes\n    label_13 = -1\n    label_24 = 1\n\n    # generate samples of circular symmetric Gaussian for 2nd and 4th quadrants\n    samples_24_r = np.sqrt(variance)*np.random.randn(first_half,1)\n    samples_24_theta = -np.random.rand(first_half,1)*(np.pi/2)\n    samples_24_x1 = samples_24_r*np.cos(samples_24_theta)\n    samples_24_x2 = samples_24_r*np.sin(samples_24_theta)\n\n    # generate samples of circular symmetric Gaussian for 1st and 3rd quadrants\n    samples_13_r = np.sqrt(variance)*np.random.randn(first_half,1)\n    samples_13_theta = np.random.rand(first_half,1)*(np.pi/2)\n    samples_13_x1 = samples_13_r*np.cos(samples_13_theta)\n    samples_13_x2 = samples_13_r*np.sin(samples_13_theta)\n\n    # combine both classes into single vectors for each coordinate\n    samples_x1 = np.append(samples_24_x1, samples_13_x1)\n    samples_x2 = np.append(samples_24_x2, samples_13_x2)\n\n    # generate label vector\n    classes = np.append(label_24*np.ones((first_half, 1)),\n                        label_13*np.ones((second_half, 1)))\n\n    # combine all into single Nx3 array\n    samples = np.stack((samples_x1,samples_x2,classes),axis=-1)\n\n    return samples\n\n","sub_path":"Project1/codes/requiredFunctions/gaussX.py","file_name":"gaussX.py","file_ext":"py","file_size_in_byte":1620,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"626807179","text":"import requests\nfrom lxml import etree \nfrom eagle_demo import Chaojiying_Client\n#编码流程:\n#1.验证码识别,获取验证码图片的文字数据\n#2.对post请求进行发送(处理请求参数)\n#3.对响应数据进行持久化存储\ndef getCodeText():\n    chaojiying = Chaojiying_Client('635072437', 'wujiawei', '917557')\t#用户中心>>软件ID 生成一个替换 96001\n    im = open('code.jpg', 'rb').read()\t\t\t\t\t\t\t\t\t\t\t\t\t#本地图片文件路径 来替换 a.jpg 有时WIN系统须要//\n    return chaojiying.PostPic(im, 1902)\t\n#1.对验证码图片进行捕获和识别\nurl = \"https://so.gushiwen.cn/user/login.aspx?from=http://so.gushiwen.cn/user/collect.aspx\"\nheaders = {\n    'User-Agent':'Mozilla/5.0 (Windows; U; Windows NT 5.1; zh-CN) AppleWebKit/523.15(KHTML, like Gecko, Safari/419.3) Arora/0.3 (Change: 287 c9dfb30)'\n}\n#创建一个session对象\nsession = requests.session()\npage_text = session.get(url=url,headers=headers).text\ntree = etree.HTML(page_text)\ncode_img_src = 'https://so.gushiwen.cn'+tree.xpath('//*[@id=\"imgCode\"]/@src')[0]\nimg_data = session.get(url=code_img_src,headers=headers).content \n#将验证码图片保存到本地\nwith open('./code.jpg','wb') as fp:\n    fp.write(img_data)\n#调用打码平台的实例程序进行验证码图片数据识别\nresult = str(getCodeText()['pic_str'])\nprint(result)\nprint(type(result))\n#post请求的发送(模拟登陆)\nlogin_url = \"https://so.gushiwen.cn/user/login.aspx?from=http%3a%2f%2fso.gushiwen.cn%2fuser%2fcollect.aspx\"\ndata = {\n    'email': '17671725832',\n    'pwd': 'wujiawei',\n    'code': result,\n    'denglu': '登录'\n}\nresp = session.post(url=login_url,headers=headers,data=data)\n#打印响应状态码 200即为成功\nprint(resp.status_code)\n#爬取当前用户个人主页对应的页面数据\ndetail_url = 'https://so.gushiwen.cn/user/collect.aspx'\n#使用携带cookies的session进行get请求发送\ndetail_page_text = session.get(url=detail_url,headers=headers).text \nwith open('robin.html','w',encoding='utf-8') as fp:\n    fp.write(detail_page_text)","sub_path":"5_requests模块高级/1.模拟登陆古诗词网.py","file_name":"1.模拟登陆古诗词网.py","file_ext":"py","file_size_in_byte":2042,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"372370219","text":"import math\nimport traceback\nfrom enum import Enum\nfrom threading import Thread\n\nimport cv2\nimport logging\nimport time\n\nfrom fishy.constants import libgps, fishyqr, lam2\nfrom fishy.engine.fullautofisher.qr_detection import get_values_from_image, get_qr_location\nfrom fishy.engine.semifisher.fishing_mode import FishingMode\n\nfrom fishy.engine import SemiFisherEngine\nfrom fishy.engine.common.window import WindowClient\nfrom fishy.engine.semifisher import fishing_mode, fishing_event\n\nfrom fishy.engine.common.IEngine import IEngine\nfrom pynput import keyboard, mouse\n\nfrom fishy.helper import hotkey, helper\nfrom fishy.helper.helper import sign\n\nmse = mouse.Controller()\nkb = keyboard.Controller()\n\n\ndef image_pre_process(img):\n    scale_percent = 100  # percent of original size\n    width = int(img.shape[1] * scale_percent / 100)\n    height = int(img.shape[0] * scale_percent / 100)\n    dim = (width, height)\n    img = cv2.resize(img, dim, interpolation=cv2.INTER_AREA)\n    return img\n\n\nclass State(Enum):\n    NONE = 0\n    PLAYING = 1\n    RECORDING = 2\n    OTHER = 3\n\n\nclass FullAuto(IEngine):\n    rotate_by = 30\n    state = State.NONE\n\n    def __init__(self, gui_ref):\n        from fishy.engine.fullautofisher.controls import Controls\n        from fishy.engine.fullautofisher import controls\n        from fishy.engine.fullautofisher.calibrator import Calibrator\n        from fishy.engine.fullautofisher.test import Test\n\n        super().__init__(gui_ref)\n        self._hole_found_flag = False\n        self._curr_rotate_y = 0\n\n        self.fisher = SemiFisherEngine(None)\n        self.calibrator = Calibrator(self)\n        self.test = Test(self)\n        self.controls = Controls(controls.get_controls(self))\n        self.show_crop = False\n\n    def run(self):\n\n        addons_req = [libgps, lam2, fishyqr]\n        for addon in addons_req:\n            if not helper.addon_exists(*addon):\n                helper.install_addon(*addon)\n\n        FullAuto.state = State.NONE\n\n        self.gui.bot_started(True)\n        self.window = WindowClient(color=cv2.COLOR_RGB2GRAY, show_name=\"Full auto debug\")\n\n        try:\n            self.window.crop = get_qr_location(self.window.get_capture())\n            if self.window.crop is None:\n                logging.warning(\"FishyQR not found\")\n                self.start = False\n                raise Exception(\"FishyQR not found\")\n\n            if not self.calibrator.all_callibrated():\n                logging.error(\"you need to calibrate first\")\n\n            self.fisher.toggle_start()\n            fishing_event.unsubscribe()\n\n            self.controls.initialize()\n            while self.start and WindowClient.running():\n                if self.show_crop:\n                    self.window.show(self.show_crop, func=image_pre_process)\n                else:\n                    time.sleep(0.1)\n        except:\n            traceback.print_exc()\n\n            if self.window.get_capture() is None:\n                logging.error(\"Game window not found\")\n\n        self.gui.bot_started(False)\n        self.controls.unassign_keys()\n        self.window.show(False)\n        logging.info(\"Quitting\")\n        self.window.destory()\n        self.fisher.toggle_start()\n\n    def get_coods(self):\n        img = self.window.processed_image(func=image_pre_process)\n        return get_values_from_image(img)\n\n    def move_to(self, target):\n        if target is None:\n            logging.error(\"set target first\")\n            return\n\n        if not self.calibrator.all_callibrated():\n            logging.error(\"you need to callibrate first\")\n            return\n\n        current = self.get_coods()\n        print(f\"Moving from {(current[0], current[1])} to {target}\")\n        move_vec = target[0] - current[0], target[1] - current[1]\n\n        dist = math.sqrt(move_vec[0] ** 2 + move_vec[1] ** 2)\n        print(f\"distance: {dist}\")\n        if dist < 5e-05:\n            print(\"distance very small skipping\")\n            return\n\n        target_angle = math.degrees(math.atan2(-move_vec[1], move_vec[0])) + 90\n        from_angle = current[2]\n\n        self.rotate_to(target_angle, from_angle)\n\n        walking_time = dist / self.calibrator.move_factor\n        print(f\"walking for {walking_time}\")\n        kb.press('w')\n        time.sleep(walking_time)\n        kb.release('w')\n        print(\"done\")\n\n    def rotate_to(self, target_angle, from_angle=None):\n        if from_angle is None:\n            _, _, from_angle = self.get_coods()\n\n        if target_angle < 0:\n            target_angle = 360 + target_angle\n        while target_angle > 360:\n            target_angle -= 360\n        print(f\"Rotating from {from_angle} to {target_angle}\")\n\n        angle_diff = target_angle - from_angle\n\n        if abs(angle_diff) > 180:\n            angle_diff = (360 - abs(angle_diff)) * sign(angle_diff) * -1\n\n        rotate_times = int(angle_diff / self.calibrator.rot_factor) * -1\n\n        print(f\"rotate_times: {rotate_times}\")\n\n        for _ in range(abs(rotate_times)):\n            mse.move(sign(rotate_times) * FullAuto.rotate_by * -1, 0)\n            time.sleep(0.05)\n\n    def look_for_hole(self):\n        self._hole_found_flag = False\n\n        if FishingMode.CurrentMode == fishing_mode.State.LOOKING:\n            return True\n\n        def found_hole(e):\n            if e == fishing_mode.State.LOOKING:\n                self._hole_found_flag = True\n\n        fishing_mode.subscribers.append(found_hole)\n\n        t = 0\n        while not self._hole_found_flag and t <= 1.25:\n            mse.move(0, FullAuto.rotate_by)\n            time.sleep(0.05)\n            t += 0.05\n        while not self._hole_found_flag and t > 0:\n            mse.move(0, -FullAuto.rotate_by)\n            time.sleep(0.05)\n            t -= 0.05\n\n        self._curr_rotate_y = t\n        fishing_mode.subscribers.remove(found_hole)\n        return self._hole_found_flag\n\n    def rotate_back(self):\n        while self._curr_rotate_y > 0.01:\n            mse.move(0, -FullAuto.rotate_by)\n            time.sleep(0.05)\n            self._curr_rotate_y -= 0.05\n\n    def toggle_start(self):\n        if self.start and FullAuto.state != State.NONE:\n            logging.info(\"Please turn off RECORDING/PLAYING first\")\n            return\n\n        self.start = not self.start\n        if self.start:\n            self.thread = Thread(target=self.run)\n            self.thread.start()\n\n\nif __name__ == '__main__':\n    logging.getLogger(\"\").setLevel(logging.DEBUG)\n    hotkey.initalize()\n    # noinspection PyTypeChecker\n    bot = FullAuto(None)\n    bot.toggle_start()\n","sub_path":"fishy/engine/fullautofisher/engine.py","file_name":"engine.py","file_ext":"py","file_size_in_byte":6516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"315254500","text":"import numpy as np\nimport pytest\nimport xarray as xr\n\nsdba = pytest.importorskip(\"xclim.sdba\")  # noqa\n\nfrom xclim.sdba.base import Grouper\nfrom xclim.sdba.base import Parametrizable\nfrom xclim.sdba.processing import normalize\n\n\ndef test_param_class():\n    gr = Grouper(group=\"time.month\")\n    in_params = dict(\n        anint=4, abool=True, astring=\"a string\", adict={\"key\": \"val\"}, group=gr\n    )\n    obj = Parametrizable(**in_params)\n\n    assert obj.parameters == in_params\n\n    repr(obj).startswith(\n        \"ParametrizableClass(anint=4, abool=True, astring='a string', adict={'key': 'val'}, \"\n        \"group=Grouper(dim='time',\"\n    )\n\n\n@pytest.mark.parametrize(\n    \"group,window,nvals\",\n    [(\"time\", 1, 366), (\"time.month\", 1, 31), (\"time.dayofyear\", 5, 1)],\n)\ndef test_grouper_group(tas_series, group, window, nvals):\n    tas = tas_series(np.ones(366), start=\"2000-01-01\")\n\n    grouper = Grouper(group, window=window)\n    grpd = grouper.group(tas)\n\n    if window > 1:\n        assert \"window\" in grpd.dims\n\n    assert grpd.count().max() == nvals\n\n\n@pytest.mark.parametrize(\n    \"group,interp,val90\",\n    [(\"time\", False, True), (\"time.month\", False, 3), (\"time.month\", True, 3.5)],\n)\ndef test_grouper_get_index(tas_series, group, interp, val90):\n    tas = tas_series(np.ones(366), start=\"2000-01-01\")\n    grouper = Grouper(group, interp=interp)\n    indx = grouper.get_index(tas)\n    # 90 is March 31st\n    assert indx[90] == val90\n\n\ndef test_grouper_apply(tas_series):\n    tas1 = tas_series(np.arange(366), start=\"2000-01-01\")\n    tas0 = tas_series(np.zeros(366), start=\"2000-01-01\")\n    tas = xr.concat((tas1, tas0), dim=\"lat\")\n\n    grouper = Grouper(\"time.month\")\n    out = grouper.apply(\"mean\", tas)\n    assert out.isel(month=0, lat=0) == 15.0\n    out = normalize(tas, group=grouper)\n\n    grouper = Grouper(\"time.month\", add_dims=[\"lat\"])\n    out = grouper.apply(\"mean\", tas)\n    assert out.ndim == 1\n    assert out.isel(month=0,) == 7.5\n    assert out.attrs[\"group\"] == \"time.month\"\n    assert out.attrs[\"group_compute_dims\"] == [\"time\", \"lat\"]\n    assert out.attrs[\"group_window\"] == 1\n\n    grouper = Grouper(\"time.month\", window=5)\n    out = grouper.apply(\"mean\", tas)\n    np.testing.assert_almost_equal(out.isel(month=0, lat=0), 15.32236842)\n\n    tas = tas.chunk({\"lat\": 1})\n    out = grouper.apply(\"mean\", tas)\n    assert out.chunks == ((1, 1), (12,))\n\n    out = normalize(tas, group=grouper)\n    assert out.chunks == ((1, 1), (366,))\n\n    def mixed_reduce(grdds, dim=None):\n        tas1 = grdds.tas1.mean(dim=dim)\n        tas0 = grdds.tas0 / grdds.tas0.mean(dim=dim)\n        tas1.attrs[\"_group_apply_reshape\"] = True\n        return xr.Dataset(data_vars={\"tas1_mean\": tas1, \"norm_tas0\": tas0})\n\n    tas1 = tas1.chunk({\"time\": -1})\n    out = grouper.apply(mixed_reduce, {\"tas1\": tas1, \"tas0\": tas0})\n    assert \"month\" not in out.norm_tas0.dims\n    assert \"month\" in out.tas1_mean.dims\n\n    assert out.tas1_mean.chunks == ((12,),)\n    assert out.norm_tas0.chunks == ((366,),)\n","sub_path":"tests/test_sdba/test_base.py","file_name":"test_base.py","file_ext":"py","file_size_in_byte":2990,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"411909759","text":"# -*- coding: utf-8 -*-\nimport os\nimport datetime\nimport const as ct\nfrom pathlib import Path\nfrom scrapy import signals\nfrom datetime import datetime\nfrom scrapy import FormRequest\nfrom base.clog import getLogger\nfrom dspider.myspider import BasicSpider\nfrom dspider.items import MyDownloadItem, ChinaSecurityIndustryValuationItem\nclass ChinaSecurityIndustryValuationSpider(BasicSpider):\n    #name = 'chinaSecurityIndustryValuationSpider'\n    name = 'cSIValuationSpider'\n    file_name = ''\n    logger = getLogger(__name__)\n    custom_settings = {\n        'ROBOTSTXT_OBEY': False,\n        'SPIDERMON_ENABLED': True,\n        'DOWNLOAD_DELAY': 1.0,\n        'CONCURRENT_REQUESTS_PER_IP': 10,\n        'CONCURRENT_REQUESTS_PER_DOMAIN': 1,\n        'RANDOMIZE_DOWNLOAD_DELAY': False,\n        'FILES_STORE': ct.CHINA_SECURITY_INDUSTRY_VALUATION_PATH,\n        'SPIDERMON_VALIDATION_ADD_ERRORS_TO_ITEMS': True,\n        'SPIDERMON_VALIDATION_ERRORS_FIELD': ct.SPIDERMON_VALIDATION_ERRORS_FIELD,\n        'SPIDERMON_EXPECTED_FINISH_REASONS': ct.SPIDERMON_EXPECTED_FINISH_REASONS,\n        'SPIDERMON_VALIDATION_MODELS': {\n            ChinaSecurityIndustryValuationItem: 'dspider.validators.PlateValuationModel',\n        },\n        'EXTENSIONS': {\n            'spidermon.contrib.scrapy.extensions.Spidermon': 500,\n        },\n        'ITEM_PIPELINES': {\n            'dspider.pipelines.PlateValuationDownloadPipeline': 100,\n            'dspider.pipelines.ChinaSecurityIndustryValuationHandlePipeline': 200,\n        },\n        'SPIDERMON_UNWANTED_HTTP_CODES': ct.DEFAULT_ERROR_CODES,\n        'SPIDERMON_SPIDER_CLOSE_MONITORS': (\n            'dspider.monitors.SpiderCloseMonitorSuite',\n        )\n    }\n    allowed_domains = ['47.97.204.47']\n    start_url = 'http://47.97.204.47/syl/'\n    def start_requests(self):\n        mformat = 'csi%Y%m%d.zip'\n        end_date = datetime.now().strftime(mformat)\n        self.file_name = end_date\n        start_date = self.get_nday_ago(end_date, 10, dformat = mformat)\n        while start_date <= end_date:\n            furl =  self.start_url + start_date\n            yield FormRequest(url = furl, method = 'GET', callback = self.parse, errback=self.errback_httpbin)\n            start_date = self.get_tomorrow_date(sdate = start_date, dformat = mformat)\n\n    def parse(self, response):\n        try:\n            if response.status == 200:\n                fname = os.path.basename(response.url)\n                yield MyDownloadItem(file_urls = [response.url], file_name = fname)\n            else:\n                self.logger.error(\"get china security industry valuation failed url:{} status:{}\".format(response.url, response.status))\n        except Exception as e:\n            self.logger.error(\"get china security industry valuation exception:{}\".format(e))\n\n    @classmethod\n    def from_crawler(cls, crawler, *args, **kwargs):\n        spider = super(ChinaSecurityIndustryValuationSpider, cls).from_crawler(crawler, *args, **kwargs)\n        crawler.signals.connect(spider.spider_closed, signal=signals.spider_closed)\n        return spider\n\n    def spider_closed(self, spider, reason):\n        mdate = datetime.now().strftime('%Y-%m-%d')\n        file_path = Path(ct.CHINA_SECURITY_INDUSTRY_VALUATION_PATH)/\"{}\".format(self.file_name)\n        if file_path.exists():\n            message = \"download china security industry valuation {} at {} succeed\".format(file_path, mdate)\n            self.status = True\n        else:\n            message = \"download china security industry valuation {} at {} failed\".format(file_path, mdate)\n            self.status = False\n        self.message = message\n        self.collect_spider_info()\n","sub_path":"crawler/dspider/spiders/chinaSecurityIndustryValuationSpider.py","file_name":"chinaSecurityIndustryValuationSpider.py","file_ext":"py","file_size_in_byte":3641,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"103396677","text":"# -*- coding: utf-8 -*-\n# @Time    : 2020/12/11 21:24\n# @Author  : hyy\n# @Email   : hyywestwood@zju.edu.cn\n# @File    : spider_2.py\n# @Software: PyCharm\nimport configparser\nimport schedule\nimport requests\nfrom selenium import webdriver\nfrom bs4 import BeautifulSoup\nimport os\nimport time\nimport random\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.action_chains import ActionChains\nimport smtplib\nfrom email.mime.text import MIMEText\nfrom email.header import Header\nimport logging\n\n\nclass Spider:\n    def __init__(self, url):\n        self.url = url\n        self.flag = 1  # 用于控制发邮件进行通知的时间\n        self.retry_counts = 3\n        # self.report_time = None\n        self.djdh = None\n        self.dxsk = None\n        self.zdysq = None\n        self.qgryl = None\n        self.path = os.path.abspath(os.path.join('.', '水利部-新版数据'))  # 数据文件存储路径\n        self.folder = os.path.exists(self.path)  # 判断存储路径文件夹是否存在，没有则创建\n        if not self.folder:\n            os.makedirs(self.path)\n        self.driver = self.getdriver(self.url)\n        self.logger = self.log_setting()\n\n    def run(self):\n        # schedule.every(10).minutes.do(self.single_process)\n        schedule.every().day.at(\"09:00\").do(self.single_process)\n        schedule.every().day.at(\"21:00\").do(self.single_process)\n        text = '水利数据爬取完成'\n        subject = '水利数据'\n        schedule.every(3).day.at(\"22:00\").do(self.email_send, text, subject)\n        # schedule.every(2).day.at(\"22:00\").do(self.email_send, text, subject)\n        while True:\n            schedule.run_pending()\n\n    def single_process(self):\n        time.sleep(10)\n        self.driver.refresh()\n\n        # 获取水利部官网数据\n        self.djdh = None\n        while self.djdh is None:\n            click_btn = self.driver.find_element_by_xpath('//a[li=\"大江大河\"]')\n            ActionChains(self.driver).click(click_btn).perform()\n            self.djdh = self.get_data()\n            self.write_data(self.djdh, '大江大河')  # 将获取的数据写入文件，简单起见，不设置新旧数据对比\n        self.logger.info(\"大江大河数据抓取完成\")\n\n        self.dxsk = None\n        while self.dxsk is None:\n            click_btn = self.driver.find_element_by_xpath('//a[li=\"大型水库\"]')\n            ActionChains(self.driver).click(click_btn).perform()\n            self.dxsk = self.get_data()\n            self.write_data(self.dxsk, '大型水库')\n            # time.sleep(10)\n        self.logger.info(\"大型水库数据抓取完成\")\n\n        self.zdysq = None\n        while self.zdysq is None:\n            click_btn = self.driver.find_element_by_xpath('//a[li=\"重点雨水情\"]')\n            ActionChains(self.driver).click(click_btn).perform()\n            self.zdysq = self.get_data()\n            self.write_data(self.zdysq, '重点雨水情')\n        self.logger.info(\"重点雨水情数据抓取完成\")\n\n        click_btn = self.driver.find_element_by_xpath('//a[li=\"全国日雨量\"]')\n        ActionChains(self.driver).click(click_btn).perform()\n        self.get_qgryl('全国日雨量')\n        self.logger.info(\"全国日雨量数据抓取完成\")\n\n        # self.driver.close()  # 浏览器关闭\n        # self.write_data(self.djdh, '大江大河')  # 将获取的数据写入文件，简单起见，不设置新旧数据对比\n        # self.write_data(self.dxsk, '大型水库')\n        # self.write_data(self.zdysq, '重点雨水情')\n\n    def getdriver(self, url, Headless=True):\n        profile = webdriver.FirefoxOptions()\n        user_agent = 'Mozilla/5.0 (Linux; Android 7.0; BND-AL10 Build/HONORBND-AL10; wv) ' \\\n                     'AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/57.0.2987.132 ' \\\n                     'MQQBrowser/6.2 TBS/044304 Mobile Safari/537.36 MicroMessenger/6.7.3.1340(0x26070331) ' \\\n                     'NetType/4G Language/zh_CN Process/tools'\n        if Headless:\n            profile.add_argument('-headless')  # 设置无头模式\n        # profile.set_preference('network.proxy.type', 1)\n        profile.set_preference('general.useragent.override', user_agent)\n        driver = webdriver.Firefox(options=profile)\n        driver.get(url)\n        return driver\n\n    def log_setting(self):\n        logger = logging.getLogger(__name__)\n        logger.setLevel(level=logging.INFO)\n        handler = logging.FileHandler(\"log.txt\")\n        handler.setLevel(logging.INFO)\n        formatter = logging.Formatter('%(asctime)s - %(filename)s - %(levelname)s - %(message)s')\n        handler.setFormatter(formatter)\n        console = logging.StreamHandler()\n        console.setLevel(logging.INFO)\n        console.setFormatter(formatter)\n        logger.addHandler(handler)\n        logger.addHandler(console)\n        return logger\n\n    def get_qgryl(self, f_str):\n        path2 = os.path.join(self.path, f_str)\n        folder = os.path.exists(path2)\n        if not folder:\n            os.makedirs(path2)\n\n        headers = {'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',\n                   'Accept - Encoding': 'gzip, deflate',\n                   'Accept-Language': 'zh-CN,zh;q=0.8,zh-TW;q=0.7,zh-HK;q=0.5,en-US;q=0.3,en;q=0.2',\n                   'Connection': 'Keep-Alive',\n                   'Host': 'xxfb.mwr.cn',\n                   'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:80.0) Gecko/20100101 Firefox/80.0',\n                   'Referer': 'http://xxfb.mwr.cn/sq_qgryl.html',\n                   'Upgrade-Insecure-Requests': '1'}\n        time.sleep(5)\n        bf = BeautifulSoup(self.driver.page_source, 'html.parser')\n        img = bf.find('div', id='hdcontent').find_all('img')\n        url = 'http://xxfb.mwr.cn' + img[0].attrs['src']\n        r = requests.get(url, headers=headers, stream=True)\n        if r.status_code == 200:\n            open(os.path.join(path2, time.strftime(\"%Y-%m-%d\", time.localtime()) + '.png'), 'wb').write(r.content)  # 将内容写入图片\n            # print(self.report_time+'.png 已成功保存')\n            time.sleep(10)\n            # open(r'D:\\pycharm\\pachong\\水利部-新版数据\\全国日雨量\\test2.png', 'wb').write(r.content)  # 将内容写入图片\n\n    def get_data(self):\n        while self.retry_counts > 0:\n            try:\n                time.sleep(random.uniform(80, 100))  # 需要停留足够长的时间确保数据加载出来\n                WebDriverWait(self.driver, 60).until(EC.presence_of_element_located((By.ID, 'hdcontent')))\n                retrytimes = 10\n                while retrytimes > 0:\n                    time.sleep(30*1)\n                    html = self.driver.page_source\n                    bf = BeautifulSoup(html, 'html.parser')\n                    # self.report_time = str(bf.find('span', id='hddate').contents[0])\n                    data_hd = self.trans(bf)\n                    retrytimes -= 1\n                    if data_hd:\n                        return data_hd\n                return None\n            except Exception:\n                print('错误发生，重新尝试获取，剩余次数{}'.format(self.retry_counts-1))\n                self.retry_counts -= 1\n        return None\n\n    def trans(self, a):\n        hd = a.find('div',id='hdtable').find_all('tr')\n        data_hd = []\n        for hang in hd:\n            zhandian = []\n            for item in hang.contents:\n                if item.name == 'td':\n                    d_str = item.text\n                    d_str = d_str.replace('↑', '')\n                    d_str = d_str.replace('↓', '')\n                    # d_str = d_str.replace('—', '')\n                    d_str = d_str.replace('*', '')\n                    d_str = d_str.replace('?', '')\n                    d_str = d_str.replace('/', '')\n                    d_str = d_str.replace('|', '')\n                    # d_str = d_str.replace(':', '')\n                    zhandian.append(d_str.strip())\n            data_hd.append(zhandian)\n        return data_hd\n\n    def write_data(self, data, f_str):\n        path2 = os.path.join(self.path, f_str)\n        folder = os.path.exists(path2)\n        if not folder:\n            os.makedirs(path2)\n\n        if f_str == '大江大河':\n            for hang in data:\n                path3 = os.path.join(self.path, f_str, hang[0])\n                folder = os.path.exists(path3)\n                if not folder:\n                    os.makedirs(path3)\n                with open(path3 + '\\\\{}-{}-{}.txt'.format(hang[1], hang[2], hang[3]),\n                                  'a+',encoding='utf-8') as f:\n                    f.write('{}\\t{}\\t{}\\t{} \\n'.format(time.strftime(\"%Y-\", time.localtime()) + hang[4],\n                                                               hang[5], hang[6], hang[7]))\n\n        if f_str == '大型水库':\n            for hang in data:\n                path3 = os.path.join(self.path, f_str, hang[0])\n                folder = os.path.exists(path3)\n                if not folder:\n                    os.makedirs(path3)\n                with open(path3 + '\\\\{}-{}-{}.txt'.format(hang[1], hang[2], hang[3]),\n                                  'a+',encoding='utf-8') as f:\n                    f.write('{}\\t{}\\t{}\\t{} \\n'.format(time.strftime(\"%Y-%m-%d\", time.localtime()), hang[4],\n                                                               hang[5], hang[6], hang[7]))\n\n        if f_str == '重点雨水情':\n            for hang in data:\n                path3 = os.path.join(self.path, f_str, hang[0])\n                folder = os.path.exists(path3)\n                if not folder:\n                    os.makedirs(path3)\n                with open(path3 + '\\\\{}-{}-{}.txt'.format(hang[1], hang[2], hang[3]),\n                                  'a+',encoding='utf-8') as f:\n                    f.write('{}\\t{}\\t{} \\n'.format(hang[4],hang[5], hang[6]))\n\n    def email_send(self, text, subject):\n        # 读取email配置\n        config = configparser.ConfigParser()\n        config.read(\"./config.cfg\")\n        conf_email = config['email_setting']\n\n        sender = conf_email['sender']\n        receivers = conf_email['receivers'].split(',')  # 接收邮件，可设置为你的QQ邮箱或者其他邮箱\n        mail_host = conf_email['mail_host']  # 设置服务器\n        mail_user = conf_email['mail_user']  # 用户名\n        mail_pass = conf_email['mail_pass']  # 口令\n\n        # 三个参数：第一个为文本内容，第二个 plain 设置文本格式，第三个 utf-8 设置编码\n        message = MIMEText(text, 'plain', 'utf-8')\n        message['From'] = Header(\"水利数据\", 'utf-8')  # 发送者\n        message['To'] = Header(\"hyy\", 'utf-8')  # 接收者\n        message['Subject'] = Header(subject, 'utf-8')\n\n        try:\n            smtpObj = smtplib.SMTP(mail_host, 25)\n            # smtpObj.connect(mail_host, 25)  # 25 为 SMTP 端口号\n            smtpObj.login(mail_user, mail_pass)\n            smtpObj.sendmail(sender, receivers, message.as_string())\n            print(\"邮件发送成功\")\n        except smtplib.SMTPException as e:\n            print(\"Error: 无法发送邮件\", e)\n\n\nif __name__ == '__main__':\n    url = 'http://xxfb.mwr.cn/sq_djdh.html'\n    Web_spider = Spider(url)\n    Web_spider.run()\n\n\n\n\n\n\n","sub_path":"spider_2.py","file_name":"spider_2.py","file_ext":"py","file_size_in_byte":11440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"380999518","text":"import torch\nfrom torchvision import datasets, transforms\n\nfrom deeprobust.image.attack.onepixel import Onepixel\nimport deeprobust.image.netmodels.resnet as resnet\nimport deeprobust.image.netmodels.CNN as CNN\nfrom deeprobust.image.config import attack_params\nimport matplotlib.pyplot as plt\n\nmodel = resnet.ResNet18().to('cuda')\nprint(\"Load network\")\n\nmodel.load_state_dict(torch.load(\"./trained_models/CIFAR10_ResNet18_epoch_50.pt\"))\nmodel.eval()\n\ntransform_val = transforms.Compose([\n                transforms.ToTensor(),\n                ])\n\ntest_loader  = torch.utils.data.DataLoader(\n                datasets.CIFAR10('deeprobust/image/data', train = False, download=True,\n                transform = transform_val),\n                batch_size = 1, shuffle=True) #, **kwargs)\n\nclasses = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n\nxx, yy = next(iter(test_loader))\nxx = xx.to('cuda').float()\n\nonepixel_params = {\n    'pixels':1\n}\nprint(xx.size())\nattack = Onepixel(model,'cuda')\nsuccess, rate = attack.generate(image = xx, label = yy, **onepixel_params)\nprint(success, rate)\n","sub_path":"examples/image/test_onepixel.py","file_name":"test_onepixel.py","file_ext":"py","file_size_in_byte":1116,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"651358660","text":"def parse_youtube_video(video, data):\n    video.etag = data[\"etag\"]\n    video.id = data[\"id\"]\n\n    # snippet\n    snippet_data = data.get(\"snippet\", False)\n    if snippet_data:\n        video.published_at = snippet_data.get(\"publishedAt\", None)\n        video.channel_id = snippet_data.get(\"channelId\", None)\n        video.title = snippet_data.get(\"title\", None)\n        video.description = snippet_data.get(\"description\", None)\n        video.channel_title = snippet_data.get(\"channelTitle\", None)\n        video.tags = snippet_data.get(\"tags\", None)\n        video.category_id = snippet_data.get(\"categoryId\", None)\n        video.live_broadcast_content = snippet_data.get(\n            \"liveBroadcastContent\", None\n        )\n        video.default_language = snippet_data.get(\"defaultLanguage\", None)\n        video.default_audio_language = snippet_data.get(\n            \"defaultAudioLanguage\", None\n        )\n\n    # contentDetails\n    content_details_data = data.get(\"contentDetails\", False)\n    if content_details_data:\n        video.duration = content_details_data.get(\"duration\", None)\n        video.dimension = content_details_data.get(\"dimension\", None)\n        video.definition = content_details_data.get(\"definition\", None)\n        video.caption = content_details_data.get(\"caption\", None)\n        video.licensed_content = content_details_data.get(\n            \"licensedContent\", None\n        )\n        video.projection = content_details_data.get(\"projection\", None)\n        video.has_custom_thumbnail = content_details_data.get(\n            \"hasCustomThumbnail\", None\n        )\n\n    # status\n    status_data = data.get(\"status\", False)\n    if status_data:\n        video.upload_status = status_data.get(\"uploadStatus\", None)\n        video.failure_reason = status_data.get(\"failureReason\", None)\n        video.rejection_reason = status_data.get(\"rejectionReason\", None)\n        video.privacy_status = status_data.get(\"privacyStatus\", None)\n        video.publish_at = status_data.get(\"publishAt\", None)\n        video.license = status_data.get(\"license\", None)\n        video.embeddable = status_data.get(\"embeddable\", None)\n        video.public_stats_viewable = status_data.get(\n            \"publicStatsViewable\", None\n        )\n\n    # statistics\n    statistics_data = data.get(\"statistics\", False)\n    if statistics_data:\n        video.view_count = statistics_data.get(\"viewCount\", None)\n        video.like_count = statistics_data.get(\"likeCount\", None)\n        video.dislike_count = statistics_data.get(\"dislikeCount\", None)\n        video.favorite_count = statistics_data.get(\"favoriteCount\", None)\n        video.comment_count = statistics_data.get(\"commentCount\", None)\n\n    # player\n    player_data = data.get(\"player\", False)\n    if player_data:\n        video.embed_html = player_data.get(\"embedHtml\", None)\n        video.embed_height = player_data.get(\"embedHeight\", None)\n        video.embed_width = player_data.get(\"embedWidth\", None)\n\n    # topicDetails\n    topic_details_data = data.get(\"topicDetails\", False)\n    if topic_details_data:\n        video.topic_ids = topic_details_data.get(\"topicIds\", None)\n        video.relevant_topic_ids = topic_details_data.get(\n            \"relevantTopicIds\", None\n        )\n        video.topic_categories = topic_details_data.get(\"topicCategories\", None)\n\n    # recordingDetails\n    recording_details_data = data.get(\"recordingDetails\", False)\n    if recording_details_data:\n        video.recording_date = recording_details_data.get(\"recordingDate\", None)\n\n    # fileDetails\n    file_details_data = data.get(\"fileDetails\", False)\n    if file_details_data:\n        video.file_name = file_details_data.get(\"fileName\", None)\n        video.file_size = file_details_data.get(\"fileSize\", None)\n        video.file_type = file_details_data.get(\"fileType\", None)\n        video.container = file_details_data.get(\"container\", None)\n        video.video_streams = file_details_data.get(\"videoStreams\", None)\n        video.audio_streams = file_details_data.get(\"audioStreams\", None)\n        video.duration_ms = file_details_data.get(\"durationMs\", None)\n        video.bitrate_bps = file_details_data.get(\"bitrateBps\", None)\n        video.creation_time = file_details_data.get(\"creationTime\", None)\n\n    # processingDetails\n    processing_details_data = data.get(\"processingDetails\", False)\n    if processing_details_data:\n        video.processing_status = processing_details_data.get(\n            \"processingStatus\", None\n        )\n        video.processing_failure_reason = processing_details_data.get(\n            \"processingFailureReason\", None\n        )\n        video.file_details_availability = processing_details_data.get(\n            \"fileDetailsAvailability\", None\n        )\n        video.processing_issues_availability = processing_details_data.get(\n            \"processingIssuesAvailability\", None\n        )\n        video.tag_suggestions_availability = processing_details_data.get(\n            \"tagSuggestionsAvailability\", None\n        )\n        video.editor_suggestions_availability = processing_details_data.get(\n            \"editorSuggestionsAvailability\", None\n        )\n        video.thumbnails_availability = processing_details_data.get(\n            \"thumbnailsAvailability\", None\n        )\n\n    # suggestions\n    suggestions_data = data.get(\"suggestions\", False)\n    if suggestions_data:\n        video.processing_errors = suggestions_data.get(\"processingErrors\", None)\n        video.processing_warnings = suggestions_data.get(\n            \"processingWarnings\", None\n        )\n        video.processing_hints = suggestions_data.get(\"processingHints\", None)\n        video.tag_suggestions = suggestions_data.get(\"tagSuggestions\", None)\n        video.editor_suggestions = suggestions_data.get(\n            \"editorSuggestions\", None\n        )\n\n    # liveStreamingDetails\n    live_streaming_details_data = data.get(\"liveStreamingDetails\", False)\n    if live_streaming_details_data:\n        video.actual_start_time = live_streaming_details_data.get(\n            \"actualStartTime\", None\n        )\n        video.actual_end_time = live_streaming_details_data.get(\n            \"actualEndTime\", None\n        )\n        video.scheduled_start_time = live_streaming_details_data.get(\n            \"scheduledStartTime\", None\n        )\n        video.scheduled_end_time = live_streaming_details_data.get(\n            \"scheduledEndTime\", None\n        )\n        video.concurrent_viewers = live_streaming_details_data.get(\n            \"concurrentViewers\", None\n        )\n        video.active_live_chat_id = live_streaming_details_data.get(\n            \"activeLiveChatId\", None\n        )\n    video.localizations = data[\"localizations\"]\n\n    return video\n","sub_path":"tools/video.py","file_name":"video.py","file_ext":"py","file_size_in_byte":6675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"34453667","text":"import numpy as np\nfrom tqdm import tqdm\nfrom gensim.models import word2vec, Word2Vec\nimport matplotlib\nfrom typing import Any, List\nfrom gensim.models import word2vec, Word2Vec\nimport csv\nfrom matplotlib import pyplot as plt\nfrom numpy.core.multiarray import ndarray\n\n\ndef positions(positiondata_PATH):\n    positions = []\n    with open(positiondata_PATH, \"r\") as f:\n        reader = csv.reader(f)\n        for row in tqdm(reader):\n            positions.append([float(data) for data in row])\n    return np.array(positions)\n\n\ndef inpeek(x, y, box):  # 引数： bboxのポジション（[Xmim, Xmax, Ymin, Ymax])　戻り値： 入ってたらTrue,それ以外はFalse\n    if box[0] < x < box[1] and box[2] < y < box[3]:\n        return True\n    else:\n        return False\n\nwords = []\nx_positions = positions('shuseibumtext_posi.csv')\nwith open('result170_1201.csv', newline='') as csvfile:\n    reader = csv.reader(csvfile, delimiter=',', quotechar='\"')\n    for row in reader:\n        words.append(row[-1])\nmodel = word2vec.Word2Vec.load(\"sample2.model\")\n\nx1 = -0.532\nx2 = 0.769\nmat = np.c_[xy_positions, obj_word_positions]\ns = []\nfor row in mat:\n    if x1 < row[0] < x2:\n        s.append(row)\na = []\nfor row in s:\n    a.append(row[2:])\n\n\n\nl = []\nfor row in tqdm(a):\n    l.append([model.most_similar([np.array(row[0:200])], [], 1)[0][0],\n              model.most_similar([np.array(row[200:400])], [], 1)[0][0]])\n\nwith open('./x_{}-{}_in_hist.csv'.format(x1, x2), 'w') as file:\n    writer = csv.writer(file, lineterminator='\\n')\n    writer.writerows(l)\n","sub_path":"sceen_text_hist_peel.py","file_name":"sceen_text_hist_peel.py","file_ext":"py","file_size_in_byte":1550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"178453298","text":"#!/usr/bin/env python\n# encoding: utf-8\n\nimport requests\nimport itchat\nimport os\nimport re\nimport shutil\nimport time\nfrom functions import get_functions, get_example, AUTO_REPLAY\nfrom threading import Timer\n\nfrom itchat.content import *\n\n# KEY = '8edce3ce905a4c1dbb965e6b35c3834d'\nKEY = 'b545f73ce82c45f5b63f3f4b20e6f4a9'\n\n\ndef get_response(msg):\n    # 构造发送给图灵机器人服务器的数据\n    api_url = 'http://www.tuling123.com/openapi/api'\n    data = {\n        'key': KEY,\n        'info': msg,\n        'userid': 'wechat-robot',\n    }\n    try:\n        r = requests.post(api_url, data=data).json()\n        # 字典的get方法在字典没有'text'值的时候会返回None���不会抛出异常\n        text = r.get('text')\n        url = r.get('url', None)\n        if url is not None:\n            return text + '\\n' + url\n        else:\n            return text\n    # 为了防止服务器没有正常响应导致程序异常退出，这里用try-except捕获了异常\n    # 如果服务器没能正常交互（返回非json或无法连接），那么就会进入下面的return\n    except IOError:\n        # 将会返回一个None\n        return\n\n\ndef is_msg_from_myself(msgFromUserName):\n    # 检查消息发送方是否为自己\n    global myName\n    return myName == msgFromUserName\n\n\n# # 注册文本消息回复函数\n# @itchat.msg_register(itchat.content.TEXT)\n# def tuling_reply(msg):\n#     global autoReplyFlag, timerSet, noReply, t  # 状态标志位\n#     print(msg['Text'])\n#     if is_msg_from_myself(msg['FromUserName']):\n#         print(\"Replied!!\")\n#         autoReplyFlag = False\n#         noReply = False\n#         try:\n#             t.cancel()\n#             print(\"Timer Canceled\")\n#             timerSet = False\n#         except:\n#             pass\n#         return None\n#\n#     if autoReplyFlag:\n#         # 为了保证在图灵Key出现问题的时候仍旧可以回复，这里设置一个默认回复\n#         default_reply = 'I received: ' + msg['Text']\n#         # 如果图灵Key出现问题，那么reply将会是None\n#         reply = get_response(msg['Text'])\n#         # a or b的意思是，如果a有内容，那么返回a，否则返回b\n#         # 有内容一般就是指非空或者非None，你可以用`if a: print('True')`来测试\n#         return '小K: ' + reply or '小K: ' + default_reply\n#     else:\n#         noReply = True\n#         if not timerSet:\n#             # if time.time()-noReplyStartTime >= 120:\n#             print(\"Timer setting\")\n#             t = Timer(12, send_busy_status, [msg['FromUserName']])\n#             t.start()\n#             timerSet = True\n#\n\ndef send_busy_status(user_name):\n    global noReply, autoReplyFlag, timerSet\n    print(\"Timer Working!\")\n    if noReply:\n        content = \"您好,我是主人的机器人小K\\n注意:以'小K:'\\t开头的都是自动回复的哦(^o^)\\n\"\n        off_auto = r'如果不希望小K自动回复请在消息前面加上@符号' + '\\n'\n        functions = '您可以回复: 所有功能 来查看小K的功能\\n'\n        if AUTO_REPLAY:\n            itchat.send(content + functions + off_auto, user_name)\n        autoReplyFlag = True\n        timerSet = False\n\n\n#\n# @itchat.msg_register([PICTURE, MAP, CARD, NOTE, SHARING, RECORDING, ATTACHMENT, VIDEO])\n# def text_reply(msg):\n#     if msg['Type'] == 'Text':\n#         reply_content = msg['Text']\n#     elif msg['Type'] == 'Picture':\n#         reply_content = r\"图片: \" + msg['FileName']\n#     elif msg['Type'] == 'Card':\n#         reply_content = r\" \" + msg['RecommendInfo']['NickName'] + r\" 的名片\"\n#     elif msg['Type'] == 'Map':\n#         x, y, location = re.search(\"<location x=\\\"(.*?)\\\" y=\\\"(.*?)\\\".*label=\\\"(.*?)\\\".*\", msg['OriContent']).group(1,\n#                                                                                                                     2,\n#                                                                                                                     3)\n#         if location is None:\n#             reply_content = r\"位置: 纬度->\" + x.__str__() + \" 经度->\" + y.__str__()\n#         else:\n#             reply_content = r\"位置: \" + location\n#     elif msg['Type'] == 'Note':\n#         reply_content = r\"通知\"\n#     elif msg['Type'] == 'Sharing':\n#         reply_content = r\"分享\"\n#     elif msg['Type'] == 'Recording':\n#         reply_content = r\"语音\"\n#     elif msg['Type'] == 'Attachment':\n#         reply_content = r\"文件: \" + msg['FileName']\n#     elif msg['Type'] == 'Video':\n#         reply_content = r\"视频: \" + msg['FileName']\n#     else:\n#         reply_content = r\"消息\"\n#\n#     friend = itchat.search_friends(userName=msg['FromUserName'])\n#     itchat.send(r\"Friend:%s -- %s    \"\n#                 r\"Time:%s    \"\n#                 r\" Message:%s\" % (friend['NickName'], friend['RemarkName'], time.ctime(), reply_content),\n#                 toUserName='filehelper')\n#\n#     itchat.send(r\"小K已经收到你在【%s】发送的消息【%s】主人稍后回复。\" % (time.ctime(), reply_content),\n#                 toUserName=msg['FromUserName'])\n\n\n# 处理群聊消息\n@itchat.msg_register(itchat.content.TEXT, isGroupChat=True)\ndef text_reply(msg):\n    # print(msg)\n    if msg['isAt']:\n        content_message = msg['Content']\n        print(content_message)\n        start_at = content_message.find('\\u2005')\n        if start_at == -1:\n            start_at = 0\n        print(start_at)\n        content = content_message[start_at:]\n        print(content)\n        # 'Content': '@阿宝\\u2005宝宝真棒',\n        smart_reply = '小K: ' + get_response(content)\n        print(smart_reply)\n        if AUTO_REPLAY:\n            itchat.send(smart_reply, msg['FromUserName'])\n\n            #  itchat.send(u'@%s\\u2005I received: %s' % (msg['ActualNickName'], msg['Content']), msg['FromUserName'])\n\n\n# {msg_id:(msg_from,msg_to,msg_time,msg_time_touser,msg_type,msg_content,msg_url,msg_from_user_name)}\nmsg_dict = {}\n\n\n# ClearTimeOutMsg用于清理消息字典，把超时消息清理掉\n# 为减少资源占用，此函数只在有新消息动态时调用\ndef clear_timeout_msg():\n    if msg_dict.__len__() > 0:\n        for msgid in list(msg_dict):  # 由于字典在遍历过程中不能删除元素，故使用此方法\n            if time.time() - msg_dict.get(msgid, None)[\"msg_time\"] > 130.0:  # 超时两分钟\n                item = msg_dict.pop(msgid)\n                # print(\"超时的消息：\", item['msg_content'])\n                # 可下载类消息，并删除相关文件\n                if item['msg_type'] == \"Picture\" \\\n                        or item['msg_type'] == \"Recording\" \\\n                        or item['msg_type'] == \"Video\" \\\n                        or item['msg_type'] == \"Attachment\":\n                    print(\"要删除的文件：\", item['msg_content'])\n                    os.remove(item['msg_content'])\n\n\n# 将接收到的消息存放在字典中，当接收到新消息时对字典中超时的消息进行清理\n# 没有注册note（通知类）消息，通知类消息一般为：红包 转账 消息撤回提醒等，不具有撤回功能\n@itchat.msg_register([TEXT, PICTURE, MAP, CARD, SHARING, RECORDING, ATTACHMENT, VIDEO, FRIENDS])\ndef revocation(msg):\n    mytime = time.localtime()  # 这儿获取的是本地时间\n    # 获取用于展示给用户看的时间 2017/03/03 13:23:53\n    msg_time_touser = mytime.tm_year.__str__() \\\n                      + \"/\" + mytime.tm_mon.__str__() \\\n                      + \"/\" + mytime.tm_mday.__str__() \\\n                      + \" \" + mytime.tm_hour.__str__() \\\n                      + \":\" + mytime.tm_min.__str__() \\\n                      + \":\" + mytime.tm_sec.__str__()\n    msg_from_user_name = msg['FromUserName']\n    msg_id = msg['MsgId']  # 消息ID\n    msg_time = msg['CreateTime']  # 消息时间\n    msg_from = itchat.search_friends(userName=msg['FromUserName'])['NickName']  # 消息发送人昵称\n    msg_type = msg['Type']  # 消息类型\n    msg_content = None  # 根据消息类型不同，消息内容不同\n    msg_url = None  # 分享类消息有url\n    # 图片 语音 附件 视频，可下载消息将内容下载暂存到当前目录\n    wx_msg_text = msg['Text']\n    if msg['Type'] == 'Text':\n\n        msg_content = wx_msg_text\n\n        global autoReplyFlag, timerSet, noReply, t  # 状态标志位\n        print(wx_msg_text)\n        if is_msg_from_myself(msg['FromUserName']):\n            print(\"Replied!!\")\n            autoReplyFlag = False\n            noReply = False\n            try:\n                t.cancel()\n                print(\"Timer Canceled\")\n                timerSet = False\n            except:\n                pass\n            return None\n\n        if autoReplyFlag:\n            # 查看功能\n            if wx_msg_text == '所有功能':\n                return '您可以使用下列功能:\\n' + get_functions()\n            elif wx_msg_text[:1] == r'#':\n                return get_example(wx_msg_text[1:])\n            elif wx_msg_text[:1] == r'@':\n                print('do nothing')\n            else:\n                # 为了保证在图灵Key出现问题的时候仍旧可以回复，这里设置一个默认回复\n                default_reply = 'I received: ' + wx_msg_text\n                # 如果图灵Key出现问题，那么reply将会是None\n                reply = get_response(wx_msg_text)\n                # a or b的意思是，如果a有内容，那么返回a，否则返回b\n                # 有内容一般就是指非空或者非None，你可以用`if a: print('True')`来测试\n                if AUTO_REPLAY:\n                    return '小K: ' + reply or '小K: ' + default_reply\n        else:\n            noReply = True\n            if not timerSet:\n                # if time.time()-noReplyStartTime >= 120:\n                print(\"Timer setting\")\n                t = Timer(12, send_busy_status, [msg['FromUserName']])\n                t.start()\n                timerSet = True\n\n    elif msg['Type'] == 'Picture':\n        msg_content = msg['FileName']\n\n        try:\n            # 下载图片\n            wx_msg_text(msg['FileName'])\n        except OSError:\n            ''\n    elif msg['Type'] == 'Card':\n        msg_content = msg['RecommendInfo']['NickName'] + r\" 的名片\"\n    elif msg['Type'] == 'Map':\n        x, y, location = re.search(\"<location x=\\\"(.*?)\\\" y=\\\"(.*?)\\\".*label=\\\"(.*?)\\\".*\", msg['OriContent']).group(1,\n                                                                                                                    2,\n                                                                                                                    3)\n        if location is None:\n            msg_content = r\"纬度->\" + x.__str__() + \" 经度->\" + y.__str__()\n        else:\n            msg_content = r\"\" + location\n    elif msg['Type'] == 'Sharing':\n        msg_content = wx_msg_text\n        msg_url = msg['Url']\n    elif msg['Type'] == 'Recording':\n        msg_content = msg['FileName']\n        wx_msg_text(msg['FileName'])\n    elif msg['Type'] == 'Attachment':\n        msg_content = r\"\" + msg['FileName']\n        wx_msg_text(msg['FileName'])\n    elif msg['Type'] == 'Video':\n        msg_content = msg['FileName']\n        wx_msg_text(msg['FileName'])\n    elif msg['Type'] == 'Friends':\n        msg_content = wx_msg_text\n\n    # friend = itchat.search_friends(userName=msg['FromUserName'])\n    # itchat.send(r\"Friend:%s -- %s    \"\n    #             r\"Time:%s    \"\n    #             r\" Message:%s\" % (friend['NickName'], friend['RemarkName'], msg_time_touser, msg_content),\n    #             toUserName='filehelper')\n\n    if msg['Type'] != 'Text':\n        if AUTO_REPLAY:\n            itchat.send(\"小K已收到您的消息,主人稍后回复!\\n\\t时间: %s\\n\\t内容: %s\" % (msg_time_touser, msg_content),\n                        toUserName=msg['FromUserName'])\n\n    # 更新字典\n    # {msg_id:(msg_from,msg_time,msg_time_touser,msg_type,msg_content,msg_url)}\n    msg_dict.update(\n        {msg_id: {\"msg_from\": msg_from, \"msg_time\": msg_time, \"msg_time_touser\": msg_time_touser, \"msg_type\": msg_type,\n                  \"msg_content\": msg_content, \"msg_url\": msg_url, \"msg_from_user_name\": msg_from_user_name}})\n    # 清理字典\n    clear_timeout_msg()\n\n\n# 收到note类消息，判断是不是撤回并进行相应操作\n@itchat.msg_register([NOTE])\ndef save_msg(msg):\n    # print(msg)\n    # 创建可下载消息内容的存放文件夹，并将暂存在当前目录的文件移动到该文件中\n    if not os.path.exists(\"Revocation\"):\n        os.mkdir(\"Revocation\")\n    if re.search(r\"\\<replacemsg\\>\\<\\!\\[CDATA\\[.*撤回了一条消息\\]\\]\\>\\<\\/replacemsg\\>\", msg['Content']) is not None:\n\n        old_msg_id = re.search(\"\\<msgid\\>(.*?)\\<\\/msgid\\>\", msg['Content']).group(1)\n\n        old_msg = msg_dict.get(old_msg_id, {})\n\n        # print(old_msg_id, old_msg)\n\n        msg_send = r'您的好友：' \\\n                   + old_msg.get('msg_from', '') \\\n                   + r\"  在 [\" + old_msg.get('msg_time_touser', '') \\\n                   + r\"], 撤回了一条 [\" + old_msg['msg_type'] + \"] 消息, 内容如下:\" \\\n                   + old_msg.get('msg_content', '')\n        if old_msg['msg_type'] == \"Sharing\":\n            msg_send += r\", 链接: \" + old_msg.get('msg_url', '')\n        elif old_msg['msg_type'] == 'Picture' \\\n                or old_msg['msg_type'] == 'Recording' \\\n                or old_msg['msg_type'] == 'Video' \\\n                or old_msg['msg_type'] == 'Attachment':\n            msg_send += r\", 存储在当前目录下Revocation文件夹中\"\n\n        # print('************' + old_msg['msg_content'])\n\n        itchat.send(msg_send, toUserName='filehelper')  # 将撤回消息的通知以及细节发送到文件助手\n        if AUTO_REPLAY:\n            itchat.send('小K : 还想撤回 ? too naive!!! 消息已经保存存啦' + r'[偷笑]',\n                        toUserName=old_msg.get('msg_from_user_name'))  # 将撤回消息的通知以及细节发送到文件助手\n        try:\n            shutil.move(old_msg['msg_content'], \"Revocation\")\n        except FileNotFoundError:\n            print('move error')\n        msg_dict.pop(old_msg_id)\n\n        clear_timeout_msg()\n\n\n# 为了让实验过程更加方便（修改程序不用多次扫码），我们使用热启动\nitchat.auto_login()\n\nautoReplyFlag, timerSet, noReply = False, False, False\nt = 0  # 定义全局变量t, 用作触发器使用，此行甚是丑陋；怎么才能更优雅呢？请大神指点。\nmyName = itchat.get_friends(update=True)[0]['UserName']\nitchat.run()\n","sub_path":"robot_smart.py","file_name":"robot_smart.py","file_ext":"py","file_size_in_byte":14567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"550769914","text":"import argparse\n\ndef parse_args():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('s1')\n    parser.add_argument('s2')\n    return parser.parse_args()\n\ndef stringOneEdit(s1, s2):\n    print('Comparing {0} & {1}'.format(s1, s2))\n    if abs(len(s1) - len(s2)) > 1:\n        print('Different by more than 1')\n        return\n\n    # always make s1 the longer string\n    if len(s1) < len(s2):\n        tmp = s1\n        s1 = s2\n        s2 = tmp\n\n    diff = 0\n    i = 0\n    j = 0\n    while i < len(s1) and j < len(s2) and diff < 2:\n        # print('s1[{0}]{1}  ?==  s2[{2}]{3}'.format(i, s1[i], j, s2[j]))\n        if s1[i] != s2[j]:\n            diff += 1\n            i += 1\n            if len(s1) == len(s2):\n                j += 1\n        else:\n            i += 1\n            j += 1\n\n    if diff < 2:\n        print('Same or different by at most 1')\n    else:\n        print('Different by more than 1')\n\n\ndef main():\n    args = parse_args()\n    stringOneEdit(args.s1, args.s2)\n\nif __name__ == '__main__':\n    main()","sub_path":"py/leetcode/string_one_edit.py","file_name":"string_one_edit.py","file_ext":"py","file_size_in_byte":1017,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"345325418","text":"import tensorflow as tf\nimport numpy as np\nfrom sklearn.cross_validation import train_test_split\n\n# Load the data\nX = np.load('X_Test.npy')\nY = np.load('Y_Test.npy')\n\n#initial value of weights\n\nlayer_size_1 = 200 # put the same number as it is in neural newtwork code\nlayer_size_2 = 10\n\n# Load the variables\nw_1 = np.load('W1.npy')\nw_2 = np.load('W2.npy')\nw_3 = np.load('W3.npy')\n\n# Model\ndef model(X, w_1, w_2,w_3):\n    h1 = tf.nn.tanh(tf.matmul(X, w_1))\n    h2 = tf.nn.tanh(tf.matmul(h1, w_2))\n    return tf.matmul(h2, w_3)\n\n# Run the optimisation\nwith tf.Session() as sess:\n    tf.initialize_all_variables().run()\n\n    Predicted = sess.run(tf.nn.sigmoid(model),feed_dict={X:X, w_1:W1, w_2:W2, w_3:W3})\n    Predicted = np.round(Predicted)\n    print('Testing', np.mean(Predicted == Y), sum(Y) / len(Y))\n\n\n","sub_path":"Prediction.py","file_name":"Prediction.py","file_ext":"py","file_size_in_byte":806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"283535901","text":"def addForceMomentProbe(context):\n\tobjList = ExtAPI.DataModel.Tree.ActiveObjects\n\tanalysis = objList[0].Parent\n\tfor obj in objList:\n\t\tif obj.InternalObject.Class == 403:\t\t\t# Load & BC\n\t\t\tif obj.InternalObject.AnsBCType in [13, 14, 15, 16, 17, 19, 20, 29]:\n\t\t\t\tprobe = analysis.Solution.AddForceReaction()\n\t\t\t\tprobe.Name = 'F_' + obj.Name\n\t\t\t\tprobe.BoundaryConditionSelection = obj\n\t\t\tif obj.InternalObject.AnsBCType in [13, 14, 15, 16, 17, 19, 20, 28, 29]:\n\t\t\t\tprobe = analysis.Solution.AddMomentReaction()\n\t\t\t\tprobe.Name = 'M_' + obj.Name\n\t\t\t\tprobe.BoundaryConditionSelection = obj\n# Class = 403\n# AnsBCType:\n# Frictionless Support 13\n# Compression Only Support 14\n# Cylindrical Support 15\n# Simply Supported 16\n# Elastic Support 17\n# Displacement 19\n# Remote Displacement 20\n# Fixed Rotation 28\n# Fixed Support 29\n# Velocity 54 (ej)","sub_path":"ForceMomentProbe/forceMomentProbe.py","file_name":"forceMomentProbe.py","file_ext":"py","file_size_in_byte":834,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"581229929","text":"from math import ceil\nimport random\nimport sys\nimport time\nfrom pyfiglet import Figlet\nfrom plumbum import cli\nimport questionary\nimport nltk\nfrom nltk.corpus import words\nnltk.download('words') # Delete me after first time running\n\n\ndef delete_last_line():\n    \"\"\"Delete the last line\"\"\"\n    sys.stdout.write('\\x1b[1A')\n    sys.stdout.write('\\x1b[2K')\n\n\nLETTERS = \"QWERTYUIOPASDFGHJKLZXCVBNM\"\nDIFFICULTIES = {'Easy': '5 12', 'Medium': '7 16', 'Hard': '10 20'}\nDIRECTIONS = {\n    0: '-1 0',\n    1: '-1 1',\n    2: '0 1',\n    3: '1 1',\n    4: '1 0',\n    5: '1 -1',\n    6: '0 -1',\n    7: '-1 -1',\n}\nINSTR = \"\"\"\nThis is a standard word search with 3 difficulties:\n    -Easy: 5 words, 12x12\n    -Medium: 7 words, 16x16\n    -Hard: 10 words, 20x20\nThe words are imported from NLTK.\n\nIf you find a word, enter the coordinates of the first letter, separated by a space.\nAlthough the margins only show the units digit, enter the full coordinate.\nThe row goes first, and then the column.\nThe numbering starts at 1 1, which represents the top left corner.\nYou can always select 'exit program' to exit the program.\nEnjoy!\n\"\"\"\n\ndef print_block(text):\n    \"\"\"Print the title\"\"\"\n    print(Figlet(font='doom', justify='center').renderText(text))\n\ndef select_difficulty():\n    \"\"\"Have user choose a difficulty\"\"\"\n    return questionary.select(\n        \"Choose a difficulty:\",\n        choices=[\n            'Easy',\n            'Medium',\n            'Hard',\n        ]).ask()\n\ndef found_words(to_find):\n    \"\"\"Display words left to find\"\"\"\n    return questionary.select(\n        'Check off when found:',\n        choices=to_find\n    ).ask()\n\ndef check_found_word(word):\n    \"\"\"Check if the user really found a word\"\"\"\n    return questionary.text(f\"Enter coordinates of the first letter of {word}:\").ask()\n\ndef create_letter_list_2d(board_size: int, random_words):\n    \"\"\"Create the 2d list of letters to \"paste\" onto board\"\"\"\n    def find_space(word, start_row, start_col, direction):\n        del_row = int(DIRECTIONS[direction].split()[0])\n        del_col = int(DIRECTIONS[direction].split()[1])\n        has_space = True\n\n        curr_row = start_row\n        curr_col = start_col\n        for i in word:\n            if letter_list[curr_row][curr_col] != ' ' and letter_list[curr_row][curr_col] != i:\n                has_space = False\n                break\n            curr_row += del_row\n            curr_col += del_col\n\n        if has_space:\n            # Add to dictionary only if there is space\n            start_pos[word] = f'{start_row + 1} {start_col + 1}'\n\n            curr_row = start_row\n            curr_col = start_col\n            for let in word:\n                # Add to 2d list only if there is space\n                letter_list[curr_row][curr_col] = let\n\n                curr_row += del_row\n                curr_col += del_col\n        return has_space\n\n    letter_list = [[' ' for i in range(board_size)] for j in range(board_size)]\n    start_pos = {}\n    for word in random_words:\n        success = False\n        while not success:\n            direction = random.randrange(8)\n            start_coords = \"\"\n            start_row = \"\"\n            start_col = \"\"\n\n            # N\n            if direction == 0:\n                start_row = random.randrange(len(word) - 1, board_size)\n                start_col = random.randrange(board_size)\n            # NE\n            elif direction == 1:\n                start_row = random.randrange(len(word) - 1, board_size)\n                start_col = random.randrange(board_size - len(word) + 1)\n            # E\n            elif direction == 2:\n                start_row = random.randrange(board_size)\n                start_col = random.randrange(board_size - len(word) + 1)\n            # SE\n            elif direction == 3:\n                start_row = random.randrange(board_size - len(word) + 1)\n                start_col = random.randrange(board_size - len(word) + 1)\n            # S\n            elif direction == 4:\n                start_row = random.randrange(board_size - len(word) + 1)\n                start_col = random.randrange(board_size)\n            # SW\n            elif direction == 5:\n                start_row = random.randrange(board_size - len(word) + 1)\n                start_col = random.randrange(len(word) - 1, board_size)\n            # W\n            elif direction == 6:\n                start_row = random.randrange(board_size)\n                start_col = random.randrange(len(word) - 1, board_size)\n            # NW\n            else:\n                start_row = random.randrange(len(word) - 1, board_size)\n                start_col = random.randrange(len(word) - 1, board_size)\n\n            start_coords = f'{start_row} {start_col}'\n            success = find_space(word, int(start_coords.split()[0]),\n                int(start_coords.split()[1]), direction)\n\n    for i in range(board_size):\n        for j in range(board_size):\n            if letter_list[i][j] == ' ':\n                letter_list[i][j] = random.choice(LETTERS)\n    return [letter_list, start_pos]\n\ndef print_board(input_list):\n    \"\"\"Paste the 2d list of letters onto a square board\"\"\"\n    v_spacing_list = [f'{(i + 1) % 10} ' for i,_ in enumerate(input_list)]\n    print(f\"\\n{''.join(v_spacing_list).rjust(2 * len(v_spacing_list) + 20)}\\n\")\n    for i,_ in enumerate(input_list):\n        h_spacing = f'{(i + 1) % 10}  '\n        print(h_spacing.rjust(20), end='')\n        for j,_ in enumerate(input_list[i]):\n            print(f'{input_list[i][j]} ', end='')\n        print(f'  {h_spacing} ')\n    print(f\"\\n{''.join(v_spacing_list).rjust(2 * len(v_spacing_list) + 20)}\\n\")\n\ndef generate_words(num_words: int, board_size: int):\n    \"\"\"Generate random words of valid length\"\"\"\n    my_words = []\n    while len(my_words) < num_words:\n        random_word = random.choice(words.words()).upper()\n        if len(random_word) in range(ceil(board_size / 4), board_size + 1):\n            my_words.append(random_word)\n    return my_words\n\nclass Game(cli.Application):\n    VERSION = \"1.0\"\n    instructions = cli.Flag(['i', 'instructions'], help=\"Read the instructions\")\n\n    def main(self):\n        print_block(\"Word Search\")\n\n        if self.instructions:\n            print(INSTR)\n            time.sleep(10)\n\n        diff = DIFFICULTIES[select_difficulty()].split()\n        random_list = generate_words(int(diff[0]), int(diff[1]))\n        random_letter_list = create_letter_list_2d(int(diff[1]), random_list)\n        my_list = random_letter_list[0]\n        my_pos = random_letter_list[1]\n\n        print_board(my_list)\n        random_list.append('exit program')\n\n        print('Remaining words:')\n        while len(random_list) > 1:\n            has_found = found_words(random_list)\n            if has_found == 'exit program':\n                sys.exit()\n            if check_found_word(has_found) == my_pos[has_found]:\n                print(\"Nice!\")\n                random_list.remove(has_found)\n            else:\n                print(\"Try again.\")\n            time.sleep(0.5)\n            for _ in range(3):\n                delete_last_line()\n        print(\"\\nCongrats! You win!\")\n\nif __name__ == \"__main__\":\n    Game()\n\ndef test_create_letter_list_2d():\n    \"\"\"Test the `create_letter_list_2d` method\"\"\"\n    my_words = generate_words(5, 12)\n    assert len(my_words) == 5\n    assert len(create_letter_list_2d(12, my_words)[1]) == 5\n\ndef test_generate_words():\n    \"\"\"Test the `generate_words` method\"\"\"\n    my_words = generate_words(3, 8)\n    for word in my_words:\n        assert len(word) in range(2, 9)\n    assert len(my_words) == 3\n","sub_path":"wordsearch.py","file_name":"wordsearch.py","file_ext":"py","file_size_in_byte":7540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"125965716","text":"\"\"\" Functions and Operations for analysing a cluster's tidal tails\n\n\"\"\"\n__author__ = \"Jeremy J Webb\"\n__all__ = [\n    \"to_tail\",\n    \"tail_path\",\n    \"tail_path_match\",\n]\n\nfrom galpy.util import bovy_conversion,_rotate_to_arbitrary_vector\nfrom galpy import potential\nfrom galpy.potential import MWPotential2014\n\nimport numpy as np\n\nfrom .orbit import orbital_path, orbital_path_match\nfrom .operations import *\nfrom ..util.recipes import binmaker\n\nfrom ..util.plots import *\n\n\ndef to_tail(cluster):\n    \"\"\"Calculate positions and velocities of stars when rotated such that clusters velocity vector\n       points along x-axis\n\n    - no change to coordinates in StarCluster\n\n    Parameters\n    ----------\n    cluster : class\n        StarCluster\n\n    Returns\n    -------\n    x_tail,y_tail,z_tail,vx_tail,vy_tail,vz_tail : float\n        rotated coordinates with cluster's velocity vector point along x-axis\n\n    History:\n    -------\n    2018 - Written - Webb (UofT)\n\n    \"\"\"\n    units0, origin0 = cluster.units, cluster.origin\n\n    cluster.to_centre()\n\n    v_vec = np.array([cluster.vxgc, cluster.vygc, cluster.vzgc])\n    new_v_vec = np.array([1.0, 0.0, 0.0])\n\n    rot = _rotate_to_arbitrary_vector(\n        np.atleast_2d(v_vec), new_v_vec, inv=False, _dontcutsmall=False\n    )\n\n    x_tail = (\n        cluster.x * rot[:, 0, 0] + cluster.y * rot[:, 1, 0] + cluster.z * rot[:, 2, 0]\n    )\n    y_tail = (\n        cluster.x * rot[:, 0, 1] + cluster.y * rot[:, 1, 1] + cluster.z * rot[:, 2, 1]\n    )\n    z_tail = (\n        cluster.x * rot[:, 0, 2] + cluster.y * rot[:, 1, 2] + cluster.z * rot[:, 2, 2]\n    )\n    vx_tail = (\n        cluster.vx * rot[:, 0, 0] + cluster.vy * rot[:, 1, 0] + cluster.vz * rot[:, 2, 0]\n    )\n    vy_tail = (\n        cluster.vx * rot[:, 0, 1] + cluster.vy * rot[:, 1, 1] + cluster.vz * rot[:, 2, 1]\n    )\n    vz_tail = (\n        cluster.vx * rot[:, 0, 2] + cluster.vy * rot[:, 1, 2] + cluster.vz * rot[:, 2, 2]\n    )\n\n    cluster.to_origin(origin0)\n\n    return x_tail,y_tail,z_tail,vx_tail,vy_tail,vz_tail\n\ndef tail_path(\n    cluster, dt=0.1, nt=100, pot=MWPotential2014, from_centre=False, ro=8.0, vo=220.0,\n    plot=False\n):\n    \"\"\"Calculate tail path +/- dt Gyr around the cluster\n\n        Parameters\n    ----------\n    cluster : class\n        StarCluster\n    dt : float\n        timestep that StarCluster is to be moved to\n    nt : int\n        number of timesteps\n    pot : class\n        galpy Potential that orbit is to be integrate in (default: MWPotential2014)\n    from_centre : bool\n        genrate orbit from cluster's exact centre instead of its assigned galactocentric coordinates (default: False)\n    ro :float \n        galpy distance scale (Default: 8.)\n    vo : float\n        galpy velocity scale (Default: 220.)\n    plot : bool\n        plot a snapshot of the cluster in galactocentric coordinates with the orbital path (defualt: False)\n\n    Returns\n    -------\n    t : float\n        times for which path is provided\n    x,y,z : float\n        tail path positions\n    vx,vy,vz : float\n        tail path velocities\n    History\n    -------\n    2018 - Written - Webb (UofT)\n    2019 - Implemented numpy array preallocation to minimize runtime - Nathaniel Starkman (UofT)\n    \"\"\"\n\n    units0, origin0 = save_cluster(cluster)\n    cluster.to_galaxy()\n    cluster.to_kpckms()\n\n    to, xo, yo, zo, vxo, vyo, vzo, o = orbital_path(\n        cluster,\n        dt=dt,\n        nt=nt,\n        pot=pot,\n        from_centre=from_centre,\n        initialize=True,\n        ro=ro,\n        vo=vo,\n    )\n    tstar, dprog, dpath = orbital_path_match(\n        cluster=cluster, dt=dt, nt=nt, pot=pot, from_centre=from_centre, ro=ro, vo=vo\n    )\n\n    t_lower, t_mid, t_upper, t_hist = binmaker(to, nbin=nt)\n    ttail = []\n    xtail = []\n    ytail = []\n    ztail = []\n    vxtail = []\n    vytail = []\n    vztail = []\n\n    for i in range(0, len(t_mid)):\n        indx = (tstar >= t_lower[i]) * (tstar <= t_upper[i])\n        if np.sum(indx) > 0:\n            ttail = np.append(ttail, t_mid[i])\n            xtail = np.append(xtail, np.mean(cluster.x[indx]))\n            ytail = np.append(ytail, np.mean(cluster.y[indx]))\n            ztail = np.append(ztail, np.mean(cluster.z[indx]))\n            vxtail = np.append(vxtail, np.mean(cluster.vx[indx]))\n            vytail = np.append(vytail, np.mean(cluster.vy[indx]))\n            vztail = np.append(vztail, np.mean(cluster.vz[indx]))\n\n    if plot:\n        filename = kwargs.pop(\"filename\", None)\n        overplot = kwargs.pop(\"overplot\", False)\n        starplot(cluster,coord='xy',overplot=overplot)\n        _lplot(xtail,ytail,overplot=True)\n\n        if filename != None:\n            plt.savefig(filename)\n\n    return_cluster(cluster, units0, origin0)\n\n    return ttail, xtail, ytail, ztail, vxtail, vytail, vztail\n\n\ndef tail_path_match(\n    cluster,\n    dt=0.1,\n    nt=100,\n    pot=MWPotential2014,\n    from_centre=False,\n    to_path=False,\n    do_full=False,\n    ro=8.0,\n    vo=220.0,\n    plot=False,\n):\n    \"\"\"Match stars to a position along the tail path of the cluster\n\n    Parameters\n    ----------\n    cluster : class\n        StarCluster\n    dt : float\n        timestep that StarCluster is to be moved to\n    nt : int\n        number of timesteps\n    pot : class\n        galpy Potential that orbit is to be integrate in (default: MWPotential2014)\n    from_centre : bool\n        genrate orbit from cluster's exact centre instead of its assigned galactocentric coordinates (default: False)\n    to_path : bool\n        measure distance to the path itself instead of distance to central point along the path (default: False)\n    do_full : bool\n        calculate dpath all at once in a single numpy array (can be memory intensive) (default:False)\n    ro :float \n        galpy distance scale (Default: 8.)\n    vo : float\n        galpy velocity scale (Default: 220.)\n    plot : bool\n        plot a snapshot of the cluster in galactocentric coordinates with the orbital path (defualt: False)\n\n    Returns\n    -------\n    tstar : float\n        orbital time associated with star\n    dprog : float\n        distance along the path to the progenitor\n    dpath : \n        distance to centre of the tail path bin (default) or the tail path (to_path = True)\n\n    History\n    -------\n    2018 - Written - Webb (UofT)\n    \"\"\"\n    units0, origin0 = save_cluster(cluster)\n    cluster.to_galaxy()\n    cluster.to_kpckms()\n\n    ts, x, y, z, vx, vy, vz = tail_path(\n        cluster, dt=dt, nt=nt, pot=pot, from_centre=from_centre, ro=ro, vo=vo\n    )\n    pindx = np.argmin(np.fabs(ts))\n\n    dx = np.tile(x, cluster.ntot).reshape(cluster.ntot, len(ts)) - np.repeat(\n        cluster.x, len(ts)\n    ).reshape(cluster.ntot, len(ts))\n    dy = np.tile(y, cluster.ntot).reshape(cluster.ntot, len(ts)) - np.repeat(\n        cluster.y, len(ts)\n    ).reshape(cluster.ntot, len(ts))\n    dz = np.tile(z, cluster.ntot).reshape(cluster.ntot, len(ts)) - np.repeat(\n        cluster.z, len(ts)\n    ).reshape(cluster.ntot, len(ts))\n    dr = np.sqrt(dx ** 2.0 + dy ** 2.0 + dz ** 2.0)\n\n    indx = np.argmin(dr, axis=1)\n    dpath = np.amin(dr, axis=1)\n    tstar = ts[indx]  # *bovy_conversion.time_in_Gyr(ro=ro,vo=vo)\n\n    dxo = x[1:] - x[0:-1]\n    dyo = y[1:] - y[0:-1]\n    dzo = z[1:] - z[0:-1]\n\n    dprogx = np.cumsum(np.fabs(dxo))\n    dprogy = np.cumsum(np.fabs(dyo))\n    dprogz = np.cumsum(np.fabs(dzo))\n\n    dprogx = np.insert(dprogx, 0, 0.0)\n    dprogy = np.insert(dprogy, 0, 0.0)\n    dprogz = np.insert(dprogz, 0, 0.0)\n\n    dprogr = np.sqrt(dprogx ** 2.0 + dprogy ** 2.0 + dprogz ** 2.0)\n    dprog = dprogr[indx] - dprogr[pindx]\n\n    # Find distance to path instead of to central point\n    if to_path:\n        dxo = np.append(dxo, dxo[-1])\n        dyo = np.append(dyo, dyo[-1])\n        dzo = np.append(dzo, dzo[-1])\n\n        if do_full:\n            # Typically it is too expensive to calculate dpath all at once, but will allow option via do_full\n\n            ovec = np.column_stack([dxo, dyo, dzo])\n            mag_ovec = np.sqrt(dxo ** 2.0 + dyo ** 2.0 + dzo ** 2.0)\n            svec = np.column_stack([dx[:, indx], dy[:, indx], dz[:, indx]])\n            mag_svec = dr[:, indx]\n            theta = np.arccos(np.dot(ovec[indx], svec) / (mag_ovec[indx] * mag_svec))\n            dpath = mag_svec * np.sin(theta)\n        else:\n            # Need to optimize this via numba\n            dpath = np.array([])\n            for i in range(0, cluster.ntot):\n                ovec = [dxo[indx[i]], dyo[indx[i]], dzo[indx[i]]]\n                mag_ovec = np.sqrt(\n                    dxo[indx[i]] ** 2.0 + dyo[indx[i]] ** 2.0 + dzo[indx[i]] ** 2.0\n                )\n\n                svec = [dx[i, indx[i]], dy[i, indx[i]], dz[i, indx[i]]]\n                mag_svec = dr[i, indx[i]]\n\n                theta = np.arccos(\n                    (ovec[0] * svec[0] + ovec[1] * svec[1] + ovec[2] * svec[2])\n                    / (mag_ovec * mag_svec)\n                )\n                dpath = np.append(dpath, mag_svec * np.sin(theta))\n\n    # Assign negative to stars with position vectors in opposite direction as local angular momentum vector\n    rgc = np.column_stack([x[indx], y[indx], z[indx]])\n    vgc = np.column_stack([vx[indx], vy[indx], vz[indx]])\n    lz = np.cross(rgc, vgc)\n\n    rstar = np.column_stack(\n        [cluster.x - x[indx], cluster.y - y[indx], cluster.z - z[indx]]\n    )\n\n    ldot = np.sum(rstar * lz, axis=1)\n    dpath[ldot < 0] *= -1\n\n    if plot:\n        filename = kwargs.pop(\"filename\", None)\n        overplot = kwargs.pop(\"overplot\", False)\n        _scatter(dprog,dpath,xlabel=\"Dprog\",ylabel=\"Dpath\",overplot=overplot)\n\n        if filename != None:\n            plt.savefig(filename)\n\n    return_cluster(cluster, units0, origin0)\n\n    return np.array(tstar), np.array(dprog), np.array(dpath)","sub_path":"clustertools/analysis/tails.py","file_name":"tails.py","file_ext":"py","file_size_in_byte":9705,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"592830275","text":"#!/usr/bin/env python3\n\n##########################################################       \ndef getFloats(string):\n   numbers_str = string.split()\n   numbers_float = [float(x) for x in numbers_str]   \n   return numbers_float\n\n\n########################################################   \ndef  getProb(kmer, probs):\n   prod = 1.0\n   ll = len(kmer)\n   for i in range(0,ll):\n      ch = kmer[i]\n      prod = prod * probs[ch][i]  \n   \n   return prod   \n########################################################################           \ndef getKMers(s, k):\n   ll = len(s)\n   kmers = [ ] \n   for i in range(0,ll-k+1):\n        kmers.append(s[i:i+k])\n\n   return kmers\n    \n\n########################################################  \ndef mostProb(s, probs, t):\n    mostp = \"\"\n    pp = 0\n    allkm = getKMers(s, t)\n    \n    for km in allkm:\n       pp1 = getProb(km, probs) \n       if pp1 > pp:\n          pp = pp1\n          mostp = km   \n\n    return mostp\n\n########################################################  \n \n\nfin = open(\"input.dat\",\"r\")\ndna2 = fin.readline()\ndna = dna2.strip()\n\nts = fin.readline()\nt = int(ts.strip())\n\ncharss = fin.readline()\ncharss2 = charss.strip()\n\nchars = charss.split()\nprint(chars)\nprobs = {}\n\nfor ch in chars:\n   probs[ch] = []\n\nfor i in range(0,t):\n   line = fin.readline()\n   floats = getFloats(line)\n   ff = 0\n   for ch in chars:\n      probs[ch].append(floats[ff])\n      ff = ff + 1\n   \nprint( mostProb(dna, probs, t))\n    \n","sub_path":"p3-3.py","file_name":"p3-3.py","file_ext":"py","file_size_in_byte":1448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"425863156","text":"import operator\n\nfull_text = input(\"Give me some text: \")\n\nremove_these = [',','.','?','(',')','-']\nfor thing in remove_these:\n  full_text = full_text.replace(thing,'')\n\n\nword_list = full_text.lower().split()\n\nword_count = {}\n\nfor word in word_list:\n  if word in word_count:\n    word_count[word] += 1\n  else:\n    word_count[word] = 1\n\nsorted_count = sorted(word_count.items(), key=operator.itemgetter(1), reverse=False)\n  \n\n\nfor word_tuple in sorted_count:\n  print(f'{word_tuple[0].capitalize()} - {word_tuple[1]}')\n\nprint(f'\\nThere are {len(sorted_count)} unique words!')","sub_path":"inputs.py","file_name":"inputs.py","file_ext":"py","file_size_in_byte":572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"563480127","text":"from telegram import KeyboardButton, InlineKeyboardButton\n################################################\n# АДМИН\n################################################\n\n# РАССЫЛКА\nmailing_photo = [[InlineKeyboardButton(\"✅ Да\", callback_data='mailing_photo_yes'), InlineKeyboardButton(\"❌ Нет\", callback_data='mailing_photo_no')],\n                 [InlineKeyboardButton('Отмена', callback_data='mail_decline')]]\n\nmailing_send = [[InlineKeyboardButton(\"❌ Отмена\", callback_data='mailing_send_no')], [InlineKeyboardButton(\"📨 Начать\", callback_data='mailing_send_yes')]]\n# РАССЫЛКА\n################################################\n# ПОЛЬЗОВАТЕЛЬ\n################################################\n\n# КНОПКИ\nd_language = {'ru': \"🇷🇺 Русский\",\n              'uz': \"🇺🇿 Узбекский\"\n              }\n\nd_menu = {'ru': {'products': \"💪 Продукция\",\n                 'about': \"☎ О нас\",\n                 'news': \"❓ Новости\",\n                 'actions': \"🎁 Акции\",\n                 'main_menu': \"⬅ Главное меню\",\n                 'change_language': \"🇷🇺🔄🇺🇿 Сменить язык\",\n                 'basket': \"🛒 Корзина\",\n                 'partnership': \"🤝 Партнерка\",\n                 'cancel': \"❌ Отмена\"},\n\n          'uz': {'products': \"💪 Продукция\",\n                 'about': \"☎ О нас\",\n                 'news': \"❓ Новости\",\n                 'actions': \"🎁 Акции\",\n                 'main_menu': \"⬅ Главное меню\",\n                 'change_language': \"🇺🇿🔄🇷🇺 Сменить язык\",\n                 'basket': \"🛒 Корзина\",\n                 'partnership': \"🤝 ��артнерка\",\n                 'cancel': \"❌ Отмена\"}\n          }\n\nd_action = {'ru': {'next': \"Далее ➡\", 'back': \"⬅ Назад\"},\n            'uz': {'next': \"Далее ➡\", 'back': \"⬅ Назад\"}\n            }\n\nd_products = {'ru': {'healthy': \"🥦 Здоровое питание\", 'sport': \"🏋️‍♂️Спортвиное питание\"},\n              'uz': {'healthy': \"🥦 Здоровое питание\", 'sport': \"🏋️‍♂️Спортвиное питание\"}\n              }\n\nback = {'ru': \"⬅ Назад\",\n        'uz': \"⬅ Назад\"}\n\nadd_basket = {'ru': '''🛒 Добавить в корзину''',\n              'uz': '''🛒 Добавить в корзину'''}\n\n\nd_basket = {'ru': {'clear_basket': \"❌ Очистить корзину\", 'order': \"🚗 Заказать\", 'promocode': \"🔲 Промокод\"},\n            'uz': {'clear_basket': \"❌ Очистить корзину\", 'order': \"🚗 Заказать\", 'promocode': \"🔲 Промокод\"}\n            }\n\nd_partnership = {'ru': {'statistic': \"📊 Статистика\", 'application': \"✏ Оставить заявку\", 'info': \"❓ О партнерстве\"},\n                 'uz': {'statistic': \"📊 Статистика\", 'application': \"✏ Оставить заявку\", 'info': \"❓ О партнерстве\"}\n                 }\n\nd_contact = {'ru': {'location': \"📍 Поделиться локацией\", 'contact': \"📞 Поделиться контактом\"},\n             'uz': {'location': \"📍 Поделиться локацией\", 'contact': \"📞 Поделиться контактом\"}\n             }\n\nd_type_delivery = {'ru': {'self': \"✋ Самовывоз\", 'delivery': \"🚗 Доставка\"},\n                   'uz': {'self': \"✋ Самовывоз\", 'delivery': \"🚗 Доставка\"}\n                   }\n\nd_type_payment = {'ru': {'cash': \"💵 Наличные\", 'payme': \"💳 PayMe\"},\n                  'uz': {'cash': \"💵 Наличные\", 'payme': \"💳 PayMe\"}}\n\nd_status_order = {'accept_order': \"✅ Принять\", 'decline_order': \"❌ Отклонить\"}\n# КНОПКИ\n\n################################################\n\n# ЯЗЫК\nlanguage = [[d_language['uz']],\n            [d_language['ru']]\n            ]\n# ЯЗЫК\n\n\n# МЕНЮ\nmain_menu = {'ru': [[d_menu['ru']['products']],\n                    [d_menu['ru']['about'], d_menu['ru']['basket']],\n                    [d_menu['ru']['news'], d_menu['ru']['actions']],\n                    [d_menu['ru']['partnership']],\n                    [d_menu['ru']['change_language']]\n                    ],\n\n             'uz': [[d_menu['uz']['products']],\n                    [d_menu['uz']['about'], d_menu['uz']['basket']],\n                    [d_menu['uz']['news'], d_menu['uz']['actions']],\n                    [d_menu['uz']['partnership']],\n                    [d_menu['uz']['change_language']]\n                    ],\n             }\n# МЕНЮ\n\n\n# ПРОДУКТЫ\nproducts = {'ru': [[d_products['ru']['healthy'], d_products['ru']['sport']],\n                   [d_menu['ru']['main_menu']]\n                   ],\n\n            'uz': [[d_products['ru']['healthy'], d_products['ru']['sport']],\n                   [d_menu['ru']['main_menu']]\n                   ],\n            }\n\nback_category_healthy = {'ru': [InlineKeyboardButton(back['ru'], callback_data='back_category_healthy')],\n                         'uz': [InlineKeyboardButton(back['uz'], callback_data='back_category_healthy')]\n                         }\n\nback_category_sport = {'ru': [InlineKeyboardButton(back['ru'], callback_data='back_category_sport')],\n                       'uz': [InlineKeyboardButton(back['uz'], callback_data='back_category_sport')]\n                       }\n# ПРОДУКТЫ\n\n\n# ДОБАВЛЕНИЕ ПРОДУКТА\ncount_product = [['1', '2', '3'],\n                 ['4', '5', '6'],\n                 ['7', '8', '9']]\n\nbasket = {'ru': [[d_basket['ru']['order']],\n                 [d_basket['ru']['clear_basket'], d_basket['ru']['promocode']],\n                 [d_menu['ru']['main_menu']]],\n\n          'uz': [[d_basket['uz']['order']],\n                 [d_basket['uz']['clear_basket'], d_basket['uz']['promocode']],\n                 [d_menu['uz']['main_menu']]]\n          }\n\ncancel = {'ru': [[d_menu['ru']['cancel']]\n                 ],\n\n          'uz': [[d_menu['uz']['cancel']]\n                 ],\n          }\n# ДОБАВЛЕНИЕ ПРОДУКТА\n\n\n# ПАРТНЕРКА\npartnership = {'ru': [[d_partnership['ru']['application']],\n                      [d_partnership['ru']['info']],\n                      [d_menu['ru']['main_menu']]\n                      ],\n\n               'uz': [[d_partnership['uz']['application']],\n                      [d_partnership['uz']['info']],\n                      [d_menu['uz']['main_menu']]\n                      ]\n               }\n\n\npartnership_already = {'ru': [[d_partnership['ru']['info']],\n                              [d_menu['ru']['main_menu']]\n                              ],\n\n                       'uz': [[d_partnership['uz']['info']],\n                              [d_menu['uz']['main_menu']]\n                              ]\n                       }\n\npartnership_statistic = {'ru': [[d_partnership['ru']['statistic']],\n                                [d_partnership['ru']['info']],\n                                [d_menu['ru']['main_menu']]\n                                ],\n\n                         'uz': [[d_partnership['ru']['statistic']],\n                                [d_partnership['uz']['info']],\n                                [d_menu['uz']['main_menu']]\n                                ]\n                       }\n# ПАРТНЕРКА\n\n\n# КОНТАКТЫ\ncontact = {'ru': [[KeyboardButton(d_contact['ru']['contact'], request_contact=True)],\n                  [d_menu['ru']['main_menu']]\n                  ],\n           'uz': [[KeyboardButton(d_contact['uz']['contact'], request_contact=True)],\n                  [d_menu['uz']['main_menu']]\n                  ]\n           }\n\nlocation = {'ru': [[KeyboardButton(d_contact['ru']['location'], request_location=True)],\n                   [d_menu['ru']['basket']],\n                   [d_menu['ru']['main_menu']]\n                  ],\n\n           'uz': [[KeyboardButton(d_contact['uz']['location'], request_location=True)],\n                  [d_menu['ru']['basket']],\n                  [d_menu['uz']['main_menu']]\n                  ]\n           }\n# КОНТАКТЫ\n\n\n# ЗАКАЗ\ntype_delivery = {'ru': [[d_type_delivery['ru']['self'], d_type_delivery['ru']['delivery']],\n                        [d_menu['ru']['basket']],\n                        [d_menu['ru']['main_menu']]\n                        ],\n\n                 'uz': [[d_type_delivery['uz']['self'], d_type_delivery['uz']['delivery']],\n                        [d_menu['uz']['basket']],\n                        [d_menu['uz']['main_menu']]\n                        ]\n                 }\n\ntype_payment = {'ru': [[d_type_payment['ru']['cash'], d_type_payment['ru']['payme']],\n                       [d_menu['ru']['basket']],\n                       [d_menu['ru']['main_menu']]\n                       ],\n\n                'uz': [[d_type_payment['uz']['cash'], d_type_payment['uz']['payme']],\n                       [d_menu['ru']['basket']],\n                       [d_menu['ru']['main_menu']]\n                       ]\n                }\n\nstatus_order = [[InlineKeyboardButton(d_status_order['accept_order'], callback_data='accept_order')],\n                [InlineKeyboardButton(d_status_order['decline_order'], callback_data='decline_order')]]\n# ЗАКАЗ","sub_path":"bot_core/keyboardbot.py","file_name":"keyboardbot.py","file_ext":"py","file_size_in_byte":9434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"390459042","text":"# -*- encoding: utf-8 -*-\nimport json\nimport requests\nfrom app.configuration import config\n\n\ndef send_message(recipient_id, message_text):\n\n    params = {\"access_token\": config['default'].PAGE_ACCESS_TOKEN\n              }\n    headers = {\"Content-Type\": \"application/json\"\n               }\n    data = json.dumps({\"recipient\": {\n                            \"id\": recipient_id\n                                    },\n                       \"message\": {\n                            \"text\": message_text\n                                  }\n                       })\n    requests.post(\"https://graph.facebook.com/v2.6/me/messages\", params=params, headers=headers, data=data)\n\n\ndef send_quick_reply(recipient_id, message_text, replies=('Yes!', 'No!')):\n\n    params = {\"access_token\": config['default'].PAGE_ACCESS_TOKEN\n              }\n    headers = {\"Content-Type\": \"application/json\"\n               }\n    quick_replies = [{\"content_type\": \"text\",\n                      \"title\": word,\n                      \"payload\": \"DEVELOPER_DEFINED_PAYLOAD_FOR_PICKING_GREEN\"\n                      } for word in replies]\n\n    data = json.dumps({\n        \"recipient\": {\n            \"id\": recipient_id\n        },\n        \"message\": {\n            \"text\": message_text,\n            \"quick_replies\": quick_replies\n\n        }\n    })\n    requests.post(\"https://graph.facebook.com/v2.6/me/messages\", params=params, headers=headers, data=data)\n\n\ndef reply(user_id, msg):\n    data = {\n        \"recipient\": {\"id\": user_id},\n        \"message\": {\"text\": msg}\n            }\n    requests.post(\"https://graph.facebook.com/v2.6/me/messages?access_token=\" + config['default'].PAGE_ACCESS_TOKEN,\n                  json=data)","sub_path":"app/utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":1686,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"231377894","text":"from django.shortcuts import render\nfrom django.views import View\nfrom Tin.models import Tin, LoaiTin, TheLoai, Comment\nfrom django.http import HttpResponse\n\nfrom chung.khongDau import convert, convert1\n\n\n# Create your views here.\n\n\nclass getTrangchu(View):\n\n    def get(self, request):\n        theloai_base = TheLoai.objects.all()\n        lt_base = dict()\n        for tl in theloai_base:\n            lt_base[tl] = LoaiTin.objects.filter(idTheLoai=tl.pk)\n        tinxemnhieu_trangChu = Tin.objects.order_by('-view')[:5]\n        tin_trangChu = Tin.objects.all()[:5]\n        return render(request, 'homepage/trangchu.html',\n                      {'theloai_base': theloai_base, 'lt_base': lt_base, 'tinxemnhieu_trangChu': tinxemnhieu_trangChu,\n                       'tin_trangChu': tin_trangChu})\n\n\nclass getTheloai(View):\n    def get(self, request, theloaiPost):\n        theloai_base = TheLoai.objects.all()\n        lt_base = dict()\n        for tl in theloai_base:\n            lt_base[tl] = LoaiTin.objects.filter(idTheLoai=tl.pk)\n        tinxemnhieu = Tin.objects.order_by('-view')[:5]\n\n        tl = TheLoai.objects.filter(tenKhongDau=theloaiPost).first()\n        tin = Tin.objects.filter(id_tl=tl.pk).order_by('-create_at')\n        # khi làm phân trang thì bỏ\n        if (len(tin) > 5):\n            tin = tin[:5]\n        return render(request, 'homepage/theloai.html',\n                      {'theloai_base': theloai_base, 'lt_base': lt_base, 'tinxemnhieu': tinxemnhieu\n                          , 'tin_theLoai': tin, 'theloai': tl})\n\n\nclass getTin(View):\n    # không hieent thị hình ảnh\n\n    def get(self, request, tentin):\n        theloai_base = TheLoai.objects.all()\n        lt_base = dict()\n        for tl in theloai_base:\n            lt_base[tl] = LoaiTin.objects.filter(idTheLoai=tl.pk)\n        tinxemnhieu = Tin.objects.order_by('-view')[:5]\n\n        tin = Tin.objects.filter(tieuDeKhongDau=tentin).first()\n        return render(request, 'homepage/tin.html',\n                      {'theloai_base': theloai_base, 'lt_base': lt_base, 'tinxemnhieu': tinxemnhieu, 'tin': tin})\n\n\nclass getLoaiTin(View):\n    def get(self, request, tenloaitin):\n        theloai_base = TheLoai.objects.all()\n        lt_base = dict()\n        for tl in theloai_base:\n            lt_base[tl] = LoaiTin.objects.filter(idTheLoai=tl.pk)\n        tinxemnhieu = Tin.objects.order_by('-view')[:5]\n\n        tl = LoaiTin.objects.filter(tenKhongDau=tenloaitin).first()\n        tin = Tin.objects.filter(id_lt=tl.pk).order_by('-create_at')\n        # khi làm phân trang thì bỏ\n        if (len(tin) > 5):\n            tin = tin[:5]\n        return render(request, 'homepage/theloai.html',\n                      {'theloai_base': theloai_base, 'lt_base': lt_base, 'tinxemnhieu': tinxemnhieu\n                          , 'tin_theLoai': tin, 'theloai': tl})\n","sub_path":"Authen/Tin/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"371645277","text":"# Slowest but it used vectorizing!!!\nimport numpy as np\n\ndef myfunc(a):\n    if a % 3 == 0 or a % 5 == 0:\n        return a\n    else:\n        return 0\n\narray = np.arange(1000)\nvfunc = np.vectorize(myfunc)\ns = np.sum(vfunc(array))\n","sub_path":"problem1/multiplevector.py","file_name":"multiplevector.py","file_ext":"py","file_size_in_byte":228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"275964441","text":"Import('env')\n\nif not env['GLUT']:\n    Return()\n\nenv = env.Clone()\n\nenv.Prepend(CPPPATH = [\n\t'../util',\n])\n\nenv.Prepend(LIBS = ['$GLUT_LIB'])\n\nprogs = [\n    'fp-tri',\n    'tri-depth',\n    'tri-depth2',\n    'tri-depthwrite',\n    'tri-depthwrite2',\n    'tri-inv',\n    'tri-param',\n    'tri-tex',\n    'point-position',\n]\n\nfor prog in progs:\n    env.Program(\n        target = prog,\n        source = [prog + '.c'],\n    )\n","sub_path":"progs/fp/SConscript","file_name":"SConscript","file_ext":"","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"5720667","text":"import flask\n\nfrom .. import auth\n\n\n@auth.bp.route(\"/logout\",methods=(\"GET\",\"POST\"))\ndef logout():\n    if flask.request.method==\"GET\":\n        return \"\"\"<script src=\"js/auth.js\"></script><script>\n            var rq=new XMLHttpRequest();\n            rq.onreadystatechange=function () {\n                if (this.readyState==4) {\n                    remove_login();\n                    window.location.replace(\"/\");\n                }\n            };\n            rq.open(\"POST\",\"/auth/logout\",true);\n            rq.setRequestHeader(\"Content-Type\",\"application/json\");\n            rq.send();\n        </script>\"\"\"\n    else:\n        if auth.check_client_session():\n            conn=auth.connectDB()\n            cur=conn.cursor()\n            \n            cur.execute(\"update users set session=%s where id=%s;\",(auth.generate_salt(),flask.session.get(\"user_id\")))\n            \n            conn.commit()\n            conn.close()\n            \n            flask.session.pop(\"user_id\")\n            flask.session.pop(\"session\")\n            \n        return \"{}\",{\"Content-Type\":\"application/json\"}","sub_path":"src/auth/logout.py","file_name":"logout.py","file_ext":"py","file_size_in_byte":1081,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"154931848","text":"import datetime, time, os\nfrom datetime import timedelta\nimport matplotlib.pyplot as plt\nimport matplotlib\nimport pandas as pd\nimport tkinter as tk\nfrom tkinter import *\nfrom tkinter import filedialog\n\nclass Buttons():\n\tdef __init__(self):\n\t\tw = tk.Tk(screenName = None, baseName = None, className = \" Main Control Panel\", useTk = 1)\n\t\tw.title(\"Control Panel\")\n\n\t\tdef opentl():\n\t\t\tw2 = Toplevel(w)\n\t\t\tw2.title(\"Select Time Period\")\n\t\t\tw2.withdraw()\n\n\t\t\tframe1 = Frame(w2)\n\t\t\tframe1.pack()\n\n\t\t\tself.v = tk.IntVar()\n\t\t\trbyes = Radiobutton(frame1, text = \"Yesterday\", variable = self.v, value = '1', command = self.v.set(\"1\"))\n\t\t\trbyes.grid(row = 1, column = 1)\n\t\t\trblw = Radiobutton(frame1, text = \"Last week\", variable = self.v, value = '2', command = self.v.set(\"2\"))\n\t\t\trblw.grid(row = 1, column = 2)\n\t\t\trblm = Radiobutton(frame1, text = \"Last month\", variable = self.v, value = '3', command = self.v.set(\"3\"))\n\t\t\trblm.grid(row = 1, column = 3)\n\t\t\tframe2 = Frame(w2)\n\t\t\tframe2.pack()\n\t\t\tself.but4 = tk.Button(frame2, text = \"Graph\", command = self.processRb)\n\t\t\tself.but4.grid(row = 1, column = 2, padx = \"10\")\n\t\t\tw2.mainloop()\n\n\n\t\tself.lab1 = tk.Label(w, text = \"Close the program.\", wraplength = \"150\")\n\t\tself.lab1.grid(row = 2, column = 3, padx = \"10\")\n\t\tself.lab2 = tk.Label(w, text = \"Graphs a chosen dataset\", wraplength = \"150\")\n\t\tself.lab2.grid(row = 2, column = 2, padx = \"10\")\n\t\tself.lab3 = tk.Label(w, text = \"See previous trends, ending with yesterday's full data set.\", wraplength = \"150\")\n\t\tself.lab3.grid(row = 2, column = 1, padx = \"10\")\n\t\tself.but1 = tk.Button(w, text = \"Close Window\", command = w.destroy)\n\t\tself.but1.grid(row = 1, column = 3, pady = \"10\", padx = \"10\")\n\t\tself.but2 = tk.Button(w, text = \"Graph 1 Day\", command = self.plot)\n\t\tself.but2.grid(row = 1, column = 2, padx = \"10\")\n\t\tself.but3 = tk.Button(w, text = \"Show Previous Trends\", command = opentl)\n\t\tself.but3.grid(row = 1, column = 1, padx = \"10\")\n\n\n\t\tw.mainloop()\n\n\tdef processRb(self):\n\t\tprint(self.v.get())\n\n\t\tif self.v.get() == 1:\n\t\t\tself.prev(1)\n\t\telif self.v.get() == 2:\n\t\t\tself.prev(7)\n\t\telif self.v.get() == 3:\n\t\t\tself.prev(30)\n\n\n\tdef prev(self, val):\n\t\tatemps = []\n\t\tahums = []\n\t\ttimes = []\n\n\t\tdate = datetime.date.today()\n\t\tlength = []\n\t\tdelta = timedelta(days = 1)\n\n\t\tfor i in range(val):\n\t\t\tdate = date - delta\n\t\t\tlength.append(date)\n\n\t\tfor j in range(len(length)):\n\t\t\tfdate = length[j].strftime(\"%Y-%m-%d\")\n\t\t\tdf = pd.DataFrame(pd.read_csv((os.environ['HOME'] + \"/TempHum_Results/\" + fdate + \"_results.csv\"), sep=',', index_col=1))\n\t\t\tdf.set_axis(['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'], axis='columns', inplace=True)\n\n\t\t\ttime = df.index\n\t\t\tcounttime = len(time)\n\t\t\tdivtime = counttime // 8\n\t\t\ttt = 0\n\n\t\t\tfdate2 = length[j].strftime(\"%d %b\")\n\t\t\ttimes.append(time[tt] + \", \" + fdate2)\n\t\t\tatemps.append(df.iloc[tt]['B'])\n\t\t\tahums.append(df.iloc[tt]['C'])\n\n\t\t\tfor k in range(7):\n\t\t\t\ttt += divtime\n\t\t\t\ttimes.append(time[tt] + \", \" + fdate2)\n\t\t\t\tatemps.append(df.iloc[tt]['B'])\n\t\t\t\tahums.append(df.iloc[tt]['C'])\n\n\t\ttimes.reverse()\n\n\t\tplt.figure()\n\t\tax1 = plt.subplot(211)\n\t\tax1.plot(times, atemps, color = \"black\", label = \"Average\", linewidth = 3.0)\n\t\tplt.ylabel('Temperature (*C)')\n\t\tplt.xticks(self.xtickval(times))\n\t\tplt.setp(ax1.get_xticklabels(), rotation = -25, ha = \"left\")\n\t\tplt.grid(True)\n\n\n\t\tax2 = plt.subplot(212)\n\t\tax2.plot(times, ahums, color = \"black\", label = \"Average\", linewidth = 3.0)\n\t\tplt.ylabel('Humidity (%)')\n\t\tplt.xticks(self.xtickval(times))\n\t\tplt.setp(ax2.get_xticklabels(), rotation = -25, ha = \"left\")\n\t\tplt.grid(True)\n\t\tval -= 1\n\t\tplt.suptitle(\"Temperature and Humidity for \" + str(length[val]) + \" to \" + str(length[0]))\n\n\n\t\tmng = plt.get_current_fig_manager()\n\t\tmng.resize(*mng.window.maxsize())\n\n\t\tplt.show()\n\n\tdef xtickval(self, value):\n\t\ttoUse = []\n\t\tfor i in range(len(value)):\n\t\t\tif len(value) <= 25:\n\t\t\t\ttoUse.append(value[i])\n\t\t\telif len(value) > 25 and len(value) < 300:\n\t\t\t\tif (i % 8 == 0) == True:\n\t\t\t\t\ttoUse.append(value[i])\n\t\t\telif len(value) >= 300:\n\t\t\t\tif (i % 50 == 0) == True:\n\t\t\t\t\ttoUse.append(value[i])\n\t\treturn toUse\n\n\tdef plot(self):\n\t\tdf = pd.DataFrame(self.newfile())\n\t\ttimes = df.index\n\t\tdf.set_axis(['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'], axis='columns', inplace=True)\n\t\tatemps = df['B']\n\t\tahums = df['C']\n\n\t\tplt.figure()\n\t\tax1 = plt.subplot(211)\n\t\tax1.plot(times, atemps, color = \"black\", label = \"Average\", linewidth = 3.0)\n\t\tax1.plot(times, df['D'], color = \"green\", label = \"Sensor 1\")\n\t\tax1.plot(times, df['F'], color = \"blue\", label = \"Sensor 2\")\n\t\tax1.plot(times, df['H'], color = \"purple\", label = \"Sensor 3\")\n\t\tplt.ylabel('Temperature (*C)')\n\t\tplt.xticks(self.xtickval(times))\n\t\tplt.setp(ax1.get_xticklabels(), rotation = -25, ha = \"left\")\n\t\toffset = -72\n\t\tbbox = dict(boxstyle=\"round\", fc=\"0.8\")\n\t\tarrowprops = dict(arrowstyle = \"->\", connectionstyle = \"angle, angleA = 0, angleB = 90, rad = 10\")\n\t\tax1.annotate((\n\t\t\t\"Maximum \" + str(atemps.max()) + '*C at' + str(atemps.idxmax())),\n\t\t\txy=(atemps.idxmax(), atemps.max()),\n\t\t\txytext=(offset, 2.5*offset), textcoords='offset points',\n\t\t\tbbox=bbox, arrowprops = arrowprops)\n\t\tplt.grid(True)\n\t\tplt.legend(bbox_to_anchor = (1.001, 1), loc = 'upper left', borderaxespad = 0)\n\n\t\tax2 = plt.subplot(212)\n\t\tax2.plot(times, ahums, color = \"black\", label = \"Average\", linewidth = 3.0)\n\t\tax2.plot(times, df['E'], color = \"green\", label = \"Sensor 1\")\n\t\tax2.plot(times, df['G'], color = \"blue\", label = \"Sensor 2\")\n\t\tax2.plot(times, df['I'], color = \"purple\", label = \"Sensor 3\")\n\t\tplt.xlabel('Time between' + str(min(times)) + 'and' + str(max(times)))\n\t\tplt.ylabel('Humidity (%)')\n\t\tplt.xticks(self.xtickval(times))\n\t\tplt.setp(ax2.get_xticklabels(), rotation = -25, ha = \"left\")\n\t\tax2.annotate((\n\t\t\t\"Maximum \" + str(ahums.max()) + '% at' + str(ahums.idxmax())),\n\t\t\txy=(ahums.idxmax(), ahums.max()),\n\t\t\txytext=(offset, 2.5*offset), textcoords='offset points',\n\t\t\tbbox = bbox, arrowprops = arrowprops)\n\t\tplt.grid(True)\n\t\tplt.legend(bbox_to_anchor = (1.001, 1), loc = 'upper left', borderaxespad = 0)\n\t\tplt.suptitle(\"Temperature and Humidity for \" + df.iloc[1]['A'])\n\n\t\tmng = plt.get_current_fig_manager()\n\t\tmng.resize(*mng.window.maxsize())\n\n\t\tplt.show()\n\n\tdef newfile(self):\n\t\tfile = pd.read_csv(filedialog.askopenfilename(\n\t\t\tinitialdir = (os.environ['HOME'] + \"/TempHum_Results\"), \n\t\t\t\ttitle = \"Select file\", \n\t\t\t\tfiletypes = [(\"Spreadsheets\", \"*.csv\")]),\n\t\t\tsep=',', index_col=1)\n\n\t\treturn file\n\nButtons()\n\n","sub_path":"Python_test_codes/weeklyplot.py","file_name":"weeklyplot.py","file_ext":"py","file_size_in_byte":6423,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"357913302","text":"# -*- coding: utf-8 -*-\n\nimport argparse\nfrom vocab_utils import Vocab\nimport namespace_utils\n\nimport tensorflow as tf\nimport TriMatchTrainer_v2 as TriMatchTrainer\nfrom TriMatchModelGraph_v2 import TriMatchModelGraph\nimport sys\n\n'''\nScript for testing.\n'''\n\ntf.logging.set_verbosity(tf.logging.ERROR) # DEBUG, INFO, WARN, ERROR, and FATAL\nnum_options=4\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--model_prefix', type=str, required=True, help='Prefix to the models.')\n    parser.add_argument('--in_path', type=str, required=True, help='the path to the test file.')\n    parser.add_argument('--out_path', type=str, required=True, help='The path to the output file.')\n    parser.add_argument('--word_vec_path', type=str, required=True, help='word embedding file for the input file.')\n    parser.add_argument('--mode', type=str, default=\"prediction\", help='prediction or probs')\n    parser.add_argument('--batch_size', default=None, help='test batch size')\n    parser.add_argument('--output_gate_probs',default=False, help='output RL gates probs.', action='store_true')\n\n\n\n    args, unparsed = parser.parse_known_args()\n    \n    model_prefix = args.model_prefix\n    in_path = args.in_path\n    out_path = args.out_path\n    word_vec_path = args.word_vec_path\n    mode = args.mode\n    out_json_path = None\n    dump_prob_path = None\n\n    # load the configuration file\n    print('Loading configurations.')\n    FLAGS = namespace_utils.load_namespace(model_prefix + \".config.json\")\n    print(FLAGS)\n\n    with_POS=False\n    if hasattr(FLAGS, 'with_POS'): with_POS = FLAGS.with_POS\n    with_NER=False\n    if hasattr(FLAGS, 'with_NER'): with_NER = FLAGS.with_NER\n    wo_char = False\n    if hasattr(FLAGS, 'wo_char'): wo_char = FLAGS.wo_char\n\n    wo_left_match = False\n    if hasattr(FLAGS, 'wo_left_match'): wo_left_match = FLAGS.wo_left_match\n\n    wo_right_match = False\n    if hasattr(FLAGS, 'wo_right_match'): wo_right_match = FLAGS.wo_right_match\n\n    wo_full_match = False\n    if hasattr(FLAGS, 'wo_full_match'): wo_full_match = FLAGS.wo_full_match\n\n    wo_maxpool_match = False\n    if hasattr(FLAGS, 'wo_maxpool_match'): wo_maxpool_match = FLAGS.wo_maxpool_match\n\n    wo_attentive_match = False\n    if hasattr(FLAGS, 'wo_attentive_match'): wo_attentive_match = FLAGS.wo_attentive_match\n\n    wo_max_attentive_match = False\n    if hasattr(FLAGS, 'wo_max_attentive_match'): wo_max_attentive_match = FLAGS.wo_max_attentive_match\n\n    max_hyp_length = 100\n    if hasattr(FLAGS, 'max_hyp_length'): max_hyp_length = FLAGS.max_hyp_length\n\n    max_choice_length=None\n    if hasattr(FLAGS, 'max_choice_length'): max_choice_length = FLAGS.max_choice_length\n\n    matching_option=0\n    if hasattr(FLAGS,'matching_option'): matching_option=FLAGS.matching_option\n\n    use_options=False\n    if hasattr(FLAGS,'use_options'): use_options=FLAGS.use_options\n\n    cond_training=False\n    if hasattr(FLAGS,'cond_training') or FLAGS.matching_option==7:\n        cond_training=True\n\n    if args.batch_size is not None:\n        FLAGS.batch_size=args.batch_size\n    gen_concat_mat=False\n    gen_split_mat=False\n    if FLAGS.matching_option==7:\n        gen_concat_mat=True\n        FLAGS.cond_training=True\n        if FLAGS.concat_context:\n            gen_split_mat=True\n        reasonet_training=False\n        if hasattr(FLAGS,'reasonet_training'):\n            reasonet_training=FLAGS.reasonet_training\n        rl_matches=FLAGS.rl_matches\n        concat_context=FLAGS.concat_context\n        print('concat_context=',concat_context)\n        tied_aggre=False\n        if hasattr(FLAGS,'tied_aggre'):\n            tied_aggre=FLAGS.tied_aggre\n        tied_match=False\n        if hasattr(FLAGS,'tied_match'):\n            tied_match=FLAGS.tied_match\n        rl_training_method=FLAGS.rl_training_method\n        efficient=FLAGS.efficient\n        reasonet_steps=5\n        reasonet_hidden_dim=128\n        reasonet_lambda=10\n        reasonet_terminate_mode='original'\n        reasonet_keep_first=True\n        reasonet_logit_combine='sum'       \n        if reasonet_training:\n            reasonet_steps=FLAGS.reasonet_steps\n            reasonet_hidden_dim=FLAGS.reasonet_hidden_dim\n            reasonet_lambda=FLAGS.reasonet_lambda\n            reasonet_terminate_mode=FLAGS.reasonet_terminate_mode\n            reasonet_keep_first=FLAGS.reasonet_keep_first\n            reasonet_logit_combine=FLAGS.reasonet_logit_combine\n\n\n\n\n\n    # load vocabs\n    print('Loading vocabs.')\n    word_vocab = Vocab(word_vec_path, fileformat='txt3',tolower=FLAGS.use_lower_letter)\n    label_vocab = Vocab(model_prefix + \".label_vocab\", fileformat='txt2',tolower=FLAGS.use_lower_letter)\n    print('word_vocab: {}'.format(word_vocab.word_vecs.shape))\n    print('label_vocab: {}'.format(label_vocab.word_vecs.shape))\n    num_classes = label_vocab.size()\n    \n    POS_vocab = None\n    NER_vocab = None\n    char_vocab = None\n    if with_POS: POS_vocab = Vocab(model_prefix + \".POS_vocab\", fileformat='txt2',tolower=FLAGS.use_lower_letter)\n    if with_NER: NER_vocab = Vocab(model_prefix + \".NER_vocab\", fileformat='txt2',tolower=FLAGS.use_lower_letter)\n    char_vocab = Vocab(model_prefix + \".char_vocab\", fileformat='txt2')\n    print('char_vocab: {}'.format(char_vocab.word_vecs.shape))\n    \n    print('Build TriMatchDataStream ... ')\n    testDataStream = TriMatchTrainer.TriMatchDataStream(in_path, word_vocab=word_vocab, char_vocab=char_vocab, \n                                              POS_vocab=POS_vocab, NER_vocab=NER_vocab, label_vocab=label_vocab, \n                                              batch_size=FLAGS.batch_size, isShuffle=False, isLoop=True, isSort=False, \n                                              max_char_per_word=FLAGS.max_char_per_word, \n                                              max_sent_length=FLAGS.max_sent_length, max_hyp_length=max_hyp_length, \n                                              max_choice_length=max_choice_length,tolower=FLAGS.use_lower_letter,\n                                              gen_concat_mat=gen_concat_mat, gen_split_mat=gen_split_mat,\n                                              efficient=efficient)\n    print('Number of instances in testDataStream: {}'.format(testDataStream.get_num_instance()))\n    print('Number of batches in testDataStream: {}'.format(testDataStream.get_num_batch()))\n\n    if wo_char: char_vocab = None\n\n    init_scale = 0.01\n    best_path = model_prefix + \".best.model\"\n    print('Decoding on the test set:')\n    with tf.Graph().as_default():\n        initializer = tf.random_uniform_initializer(-init_scale, init_scale)\n        with tf.variable_scope(\"Model\", reuse=False, initializer=initializer):\n            if matching_option!=7:\n                valid_graph =  TriMatchModelGraph(num_classes, word_vocab=word_vocab, char_vocab=char_vocab,POS_vocab=POS_vocab, NER_vocab=NER_vocab, \n                     dropout_rate=0.0, learning_rate=FLAGS.learning_rate, optimize_type=FLAGS.optimize_type,\n                     lambda_l2=FLAGS.lambda_l2, char_lstm_dim=FLAGS.char_lstm_dim, context_lstm_dim=FLAGS.context_lstm_dim, \n                     aggregation_lstm_dim=FLAGS.aggregation_lstm_dim, is_training=False, MP_dim=FLAGS.MP_dim, \n                     context_layer_num=FLAGS.context_layer_num, aggregation_layer_num=FLAGS.aggregation_layer_num, \n                     fix_word_vec=FLAGS.fix_word_vec, with_highway=FLAGS.with_highway,\n                     word_level_MP_dim=FLAGS.word_level_MP_dim,\n                     with_match_highway=FLAGS.with_match_highway, with_aggregation_highway=FLAGS.with_aggregation_highway,\n                     highway_layer_num=FLAGS.highway_layer_num,\n                     match_to_question=FLAGS.match_to_question, match_to_passage=FLAGS.match_to_passage, match_to_choice=FLAGS.match_to_choice,\n                     with_full_match=(not FLAGS.wo_full_match), with_maxpool_match=(not FLAGS.wo_maxpool_match), \n                     with_attentive_match=(not FLAGS.wo_attentive_match), with_max_attentive_match=(not FLAGS.wo_max_attentive_match), \n                     use_options=use_options, num_options=num_options, with_no_match=FLAGS.with_no_match, matching_option=matching_option,\n                     cond_training=cond_training)\n            else:\n                valid_graph = TriMatchModelGraph(num_classes, word_vocab=word_vocab, char_vocab=char_vocab,POS_vocab=POS_vocab, NER_vocab=NER_vocab, \n                     dropout_rate=FLAGS.dropout_rate, learning_rate=FLAGS.learning_rate, optimize_type=FLAGS.optimize_type,\n                     lambda_l2=FLAGS.lambda_l2, char_lstm_dim=FLAGS.char_lstm_dim, context_lstm_dim=FLAGS.context_lstm_dim, \n                     aggregation_lstm_dim=FLAGS.aggregation_lstm_dim, is_training=False, MP_dim=FLAGS.MP_dim, \n                     context_layer_num=FLAGS.context_layer_num, aggregation_layer_num=FLAGS.aggregation_layer_num, \n                     fix_word_vec=FLAGS.fix_word_vec, with_highway=FLAGS.with_highway,\n                     word_level_MP_dim=FLAGS.word_level_MP_dim,\n                     with_match_highway=FLAGS.with_match_highway, with_aggregation_highway=FLAGS.with_aggregation_highway,\n                     highway_layer_num=FLAGS.highway_layer_num,\n                     match_to_question=FLAGS.match_to_question, match_to_passage=FLAGS.match_to_passage, match_to_choice=FLAGS.match_to_choice,\n                     with_full_match=(not FLAGS.wo_full_match), with_maxpool_match=(not FLAGS.wo_maxpool_match), \n                     with_attentive_match=(not FLAGS.wo_attentive_match), with_max_attentive_match=(not FLAGS.wo_max_attentive_match), \n                     use_options=use_options, num_options=num_options, with_no_match=FLAGS.with_no_match, \n                     matching_option=matching_option, concat_context=concat_context, \n                     tied_aggre=tied_aggre, rl_training_method=rl_training_method, rl_matches=rl_matches, \n                     cond_training=cond_training,reasonet_training=reasonet_training, reasonet_steps=reasonet_steps, \n                     reasonet_hidden_dim=reasonet_hidden_dim, reasonet_lambda=reasonet_lambda, \n                     reasonet_terminate_mode=reasonet_terminate_mode, reasonet_keep_first=reasonet_keep_first, \n                     efficient=efficient, tied_match=tied_match, reasonet_logit_combine=reasonet_logit_combine)\n\n#             saver = tf.train.Saver()\n        # remove word _embedding\n        vars_ = {}\n        for var in tf.global_variables():\n            if \"word_embedding\" in var.name: continue\n            if not var.name.startswith(\"Model\"): continue\n            vars_[var.name.split(\":\")[0]] = var\n        saver = tf.train.Saver(vars_)\n                \n        config = tf.ConfigProto()\n        config.gpu_options.allow_growth = True \n        sess = tf.Session(config=config)\n\n        sess.run(tf.global_variables_initializer())\n        step = 0\n        saver.restore(sess, best_path)\n\n        accuracy = TriMatchTrainer.evaluate(testDataStream, valid_graph, sess, outpath=out_path, label_vocab=label_vocab,mode=args.mode,\n                                                 char_vocab=char_vocab, POS_vocab=POS_vocab, NER_vocab=NER_vocab, use_options=use_options, \n                                                 cond_training=cond_training,output_gate_probs=args.output_gate_probs,efficient=efficient)\n        print(\"Accuracy for test set is %.2f\" % accuracy)\n\n\n","sub_path":"src/TriMatchDecoder_v2.py","file_name":"TriMatchDecoder_v2.py","file_ext":"py","file_size_in_byte":11409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"462775549","text":"# -*- coding: utf-8 -*-\nfrom __future__ import annotations\nfrom typing import Dict, List, Tuple\n\nfrom fcatools import EMPTY_VALUE\nfrom .dyadic_link import DyadicLink\nfrom .dyadic_concept import DyadicConcept\nfrom .dyadic_generator import DyadicGenerator\nfrom .dyadic_association_rule import DyadicAssociationRule\n\nclass DyadicLattice:\n    def __init__(self):\n        self.lattice: Dict[frozenset, DyadicConcept] = {}\n\n    def add_connection(self, intent_1, intent_2, concepts):\n        concepts_reversed = {i.attrs: i.objects for i in concepts}\n\n        intent_1 = frozenset(intent_1)\n        intent_2 = frozenset(intent_2)\n\n        if intent_1 in concepts_reversed and intent_2 not in concepts_reversed:\n            extent = concepts_reversed[intent_1]\n            if extent not in self.lattice:\n                self.lattice[extent] = DyadicConcept(extent, intent_1)\n\n        elif intent_2 in concepts_reversed and intent_1 not in concepts_reversed:\n            extent = concepts_reversed[intent_2]\n            if extent not in self.lattice:\n                self.lattice[extent] = DyadicConcept(extent, intent_2)\n\n        elif intent_2 in concepts_reversed and intent_1 in concepts_reversed:\n            extent_1 = concepts_reversed[intent_1]\n            extent_2 = concepts_reversed[intent_2]\n\n            if extent_1 in self.lattice and extent_2 in self.lattice:\n                self.lattice[extent_1].add_connection(self.lattice[extent_2])\n            elif extent_1 not in self.lattice and extent_2 in self.lattice:\n                c = DyadicConcept(extent_1, intent_1)\n                c.add_connection(self.lattice[extent_2])\n                self.lattice[extent_1] = c\n            elif extent_1 in self.lattice and extent_2 not in self.lattice:\n                c = DyadicConcept(extent_2, intent_2)\n                c.add_connection(self.lattice[extent_1])\n                self.lattice[extent_2] = c\n            else:\n                c1 = DyadicConcept(extent_1, intent_1)\n                c2 = DyadicConcept(extent_2, intent_2)\n                c1.add_connection(c2)\n                self.lattice[extent_1] = c1\n                self.lattice[extent_2] = c2\n\n    def get_objects_count(self) -> int:\n        unique_objects = []\n        for objects, concept in self.lattice.items():\n            for o in objects:\n                if o not in unique_objects:\n                    unique_objects.append(o)\n\n        if EMPTY_VALUE in unique_objects:\n            unique_objects.remove(EMPTY_VALUE)\n\n        return len(unique_objects)\n\n    def compute_association_rules(self, generators: List[DyadicGenerator]) -> List[DyadicAssociationRule]:\n        rules = []\n        objects_count = self.get_objects_count()\n        for extent, concept in self.lattice.items():\n\n            for g in generators:\n                if concept.attrs == g.attrs:\n                    gen = g\n\n                    ant_support = len(concept.objects) / objects_count\n                    children = self.lattice[concept.objects].children\n\n                    for attrs in gen.generator:\n                        if len(children) != 0 and len(concept.attrs) != 0:\n                            for child in children:\n\n                                cons_support = len(child.objects - frozenset({EMPTY_VALUE})) / objects_count\n                                potential_cons = [i for i in child.attrs if i not in concept.attrs]\n\n                                rule_conf = cons_support / ant_support\n\n                                if len(potential_cons) != 0:\n                                    rules.append(DyadicAssociationRule(attrs, potential_cons, cons_support, rule_conf))\n\n                        rule_support = len(concept.objects - frozenset({EMPTY_VALUE})) / objects_count\n                        potential_cons = [i for i in concept.attrs if i not in attrs]\n\n                        if len(potential_cons) != 0:\n                            rules.append(DyadicAssociationRule(attrs, potential_cons, rule_support,  1.0))\n\n        return rules\n\n    def compute_generators(self) -> List[DyadicGenerator]:\n        generators: List[DyadicGenerator] = []\n\n        for extent, concept in self.lattice.items():\n            gen = []\n\n            if len(concept.attrs) > 0:\n                parents = concept.parents\n                faces = []\n                for p in parents:\n                    faces.append(concept.attrs - p.attrs)\n\n                if len(faces) > 0:\n                    first_face = faces.pop(0)\n                    for f in first_face:\n                        gen.append(frozenset({f}))\n\n                    if len(faces) > 0:\n                        for f in faces:\n                            min_blockers = []\n                            blockers = []\n\n                            for g in gen:\n                                if len(g & f) == 0:\n                                    for element in f:\n                                        union = frozenset({element}) | g\n                                        if union not in blockers:\n                                            blockers.append(union)\n                                else:\n                                    if frozenset(g) not in min_blockers:\n                                        min_blockers.append(frozenset(g))\n\n                            if len(blockers) == 0:\n                                gen = min_blockers\n                            elif len(min_blockers) == 0:\n                                gen = blockers\n                            else:\n                                result = []\n                                for b in blockers:\n                                    for min_b in min_blockers:\n                                        if min_b <= b:\n                                            if b not in result:\n                                                result.append(b)\n                                                break\n                                gen = list(frozenset(min_blockers) | (frozenset(blockers) - frozenset(result)))\n                else:\n                    gen = [frozenset({i}) for i in concept.attrs]\n\n            generators.append(DyadicGenerator(concept.attrs, gen))\n\n        return generators\n    \n    @staticmethod\n    def build_lattice_iPred(concepts: List[DyadicConcept]) -> Tuple[DyadicLattice, List[DyadicLink]]:\n        attributes = [c.attrs for c in concepts]\n\n        attributes.sort(key=len)\n\n        if frozenset({EMPTY_VALUE}) not in attributes:\n            attributes.insert(0, frozenset({EMPTY_VALUE}))\n\n        empty_set = {EMPTY_VALUE}\n        faces = {}\n        links = []\n        concepts_lattice = DyadicLattice()\n\n        for i in attributes:\n            faces[i] = empty_set\n\n        border = attributes.pop(0)\n\n        for Ci in attributes:\n            candidates = set({})\n\n            for element in border:\n                candidates = candidates | frozenset({(Ci & frozenset(element))})\n\n            candidates = (candidates - frozenset({frozenset({})})) | empty_set\n\n            for element in candidates:\n                delta_intersection = Ci & faces[frozenset(element)]\n                if len(delta_intersection) == 0 or delta_intersection == empty_set:\n                    concepts_lattice.add_connection(Ci, set(element), concepts)\n\n                    links.append(\n                        DyadicLink(\n                            DyadicLattice.__getObjectFromAttr(Ci, concepts),\n                            DyadicLattice.__getObjectFromAttr(frozenset(element), concepts)\n                        )\n                    )\n                    faces[frozenset(element)] = (faces[frozenset(element)] | (Ci - set(element))) - empty_set\n                    border = (border - frozenset({element})) - empty_set\n\n            border = border | {Ci}\n\n        return concepts_lattice, links\n\n    @staticmethod\n    def __getObjectFromAttr(attrs: frozenset, concepts: List[DyadicConcept]) -> frozenset:\n        for c in concepts:\n            if c.attrs == attrs:\n                return c.objects\n\n        return frozenset(EMPTY_VALUE)\n\n    def __repr__(self) -> str:\n        return f'DyadicLattice({self.lattice})'\n","sub_path":"src/fcatools/dyadic/dyadic_lattice.py","file_name":"dyadic_lattice.py","file_ext":"py","file_size_in_byte":8153,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"384131513","text":"import numpy as np\nfrom madmom.utils.midi import MIDIFile\n\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import LSTM, Activation\n\nimport random\nimport time, os\n\n\nnum_songs = 500\nnum_timesteps = 1000\nnote_vec_size = 5\n\nnum_epochs = 100\ncheckpoint_distance = 10\n\narchitecture = [50,50,50]\n\ndataset = \"1\"\nlayers = \"\"\nfor layer in architecture:\n\tlayers += str(layer) + \"x\"\nactivation = 'relu'\n\nrun_name = \"ds:_\" + dataset + \"__arch:_\" + layers + \"__act:_\" + activation\n\nprint(run_name)\n\n\n################################################# Import music files\n\nmusic_files = []\n\ndirectory =  \"datasets/\" + dataset + \"/\"\n\nfor filename in os.listdir(directory):\n\t# generate a MIDIFile object from a midi file\n\tmusic_files.append(MIDIFile.from_file(directory + filename).notes())\n\t#note structure: (onset time, pitch, duration, velocity, channel)\n\nmusic_files = np.array(music_files)\n\n#print(music_files[0,:10])\nimport sys\n#sys.exit(0)\n#################################################\n\n\nmodel = Sequential()\nfirst = True\nfor layer in architecture:\n\tif(first):\n\t\tmodel.add(LSTM(50, batch_input_shape = (1, 1, note_vec_size), stateful=True, return_sequences=True))#, init=init),\n\t\tmodel.add(Activation(activation))\n\t\tfirst = False\n\telse:\n\t\tmodel.add(LSTM(50, stateful=True, return_sequences=True))\n\t\tmodel.add(Activation(activation))\nmodel.add(LSTM(note_vec_size, stateful=True, return_sequences=True))\n\n\nmodel.compile('adam', loss='mse')\n\n#save network architecture\nmodel.save(\"models/\" + run_name)\n#create checkpoint folder if it doesn't already exist\ncheckpoint_path = \"model_checkpoints/\" + run_name\nif not os.path.exists(checkpoint_path):\n\tos.makedirs(checkpoint_path)\n\nloss_list = []\n\n# training loop\nfor epoch in range(num_epochs):\t\t\t\t\t# epochs through all songs\n\tloss_this_epoch = 0\n\tprint(\"\\n\\nEPOCH:\\t\"+str(epoch)+\" / \" + str(num_epochs) + \"\\n\\n\")\n\tfor i in range(music_files.shape[0]):\t\t# songs\n\t\tfor note in range(music_files.shape[1]-1):\t# timesteps\n\t\t\tprint(\"\\nnote:\\t\"+str(note)+\" / \"+str(music_files.shape[1]-1))\n\n\t\t\tloss_this_epoch += model.train_on_batch(np.reshape(music_files[i, note],(1,1,5)), np.reshape(music_files[i, note+1], (1,1,5)))\n\n\t\tmodel.reset_states()\n\tprint(loss_this_epoch)\n\tloss_list.append(loss_this_epoch)\n\n\tif(epoch % checkpoint_distance == 0):\n\n\t\tmodel.save_weights(checkpoint_path + \"/\" + str(epoch) + \"_epochs_weights\")\n\n\t\tgenerated_song = []\n\n\t\tgenerated_song.append( model.predict_on_batch( np.reshape(music_files[0,0],(1,1,5)) ) )#np.random.randint(2, size=num_notes) ) )\n\n\t\tfor i in range(len(music_files[0])):\n\t\t\tgenerated_song.append(model.predict_on_batch(np.reshape(generated_song[-1], (1,1,5))))\n\n\t\t#convert to np array and round pitch, velocity, and channel to integer values, then clip them to the appropriate range\n\t\tgenerated_song = np.array(generated_song)\n\n\t\t\n\t\tgenerated_song = generated_song[:,0,0,:] #manual reshaping\n\t\t\n\t\tgenerated_song[:, (1,3,4)] = np.round(generated_song[:,(1,3,4)])\n\t\tgenerated_song[:, (1,3,4)] = np.clip(generated_song[:,(1,3,4)], 0, 127)\n\t\tgenerated_song = np.nan_to_num(generated_song)\n\n\t\tprint(generated_song[:10])\n\n\t\tnew_midifile = MIDIFile.from_notes(generated_song)\n\n\t\tnew_midifile.write(\"generated_song_attempts/\" + run_name + str(epoch) + \".mid\")\n\nnp.savetxt((\"models/\" + run_name + \"__loss_trend\"), loss_list)\n\n#  T O  D O\n#\t\t\t\tdetermine which data to feed in\n#\t\t\t\ttweak neural net\n#\t\t\t\tget and save error rates\n\n#\t\t\t\tset up reinforcement learning","sub_path":"Desktop/music_generation_AI-master/music_generating_RNN.py","file_name":"music_generating_RNN.py","file_ext":"py","file_size_in_byte":3448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"93585146","text":"import allure\nimport pytest\nfrom seleniumbase import BaseCase\nfrom pages.store_page import StorePage\nfrom pages.login_page import LoginPage\nfrom pages.popup_page import PopupPage\nimport os\n\n\n@allure.feature(\"Home page\")\nclass TestHome:\n    @classmethod\n    @pytest.fixture(scope=\"function\", autouse=True)\n    def setup(cls, base_driver):\n        \"\"\"\n        前置步骤\n        :return:\n        \"\"\"\n        cls.login_page = LoginPage(base_driver)\n        cls.popup_page = PopupPage(base_driver)\n        cls.store_page = StorePage(base_driver)\n        cls.driver = BaseCase(base_driver)\n\n    @allure.story(\"检查已有popup是否能成功保存\")\n    def test_popup_save(self):\n        self.login_page.login_in()\n        self.driver.sleep(2)\n        self.home_page.go_to_popup_page()\n        self.home_page.go_to_popup_edit()\n        self.home_page.go_to_popup_test()\n        self.home_page.reset_popup_test()\n\n    @allure.story(\"检查C端页面popup是否与配置一致\")\n    def test_toc_popup(self):\n        self.login_page.login_in()\n        self.driver.sleep(2)\n        self.home_page.go_to_store()\n        self.store_page.login_in()\n        self.store_page.check_popup_header()\n        self.store_page.check_popup_description()\n\n\nif __name__ == \"__main__\":\n    pytest.main([\"-s\", os.path.abspath(__file__)])\n","sub_path":"tests/test_popup.py","file_name":"test_popup.py","file_ext":"py","file_size_in_byte":1317,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"152029937","text":"# Author: wonder\nimport os\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport xgboost as xgb\nimport numpy as np\nfrom xgboost import plot_importance\nfrom sklearn.preprocessing import Imputer\n\nfrom sklearn.metrics import mean_absolute_error\n\n\ndef loadDataset():\n    dataset_path = './dataset/train'\n    noises = [1, 5, 6, 12, 13, 19, 20]\n    datas = []\n    for i in range(25):\n        if i + 1 in noises: continue\n        fpath = os.path.join(dataset_path, '{}.npy'.format(i + 1))\n        data = np.load(fpath) # shape: 144 * 81 * 2\n        data = data.swapaxes(0,1)\n        #data = data.reshape((81*144,2))\n        #print(\"sssss\", data.shape)\n        datas.append(data)\n    #datas = np.concatenate(datas, axis=0)\n    #print(datas.shape)\n    return np.array(datas)\n\n\ndef loadTrainData(data):\n    data_num = len(data)\n    #print(data_num)\n    #print(data.shape)\n    X = []\n    Y = []\n    for row in range(0, data_num-1):\n        X.append(data[row, :, :, :])\n        Y.append(data[row+1, :, :, :])\n    X = np.array(X)\n    Y = np.array(Y)\n    X = X.reshape((-1, 144, 2))\n    Y = Y.reshape((-1, 144, 2))\n    X = X.reshape(-1, 288)\n    Y = Y.reshape(-1, 288)\n    return X, Y\n\ndef loadTestData():\n    dataset_path = './dataset/h_train'\n    fpath = os.path.join(dataset_path, '28.npy')\n    data = np.load(fpath) \n    data = data.swapaxes(0,1)\n\n    data[:,:,0], data[:,:,1] = data[:,:,1], data[:,:,0]\n    return data\n    \ndef train(X, Y):\n    # XGBoost训练过程\n    models = []\n    for i in range(288):\n        model = xgb.XGBRegressor(max_depth=5, learning_rate=0.1, n_estimators=160, silent=False, objective='reg:gamma')\n        models.append(model)\n    \n    for t in range(144):\n        in_id = 2 * t\n        out_id = 2 * t + 1\n        models[in_id].fit(X, Y[:, in_id])\n        models[out_id].fit(X, Y[:, out_id])\n    # 对测试集进行预测\n    return models\n\n    #pred = model.predict(val)\n    \n    # 评估预测结果  \n    #print(\"MAE: %.2f%%\" % (mean_absolute_error(Y_val,Y_pred)))\ndef test(models, X):\n    X = X.reshape(81, 288)\n    Y = np.zeros((81,144,2))\n    for t in range(144):\n        in_id = 2 * t\n        out_id = 2 * t + 1\n        in_pred = models[in_id].predict(X)\n        out_pred = models[out_id].predict(X)\n        Y[:,t,0] = in_pred\n        Y[:,t,1] = out_pred\n    return Y\n        #print(\"MAE: %.2f%%\" % (mean_absolute_error(Y_val,Y_pred)))\n    \nif __name__ == '__main__':\n    #data = loadDataset()\n    #X, Y = loadTrainData(data)\n    X_val = loadTestData()\n    #models = train(X, Y)\n\n    import pickle\n    #with open('xgb_model.pkl', 'wb') as f:\n    #    pickle.dump(models, f)\n    with open('xgb_model.pkl','rb') as f:\n        models = pickle.load(f)\n    res = test(models,X_val)\n\n\n    import time, datetime\n    def time2str(id, date):\n        dt = datetime.datetime.strptime(date, \"%Y-%m-%d\")\n        t1 = time.mktime(dt.timetuple()) + int(id) * 10 * 60\n        t2 = t1 + 10 * 60\n        t1_str = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(t1))\n        t2_str = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(t2))\n\n        return t1_str, t2_str\n\n    date = '2019-01-29'\n    with open('./results/xgb-ly/29.csv', 'w') as f:\n        title = 'stationID,startTime,endTime,inNums,outNums'\n        print(title, file=f)\n        x, y, z = res.shape\n        print(res[0][0])\n        for j in range(y):\n            for i in range(x):\n                t1, t2 = time2str(i, date)\n                in_num, out_num = res[i][j]  \n                print(j, t1, t2, in_num, out_num, sep=',', file=f)\n","sub_path":"xgb.py","file_name":"xgb.py","file_ext":"py","file_size_in_byte":3521,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"274459742","text":"class Solution:\n    def networkDelayTime(self, times, N, K):\n        from collections import defaultdict\n        import math\n        g = defaultdict(dict)\n        for s, d, t in times:\n            g[s][d] = t\n\n        distances = dict()\n        queue = []\n        for n in range(1, N+1):\n            distances[n] = math.inf\n            queue.append(n)\n        distances[K] = 0\n\n        while queue:\n            queue = list(reversed(sorted(queue, key=lambda k: distances[k])))\n            node = queue.pop()\n\n            for neighbor, d in g[node].items():\n                new_distance = min(\n                        d + distances[node],\n                        distances[neighbor]\n                        )\n                distances[neighbor] = new_distance\n        return max(distances.values())\n\nif __name__ == '__main__':\n    inputs = [[2,1,1],[2,3,1],[3,4,1]]\n    N = 4\n    K = 2\n    print(Solution().networkDelayTime(inputs, N, K))\n","sub_path":"leetcode/743_network_delay_time.py","file_name":"743_network_delay_time.py","file_ext":"py","file_size_in_byte":938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"293794610","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri May  5 14:12:38 2017\n\n@author: admin\n\"\"\"\nimport numpy as np\nimport matplotlib.pylab as plt\n\n\ndef numerical_diff(f, x):\n    h = 1e-4\n    return (f(x+h) - f(x-h)) / (2*h)\n    \ndef function_1(x):\n    return 0.01 * x **2 + 0.1*x\n    \ndef function_2(x):\n    return x[0]**2 + x[1]**2    \n\ndef numerical_gradient(f, x):\n    h = 1e-4\n    grad = np.zeros_like(x)\n    \n    for idx in range(x.size):\n        tmp_val = x[idx]\n        \n        #f(x+h) 계산\n        x[idx] = tmp_val + h\n        fxh1 = f(x)\n        \n        #f(x-h) 계산\n        x[idx] = tmp_val - h\n        fxh2 = f(x)\n       \n        grad[idx] = (fxh1 - fxh2) / (2*h)\n        x[idx] = tmp_val\n        \n    return grad\n\ndef gradient_descent(f, init_x, lr=0.01, step_num=100):\n    x = init_x\n    \n    for i in range(step_num):\n        grad = numerical_gradient(f,x)\n        x -= lr * grad\n        print (grad, x)\n    return x\n\n    \nx = np.arange(0.0, 20.0, 0.1)\ny = function_1(x)\nplt.xlabel(\"x\")\nplt.ylabel(\"f(x)\")\nplt.plot(x,y)\nplt.show()\n\n\nprint(numerical_gradient(function_2, np.array([3.0, 4.0])))\nprint(numerical_gradient(function_2, np.array([0.0, 2.0])))\nprint(numerical_gradient(function_2, np.array([3.0, 0.0])))","sub_path":"ch04/num_diff.py","file_name":"num_diff.py","file_ext":"py","file_size_in_byte":1220,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"333357847","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nfrom builtins import str\nfrom builtins import object\nfrom qgis.core import  QgsProject,QgsMapLayer,QgsVectorLayer,QgsDataProvider,QgsDataSourceUri\nfrom PyQt5.QtCore import Qt\n\nclass run(object):\n    def __init__(self, id, gtotool, config, debug):\n        super(run, self).__init__()\n\n        try:\n            #tool data\n            activelayer = config.get('active_layer',None)\n            layers = config['layers']\n            #init\n            iface=  gtotool.iface\n            prj = QgsProject.instance()\n            root = prj.layerTreeRoot()\n            for g in layers:\n                try:\n                    group=g['name']\n                    #state=g['state']\n                    state = g['visible']\n                    if debug: gtotool.info.log(group + \"/\" + str(state))\n                    node=prj.layerTreeRoot().findGroup(group)#QgsLayerTreeNode\n                    if node: node.setItemVisibilityCheckedRecursive(state)\n                except ValueError as e:\n                    gtotool.info.err(e)\n            #set layers\n            for lyr in layers:\n                layer = prj.mapLayersByName(lyr['name'])\n                if layer:\n                    layer = layer[0]  # duplicte names => take the first\n                    visible = lyr['visible']\n                    if visible:\n                        prj.layerTreeRoot().findLayer(layer.id()).setItemVisibilityCheckedParentRecursive(True)\n                    else:\n                        prj.layerTreeRoot().findLayer(layer.id()).setItemVisibilityChecked(False)\n                    #symbology\n                    symbology = lyr.get(\"symbology\",None)#list\n                    if symbology is not None:\n                        if isinstance(symbology,list):\n                            ltl = root.findLayer(layer.id())#QgsLayerTreeLayer\n                            ltm = iface.layerTreeView().model()\n                            legendNodes = ltm.layerLegendNodes(ltl)\n                            for ln in legendNodes:  # QgsLayerTreeModelLegendNode\n                                for d in symbology:#dic\n                                    symLayerName= d.get(\"name\")\n                                    if symLayerName == ln.data(0):\n                                        if d.get(\"visible\",True):\n                                            ln.setData(Qt.Checked, Qt.CheckStateRole)\n                                        else:\n                                            ln.setData(Qt.Unchecked, Qt.CheckStateRole)\n                                        break\n                        else:\n                            ltl = root.findLayer(layer.id())#QgsLayerTreeLayer\n                            ltm = iface.layerTreeView().model()\n                            legendNodes = ltm.layerLegendNodes(ltl)\n                            for ln in legendNodes:  # QgsLayerTreeModelLegendNode\n                                if symbology:\n                                    ln.setData(Qt.Checked, Qt.CheckStateRole)\n                                else:\n                                    ln.setData(Qt.Unchecked, Qt.CheckStateRole)\n            #set active layer:\n            if activelayer:\n                layer = prj.mapLayersByName(activelayer)\n                if layer:\n                    layer = layer[0]  # duplicte names => take the first\n                    prj.layerTreeRoot().findLayer(layer.id()).setItemVisibilityCheckedParentRecursive(True)\n                    iface.setActiveLayer(layer)\n        except Exception as e:\n            gtotool.info.err(e)\n\n","sub_path":"GZP_GTO_QGIS/INSTALLATION/GeoTaskOrganizer/mActionGTOmas.py","file_name":"mActionGTOmas.py","file_ext":"py","file_size_in_byte":3593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"513597732","text":"import run_full\r\nimport cv2\r\nimport os, shutil\r\n\r\ndef clear_folder():\r\n    folder = os.path.join(os.path.dirname(os.path.abspath(__file__)),'..','..','pic')\r\n    for the_file in os.listdir(folder):\r\n        file_path = os.path.join(folder, the_file)\r\n        try:\r\n            if os.path.isfile(file_path):\r\n                os.unlink(file_path)\r\n            #elif os.path.isdir(file_path): shutil.rmtree(file_path)\r\n        except Exception as e:\r\n            print(e)\r\n\r\nvidcap = cv2.VideoCapture('../../../4/vid4.mp4')\r\nsuccess,image = vidcap.read()\r\ncount = 0\r\n\r\nwhile success:\r\n    cv2.imwrite(\"../../pic/frame%s.jpg\" % str(count).zfill(5), image)\r\n    if count%40 == 0 and count != 0:\r\n        run_full.demo_images()\r\n        clear_folder()\r\n    success,image = vidcap.read()\r\n    print(' Read a new frame: ', success)\r\n    count += 1","sub_path":"src/create_dataset.py","file_name":"create_dataset.py","file_ext":"py","file_size_in_byte":839,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"197301780","text":"# name = \"Siddhesh\"\n\n# new_list = [letter.upper() for letter in name]\n\n# print(new_list)\n\n# double_list = [2*n for n in range(1,25)]\n\n# print(double_list)\n\n# names = [\"Alex\", \"Annemarie\", \"Jeaneatte\", \"Sarah\", \"Robert\", \"Patrick\", \"Jenny\"]\n\n# long_names = [name.upper() for name in names if len(name) > 5]\n\n# print(long_names)\n\nnumbers = [1, 1, 2, 3, 5, 8, 13, 21, 34, 55]\n# 🚨 Do Not Change the code above 👆\n\n#Write your 1 line code 👇 below:\n\nsquared_numbers = [n**2 for n in numbers]\n\n\n\n#Write your code 👆 above:\n\nprint(squared_numbers)\n\n\n","sub_path":"day_26/main_1.py","file_name":"main_1.py","file_ext":"py","file_size_in_byte":552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"126791030","text":"import os.path\nimport re\n\nimport PyQt5.uic\nfrom PyQt5 import QtCore, QtWidgets\n\nROOT = QtCore.QFileInfo(__file__).absolutePath()\n\ndef getImgPath(name):\n    return os.path.join(ROOT, \"resources\", \"img\", name)\n\ndef getTranslationPath(name):\n    return os.path.join(ROOT, \"resources\", \"ts\", name)\n\ndef getOpenFilename(parent=None, root=\".\", title=\"\"):\n    if not title: \n        title = translate(\"Global\", \"Open\")\n    \n    return QtWidgets.QFileDialog.getOpenFileName(parent, title, root)\n\ndef getSaveFilename(parent=None, root=\".\", title=\"\"):\n    if not title: \n        title = translate(\"Global\", \"Save\")\n    \n    return QtWidgets.QFileDialog.getSaveFileName(parent, title, root)\n\ndef loadUi(widget):\n    basepath = os.path.join(ROOT, \"views\")\n\n    basename = [a for a in re.split(\n        r\"([A-Z][a-z]*)\", \n        widget.__class__.__name__\n    ) if a] # Split on capital letters\n    basename = \"_\".join(basename).lower()\n\n    uifile = os.path.join(basepath, \"{basename}.ui\".format(basename=basename))\n    uipath = os.path.dirname(uifile)\n\n    currdir = QtCore.QDir.currentPath()\n    QtCore.QDir.setCurrent(uipath)\n\n    PyQt5.uic.loadUi(uifile, widget)\n\n    QtCore.QDir.setCurrent(currdir)\n\ndef showPopup(msg, parent=None):\n    popup = QtWidgets.QMessageBox(parent)\n    popup.setText(msg)\n    popup.show()\n\ntranslate = QtCore.QCoreApplication.translate\n","sub_path":"orchardtreatmentcreator/globals.py","file_name":"globals.py","file_ext":"py","file_size_in_byte":1355,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"107886753","text":"# EX 2.3\ndef Mode(Li):\n    '''\n    takes a list and return the most freq value\n    \n    >>> L = [1,1,2,2,3,4,4,3,3]\n    '''\n    val_c = {}\n    for i in Li:\n        if i in val_c.keys():\n            val_c[i] += 1\n        else:\n            val_c[i] = 1\n    # now we have a histogram\n    # want to return key in histo w the greatest val\n    high_freq = 0\n    modes = []\n    for key in val_c:\n        if val_c[key] > high_freq:\n            high_freq = val_c[key]\n            modes = [(key, high_freq)]\n        elif val_c[key] == high_freq:\n            modes.append((key, high_freq))\n    #mode = (modes[:][1].sum()) / len(modes)\n    '''\n    modes[:][1]\n    (5, 3)\n    modes[:]\n    [(2, 3), (5, 3), (6, 3)]'''\n    # there can be > 1 mode\n    # thus =>\n    # if known beforehand would have been easier to use dic and call keys()\n    # but since were already this far\n    mode = []\n    for i in modes:\n        mode.append(i[0])\n    return mode\n\ndef histo(Li):\n    val_c = {}\n    for i in Li:\n        if i in val_c.keys():\n            val_c[i] += 1\n        else:\n            val_c[i] = 1    \n    return val_c\n    \ndef AllModes(Li):\n    '''\n    Returns a list val/freq pairs in descending order of freq\n    <=> greatest to least\n    '''\n    hist = histo(Li)\n    #return sorted(hist.items(), reverse=True)\n    # this is true for sorting by value size but not freq\n    # lets just flip the tuples sort and then flip back\n    L = []\n    for i in hist.items():\n        L.append((i[1],i[0]))\n    M = sorted(L)    \n    modes = []\n    for j in M:\n        #j[0],j[1] = j[1],j[0]\n        #'tuple' object does not support item assignment\n        modes.append((j[1],j[0]))\n    return modes\n\n# EX 2.4\nimport nsfg\nimport math\n# using totalwgt_lb investigate whether first babies are heavier or lighter than others\n\ndf = nsfg.ReadFemPreg()\n# now were in business\n\n# lets begin by creating 2 DFs one for first babes one for others\n#df_1st = df[pregordr == 1]\n#df_oth = df[pregordr != 1]\ndf_1st = df[df.pregordr == 1]\ndf_oth = df[df.pregordr != 1]\ndiff_in_means = df_1st.totalwgt_lb.mean() - df_oth.totalwgt_lb.mean()\n# lets begin with the mean for each\nprint('the mean for 1st babes is',\ndf_1st.totalwgt_lb.mean(), 'mean for others is', df_oth.totalwgt_lb.mean(), \n'thus => on avg difference between weight of 1st babies vs others is', diff_in_means)\n\n# lets eval the stds of each\nprint('std for 1st babes is', df_1st.totalwgt_lb.std(), 'and for other babies is', df_oth.totalwgt_lb.std())\n\n# => barely any deviation either, so we can conclude that 1st babies are \n# likely to weigh less than others\n\n# lets compute Cohen's d now\n# which is diff in means / pooled std\n# and is used to compare difference between groups according to the \n# variability between groups\n\ndef Coh_d(col1, col2):\n    diff_in_u = col1.mean() - col2.mean()\n    EV1_var = col1.var() * (len(col1) / (len(col1) + len(col2)))\n    EV2_var = col2.var() * (len(col2) / (len(col1) + len(col2)))\n    pooled_var = (EV1_var + EV2_var)\n    d = diff_in_u / (math.sqrt(pooled_var))\n    return d\n\nd_diff = Coh_d(df_1st.totalwgt_lb, df_oth.totalwgt_lb)\n\nprint('Cohens d between 1st and others for baby weight is', d_diff)\nprint('Cohens d between 1st and others for preg length is', \n      Coh_d(df_1st.prglngth, df_oth.prglngth))\n","sub_path":"brydon_chap02ex.py","file_name":"brydon_chap02ex.py","file_ext":"py","file_size_in_byte":3264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"65384104","text":"\nimport logging\nimport authorized\nfrom base_request_handler import BaseRequestHandler\n\nclass AddBlogReaction(BaseRequestHandler):\n  def post(self,randomID):\n\n    self.session = True\n    user = UserInfo()\n    user.whoIs(self)\n\n\n    # this key is actually of fixed length\n    theKey = \"-\" + randomID + Sketch.generateKey()\n    sketchComment = SketchComment(key_name = theKey)\n    sketchComment.randomID = randomID\n    sketchComment.body = self.request.get('text_input')\n\n    #sketchComment.author_user = user.user\n    sketchComment.author_email = user.email\n    sketchComment.author_user_id = user.user_id\n    sketchComment.author_string_user_id = util.convDecToBase(string._long(user.user_id),62)\n    sketchComment.author_nickname = user.nickname\n\n    # This is necessary cause we need to store in the comment who is the author of the sketch\n    # cause we'll have the client browser to independently check whether to allow the author of the sketch to delete any of the comments\n    # we could do this check on the server side without storing the sketch author in the comment    \n    # BUT we can't do the check on the client side without passing a parameter accross three pages...    \n    sketch = Sketch.get_by_randomID(randomID)\n    if sketch is None: self.redirect(\"/403.html\")\n    sketchComment.sketch_author_user_id = sketch.author_user_id\n        \n    sketchComment.save()\n    self.redirect('/view/'+randomID+\"/\")\n\n\n\nclass DeleteBlogReaction(BaseRequestHandler):\n  @authorized.role(\"user\")\n  def get(self,commentId):\n\n\t\t  logging.info('got in')\n\n\t\t  util.insertUsersideCookies(self)\n\n\t\t  self.session = True\n\t\t  user = UserInfo()\n\t\t  user.whoIs(self)\n\n\t\t  # anonymous users can't delete comments or sketches\n\t\t  if not user.user:\n\t\t  \tself.redirect(\"/403.html\")\n\t\t  \treturn\n\n\t\t  # does the comment exist?\n\t\t  q = SketchComment.get_by_key_name(commentId)\n\t\t  if not q:\n\t\t  \tlogging.info('no such comment')\n\t\t  \tself.redirect(\"/403.html?no such comment\")\n\t\t  \treturn\n\n\t\t  # is the user a) an admin b) the owner of the sketch c) the owner of the comment?\n\t\t  if ((user.user_id == q.author_user_id) or (user.is_current_user_admin) or (user.user_id == q.sketch_author_user_id)):\n\t\t  \tlogging.info('ok, deleting now')\n\t\t  \tq.delete()\n\t\t  else:\n\t\t  \tlogging.info('wrong permissions')\n\t\t  \tself.redirect(\"/403.html?you cant do that\")\n\t\t  \treturn\n\n\t\t  logging.info('redirecting to: ' + self.request.get(\"backTo\"))\n\t\t  self.redirect(self.request.get(\"backTo\"))\n\t\t  return\n\n\n","sub_path":"blog_reactions.py","file_name":"blog_reactions.py","file_ext":"py","file_size_in_byte":2470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"53693647","text":"#! /usr/bin/python\n\"\"\"\n\n@author: Pablo ALINGERY for IAS 07-05-2013\n\"\"\"\nfrom gaia_client_idoc import *\n\nd1 = datetime(2012,8,10,0,0,0)\nd2 = d1 + timedelta(days=1)\n\ngaia_data_list = search( DATES=[d1,d2], nb_res_max=1 )  \n\n#for item in gaia_data_list :\n#\tprint item\n\n#the fastest way to retrieve data\n#PS : The directory 'results' has to be created !\nget(GAIA_LIST=gaia_data_list, TARGET_DIR=\"results\")\n\n#specify TYPE you want to  retrieve , it should be in list 'temp','em','width','chi2' (TYPE=['all'] will do as well ), FILENAME would be the default one \n#get(GAIA_LIST=gaia_data_list, TARGET_DIR=\"results\", TYPE=['temp','em'])\n\n#specify FILENAME you want to retrieve , it should be a dictionary with key within 'temp','em','width','chi2' and value can be whatever you want\n#get(GAIA_LIST=gaia_data_list, TARGET_DIR=\"results\", FILENAME={'temp' :'temp.fits','em':'em.fits'})\n\n#Need to do it quietly \n#get(GAIA_LIST=gaia_data_list, TARGET_DIR=\"results\",QUIET=True)\n\n#########################Warning###########################\n#specify both FILENAME and TYPE is not allowed \n#get(GAIA_LIST=gaia_data_list, TARGET_DIR=\"results\", FILENAME={'temp' :'temp.fits','em':'em.fits'}, TYPE=['temp','em'])\n###########################################################\n\n#Need to get a tar ball do sthg like :\n#get_selection(GAIA_LIST=gaia_data_list,DOWNLOAD_TYPE=\"tar\", target_dir=\"results\" ,FILENAME=\"my_dowload_file.tar\")\n\n","sub_path":"example_gaia.py","file_name":"example_gaia.py","file_ext":"py","file_size_in_byte":1409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"4161595","text":"import sys\nimport psycopg2\nfrom filmie2ML import *\n##\n## Script to add a USER & MOVIE_ID and RATINGS to end of a datafile\n##\n##USAGE ::\n## $ python3 dumpUser2File.py filename User_ID\n##\n##  DOES NOT NORMALIZE THE ADDED USERS RATINGS\n\n\nfilename = sys.argv[1]\nuser_id = sys.argv[2]\n\n\nf_data = open(filename,'a')\n\ncurML = openMovieLensDB()\nmovies = loadDBData(curML, \"movies_large\")\n\ncurFilmie = openFilmieDB()\n\n\n### GRAB USERS MOVIES\n\nuser_filmie_Movies = grabLikes(curFilmie,[int(user_id)])\nwrong_count = 0\nfor item in user_filmie_Movies:\n    movie_id = item[1]\n    try:\n        print(str(user_id) + \",\" + str(movie_id) + \",\" + \"4.5\" + \",\" + \"123456\", file = f_data)\n    except:\n        print(\"Failed out of something\", movie_id)\n\n\nuser_filmie_Movies = grabDisLikes(curFilmie,[int(user_id)])\nfor item in user_filmie_Movies:\n    movie_id = item[1]\n    try:\n        print(str(user_id) + \",\" + str(movie_id) + \",\" + \"1.5\" + \",\" + \"123456\", file = f_data)\n    except:\n        print(\"Failed out of something negative\", movie_id)\n                        \n\ncurML.close()\ncurFilmie.close()\n\nf_data.close()\n","sub_path":"dumpUsers2CSVFile.py","file_name":"dumpUsers2CSVFile.py","file_ext":"py","file_size_in_byte":1097,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"40608208","text":"# Definition for a binary tree node.\nclass TreeNode:\n    def __init__(self, x):\n        self.val = x\n        self.left = None\n        self.right = None\n\n\nclass Solution:\n    # 递归\n    def inorderTraversal1(self, root: TreeNode):\n        nums = []\n        return self.method(root, nums)\n\n    def method(self, root: TreeNode, nums):\n        if root is not None:\n            self.method(root.left, nums)\n            nums.append(root.val)\n            self.method(root.right, nums)\n            return nums\n\n    # 栈\n    def inorderTraversal2(self, root: TreeNode):\n        res = []\n        if not root:\n            return res\n        stack = []\n        cur = root\n        while stack or cur:\n            while cur:\n                stack.append(cur)\n                cur = cur.left\n            cur = stack.pop()\n            res.append(cur.val)\n            cur = cur.right\n        return res\n\n    # 染色方法\n    def inorderTraversal(self, root: TreeNode):\n        stack = list()\n        stack.append((0, root))\n        result = list()\n        while stack:\n            color, cursor = stack.pop()\n            if cursor is None:\n                continue\n            if color == 0:\n                stack.append((0, cursor.right))\n                # 染色为1，表示已经到过该节点，下一次路过取出value进行存储\n                stack.append((1, cursor))\n                # 由于使用栈，所以这里倒序，这样会从左节点开始取出\n                stack.append((0, cursor.left))\n            else:\n                result.append(cursor.val)\n\n        return result\n\n\nif __name__ == '__main__':\n    x = Solution()\n    a = TreeNode(1)\n    b = TreeNode(2)\n    c = TreeNode(3)\n    a.left = b\n    a.right = c\n    x.inorderTraversal(a)\n    print(x.inorderTraversal(a))\n","sub_path":"94. 二叉树的中序遍历.py","file_name":"94. 二叉树的中序遍历.py","file_ext":"py","file_size_in_byte":1788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"548684991","text":"\n\"\"\"\nA resource defines an object that exposes functionality. It allows the\ndefinition of handler that can be called on the resource.\n\"\"\"\nimport six\nfrom whistle.request import Request\nfrom whistle.response import Response\nfrom functools import partial\n\n\nclass ResourceOptions(object):\n    \"\"\"\n    A configuration class for the resource. Provides sane defaults for options\n    that can be over-ridden by the Meta class in a resource\n\n    @handlers: a map of handler_name to a list of callables.\n    resource.handler_name will call the callable with a request object.\n    The handler is expected to return a response object\n\n    @serializer (Class): object of this class should be able to serialize the\n    response from the handlers\n\n    @validator (Class): object of this class should be able to validate the\n    request. Authorization, update and create request data validation,\n    business validations (is this update even allowed etc) are all expected\n    to be handled by a validator\n    \"\"\"\n\n    def __init__(self, options):\n        self.options = options\n        self.handlers = {}\n        self.serializer = None  # set this to default serializer later on\n        self.validator = None\n\n        for opt_name in dir(options):\n            if not opt_name.startswith('_'):\n                setattr(self, opt_name, getattr(options, opt_name))\n\n\nclass ResourceMetaClass(type):\n\n    def __new__(cls, name, bases, attrs):\n        new_class = super(ResourceMetaClass, cls).__new__(\n            cls, name, bases, attrs)\n        options = getattr(new_class, 'Meta', None)\n        new_class._meta = ResourceOptions(options)\n\n        return new_class\n\n\nclass Resource(six.with_metaclass(ResourceMetaClass)):\n\n    def get_validation_object(self, request):\n        if self._meta.validator:\n            return self._meta.validator(request=request)\n        else:\n            return None\n\n    def validate_and_call(self, fn, validation_object, request):\n        \"\"\"\n        validation_object.pre_function_name and\n        validation_object.post_function_name are called before and after the\n        actual function call. If pre/post return a response, it is immediately\n        returned. If the function call returns a response, it is passed to the\n        post_validator to modify the response if necessary.\n        \"\"\"\n        if validation_object is not None:\n            fn_name = fn.__name__\n            pre_validator = getattr(validation_object,\n                                    \"pre_%s\" % fn_name, None)\n            post_validator = getattr(validation_object,\n                                     \"post_%s\" % fn_name, None)\n        else:\n            pre_validator = None\n            post_validator = None\n\n        if pre_validator:\n            # can raise a validation error and exit\n            pre_validator_response = pre_validator(request=request)\n            if pre_validator_response is not None:\n                return pre_validator_response\n\n        response = fn(request=request)\n\n        if post_validator:\n            if isinstance(response, Response):\n                post_validator_response = post_validator(response=response)\n            else:\n                post_validator_response = post_validator(request=request)\n\n            if post_validator_response is not None:\n                return post_validator_response\n\n        return response\n\n    def call_handler(self, pipeline, call, user=None, **kwargs):\n        \"\"\"\n        used when \"resource_obj\".\"handler_name\" is accessed. A partial of this\n        function is returned on attribute access.\n        \"\"\"\n        pipeline = [fn for fn in pipeline if callable(fn)]\n        request = Request(user=user, params=kwargs, call=call)\n        validation_object = self.get_validation_object(request)\n\n        for fn in pipeline:\n            response = self.validate_and_call(fn, validation_object, request)\n            if isinstance(response, Response):\n                # fn has returned a response. Break and return the value\n                break\n\n        return response  # this should be serialized and returned later on\n\n    def __getattr__(self, name):\n        \"\"\"\n        check if a handler is being accessed. If yes check if it is a callable\n        and return a partial that will construct the request and call the\n        handler with it.\n        \"\"\"\n        handlers = self._meta.handlers\n        if name in handlers:\n            handler_pipeline = handlers[name]\n            return partial(self.call_handler,\n                           pipeline=handler_pipeline,\n                           call=name)\n        else:\n            return object.__getattribute__(self, name)\n","sub_path":"whistle/resources.py","file_name":"resources.py","file_ext":"py","file_size_in_byte":4644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"181856829","text":"import os\n\nfrom house_page_processor import HousePageProcessor\nfrom requester import Requester, RequestsLibrary, RetryError\nfrom time_util import timeit\n\n\nclass HouseDownloader:\n    \"\"\"Class that takes care of downloading the images for the given range of houses from the API.\n\n    It does so by iterating through the first 10 pages of the API endpoint and processing each page\n    to download the images.\n    \"\"\"\n    NUMBER_OF_PAGES = 10\n    API_URL = \"http://app-homevision-staging.herokuapp.com/api_project/houses\"\n\n    def __init__(self, requests_library: Requester, house_page_processor: HousePageProcessor):\n        self._requests_library = requests_library\n        self._house_page_processor = house_page_processor\n\n    @classmethod\n    def build(cls):\n        return cls(RequestsLibrary(), HousePageProcessor.build())\n\n    @timeit\n    def download_houses(self):\n        self._ensure_dir()\n\n        results = []\n        for i in range(1, self.NUMBER_OF_PAGES+1):\n            # I think this could also be parallelized as a further improvement, no real reason we\n            # have to access the pages sequentially\n            url = f\"{self.API_URL}?page={i}\"\n            try:\n                response = self._requests_library.get(url)\n            except RetryError:  # We allow for 5 retries which should always be enough but if not we log it\n                print(f\"Page {i} could not be loaded after the maximum amount of retries.\")\n                continue\n\n            page_results = self._house_page_processor.download_all(response.json())\n            for page_result in page_results:\n                results.append(page_result)\n\n        return results\n\n    def _ensure_dir(self):\n        if not os.path.isdir(\"output\"):\n            os.mkdir(\"output\")\n","sub_path":"src/house_downloader.py","file_name":"house_downloader.py","file_ext":"py","file_size_in_byte":1763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"502712280","text":"LEVE_ITEMS = {\"name\": {}, \"id\": {}}\nQUEST_TEXT = {\"text\": {}}\nQUESTS = {\"name\": {}, \"genre_name\": {}, \"npc_name\": {}, \"patch\": {\"name\": {}, \"number\": {}}}\nITEM_CLASSES = {} # TODO: Add all item classes.\n\ndef items(args):\n    d = {}\n    for cls in ITEM_CLASSES:\n        sd = {}\n        for subcls in cls:\n            sd[subcls] = args\n        d[cls] = sd\n    return d\n\ntable = {\n    # TODO: Gathering, crafting\n\t\"achievement\": {\n\t\t\"url\": \"https://api.xivdb.com/achievement\",\n\t\t\"keys\": {\n\t\t\t'name': {},\n                        'name_ja': {},\n\t\t\t'patch': {'number': {}},\n\t\t\t'kind_name': {},\n                        'type_name': {},\n                        'category_name': {},\n                        'help': {},\n                        'item': {},\n                        'title': {}\n\t\t}\n\t},\n        \"action\": {\n            \"url\": \"https://api.xivdb.com/action\",\n            \"keys\": {\n                'name': {},\n                'name_jp': {},\n                'help_en': {},\n                'level': {},\n                'cost_cp': {},\n                'cost_hp': {},\n                'cost_mp': {},\n                'cost_tp': {},\n                'cast_range': {},\n                'cast_time': {},\n                'recast_time': {},\n                'is_trait': {},\n                'type_name': {},\n                'patch': {'number': {}},\n            },\n        },\n        \"emote\": {\n            \"url\": \"https://api.xivdb.com/emote\",\n            \"keys\": {\n                'name': {},\n                'name_jp': {},\n                'text_command': {'command_1': {}, 'command_2': {}, 'command_3': {}, 'command_4': {}},\n                'patch': {'number': {}},\n            },\n        },\n        \"enemy\": {\n            \"url\": \"https://api.xivdb.com/enemy\",\n            \"keys\": {\n                'name': {},\n                'name_jp': {},\n                'map_data': {\n                    'maps': {\"placename_name\": {}, \"zone_name\": {}},\n                    'stats': {'hpAvg': {}, 'hpMax': {}, 'hpMin': {}, 'levelMax': {}, 'levelMin': {}, 'mpAvg': {}, 'mpMax': {}, 'mpMin': {}},\n                },\n                'patch': {'number': {}},\n            },\n        },\n        \"fate\": {\n            \"url\": \"https://api.xivdb.com/fate\",\n            \"keys\": {\n                'name': {},\n                'name_jp': {},\n                'help_en': {},\n                'class_level': {},\n                'class_level_max': {},\n                'map': {'placename': {'name': {}}},\n                'patch': {'number': {}},\n            },\n        },\n        \"instance\": {\n            \"url\": \"https://api.xivdb.com/instance\",\n            \"keys\": {\n                'name': {},\n                'name_ja': {},\n                'help_en': {},\n                'level': {},\n                'level_sync': {},\n                'time_limit': {},\n                'tanks_per_party': {},\n                'healers_per_party': {},\n                'dps_per_party': {},\n                'item_level': {},\n                'item_level_sync': {},\n                'content_name': {},\n                'patch': {'number': {}},\n            },\n        },\n        \"item\": {\n            \"url\": \"https://api.xivdb.com/item\",\n            \"keys\": {\n                'name': {},\n                'name_ja': {},\n                'help_en': {},\n                'classjob_category': {},\n                'level_equip': {},\n                'level_item': {},\n                'category_name': {},\n                'attributes_base': {'auto_attack': {}, 'auto_attack_hq': {}, 'block_rate': {}, 'block_rate_hq': {}, 'block_strength': {}, 'block_strength_hq': {},\n                    'damage': {}, 'damage_hq': {}, 'defense': {}, 'defense_hq': {}, 'delay': {}, 'delay_hq': {}, 'magic_damage': {}, 'magic_damage_hq': {},\n                    'magic_defense': {}, 'magic_defense_hq': {}},\n                'attributes_params': {\"name\": {}, \"value\": {}, \"value_hq\": {}},\n                'patch': {'number': {}},\n            },\n        },\n        \"leve\": {\n            \"url\": \"https://api.xivdb.com/leve\",\n            \"keys\": {\n                'name': {},\n                'name_ja': {},\n                'help_en': {},\n                'gil_reward': {},\n                'exp_reward': {},\n                'leve_client': {},\n                'class_level': {},\n                'classjob_category': {},\n                'time_limit': {},\n                'placename': {'name': {}},\n                'patch': {'number': {}},\n                #'items': items(LEVE_ITEMS),\n                #TODO: This\n            },\n        },\n        \"minion\": {\n            \"url\": \"https://api.xivdb.com/minion\",\n            \"keys\": {\n                'name': {},\n                'name_ja': {},\n                'info1': {},\n                'info2': {},\n                'summon': {},\n                'behavior': {},\n                'action': {},\n                'attack': {},\n                'cost': {},\n                'hp': {},\n                'defense': {},\n                'skill_cost': {},\n                'speed': {},\n                'patch': {'number': {}},\n            },\n        },\n        \"npc\": {\n            \"norepeat\": True,\n            \"url\": \"https://api.xivdb.com/npc\",\n            \"keys\": {\n                'name': {},\n                'name_jp': {},\n                'title': {},\n                'title_de': {},\n                'title_fr': {},\n                'title_jp': {},\n                'patch': {'number': {}},\n            },\n        },\n        \"placename\": {\n            \"url\": \"https://api.xivdb.com/placename\",\n            \"keys\": {\n                'name': {},\n                'name_jp': {},\n                'enemies': {\"name\": {}, \"positions\": {\"hp\": {}, \"level\": {}, \"x\": {}, \"y\": {}}},\n                'instances': {\"name\": {}, \"level\": {}, \"content_type\": {}, \"help\": {}},\n                'npcs': {\"name\": {}, \"position\": {\"x\": {}, \"y\": {}}},\n                'quests': QUESTS,\n                'patch': {'number': {}},\n            },\n        },\n        \"quest\": {\n            \"url\": \"https://api.xivdb.com/quest\",\n            \"keys\": {\n                'category_name': {},\n                'exp_reward': {},\n                'genre_name': {},\n                'gil_reward': {},\n                'name': {},\n                'name_jp': {},\n                'npc_start': {'name': {}},\n                'npc_end': {'name': {}},\n                'patch': {'number': {}},\n                'pre_quests': QUESTS,\n                'post_quests': QUESTS,\n                'text': {'journal': QUEST_TEXT, 'todo': QUEST_TEXT},\n            },\n        },\n        \"status\": {\n            \"url\": \"https://api.xivdb.com/status\",\n            \"keys\": {\n                'name': {},\n                'name_jp': {},\n                'help': {},\n                'patch': {'number': {}},\n            },\n        },\n        \"title\": {\n            \"url\": \"https://api.xivdb.com/title\",\n            \"keys\": {\n                'name': {},\n                'name_jp': {},\n                'name_female': {},\n                'name_female_jp': {},\n                'achivements': {\"name\": {}},\n                'patch': {'number': {}},\n            },\n        },\n        \"weather\": {\n            \"url\": \"https://api.xivdb.com/weather\",\n            \"keys\": {\n                'name': {},\n                'name_jp': {},\n            },\n        },\n}\n\ndef databases():\n\treturn table\n","sub_path":"core/scripts/databases/ffxiv.py","file_name":"ffxiv.py","file_ext":"py","file_size_in_byte":7348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"349727913","text":"# -*- coding: utf-8 -*-\n\nfrom random import randint\n\n# Доработать практическую часть урока lesson_007/python_snippets/08_practice.py\n\n# Необходимо создать класс кота. У кота есть аттрибуты - сытость и дом (в котором он живет).\n# Кот живет с человеком в доме.\n# Для кота дом характеризируется - миской для еды и грязью.\n# Изначально в доме нет еды для кота и нет грязи.\n\n# Доработать класс человека, добавив методы\n#   подобрать кота - у кота появляется дом.\n#   купить коту еды - кошачья еда в доме увеличивается на 50, деньги уменьшаются на 50.\n#   убраться в доме - степень грязи в доме уменьшается на 100, сытость у человека уменьшается на 20.\n# Увеличить кол-во зарабатываемых человеком денег до 150 (он выучил пайтон и устроился на хорошую работу :)\n\n# Кот может есть, спать и драть обои - необходимо реализовать соответствующие методы.\n# Когда кот спит - сытость уменьшается на 10\n# Когда кот ест - сытость увеличивается на 20, кошачья еда в доме уменьшается на 10.\n# Когда кот дерет обои - сытость уменьшается на 10, степень грязи в доме увеличивается на 5\n# Если степень сытости < 0, кот умирает.\n# Так же надо реализовать метод \"дейс��вуй\" для кота, в котором он принимает решение\n# что будет делать сегодня\n\n# Человеку и коту надо вместе прожить 365 дней.\n\n# TODO Изменил начальные условия для большей правдоподобности: Челокеп подбирает сытых животных,\n# TODO но приходит в пустой дом. Изменил логику подыхания от голода - животное умирает в конце дня\n# TODO и уже не участвует в дальнейшей \"жизни\". Собака инициирует драку, но только с сытым котом\n\nfrom random import randint\n\n# Реализуем модель человека.\n# Человек может есть, работать, играть, ходить в магазин.\n# У человека есть степень сытости, немного еды и денег.\n# Если сытость < 0 единиц, человек умирает.\n# Человеку надо прожить 365 дней.\nfrom termcolor import cprint\n\n\nclass House:\n    def __init__(self):\n        self.food = 0\n        self.animal_food = 0\n        self.money = 0\n        self.debris = 0\n        self.number_fighting_cat = 0\n\n    def __str__(self):\n        return 'В доме еды для людей осталось {}, еды для животных -- {}, денег -- {}, грязи -- {}'.format(\n            self.food, self.animal_food, self.money, self.debris)\n\n\nclass Man:\n\n    def __init__(self, name):\n        self.name = name\n        self.fullness = 50\n        self.house = None\n        self.living = True\n\n    def __str__(self):\n        return 'Я - {}, сытость {}'.format(\n            self.name, self.fullness)\n\n    def eat(self):\n        if self.house.food >= 10:\n            cprint('{} поел'.format(self.name), color='yellow')\n            self.fullness += 10\n            self.house.food -= 10\n        else:\n            cprint('{} нет еды'.format(self.name), color='red')\n\n    def work(self):\n        cprint('{} сходил на работу'.format(self.name), color='blue')\n        self.house.money += 150\n        self.fullness -= 10\n\n    def watch_MTV(self):\n        cprint('{} смотрел MTV целый день'.format(self.name), color='green')\n        self.fullness -= 10\n\n    def shopping(self):\n        if self.house.money >= 50:\n            cprint('{} сходил в магазин за своей едой'.format(self.name), color='magenta')\n            self.house.money -= 50\n            self.house.food += 50\n        else:\n            cprint('{} не хватает денег на покупку своей еды!'.format(self.name), color='red')\n\n    def shopping_animal_food(self):\n        if self.house.money >= 50:\n            cprint('{} сходил в магазин за едой для животных'.format(self.name), color='magenta')\n            self.house.money -= 50\n            self.house.animal_food += 50\n        else:\n            cprint('{} не хватает денег на покупку еды для животных!'.format(self.name), color='red')\n\n    def clearn_house(self):\n        self.house.debris -= 100\n        self.fullness -= 20\n        cprint('{} убирался в доме'.format(self.name), color='cyan')\n\n    def go_to_the_house(self, house):\n        self.house = house\n        self.fullness -= 10\n        cprint('{} Вьехал в дом'.format(self.name), color='cyan')\n\n    def pick_up_cat(self, cat):\n        cat.house = self.house\n        cprint('Кота {} подобрал {} и принес в дом'.format(cat.name, self.name), color='cyan')\n\n    def pick_up_dog(self, dog):\n        dog.house = self.house\n        cprint('Собаку {} подобрал {} и принес в дом'.format(dog.name, self.name), color='cyan')\n\n    def act(self):\n        if self.living:\n            dice = randint(1, 6)\n            if self.fullness < 0:\n                cprint('{} умер...'.format(self.name), color='red')\n                self.living = False\n                return\n            if self.fullness < 20:\n                self.eat()\n            elif self.house.money <= 50:\n                self.work()\n            elif self.house.food <= 10:\n                self.shopping()\n            elif self.house.animal_food <= 30:\n                self.shopping_animal_food()\n            elif self.house.debris >= 100:\n                self.clearn_house()\n            elif dice == 1:\n                self.work()\n            elif dice == 2:\n                self.eat()\n            else:\n                self.watch_MTV()\n        else:\n            cprint('==================', color='red')\n\n\nclass Cat:\n\n    def __init__(self, name):\n        self.name = name\n        self.fullness = 50\n        self.house = None\n        self.living = True\n\n    def __str__(self):\n        if self.living:\n            if self.fullness < 0:\n                cprint('Кот {} умер...'.format(self.name), color='red')\n                self.living = False\n\n            str_print = 'Я кот - {}, сытость {}'.format(self.name, self.fullness)\n        else:\n            str_print = '========================='\n        return str_print\n\n    def eat(self):\n        if self.house.animal_food >= 10:\n            cprint('Кот {} поел'.format(self.name), color='yellow')\n            self.fullness += 20\n            self.house.animal_food -= 10\n        else:\n            self.fullness -= 10\n            cprint('У кота {} нет еды. Остался голодный на всесь день'.format(self.name), color='red')\n\n    def sleep(self):\n        self.fullness -= 10\n        cprint('Кот {} дрых весь день'.format(self.name), color='blue')\n\n    def strip_wallpaper(self):\n        self.fullness -= 10\n        self.house.debris += 5\n        cprint('Кот {} подрал обои'.format(self.name), color='red')\n\n    def fight_with_dog(self, cat, dog):\n        self.fullness -= 10\n        self.house.debris += 5\n        self.house.number_fighting_cat = 0\n        cprint('Кот {} дрался с собакой {}'.format(cat.name, dog.name), color='red')\n\n    def act(self, cat=None):\n        if self.living:\n            dice = randint(1, 6)\n            if self.house.number_fighting_cat == 1 and cat == cat1:\n                self.fight_with_dog(cat=cat1, dog=dog1)\n            elif self.house.number_fighting_cat == 2 and cat == cat2:\n                self.fight_with_dog(cat=cat2, dog=dog1)\n            else:\n                if self.fullness < 20:\n                    self.eat()\n                elif dice in (1, 3, 5):\n                    self.sleep()\n                else:\n                    self.strip_wallpaper()\n        else:\n            cprint('=======================', color='red')\n\n\nclass Dog:\n\n    def __init__(self, name):\n        self.name = name\n        self.fullness = 50\n        self.house = None\n        self.living = True\n\n    def __str__(self):\n        if self.living:\n            if self.fullness < 0:\n                cprint('Собака {} умерла...'.format(self.name), color='red')\n                self.living = False\n            str_print = 'Я собака - {}, сытость {}'.format(self.name, self.fullness)\n        else:\n            str_print = '========================='\n        return str_print\n\n    def eat(self):\n        if self.house.animal_food >= 10:\n            cprint('Собака {} поела'.format(self.name), color='yellow')\n            self.fullness += 20\n            self.house.animal_food -= 10\n        else:\n            self.fullness -= 10\n            cprint('У собаки {} нет еды, осталась голодная на весь день'.format(self.name), color='red')\n\n    def crew_furniture(self):\n        self.fullness -= 10\n        self.house.debris += 5\n        cprint('Собака {} грызла мебель'.format(self.name), color='red')\n\n    def fight_with_cat(self, cat):\n        self.fullness -= 10\n        self.house.debris += 5\n        cprint('Собака {} дралась с котом {}'.format(self.name, cat.name), color='red')\n\n    def act(self):\n        if self.living:\n            dice = randint(1, 4)\n            self.house.number_fighting_cat = 0\n            if self.fullness < 20:\n                self.eat()\n            elif dice == 1 and cat1.living and cat1.fullness > 10:\n                self.house.number_fighting_cat = 1\n                self.fight_with_cat(cat=cat1)\n            elif dice == 2 and cat2.living and cat2.fullness > 10:\n                self.house.number_fighting_cat = 2\n                self.fight_with_cat(cat=cat2)\n            else:\n                self.crew_furniture()\n        else:\n            cprint('=======================', color='red')\n\n\nmark = Man(name='Марк')\nmy_sweet_home = House()\ndog1 = Dog(name='Жучка')\ncat1 = Cat(name='Мурзик')\ncat2 = Cat(name='Барсик')\nmark.go_to_the_house(house=my_sweet_home)\nmark.pick_up_dog(dog=dog1)\nmark.pick_up_cat(cat=cat1)\nmark.pick_up_cat(cat=cat2)\nfor day in range(1, 366):\n    print('================ день {} =================='.format(day))\n    mark.act()\n    dog1.act()\n    cat1.act(cat=cat1)\n    cat2.act(cat=cat2)\n    print('--- в конце дня ---')\n    print(mark)\n    print(dog1)\n    print(cat1)\n    print(cat2)\n    print(my_sweet_home)\n\n# Усложненное задание (делать по желанию)\n# Создать несколько (2-3) котов и подселить их в дом к человеку.\n# Им всем вместе так же надо прожить 365 дней.\n# (Можно определить критическое количество котов, которое может прокормить человек...)\n","sub_path":"lesson_007/03_man_ans_cat.py","file_name":"03_man_ans_cat.py","file_ext":"py","file_size_in_byte":11829,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"161882479","text":"import math\nimport random\n\n# returns the probability of the value given the mean and standard\n# deviation of a gaussian distribution\ndef gaussianProbability(mean, standardDeviation, value):\n    variance = standardDeviation * standardDeviation\n    coef = 1.0 / math.sqrt(2.0 * math.pi * variance)\n    diffFromMean = float(value) - float(mean)\n    innerExp = (-1.0 * diffFromMean * diffFromMean) / (2.0 * variance)\n    return coef * math.exp(innerExp)\n\ndef mean(values):\n    if len(values) > 0:\n        num = float(len(values))\n        return sum(values) / num\n    else:\n        return 0\n\n# fundamental theory of probability:\n# variance([randomGaussian(0.0, 1.0) for n in xrange(0, 200)]) => 1.1129958700263398\n# variance([randomGaussian(0.0, 1.0) for n in xrange(0, 200000)]) => 0.99666458910502709\ndef variance(values):\n    avg = mean(values)\n    sumOfSquareDistancesToMean = 0.0\n    for v in values:\n        delta = avg - v\n        deltaSqr = delta * delta\n        sumOfSquareDistancesToMean += deltaSqr\n    return sumOfSquareDistancesToMean / float(len(values))\n\n# returns a random number distributed according to the normal distribution with\n# the provided mean and sigma\ndef randomGaussian(mean, standardDeviation):\n    # From Knuth v2, 3rd ed, p122\n    w = 0\n    y1 = 0\n    y2 = 0\n    while True:\n        x1 = 2.0 * random.random() - 1.0;\n        x2 = 2.0 * random.random() - 1.0;\n        w = x1 * x1 + x2 * x2; # square it\n        if w < 1.0 and w > 0.0:\n            break\n    return mean + standardDeviation * x1 * math.sqrt (-2.0 * math.log (w) / w);\n\n# given an array of N pairs (where first is mean and second is sigma), returns\n# a list of length N with N random gaussians\ndef randomMultivariateGaussian(arrayOfMeansAndSigmas):\n    return [ randomGaussian(u, sigma) for [u, sigma] in arrayOfMeansAndSigmas];\n\n\ndef randomUniform(min, max):\n    return (max - min) * random.random() + min\n\ndef randomMultivariateUniform(arrayOfMinMaxes):\n    return [ randomUniform(min, max) for [min, max] in arrayOfMinMaxes]\n\ndef ex():\n    randomMultivariateGaussian([ (1.0, 10.0), [-5.0, 2.0], [6.0, .001]])\n    return randomGaussian(0.0, 1.0)\n\n# usage:\n# [x for x in lowVarianceSample([\"Common\", \"Uncommon\"], [10.0, 1.0], 10)]\n# draws newSize # of objects from objects with corresponding weights\n# yields for each object\n# credit: http://www.google.com/codesearch/p?hl=en&sa=N&cd=2&ct=rc#D6Z80snicPI/trunk/fvision_modules/prob/src/fvision/prob/resampling.cpp&q=low%20variance%20resampling\ndef lowVarianceSample2(objects, weights, newSize):\n    if newSize > 0:\n        sumWeights = float(sum(weights))\n        weights = [float(x) / sumWeights for x in weights]\n        invSize = 1.0 / float(newSize)\n        r = random.random() * invSize\n        i = 0\n        c = weights[0]\n        for m in xrange(0, newSize):\n            u = r + float(float(m) - 1.0 ) * invSize\n            while u > c:\n                i = i + 1\n                c = c + weights[i]\n            yield objects[i]\n\ndef lowVarianceSampleBuggy(objects, samples = None):\n    if not samples or samples <= 0:\n        if samples <= 0 or len(objects) == 0:\n            raise Exception(\"Invalid number of samples provided.\")\n        samples = len(objects)\n    totalWeight = sum([o.weight for o in objects])\n    result = []\n    offset = random.random()*totalWeight/float(samples)\n    increment = float(totalWeight)/float(samples)\n    cumulativeWeight = objects[0].weight\n    nextSample = offset\n    index = 0\n    for i in range(samples):\n        while nextSample > cumulativeWeight:\n            index += 1\n            cumulativeWeight += objects[index].weight\n        nextSample += increment\n        result.append(objects[index])\n    assert(samples == len(result))        \n    for r in result:\n        r.weight = 1.0 / float(len(result))\n    return result\n","sub_path":"hw4/statutil.py","file_name":"statutil.py","file_ext":"py","file_size_in_byte":3807,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"462101218","text":"try:\n class Card:\n  def __init__(self,suit,number):\n    self._suit = suit\n    self._number = number\n  def __repr__(self):\n    return self.number + \" of\" + self.suit\n  @property\n  def suit(self):\n    return self._suit\n  @property\n  def number(self):\n    return self._number\n  @suit.setter\n  def suit(self,suit):\n      if suit in [\"hearts\", \"clubs\", \"diamonds\",\"spades\"]:\n           self._suit=suit\n      else:\n        print(\"Invalid Suit\")\n  @number.setter\n  def number(self,number):\n      if number in [\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"Jack\",\"Queen\",\"King\",\"Ace\"]:\n         self._number= number\n\n      else:\n        print(\"Invalid Face Value\")\n\n my_card = Card(\"spades\",\"Ace\")\n my_card.suit = \"dinosaur\"\n my_card.number = 3.67\n print(my_card)\nexcept:\n  pass\n\n\n","sub_path":"cards.py","file_name":"cards.py","file_ext":"py","file_size_in_byte":765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"379369324","text":"# Tests of obsolete or not-yet-used functionality\n# To run this test, you must be at the package root directory and run:\n# pytest tests/test_aux.py\n\n# This is necessary only when using \"pytest.raises\" tests\n# import pytest\n\nimport os\nimport warnings\n\ntestid='ci37511872'\nconfigfile='tests/testconfig.yml'\n\ndef test_aggregate():\n    from dyfi import aggregate\n\n    assert aggregate.myCeil(17,10)==20\n\n\ndef test_ipe():\n    from dyfi import ipes\n\n    func=ipes.aww2014wna # Test this ipe\n\n    assert 8.3<func(7,0.1)<8.4\n    assert func(3.5,50,fine=False)==2\n    assert 6.3<func(7,10,inverse=True)<6.4\n\n    func=ipes.aww2014ena # Test this ipe\n\n    assert 9.0<func(7,0.1)<9.1\n    assert func(2.5,100,fine=False)==2\n    assert 5.8<func(7,10,inverse=True)<5.9\n\n\ndef test_privatekey():\n\n    # To add this environmental variable using conda,\n    # Create the file [conda]/envs/dyfi/activate.d/env_vars.sh\n    # For example:\n    #\n    #    #!/bin/sh\n    #    export SECREET_SERVER=good\n\n    key=os.environ.get('SECRET_SERVER')\n    if key and key=='good':\n        return\n\n    warnings.warn('Test of environmental variable SECRET_SERVER failed. To remove this warning, set SECRET_SERVER=good in your environment setup.')\n\n\n#def test_mail():\n#    from dyfi import mail\n#\n#    sample=['--subject','test subject','--to','nobody','--text','test body']\n#\n#    # This looks for /bin/mail and should fail under Travis\n#    with pytest.raises(FileNotFoundError) as exception:\n#        mail.main(sample)\n#    assert 'No such file or directory' in str(exception.value)\n\n\n","sub_path":"tests/test_aux.py","file_name":"test_aux.py","file_ext":"py","file_size_in_byte":1550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"57896604","text":"# -*- coding:utf-8 -*-  \r\n'''\r\nCreated on 2017年12月26日\r\n\r\n@author: misldy\r\n'''\r\n'''\r\npush脚本进室内机，并运行\r\n'''\r\nimport os\r\nimport re\r\nimport subprocess\r\n\r\n#执行脚本\r\ndef push_apk(path):\r\n    filelist=os.listdir(path)\r\n    cmd=\"adb remount\"\r\n    p=subprocess.Popen(cmd,shell=True)\r\n    p.wait()\r\n    if p.returncode!=0:\r\n        return -1\r\n    for filename in filelist:\r\n        filepath=os.path.join(path,filename)\r\n        filename,extension=os.path.splitext(filename)\r\n        if extension==\".apk\":\r\n            #print(\".apk\")\r\n            cmd=\"adb push \"+filepath+\" /system/app\"\r\n            p=subprocess.Popen(cmd,shell=True)\r\n            p.wait()\r\n            if p.returncode!=0:\r\n                return -1\r\n        if extension==\".so\":\r\n            #print(\".so\")\r\n            cmd=\"adb push \"+filepath+\" /system/lib\"\r\n            p=subprocess.Popen(cmd,shell=True)\r\n            p.wait()\r\n            if p.returncode!=0:\r\n                return -1        \r\n    cmd=\"adb reboot\"\r\n    p=subprocess.Popen(cmd,shell=True)\r\n    p.wait()\r\n    if p.returncode!=0:\r\n        return -1    \r\n\r\nif __name__==\"__main__\":\r\n    path=r\"C:\\Users\\Administrator\\Desktop\\pythoncmd\\pushapk\"\r\n    push_apk(path)\r\n    print(\"执行完毕，正在重启\")   ","sub_path":"src/lTools/adb/push-system-app.py","file_name":"push-system-app.py","file_ext":"py","file_size_in_byte":1261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"615157960","text":"\"\"\"Setup for mediasite XBlock.\"\"\"\n\nimport os\nfrom setuptools import setup\n\n\ndef package_data(pkg, root):\n    \"\"\"Generic function to find package_data for `pkg` under `root`.\"\"\"\n    data = []\n    for dirname, _, files in os.walk(os.path.join(pkg, root)):\n        for fname in files:\n            data.append(os.path.relpath(os.path.join(dirname, fname), pkg))\n    return {pkg: data}\n\n\nsetup(\n    name='xblock-mediasite',\n    version='0.1.9.7',\n    description='''\n    This XBlock gives the ability to access TU Delft Collegerama where a lot\n    of recorded lectures, presentations and live events are stored.\n    With this XBlock a teacher or course maker can select a part of a\n    recording to use in an edX online course.\n    Collegerama runs under mediasite software from sonicfoundry.\n    ''',\n    packages=[\n        'mediasite', 'xblockmediasite'\n    ],\n    py_modules=['mediasite_settings'],\n    install_requires=[\n        'XBlock',\n        'requests',\n    ],\n    entry_points={\n        'xblock.v1': [\n            'xblockmediasite = xblockmediasite:XBlockMediasite',\n        ]\n    },\n    package_data=package_data(\"xblockmediasite\", \"static\"),\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1151,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"128710662","text":"## 1. Geographic Data ##\n\nimport pandas as pd\nairlines=pd.read_csv(\"airlines.csv\")\nairports=pd.read_csv(\"airports.csv\")\nroutes=pd.read_csv(\"routes.csv\")\nprint(airlines.iloc[0])\nprint(airports.iloc[0])\nprint(routes.iloc[0])\n                 \n\n## 4. Workflow With Basemap ##\n\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.basemap import Basemap\nm = Basemap(projection=\"merc\",llcrnrlat=-80,urcrnrlat=80,llcrnrlon=-180,urcrnrlon=180  )\n\n\n## 5. Converting From Spherical to Cartesian Coordinates ##\n\nm = Basemap(projection='merc', llcrnrlat=-80, urcrnrlat=80, llcrnrlon=-180, urcrnrlon=180)\nlatti=airports[\"latitude\"].tolist()\nlongi=airports[\"longitude\"].tolist()\nx,y=m(longi,latti)\nprint(x)\nprint(y)\n\n\n## 6. Generating A Scatter Plot ##\n\nm = Basemap(projection='merc', llcrnrlat=-80, urcrnrlat=80, llcrnrlon=-180, urcrnrlon=180)\nx, y = m(longitudes, latitudes)\nm.scatter(x,y, s=1)\nplt.show()\n\n## 7. Customizing The Plot Using Basemap ##\n\nm = Basemap(projection='merc', llcrnrlat=-80, urcrnrlat=80, llcrnrlon=-180, urcrnrlon=180)\nlongitudes = airports[\"longitude\"].tolist()\nlatitudes = airports[\"latitude\"].tolist()\nx, y = m(longitudes, latitudes)\nm.scatter(x, y, s=1)\nm.drawcoastlines()\nplt.show()\n\n## 8. Customizing The Plot Using Matplotlib ##\n\n# Add code here, before creating the Basemap instance.\n\nm = Basemap(projection='merc', llcrnrlat=-80, urcrnrlat=80, llcrnrlon=-180, urcrnrlon=180)\nlongitudes = airports[\"longitude\"].tolist()\nlatitudes = airports[\"latitude\"].tolist()\nx, y = m(longitudes, latitudes)\nfig=plt.figure(figsize=(15,20))\nchart=fig.add_subplot(1,1,1)\nm.scatter(x, y, s=1)\nm.drawcoastlines()\nchart.set_title(\"Scaled Up Earth With Coastlines\")\nplt.show()\n\n## 9. Introduction to Great Circles ##\n\nimport pandas as pd\ngeo_routes=pd.read_csv(\"geo_routes.csv\")\ngeo_routes[0:5]\n\n## 10. Displaying Great Circles ##\n\n\n\ndef create_great_circles(df):\n    for indes,row_list in df.iterrows():\n        if abs(row_list[\"end_lon\"]-row_list[\"start_lon\"])<180:\n            if abs(row_list[\"start_lat\"]-row_list[\"end_lat\"])<180:\n                \n#                return(None)\n                m.drawgreatcircle(row_list[\"start_lon\"],row_list[\"start_lat\"], row_list[\"end_lon\"],row_list[\"end_lat\"])\n                \ndf=geo_routes[(geo_routes[\"source\"]==\"DFW\")][[\"start_lon\",\"start_lat\",\"end_lon\",\"end_lat\"]]\n#print(df)\nfig, ax = plt.subplots(figsize=(15,20))\nm = Basemap(projection='merc', llcrnrlat=-80, urcrnrlat=80,llcrnrlon=-180,urcrnrlon=180)\nm.drawcoastlines()\ncreate_great_circles(df)\n\n\n","sub_path":"Visualizing Geographic Data-223.py","file_name":"Visualizing Geographic Data-223.py","file_ext":"py","file_size_in_byte":2495,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"461430724","text":"import sys\n\n\ndef dfs(prev, now, visited, flag):\n    visited[now] = True\n    for nxt in G[now]:\n        if nxt == prev or visited[nxt]:\n            continue\n        res[nxt] = not flag\n        dfs(now, nxt, visited, not flag)\n\n\nsys.setrecursionlimit(10**6)\n\nn, q = map(int, input().split())\na, b = zip(*[tuple(map(int, input().split())) for _ in range(n - 1)])\nc, d = zip(*[tuple(map(int, input().split())) for _ in range(q)])\n\n# 距離の偶奇が重要？\n# 2色？に分ける\n\nG = [[] for _ in range(n)]\nfor i in range(n - 1):\n    G[a[i] - 1].append(b[i] - 1)\n    G[b[i] - 1].append(a[i] - 1)\n\nvisited = [False] * n\nres = [False] * n\ndfs(0, 0, visited, False)\nfor i in range(q):\n    if res[c[i] - 1] ^ res[d[i] - 1]:\n        print(\"Road\")\n    else:\n        print(\"Town\")\n","sub_path":"src/data/248.py","file_name":"248.py","file_ext":"py","file_size_in_byte":774,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"30125936","text":"\"\"\"\noriginal code for the hidden layer is taken from http://deeplearning.net/tutorial/code/mlp.py and \ndropout added\n\n\n\"\"\"\n# __docformat__ = 'restructedtext en'\n\n\n# import cPickle\n# import gzip\n# import os\n# import sys\n# import time\nimport numpy\nimport theano\nimport theano.tensor as T\n\n\nclass HiddenLayerDropoutTanh(object):\n    def __init__(self, rng, srng, dropRate, input, n_in, n_out, n_examples=1, C=1, W=None, b=None, \n                 activation=T.tanh):\n        \"\"\"\n        Typical hidden layer of a MLP: units are fully-connected and have\n        sigmoidal activation function. Weight matrix W is of shape (n_in,n_out)\n        and the bias vector b is of shape (n_out,).\n\n        NOTE : The nonlinearity used here is tanh\n\n        Hidden unit activation is given by: tanh(dot(input,W) + b)\n\n        :type rng: numpy.random.RandomState\n        :param rng: a random number generator used to initialize weights\n\n        :type input: theano.tensor.dmatrix\n        :param input: a symbolic tensor of shape (n_examples, n_in)\n\n        :type n_in: int\n        :param n_in: dimensionality of input\n\n        :type n_out: int\n        :param n_out: number of hidden units\n        \n        :type C: int\n        :param C: norm of each column in W has to smaller or equal to C\n        \n        :type activation: theano.Op or function\n        :param activation: Non linearity to be applied in the hidden\n                           layer\n        \"\"\"\n        self.input = input\n\n        # `W` is initialized with `W_values` which is uniformely sampled\n        # from sqrt(-6./(n_in+n_hidden)) and sqrt(6./(n_in+n_hidden))\n        # for tanh activation function\n        # the output of uniform if converted using asarray to dtype\n        # theano.config.floatX so that the code is runable on GPU\n        # Note : optimal initialization of weights is dependent on the\n        #        activation function used (among other things).\n        #        For example, results presented in [Xavier10] suggest that you\n        #        should use 4 times larger initial weights for sigmoid\n        #        compared to tanh\n        #        We have no info for other function, so we use the same as\n        #        tanh.\n        if W is None:\n            W_values = numpy.asarray(rng.uniform(\n                    low=-numpy.sqrt(6. / (n_in + n_out)),\n                    high=numpy.sqrt(6. / (n_in + n_out)),\n                    size=(n_in, n_out)), dtype=theano.config.floatX)\n            # if activation == theano.tensor.nnet.sigmoid:\n            #     W_values *= 4\n\n            self.W = theano.shared(value=W_values, name='W', borrow=True)\n\n        if b is None:\n            b_values = numpy.zeros((n_out,), dtype=theano.config.floatX)\n            self.b = theano.shared(value=b_values, name='b', borrow=True)\n\n        self.delta_W = theano.shared(numpy.zeros((n_in, n_out),dtype=theano.config.floatX), borrow=True)\n                                                                         \n                \n        delta_b_values = numpy.zeros((n_out,), dtype=theano.config.floatX)\n        self.delta_b = theano.shared(value=delta_b_values, borrow=True)\n\n        self.inputDropped, self.randMatrix = dropout(input, srng, drop_rate=dropRate)\n\n        lin_output = T.dot(self.inputDropped, self.W) + self.b\n        self.output = (lin_output if activation is None\n                       else activation(lin_output))\n                 \n        # rand params \n        self.randParams = [self.randMatrix]            \n        # parameters of the model\n        self.params = [self.W, self.b]\n        # weight matrix of the model\n        self.param = [self.W]\n        # bias of the layer\n        self.bias = [self.b]\n\n        self.deltas = [self.delta_W, self.delta_b]\n\n    def out(self):\n        return self.output\n    def outmat(self):\n        return self.W\n\ndef dropout(input, srng, drop_rate=0.5): \n        \"\"\"\n        :type input: theano.tensor.dtensor4\n        :param input: symbolic image tensor of shape image_shape\n        \n        :type drop_rate: double between 0. and 1. \n        :param drop_rate: drop_rate probability of dropping a unit\n        \n        \"\"\"          \n        mask = srng.binomial(n=1, p=1-drop_rate, size=input.shape, dtype=theano.config.floatX)\n        return input * mask, mask      \n","sub_path":"HiddenLayerDropoutTanh.py","file_name":"HiddenLayerDropoutTanh.py","file_ext":"py","file_size_in_byte":4295,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"378384011","text":"\n# Import\nimport os\nimport sys\nfrom glob import glob\n\n# Fetch TF information\ntfFileName = \"/hpcwork/izkf/projects/TfbsPrediction/Data/PWM/PIQ/Expression_Analysis/PIQjaspar_translate.txt\"\ntfFile = open(tfFileName,\"r\")\ntfDict = dict()\nfor line in tfFile:\n  ll = line.strip().split(\"\\t\")\n  tfDict[ll[0]] = ll[1]\ntfFile.close()\n\ntfDict = {\"107\": \"PRRX2\"}\n\n# Cell Loop\n#cellList = [\"H1hesc\",\"K562\",\"GM12878\"]\ncellList = [\"GM12878\"]\nfor cell in cellList:\n    \n  # TF Loop\n  keyList = sorted(tfDict.keys())\n  for k in keyList:\n  \n    # Parameters\n    pwnN = k\n    factor = tfDict[k]\n    pwmLoc = \"/hpcwork/izkf/projects/TfbsPrediction/Results/MPBSAWG/PIQ_batch/\"\n    cutFileName=\"/hpcwork/izkf/projects/TfbsPrediction/Results/Signals/Counts/DU_\"+cell+\"/PIQ/DNase.RData\"\n    outputLocation=\"/hpcwork/izkf/projects/TfbsPrediction/Results/Footprints/Predictions/\"+cell+\"/PIQ_batch/\"\n    os.system(\"mkdir -p \"+outputLocation)\n\n    # Execution\n    myL = \"_\".join([cell,factor,\"APQ\"])\n    clusterCommand = \"bsub \"\n    clusterCommand += \"-J \"+myL+\" -o \"+myL+\"_out.txt -e \"+myL+\"_err.txt \"\n    clusterCommand += \"-W 100:00 -M 24000 -S 100 -P izkf -R \\\"select[hpcwork]\\\" ./applyPIQ_pipeline.zsh \"\n    clusterCommand += pwnN+\" \"+factor+\" \"+pwmLoc+\" \"+cutFileName+\" \"+outputLocation\n    os.system(clusterCommand) \n# \n\n\n","sub_path":"Code/CompetingMethods/Pipeline/applyPIQ_batch.py","file_name":"applyPIQ_batch.py","file_ext":"py","file_size_in_byte":1301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"516787089","text":"'''\nShahzeb Khalid\n\n'''\nimport statistics as stats\nimport xlrd\nimport numpy as num\nimport scipy\nfrom scipy.stats import skew\nimport math\nimport matplotlib.pyplot as plt\n\n# _-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n\n# This is define a funtion for find Outliers\noutliers = []\ndef detect_outlier(data):\n    checkpoint = 3\n    mean = num.mean(data)\n    std =num.std(data)\n    for y in data:\n        z_score= (y - mean)/std\n        if num.abs(z_score) > checkpoint:\n            outliers.append(y)\n    return outliers\n\n# _-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n\n# inisalizing file to vaiable\nfilename = \"HmEqty.xlsx\"\n# This line is used to open the file and passing the info and storing it in the variable data\ndata = xlrd.open_workbook(filename)\n\n# copy = pd.read_csv(gapminder_csv_url)\n\nsheet = data.sheet_by_index(0)\n# These variables are used to store the columns from the data set from the excel sheet.\nloan, mortdue, value, yol, derog, delinq, clage, ninq, clno, debtinc = ([] for i in range(10))\n# These declared vaiables are used for storing the data passed form the Outliers Funtion.\nloan_outlier, mort_outlier, value_outlier, yol_outlier, derog_outlier = ([] for i in range(5))\ndelinq_outlier,clage_outlier, ninq_outlier, clno_outlier, debtinc_outlier = ([] for i in range(5))\n\n# ∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞∞\n''' This is loop is used to iterate thorugh the columns from the sheet and\n    appends the colum one by one from the sheet when it called by hardcoding col numerbs '''\nfor i in range(sheet.nrows):\n   if (i > 0):\n       loan.append(sheet.cell_value(i,1))\n       mortdue.append(sheet.cell_value(i,2))\n       value.append(sheet.cell_value(i,3))\n       yol.append(sheet.cell_value(i,6))\n       derog.append(sheet.cell_value(i,7))\n       delinq.append(sheet.cell_value(i,8))\n       clage.append(sheet.cell_value(i,9))\n       ninq.append(sheet.cell_value(i,10))\n       clno.append(sheet.cell_value(i,11))\n       debtinc.append(sheet.cell_value(i,12))\n\n\n\n#_-_-_-_-_-_-_-_-_-_-_-_-_-_- Loan _-_-_-_-_-_-_-_-_-_-_-_-_-_-\nloan_mean = num.mean(loan)\nloan_MID = num.median(loan)\nloan_SD = num.std(loan)\nloan_var = num.var(loan)\nloan_skew = skew(loan)\n\nloan_min_qurt= num.quantile(loan, 0.25)\nloan_max_qurt = num.quantile(loan, 0.75)\n\nfor i in range(len(loan)):\n    min_loan = min(loan)\n    max_loan = max(loan)\n    loan_ran = max_loan - min_loan\n\n# loan_cor = num.corrcoef(loan,value)\n\nprint(\"\\n\")\nprint(25*' ',\"OUTLIERS\")\nloan_outlier = detect_outlier(loan)\nprint(\"Loan Outlier\", 8*' ', loan_outlier)\noutliers.clear()\n\n#_-_-_-_-_-_-_-_-_-_-_-_-_-_- Mortdue _-_-_-_-_-_-_-_-_-_-_-_-_-_-\nmort_mean = num.mean(mortdue)\nmort_MID = num.median(mortdue)\nmort_SD = num.std(mortdue)\nmort_var = num.var(mortdue)\nmort_skew = skew(mortdue)\n\nmort_min_qurt= num.quantile(mortdue, 0.25)\nmort_max_qurt = num.quantile(mortdue, 0.75)\n\nfor i in range(len(mortdue)):\n    min_loan = min(mortdue)\n    max_loan = max(mortdue)\n    mort_ran = max_loan - min_loan\n\nmort_outlier = detect_outlier(mortdue)\nprint(\"Mortdue Outlier\", 5*' ', mort_outlier)\noutliers.clear()\n\n# _-_-_-_-_-_-_-_-_-_-_-_-_-_- Value _-_-_-_-_-_-_-_-_-_-_-_-_-_-\nvalue_mean = num.mean(value)\nvalue_MID = num.median(value)\nvalue_SD = num.std(value)\nvalue_var = num.var(value)\nvalue_skew = skew(value)\n\nvalue_min_qurt= num.quantile(value, 0.25)\nvalue_max_qurt = num.quantile(value, 0.75)\n\nfor i in range(len(value)):\n    min_loan = min(value)\n    max_loan = max(value)\n    value_ran = max_loan - min_loan\n\nvalue_outlier = detect_outlier(value)\nprint(\"Value Outlier\",  7*' ',value_outlier)\noutliers.clear()\n\n# _-_-_-_-_-_-_-_-_-_-_-_-_-_- YoJ _-_-_-_-_-_-_-_-_-_-_-_-_-_-\n\n\n# this loop and chekc if there's any white space and reaplce it with zero\nfor j in range(len(yol)):\n    if (yol[j] == '' or yol[j] == ' '):\n        yol[j] = 0\n\nyol_mean = num.mean(yol)\n\nyol_MID = num.median(yol)\nyol_SD = num.std(yol)\nyol_var = num.var(yol)\nyol_skew = skew(yol)\n\nyol_min_qurt= num.quantile(yol, 0.25)\nyol_max_qurt = num.quantile(yol, 0.75)\n\n\n#  This loop is used to find the range\nfor i in range(len(yol)):\n    min_loan = min(yol)\n    max_loan = max(yol)\n    yol_ran = max_loan - min_loan\n\nyol_outlier = detect_outlier(yol)\nprint(\"YOJ Outlier\", 9*' ', yol_outlier)\noutliers.clear()\n\n# _-_-_-_-_-_-_-_-_-_-_-_-_-_- Derog _-_-_-_-_-_-_-_-_-_-_-_-_-_-\nderog_mean = num.mean(derog)\nderog_MID = num.median(derog)\nderog_SD = num.std(derog)\nderog_var = num.var(derog)\nderog_skew = skew(derog)\n\nderog_min_qurt= num.quantile(derog, 0.25)\nderog_max_qurt = num.quantile(derog, 0.75)\n\nfor i in range(len(derog)):\n    min_loan = min(derog)\n    max_loan = max(derog)\n    derog_ran = max_loan - min_loan\n\nderog_outlier = detect_outlier(derog)\nprint(\"DEROG Outlier\", 7*' ', derog_outlier)\noutliers.clear()\n\n# _-_-_-_-_-_-_-_-_-_-_-_-_-_- Delinq _-_-_-_-_-_-_-_-_-_-_-_-_-_-\ndelinq_mean = num.mean(delinq)\ndelinq_MID = num.median(delinq)\ndelinq_SD = num.std(delinq)\ndelinq_var = num.var(delinq)\ndelinq_skew = skew(delinq)\n\ndelinq_min_qurt= num.quantile(delinq, 0.25)\ndelinq_max_qurt = num.quantile(delinq, 0.75)\n\nfor i in range(len(delinq)):\n    min_loan = min(delinq)\n    max_loan = max(delinq)\n    delinq_ran = max_loan - min_loan\n\ndelinq_outlier = detect_outlier(delinq)\nprint(\"DELINQ Outlier\",  6*' ',delinq_outlier)\noutliers.clear()\n\n# _-_-_-_-_-_-_-_-_-_-_-_-_-_- Clage _-_-_-_-_-_-_-_-_-_-_-_-_-_-\nclage_mean = num.mean(clage)\nclage_MID = num.median(clage)\nclage_SD = num.std(clage)\nclage_var = num.var(clage)\nclage_skew = skew(clage)\n\nclage_min_qurt= num.quantile(clage, 0.25)\nclage_max_qurt = num.quantile(clage, 0.75)\n\nfor i in range(len(clage)):\n    min_loan = min(clage)\n    max_loan = max(clage)\n    clage_ran = max_loan - min_loan\n\nclage_outlier = detect_outlier(clage)\nprint(\"CLAGE Outlier\",  7*' ',clage_outlier)\noutliers.clear()\n\n# _-_-_-_-_-_-_-_-_-_-_-_-_-_- Ninq_-_-_-_-_-_-_-_-_-_-_-_-_-_-\nninq_mean = num.mean(ninq)\nninq_MID = num.median(ninq)\nninq_SD = num.std(ninq)\nninq_var = num.var(ninq)\nninq_skew = skew(ninq)\n\nninq_min_qurt= num.quantile(ninq, 0.25)\nninq_max_qurt = num.quantile(ninq, 0.75)\n\nfor i in range(len(ninq)):\n    min_loan = min(ninq)\n    max_loan = max(ninq)\n    ninq_ran = max_loan - min_loan\n\nninq_outlier = detect_outlier(ninq)\nprint(\"NINQ Outlier\", 8*' ', ninq_outlier)\noutliers.clear()\n\n# _-_-_-_-_-_-_-_-_-_-_-_-_-_- Clno _-_-_-_-_-_-_-_-_-_-_-_-_-_-\nclno_mean = num.mean(clno)\nclno_MID = num.median(clno)\nclno_SD = num.std(clno)\nclno_var = num.var(clno)\nclno_skew = skew(clno)\n\nclno_min_qurt= num.quantile(clno, 0.25)\nclno_max_qurt = num.quantile(clno, 0.75)\n\nfor i in range(len(clno)):\n    min_loan = min(clno)\n    max_loan = max(clno)\n    clno_ran = max_loan - min_loan\n\nclno_outlier = detect_outlier(clno)\nprint(\"CLNO Outlier\", 8*' ', clno_outlier)\noutliers.clear()\n\n# _-_-_-_-_-_-_-_-_-_-_-_-_-_- Debtinc _-_-_-_-_-_-_-_-_-_-_-_-_-\ndebtinc_mean = num.mean(debtinc)\ndebtinc_MID = num.median(debtinc)\ndebtinc_SD = num.std(debtinc)\ndebtinc_var = num.var(debtinc)\ndebtinc_skew = skew(debtinc)\n\ndebtinc_min_qurt= num.quantile(debtinc, 0.25)\ndebtinc_max_qurt = num.quantile(debtinc, 0.75)\n\nfor i in range(len(debtinc)):\n    min_loan = min(debtinc)\n    max_loan = max(debtinc)\n    debtinc_ran = max_loan - min_loan\n\n# debtincoutlier = []\ndebtinc_outlier = detect_outlier(debtinc)\nprint(\"DEBTINC Outlier\", 5*' ', debtinc_outlier)\noutliers.clear()\n\n# _-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-\n''' All of these are print Statments are used inorder to print the data calculated\n    for example : MEAN, MEDAIN, Standarad Deviation, VARINCE, RAGE, SKEW '''\n\nprint(\"\\n\")\nprint(\"øøøøøøøøøøøøøøøøøøøøøøøøøøøøøøø  •••••••••••••••••••••••••••••••••••••••••  øøøøøøøøøøøøøøøøøøøøøøøøøøøøøøøø\")\nprint(\"\\n\")\nprint(\"         \",\"LOAN\",'    ',\"MORTDUE\",'    ',\"VALUE\",'     ',\"YOJ\",'   ',\"DEROG\",'  ' +\\\n\"DELINQ\",'  ',\"CLAGE\", '   ', \"NINQ\", '     ', \"CLNO\", '     ',\"DEBTINC\" )\n\nprint(\"MEAN     \",round(loan_mean,2), '  ',round(mort_mean,2),'  ', +\\\nround(value_mean,2),'  ',round(yol_mean,2),'  ',round(derog_mean,2),'  ',round(delinq_mean,2),'   ',+\\\nround(clage_mean,2),'   ',round(ninq_mean,2),'     ', +\\\nround(clno_mean,2),'     ',round(debtinc_mean,2))\n\n# Medain Statment\nprint(\"MEDIAN   \",round(loan_MID,2),'   ' ,round(mort_MID,2),'   ',+\\\nround(value_MID,2),'   ',round(yol_MID,2),'   ',round(derog_MID,2),'   ',round(delinq_MID,2),'    ', +\\\nround(clage_MID,2),'   ',round(ninq_MID,2),'      ', +\\\nround(clno_MID,2),'      ',round(debtinc_MID,2))\n\n# Standarad Deviation\nprint(\"Stan.Dev \",round(loan_SD,2),'  ',round(mort_SD,2),'  ',+\\\nround(value_SD,2), '   ',round(yol_SD,2),'  ',round(derog_SD,2),'  ',round(delinq_SD,2), +\\\n2*'  ',round(clage_SD,2),'   ',round(ninq_SD,2),2*'   ', +\\\nround(clno_SD,2),2*'   ' ,round(debtinc_SD,2))\n\n# Range\nprint(\"Range    \",round(loan_ran,2),'   ', round(mort_ran,2),'  ', round(value_ran,2),'  ', +\\\nround(yol_ran,2),'  ',round(derog_ran,2),'   ' ,round(delinq_ran,2),'    ',round(clage_ran,2),'   ',+\\\nround(ninq_ran,2),'     ', round(clno_ran,2),'      ',round(debtinc_ran,2))\n\n# Skew\nprint(\"Skewness\", round(loan_skew,2),'     ', round(mort_skew,2),'      ', round(value_skew,2),'      ', +\\\nround(yol_skew,2),'  ',round(derog_skew,2),'  ' ,round(delinq_skew,2),'   ',round(clage_skew,2),'     ' , round(ninq_skew,2),'     ',+\\\nround(clno_skew,2),'      ',round(debtinc_skew,2))\n\n# print(\"\\n\")\n# print(\" øøøøøøøøøøøøøøøøøøøøøøøøøøøøø  •••••••••••••••••••••••••••••••••••••••  øøøøøøøøøøøøøøøøøøøøøøøøøøøøøø \")\n# print(\"\\n\")\n\nprint(\"\\n\")\nprint(\"  ∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚ VARIANCE ˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚ \")\nprint(\"\\n\")\n# Varicance\nprint(18*' ',\"Variance \")\nprint(\" Loan\",10*' ',round(loan_var,2),\"\\n\",\"MORTDUE\",5*' ',round(mort_var,2),\"\\n\",\"VALUE\",6*' ',+\\\nround(value_var,2),\"\\n\",\"YOJ\",16*' ',round(yol_var,2),\"\\n\",\"DEROG\",15*' ',round(derog_var,2),\"\\n\",\"DELINQ\",14*' ',+\\\nround(delinq_var,2),\"\\n\",\"CLAGE\",12*' ', round(clage_var,2),\"\\n\",\"NINQ\",16*' ',round(ninq_var,2),\"\\n\",\"CLNO\",15*' ', +\\\nround(clno_var,2),\"\\n\",\"DEBTINC\",12*' ',round(debtinc_var,2))\nprint(\"\\n\")\nprint(\" ˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚∆˚  \")\nprint(\"\\n\")\nprint(10*' ',\"  Low Quantile\",10*' ', \"High Quantile\")\n# Quantile\nprint(\"Loan\", 10*' ',loan_min_qurt,16*' ' ,loan_max_qurt)\nprint(\"MORTDUE\", 6*' ',mort_min_qurt,15*' ' ,mort_max_qurt)\nprint(\"VALUE\", 8*' ',value_min_qurt,15*' ' ,value_max_qurt)\nprint(\"YOJ\", 14*' ',yol_min_qurt,18*' ' ,yol_max_qurt)\nprint(\"DEROG\", 12*' ',derog_min_qurt,19*' ' ,derog_max_qurt)\nprint(\"DELINQ\", 11*' ',delinq_min_qurt,19*' ' ,delinq_max_qurt)\nprint(\"CLAGE\", 3*' ',clage_min_qurt,10*' ' ,clage_max_qurt)\nprint(\"NINQ\", 13*' ',ninq_min_qurt,19*' ' ,ninq_max_qurt)\nprint(\"CLNO\", 12*' ',clno_min_qurt,18*' ' ,clno_max_qurt)\nprint(\"DEBTINC\", 1*' ',debtinc_min_qurt,11*' ' ,debtinc_max_qurt)\nprint(\"\\n\")\n\nprint(\" ˆ¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬ˆ CORRALATION ˆ¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚¬˚ˆ\")\ncorraltion_1 = num.corrcoef(mortdue ,value)\nprint(\" Correlation:\", \"Mortdue & Value \\n\",corraltion_1 )\nprint(\" Correlation:\", \"Mortdue & Value \\n\", corraltion_1[0][1] )\n\n# Scatter Plot for Mortdue and value\n# plt.scatter(mortdue, value)\n# plt.xlabel('Mortdue')\n# plt.ylabel('value')\n# plt.title('Mortgage & Value')\n# plt.show()\n\n# # the histogram of the Loan\n# plt.hist(loan, facecolor='g')\n# plt.xlabel('Loan')\n# plt.ylabel('Frequency')\n# plt.title('Histogram of Loan')\n# plt.show()\n\n# Box Plot for\n# plt.boxplot(value)\n# plt.xlabel('Value')\n# plt.ylabel('value')\n# plt.title('Mortgag & Value')\n# plt.show()\n\n# Bar Plot for\n# width = 1/2.109288\n# plt.bar(yol, clno, align = 'center', alpha =.35)\n# plt.xlabel('Value')\n# plt.show()\n# plt.xlabel('Mortdue')\n# plt.ylabel('value')\n# plt.title('Mortgag & Value')\n# plt.show()\n","sub_path":"Analysis.py","file_name":"Analysis.py","file_ext":"py","file_size_in_byte":12926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"501018997","text":"#!/usr/bin/env python\nimport os\nimport sys\nimport logging\nimport numpy as np\nimport time\nimport scipy.io\nimport glob\nimport pickle\nfrom keras.utils.np_utils import to_categorical\nfrom keras import backend as K\nimport tensorflow as tf\nimport keras\n# import cv2\nfrom keras.backend.tensorflow_backend import set_session\nsys.path.append(\"../../model/\")\nimport cnn_sigmoid_leaky_1024 as cnn\nprint('example 262144 ../../../processedData/real-pd.training_data/smartphone_accelerometer/')\n(the_file, the_path)=(sys.argv[1],sys.argv[2])\nsize=512\n\n# set fraction of GPU the program can use\n#config = tf.ConfigProto()\n#config.gpu_options.per_process_gpu_memory_fraction = 0.4\n#set_session(tf.Session(config=config))\n\n# set the value of data format convention\nK.set_image_data_format('channels_last')  # TF dimension ordering in this code\n\n# handles for txt files\nall_ids=open(the_file,'r')\nthe_max=-100\nthe_min=100\nall_line=[]\nfor line in all_ids:\n    all_line.append(line.rstrip())\n    table=line.split('\\t')\n    if (float(table[0])>the_max):\n        the_max=float(table[0])\n    if (float(table[0])<the_min):\n        the_min=float(table[0])\nall_ids.close()\n\n# read into arousal data (arousal data is only helper data, help to determine the sleep length)\n\n# load models\nall_models=glob.glob('weights_*.h5')\nfor the_model in all_models:\n    model = cnn.cnn1d(size,3)\n    model.load_weights(the_model)\n    path1=the_path\n    the_file=the_file.replace('train','test')\n    all_test_files=open(the_file,'r')\n    PRED=open(('prediction.dat.'+the_model),'w')\n    for test_line in all_test_files:\n        sample = test_line\n        table=sample.split('\\t')\n        the_id=table[1]\n        image = np.loadtxt((path1 + the_id),delimiter=',',skiprows=1)[:,1:4]\n\n        image_pad=np.zeros((size,3))\n        image_pad[0:image.shape[0],:]=image\n        image_batch=[]\n        image_batch.append(image_pad)\n        image_batch=np.asarray(image_batch)\n\n        output = model.predict(image_batch)\n        if (the_max-the_min)==0:\n            output=the_max\n        else:\n            output=output*(the_max-the_min)+the_min\n            # below are not modified\n        PRED.write('%.4f\\n' % output)\n    PRED.close()\n","sub_path":"REAL_tremor/final_long_smartwatch_gyroscope_whole_sigmoid_leaky_seg512_split128_10/predict_deeplearning.py","file_name":"predict_deeplearning.py","file_ext":"py","file_size_in_byte":2189,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"273225772","text":"from bearlibterminal  import terminal\n\n\nTITLE = \"Drag Dungeons\"\n\nSCREEN_WIDTH = 80\nSCREEN_HEIGHT = 50\n\nMAP_WIDTH = 80\nMAP_HEIGHT = 43\n\nFONT_PATH = \"data/consolas10x10.png\"\n\nMAP_SETTINGS = {\n    \"map_height\": 43,\n    \"map_width\": 80,\n    \"tile_size\": 10,\n    \"num_rooms\": 100,\n    \"min_room_size\": 5,\n    \"max_room_size\": 11,\n    \"room_margin\": 1,\n}\n\nCOLORS = {\n    \"transparent\",\n    \"none\",\n    \"black\",\n    \"white\",\n    \"grey\",\n    \"gray\",\n    \"red\",\n    \"flame\",\n    \"orange\",\n    \"amber\",\n    \"yellow\",\n    \"lime\",\n    \"chartreuse\",\n    \"green\",\n    \"sea\",\n    \"turquoise\",\n    \"cyan\",\n    \"sky\",\n    \"azure\",\n    \"blue\",\n    \"han\",\n    \"violet\",\n    \"purple\",\n    \"fuchsia\",\n    \"magenta\",\n    \"pink\",\n    \"crimson\",\n    }\n\n\nclass Layers:\n    BACKGROUND = 0\n    MAP = 10\n    PLAYER = 20\n    LIGHT = 30\n\n\nclass Tiles:\n    UNSEEN = \"\\uE004\"\n    FLOOR = \"\\uE000\"\n    CORRIDOR = \"\\uE001\"\n    WALL = \"\\uE002\"\n    CONNECT = \"\\uE003\"\n","sub_path":"src/const.py","file_name":"const.py","file_ext":"py","file_size_in_byte":932,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"561140715","text":"'''\nCode for plotting  QSO spectra   vs.  NIRSpec   filters\n\n\n'''\n\nimport math\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom pylab import * \nfrom matplotlib.ticker import ScalarFormatter \nfrom astropy.io import ascii\nfrom matplotlib import colors as mcolors\n\n\n## MIRI Filters\npath = '/cos_pc19a_npr/data/filter_curves/JWST/NIRSpec/'\n#file = 'jwst_miri_mirifushort_qe.fits'\ntable = path+file\n#MIRI_IFU_short =  \n\n\n\n##\n## QSO Composite spectra\n##\n## Vanden Berk et al. 2001\npath = '/cos_pc19a_npr/data/SDSS/VdB01/'\nfile = 'Vanden_Berk_2001_Table1.dat'\ntable = path+file\nVdB01_comp = ascii.read(table)\n#VdB01_comp\n\n## Glikman et al. 2006. \n## TABLE 3:\n##  Composite Quasar Spectrum, Arithmetic and Geometric Mean\npath = '/cos_pc19a_npr/data/Glikman2006/'\nfile = 'Glikman_2006_ApJ_Table3.dat'\ntable = path+file\nGlikman_tab3 = ascii.read(table)\n## TABLE 7:\n## Optical-to-Near-Infrared Composite Quasar Spectrum, Arithmetic and Geometric Means\npath = '/cos_pc19a_npr/data/Glikman2006/'\nfile = 'Glikman_2006_ApJ_Table7.dat'\ntable = path+file\nGlikman_tab7 = ascii.read(table)\n\n## IR templates at::\n## /cos_pc19a_npr/BOSS/WISE/templates\n## http://www.iasf-milano.inaf.it/~polletta/templates/swire_templates.html\npath = '/cos_pc19a_npr/BOSS/WISE/templates/Polletta_templates/'\nfile_qso1 = 'QSO1_template_norm.sed'\nfile_qso2 = 'QSO2_template_norm.sed'\nfile_sey2 = 'Sey2_template_norm.sed'\ntable_qso1 = path+file_qso1\ntable_qso2 = path+file_qso2\ntable_sey2 = path+file_sey2\nQSO1_template = ascii.read(table_qso1)\nQSO2_template = ascii.read(table_qso2)\nSey2_template = ascii.read(table_sey2)\n\n\n'''\nCharacterizing quasars in the mid-infrared: high signal-to-noise ratio\nspectral templates\nAllison R. Hill  S. C. Gallagher  R. P. Deo  E. Peeters  Gordon T. Richards\n2014, MNRAS, 438, 2317\nhttps://doi.org/10.1093/mnras/stt2346\n\n Table 2 of Allison R. Hill et al (2013)\n Entries which contain a '-1' indicate no wavelength coverage for that template.\ncol 1: wavelength [micron]\ncol 2: total template (spectral average of all objects) in units of L_{nu}\ncol 3,4,5: the most luminous, intermediate luminosity and least luminous template (corresponding to values 1, 2 and 3 from col 10 of Table 1) in units if L_{nu}\n'''\n\npath = '/cos_pc19a_npr/data/Spitzer/Hill2014_IRS/'\nHill2014_data =  ascii.read(path+'table2.txt')\n##data =  ascii.read('table2.txt')\n\nIRS_wavelength = Hill2014_data['col1']\nIRS_total      = Hill2014_data['col2']\nIRS_most_lum   = Hill2014_data['col3']\nIRS_medium_lum = Hill2014_data['col4']\nIRS_least_lum  = Hill2014_data['col5']\n\n\n## Setting up the composites up...\n## wavelengths now into um\nVdB01_wave = (VdB01_comp['Wavelength']/10000.)\nVdB01_flux =  VdB01_comp['Flux']\n\nGlik_wave = (Glikman_tab7['Wavelength']/10000.)\nGlik_flux =  Glikman_tab7['Flux']\n\nQSO1_wave = (QSO1_template['col1']/10000.)\nQSO1_flux =  QSO1_template['col2']\nQSO2_wave = (QSO2_template['col1']/10000.)\nQSO2_flux =  QSO2_template['col2']\nSey2_wave = (Sey2_template['col1']/10000.)\nSey2_flux =  Sey2_template['col2']\n\n\n##  R E D S H I F T  !!!!\nredshift = 0.\nredshift = float(input(\"What redshift are we at??   \"))\n\n\n\n## Plotting things up...\nplt.rcParams.update({'font.size': 24})\nfig, ax = plt.subplots(figsize=(12.0, 8.0))\n\n#print(plt.style.available)\nplt.style.use('seaborn-poster')\n\ncmap=plt.get_cmap('viridis')\n\n\n## Setting the limits.\n## If log\nxmin =   .400           ##  0.1um = 1e-7 = 100e-9 = 1000Ang\nxmax = 60.              ## 30.0um\n##\nxmin = 4.4\nxmax = 30.0\n#ax.set_xscale(\"log\", nonposx='clip')\n\nymin = -0.05  \nymax = 1.2    \n\n\n## Plotting the Vanden Berk et al. (2001) QSO composite\nplt.plot((VdB01_wave*(1.+redshift)), (VdB01_flux/VdB01_flux.max()))\n\n## Plotting the Glikman et al. (2006) QSO composite\nplt.plot((Glik_wave*(1.+redshift)), (Glik_flux/Glik_flux.max()))\n\n## Plotting the Polletta et al. templates\nplt.plot((QSO1_wave*(1.+redshift)), (QSO1_flux/QSO1_flux.max()))\nplt.plot((QSO2_wave*(1.+redshift)), (QSO2_flux/QSO2_flux.max()))\nplt.plot((Sey2_wave*(1.+redshift)), (Sey2_flux/Sey2_flux.max()))\n\n## Hill et al. 2014 IRS spectra\n#plt.plot((IRS_wavelength*(1.+redshift)), ((IRS_least_lum/IRS_least_lum.max())*2.6), linestyle='solid', linewidth=4)\n#plt.plot((IRS_wavelength*(1.+redshift)), ((IRS_medium_lum/IRS_medium_lum.max())*2.2), linestyle='dotted', linewidth=4)\n#plt.plot((IRS_wavelength*(1.+redshift)), ((IRS_most_lum/IRS_most_lum.max())*1.25), linestyle='dashed', linewidth=4)\n\n#ax.text((xmin+(xmin/10.)), (0.90*ymax), '$z=5.0$', fontsize=26)\n\nplt.legend([\n             'Vanden Berk (2001)',\n         'Glikman et al. (2006)', \n            'Polletta QSO1',\n            'Polletta QSO2',\n            'Polletta Sey2' \n            ],\n#           loc=\"lower left\", ncol=3, shadow=True, fancybox=True,\n           loc=\"upper right\", ncol=1, shadow=True, fancybox=True,\n          fontsize=22, frameon=True)\n\nls = 'solid'\nlw = 1.0\nax.set_xlim((xmin, xmax))\nax.set_ylim((ymin, ymax))\nax.tick_params('x',which='major', top='on', direction='in', length=12, width=2)\nax.tick_params('x',which='minor', top='on', direction='in', length=6, width=2)\n#ax.tick_params('y', direction='in')\nax.tick_params('y', which='major',right='on', direction='in', length=12, width=2)\nax.tick_params('y',which='minor', right='on', direction='in', length=6, width=2)\n\n##ax.ticklabel_format(style='plain', axis='x')\n\n## https://matplotlib.org/gallery/ticks_and_spines/scalarformatter.html\n## https://stackoverflow.com/questions/21920233/matplotlib-log-scale-tick-label-number-formatting\nfrom matplotlib.ticker import ScalarFormatter\nfor axis in [ax.xaxis, ax.yaxis]:\n    axis.set_major_formatter(ScalarFormatter())\n    axis.set_minor_formatter(ScalarFormatter())\n\nplt.xlabel(r'wavelength [$\\mu$m]', fontsize=22)\nplt.ylabel('Relative flux',        fontsize=22)\n#ax.xaxis.label.set_size(32)\n#ax.xtick.label.set_size(32)\n\nplt.show()\n","sub_path":"Figures/Filter_SEDcoverage/QSOspectra_NIRSpec_filters.py","file_name":"QSOspectra_NIRSpec_filters.py","file_ext":"py","file_size_in_byte":5815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"79414535","text":"# Copyright 2016 - Nokia\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n#      http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport json\n\nfrom vitrage.api_handler.apis.alarm import AlarmApis\nfrom vitrage.api_handler.apis.rca import RcaApis\nfrom vitrage.api_handler.apis.topology import TopologyApis\nfrom vitrage.common.constants import EntityCategory\nfrom vitrage.common.constants import VertexProperties as VProps\nfrom vitrage.datasources import NOVA_HOST_DATASOURCE\nfrom vitrage.datasources import NOVA_INSTANCE_DATASOURCE\nfrom vitrage.datasources import NOVA_ZONE_DATASOURCE\nfrom vitrage.datasources import OPENSTACK_CLUSTER\nfrom vitrage.graph.driver.networkx_graph import NXGraph\nimport vitrage.graph.utils as graph_utils\nfrom vitrage.tests.unit.entity_graph.base import TestEntityGraphUnitBase\n\n\nclass TestApis(TestEntityGraphUnitBase):\n\n    def test_get_alarms_with_admin_project(self):\n        # Setup\n        graph = self._create_graph()\n        apis = AlarmApis(graph, None)\n        ctx = {'tenant': 'project_1', 'is_admin': True}\n\n        # Action\n        alarms = apis.get_alarms(ctx, vitrage_id='all', all_tenants='0')\n        alarms = json.loads(alarms)['alarms']\n\n        # Test assertions\n        self.assertEqual(3, len(alarms))\n        self._check_projects_entities(alarms, 'project_1', True)\n\n    def test_get_alarms_with_not_admin_project(self):\n        # Setup\n        graph = self._create_graph()\n        apis = AlarmApis(graph, None)\n        ctx = {'tenant': 'project_2', 'is_admin': False}\n\n        # Action\n        alarms = apis.get_alarms(ctx, vitrage_id='all', all_tenants='0')\n        alarms = json.loads(alarms)['alarms']\n\n        # Test assertions\n        self.assertEqual(2, len(alarms))\n        self._check_projects_entities(alarms, 'project_2', True)\n\n    def test_get_alarms_with_all_tenants(self):\n        # Setup\n        graph = self._create_graph()\n        apis = AlarmApis(graph, None)\n        ctx = {'tenant': 'project_1', 'is_admin': False}\n\n        # Action\n        alarms = apis.get_alarms(ctx, vitrage_id='all', all_tenants='1')\n        alarms = json.loads(alarms)['alarms']\n\n        # Test assertions\n        self.assertEqual(5, len(alarms))\n        self._check_projects_entities(alarms, None, True)\n\n    def test_get_rca_with_admin_project(self):\n        # Setup\n        graph = self._create_graph()\n        apis = RcaApis(graph, None)\n        ctx = {'tenant': 'project_1', 'is_admin': True}\n\n        # Action\n        graph_rca = apis.get_rca(ctx, root='alarm_on_host', all_tenants='0')\n        graph_rca = json.loads(graph_rca)\n\n        # Test assertions\n        self.assertEqual(3, len(graph_rca['nodes']))\n        self._check_projects_entities(graph_rca['nodes'], 'project_1', True)\n\n    def test_get_rca_with_not_admin_project(self):\n        # Setup\n        graph = self._create_graph()\n        apis = RcaApis(graph, None)\n        ctx = {'tenant': 'project_2', 'is_admin': False}\n\n        # Action\n        graph_rca = apis.get_rca(ctx,\n                                 root='alarm_on_instance_3',\n                                 all_tenants='0')\n        graph_rca = json.loads(graph_rca)\n\n        # Test assertions\n        self.assertEqual(2, len(graph_rca['nodes']))\n        self._check_projects_entities(graph_rca['nodes'], 'project_2', True)\n\n    def test_get_rca_with_not_admin_bla_project(self):\n        # Setup\n        graph = self._create_graph()\n        apis = RcaApis(graph, None)\n        ctx = {'tenant': 'project_2', 'is_admin': False}\n\n        # Action\n        graph_rca = apis.get_rca(ctx, root='alarm_on_host', all_tenants='0')\n        graph_rca = json.loads(graph_rca)\n\n        # Test assertions\n        self.assertEqual(3, len(graph_rca['nodes']))\n        self._check_projects_entities(graph_rca['nodes'], 'project_2', True)\n\n    def test_get_rca_with_all_tenants(self):\n        # Setup\n        graph = self._create_graph()\n        apis = RcaApis(graph, None)\n        ctx = {'tenant': 'project_1', 'is_admin': False}\n\n        # Action\n        graph_rca = apis.get_rca(ctx, root='alarm_on_host', all_tenants='1')\n        graph_rca = json.loads(graph_rca)\n\n        # Test assertions\n        self.assertEqual(5, len(graph_rca['nodes']))\n        self._check_projects_entities(graph_rca['nodes'], None, True)\n\n    def test_get_topology_with_admin_project(self):\n        # Setup\n        graph = self._create_graph()\n        apis = TopologyApis(graph, None)\n        ctx = {'tenant': 'project_1', 'is_admin': True}\n\n        # Action\n        graph_topology = apis.get_topology(\n            ctx,\n            graph_type='graph',\n            depth=10,\n            query=None,\n            root='RESOURCE:openstack.cluster:OpenStack Cluster',\n            all_tenants=0)\n        graph_topology = json.loads(graph_topology)\n\n        # Test assertions\n        self.assertEqual(8, len(graph_topology['nodes']))\n        self._check_projects_entities(graph_topology['nodes'],\n                                      'project_1',\n                                      False)\n\n    def test_get_topology_with_not_admin_project(self):\n        # Setup\n        graph = self._create_graph()\n        apis = TopologyApis(graph, None)\n        ctx = {'tenant': 'project_2', 'is_admin': False}\n\n        # Action\n        graph_topology = apis.get_topology(\n            ctx,\n            graph_type='graph',\n            depth=10,\n            query=None,\n            root='RESOURCE:openstack.cluster:OpenStack Cluster',\n            all_tenants=0)\n        graph_topology = json.loads(graph_topology)\n\n        # Test assertions\n        self.assertEqual(7, len(graph_topology['nodes']))\n        self._check_projects_entities(graph_topology['nodes'],\n                                      'project_2',\n                                      False)\n\n    def test_get_topology_with_all_tenants(self):\n        # Setup\n        graph = self._create_graph()\n        apis = TopologyApis(graph, None)\n        ctx = {'tenant': 'project_1', 'is_admin': False}\n\n        # Action\n        graph_topology = apis.get_topology(\n            ctx,\n            graph_type='graph',\n            depth=10,\n            query=None,\n            root='RESOURCE:openstack.cluster:OpenStack Cluster',\n            all_tenants=1)\n        graph_topology = json.loads(graph_topology)\n\n        # Test assertions\n        self.assertEqual(12, len(graph_topology['nodes']))\n\n    def _check_projects_entities(self,\n                                 alarms,\n                                 project_id,\n                                 check_alarm_category):\n        for alarm in alarms:\n            tmp_project_id = alarm.get(VProps.PROJECT_ID, None)\n            condition = True\n            if check_alarm_category:\n                condition = alarm[VProps.CATEGORY] == EntityCategory.ALARM\n            if project_id:\n                condition = condition and \\\n                    (not tmp_project_id or\n                     (tmp_project_id and tmp_project_id == project_id))\n            self.assertEqual(True, condition)\n\n    def _create_graph(self):\n        graph = NXGraph('Multi tenancy graph')\n\n        # create vertices\n        cluster_vertex = self._create_resource(\n            'RESOURCE:openstack.cluster:OpenStack Cluster',\n            OPENSTACK_CLUSTER)\n        zone_vertex = self._create_resource('zone_1',\n                                            NOVA_ZONE_DATASOURCE)\n        host_vertex = self._create_resource('host_1',\n                                            NOVA_HOST_DATASOURCE)\n        instance_1_vertex = self._create_resource('instance_1',\n                                                  NOVA_INSTANCE_DATASOURCE,\n                                                  project_id='project_1')\n        instance_2_vertex = self._create_resource('instance_2',\n                                                  NOVA_INSTANCE_DATASOURCE,\n                                                  project_id='project_1')\n        instance_3_vertex = self._create_resource('instance_3',\n                                                  NOVA_INSTANCE_DATASOURCE,\n                                                  project_id='project_2')\n        instance_4_vertex = self._create_resource('instance_4',\n                                                  NOVA_INSTANCE_DATASOURCE,\n                                                  project_id='project_2')\n        alarm_on_host_vertex = self._create_alarm('alarm_on_host',\n                                                  'alarm_on_host')\n        alarm_on_instance_1_vertex = self._create_alarm('alarm_on_instance_1',\n                                                        'deduced_alarm',\n                                                        project_id='project_1')\n        alarm_on_instance_2_vertex = self._create_alarm('alarm_on_instance_2',\n                                                        'deduced_alarm')\n        alarm_on_instance_3_vertex = self._create_alarm('alarm_on_instance_3',\n                                                        'deduced_alarm',\n                                                        project_id='project_2')\n        alarm_on_instance_4_vertex = self._create_alarm('alarm_on_instance_4',\n                                                        'deduced_alarm')\n\n        # create links\n        edges = list()\n        edges.append(graph_utils.create_edge(\n            cluster_vertex.vertex_id,\n            zone_vertex.vertex_id,\n            'contains'))\n        edges.append(graph_utils.create_edge(\n            zone_vertex.vertex_id,\n            host_vertex.vertex_id,\n            'contains'))\n        edges.append(graph_utils.create_edge(\n            host_vertex.vertex_id,\n            instance_1_vertex.vertex_id,\n            'contains'))\n        edges.append(graph_utils.create_edge(\n            host_vertex.vertex_id,\n            instance_2_vertex.vertex_id,\n            'contains'))\n        edges.append(graph_utils.create_edge(\n            host_vertex.vertex_id,\n            instance_3_vertex.vertex_id,\n            'contains'))\n        edges.append(graph_utils.create_edge(\n            host_vertex.vertex_id,\n            instance_4_vertex.vertex_id,\n            'contains'))\n        edges.append(graph_utils.create_edge(\n            alarm_on_host_vertex.vertex_id,\n            host_vertex.vertex_id,\n            'on'))\n        edges.append(graph_utils.create_edge(\n            alarm_on_instance_1_vertex.vertex_id,\n            instance_1_vertex.vertex_id,\n            'on'))\n        edges.append(graph_utils.create_edge(\n            alarm_on_instance_2_vertex.vertex_id,\n            instance_2_vertex.vertex_id,\n            'on'))\n        edges.append(graph_utils.create_edge(\n            alarm_on_instance_3_vertex.vertex_id,\n            instance_3_vertex.vertex_id,\n            'on'))\n        edges.append(graph_utils.create_edge(\n            alarm_on_instance_4_vertex.vertex_id,\n            instance_4_vertex.vertex_id,\n            'on'))\n        edges.append(graph_utils.create_edge(\n            alarm_on_host_vertex.vertex_id,\n            alarm_on_instance_1_vertex.vertex_id,\n            'causes'))\n        edges.append(graph_utils.create_edge(\n            alarm_on_host_vertex.vertex_id,\n            alarm_on_instance_2_vertex.vertex_id,\n            'causes'))\n        edges.append(graph_utils.create_edge(\n            alarm_on_host_vertex.vertex_id,\n            alarm_on_instance_3_vertex.vertex_id,\n            'causes'))\n        edges.append(graph_utils.create_edge(\n            alarm_on_host_vertex.vertex_id,\n            alarm_on_instance_4_vertex.vertex_id,\n            'causes'))\n\n        # add vertices to graph\n        graph.add_vertex(cluster_vertex)\n        graph.add_vertex(zone_vertex)\n        graph.add_vertex(host_vertex)\n        graph.add_vertex(instance_1_vertex)\n        graph.add_vertex(instance_2_vertex)\n        graph.add_vertex(instance_3_vertex)\n        graph.add_vertex(instance_4_vertex)\n        graph.add_vertex(alarm_on_host_vertex)\n        graph.add_vertex(alarm_on_instance_1_vertex)\n        graph.add_vertex(alarm_on_instance_2_vertex)\n        graph.add_vertex(alarm_on_instance_3_vertex)\n        graph.add_vertex(alarm_on_instance_4_vertex)\n\n        # add links to graph\n        for edge in edges:\n            graph.add_edge(edge)\n\n        return graph\n","sub_path":"vitrage-1.5.2/vitrage/tests/functional/api_handler/test_apis.py","file_name":"test_apis.py","file_ext":"py","file_size_in_byte":12733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"428056170","text":"from django.http import JsonResponse\nfrom django.shortcuts import render\nimport logic\n\n\ndef index(request):\n    return render(request, template_name=\"index.html\")\n\n\ndef ciphertext(request):\n    if request.is_ajax():\n        text = request.POST.get(\"text\")\n        number = request.POST.get(\"number\")\n        if not is_english_string(text):\n            crypted_text = \"Only english character are allowed\"\n        elif text and number:\n            crypted_text = logic.get_crypt_text(text, number)\n        else:\n            crypted_text = text\n        context = {\n            \"code_text\": crypted_text\n        }\n        return JsonResponse(context)\n\n\ndef chart(request):\n    if request.is_ajax():\n        text = request.POST.get(\"text\")\n        number = request.POST.get(\"number\")\n        if not is_english_string(text):\n            crypted_text = \"Only english character are allowed\"\n        elif text and number:\n            crypted_text = logic.get_crypt_text(text, number)\n        else:\n            crypted_text = text\n        chart_text = logic.symbol_count(crypted_text)\n        context = {\n            \"chart_text\": chart_text\n        }\n        return JsonResponse(context)\n\n\ndef is_english_string(text):\n    try:\n        text.decode(\"ascii\")\n    except UnicodeEncodeError:\n        return False\n    else:\n        return True\n","sub_path":"caesar/crypter/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"22449495","text":"import os\nimport argparse\n\n\ndef get_mode(target):\n\tmode = oct(os.stat(target).st_mode & 0o7777)\n\tmode = str(mode).replace('0o', '')\n\n\t# print to stdout\n\tprint(mode)\n\n\treturn mode\n\n\ndef parse_arguments():\n\tparser = argparse.ArgumentParser()\n\n\tparser.add_argument('target')\n\n\treturn parser.parse_args()\n\n\nif __name__ == '__main__':\n\targs = parse_arguments()\n\tget_mode(args.target)\n","sub_path":"confiles/package/helper/get_mode.py","file_name":"get_mode.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"493256835","text":"\nfrom pandas import DataFrame\nimport sys\n\nimport datetime\nfrom sqlalchemy import create_engine\nimport library.cf\nimport pymysql\nimport pymysql.cursors\n\n\npymysql.install_as_MySQLdb()\nimport MySQLdb\n\n\n\n# jango_data 테이블 이외 추가 컬럼 설명\n# account : 시뮬레이션 번호\n# total_date_count : 총 매수한 일자 수\n# total_profitcut_rate : 익절률 ( 총익절종목수 / 총매수종목수 ) * 100\n# total_buy_count : 총 매수 종목수\n\n\nclass simul_scraper():\n    def __init__(self):\n        self.cf = library.cf\n        if len(sys.argv) == 1:\n            self.start_db_num = int(input(\"start_db_num: \"))\n        # db_setting\n        self.db_setting()\n        # simul_scrap db생성\n        if not self.is_database_exist('simul_scrap'):\n            self.create_database('simul_scrap')\n\n        # 몇 번째 db까지 정렬할지 설정\n        self.simul_db_range = 1000\n\n        # simul_scrap db에 ranking 테이블이 있는지 확인 후 있으면 삭제\n        if self.is_scrap_table_exist(\"ranking\"):\n            self.drop_table('ranking')\n\n        # 데이터프레임을 만들 때 사용할 컬럼 리스트 설정\n        self.set_jango_list()\n\n        # simulator db의 최종 수익률을 (jango_data의 가장 최신 row) ranking table에 넣는다\n        self.set_scraped_db()\n\n        # ranking 테이블을 수��률 순 재정렬\n        self.set_orderby_scraped_db()\n\n\n    # 데이터 베이스를 만드는 함수\n    def create_database(self,db_name):\n        print(\"create_database!!! {}\".format(db_name))\n        sql = 'CREATE DATABASE {}'\n        self.db_conn.cursor().execute(sql.format(db_name))\n\n    # 봇 데이터 베이스 존재 여부 확인 함수\n    def is_database_exist(self,db_name):\n        sql = \"SELECT 1 FROM Information_schema.SCHEMATA WHERE SCHEMA_NAME = '{}'\"\n        rows = self.db_conn.cursor().execute(sql.format(db_name))\n        if rows:\n            print(f\"{db_name} 데이터 베이스가 존재한다! \")\n            return True\n        else:\n            print(f\"{db_name} 데이터 베이스가 존재하지 않는다! \")\n            return False\n\n    # 스크랩 시작하는 simulator의 가장 최종 jango_data일자를 가져온다\n    def get_jango_data_last_date(self):\n        self.engine_start_db = create_engine(\n            \"mysql+mysqldb://\" + self.cf.db_id + \":\" + self.cf.db_passwd + \"@\" + self.cf.db_ip + \":\" + self.cf.db_port + \"/\" + \"simulator\" + str(\n                self.start_db_num),\n            encoding='utf-8')\n        sql = \"SELECT date from jango_data order by date desc limit 1\"\n        return self.engine_start_db.execute(sql).fetchall()[0][0]\n\n\n\n    # simul_scrap에 jango_data 테이블이 있는지 확인\n    def is_scrap_table_exist(self, table_name):\n        sql = \"select 1 from information_schema.tables where table_schema = '%s' and table_name = '%s'\"\n        rows = self.engine_simul_scrap.execute(sql % ('simul_scrap', table_name)).fetchall()\n\n        if len(rows) == 1:\n            return True\n        else:\n            return False\n\n    # 각 simulator db에 jango_data 테이블이 있는지 확인\n    def is_simul_table_exist(self, db_name, table_name):\n        sql = \"select 1 from information_schema.tables where table_schema = '%s' and table_name = '%s'\"\n        rows = self.engine_simulator.execute(sql % (db_name, table_name)).fetchall()\n\n        if len(rows) == 1:\n            return True\n        else:\n            return False\n    # 데이터프레임을 만들 때 사용할 컬럼 리스트 설정\n    def set_jango_list(self):\n        self.jango_list = ['account', 'date', 'today_earning_rate', 'sum_valuation_profit', 'total_profit',\n                           'today_profit',\n                           'today_profitcut_count', 'today_losscut_count', 'today_profitcut', 'today_losscut',\n                           'd2_deposit', 'total_possess_count', 'today_buy_count', 'today_buy_list_count',\n                           'today_reinvest_count',\n                           'today_cant_reinvest_count'\n                            , 'total_asset',\n                           'total_invest',\n                           'sum_item_total_purchase', 'total_evaluation', 'today_rate',\n                           'today_invest_price', 'today_reinvest_price',\n                           'today_sell_price', 'volume_limit', 'reinvest_point', 'sell_point',\n                           'max_reinvest_count', 'invest_limit_rate', 'invest_unit',\n                           'rate_std_sell_point', 'limit_money', 'total_profitcut', 'total_losscut',\n                           'total_profitcut_count',\n                           'total_losscut_count', 'loan_money', 'start_kospi_point',\n                           'start_kosdaq_point', 'end_kospi_point', 'end_kosdaq_point',\n                           'today_buy_total_sell_count',\n                           'today_buy_total_possess_count', 'today_buy_today_profitcut_count',\n                           'today_buy_today_profitcut_rate', 'today_buy_today_losscut_count',\n                           'today_buy_today_losscut_rate',\n                           'today_buy_total_profitcut_count', 'today_buy_total_profitcut_rate',\n                           'today_buy_total_losscut_count', 'today_buy_total_losscut_rate',\n                           'today_buy_reinvest_count0_sell_count',\n                           'today_buy_reinvest_count1_sell_count', 'today_buy_reinvest_count2_sell_count',\n                           'today_buy_reinvest_count3_sell_count', 'today_buy_reinvest_count4_sell_count',\n                           'today_buy_reinvest_count4_sell_profitcut_count',\n                           'today_buy_reinvest_count4_sell_losscut_count',\n                           'today_buy_reinvest_count5_sell_count',\n                           'today_buy_reinvest_count5_sell_profitcut_count',\n                           'today_buy_reinvest_count5_sell_losscut_count',\n                           'today_buy_reinvest_count0_remain_count',\n                           'today_buy_reinvest_count1_remain_count', 'today_buy_reinvest_count2_remain_count',\n                           'today_buy_reinvest_count3_remain_count', 'today_buy_reinvest_count4_remain_count',\n                           'today_buy_reinvest_count5_remain_count']\n\n        self.jango_list_temp = ['account', 'date', 'today_earning_rate', 'sum_valuation_profit', 'total_profit',\n                                'today_profit',\n                                'today_profitcut_count', 'today_losscut_count', 'today_profitcut', 'today_losscut',\n                                'd2_deposit', 'total_possess_count', 'today_buy_count', 'today_buy_list_count',\n                                'today_reinvest_count',\n                                'today_cant_reinvest_count'\n                                , 'total_asset',\n                                'total_invest',\n                                'sum_item_total_purchase', 'total_evaluation', 'today_rate',\n                                'today_invest_price', 'today_reinvest_price',\n                                'today_sell_price', 'volume_limit', 'reinvest_point', 'sell_point',\n                                'max_reinvest_count', 'invest_limit_rate', 'invest_unit',\n                                'rate_std_sell_point', 'limit_money', 'total_profitcut', 'total_losscut',\n                                'total_profitcut_count',\n                                'total_losscut_count', 'loan_money', 'start_kospi_point',\n                                'start_kosdaq_point', 'end_kospi_point', 'end_kosdaq_point',\n                                'today_buy_total_sell_count',\n                                'today_buy_total_possess_count', 'today_buy_today_profitcut_count',\n                                'today_buy_today_profitcut_rate', 'today_buy_today_losscut_count',\n                                'today_buy_today_losscut_rate',\n                                'today_buy_total_profitcut_count', 'today_buy_total_profitcut_rate',\n                                'today_buy_total_losscut_count', 'today_buy_total_losscut_rate',\n                                'today_buy_reinvest_count0_sell_count',\n                                'today_buy_reinvest_count1_sell_count', 'today_buy_reinvest_count2_sell_count',\n                                'today_buy_reinvest_count3_sell_count', 'today_buy_reinvest_count4_sell_count',\n                                'today_buy_reinvest_count4_sell_profitcut_count',\n                                'today_buy_reinvest_count4_sell_losscut_count',\n                                'today_buy_reinvest_count5_sell_count',\n                                'today_buy_reinvest_count5_sell_profitcut_count',\n                                'today_buy_reinvest_count5_sell_losscut_count',\n                                'today_buy_reinvest_count0_remain_count',\n                                'today_buy_reinvest_count1_remain_count', 'today_buy_reinvest_count2_remain_count',\n                                'today_buy_reinvest_count3_remain_count', 'today_buy_reinvest_count4_remain_count',\n                                'today_buy_reinvest_count5_remain_count', 'total_date_count', 'total_profitcut_rate',\n                                'total_buy_count']\n\n\n    def set_jango_df(self, rows):\n        jango_df = DataFrame(rows, columns=self.jango_list)\n\n        sql = \"SELECT count(*) FROM `\" + self.simul_db_name + \"`.jango_data\"\n        rows = self.engine_simulator.execute(sql).fetchall()\n\n        # 그냥 이게 추가하는방식  self.jango[0]['total_date_count'] 이거아님, loc도 넣는거 아님\n        # 총 매수한 일자 수\n        jango_df['total_date_count'] = rows[0][0]\n\n        if len(jango_df):\n            total_profitcut_count = jango_df.loc[0, 'total_profitcut_count']\n            total_buy_count = total_profitcut_count + jango_df.loc[0, 'total_losscut_count']\n            # 익절률 ( 익절종목수 / 총매수종목수 ) * 100\n            jango_df['total_profitcut_rate'] = round(\n                total_profitcut_count / (total_buy_count) * 100, 2)\n            # 총매수종목수\n            jango_df['total_buy_count'] = total_buy_count\n\n        # astype(str) str all\n        jango_df = jango_df.astype(str)\n        return jango_df\n\n    def db_setting(self):\n        self.db_conn = pymysql.connect(host=self.cf.db_ip, port=int(self.cf.db_port), user=self.cf.db_id, password=self.cf.db_passwd,\n                                       charset='utf8')\n\n\n        self.engine_simul_scrap = create_engine(\n            \"mysql+mysqldb://\" + self.cf.db_id + \":\" + self.cf.db_passwd + \"@\" + self.cf.db_ip + \":\" + self.cf.db_port + \"/\" + \"simul_scrap\",\n            encoding='utf-8')\n\n    # 매일 새롭게 받아야 하기 때문에 매번 삭제해준다.\n    def drop_table(self, table):\n        print(\"drop ranking 테이블!\")\n        sql = f\"drop table {table}\"\n        self.engine_simul_scrap.execute(sql)\n\n    def is_simul_database_exist(self, db_name):\n\n        sql = \"SHOW DATABASES LIKE '%s'\"\n\n        rows = self.db_conn.cursor().execute(sql % (db_name))\n\n        if rows == 1:\n            return True\n        else:\n            return False\n\n    # sum_valuation_profit 순으로 정렬 (수익이 높은 순서)\n    def set_orderby_scraped_db(self):\n        sql = \"select * from ranking order by cast(sum_valuation_profit as signed INTEGER) desc\"\n        rows = self.engine_simul_scrap.execute(sql).fetchall()\n        # print(rows)\n        if len(rows) == 0:\n            print(\"scrap_date 가 존재하지 않는다!! \")\n\n        else:\n            jango_temp = DataFrame(rows, columns=self.jango_list_temp)\n\n            # 컬럼 순서 변경\n            cols = jango_temp.columns.tolist()\n            # print(\"cols\", cols)\n            # [start : end] , -1 이면 가장 끝, 비어있으면 가장 처음\n            # cols[-1:] 는 가장 마지막 부터 시작해서 끝까지 니까 마지막 하나임\n            # cols[:-1] 는 처음부터 가장 끝에서 하나 빼고 다 가져오는거\n            # cols[:1] account 하나\n            cols = cols[:1] + cols[-3:] + cols[1:-3]\n            # print(\"cols\", cols)\n            # 순서변경\n            jango_temp = jango_temp[cols]\n\n            jango_temp.to_sql('ranking', self.engine_simul_scrap, if_exists='replace')\n            print(\"simul scrap success!\")\n\n    # 각각의 simulator db의 최종 jango_data row를 simul_scrap db의 ranking 테이블에 넣는다\n    def set_scraped_db(self):\n        print(\"set_scraped_db!!\")\n        # 스크랩 시작하는 simulator db 기준으로 jango_data의 가장 마지막 날을 가져온다\n        scrap_date = self.get_jango_data_last_date()\n\n        print(\"scrap_date!!!: \" + scrap_date)\n        print(datetime.datetime.today().strftime(\"start  ******* %H : %M : %S *******\"))\n        # 스크랩 시작하는 simulator 부터 모든 simulator의 최종 jango_data row를 가져온다\n        for i in range(self.start_db_num, self.simul_db_range):\n            db_num = str(i)\n            print(db_num)\n            # 스크랩할 시뮬레이터 번호\n            self.simul_db_name = \"simulator\" + str(db_num)\n\n            if self.is_simul_database_exist(self.simul_db_name) == False:\n                print(self.simul_db_name + \" not exist !!! \")\n                continue;\n\n            self.engine_simulator = create_engine(\n                \"mysql+mysqldb://\" + self.cf.db_id + \":\" + self.cf.db_passwd + \"@\" + self.cf.db_ip + \":\" + self.cf.db_port + \"/\" + str(\n                    self.simul_db_name),\n                encoding='utf-8')\n\n            if self.is_simul_table_exist(self.simul_db_name, \"jango_data\") == False:\n                print(\"jango_data 존재하지 않는다!!!\")\n\n            else:\n                sql = \"select * from jango_data where date='%s' group by date\"\n                rows = self.engine_simulator.execute(sql % (scrap_date)).fetchall()\n                jango_df = self.set_jango_df(rows)\n\n                # account 컬럼 추가 (simulator number)\n                jango_df.loc[0, 'account'] = int(db_num)\n                jango_df = jango_df.set_index(\"account\")\n                # ranking table에 넣는다\n                jango_df.to_sql('ranking', self.engine_simul_scrap, if_exists='append')\n\n        print(datetime.datetime.today().strftime(\"end  ******* %H : %M : %S *******\"))\n\n\nif __name__ == \"__main__\":\n    simul_scraper()\n","sub_path":"scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":14455,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"587488307","text":"import sys\nfrom datetime import datetime\nimport logging\nfrom logging.handlers import RotatingFileHandler\nfrom flask import Flask, request\n\napp = Flask(__name__)\n\n@app.route('/')\ndef main_form():\n    app.logger.info('main_form')\n    return '<form action=\"submit\" id=\"textform\" method=\"post\"><textarea name=\"text\">Hi</textarea><input type=\"submit\" value=\"Submit\"></form>'\n\n@app.route('/submit', methods=['POST'])\ndef submit_textarea():\n    app.logger.info('{} Submitted: {}'.format(datetime.now(), request.form[\"text\"]))\n    return '{}'.format(request.form[\"text\"])\n\n\n'''\ndef write_notes():\n    # print('write_notes', file=sys.stderr)\n    app.logger.info('{} write_notes'.format(datetime.now()))\n    with open ('notes.txt', 'w') as notes:\n        notes.write(submit_textarea())\n'''\n\n\nif __name__ == '__main__':\n    handler = RotatingFileHandler('foo.log', maxBytes=10000, backupCount=1)\n    handler.setLevel(logging.INFO)\n    app.logger.addHandler(handler)\n    app.run()","sub_path":"Flask_fu/submit.py","file_name":"submit.py","file_ext":"py","file_size_in_byte":968,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"255078120","text":"# Copyright 2014: Mirantis Inc.\n# All Rights Reserved.\n#\n#    Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n#    not use this file except in compliance with the License. You may obtain\n#    a copy of the License at\n#\n#         http://www.apache.org/licenses/LICENSE-2.0\n#\n#    Unless required by applicable law or agreed to in writing, software\n#    distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n#    WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n#    License for the specific language governing permissions and limitations\n#    under the License.\n\nimport mock\n\nfrom rally.benchmark.scenarios.heat import stacks\nfrom tests import test\n\nHEAT_STACKS = \"rally.benchmark.scenarios.heat.stacks.HeatStacks\"\n\n\nclass HeatStacksTestCase(test.TestCase):\n\n    @mock.patch(HEAT_STACKS + \"._generate_random_name\")\n    @mock.patch(HEAT_STACKS + \"._list_stacks\")\n    @mock.patch(HEAT_STACKS + \"._create_stack\")\n    def test_create_and_list_stack(self, mock_create, mock_list,\n                                   mock_random_name):\n        heat_scenario = stacks.HeatStacks()\n        mock_random_name.return_value = \"test-rally-stack\"\n        heat_scenario.create_and_list_stack()\n        self.assertEqual(1, mock_create.called)\n        mock_list.assert_called_once_with()\n\n    @mock.patch(HEAT_STACKS + \"._generate_random_name\")\n    @mock.patch(HEAT_STACKS + \"._delete_stack\")\n    @mock.patch(HEAT_STACKS + \"._create_stack\")\n    def test_create_and_delete_stack(self, mock_create, mock_delete,\n                                     mock_random_name):\n        heat_scenario = stacks.HeatStacks()\n        fake_stack = object()\n        mock_create.return_value = fake_stack\n        mock_random_name.return_value = \"test-rally-stack\"\n        heat_scenario.create_and_delete_stack()\n\n        self.assertEqual(1, mock_create.called)\n        mock_delete.assert_called_once_with(fake_stack)\n","sub_path":"tests/benchmark/scenarios/heat/test_stacks.py","file_name":"test_stacks.py","file_ext":"py","file_size_in_byte":1947,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"421985341","text":"import json\nimport tornado.web\nimport tornado.httpclient\nimport tornado\nimport momoko\n\n\nclass MainHandler(tornado.web.RequestHandler):\n\n    @tornado.gen.coroutine\n    def get(self):\n        http_client = tornado.httpclient.AsyncHTTPClient()\n        cursor, response = yield [\n            self.application.db.execute('SELECT 42, pg_sleep(0.300)'),\n            http_client.fetch('http://127.0.0.1:8000/'),\n        ]\n        db_value = cursor.fetchone()[0]\n        json_data = json.loads(response.body.decode())\n        result = db_value - json_data['value']\n        self.write(\"Result: %s\" % result)\n        self.finish()\n\n\napplication = tornado.web.Application([\n    (r\"/\", MainHandler),\n])\n\nif __name__ == \"__main__\":\n    print(\"Serve http://127.0.0.1:8888/\")\n    ioloop = tornado.ioloop.IOLoop.instance()\n    application.db = momoko.Pool(dsn='dbname=al user=al password=al', size=10, ioloop=ioloop)\n    future = application.db.connect()\n    ioloop.add_future(future, lambda f: ioloop.stop())\n    ioloop.start()\n    future.result()  # raises exception on connection error\n    application.listen(8888)\n    tornado.ioloop.IOLoop.instance().start()\n","sub_path":"python/async/demo/tornado-momoko-http-coroutine-multi.py","file_name":"tornado-momoko-http-coroutine-multi.py","file_ext":"py","file_size_in_byte":1146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"495664532","text":"#\n# Copyright 2021 Lukas Schmelzeisen\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\n\nimport gzip\nfrom collections import defaultdict\nfrom datetime import datetime, timedelta\nfrom logging import getLogger\nfrom pathlib import Path\nfrom sys import argv\nfrom typing import AbstractSet, Mapping, MutableMapping, MutableSequence, Optional, cast\n\nfrom nasty_utils import ColoredBraceStyleAdapter, ProgramConfig\nfrom overrides import overrides\nfrom pydantic import BaseModel as PydanticModel\n\nimport wikidata_history_analyzer\nfrom wikidata_history_analyzer._paths import wikidata_incremental_rdf_revision_dir\nfrom wikidata_history_analyzer._utils import (\n    ParallelizeCallback,\n    ParallelizeProgressCallback,\n    parallelize,\n)\nfrom wikidata_history_analyzer.cli._wikidata_rdf_revision_program import (\n    WikidataRdfRevisionProgram,\n)\nfrom wikidata_history_analyzer.datamodel.wikidata_incremental_rdf_revision import (\n    WikidataIncrementalRdfRevision,\n)\nfrom wikidata_history_analyzer.datamodel.wikidata_rdf_revision import WikidataRdfTriple\nfrom wikidata_history_analyzer.dumpfiles.wikidata_dump_manager import (\n    WikidataDumpManager,\n)\nfrom wikidata_history_analyzer.dumpfiles.wikidata_meta_history_dump import (\n    WikidataMetaHistoryDump,\n)\n\n_LOGGER = ColoredBraceStyleAdapter(getLogger(__name__))\n\n\nclass WikidataRevisionStatistics(PydanticModel):\n    time_since_last_revision: Optional[timedelta] = None\n    num_added_triples: int = 0\n    num_deleted_triples: int = 0\n    subject_counter: Mapping[str, int] = defaultdict(int)\n    predicate_counter: Mapping[str, int] = defaultdict(int)\n    object_counter: Mapping[str, int] = defaultdict(int)\n    num_triple_changes_to_time_since_last_change: Mapping[\n        int, MutableSequence[timedelta]\n    ] = defaultdict(list)\n\n\nclass WikidataDumpStatistics(PydanticModel):\n    num_revisions_per_page: MutableSequence[int] = []\n\n    per_month: MutableMapping[str, MutableSequence[WikidataRevisionStatistics]] = {}\n\n\nclass WikidataCollectStatisticsProgram(WikidataRdfRevisionProgram):\n    class Config(ProgramConfig):\n        title = \"wikidata-collect-statistics\"\n        version = wikidata_history_analyzer.__version__\n        description = \"Collect statistics from incremental RDF revision stream.\"\n\n    @overrides\n    def run(self) -> None:\n        settings = self.settings.wikidata_history_analyzer\n\n        dump_manager = WikidataDumpManager(\n            settings.data_dir,\n            settings.wikidata_dump_version,\n            settings.wikidata_dump_mirror_base,\n        )\n\n        page_ids, meta_history_dumps = self._prepare_args(dump_manager)\n\n        _LOGGER.info(\"Collection statistics from each dump individually...\")\n        for _ in parallelize(\n            cast(\n                ParallelizeCallback[WikidataMetaHistoryDump, None], self._process_dump\n            ),\n            meta_history_dumps,\n            extra_arguments={\n                \"data_dir\": settings.data_dir,\n                \"page_ids\": page_ids,\n            },\n            total=len(meta_history_dumps),\n            max_workers=settings.num_workers,\n        ):\n            pass\n\n    @classmethod\n    def _process_dump(\n        cls,\n        meta_history_dump: WikidataMetaHistoryDump,\n        *,\n        data_dir: Path,\n        page_ids: AbstractSet[int],\n        progress_callback: ParallelizeProgressCallback,\n        **kwargs: object,\n    ) -> None:\n        dump_dir = (\n            wikidata_incremental_rdf_revision_dir(data_dir)\n            / meta_history_dump.path.name\n        )\n\n        out_file = dump_dir.parent / (\n            meta_history_dump.path.name + \".statistics.json.gz\"\n        )\n        if out_file.exists():\n            _LOGGER.info(\"File {} already exists, skipping...\", out_file)\n            return\n\n        dump_dir_files = sorted(dump_dir.iterdir())\n        num_dump_dir_files = len(dump_dir_files)\n\n        progress_callback(meta_history_dump.path.name, 0, num_dump_dir_files)\n\n        dump_statistics = WikidataDumpStatistics(\n            num_revisions_per_page=[],\n            per_month=defaultdict(list),\n        )\n\n        for i, page_revisions_file in enumerate(dump_dir_files):\n            progress_callback(meta_history_dump.path.name, i, num_dump_dir_files)\n\n            if not page_revisions_file.name.endswith(\".jsonl.gz\"):\n                continue\n\n            page_id_str = page_revisions_file.name[: -len(\".jsonl.gz\")]\n            if not page_id_str.isdigit():\n                continue\n\n            page_id = int(page_id_str)\n            if page_ids and page_id not in page_ids:\n                continue\n\n            num_revisions = 0\n            first_revision_timestamp: Optional[datetime] = None\n            last_revision_timestamp: Optional[datetime] = None\n            num_times_triples_changed: MutableMapping[WikidataRdfTriple, int] = {}\n            last_time_triples_changed: MutableMapping[WikidataRdfTriple, datetime] = {}\n\n            for revision in WikidataIncrementalRdfRevision.load_iter_from_file(\n                dump_dir, page_id\n            ):\n                num_revisions += 1\n                revision_statistics = WikidataRevisionStatistics()\n\n                if first_revision_timestamp is None:\n                    first_revision_timestamp = revision.timestamp\n                else:\n                    assert last_revision_timestamp is not None\n                    revision_statistics.time_since_last_revision = (\n                        revision.timestamp - last_revision_timestamp\n                    )\n\n                revision_statistics.num_added_triples += len(revision.triple_additions)\n                revision_statistics.num_deleted_triples += len(\n                    revision.triple_deletions\n                )\n\n                for triple in revision.triple_additions:\n                    if triple not in num_times_triples_changed:\n                        revision_statistics.subject_counter[triple.subject] += 1\n                        revision_statistics.predicate_counter[triple.predicate] += 1\n                        revision_statistics.object_counter[triple.object_] += 1\n                        num_times_triples_changed[triple] = 1\n                    else:\n                        assert num_times_triples_changed[triple] % 2 == 0\n                        num_times_triples_changed[triple] += 1\n\n                    time_since_last_change = revision.timestamp - (\n                        last_time_triples_changed.get(triple)\n                        or first_revision_timestamp\n                    )\n                    revision_statistics.num_triple_changes_to_time_since_last_change[\n                        num_times_triples_changed[triple]\n                    ].append(time_since_last_change)\n                    last_time_triples_changed[triple] = revision.timestamp\n\n                for triple in revision.triple_deletions:\n                    assert num_times_triples_changed[triple] % 2 == 1\n                    num_times_triples_changed[triple] += 1\n\n                    time_since_last_change = revision.timestamp - (\n                        last_time_triples_changed.get(triple)\n                        or first_revision_timestamp\n                    )\n                    revision_statistics.num_triple_changes_to_time_since_last_change[\n                        num_times_triples_changed[triple]\n                    ].append(time_since_last_change)\n                    last_time_triples_changed[triple] = revision.timestamp\n\n                month = revision.timestamp.strftime(\"%Y-%m\")\n                if month not in dump_statistics.per_month:\n                    dump_statistics.per_month[month] = []\n                dump_statistics.per_month[month].append(revision_statistics)\n\n                last_revision_timestamp = revision.timestamp\n\n                # Divide statistics into \"evolution of Wikidata\" (i.e. content-wise\n                # on a macro scale) and \"editing behavior of Wikidata\" (i.e. meta-wise\n                # on a micro scale)?\n                #\n                # Statistics for both all statements and only the filtered ones we would\n                # care about for KG embedding.\n                #\n                # Statistics about simple vs full statements?\n                # Statistics about redirects?\n                #\n                # Proportion of labels, descriptions, aliases, sitelinks, simple\n                # statements?\n                # What are datatypes and property links?\n                # Number of items vs number of properties?\n                # Languages used in wikidata literals.\n                # Languages used in wikidata sitelinks.\n                # Simple statements with literal as target.\n                # Simple statement with non-literal as target.\n                # Simple statement with in-wikidata entity as target.\n                # Simple statement with out-of-wikidata entity as target.\n                # links to wikimedia commons?\n                #\n                # Time between revision of each entity\n                # Frequency of each entity/relation\n                # Fraction of triples that are deleted\n                # Fraction of triples that oscillate\n                # (and are positive/negative at the end?)\n                # Life-time of deleted triples\n                #\n                # Probably not interesting:\n                # distribution of ranks (e.g. normalrank, bestrank, deprecatedrank,\n                # etc.)?\n\n            dump_statistics.num_revisions_per_page.append(num_revisions)\n\n        with gzip.open(out_file, \"wt\", encoding=\"UTF\") as fout:\n            fout.write(dump_statistics.json() + \"\\n\")\n\n        progress_callback(\n            meta_history_dump.path.name, num_dump_dir_files, num_dump_dir_files\n        )\n\n\ndef main(*args: str) -> None:\n    if not args:\n        args = tuple(argv[1:])\n    WikidataCollectStatisticsProgram.init(*args).run()\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"src/wikidata_history_analyzer/cli/wikidata_collect_statistics_program.py","file_name":"wikidata_collect_statistics_program.py","file_ext":"py","file_size_in_byte":10429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"190026639","text":"#-------------------------------------------------------------------------------\n# Name:        User\n# Purpose:\n#\n# Author:      Administrator\n#\n# Created:     03/08/2016\n# Copyright:   (c) Administrator 2016\n# Licence:     <your licence>\n#-------------------------------------------------------------------------------\n\nclass User:\n    ''' User object. '''\n\n    UserPartsName = 0\n    UserPartsId = 1\n    UserPartsLink = 2\n\n    def InitWithJson(self, userJson):\n        ''' Initialise new Github user object from JSON.\n\n            Arguments:\n                userJson:   user info as JSON object. '''\n\n        self.UserName = userJson['login']\n        self.UserId = userJson['id']\n        self.UserLink = userJson['url']\n\n    def  InitWithCsv(self, userCsv):\n        ''' Initialise new Guthub user object from CSV.\n\n            Arguments:\n                userCsv:    user info as CSV.\n\n            Exceptions:\n                ValueError: Thrown if csv object is invalid. '''\n\n        userParts = userCsv.split(',')\n\n        if len(userParts) != 3:\n            raise ValueError('Invalid user object.')\n\n        self.UserName = userParts[self.UserPartsName]\n        self.UserId = userParts[self.UserPartsId]\n        self.UserLink = userParts[self.UserPartsLink]\n\n    def GetPropertiesAsList(self):\n        ''' Get the properties of the user as a list. \n        \n            Returns:\n                List of User properties. '''\n        \n        rep = []\n        rep.append(self.UserName)\n        rep.append(self.UserId)\n        rep.append(self.UserLink)\n        \n        return rep\n        ","sub_path":"src/UserTracker/User.py","file_name":"User.py","file_ext":"py","file_size_in_byte":1588,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"59342159","text":"# _*_ coding\" utf-8  _*_\n\nfrom keras.layers import Input, Conv2D, MaxPooling2D, Conv2DTranspose, UpSampling2D\nfrom keras.models import Model\nimport numpy as np\n\nfrom mod_gendata import *\nfrom mod_cutregion import *\n\n# usr-settings\nkeyward='pwat'\nepochs=1000\n#epochs=100\n#epochs=50\n#epochs=30\n#epochs=10\nbatch_size=256\n#inputdir='/home/kurihana/ml_model/work_mymodel/ex4/data/train_data'\ninputdir='/home/kurihana/ml_model/work_mymodel/ex4/data/train_aug_data'\ntestdir='/home/kurihana/ml_model/work_mymodel/ex4/data/test_data'\n\n# original data shape\nlon=360\nlat=181\n\n# convert/plot-settings\nnlat = 64\nnlon = 64\nn = 2 # number of pics on screen\n\n#get data\ngd = gen_grads_data()\nx_train  = gd.load_key_data(inputdir, keyward)\nx_test   = gd.load_key_data(testdir, keyward)\nx_train  = x_train.reshape(x_train.shape[0],lat, lon)\nx_test   = x_test.reshape(x_test.shape[0],lat, lon)\n\n# adjust minst data\nx_train  = x_train.astype('float32')\nx_test   = x_test.astype('float32')\n\n# select region for square shape image\nnum_of_dim = 3  # dimension of load_data\nsg = sl_region()\n# north lat, south lat, weat lon, east lon\nx_train   = sg.get_region(x_train,3,lat,lon,64,0,225,289 )\nx_test    = sg.get_region(x_test,3,lat,lon,64,0,225,289 )\n#x_train   = sg.get_region(x_train,3,lat,lon,90,-38,200,328 )\n#x_test    = sg.get_region(x_test,3,lat,lon,90,-38,200,328 )\nx_train   = x_train.reshape(x_train.shape[0],nlat, nlon,1)\nx_test    = x_test.reshape(x_test.shape[0],nlat, nlon,1)\n\n\n### compile conv-model\ninput_img = Input(shape=(nlat,nlon,1)) # reshape by addingannel\n\n# Encoding part\n\nencoded = Conv2D(4,(2,2), activation='relu', padding='same')(input_img)\nencoded = Conv2D(8,(2,2), activation='relu', subsample=(2,2))(input_img)\nencoded = Conv2D(16,(2,2), activation='relu', subsample=(2,2))(encoded)\nencoded = Conv2D(32,(2,2), activation='relu', subsample=(2,2))(encoded)\nencoded = Conv2D(64,(2,2), activation='relu', padding='same')(encoded)\n\n# Decoding part\n\ndecoded = Conv2DTranspose(32,(2,2), activation='relu', subsample=(2,2))(encoded)\ndecoded = Conv2D(16,(2,2), activation='relu', padding='same')(decoded)\ndecoded = Conv2DTranspose(8,(2,2), activation='relu', subsample=(2,2))(decoded)\ndecoded = Conv2D(8,(2,2), activation='relu', padding='same')(decoded)\ndecoded = Conv2DTranspose(4,(2,2), activation='relu', subsample=(2,2))(decoded)\ndecoded = Conv2D(1,(2,2), activation='relu', padding='same')(decoded)\n#decoded = Conv2D(1,(2,2), activation='sigmoid', padding='same')(decoded)\n\n#\nautoencoder = Model(input=input_img, output=decoded)\nautoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')\n\n# model summary\nprint(autoencoder.summary())\n#stop\n# learning\nautoencoder.fit(x_train, x_train,\n                epochs = epochs,\n                batch_size = batch_size,\n                shuffle= 'True',\n                verbose = 1,\n                validation_data=(x_test, x_test)\n               )\n\n# save trained model\nautoencoder.save('./'+keyward+'_convae'+str(epochs)+'_2knldeeper_peraug.h5')\n\n#### plot\nimport matplotlib.pyplot as plt\n\n# comvert test-images\ndecoded_imgs = autoencoder.predict(x_test) \n\n# show\nplt.figure(figsize=(16,8))\nimage_list = [0, 12]\n#for i in range(n):\nfor index, i in enumerate(image_list):\n    ax = plt.subplot(2,n,index+1)\n    plt.imshow(x_test[i].reshape(nlat,nlon))\n    plt.gray()\n    itime = int(i*6)\n    plt.title('+ %d hour'% itime )\n    ax.get_xaxis().set_visible(False)\n    ax.get_yaxis().set_visible(False)\n\n    ax = plt.subplot(2,n,index+1+n)\n    plt.imshow(decoded_imgs[i].reshape(nlat,nlon))\n    plt.gray()\n    ax.get_xaxis().set_visible(False)\n    ax.get_yaxis().set_visible(False)\n\nplt.show()\n","sub_path":"models8.py","file_name":"models8.py","file_ext":"py","file_size_in_byte":3642,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"4125701","text":"import logging\nimport os\nimport re\n\nlogger = logging.getLogger(\"haproxy\")\n\n\nclass RunningMode():\n    LegacyMode, ComposeMode, SwarmMode, CloudMode = range(4)\n\n\ndef parse_extra_bind_settings(extra_bind_settings):\n    bind_dict = {}\n    if extra_bind_settings:\n        settings = re.split(r'(?<!\\\\),', extra_bind_settings)\n        for setting in settings:\n            term = setting.split(\":\", 1)\n            if len(term) == 2:\n                bind_dict[term[0].strip().replace(\"\\,\", \",\")] = term[1].strip().replace(\"\\,\", \",\")\n    return bind_dict\n\n\ndef parse_extra_frontend_settings(envvars):\n    settings_dict = {}\n    if isinstance(envvars, os._Environ) or isinstance(envvars, dict):\n        frontend_settings_pattern = re.compile(r\"^EXTRA_FRONTEND_SETTINGS_(\\d{1,5})$\")\n        frontend_settings_file_pattern = re.compile(r\"^EXTRA_FRONTEND_SETTINGS_FILE_(\\d{1,5})$\")\n        for k, v in envvars.iteritems():\n            settings = []\n            match = frontend_settings_pattern.match(k)\n            file_match = frontend_settings_file_pattern.match(k)\n            if match:\n                port = match.group(1)\n                settings.extend([x.strip().replace(\"\\,\", \",\") for x in re.split(r'(?<!\\\\),', v.strip())])\n            elif file_match:\n                port = file_match.group(1)\n                try:\n                    with open(v) as file:\n                        for line in file:\n                            settings.append(line.strip())\n                except Exception as e:\n                    logger.info(\"Error reading %s at '%s', error %s\" % (k, v, e))\n\n            if len(settings) > 0:\n                if port in settings_dict:\n                    settings_dict[port].extend(settings)\n                else:\n                    settings_dict[port] = settings\n    return settings_dict\n\n\ndef parse_additional_backend_settings(envvars):\n    settings_dict = {}\n    if isinstance(envvars, os._Environ) or isinstance(envvars, dict):\n        additional_backend_pattern = re.compile(r\"^ADDITIONAL_BACKEND_(\\w+)$\")\n        additional_backend_file_pattern = re.compile(r\"^ADDITIONAL_BACKEND_FILE_(\\w+)$\")\n        for k, v in envvars.iteritems():\n            settings = []\n            match = additional_backend_pattern.match(k)\n            file_match = additional_backend_file_pattern.match(k)\n\n            if file_match:\n                server = file_match.group(1)\n                try:\n                    with open(v) as f:\n                        for line in f:\n                            settings.append(line.strip())\n                except Exception as e:\n                    logger.info(\"Error reading %s at '%s', error %s\" % (k, v, e))\n            elif match:\n                server = match.group(1)\n                settings.extend([x.strip().replace(\"\\,\", \",\") for x in re.split(r'(?<!\\\\),', v.strip())])\n\n            if len(settings) > 0:\n                if server in settings_dict:\n                    settings_dict[server].extend(settings)\n                else:\n                    settings_dict[server] = settings\n    return settings_dict\n\n\n# envvar\nADDITIONAL_BACKENDS = parse_additional_backend_settings(os.environ)\nADDITIONAL_SERVICES = os.getenv(\"ADDITIONAL_SERVICES\")\nAPI_AUTH = os.getenv(\"DOCKERCLOUD_AUTH\")\nBALANCE = os.getenv(\"BALANCE\", \"roundrobin\")\nCA_CERT_FILE = os.getenv(\"CA_CERT_FILE\")\nCERT_FOLDER = os.getenv(\"CERT_FOLDER\")\nDEBUG = os.getenv(\"DEBUG\", False)\nDEFAULT_CA_CERT = os.getenv(\"CA_CERT\")\nDEFAULT_SSL_CERT = os.getenv(\"DEFAULT_SSL_CERT\") or os.getenv(\"SSL_CERT\")\nEXTRA_BIND_SETTINGS = parse_extra_bind_settings(os.getenv(\"EXTRA_BIND_SETTINGS\"))\nEXTRA_DEFAULT_SETTINGS = os.getenv(\"EXTRA_DEFAULT_SETTINGS\")\nEXTRA_DEFAULT_SETTINGS_FILE = os.getenv(\"EXTRA_DEFAULT_SETTINGS_FILE\")\nEXTRA_FRONTEND_SETTINGS = parse_extra_frontend_settings(os.environ)\nEXTRA_GLOBAL_SETTINGS = os.getenv(\"EXTRA_GLOBAL_SETTINGS\")\nEXTRA_GLOBAL_SETTINGS_FILE = os.getenv(\"EXTRA_GLOBAL_SETTINGS_FILE\")\nEXTRA_SSL_CERT = os.getenv(\"EXTRA_SSL_CERTS\")\nEXTRA_ROUTE_SETTINGS = os.getenv(\"EXTRA_ROUTE_SETTINGS\", \"\")\nFORCE_DEFAULT_BACKEND = os.getenv(\"FORCE_DEFAULT_BACKEND\", \"True\")\nHAPROXY_CONTAINER_URI = os.getenv(\"DOCKERCLOUD_CONTAINER_API_URI\")\nHAPROXY_SERVICE_URI = os.getenv(\"DOCKERCLOUD_SERVICE_API_URI\")\nHEALTH_CHECK = os.getenv(\"HEALTH_CHECK\", \"check inter 2000 rise 2 fall 3\")\nHTTP_BASIC_AUTH = os.getenv(\"HTTP_BASIC_AUTH\")\nHTTP_BASIC_AUTH_SECURE = os.getenv(\"HTTP_BASIC_AUTH_SECURE\")\nMAXCONN = os.getenv(\"MAXCONN\", \"4096\")\nMODE = os.getenv(\"MODE\", \"http\")\nMONITOR_PORT = os.getenv(\"MONITOR_PORT\")\nMONITOR_URI = os.getenv(\"MONITOR_URI\")\nOPTION = os.getenv(\"OPTION\", \"redispatch, httplog, dontlognull, forwardfor\")\nRSYSLOG_DESTINATION = os.getenv(\"RSYSLOG_DESTINATION\", \"127.0.0.1\")\nSKIP_FORWARDED_PROTO = os.getenv(\"SKIP_FORWARDED_PROTO\")\nSSL_BIND_CIPHERS = os.getenv(\"SSL_BIND_CIPHERS\")\nSSL_BIND_OPTIONS = os.getenv(\"SSL_BIND_OPTIONS\")\nSTATS_AUTH = os.getenv(\"STATS_AUTH\", \"stats:stats\")\nSTATS_PORT = os.getenv(\"STATS_PORT\", \"1936\")\nTIMEOUT = os.getenv(\"TIMEOUT\", \"connect 5000, client 50000, server 50000\")\nNBPROC = int(os.getenv(\"NBPROC\", 1))\nSWARM_MODE_POLLING_INTERVAL = int(os.getenv(\"SWARM_MODE_POLLING_INTERVAL\", 5))\nHAPROXY_USER = os.getenv(\"HAPROXY_USER\", \"root\")\nHAPROXY_GROUP = os.getenv(\"HAPROXY_GROUP\", \"root\")\nRELOAD_TIMEOUT = os.getenv(\"RELOAD_TIMEOUT\", \"0\")\n\n# global\nRUNNING_MODE = None\n\n# const\nCERT_DIR = \"/certs/\"\nCACERT_DIR = \"/cacerts/\"\nHAPROXY_CONFIG_FILE = \"/haproxy.cfg\"\nHAPROXY_RUN_COMMAND = ['haproxy', '-f', HAPROXY_CONFIG_FILE, '-db', '-q']\nHAPROXY_CONFIG_CHECK_COMMAND = ['haproxy', '-c', '-f', HAPROXY_CONFIG_FILE]\nAPI_RETRY = 10  # seconds\nPID_FILE = \"/tmp/dockercloud-haproxy.pid\"\nSERVICE_PORTS_ENVVAR_NAME = \"SERVICE_PORTS\"\nLABEL_SWARM_MODE_DEACTIVATE = \"com.docker.dockercloud.haproxy.deactivate\"\n\n# regular expressions\nSERVICE_NAME_MATCH = re.compile(r\"(.+)_\\d+$\")\nBACKEND_MATCH = re.compile(r\"(?P<proto>tcp|udp):\\/\\/(?P<addr>[^:]*):(?P<port>.*)\")\nSERVICE_ALIAS_MATCH = re.compile(r\"_PORT_\\d{1,5}_(TCP|UDP)$\")\nDETAILED_SERVICE_ALIAS_MATCH = re.compile(r\"_\\d+_PORT_\\d{1,5}_(TCP|UDP)$\")\nENV_SERVICE_ALIAS_MATCH = re.compile(r\"_ENV_\")\nENV_DETAILED_SERVICE_ALIAS_MATCH = re.compile(r\"_\\d+_ENV\")\n","sub_path":"haproxy/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":6156,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"592190638","text":"# %%\nimport sys\nimport os\nimport ast\nimport argparse\n\nimport pandas as pd\n\nif \"__file__\" in globals():\n    os.chdir(os.path.dirname(__file__) + \"/..\")\nelif \"pkg\" not in os.listdir(\".\"):\n    os.chdir(\"..\")\nsys.path.append(\".\")\n\nfrom pkg.utils.pandas import multiindex_pivot\n\n\ndef get_parser():\n\n    parser = argparse.ArgumentParser(description=\"Summarize results\")\n\n    parser.add_argument(\"dataset\", type=str, help=\"Which dataset to consider.\")\n    parser.add_argument(\"model\", type=str, help=\"Which model to consider.\")\n    parser.add_argument(\n        \"hparams\",\n        type=str,\n        nargs=\"+\",\n        default=None,\n        help=\"Which method to consider. None\",\n    )\n\n    parser.add_argument(\n        \"--input_path\",\n        type=str,\n        default=\"data/output/results.csv\",\n        help=\"The path to write the result. Default: data/output/results.csv\",\n    )\n\n    return parser\n\n\nargs = get_parser().parse_args()\n\n# %%\ndf = pd.read_csv(args.input_path)\nassert tuple(df.columns) == (\n    \"timestamp\",\n    \"dataset\",\n    \"split_id\",\n    \"model\",\n    \"metric\",\n    \"value\",\n    \"config\",\n)\n\ndf = df.query(f\"dataset=='{args.dataset}' and model=='{args.model}'\").drop(\n    [\"dataset\", \"model\"], axis=1\n)\n# extract relevant hyperamters\ndf.config = df.config.apply(ast.literal_eval)\nfor p in args.hparams:\n    df[p] = df.config.apply(lambda x: x.get(p, None))\n\ndf = df.drop_duplicates([\"split_id\", \"metric\", *args.hparams], keep=\"last\")\ndf.head()\n\n# %%\nt = df.groupby([*args.hparams, \"metric\"]).value.agg([\"mean\", \"std\"])\nt[\"agg\"] = t.apply(lambda x: f\"{x[0]:.3f}+/-{x[1]:.3f}\", axis=1)\nt = t.reset_index()\nres = multiindex_pivot(t, args.hparams, columns=\"metric\", values=\"agg\")\n\nprint(res)\n","sub_path":"postprocessing/compare_hparams.py","file_name":"compare_hparams.py","file_ext":"py","file_size_in_byte":1698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"401048234","text":"# Simple enough, just import everything from tkinter.\nfrom tkinter import *\n\n\n#download and install pillow:\n# http://www.lfd.uci.edu/~gohlke/pythonlibs/#pillow\nfrom PIL import Image, ImageTk\n\n\n# Here, we are creating our class, Window, and inheriting from the Frame\n# class. Frame is a class from the tkinter module. (see Lib/tkinter/__init__)\nclass Window(Frame):\n\n    # Define settings upon initialization. Here you can specify\n    def __init__(self, master=None):\n        \n        # parameters that you want to send through the Frame class. \n        Frame.__init__(self, master)   \n\n        #reference to the master widget, which is the tk window                 \n        self.master = master\n\n        #with that, we want to then run init_window, which doesn't yet exist\n        self.init_window()\n\n    #Creation of init_window\n    def init_window(self):\n\n        # changing the title of our master widget      \n        self.master.title(\"GUI\")\n\n        # allowing the widget to take the full space of the root window\n        self.pack(fill=BOTH, expand=1)\n\n        # creating a menu instance\n        menu = Menu(self.master)\n        self.master.config(menu=menu)\n\n        # create the file object)\n        file = Menu(menu, tearoff=0)\n\n        # adds a command to the menu option, calling it exit, and the\n        # command it runs on event is client_exit\n        file.add_command(label=\"All you can Eat\", command=lambda: self.eat(lChoice, rChoice, img, text))\n        file.add_command(label=\"Lost at Sea\", command=lambda: self.ownPath(lChoice, rChoice, img, text))\n        file.add_command(label=\"Elf Overboard\", command=lambda: self.overboard(lChoice, rChoice, img, text))\n        file.add_command(label=\"Sharkboait ooo aha\", command=lambda: self.sharkBait(lChoice, rChoice, img, text))\n        file.add_command(label=\"Sticky Business\", command=lambda: self.sticky(lChoice, rChoice, img, text))\n        file.add_command(label=\"Bad Day Bidet\", command=lambda: self.badDay(lChoice, rChoice, img, text))\n        file.add_command(label=\"Choked Up\", command=lambda: self.kareoke(lChoice, rChoice, img, text))\n        file.add_command(label=\"Coldfeet\", command= lambda: self.coldfeet(lChoice, rChoice, img, text))\n        file.add_command(label=\"The End\", command=lambda: self.theEnd(lChoice, rChoice, img, text))\n        file.add_command(label=\"The Bunny!\", command=lambda: self.runAway(lChoice, rChoice, img, text))\n        file.add_command(label=\"Exit Game\", command=self.client_exit)\n\n\n        #added \"file\" to our menu\n        menu.add_cascade(label=\"Level Select\", menu=file)\n\n\n        # create the file object)\n        edit = Menu(menu, tearoff=0)\n\n        # adds a command to the menu option, calling it exit, and the\n        # command it runs on event is client_exit\n        edit.add_command(label=\"Show Img\", command=self.showImg)\n        edit.add_command(label=\"Show Text\", command=self.showText)\n\n        #added \"file\" to our menu\n        menu.add_cascade(label=\"Edit\", menu=edit)\n        # creating a button instance\n\n        img=self.showImg(\"MainTitle.png\")\n        text = Label(self, text=\"The Many Adventures of Mr. Gonzales\", font=('Sans Serif', 25))\n        text.pack()\n        text.place(x=45, y=0)\n        lChoice = Button(self, text=\"BEGIN\", command=lambda: self.how(lChoice, rChoice, img, text), font=('Sans Serif', 25))\n        rChoice = Button(self, text=\"RUN AWAY\", command=self.client_exit, font=('Sans Serif', 25))\n        lChoice.place(x=50, y=500)\n        rChoice.place(x= 400, y=500)\n\n    def how(self, button1, button2,img,text):\n        self.dB(button1, button2, img, text)\n        img=self.showImg(\"Scene1.png\")\n        text=self.showText(\"Dad asks you to go on a cruise with him. \\n Do you go?\")\n        lChoice = self.lButton(\"Go with Dad.\", lambda: self.eat(lChoice, rChoice, img, text))\n        rChoice = self.rButton(\"Don't go with Dad\", lambda: self.die(lChoice, rChoice, img, text))\n\n    def how3(self, button1, img):\n        self.dB3(button1, img)\n        img=self.showImg(\"Scene1.png\")\n        text=self.showText(\"Dad asks you to go on a cruise with him. \\n Do you go?\")\n        lChoice = self.lButton(\"Go with Dad.\", lambda: self.eat(lChoice, rChoice, img, text))\n        rChoice = self.rButton(\"Don't go with Dad\", lambda: self.die(lChoice, rChoice, img, text))\n        \n    def how2(self, button1, button2, button3, img,text):\n        self.dB2(button1, button2, button3, img, text)\n        img=self.showImg(\"Scene1.png\")\n        text=self.showText(\"Dad asks you to go on a cruise with him. \\n Do you go?\")\n        lChoice = self.lButton(\"Go with Dad.\", lambda: self.eat(lChoice, rChoice, img, text))\n        rChoice = self.rButton(\"Don't go with Dad\", lambda: self.die(lChoice, rChoice, img, text))\n        \n        \n    def eat(self, button1, button2,img,text):\n        self.dB(button1, button2, img, text)\n        img=self.showImg(\"Hallway Scene.png\")\n        text=self.showText(\"When you and Dad get on the cruise, he smells a delicious breakfast buffet. \\n Dad decides to go to the buffet. Do you go?\")\n        lChoice = self.lButton(\"Follow Dad\", lambda: self.die(lChoice, rChoice, img, text))\n        rChoice = self.rButton(\"Go your own path.\", lambda: self.ownPath(lChoice, rChoice, img, text))\n    \n    \n\n    def ownPath(self, button1, button2,img,text):\n        self.dB(button1, button2, img, text)\n        img=self.showImg(\"Hallway Scene.png\")\n        text=self.showText(\"You decided to go your own path. Do you ...\")\n        lChoice = self.lButton(\"Go Left.\", lambda: self.left(lChoice, rChoice, img, text))\n        rChoice = self.rButton(\"Go Right.\", lambda: self.right(lChoice, rChoice, img, text))\n        \n        \n    def left(self, button1, button2,img,text):\n        self.dB(button1, button2, img, text)\n        img=self.showImg(\"Hallway Scene.png\")\n        text=self.showText(\"After going left. Do you ... \")\n        lChoice = self.lButton(\"Go Left.\", lambda: self.overboard(lChoice, rChoice, img, text))\n        rChoice = self.rButton(\"Go Right.\", lambda: self.die(lChoice, rChoice, img, text))\n       \n        \n    def right(self, button1, button2,img,text):\n        self.dB(button1, button2, img, text)\n        img=self.showImg(\"Hallway Scene.png\")\n        text=self.showText(\"After going Right. Do you...\")\n        lChoice = self.lButton(\"Go Left.\", lambda: self.overboard(lChoice, rChoice, img, text))\n        rChoice = self.rButton(\"Go Right.\", lambda: self.die(lChoice, rChoice, img, text))\n  \n\n\n    def overboard(self, button1, button2,img,text):\n        self.dB(button1, button2, img, text)\n        img=self.showImg(\"ocean.png\")\n        text=self.showText(\" You decide that you need to relax a bit after walking around for a bit. \\n Do you want to relax by the pool or look out over the sea?\")\n        lChoice = self.lButton(\"Look out over the sea\", lambda: self.die(lChoice, rChoice, img, text))\n        rChoice = self.rButton(\"Relax by the pool\", lambda: self.sharkBait(lChoice, rChoice, img, text))\n        \n\n    def sharkBait(self, button1, button2,img,text):\n        self.dB(button1, button2, img, text)\n        img=self.showImg(\"Pool Scene1.png\")\n        text=self.showText(\"Good choice to decide to relax by the pool. \\n While relaxing, you see a hammerhead shark. \\n Do you decide to…\")\n        lChoice = self.lButton(\"Run Away\", lambda: self.die(lChoice, rChoice, img, text))\n        rChoice = self.rButton(\"Freeze\", lambda: self.sticky(lChoice, rChoice, img, text))\n      \n\n\n    def sticky(self, button1, button2,img,text):\n        self.dB(button1, button2, img, text)\n        img=self.showImg(\"Pool Scene 2.png\")\n        text=self.showText(\"The hammerhead shark sees that you are scared and comes up to you. \\n He introduces himself as Ceasar and you become best friends. \\nAs your wingman, he spots a pretty lady across the pool. Do you…\")\n        lChoice = self.lButton(\"Buy her a drink\", lambda: self.badDay(lChoice, rChoice, img, text))\n        rChoice = self.rButton(\"Spill your drink on yourself\", lambda: self.die(lChoice, rChoice, img, text))\n        \n\n    def badDay(self, button1, button2,img,text):\n        self.dB(button1, button2, img, text)\n        img=self.showImg(\"BadDayBide.png\")\n        text=self.showText(\"After having a few drinks, you start feeling sick. Do you..\")\n        lChoice = self.lButton(\"Go to the bathroom\", lambda: self.die(lChoice, rChoice, img, text))\n        rChoice = self.rButton(\"Get over yourself\", lambda: self.kareoke(lChoice, rChoice, img, text))\n       \n        \n        \n    def kareoke(self, button1, button2,img,text):\n        self.dB(button1, button2, img, text)\n        img=self.showImg(\"Kareoke.png\")\n        text=self.showText(\"To thank you for her a drink, Auntie Jemima invites you to a karaoke contest. \\n Do you..\")\n        lChoice = self.lButton(\"Sing Hymn #602\", lambda: self.die(lChoice, rChoice, img, text))\n        rChoice = self.rButton(\"Sing a Backstreet Boys song\", lambda: self.coldfeet(lChoice, rChoice, img, text))\n       \n        \n    def coldfeet(self, button1, button2,img,text):\n        self.dB(button1, button2, img, text)\n        img=self.showImg(\"Wedding.png\")\n        text=self.showText(\"You wooed Auntie Jemima with your incredible singing skills and propose to her. \\n She says yes! Later that night, you are getting ready for the wedding. \\n Do you…\")\n        lChoice = self.lButton(\"Get there on time.\", lambda: self.theEnd(lChoice, rChoice, img, text))\n        rChoice = self.rButton(\"Show up Late\", lambda: self.die(lChoice, rChoice, img, text))\n        \n\n    def theEnd(self, button1, button2,img,text):\n        self.dB(button1, button2, img, text)\n        img=self.showImg(\"Wedding.png\")\n        secret=Button(self,text= \"Congratulations!\", command=lambda: self.runAway(lChoice, rChoice, img, text), font=('Sans Serif', 25), width= 54)\n        secret.place(x=100, y=610)\n        text=self.showText(\" \\n You and Auntie Jemima got married and had Ceasar as your best man. \\n Do you want to..\")\n        lChoice = self.lButton(\"Exit the Game\", lambda: self.client_exit())\n        rChoice = self.rButton(\"Play Again\", lambda: self.how2(lChoice, rChoice, secret, img, text))\n        \n\n    def runAway(self, button1, button2,img,text):\n        self.dB(button1, button2, img, text)\n        img=self.showImg(\"RUN AWAY.png\")\n        text=self.showText(\"You go outside to sunbathe and you see a bunny in the corner of your yard. Do you..\")\n        lChoice = self.lButton(\"MURDER\", self.win)\n        rChoice = self.rButton(\"murder\", lambda: self.die(lChoice, rChoice, img, text))\n\n    def die2(self, button1, button2, button, img, text):\n        self.dB2(button1, button2, button3, img, text) \n        img=self.showImg(\"GameOver.png\")\n        new= Button(self, text=\"Restart\", command=lambda: self.how3(new,img),font=('Sans Serif', 30))\n        new.place(x= 300, y= 200)\n\n    def die(self, button1, button2, img, text):\n        self.dB(button1, button2, img, text) \n        img=self.showImg(\"GameOver.png\")\n        new= Button(self, text=\"Restart\", command=lambda: self.how3(new,img),font=('Sans Serif', 30))\n        new.place(x= 300, y= 200)\n        \n    def win(self):\n        img=self.showImg(\"RUN AWAY.png\")\n        text = Label(self, text=\"YOU WIN- \\n THE PINK BELT IS YOURS FOR THE TAKING\", font=('Sans Serif', 50))\n        text.pack()\n        text.place(x=0, y=300)\n\n\n    def dB3(self, button1, img):\n        button1.destroy()\n        img.destroy()\n        \n    def dB2(self, button1, button2, button3, img, text):\n        button1.destroy()\n        button2.destroy()\n        button3.destroy()\n        img.destroy()\n        text.destroy()\n        \n        \n    def dB(self, button1, button2, img, text):\n        button1.destroy()\n        button2.destroy()\n        img.destroy()\n        text.destroy()\n        \n    def rButton(self, text2, command2):\n        Choice = Button(self, text=text2, command=command2,font=('Sans Serif', 30))\n        Choice.place(x=650, y=0)\n        return Choice\n\n    def lButton(self, text2, command2):\n        Choice = Button(self, text=text2, command=command2, font=('Sans Serif', 30))\n        Choice.place(x=100, y=0)\n        return Choice\n        \n    def showImg(self, pic):\n        load = Image.open(pic)\n        render = ImageTk.PhotoImage(load)\n\n        # labels can be text or images\n        img = Label(self, image=render)\n        img.image = render\n        img.place(x=0, y=0)\n        return img\n\n\n    def showText(self,word):\n        text = Label(self, text=word, font=('Sans Serif', 25))\n        text.pack()\n        text.place(x=100, y=675)\n        return text\n\n        \n\n    def client_exit(self):\n        exit()\n        \n    \n\n\n# root window created. Here, that would be the only window, but\n# you can later have windows within windows.\nroot = Tk()\n\n\nroot.geometry(\"{0}x{1}+0+0\".format(root.winfo_screenwidth(), root.winfo_screenheight()))\n\n#creation of an instance\napp = Window(root)\n\n\n#mainloop \nroot.mainloop()  \n","sub_path":"Hack2020.py","file_name":"Hack2020.py","file_ext":"py","file_size_in_byte":12926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"412100451","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*- pyversions=3.3+\n\nimport logging.config\nimport os\n\nimport time\n\nimport buoy.client.utils.argsparse as args_parse\nimport buoy.client.utils.config as load_config\nfrom buoy.client.network.connection import is_connected_to_internet\nfrom buoy.client.service.daemon import Daemon\n\nDAEMON_NAME = 'check-dongle-connectivity'\n\nlogger = logging.getLogger(__name__)\n\n\nclass IsInternetConectionDaemon(Daemon):\n    def __init__(self, name, config):\n        Daemon.__init__(self,  daemon_name=name, daemon_config=config['service'])\n\n        conf = config['connection']['check']\n\n        self.time = conf['time']\n        self.num_attempts = conf['num_attempts']\n        self.time_between_attempts = conf['time_between_attempts']\n        self.ip = conf['ip']\n        self.start_timeout = conf['start_timeout']\n        self.path_reboot_files = config['service']['path_reboot_files']\n\n    def run(self):\n        first_loop = True\n        while self.is_active():\n            if not is_connected_to_internet(ip=self.ip, max_attempts=self.num_attempts,\n                                            time_between_attempts=self.time_between_attempts):\n                self.error()\n            else:\n                if first_loop:\n                    self.delete_file_lifecycle()\n                    first_loop = False\n\n                time.sleep(self.time)\n\n    '''\n    Elimina los ficheros creados, cuando no se tiene conexión. Esto solo\n    ocurre cuando se obtiene conexión a internet.    \n    '''\n    def delete_file_lifecycle(self):\n        for file in os.listdir(self.path_reboot_files):\n            logger.info(\"Delete file \" + file)\n            os.remove(os.path.join(self.path_reboot_files, file))\n\n\ndef run(config: str, config_log_file: str):\n    logging.config.dictConfig(load_config.load_config_logger(path_config=config_log_file))\n    buoy_config = load_config.load_config(path_config=config)\n\n    daemon = IsInternetConectionDaemon(name=DAEMON_NAME, config=buoy_config)\n    daemon.start()\n\n\ndef main():\n    args = args_parse.parse_args()\n    run(config=args.config_file, config_log_file=args.config_log_file)\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"buoy/client/internet_connection.py","file_name":"internet_connection.py","file_ext":"py","file_size_in_byte":2186,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"461708290","text":"\"\"\"Test TAF Parsing\"\"\"\nimport os\nimport unittest\nimport datetime\n\nimport pytz\nfrom pyiem.nws.products.taf import parser as tafparser\n\n\ndef get_file(name):\n    ''' Helper function to get the text file contents '''\n    basedir = os.path.dirname(__file__)\n    fn = \"%s/../../../../data/product_examples/TAF/%s\" % (basedir, name)\n    return open(fn).read()\n\n\nclass TestProducts(unittest.TestCase):\n    \"\"\" Tests \"\"\"\n\n    def test_parse(self):\n        \"\"\"TAF type\"\"\"\n        utcnow = datetime.datetime(2017, 7, 25).replace(tzinfo=pytz.utc)\n        prod = tafparser(get_file(\"TAFJFK.txt\"), utcnow=utcnow)\n        j = prod.get_jabbers(\"http://localhost\", \"http://localhost\")\n        self.assertEquals(j[0][0],\n                          (\"OKX issues Terminal Aerodrome Forecast (TAF) \"\n                           \"at Jul 25, 13:41 UTC for JFK http://localhost?\"\n                           \"pid=201707251341-KOKX-FTUS41-TAFJFK\"))\n        assert \"TAFJFK\" in j[0][2]['channels'].split(\",\")\n","sub_path":"pyiem/nws/products/tests/test_taf.py","file_name":"test_taf.py","file_ext":"py","file_size_in_byte":979,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"112014648","text":"import matplotlib.pyplot as plt\nimport numpy\nimport sys # cmd line arguments\nimport pandas\n\nif len(sys.argv) != 2:\n\traise Exception('Missing csv file name.')\n\ncsv_file_path = sys.argv[1]\nresults = pandas.read_csv(csv_file_path)\n\ny_snr = [float(i) for i in results.snr]\ny_snr_std_dev = [float(i) for i in results.std_dev_snr]\ny_time_std_dev = [int(i) for i in results.std_dev_time]\ny_pkt_loss = (numpy.array([(int(results.msgs_per_setting[0]) - int(i)) for i in results['pkt_count']]) / float(results.msgs_per_setting[0])) * 100.0\ny_time = [int(i) for i in results['avg_time']]\n\nf, (snr_ax, pkt_loss_ax, time_ax) = plt.subplots(3, sharex=True)\nf.subplots_adjust(bottom=0.15)\n\nfixed_params = 'Messages per setting: ' + str(results.msgs_per_setting[0])\n\nif results.test_type[0] == 0: # POW test\n\ttest_title = 'TX Power test'\n\tx = [int(i) for i in results['pow']]\n\tplt.xlabel(\"TX Power (in dB)\")\nelse:\n\tfixed_params += ', TX power: ' + str(results['pow'][0]) + ' dB'\n\nif results.test_type[0] == 1: # BW test\n\ttest_title = 'Bandwidth test'\n\tlabels = ['BW125', 'BW250']\n\tx = range(1, 2+1)\n\tplt.xticks(x, labels, rotation='horizontal')\n\tplt.xlabel(\"Bandwidth (in kHz)\")\nelse:\n\tfixed_params += ', Bandwith: ' + str(results.bw[0]) + ' kHz'\n\nif results.test_type[0] == 2: # SF test\n\ttest_title = 'Spreading Factor test'\n\tlabels = ['SF7', 'SF8', 'SF9', 'SF10', 'SF11', 'SF12']\n\tx = range(1, 6+1)\n\tplt.xticks(x, labels, rotation='horizontal')\n\tplt.xlabel(\"Spreading Factor\")\nelse:\n\tfixed_params += ', Spreading factor: ' + str(results.dr[0])\n\nif results.test_type[0] == 3: # CRC test\n\ttest_title = 'CRC Coding test'\n\tlabels = ['4/5', '2/3', '4/7', '1/2']\n\tx = range(1, 4+1)\n\tplt.xticks(x, labels, rotation='horizontal')\n\tplt.xlabel(\"CRC coding\")\nelse:\n\tfixed_params += ', CRC coding: ' + str(results.crc[0])\n\nif results.test_type[0] == 4: # SIZE test\n\ttest_title = 'Packet Size test'\n\tx = [int(i) for i in results['size']]\n\tplt.xlabel(\"Packet size (in bytes)\")\nelse:\n\tfixed_params += ', Packet size: ' + str(results.size[0]) + ' bytes'\n\nf.suptitle(test_title +\" \\nUplink mode\", fontsize=16, fontweight='bold')\n\nsnr_ax.set_title(\"Signal-to-noise ratio\")\neb1 = snr_ax.errorbar(x, y_snr, yerr=y_snr_std_dev, marker='o', clip_on=False)\neb1[-1][0].set_linestyle('--')\n#snr_ax.plot(x, y_snr, '-ro')\nsnr_ax.set_ylabel(\"SNR (in dB)\")\n\n#snr_ax.vlines(x, [-4.0 ], y_snr,  linestyles='dashed')\n\npkt_loss_ax.set_title(\"Packets lost\")\npkt_loss_ax.plot(x, y_pkt_loss, '-ro')\npkt_loss_ax.set_ylabel(\"Packet loss (%)\")\n\ntime_ax.set_title(\"Average transmission time\")\n#time_ax.plot(x, y_time, '-ro')\neb1 = time_ax.errorbar(x, y_time, yerr=y_time_std_dev, marker='o', clip_on=False)\neb1[-1][0].set_linestyle('--')\ntime_ax.set_ylabel(\"Average tx time (in ms)\")\n\nplt.figtext(0.5, 0.05, 'Fixed parameters:\\n' + fixed_params, horizontalalignment='center', fontsize=15)\n\n\n\n# leaving margins near the axis so that error plot is shown for terminal points\nxmin, xmax = plt.xlim()\nplt.xlim(xmin=xmin-0.1,xmax=xmax+0.1)\n\nplt.show()","sub_path":"uplink/gen_uplink.py","file_name":"gen_uplink.py","file_ext":"py","file_size_in_byte":2991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"325457800","text":"#------TRIGGER__PY_START------\n\nclass Car:\n    __color = \"\"\n    weight = 0\n\n    def set(self, color, weight):\n        self.__color = color\n        self.weight = weight\n\nclass BMW (Car):\n    isM_model = False\n\n    def set(self, color, weight, isM_model):\n        self._Car__color = color\n        self.weight = weight\n        self.isM_model = isM_model\n\nclass Mercedes (Car):\n    isMaybach = False\n\nx3 = BMW()\nx3.set(\"Белый\", 2400, False)\nprint(x3._Car__color)\n\nm3 = BMW()\nm3.set(\"Синий\", 1400, True)\nprint(m3.isM_model)\n\n\n#-------HOMEWORK--------\n\n\n# TODO Простое наследование\n#  Есть класс Автомобиль:\n\nclass Car:  # Создание класса\n    wheels = 4  # Несколько полей\n    model = \"Some\"\n    speed = 123.5\n\n    def set(self, wheels, model, speed):  # Метод для установки значений\n        self.wheels = wheels\n        self.model = model\n        self.speed = speed\n\n    def getAll(self):  # Метод для вывода значений\n        print(\"Автомобиль \", self.model, \" может ехать со скоростью \", self.speed, \" на всех \", self.wheels,\n              \" колесах!\")\n        pass\n\n# TODO Создайте класс-наследник Мотоцикл с параметрами:\n#  поле с описанием двигателя - Engine;\n#  метод для установки значения в поле Engine.\n#  Создание объектов:\n#  Создайте два объекта для класс Автомобиль и используйте оба метода для каждого из объектов.\n#  Создайте объект на основе класса-наследника и используйте методы из класса Мотоцикл, а также из класса Автомобиль.\n\n\n# TODO Переопределение конструктора(Повышенная сложность)\n#  Есть код:\n\nclass Car_2:\n    wheels = 4\n    model = \"Some\"\n    speed = 123.5\n\n    def __init__(self, wheels, model, speed):\n        self.wheels = wheels\n        self.model = model\n        self.speed = speed\n\n    def set(self, wheels, model, speed):\n        self.wheels = wheels\n        self.model = model\n        self.speed = speed\n\n    def getAll(self):\n        self.__protected()\n\n    def __protected(self):\n        print(\"Транспорт \", self.model, \" может ехать со скоростью \", self.speed, \" на всех \", self.wheels, \" колесах!\")\n\n\nclass Motorcycle(Car_2):\n    engine = \"Default\"\n\n    def getAll(self):\n        super().getAll()\n        print(\"Его двигатель:\", self.engine)\n\n    def change(self, engine):\n        self.engine = engine\n        print(\"Двигатель мотоцикла установлен как: \" + engine)\n\n\nshkoda = Car_2(4, \"Shkoda\", 125.45)\nshkoda.getAll()\n\naudi = Car_2(4, \"Audi\", 250.9)\naudi.getAll()\n\nprint(\"\")  # Просто пропуск строки\n\nharley = Motorcycle(2, \"Harley Davidson\", 200, \"Powerful\")\nharley.getAll()\n\n# TODO Создайте в классе наследнике конструктор, который будет вызывать конструктор главного класса,\n#  а также добавлять значение engine для класса объектов Motorcycle.\n","sub_path":"lessons_finish/example.py","file_name":"example.py","file_ext":"py","file_size_in_byte":3413,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"568382876","text":"import operator\nimport numba\nfrom numba.extending import (box, unbox, typeof_impl, register_model, models,\n                             NativeValue, lower_builtin, lower_cast, overload,\n                             type_callable, overload_method)\nfrom numba.targets.imputils import lower_constant, impl_ret_new_ref, impl_ret_untracked\nfrom numba import types, typing\nfrom numba.typing.templates import (signature, AbstractTemplate, infer, infer_getattr,\n                                    ConcreteTemplate, AttributeTemplate, bound_function, infer_global)\nfrom numba import cgutils\nimport llvmlite.llvmpy.core as lc\nfrom llvmlite import ir as lir\nimport llvmlite.binding as ll\nimport hpat\n#from hpat.utils import unliteral_all\n# TODO: resolve import conflict\ndef unliteral_all(args):\n    return tuple(types.unliteral(a) for a in args)\n\nimport hstr_ext\nll.add_symbol('get_char_from_string', hstr_ext.get_char_from_string)\nll.add_symbol('get_char_ptr', hstr_ext.get_char_ptr)\nll.add_symbol('del_str', hstr_ext.del_str)\nll.add_symbol('_hash_str', hstr_ext.hash_str)\n\nclass StringType(types.Opaque):\n    def __init__(self):\n        super(StringType, self).__init__(name='StringType')\n\nstring_type = StringType()\n\n\n@typeof_impl.register(str)\ndef _typeof_str(val, c):\n    return string_type\n\n\nregister_model(StringType)(models.OpaqueModel)\n\n# XXX: should be subtype of StringType?\nclass CharType(types.Type):\n    def __init__(self):\n        super(CharType, self).__init__(name='CharType')\n        self.bitwidth = 8\n\nchar_type = CharType()\nregister_model(CharType)(models.IntegerModel)\n\n@overload(operator.getitem)\ndef char_getitem_overload(_str, ind):\n    if _str == string_type and isinstance(ind, types.Integer):\n        sig = char_type(\n                    string_type,   # string\n                    types.intp,    # index\n                    )\n        get_char_from_string = types.ExternalFunction(\"get_char_from_string\", sig)\n        def impl(s, i):\n            return get_char_from_string(s, i)\n\n        return impl\n\n# XXX: fix overload for getitem and use it\n@lower_builtin(operator.getitem, StringType, types.Integer)\ndef getitem_string(context, builder, sig, args):\n    fnty = lir.FunctionType(lir.IntType(8),\n                            [lir.IntType(8).as_pointer(), lir.IntType(64)])\n    fn = builder.module.get_or_insert_function(fnty, name=\"get_char_from_string\")\n    return builder.call(fn, args)\n\n\n@box(CharType)\ndef box_char(typ, val, c):\n    \"\"\"\n    \"\"\"\n    fnty = lir.FunctionType(lir.IntType(8).as_pointer(),\n                            [lir.IntType(8)])\n    fn = c.builder.module.get_or_insert_function(fnty, name=\"get_char_ptr\")\n    c_str = c.builder.call(fn, [val])\n    pystr = c.pyapi.string_from_string_and_size(c_str, c.context.get_constant(types.intp, 1))\n    # TODO: delete ptr\n    return pystr\n\ndel_str = types.ExternalFunction(\"del_str\", types.void(string_type))\n_hash_str = types.ExternalFunction(\"_hash_str\", types.int64(string_type))\nget_c_str = types.ExternalFunction(\"get_c_str\", types.voidptr(string_type))\n\n@overload_method(StringType, 'c_str')\ndef str_c_str(str_typ):\n    return lambda s: get_c_str(s)\n\n@overload_method(StringType, 'join')\ndef str_join(str_typ, iterable_typ):\n    # TODO: more efficient implementation (e.g. C++ string buffer)\n    def str_join_impl(sep_str, str_container):\n        res = \"\"\n        counter = 0\n        for s in str_container:\n            if counter != 0:\n                res += sep_str\n            counter += 1\n            res += s\n        return res\n    return str_join_impl\n\n@overload(hash)\ndef hash_overload(str_typ):\n    if str_typ == string_type:\n        return lambda s: _hash_str(s)\n\n@infer\n@infer_global(operator.add)\n@infer_global(operator.iadd)\nclass StringAdd(ConcreteTemplate):\n    key = \"+\"\n    cases = [signature(string_type, string_type, string_type)]\n\n\n@infer\n@infer_global(operator.eq)\n@infer_global(operator.ne)\n@infer_global(operator.ge)\n@infer_global(operator.gt)\n@infer_global(operator.le)\n@infer_global(operator.lt)\nclass StringOpEq(AbstractTemplate):\n    key = '=='\n\n    def generic(self, args, kws):\n        assert not kws\n        (arg1, arg2) = args\n        if isinstance(arg1, StringType) and isinstance(arg2, StringType):\n            return signature(types.boolean, arg1, arg2)\n        if arg1 == char_type and arg2 == char_type:\n            return signature(types.boolean, arg1, arg2)\n\n\n@infer\nclass StringOpNotEq(StringOpEq):\n    key = '!='\n\n@infer\nclass StringOpGE(StringOpEq):\n    key = '>='\n\n@infer\nclass StringOpGT(StringOpEq):\n    key = '>'\n\n@infer\nclass StringOpLE(StringOpEq):\n    key = '<='\n\n@infer\nclass StringOpLT(StringOpEq):\n    key = '<'\n\n\n@infer_getattr\nclass StringAttribute(AttributeTemplate):\n    key = StringType\n\n    @bound_function(\"str.split\")\n    def resolve_split(self, dict, args, kws):\n        assert not kws\n        assert len(args) == 1\n        return signature(types.List(string_type), types.unliteral(args[0]))\n\n\n# @infer_global(operator.getitem)\n# class GetItemString(AbstractTemplate):\n#     key = operator.getitem\n#\n#     def generic(self, args, kws):\n#         assert not kws\n#         if (len(args) == 2 and isinstance(args[0], StringType)\n#                 and isinstance(args[1], types.Integer)):\n#             return signature(args[0], *args)\n\n\n@infer_global(len)\nclass LenStringArray(AbstractTemplate):\n    def generic(self, args, kws):\n        if not kws and len(args) == 1 and args[0] == string_type:\n            return signature(types.intp, *args)\n\n\n@infer_global(int)\nclass StrToInt(AbstractTemplate):\n    def generic(self, args, kws):\n        assert not kws\n        [arg] = args\n        if isinstance(arg, StringType):\n            return signature(types.intp, arg)\n\n\n@infer_global(float)\nclass StrToFloat(AbstractTemplate):\n    def generic(self, args, kws):\n        assert not kws\n        [arg] = args\n        if isinstance(arg, StringType):\n            return signature(types.float64, arg)\n\n\n@infer_global(str)\nclass StrConstInfer(AbstractTemplate):\n    def generic(self, args, kws):\n        assert not kws\n        assert len(args) == 1\n        assert args[0] in [types.int32, types.int64, types.float32, types.float64, string_type]\n        return signature(string_type, *args)\n\n\nclass RegexType(types.Opaque):\n    def __init__(self):\n        super(RegexType, self).__init__(name='RegexType')\n\n\nregex_type = RegexType()\n\nregister_model(RegexType)(models.OpaqueModel)\n\n\ndef compile_regex(pat):\n    return 0\n\n\n@infer_global(compile_regex)\nclass CompileRegexInfer(AbstractTemplate):\n    def generic(self, args, kws):\n        assert not kws\n        assert len(args) == 1\n        return signature(regex_type, *unliteral_all(args))\n\n\ndef contains_regex(str, pat):\n    return False\n\n\ndef contains_noregex(str, pat):\n    return False\n\n\n@infer_global(contains_regex)\n@infer_global(contains_noregex)\nclass ContainsInfer(AbstractTemplate):\n    def generic(self, args, kws):\n        assert not kws\n        assert len(args) == 2\n        return signature(types.boolean, *unliteral_all(args))\n\nll.add_symbol('init_string', hstr_ext.init_string)\nll.add_symbol('init_string_const', hstr_ext.init_string_const)\nll.add_symbol('get_c_str', hstr_ext.get_c_str)\nll.add_symbol('str_concat', hstr_ext.str_concat)\nll.add_symbol('str_compare', hstr_ext.str_compare)\nll.add_symbol('str_equal', hstr_ext.str_equal)\nll.add_symbol('str_equal_cstr', hstr_ext.str_equal_cstr)\nll.add_symbol('str_split', hstr_ext.str_split)\nll.add_symbol('str_substr_int', hstr_ext.str_substr_int)\nll.add_symbol('str_to_int64', hstr_ext.str_to_int64)\nll.add_symbol('str_to_float64', hstr_ext.str_to_float64)\nll.add_symbol('get_str_len', hstr_ext.get_str_len)\nll.add_symbol('compile_regex', hstr_ext.compile_regex)\nll.add_symbol('str_contains_regex', hstr_ext.str_contains_regex)\nll.add_symbol('str_contains_noregex', hstr_ext.str_contains_noregex)\nll.add_symbol('str_from_int32', hstr_ext.str_from_int32)\nll.add_symbol('str_from_int64', hstr_ext.str_from_int64)\nll.add_symbol('str_from_float32', hstr_ext.str_from_float32)\nll.add_symbol('str_from_float64', hstr_ext.str_from_float64)\n\n\n@unbox(StringType)\ndef unbox_string(typ, obj, c):\n    \"\"\"\n    \"\"\"\n    ok, buffer, size = c.pyapi.string_as_string_and_size(obj)\n\n    fnty = lir.FunctionType(lir.IntType(8).as_pointer(),\n                            [lir.IntType(8).as_pointer(), lir.IntType(64)])\n    fn = c.builder.module.get_or_insert_function(fnty, name=\"init_string\")\n    ret = c.builder.call(fn, [buffer, size])\n\n    return NativeValue(ret, is_error=c.builder.not_(ok))\n\n\n@box(StringType)\ndef box_str(typ, val, c):\n    \"\"\"\n    \"\"\"\n    fnty = lir.FunctionType(lir.IntType(8).as_pointer(),\n                            [lir.IntType(8).as_pointer()])\n    fn = c.builder.module.get_or_insert_function(fnty, name=\"get_c_str\")\n    c_str = c.builder.call(fn, [val])\n    pystr = c.pyapi.string_from_string(c_str)\n    return pystr\n\ndef getpointer(str):\n    pass\n\n@type_callable(getpointer)\ndef type_string_getpointer(context):\n    def typer(val):\n        return types.voidptr\n    return typer\n\n@lower_builtin(getpointer, StringType)\ndef getpointer_from_string(context, builder, sig, args):\n    val = args[0]\n    fnty = lir.FunctionType(lir.IntType(8).as_pointer(),\n                            [lir.IntType(8).as_pointer()])\n    fn = builder.module.get_or_insert_function(fnty, name=\"get_c_str\")\n    c_str = builder.call(fn, [val])\n    return c_str\n\n@lower_builtin(getpointer, types.StringLiteral)\ndef getpointer_from_string_literal(context, builder, sig, args):\n    cstr = context.insert_const_string(builder.module, sig.args[0].literal_value)\n    return cstr\n\n@lower_cast(StringType, types.StringLiteral)\ndef string_type_to_const(context, builder, fromty, toty, val):\n    # calling str() since the const value can be non-str like tuple const (CSV)\n    cstr = context.insert_const_string(builder.module, str(toty.literal_value))\n    # check to make sure Const value matches stored string\n    # call str == cstr\n    fnty = lir.FunctionType(lir.IntType(1),\n                            [lir.IntType(8).as_pointer(), lir.IntType(8).as_pointer()])\n    fn = builder.module.get_or_insert_function(fnty, name=\"str_equal_cstr\")\n    match = builder.call(fn, [val, cstr])\n    with cgutils.if_unlikely(builder, builder.not_(match)):\n        # Raise RuntimeError about the assumption violation\n        usermsg = \"constant string assumption violated\"\n        errmsg = \"{}: expecting {}\".format(usermsg, toty.literal_value)\n        context.call_conv.return_user_exc(builder, RuntimeError, (errmsg,))\n\n    return impl_ret_untracked(context, builder, toty, cstr)\n\n\n@lower_constant(StringType)\ndef const_string(context, builder, ty, pyval):\n    cstr = context.insert_const_string(builder.module, pyval)\n\n    fnty = lir.FunctionType(lir.IntType(8).as_pointer(),\n                            [lir.IntType(8).as_pointer()])\n    fn = builder.module.get_or_insert_function(fnty, name=\"init_string_const\")\n    ret = builder.call(fn, [cstr])\n    return ret\n\n@lower_cast(types.StringLiteral, StringType)\ndef const_to_string_type(context, builder, fromty, toty, val):\n    cstr = context.insert_const_string(builder.module, fromty.literal_value)\n\n    fnty = lir.FunctionType(lir.IntType(8).as_pointer(),\n                            [lir.IntType(8).as_pointer()])\n    fn = builder.module.get_or_insert_function(fnty, name=\"init_string_const\")\n    ret = builder.call(fn, [cstr])\n    return ret\n\n@lower_builtin(str, types.Any)\ndef string_from_impl(context, builder, sig, args):\n    in_typ = sig.args[0]\n    if in_typ == string_type:\n        return args[0]\n    ll_in_typ = context.get_value_type(sig.args[0])\n    fnty = lir.FunctionType(lir.IntType(8).as_pointer(), [ll_in_typ])\n    fn = builder.module.get_or_insert_function(\n        fnty, name=\"str_from_\" + str(in_typ))\n    return builder.call(fn, args)\n\n\n@lower_builtin(operator.add, string_type, string_type)\n@lower_builtin(\"+\", string_type, string_type)\ndef impl_string_concat(context, builder, sig, args):\n    fnty = lir.FunctionType(lir.IntType(8).as_pointer(),\n                            [lir.IntType(8).as_pointer(), lir.IntType(8).as_pointer()])\n    fn = builder.module.get_or_insert_function(fnty, name=\"str_concat\")\n    return builder.call(fn, args)\n\n\n@lower_builtin(operator.eq, string_type, string_type)\n@lower_builtin('==', string_type, string_type)\ndef string_eq_impl(context, builder, sig, args):\n    fnty = lir.FunctionType(lir.IntType(1),\n                            [lir.IntType(8).as_pointer(), lir.IntType(8).as_pointer()])\n    fn = builder.module.get_or_insert_function(fnty, name=\"str_equal\")\n    return builder.call(fn, args)\n\n@lower_builtin(operator.eq, char_type, char_type)\n@lower_builtin('==', char_type, char_type)\ndef char_eq_impl(context, builder, sig, args):\n    def char_eq_impl(c1, c2):\n        return c1==c2\n    new_sig = signature(sig.return_type, types.uint8, types.uint8)\n    res = context.compile_internal(builder, char_eq_impl, new_sig, args)\n    return res\n\n\n@lower_builtin(operator.ne, string_type, string_type)\n@lower_builtin('!=', string_type, string_type)\ndef string_neq_impl(context, builder, sig, args):\n    fnty = lir.FunctionType(lir.IntType(1),\n                            [lir.IntType(8).as_pointer(), lir.IntType(8).as_pointer()])\n    fn = builder.module.get_or_insert_function(fnty, name=\"str_equal\")\n    return builder.not_(builder.call(fn, args))\n\n@lower_builtin(operator.ge, string_type, string_type)\n@lower_builtin('>=', string_type, string_type)\ndef string_ge_impl(context, builder, sig, args):\n    fnty = lir.FunctionType(lir.IntType(32),\n                            [lir.IntType(8).as_pointer(), lir.IntType(8).as_pointer()])\n    fn = builder.module.get_or_insert_function(fnty, name=\"str_compare\")\n    comp_val = builder.call(fn, args)\n    zero = context.get_constant(types.int32, 0)\n    res = builder.icmp(lc.ICMP_SGE, comp_val, zero)\n    return res\n\n@lower_builtin(operator.gt, string_type, string_type)\n@lower_builtin('>', string_type, string_type)\ndef string_gt_impl(context, builder, sig, args):\n    fnty = lir.FunctionType(lir.IntType(32),\n                            [lir.IntType(8).as_pointer(), lir.IntType(8).as_pointer()])\n    fn = builder.module.get_or_insert_function(fnty, name=\"str_compare\")\n    comp_val = builder.call(fn, args)\n    zero = context.get_constant(types.int32, 0)\n    res = builder.icmp(lc.ICMP_SGT, comp_val, zero)\n    return res\n\n@lower_builtin(operator.le, string_type, string_type)\n@lower_builtin('<=', string_type, string_type)\ndef string_le_impl(context, builder, sig, args):\n    fnty = lir.FunctionType(lir.IntType(32),\n                            [lir.IntType(8).as_pointer(), lir.IntType(8).as_pointer()])\n    fn = builder.module.get_or_insert_function(fnty, name=\"str_compare\")\n    comp_val = builder.call(fn, args)\n    zero = context.get_constant(types.int32, 0)\n    res = builder.icmp(lc.ICMP_SLE, comp_val, zero)\n    return res\n\n@lower_builtin(operator.lt, string_type, string_type)\n@lower_builtin('<', string_type, string_type)\ndef string_lt_impl(context, builder, sig, args):\n    fnty = lir.FunctionType(lir.IntType(32),\n                            [lir.IntType(8).as_pointer(), lir.IntType(8).as_pointer()])\n    fn = builder.module.get_or_insert_function(fnty, name=\"str_compare\")\n    comp_val = builder.call(fn, args)\n    zero = context.get_constant(types.int32, 0)\n    res = builder.icmp(lc.ICMP_SLT, comp_val, zero)\n    return res\n\n\n@lower_builtin(\"str.split\", string_type, string_type)\ndef string_split_impl(context, builder, sig, args):\n    nitems = cgutils.alloca_once(builder, lir.IntType(64))\n    # input str, sep, size pointer\n    fnty = lir.FunctionType(lir.IntType(8).as_pointer().as_pointer(),\n                            [lir.IntType(8).as_pointer(), lir.IntType(8).as_pointer(),\n                             lir.IntType(64).as_pointer()])\n    fn = builder.module.get_or_insert_function(fnty, name=\"str_split\")\n    ptr = builder.call(fn, args + [nitems])\n    size = builder.load(nitems)\n    # TODO: use ptr instead of allocating and copying, use NRT_MemInfo_new\n    # TODO: deallocate ptr\n    _list = numba.targets.listobj.ListInstance.allocate(context, builder,\n                                                        sig.return_type, size)\n    _list.size = size\n    with cgutils.for_range(builder, size) as loop:\n        value = builder.load(cgutils.gep_inbounds(builder, ptr, loop.index))\n        # TODO: refcounted str\n        _list.setitem(loop.index, value, incref=False)\n    return impl_ret_new_ref(context, builder, sig.return_type, _list.value)\n\n\n# @lower_builtin(operator.getitem, StringType, types.Integer)\n# def getitem_string(context, builder, sig, args):\n#     fnty = lir.FunctionType(lir.IntType(8).as_pointer(),\n#                             [lir.IntType(8).as_pointer(), lir.IntType(64)])\n#     fn = builder.module.get_or_insert_function(fnty, name=\"str_substr_int\")\n#     # TODO: handle reference counting\n#     # return impl_ret_new_ref(builder.call(fn, args))\n#     return (builder.call(fn, args))\n\n\n@lower_cast(StringType, types.int64)\ndef cast_str_to_int64(context, builder, fromty, toty, val):\n    fnty = lir.FunctionType(lir.IntType(64), [lir.IntType(8).as_pointer()])\n    fn = builder.module.get_or_insert_function(fnty, name=\"str_to_int64\")\n    return builder.call(fn, (val,))\n\n\n@lower_cast(StringType, types.float64)\ndef cast_str_to_float64(context, builder, fromty, toty, val):\n    fnty = lir.FunctionType(lir.DoubleType(), [lir.IntType(8).as_pointer()])\n    fn = builder.module.get_or_insert_function(fnty, name=\"str_to_float64\")\n    return builder.call(fn, (val,))\n\n\n@lower_builtin(len, StringType)\ndef len_string(context, builder, sig, args):\n    fnty = lir.FunctionType(lir.IntType(64),\n                            [lir.IntType(8).as_pointer()])\n    fn = builder.module.get_or_insert_function(fnty, name=\"get_str_len\")\n    return (builder.call(fn, args))\n\n\n@lower_builtin(compile_regex, string_type)\ndef lower_compile_regex(context, builder, sig, args):\n    fnty = lir.FunctionType(lir.IntType(8).as_pointer(),\n                            [lir.IntType(8).as_pointer()])\n    fn = builder.module.get_or_insert_function(fnty, name=\"compile_regex\")\n    return builder.call(fn, args)\n\n\n@lower_builtin(contains_regex, string_type, regex_type)\ndef impl_string_contains_regex(context, builder, sig, args):\n    fnty = lir.FunctionType(lir.IntType(1),\n                            [lir.IntType(8).as_pointer(), lir.IntType(8).as_pointer()])\n    fn = builder.module.get_or_insert_function(fnty, name=\"str_contains_regex\")\n    return builder.call(fn, args)\n\n\n@lower_builtin(contains_noregex, string_type, string_type)\ndef impl_string_contains_noregex(context, builder, sig, args):\n    fnty = lir.FunctionType(lir.IntType(1),\n                            [lir.IntType(8).as_pointer(), lir.IntType(8).as_pointer()])\n    fn = builder.module.get_or_insert_function(\n        fnty, name=\"str_contains_noregex\")\n    return builder.call(fn, args)\n","sub_path":"hpat/str_ext.py","file_name":"str_ext.py","file_ext":"py","file_size_in_byte":18932,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"271690536","text":"from flask import Blueprint, render_template, request, jsonify, Markup, flash, redirect, url_for\nfrom login.decorators import is_logged_in\nfrom login.views import session\n#from settings import ENGINE as engine\nimport pandas as pd\nimport json\nimport datetime\nimport numpy as np\nimport html.parser\nimport sqlalchemy as sa\nengine = sa.create_engine('mssql+pyodbc://supermegaadmin:123QWEasd@sql541', poolclass=sa.pool.NullPool)\ntrends_app = Blueprint('trends_app', __name__)\n\nTABLE_CHANGES = 'AI_Role_transformation'\nTABLE_CHANGES_TEMP = 'AI_Role_transformation_temp'\n\nTABLE_CHANGES_INITIATIVES = 'AI_Initiatives'\nTABLE_CHANGES_INITIATIVES_TEMP = 'AI_Initiatives_temp'\n\n\n# Справочник Код сектора - Название столбца. Заполняется только 1 раз за сессию.\n# code_sector_dict = {}\n\n\n# TODO: function\n# def replacer(df, symbol):\n#     df = df.str.replace(symbol)\n\ndef getChartPoints(idName, calc_df):\n    # print('calc_df',calc_df,'idName',idName  )\n    chart_points = {'id': idName, \\\n                    'pointsArr': [\n                        {'name': 'population', 'qty': calc_df['population'].sum().item(), 'date': '2019-01-01',\n                         'value': 0}, \\\n                        {'name': 'wave_1', 'qty': calc_df['wave_1'].sum().item(), 'date': '2020-01-01', 'value': 100 * (\n                                calc_df['population'].sum().item() - calc_df['wave_1'].sum().item()) /\n                                                                                                                 calc_df[\n                                                                                                                     'population'].sum().item()}, \\\n                        {'name': 'wave_2', 'qty': calc_df['wave_2'].sum().item(), 'date': '2021-01-01', 'value': 100 * (\n                                calc_df['population'].sum().item() - calc_df['wave_2'].sum().item()) /\n                                                                                                                 calc_df[\n                                                                                                                     'population'].sum().item()}, \\\n                        {'name': 'wave_3', 'qty': calc_df['wave_3'].sum().item(), 'date': '2022-01-01', 'value': 100 * (\n                                calc_df['population'].sum().item() - calc_df['wave_3'].sum().item()) /\n                                                                                                                 calc_df[\n                                                                                                                     'population'].sum().item()}]}\n    return chart_points\n\n\ndef sort_df(df, column_idx, key):\n    '''Takes a dataframe, a column index and a custom function for sorting, \n    returns a dataframe sorted by that column using that function'''\n    col = df.ix[:, column_idx]\n    df = df.ix[[i[1] for i in sorted(zip(col, range(len(col))), key=key)]]\n    return df\n\n\n@trends_app.route('/rolefunction/', defaults={'id_scenario': '1'}, methods=['GET', 'POST'])\n@trends_app.route('/rolefunction/<id_scenario>', methods=['GET', 'POST'])\ndef rolefunction(id_scenario):\n    code_scenario = id_scenario\n    print(code_scenario)\n    print(session['block'])\n\n    role_df = pd.read_sql(\n        \"SELECT [KOD_role] as code_role,[2020] as population FROM [New_Structure].[dbo].[Values_View_temp]\", engine)\n    ai_population = pd.read_sql(\n        \"SELECT [KOD_Role] as code_role,[1] as Algorithmic,[2] as Augmentation,[3] as Autonomous\"\n        \" FROM [New_Structure].[dbo].[AI_population] WHERE KOD_versio_AI = '1'\", engine)\n    print('sum')\n    print(role_df['population'].sum())\n    if str(code_scenario) == '1':\n        df = pd.read_sql(\"SELECT [KOD_role] as code_role, \"\n                         \"[Optimized] as [Автомат. задач], [1] as [Ручные задачи], \"\n                         \"[2] as [Рутинные задачи],[3] as [Аналит. задачи], [4] as [Менеджмент], \"\n                         \"[5] as [Навыки общения], [6] as [Академ. знания], \"\n                         \"Block, LVL_2, LVL_3, LVL_4, LVL_5,  LVL_org, KOD_versio_AI \"\n                         \" FROM [New_Structure].[dbo].[AI_view] \"\n                         \"WHERE\" + \" KOD_versio_AI = '1'\", engine)\n\n        result_df = pd.merge(df, role_df, how='left')\n        print(result_df['population'].sum())\n        result_df = pd.merge(pd.merge(df, role_df, how='left'), ai_population, how='outer').fillna(0)\n        result_df['wave_1'] = (result_df['population'] - result_df['population'] * result_df['Algorithmic']) * (\n                1 - result_df['Автомат. задач'] / 10)\n        result_df['wave_2'] = (result_df['population'] - result_df['population'] * (\n                result_df['Algorithmic'] + result_df['Augmentation'])) * (1 - result_df['Автомат. задач'] / 10)\n        result_df['wave_3'] = (result_df['population'] - result_df['population'] * (\n                result_df['Algorithmic'] + result_df['Augmentation'] + result_df['Autonomous'])) * (\n                                      1 - result_df['Автомат. задач'] / 10)\n        print('result_df')\n        print(result_df['population'].sum())\n        chart_points = [getChartPoints('defaultGraph', result_df)]\n        print('typetype', df.iloc[0]['KOD_versio_AI'] == 1)\n    else:\n        if session['block'] is not None:\n            df = pd.read_sql(\"SELECT [KOD_role] as code_role, \"\n                             \"[Optimized] as [Автомат. задач], [1] as [Ручные задачи], \"\n                             \"[2] as [Рутинные задачи],[3] as [Аналит. задачи], [4] as [Менеджмент], \"\n                             \"[5] as [Навыки общения], [6] as [Академ. знания], \"\n                             \"Block, LVL_2, LVL_3, LVL_4, LVL_5,  LVL_org, KOD_versio_AI \"\n                             \" FROM [New_Structure].[dbo].[AI_view] \"\n                             \"WHERE  Block = '\" + session['block'] + \"'\", engine)\n        else:\n            df = pd.read_sql(\"SELECT [KOD_role] as code_role, \"\n                             \"[Optimized] as [Автомат. задач], [1] as [Ручные задачи], \"\n                             \"[2] as [Рутинные задачи],[3] as [Аналит. задачи], [4] as [Менеджмент], \"\n                             \"[5] as [Навыки общения], [6] as [Академ. знания], \"\n                             \"Block, LVL_2, LVL_3, LVL_4, LVL_5,  LVL_org, KOD_versio_AI \"\n                             \" FROM [New_Structure].[dbo].[AI_view] \"\n                             \"WHERE\" + \" KOD_versio_AI = '1'\", engine)\n\n        df_default = df[df['KOD_versio_AI'] == 1]\n        df_default = df[df['KOD_versio_AI'] == 1]\n        print('df_default', df_default)\n        result_df_default = pd.merge(pd.merge(df_default, role_df, how='left'), ai_population, how='outer').fillna(0)\n        result_df_default['wave_1'] = (result_df_default['population'] - result_df_default['population'] * \\\n                                       result_df_default['Algorithmic']) * (\n                                              1 - result_df_default['Автомат. задач'] / 10)\n        result_df_default['wave_2'] = (result_df_default['population'] - result_df_default['population'] * (\n                result_df_default['Algorithmic'] + result_df_default['Augmentation'])) * (\n                                              1 - result_df_default['Автомат. задач'] / 10)\n        result_df_default['wave_3'] = (result_df_default['population'] - result_df_default['population'] * (\n                result_df_default['Algorithmic'] + result_df_default['Augmentation'] + result_df_default[\n            'Autonomous'])) * (1 - result_df_default['Автомат. задач'] / 10)\n\n        cmp = lambda x: 2 if x[0] == code_scenario else 1\n        df_modeling = sort_df(df, 'KOD_versio_AI', cmp).drop_duplicates('code_role', keep='last')\n        ai_population_modeling = pd.read_sql(\n            \"SELECT [KOD_versio_AI], [KOD_Role] as code_role,[1] as Algorithmic,[2] as Augmentation,[3] as Autonomous\"\n            \" FROM [New_Structure].[dbo].[AI_population] WHERE KOD_versio_AI = '1' OR KOD_versio_AI = '\" + str(\n                code_scenario) + \"'\", engine)\n        print('df_modeling', df_modeling)\n        print('ai_population_modeling', ai_population_modeling, 'code_scenario', code_scenario)\n        result_df = pd.merge(pd.merge(df_modeling, role_df, how='left'), ai_population_modeling, how='outer').fillna(0)\n        result_df['wave_1'] = ((result_df['population'] - result_df['population'] * result_df['Algorithmic'])) * (\n                1 - result_df['Автомат. задач'] / 10)\n        result_df['wave_2'] = (result_df['population'] - result_df['population'] * (\n                result_df['Algorithmic'] + result_df['Augmentation'])) * (1 - result_df['Автомат. задач'] / 10)\n        result_df['wave_3'] = (result_df['population'] - result_df['population'] * (\n                result_df['Algorithmic'] + result_df['Augmentation'] + result_df['Autonomous'])) * (\n                                      1 - result_df['Автомат. задач'] / 10)\n\n        chart_points = [getChartPoints('defaultGraph', result_df_default), getChartPoints('defaultModeling', result_df)]\n\n    if request.method == 'POST':\n        if 'Content-Type' in request.headers and request.headers['Content-Type'] == 'application/json':\n            return json.dumps(chart_points)\n\n    hidden_items = ['code_role', 'population', 'Algorithmic', 'Augmentation', 'Autonomous', 'wave_1',\n                    'wave_2', 'wave_3', 'KOD_versio_AI']\n    text_df = ['Block', 'LVL_2', 'LVL_3', 'LVL_4', 'LVL_5', 'LVL_org']\n    result_df['Role'] = result_df[text_df].apply(lambda x: ' \\u2022 '.join(str(item) for item in x), axis=1)\n    result_df['Role'] = result_df['Role'].str.replace('\\u2022  \\u2022', '\\u2022')\n    result_df['Role'] = result_df['Role'].str.replace('\\u2022  \\u2022', '\\u2022')\n    result_df['Role'] = result_df['Role'].str.replace('\\u2022  \\u2022', '\\u2022')\n\n    attr_kod_role = pd.Series(result_df['code_role'].values, index=result_df['Role']).to_dict()\n    for item in text_df + hidden_items:\n        del result_df[item]\n    result_df.rename(\n        columns={'Role': 'Роль'}, inplace=True)\n    columns = result_df.columns.tolist()\n    columns = columns[-1:] + columns[:-1]\n    result_df = result_df[columns]\n\n    return render_template('trends/rolefunction.html', table=json.loads(result_df.to_json(orient='records')),\n                           columns=columns,\n                           attribute_role=attr_kod_role,\n                           count_rows=int(df.shape[0]), chart_points=chart_points, code_scenario=code_scenario)\n\n\n@trends_app.route('/changetrends', methods=['GET', 'POST'])\ndef changetrends():\n    if request.method == 'POST':\n        getForm = json.loads(request.form['data'])\n        changed_rows = getForm['changed_rows']\n        code_scenario = getForm['code_scenario']\n        accept_change = getForm['accept_change']\n        # print('changed_rows',changed_rows,'code_scenario',code_scenario,'accept_change',accept_change)\n        # print(changed_rows)\n        # if not code_sector_dict:\n        #     for index, item in enumerate(changed_rows[list(changed_rows.keys())[0]]):\n        #         code_sector_dict[index] = item\n        df_data = []\n        # print(code_sector_dict)\n        for k in changed_rows:\n            changed_rows[k]['report_dt'] = datetime.datetime.now()\n            changed_rows[k]['login'] = session['username']\n            changed_rows[k]['code_role'] = ('0' if k == '' else k)\n            changed_rows[k]['KOD_versio_AI'] = str(code_scenario)\n            df_data.append(changed_rows[k])\n        df_update = pd.DataFrame(data=df_data)\n        print(df_update.columns.values)\n        df_update.rename(\n            columns={'code_role': 'KOD_Role', 'value': 'Value', 'Manual': '1', 'Routine': '2', 'Computation': '3',\n                     'Management': '4', 'Social': '5', 'Literacy': '6'}, inplace=True)\n        df_update = pd.melt(df_update, id_vars=['KOD_Role', 'Optimized', 'KOD_versio_AI', 'report_dt', 'login'],\n                            value_vars=['1', '2', '3', '4', '5', '6'],\n                            var_name='KOD_Sector')\n        print(df_update['report_dt'])\n        print(df_update)\n        df_update.to_sql((TABLE_CHANGES if accept_change else TABLE_CHANGES_TEMP), schema='New_Structure.dbo',\n                         con=engine, if_exists='append',\n                         index=False)\n\n        return jsonify(status=201)\n\n\n@trends_app.route('/deletechangestrends', methods=['GET', 'POST'])\ndef deletechangestrends():\n    if request.method == 'POST':\n        getForm = json.loads(request.form['data'])\n        code_scenario = getForm['code_scenario']\n        delete_connect = engine.connect()\n        rowcount = delete_connect.execute(\n            \"DELETE  FROM [New_Structure].[dbo].[\" + TABLE_CHANGES_TEMP + \"] WHERE login='\" + session[\n                'username'] + \"' AND KOD_versio_AI='\" + str(code_scenario) + \"'\").rowcount\n\n    return jsonify(status=200)\n\n\n@trends_app.route('/initiatives', methods=['GET', 'POST'])\ndef initiatives():\n    if request.method == 'POST':\n        print(datetime.datetime.now())\n        # scenario = request.form['scenario']\n        # version = request.form['version']\n        df = pd.read_sql('''\n                SELECT [id] as 'id',[Block] as 'block',[Подразделение] as 'subdivision',[Высокоуровневая цель] 'highLevelTarget',[Эффет(P&L ежегодно) ИТОГОВАЯ  СУММА] as 'effectP&L'\n                ,[Эффект (сокр. ПШЕ)] as 'effectPSHE',[Эффект (снижение нагрузки КЦ)] 'effectKC',[Цель] as 'purpose'\n                ,[Текущая формулировка целей 2019] as 'curPurposes2019',[Новая амбиция под запрашиваемые доп. Ресурсы] as 'newAmbitions'\n                ,[Учтено в Бизнес-плане] as 'includeBusiness',[2019(эффект)] as 'effect2019',[2020 (эффект)] as 'effect2020',[2021(эффект)] as 'effect2021'\n                ,[год] as 'year',[Платформа/ бизнес-сервис] as 'platform',[Требуемые платформы (выбрать из списка)] as 'needsPlatfotmSelect',[AS IS: Всего] as 'ASISTotal'\n                ,[Доп.потребность: Всего] as 'extraNeedsTotal',[AS IS: DS] as 'ASISDS',[Доп.потребность: DS] as 'extraNeedsDS',[AS IS: DE] as 'ASISDE'\n                ,[Доп.потребность: DE] as 'extraNeedsDE',[AS IS: разметчики] as 'ASISMarkers',[Доп.потребность: разметчики] as 'extraNeedsMarkers'\n                ,[AS IS: другие профили] as 'ASISOthers',[Доп.потребность: другие профили] as 'extraNeedsOthers',[BA/SA] as 'BASA'\n                ,[PO/TL] as 'POTL',[Developer] as 'developer',[DA],[Data ops] as 'dataOps',[Architect] as 'architect',[UX/UI дизайнер] as 'UXUIDesigner'\n                FROM [New_Structure].[dbo].[AI_Initiatives_View]\n            ''', engine)\n        accordanceDict = {'id': 'Идентификатор', 'block': 'Блок', 'subdivision': 'Подразделение',\n                          'highLevelTarget': 'Высокоуровневая цель', 'effectP&L': 'Эффект P&L',\n                          'effectPSHE': 'Эффект ПШЕ', 'effectKC': 'Снижение нагрузки КЦ', 'purpose': 'Цель', \\\n                          'curPurposes2019': 'Текущая формулировка целей 2019 года',\n                          'newAmbitions': 'Новая амбиция под запрашиваемые доп. Ресурсы',\n                          'includeBusiness': 'Учтено в Бизнес-плане', 'effect2019': 'Эффект 2019',\n                          'effect2020': 'Эффект 2020', \\\n                          'effect2021': 'Эффект 2021', 'year': 'год', 'platform': 'Платформа/Бизнес-сервис',\n                          'needsPlatfotmSelect': 'Требуемые платформы', 'ASISTotal': 'AS IS: Всего',\n                          'extraNeedsTotal': 'Доп.потребность: Всего', 'ASISDS': 'Data Scientist', \\\n                          'extraNeedsDS': 'Доп.потребность: Data Scientist', 'ASISDE': 'Data Engineer',\n                          'extraNeedsDE': 'Доп.потребность: Data Engineer', 'ASISMarkers': 'Разметчики',\n                          'extraNeedsMarkers': 'Доп.потребность: Разметчики', 'ASISOthers': 'Остальные', \\\n                          'extraNeedsOthers': 'Доп.потребность: Остальные', 'BASA': 'BA/SA', 'POTL': 'PO/TL',\n                          'developer': 'Разработчик', 'DA': 'Data Analytic', \\\n                          'dataOps': 'DataOps', 'architect': 'Архитектура', 'UXUIDesigner': 'UX/UI Дизайнер'}\n        requiredFields = ['block', 'subdivision', 'highLevelTarget', 'purpose', 'platform', 'ASISTotal',\n                          'extraNeedsTotal']\n        # print('df',df)\n        incr = 0\n        return jsonify(\n            table=json.loads(df.to_json(orient=\"records\")),\n            columns=[{\"title\": accordanceDict[str(col)], 'data': str(col), 'main': (str(col) in requiredFields)} for col\n                     in json.loads(df.to_json(orient=\"split\"))[\"columns\"]])\n    else:\n        return render_template('trends/initiatives.html')\n\n\n@trends_app.route('/initiativesChange', methods=['GET', 'POST'])\ndef initiativesChange():\n    if request.method == 'POST':\n        getForm = json.loads(request.form['data'])\n        changed_row = getForm['changed_row']\n        accept_change = getForm['accept_change']\n        print('changed_row', changed_row)\n\n        df_block = pd.read_sql('''\n        SELECT KOD_Block\n        FROM \n        [New_Structure].[dbo].[KOD_Block]\n        WHERE Block = '%s'\n        ''' % (changed_row['block']), engine)\n\n        print('df_block', df_block['KOD_Block'].values[0])\n        accordanceDict = {'id': 'id', 'block': 'block', 'subdivision': 'Подразделение',\n                          'highLevelTarget': 'Высокоуровневая цель', 'effectP&L': 'Эффет(P&L ежегодно) ИТОГОВАЯ  СУММА', \\\n                          'effectPSHE': 'Эффект (сокр. ПШЕ)', 'effectKC': 'Эффект (снижение нагрузки КЦ)',\n                          'purpose': 'Цель', \\\n                          'curPurposes2019': 'Текущая формулировка целей 2019',\n                          'newAmbitions': 'Новая амбиция под запрашиваемые доп. Ресурсы', 'includeBusiness': \\\n                              'Учтено в Бизнес-плане', 'effect2019': '2019(эффект)', 'effect2020': '2020 (эффект)', \\\n                          'effect2021': '2021(эффект)', 'year': 'год', 'platform': 'Платформа/ бизнес-сервис',\n                          'needsPlatfotmSelect': 'Требуемые платформы (выбрать из списка)', \\\n                          'ASISTotal': 'AS IS: Всего', 'extraNeedsTotal': 'Доп.потребность: Всего',\n                          'ASISDS': 'AS IS: DS', \\\n                          'extraNeedsDS': 'Доп.потребность: DS', 'ASISDE': 'AS IS: DE',\n                          'extraNeedsDE': 'Доп.потребность: DE', 'ASISMarkers': 'AS IS: разметчики',\n                          'extraNeedsMarkers': 'Доп.потребность: разметчики', 'ASISOthers': 'AS IS: другие профили', \\\n                          'extraNeedsOthers': 'Доп.потребность: другие профили', 'BASA': 'BA/SA', 'POTL': 'PO/TL',\n                          'developer': 'Developer', 'DA': 'DA', \\\n                          'dataOps': 'Data ops', 'architect': 'Architect', 'UXUIDesigner': 'UX/UI дизайнер'}\n\n        df_data = []\n        newRow = {}\n        for k in changed_row:\n            newRow[accordanceDict[k]] = str(changed_row[k])\n\n        del newRow['block']\n        newRow['KOD_Block'] = df_block['KOD_Block'].values[0]\n        newRow['report_dt'] = datetime.datetime.now()\n        df_data.append(newRow)\n\n        '''df_update = pd.DataFrame(data=df_data, columns=['id','KOD_Block','Подразделение','Высокоуровневая цель','Эффет(P&L ежегодно) ИТОГОВАЯ  СУММА',\\\n            'Эффект (сокр. ПШЕ)','Эффект (снижение нагрузки КЦ)','Цель','Текущая формулировка целей 2019','Новая амбиция под запрашиваемые доп. Ресурсы',\\\n            'Учтено в Бизнес-плане','2019(эффект)','2020 (эффект)','2021(эффект)','год','Платформа/ бизнес-сервис','Требуемые платформы (выбрать из списка)',\\\n           'AS IS: Всего','Доп.потребность: Всего','AS IS: DS','Доп.потребность: DS','AS IS: DE','Доп.потребность: DE','AS IS: разметчики','Доп.потребность: разметчики',\\\n            'AS IS: другие профили','Доп.потребность: другие профили','BA/SA','PO/TL','Developer','DA','Data ops','Architect','UX/UI дизайнер','report_dt'])\n        '''\n        df_update = pd.DataFrame(data=df_data)\n        print('df_update', df_update, df_update['KOD_Block'], df_update['report_dt'], df_update['id'],\n              df_update.columns)\n        df_update.to_sql((TABLE_CHANGES_INITIATIVES if accept_change else TABLE_CHANGES_INITIATIVES_TEMP),\n                         schema='New_Structure.dbo', con=engine, if_exists='append', index=False)\n        return jsonify(status=200)\n","sub_path":"trends/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":22462,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"515169410","text":"import logging\nfrom argparse import ArgumentParser\nfrom datetime import datetime\nfrom logging import Logger\nfrom pathlib import Path\nfrom typing import Any, Mapping, Optional, Union\n\nimport ignite.distributed as idist\nimport torch\nimport yaml\nfrom ignite.contrib.engines import common\nfrom ignite.engine import Engine\nfrom ignite.engine.events import Events\nfrom ignite.handlers import Checkpoint, DiskSaver, global_step_from_engine\nfrom ignite.handlers.early_stopping import EarlyStopping\nfrom ignite.handlers.terminate_on_nan import TerminateOnNan\nfrom ignite.handlers.time_limit import TimeLimit\nfrom ignite.utils import setup_logger\n\n\ndef setup_parser():\n    with open(\"config.yaml\", \"r\") as f:\n        config = yaml.safe_load(f.read())\n\n    parser = ArgumentParser()\n    parser.add_argument(\"--backend\", default=None, type=str)\n    for k, v in config.items():\n        if isinstance(v, bool):\n            parser.add_argument(f\"--{k}\", action=\"store_true\")\n        else:\n            parser.add_argument(f\"--{k}\", default=v, type=type(v))\n\n    return parser\n\n\ndef log_metrics(engine: Engine, tag: str) -> None:\n    \"\"\"Log `engine.state.metrics` with given `engine` and `tag`.\n\n    Parameters\n    ----------\n    engine\n        instance of `Engine` which metrics to log.\n    tag\n        a string to add at the start of output.\n    \"\"\"\n    metrics_format = \"{0} [{1}/{2}]: {3}\".format(\n        tag, engine.state.epoch, engine.state.iteration, engine.state.metrics\n    )\n    engine.logger.info(metrics_format)\n\n\ndef resume_from(\n    to_load: Mapping,\n    checkpoint_fp: Union[str, Path],\n    logger: Logger,\n    strict: bool = True,\n    model_dir: Optional[str] = None,\n) -> None:\n    \"\"\"Loads state dict from a checkpoint file to resume the training.\n\n    Parameters\n    ----------\n    to_load\n        a dictionary with objects, e.g. {“model”: model, “optimizer”: optimizer, ...}\n    checkpoint_fp\n        path to the checkpoint file\n    logger\n        to log info about resuming from a checkpoint\n    strict\n        whether to strictly enforce that the keys in `state_dict` match the keys\n        returned by this module’s `state_dict()` function. Default: True\n    model_dir\n        directory in which to save the object\n    \"\"\"\n    if isinstance(checkpoint_fp, str) and checkpoint_fp.startswith(\"https://\"):\n        checkpoint = torch.hub.load_state_dict_from_url(\n            checkpoint_fp,\n            model_dir=model_dir,\n            map_location=\"cpu\",\n            check_hash=True,\n        )\n    else:\n        if isinstance(checkpoint_fp, str):\n            checkpoint_fp = Path(checkpoint_fp)\n\n        if not checkpoint_fp.exists():\n            raise FileNotFoundError(\n                f\"Given {str(checkpoint_fp)} does not exist.\"\n            )\n        checkpoint = torch.load(checkpoint_fp, map_location=\"cpu\")\n\n    Checkpoint.load_objects(\n        to_load=to_load, checkpoint=checkpoint, strict=strict\n    )\n    logger.info(\"Successfully resumed from a checkpoint: %s\", checkpoint_fp)\n\n\ndef setup_output_dir(config: Any, rank: int) -> Path:\n    \"\"\"Create output folder.\"\"\"\n    if rank == 0:\n        now = datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n        name = f\"{now}-backend-{config.backend}-lr-{config.lr}\"\n        path = Path(config.output_dir, name)\n        path.mkdir(parents=True, exist_ok=True)\n        config.output_dir = path.as_posix()\n\n    return Path(idist.broadcast(config.output_dir, src=0))\n\n\ndef setup_logging(config: Any) -> Logger:\n    \"\"\"Setup logger with `ignite.utils.setup_logger()`.\n\n    Parameters\n    ----------\n    config\n        config object. config has to contain `verbose` and `output_dir` attribute.\n\n    Returns\n    -------\n    logger\n        an instance of `Logger`\n    \"\"\"\n    green = \"\\033[32m\"\n    reset = \"\\033[0m\"\n    logger = setup_logger(\n        name=f\"{green}[ignite]{reset}\",\n        level=logging.DEBUG if config.debug else logging.INFO,\n        format=\"%(name)s: %(message)s\",\n        filepath=config.output_dir / \"training-info.log\",\n    )\n    return logger\n\n\n#::: if (it.save_training || it.save_evaluation || it.patience || it.terminate_on_nan || it.limit_sec) { :::#\n\n\ndef setup_handlers(\n    trainer: Engine,\n    evaluator: Engine,\n    config: Any,\n    to_save_train: Optional[dict] = None,\n    to_save_eval: Optional[dict] = None,\n):\n    \"\"\"Setup Ignite handlers.\"\"\"\n\n    ckpt_handler_train = ckpt_handler_eval = None\n    #::: if (it.save_training || it.save_evaluation) { :::#\n    # checkpointing\n    saver = DiskSaver(config.output_dir / \"checkpoints\", require_empty=False)\n    #::: if (it.save_training) { :::#\n    ckpt_handler_train = Checkpoint(\n        to_save_train,\n        saver,\n        filename_prefix=config.filename_prefix,\n        n_saved=config.n_saved,\n    )\n    trainer.add_event_handler(\n        Events.ITERATION_COMPLETED(every=config.save_every_iters),\n        ckpt_handler_train,\n    )\n    #::: } :::#\n    #::: if (it.save_evaluation) { :::#\n    global_step_transform = None\n    if to_save_train.get(\"trainer\", None) is not None:\n        global_step_transform = global_step_from_engine(\n            to_save_train[\"trainer\"]\n        )\n    ckpt_handler_eval = Checkpoint(\n        to_save_eval,\n        saver,\n        filename_prefix=\"best\",\n        n_saved=config.n_saved,\n        global_step_transform=global_step_transform,\n        score_name=\"eval_accuracy\",\n        score_function=Checkpoint.get_default_score_fn(\"eval_accuracy\"),\n    )\n    evaluator.add_event_handler(\n        Events.EPOCH_COMPLETED(every=1), ckpt_handler_eval\n    )\n    #::: } :::#\n    #::: } :::#\n\n    #::: if (it.patience) { :::#\n    # early stopping\n    def score_fn(engine: Engine):\n        return -engine.state.metrics[\"eval_loss\"]\n\n    es = EarlyStopping(config.patience, score_fn, trainer)\n    evaluator.add_event_handler(Events.EPOCH_COMPLETED, es)\n    #::: } :::#\n\n    #::: if (it.terminate_on_nan) { :::#\n    # terminate on nan\n    trainer.add_event_handler(Events.ITERATION_COMPLETED, TerminateOnNan())\n    #::: } :::#\n\n    #::: if (it.limit_sec) { :::#\n    # time limit\n    trainer.add_event_handler(\n        Events.ITERATION_COMPLETED, TimeLimit(config.limit_sec)\n    )\n    #::: } :::#\n    #::: if (it.save_training || it.save_evaluation) { :::#\n    return ckpt_handler_train, ckpt_handler_eval\n    #::: } :::#\n\n\n#::: } :::#\n\n#::: if (it.logger) { :::#\n\n\ndef setup_exp_logging(config, trainer, optimizers, evaluators):\n    \"\"\"Setup Experiment Tracking logger from Ignite.\"\"\"\n\n    #::: if (it.logger === 'clearml') { :::#\n    logger = common.setup_clearml_logging(\n        trainer, optimizers, evaluators, config.log_every_iters\n    )\n    #::: } else if (it.logger === 'mlflow') { :::#\n    logger = common.setup_mlflow_logging(\n        trainer, optimizers, evaluators, config.log_every_iters\n    )\n    #::: } else if (it.logger === 'neptune') { :::#\n    logger = common.setup_neptune_logging(\n        trainer, optimizers, evaluators, config.log_every_iters\n    )\n    #::: } else if (it.logger === 'polyaxon') { :::#\n    logger = common.setup_plx_logging(\n        trainer, optimizers, evaluators, config.log_every_iters\n    )\n    #::: } else if (it.logger === 'tensorboard') { :::#\n    logger = common.setup_tb_logging(\n        config.output_dir,\n        trainer,\n        optimizers,\n        evaluators,\n        config.log_every_iters,\n    )\n    #::: } else if (it.logger === 'visdom') { :::#\n    logger = common.setup_visdom_logging(\n        trainer, optimizers, evaluators, config.log_every_iters\n    )\n    #::: } else if (it.logger === 'wandb') { :::#\n    logger = common.setup_wandb_logging(\n        trainer, optimizers, evaluators, config.log_every_iters\n    )\n    #::: } :::#\n    return logger\n\n\n#::: } :::#\n","sub_path":"src/templates/template-vision-classification/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":7688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"305385124","text":"import pandas as pd\nimport numpy as np\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import average_precision_score\nfrom sklearn import svm, metrics\nfrom sklearn.model_selection import StratifiedShuffleSplit\nfrom sklearn.model_selection import GridSearchCV\nimport matplotlib.pyplot as plt\nfrom sklearn.externals import joblib\nfrom xgboost import XGBClassifier\nfrom sklearn.preprocessing import LabelEncoder\n\n\ndef train_xgboost_models(yTrain, xTrain, yTest, xTest):\n    '''\n    Evaluates the best number of neighbors on KNN model.\n    yTrain: target train\n    xTrain: features train\n    yTest: target test\n    xTest: features test\n    '''\n    for i in range(500):\n\n        modelX = XGBClassifier(max_depth=10,\n                               learning_rate=0.05,\n                               n_estimators=i)\n        modelX.fit(xTrain, yTrain)\n\n        # Make predictions for test data.\n        y_pred = modelX.predict(xTest)\n        predictions = [round(value) for value in y_pred]\n\n        # Evaluate predictions.\n        accuracy = average_precision_score(yTest, predictions)\n        print(\"Parameters:: (\"\n              + \"n_estimators: \"\n              + str(i)\n              + \". Average_precision_score:: \"\n              + str(accuracy))\n\n\ndef train_KNN_models(yTrain, xTrain, yTest, xTest):\n    '''\n    Evaluates the best number of neighbors on KNN model.\n    yTrain: target train\n    xTrain: features train\n    yTest: target test\n    xTest: features test\n    '''\n    number_of_neighbors = len(yTrain)\n    for i in range(number_of_neighbors):\n        classifier = KNeighborsClassifier(n_neighbors=i + 1,\n                                          weights='distance')\n        classifier.fit(xTrain, yTrain)\n        # predicted = classifier.predict_proba(xTest)\n        predicted = classifier.predict(xTest)\n\n        # Calculates precision.\n        avrPrec = average_precision_score(yTest, predicted)\n        print(\"Parameters:: (\"\n              + \"number_of_neighbors: \"\n              + str(i)\n              + \". Average_precision_score:: \"\n              + str(avrPrec))\n\n\ndef train_SVM_models(yTrain, xTrain, yTest, xTest):\n    '''\n    Evaluates the best parameter gamma and C on SVM model.\n    yTrain: target train\n    xTrain: features train\n    yTest: target test\n    xTest: features test\n    '''\n    c_range = np.logspace(-2, 10, 18)\n    gamma_range = np.logspace(-12, 3, 13)\n    param_grid = dict(gamma=gamma_range, C=c_range)\n    print(param_grid)\n    cv = StratifiedShuffleSplit(n_splits=5, test_size=0.2, random_state=42)\n    grid = GridSearchCV(svm.SVC(), param_grid=param_grid, cv=cv)\n    grid.fit(xTrain, yTrain)\n    c_range_eval = [1e-1, 10, 1e1]\n    gamma_range_eval = [1e-1, 10, 1e1]\n    print(\"cv\")\n    print(cv)\n    classifiers = []\n    for C in c_range_eval:\n        print(C)\n        for gamma in gamma_range_eval:\n            clf = svm.SVC(C=C, gamma=gamma)\n            clf.fit(xTrain, yTrain)\n            classifiers.append((C, gamma, clf))\n\n    scores = grid.cv_results_['mean_test_score'].reshape(len(c_range),\n                                                         len(gamma_range))\n\n    # Plot parameters and score matrix.\n    plt.figure(figsize=(8, 6))\n    plt.subplots_adjust(left=.2, right=0.95, bottom=0.15, top=0.95)\n    plt.imshow(scores,\n               interpolation='nearest',\n               cmap=plt.cm.get_cmap('Blues', 6))\n    plt.xlabel('gamma')\n    plt.ylabel('C')\n    plt.colorbar()\n    plt.xticks(np.arange(len(gamma_range)), gamma_range, rotation=45)\n    plt.yticks(np.arange(len(c_range)), c_range)\n    plt.title('Validation accuracy')\n    plt.show()\n    # plt.savefig('SVM_evaluation_parameters.png')\n\n    # Save model with best paramater\n    classifier = svm.SVC(C=1e16, gamma=1e-11, kernel='rbf', probability=True)\n    classifier.fit(xTrain, yTrain)\n    joblib.dump(classifier,\n                'SVM_C_' +\n                str(1e16) +\n                '_gamma_' +\n                str(1e-11) +\n                '.pkl')\n\n\ndef train_ANN_models(yTrain, xTrain, yTest, xTest):\n    '''\n    Obtain best ANN models from comparing the highest precision.\n    It is evaluate the modification through change the number\n    of neurons on first hidden layer.\n    Select model with precision highrer than 85%.\n    Source:\n        Graber, Mark L. \"The incidence of diagnostic error in medicine.\"\n        BMJ Qual Saf 22.Suppl 2 (2013): ii21-ii27.\n    yTrain: target train\n    xTrain: features train\n    yTest: target test\n    xTest: features test\n    '''\n\n    # Simulation range parameters (first hidden layer).\n    parameters = np.linspace(1, 10000, 10000)\n\n    for i in range(len(parameters)):\n\n        classifier = MLPClassifier(hidden_layer_sizes=(i+1, 100, 500, 50),\n                                   max_iter=500, alpha=1e-4,\n                                   solver='lbfgs', verbose=0,\n                                   tol=1e-4, random_state=1,\n                                   learning_rate_init=.1)\n\n        classifier.fit(xTrain, yTrain)\n        predicted = classifier.predict_proba(xTest)\n\n        # Calculates precision.\n        avrPrec = average_precision_score(yTest, predicted[:, 1])\n        print(\"Parameters:: (\"\n              + str(i + 1)\n              + \", 100, 500, 50). Average_precision_score:: \"\n              + str(avrPrec))\n\n        # Filter condition.\n        if (100 * avrPrec > 80):\n            joblib.dump(classifier, 'ANN_nlayer_' + str(i) + '.pkl')\n\n\ndef substrings_in_string(big_string, substrings):\n    '''\n    Segments the input string based on a dictionary.\n    big_string: string applied\n    substrings: dictionary to be found\n    '''\n    for substring in substrings:\n        if big_string.find(substring) != -1:\n            return substring\n    print(big_string)\n\n    return np.nan\n\n\ndef replace_titles(x):\n    '''\n    Replacing all titles with mr, mrs, miss, master.\n    x: title input\n    '''\n    title = x['Title']\n    if title in ['Don', 'Major', 'Capt', 'Jonkheer', 'Rev', 'Col']:\n        return 'Mr'\n    elif title in ['Countess', 'Mme']:\n        return 'Mrs'\n    elif title in ['Mlle', 'Ms']:\n        return 'Miss'\n    elif title == 'Dr':\n        if x['Sex'] == 'Male':\n            return 'Mr'\n        else:\n            return 'Mrs'\n    else:\n        return title\n\n\ndef phase1clean(df):\n    '''\n    Feature enginering extraction. Get from:\n    https://triangleinequality.wordpress.com/2013/09/08/basic-feature-engineering-with-the-titanic-data/\n    df: train or test set of data\n    '''\n    # Setting silly values to nan.\n    df.Fare = df.Fare.map(lambda x: np.nan if x == 0 else x)\n\n    # Special case for cabins as nan may be signal.\n    df.Cabin = df.Cabin.fillna('Unknown')\n\n    # Creating a title column from name.\n    title_list = ['Mrs', 'Mr', 'Master', 'Miss', 'Major', 'Rev',\n                  'Dr', 'Ms', 'Mlle', 'Col', 'Capt', 'Mme', 'Countess',\n                  'Don', 'Jonkheer']\n\n    df['Title'] = df['Name'].map(lambda x: substrings_in_string(x, title_list))\n\n    # Replacing all titles with mr, mrs, miss, master.\n    df['Title'] = df.apply(replace_titles, axis=1)\n\n    # Turning cabin number into Deck.\n    cabin_list = ['A', 'B', 'C', 'D', 'E', 'F', 'T', 'G', 'Unknown']\n    df['Deck'] = df['Cabin'].map(lambda x: substrings_in_string(x, cabin_list))\n\n    # Creating new family_size column.\n    df['Family_Size'] = df['SibSp'] + df['Parch']\n\n    return df\n\n\ndef return_new_features(train_data):\n    '''\n    Return features used during train process.\n    train_data: Train data\n    Output:     New features\n    '''\n    new_features = phase1clean(train_data)\n\n    return new_features\n\n\ndef import_raw_data():\n    '''\n    Get file and export raw data.\n    Output: Test and train data\n    '''\n    trainpath = '~/Documents/GitRepository/TitanicRepo/titanic/train.csv'\n    testpath = '~/Documents/GitRepository/TitanicRepo/titanic/test.csv'\n    train_data = pd.read_csv(trainpath)\n    test_data = pd.read_csv(testpath)\n\n    return[train_data, test_data]\n\n\ndef main():\n    '''\n    Main function\n    '''\n    # First: load raw data.\n    train_data, test_data = import_raw_data()\n\n    # Get features.\n    new_features = return_new_features(train_data)\n\n    # Apply one-hoted encode for each string column.\n    classes = LabelEncoder().fit(new_features['Sex'])\n    classes_transformed = classes.transform(new_features['Sex'])\n    new_features['SexLabel'] = classes_transformed\n\n    classes = LabelEncoder().fit(new_features['Deck'])\n    classes_transformed = classes.transform(new_features['Deck'])\n    new_features['DeckLabel'] = classes_transformed\n\n    classes = LabelEncoder().fit(new_features['Title'])\n    classes_transformed = classes.transform(new_features['Title'])\n    new_features['TitleLabel'] = classes_transformed\n\n    classes = LabelEncoder().fit(new_features['Cabin'])\n    classes_transformed = classes.transform(new_features['Cabin'])\n    # new_features['CabinLabel'] = classes_transformed\n\n    # Create vector of input and targets samples.\n    input_samples = new_features.drop(columns=['Cabin',\n                                               'Deck',\n                                               'Embarked',\n                                               'Name',\n                                               'Sex',\n                                               'Survived',\n                                               'Ticket',\n                                               'Title'])\n\n    # Target vector.\n    target_samples_tmp = new_features['Survived']\n\n    # Eliminates nan numbers (change to zero).\n    input_samples = input_samples.fillna(0)\n\n    # Normalize data.\n    scaler = StandardScaler()\n    scaler.fit(input_samples)\n    input_samples_normalized = scaler.transform(input_samples)\n\n    # Split dataset into train and test data.\n    X_train, X_test, y_train, y_test = train_test_split(input_samples_normalized,\n                                                        target_samples_tmp,\n                                                        test_size=0.2,\n                                                        random_state=42)\n                                                        # stratify=y)\n\n    # Get best hiperparameters of SVM model.\n    train_KNN_models(y_train.values, X_train, y_test.values, X_test)\n    train_xgboost_models(y_train.values, X_train, y_test.values, X_test)\n    train_ANN_models(y_train.values, X_train, y_test.values, X_test)\n    train_SVM_models(y_train.values, X_train, y_test.values, X_test)\n\n\nif __name__ == '__main__':\n    '''\n    Main function\n    '''\n    main()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":10702,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"455802896","text":"import requests\r\nfrom bs4 import BeautifulSoup\r\nimport urllib.parse\r\nfrom urllib.parse import urlparse\r\nimport re\r\n\r\n#function which take user question as input string,makes google search and returns a list of links appeared n the first page\r\n\r\ndata=[]\r\n\r\ndef search_question(question):\r\n    \r\n    URL = 'https://google.com/search?q='+question\r\n    page = requests.get(URL)\r\n    soup = BeautifulSoup(page.content, 'html.parser')\r\n    a = soup.find_all('a')\r\n    g_clean = []\r\n    for i in a:\r\n        k = i.get('href')\r\n        pattern = \"/url\\?q=\"\r\n        m = re.match(pattern,k)\r\n       # print(m)\r\n        if(m == None):\r\n            continue;\r\n           # print(m)\r\n        n = m.group(0)\r\n        #print(\"here\")\r\n        temp = k[len(n):]\r\n        #print(temp)\r\n        rul = temp.split('&')[0]\r\n       # print(rul)\r\n        domain = urlparse(rul)\r\n        if(re.search('google.com', domain.netloc)):\r\n            continue\r\n        else:\r\n            g_clean.append(rul)\r\n            \r\n    return g_clean\r\n    \r\n\r\n    \r\ndef open_link_and_extract(link):\r\n    page = requests.get(link)\r\n    soup = BeautifulSoup(page.content, 'html.parser')\r\n    x=soup.find_all('p')\r\n    for i in range (len(x)):\r\n        data.append(soup.find_all('p')[i].get_text())\r\n\r\n    \r\n\r\ndef get_all_data():\r\n    all_links=search_question(\"Covid19 cases in India\")\r\n    for i in range (len(all_links)):\r\n        open_link_and_extract(all_links[i])\r\n\r\n\r\nget_all_data()\r\nprint(data)","sub_path":"flask app/searchQuestion.py","file_name":"searchQuestion.py","file_ext":"py","file_size_in_byte":1460,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"84915130","text":"from ANN import ANN \nimport math\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib import rcParams\nrcParams.update({'figure.autolayout': True})\nimport numpy as np\n\ndef f (x):\n    return [x[0]**2]\n\ndef g (x):\n    return [math.sin(1.5*math.pi *x[0])]\n\ndef h(entrada):    \n    if ((entrada[0]**2 +entrada[1]**2) <0.5):\n        return [1]\n    return [0]\n\npuntos =np.array( sorted([ [(n*(0.05))*x for x in range(1,2) ] for n in range(-20,20)]))\npuntosEx =np.array( ([[ 1, (n*(0.05))*x] for x in range(1,2)  for n in range(-20,20)]))\ndoble= np.array ([ [x, y ]for x in (0.1,0.3,0.5,0.7,0.9) for y in (0.2,0.4,0.6,0.8)])\ndobleEx= np.array ([ [1,x, y ]for x in (0.1,0.3,0.5,0.7,0.9) for y in (0.2,0.4,0.6,0.8)])\n\n\nsol_f = np.array(list(map(f,puntos)))\nsol_g= np.array(list(map(g,puntos)))\nsol_h= np.array(list(map(h,doble)))\n\n\n#Configuracion f\nsizeoculta = 5\nentradas=1\nsalidas=1\nni=0.1\nk = 7\nalfa = 0.1\ntang = 0\nmax_It=10000\n\n#a = ANN(puntos,puntosEx ,sol_f ,sizeoculta,entradas,salidas,ni,k,alfa,tang,max_It)\n#a.entrenar()\n\n#Configuracion g\nsizeoculta = 3\nentradas=1\nsalidas=1\nni=0.05\nk = 1\nalfa = 0.1\ntang = 1\nmax_It=100000\n#b = ANN(puntos,puntosEx ,sol_g ,sizeoculta,entradas,salidas,ni,k,alfa,tang,max_It)\n#b.entrenar()\n\n#Configuracion h\nsizeoculta = 3\nentradas=2\nsalidas=1\nni=0.1\nk = 1\nalfa = 0.1\ntang = 0\nmax_It=100000\nc = ANN(doble,dobleEx ,sol_h, sizeoculta,entradas,salidas,ni,k,alfa,tang,max_It)\nc.entrenar()","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":1449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"419713154","text":"# -*- coding: utf-8 -*-\nimport scrapy\nimport time\nimport json\nimport pymongo\nimport pandas as pd\nimport datetime\nimport re\n\nfrom carbuisness_new.items import XiaoZhuItem\n\nsettings = {\n    \"MONGODB_SERVER\": \"192.168.1.94\",\n    \"MONGODB_PORT\": 27017,\n    \"MONGODB_DB\": \"residual_value\",\n    \"MONGODB_COLLECTION\": \"xiaozhu_modellist\",\n}\nuri = f'mongodb://{settings[\"MONGODB_SERVER\"]}:{settings[\"MONGODB_PORT\"]}/'\n\nconnection = pymongo.MongoClient(uri)\ndb = connection[settings['MONGODB_DB']]\ncollection = db[settings['MONGODB_COLLECTION']]\n\n\nclass XiaozhuGzSpider(scrapy.Spider):\n    name = 'xiaozhu_gz'\n\n    # allowed_domains = ['xiaozhu2.com']\n\n    # start_urls = ['http://xiaozhu2.com/']\n\n    @classmethod\n    def update_settings(cls, settings):\n        settings.setdict(\n            getattr(cls, 'custom_debug_settings' if getattr(cls, 'is_debug', False) else 'custom_settings', None) or {},\n            priority='spider')\n\n    def __init__(self, **kwargs):\n        super(XiaozhuGzSpider, self).__init__(**kwargs)\n        self.counts = 0\n        self.city_list = [\"beijing\", \"tianjin\", \"shijiazhuang\", \"taiyuan\", \"huhehaote\", \"shanghai\", \"qingdao\", \"suzhou\",\n                          \"hangzhou\", \"hefei\", \"shenzhen\", \"fuzhou\", \"nanning\", \"haikou\", \"zhengzhou\", \"wuhan\",\n                          \"changsha\", \"nanchang\", \"shenyang\", \"haerbin\", \"changchun\", \"chongqing\", \"chengdu\", \"kunming\",\n                          \"guiyang\", \"lasa\", \"xian\", \"wulumuqi\", \"lanzhou\", \"yinchuan\", \"xining\"]\n        data = pd.DataFrame(list(collection.find()))\n        data = data[data[\"output\"].notnull()]\n        self.data = data.loc[:, [\"model_id\", \"year\"]]\n        self.data[\"year\"].astype('int')\n        self.now_year = datetime.datetime.now().year\n        now_month = datetime.datetime.now().month\n        self.now_month = f\"0{str(now_month)}\" if now_month < 10 else now_month\n        self.headers = {\n            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36',\n        }\n\n    is_debug = True\n    custom_debug_settings = {\n        'MYSQL_SERVER': '192.168.1.94',\n        'MYSQL_DB': 'residual_value',\n        'MYSQL_TABLE': 'xiaozhu_gz',\n        'MONGODB_SERVER': '192.168.1.94',\n        'MONGODB_DB': 'residual_value',\n        'MONGODB_COLLECTION': 'xiaozhu_gz',\n        'CrawlCar_Num': 800000,\n        'CONCURRENT_REQUESTS': 8,\n        'DOWNLOAD_DELAY': 0,\n        'LOG_LEVEL': 'DEBUG',\n        # 'SCHEDULER': '',\n        # 'DUPEFILTER_CLASS': '',\n        # 'REDIS_URL': ''\n\n    }\n\n    def start_requests(self):\n        url = \"https://www.baidu.com/\"\n        yield scrapy.Request(\n            url=url,\n        )\n\n    def parse(self, response):\n        for index, rows in self.data.iterrows():\n            if rows[\"year\"] < self.now_year:\n                year_list = [i for i in range(rows[\"year\"], self.now_year + 1)]\n                year_dic = {year: (self.now_year - year) * 20000 for year in year_list}\n                for k, v in year_dic.items():\n                    if v == 0:\n                        v = 1000\n                    for city in self.city_list:\n                        url = f\"https://www.xiaozhu2.com/appraisal/w{city}-x{rows['model_id']}-y{k}{self.now_month}-z{v}.html\"\n                        if k == 2020:\n                            print(url)\n                        yield scrapy.Request(\n                            url=url,\n                            callback=self.parse_detail,\n                            headers=self.headers,\n                            dont_filter=True\n                        )\n\n    def parse_detail(self, response):\n        item = XiaoZhuItem()\n        price_list = response.xpath(\"//div[@class='holder']/span/text()\").getall()\n        tag_list = response.xpath(\"//div[@class='content-tab']/ul/li/text()\").getall()\n        data_dict = dict()\n        for tag in tag_list:\n            price = [price_list[i:i + 3] for i in range(0, len(price_list), 3)]\n            data_dict[tag] = price[tag_list.index(tag)]\n\n        item[\"prices\"] = json.dumps(data_dict, ensure_ascii=False)\n        item[\"url\"] = response.url\n        item[\"model_id\"] = re.findall('-x(.*?)-y', response.url)[0]\n        info = response.xpath(\"//p[@class='price']/following-sibling::p/text()\").get().replace(' ', '').replace('\\xa0',\n                                                                                                                '')\n        info_list = info.split('|')\n        item[\"city\"] = info_list[0]\n        item[\"registerdate\"] = info_list[1]\n        item[\"mile\"] = info_list[2]\n        item[\"desc\"] = response.xpath(\"//h3[@class='name']/text()\").get()\n        item[\"status\"] = response.url + \"-\" + str(datetime.datetime.now().year) + \"-\" + str(\n            datetime.datetime.now().month)\n        item[\"grabtime\"] = time.strftime('%Y-%m-%d %X', time.localtime())\n        yield item\n        # print(item)\n","sub_path":"cagey/carbuisness_new/carbuisness_new/spiders/xiaozhu_gz.py","file_name":"xiaozhu_gz.py","file_ext":"py","file_size_in_byte":4912,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"131108112","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue May 17 10:17:51 2016\n\n@author: Li Gong\n\n@description:这是用jieba分词来分词的\n\"\"\"\nfrom __future__ import unicode_literals\nfrom WordCut import WordCut\nimport jieba\nimport jieba.posseg as pseg\nclass Jieba(WordCut):\n    def __init__(self):\n        super(Jieba,self).__init__()\n        self.name = 'jieba'\n    \n    def cut(self,content):\n        '''这是用来分词的函数，返回的是词'''\n        return jieba.cut(content,cut_all = True)\n    \n    def nominal_cut(self,content):\n        '''这带有词性标注的分词函数'''\n        return map(lambda x:(x.word,x.flag),pseg.cut(content))\n    \n    def load_user_dict(self,user_dict):\n        '''加载用户词典'''\n        jieba.load_userdict(user_dict)\n    \n\nif __name__ == '__main__':\n    jb = Jieba()\n    r = set()\n    with open(r'../../../BabySpyder/nobaby.txt','r') as inf:\n        line = inf.readline()\n        while line:\n            words = jb.cut(line.strip().decode('gbk','ignore'))\n            words = filter(lambda x:len(x.strip()) > 0,words)\n            if len(words) > 3:\n                r.add((' '.join(words)+'\\n').encode('utf8'))\n            line = inf.readline()\n            \n    r = list(r)\n    with open(r'b.txt','w') as outf:\n        for l in r:\n            outf.write(l)\n    \n        \n","sub_path":"Husky/Segment/WordCutter/Jieba.py","file_name":"Jieba.py","file_ext":"py","file_size_in_byte":1318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"493782891","text":"# -*- coding: utf-8 -*-\nfrom django.shortcuts import render, redirect, HttpResponse\nfrom django.contrib.auth import login, authenticate, logout\nfrom django.contrib.auth.models import User, Group\nfrom testapp.models import Device, UserProfile\nfrom django.http import Http404\nfrom django.contrib.auth.decorators import login_required\nfrom django.core.context_processors import csrf\nfrom testapp.forms import UserCreateForm,  UserDeleteForm, DeviceCreateForm, DeviceDeleteForm, CreateNumberBank\n\n\ndef home(request):\n    if request.user.is_authenticated(): \n        return manage(request, error_message = None)  \n    return render(request, 'home.html')  \n\n@login_required\ndef manage(request, error_message = None):\n    if request.user.has_perm('auth.add_user'):\n        device_form = DeviceCreateForm()\n        delete_device_form = DeviceDeleteForm()\n        user_form = UserCreateForm()\n        delete_user_form = UserDeleteForm()\n        bank_form = CreateNumberBank()\n        clients =  Device.objects.all()\n        context = {'clients': clients, 'user_form': user_form, 'device_form': device_form, \n        'delete_user_form': delete_user_form, 'delete_device_form': delete_device_form,\n        'bank_form': bank_form, 'error_message': error_message\n        }\n        return render(request, 'manage.html', context)\n    else:\n        clients =  Device.objects.filter(client = request.user)\n        context = {'clients': clients}\n        return render(request, 'client.html', context)\n\ndef logout_view(request):\n    logout(request)\n    return redirect('/')\n\n@login_required\ndef signup(request):\n    user_form = UserCreateForm(data=request.POST)\n    if request.method == 'POST':\n        if user_form.is_valid():\n            username = user_form.clean_username()\n            password = user_form.clean_password2()\n            user_form.save()\n            g = Group.objects.get(name ='Clients') \n            user = User.objects.get(username = username) \n            return redirect('/manage')\n        else:\n            user_form = UserCreateForm()\n            return manage(request, error_message = 'Ошибка в форме')\n    \n\n\n\n#Удаляет пользователя\n@login_required\ndef delete_user(request):\n    delete_user_form = UserDeleteForm(data=request.POST)\n    if request.method == 'POST':\n        if delete_user_form.is_valid():\n            username = delete_user_form.cleaned_data.get('username')\n            User.objects.filter(username=username).delete()\n            return redirect('/manage')\n        else:\n            delete_user_form = UserDeleteForm()\n            return manage(request, error_message = 'Ошибка в форме')\n    \n\n#Добавляет устройство\n#Добавить полей\n@login_required\ndef add_device(request):\n    device_form = DeviceCreateForm(data=request.POST)\n\n    if request.method == 'POST':\n        if device_form.is_valid():\n            client = device_form.cleaned_data.get('client')\n            name_device  = device_form.cleaned_data.get('name_device')\n            device_form.save()\n            return redirect('/manage')\n        else:\n            device_form = DeviceCreateForm()\n            return manage(request, error_message = 'Ошибка в форме') \n    \n\n#Удаляет устройство\n@login_required\ndef delete_device(request):\n    delete_device_form = DeviceDeleteForm(data=request.POST)\n    if request.method == 'POST':\n        if delete_device_form.is_valid():\n            client = delete_device_form.cleaned_data.get('client')\n            Device.objects.filter(id=client.id).delete()\n            return redirect('/manage')\n        else:\n            delete_device_form = DeviceDeleteForm()\n            return manage(request, error_message = 'Ошибка в форме')  \n    \n\n@login_required\ndef add_number_bank(request):\n    bank_form = CreateNumberBank(data=request.POST)\n\n    if request.method == 'POST':\n        if bank_form.is_valid():\n            user = bank_form.cleaned_data.get('user')\n            number_bank_account = bank_form.cleaned_data.get('number_bank_account')\n            bank_form.save()\n            return redirect('/manage')\n        else:\n            bank_form = CreateNumberBank()\n            return manage(request, error_message = 'Ошибка в форме') \n    \n\n\n\n","sub_path":"testapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4297,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"547699318","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu May  6 10:06:46 2021\n\n@author: asus\n\"\"\"\nDIM=224\nIMG_DIM = (DIM, DIM)\ninput_shape = (DIM, DIM, 3)\nimport os,cv2\nimport numpy as np\nfrom sklearn.linear_model import LogisticRegression\nimport pickle\nimport time\nfrom sklearn.model_selection import StratifiedShuffleSplit\nimport pandas as pd\nimport glob\n\n\ndef createMobileNet():\n    from keras.applications.mobilenet import MobileNet\n    import keras\n    from keras.models import Model\n    \n    mobileNet =  MobileNet(include_top=False, weights='imagenet', \n                                     input_shape=input_shape)\n\n    output    = mobileNet.layers[-1].output\n    output    = keras.layers.Flatten()(output)\n    ModelmobileNet = Model(inputs=mobileNet.input, outputs=output)# base_model.get_layer('custom').output)\n\n    ModelmobileNet.trainable = False\n    for layer in ModelmobileNet.layers:\n        layer.trainable = False\n    return ModelmobileNet\n\ndef LoadImage(path,huruf,ModelmobileNet): \n    print(\"Huruf ....\" + huruf)     \n    fullPath = path + \"\\\\\" + huruf + \"\\\\*.png\"    \n    images   = [cv2.resize(cv2.imread(file), IMG_DIM) for file in glob.glob(fullPath)]\n    df = pd.DataFrame(ModelmobileNet.predict(np.array(images), verbose=1))\n    df['huruf']=huruf\n    return df\nif __name__ == '__main__':\n    FILE_ZIP = \"all_huruf.gzip\"\n    t = time.time()\n    if not os.path.exists(FILE_ZIP):\n        print(\"Create data ...%s\" %FILE_ZIP)    \n        ModelmobileNet = createMobileNet()\n        # path = \"D:\\TA\\TA_Welearn\\TA_PutriEndah\\\\dataset\"\n        path = \"C:\\\\xampp\\\\htdocs\\\\welearn_web\\\\public\\dataset\"            \n        df    = pd.concat((LoadImage(path,huruf,ModelmobileNet) for huruf in os.listdir(path)))        \n        print(\"Saving ... %s\"%FILE_ZIP)        \n        df.to_pickle(FILE_ZIP)        \n        elapsed = time.time() - t\n        print(\"Time created & saved = {:.3f}\".format(elapsed)) \n    else:\n        print(\"File exist %s ... \\nWaiting for load ...\"%FILE_ZIP)\n        df = pd.read_pickle(FILE_ZIP)\n        elapsed = time.time() - t\n        print(\"Time Load DB  = {:.3f}\".format(elapsed))                   \n            \n    \n    sss = StratifiedShuffleSplit(n_splits=1, test_size=0.5, random_state=0)    \n    for train_index, test_index in sss.split(df.iloc[:,:-1], df.iloc[:,-1]):\n        x_train, x_test = df.iloc[train_index,:-1], df.iloc[test_index,:-1]        \n        y_train, y_test = df.iloc[train_index,-1], df.iloc[test_index,-1]\n        print(\"Fitting model ....\")        \n        modelLR = LogisticRegression(solver='lbfgs',n_jobs=-1, multi_class='auto',tol=0.8)\n        modelLR.fit(x_train,y_train)    \n        scores = modelLR.score(x_test,y_test)\n        with open('modelTR_Huruf.pkl', 'wb') as f:\n            pickle.dump(modelLR, f)\n            elapsed = time.time() - t\n        print (\"Save Model succeded Time Elapsed = %g\"%elapsed)\n        print(scores)\n    \n    \n    \n    ","sub_path":"public/code/doTrainHurufBaru.py","file_name":"doTrainHurufBaru.py","file_ext":"py","file_size_in_byte":2914,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"530338916","text":"import aiohttp\nimport discord\nfrom discord.ext import commands\nimport sys\nimport subprocess\n\nfrom utils.config import config\nfrom utils import formatting\nfrom utils import checks\n\n\nclass PrimBot:\n\n    def __init__(self, bot):\n        self.bot = bot\n\n    @commands.command(pass_context=True)\n    @checks.is_owner()\n    async def set_username(self, ctx):\n        username = ctx.message.content.split(\" \", 1)[1]\n        try:\n            await self.bot.edit_profile(username=username)\n        except discord.HTTPException:\n            await self.bot.say(\"Failed to set username.\")\n\n    @commands.command(pass_context=True)\n    @checks.is_owner()\n    async def set_nickname(self, ctx):\n        arg_list = ctx.message.content.split(\" \", 1)\n        if len(arg_list) == 2:\n            nickname = arg_list[1]\n        else:\n            nickname = None\n        member = ctx.message.server.me\n\n        try:\n            await self.bot.change_nickname(member=member, nickname=nickname)\n        except discord.Forbidden:\n            await self.bot.say(\"No permission to change the nickname.\")\n        except discord.HTTPException:\n            await self.bot.say(\"Failed to set nickname.\")\n\n    @commands.command(pass_context=True)\n    @checks.is_owner()\n    async def set_avatar(self, ctx):\n        url = ctx.message.content.split(\" \", 1)[1]\n\n        async with aiohttp.get(url) as r:\n            if r.status == 200:\n                data = await r.read()\n                await self.bot.edit_profile(avatar=data)\n            else:\n                raise aiohttp.HttpProcessingError(\n                    code=r.status,\n                    message=r.reason,\n                    headers=r.headers\n                )\n\n    async def _set_status(self, game_name=\"\", url=None, game_type=0, idle=False):\n        game = discord.Game(\n            name=game_name,\n            url=url,\n            type=game_type\n        )\n\n        await self.bot.change_status(game=game, idle=idle)\n\n    @commands.command(pass_context=True)\n    @checks.is_owner()\n    async def set_status(self, ctx):\n        game_name = ctx.message.content.split(\" \", 1)[1]\n\n        try:\n            await self._set_status(game_name)\n        except discord.InvalidArgument:\n            await self.bot.say(\"Invalid game parameter.\")\n\n    async def load_extension(self, name):\n        self.bot.load_extension(name)\n\n    async def unload_extension(self, name):\n        self.bot.unload_extension(name)\n\n    @commands.command()\n    @checks.is_owner()\n    async def exit(self):\n        await self.bot.say(formatting.code_block(\"Exiting\"))\n        await self.bot.logout()\n        await self.bot.close()\n        config.write_config()\n\n    @commands.command()\n    @checks.is_owner()\n    async def restart(self):\n        await self.bot.say(formatting.code_block(\"Restarting\"))\n        await self.bot.logout()\n        await self.bot.close()\n        config.write_config()\n\n        subprocess.call([sys.executable, sys.argv[0]])\n\n    @commands.command(pass_context=True)\n    @checks.is_owner()\n    async def prune(self, ctx):\n        try:\n            count = int(ctx.message.content.split(\" \", 1)[1])\n        except IndexError:\n            print(\"Invalid value specified, set to 1.\")\n            count = 1\n        except ValueError:\n            print(\"Invalid value specified, set to 1.\")\n            count = 1\n\n        try:\n            async for message in self.bot.logs_from(ctx.message.channel,limit=500):\n\n                if message.author == self.bot.user:\n                    await self.bot.delete_message(message)\n                    count -= 1\n\n                if count <= 0:\n                    break\n        except discord.Forbidden:\n            self.bot.say(formatting.code_block(\"No Permission\"))\n\n\ndef setup(bot):\n    bot.add_cog(PrimBot(bot))\n\n","sub_path":"cmd/primbot.py","file_name":"primbot.py","file_ext":"py","file_size_in_byte":3783,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"378488296","text":"from flask import request #first you import things from outside\nfrom flask import jsonify, request\n\n\nfrom project import app #siehe init-file\nfrom project import db\nfrom project.models import User\nfrom project.serializers import UserSerializer\n\n\n@app.route('/')\ndef hello():\n    return \n\n@app.route('/about')\ndef about():\n    return \"<h2>About me</h2>\"\n\n\n@app.route('/users', methods=[\"GET\"])\ndef show_users():\n    users=User.query.all()\n    results=[]\n    for user in users:\n        serialized_user=UserSerializer.serialize(user)\n        results.append(serialized_user)\n    return jsonify(results)\n\n@app.route('/users', methods=[\"POST\"])\ndef create_user():\n    body = request.get_json()\n    name=body.get(\"name\")\n    pin=body.get(\"pin\")\n    balance=body.get(\"balance\")\n    \n    new_user=User(name=name, pin=pin, balance=balance)\n    db.session.add(new_user)\n    db.session.commit()\n    return \"OK\"\n\n@app.route('/users/<name>', methods=[\"DELETE\"])\ndef delete_user(name):\n    User.query.filter_by(name=name).delete()\n    db.session.commit()\n    return \"Deleted\"\n\n@app.route('/balance')\ndef display_balance():\n    pin = int(request.args.get('pin'))\n    user = request.args.get('name')\n    user_data=User.query.filter_by(name=user).first()\n    if pin == user_data.pin:\n        return \"Current amount of money: {}.\".format(user_data.balance)\n    else:\n        return jsonify({\"Error\":\"INCORRECT PIN\"})\n\n@app.route('/withdraw', methods=[\"PUT\"])\ndef withdraw_money():\n    body = request.get_json()\n    name=body.get(\"name\")\n    pin=body.get(\"pin\")\n    amount_to_withdraw=body.get(\"amount to withdraw\")\n    user_data=User.query.filter_by(name=name).first()\n    if pin == user_data.pin:\n        if amount_to_withdraw <= user_data.balance:\n            user_data.balance=user_data.balance-amount_to_withdraw\n            db.session.add(user_data)\n            db.session.commit()\n            return jsonify(UserSerializer.serialize(user_data))    \n        else:\n            return \"Insufficient funds\"\n    else:\n        return \"INCORRECT PIN\"\n\n#endpoint looking for a user, using id - get request - endpoint would be users/id - serializer needs to give us the id of the user\n\n@app.route('/search_by_id', methods=['GET'])\ndef show_user_by_id():\n    user = request.args.get('id')\n    user_data=User.query.filter_by(id=user).first()\n    return \"The name of the user is: {}.\".format(user_data.name)\n    \n","sub_path":"project/controllers.py","file_name":"controllers.py","file_ext":"py","file_size_in_byte":2388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"369525260","text":"# Part of the source code is based on the code base developed by coMind in 2018\n# GitHub link: https://github.com/coMindOrg/federated-averaging-tutorials\n# ==============================================================================\n\n# Helper libraries\nimport argparse\nfrom library.util import DeviceUtil, NetUtil, DatasetUtil, FileSystemUtil\nfrom event_handler import EventHandler\nfrom training import TrainingSession\nfrom logger import Logger\nimport json\nfrom library.datasets import *\n\n# Disable usage of GPU\nos.environ['CUDA_VISIBLE_DEVICES'] = ''\n\n# Disable tensorflow warnings and log information\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\nos.environ['TF_CPP_MIN_VLOG_LEVEL'] = '0'\n\n\ndef _get_parser():\n    \"\"\"\n    Get the arguments passed to python command, which is executed when a node instance starts. The python command\n    with it's arguments will be generated automatically along with the docker-compose file\n    :return:the argument parser object\n    \"\"\"\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument('--device_type', type=str, required=True,\n                        help='Device type [client_node/edge_node/mid_node/cloud]')\n\n    parser.add_argument('--log_dir', type=str, required=True, help='Log directory path')\n    parser.add_argument('--data_dir', type=str, required=True, help='Dataset directory path')\n    parser.add_argument('--dataset', type=str, required=True, help=\"Dataset [mnist/fashion-mnist]\")\n    parser.add_argument('--n_nodes', type=int, required=True, help=\"Total number of nodes\")\n    parser.add_argument('--net_config', type=str, required=True,\n                        help=\"Deep Neural Network to be used for classification [simple/inception]\")\n\n    parser.add_argument('--n_child_nodes', type=int,\n                        help=\"Number of child nodes [sum(edge_nodes)/sum(mid_nodes)/sum(client_nodes)]. Only required for edge nodes / mid nodes / cloud\")\n    parser.add_argument('--client_group_start_index', type=int,\n                        help=\"Start index number for indexing the clients in the group. Only required for client nodes\")\n    parser.add_argument('--n_higher_nodes', type=int,\n                        help=\"Number of higher level nodes [sum(edge_nodes, mid_nodes, cloud)]\")\n\n    parser.add_argument('--dataset_config_file', type=str, required=True,\n                        help=\"JSON file name to be processed. This file is used for any nodes reading the training / testing dataset configuration\")\n\n    parser.add_argument('--architecture_setting', required=True, type=str,\n                        help=\"Complexity of architecture used to run. [simple/complex]\")\n    parser.add_argument('--experiment', required=True, type=str,\n                        help=\"Type of experiment to run. [case_a/case_b/case_c]\")\n\n    parser.add_argument('--lr', type=float, required=True, help=\"Learning rate\")\n    parser.add_argument('--epochs', type=int, required=True, help=\"Epochs\")\n    parser.add_argument('--batch_size', type=int, required=True, help=\"Mini-batch size\")\n\n    parser.add_argument('--published_port', type=int,\n                        help=\"Node's published port, used for opening socket server. Only required for edge/mid node and cloud\")\n\n    parser.add_argument('--parent', type=str,\n                        help=\"ID of the higher level node, to which the current node connect. Not required for cloud\")\n\n    parser.add_argument('--mqtt_broker_ip', type=str, required=True,\n                        help=\"IP of the MQTT broker, to which all nodes in the network connect. The MQTT broker is used for simple message transmission between nodes\")\n    parser.add_argument('--mqtt_broker_port', type=int, required=True,\n                        help=\"Port of the MQTT broker\")\n\n    parser.add_argument('--interval_steps', type=int, default=100,\n                        help=\"How many mini-batches should be processed before performing aggregation step. Not required for cloud\")\n\n    return parser\n\n\ndef _mkdirs(dir_path):\n    \"\"\"\n    Utility function to make directory if it doesn't exist\n    :param dir_path:\n    :return:\n    \"\"\"\n    try:\n        os.makedirs(dir_path)\n    except Exception as _:\n        pass\n\n\ndef _init_dataset(dataset, experiment, device_type, dataset_config_file, data_dir, node_index, device_index, n_nodes):\n    \"\"\"\n    Initialize the dataset for both training and testing\n    :param dataset: the dataset chosen for experiment [fashion-mnist]\n    :param experiment: the type of experiment to run. [case_a/case_b/random]\n    :param device_type: device type [client_node/mid_node/edge_node/cloud]\n    :param dataset_config_file: the name of json config file, which defines the dataset distribution configuration\n    :param data_dir: the device's data directory. This directory is used to store the\n    :param node_index: the node unique index, with regard to the whole system\n    :param device_index: the device unique index, with regard to it's belonging layer\n    :param n_nodes: total number of nodes in the system, i.e. sum of client nodes, edge nodes, mid nodes and a single cloud\n    :return:\n    \"\"\"\n    dataset_util = DatasetUtil()\n    dataset_map = None\n    if dataset == \"fashion-mnist\":\n        fashion_mnist = FashionMNIST(data_dir, experiment, device_type, device_index)\n        train_ds =fashion_mnist.build_train_dataset()\n        test_ds = fashion_mnist.build_test_dataset()\n        if \"case_c\" not in experiment:\n            print(\"Parsing dataset distribution configuration \\n\")\n            dataset_map = dataset_util.parse_fashion_mnist_dataset_dist(device_type=device_type,\n                                                                        device_index=device_index,\n                                                                        config_file=dataset_config_file)\n        if device_type == \"client_node\":\n            X_train, y_train, X_valid, y_valid = dataset_util.load_fashion_mnist_dataset(experiment=experiment,\n                                                                                         train_ds=train_ds,\n                                                                                         device_type=device_type,\n                                                                                         node_index=node_index,\n                                                                                         n_nodes=n_nodes,\n                                                                                         dataset_map=dataset_map)\n            return X_train, y_train, X_valid, y_valid, test_ds\n        else:\n            X_valid, y_valid = dataset_util.load_fashion_mnist_dataset(experiment=experiment, train_ds=train_ds,\n                                                                       device_type=device_type, node_index=node_index,\n                                                                       n_nodes=n_nodes, dataset_map=dataset_map)\n            return None, None, X_valid, y_valid, test_ds\n\n\ndef _init_event_handler(args, device_index, device_ip_addr):\n    \"\"\"\n    Initialize the event handler for communication to other nodes (parent nodes and child nodes)\n    :param args:\n    :param device_index:\n    :param device_ip_addr:\n    :return:\n    \"\"\"\n    event_handler = EventHandler(args.device_type, args.mqtt_broker_ip, args.mqtt_broker_port, device_index,\n                                 device_ip_addr, args.published_port,\n                                 args.parent, args.n_child_nodes)\n    event_handler.initialize()\n    event_handler.run()\n    return event_handler\n\n\ndef run_exp(mode=\"train\"):\n    \"\"\"\n    Main entry point when a node instance (a docker container) starts\n    :return:\n    \"\"\"\n    parser = _get_parser()\n    # Getting the argument dictionary from parser object\n    args = parser.parse_args()\n\n    if args.n_higher_nodes is None:\n        parser.error(\"--n_higher_nodes is required.\")\n    if args.device_type != \"client_node\" and (args.published_port is None or args.n_child_nodes is None):\n        parser.error(\"--published_port and --n_child_nodes are required.\")\n    if args.device_type == \"client_node\" and args.client_group_start_index is None:\n        parser.error(\"--client_group_start_index is required.\")\n    if args.device_type != \"cloud\" and (args.parent is None or args.interval_steps is None):\n        parser.error(\"--parent and --interval_steps and --n_higher_nodes are required\")\n\n    info_log_dir = os.path.join(args.log_dir, \"info\", args.architecture_setting, args.experiment)\n    FileSystemUtil().make_dirs(info_log_dir)\n    train_log_dir = os.path.join(args.log_dir, \"train_log\", args.architecture_setting, args.experiment)\n    FileSystemUtil().make_dirs(train_log_dir)\n    test_log_dir = os.path.join(args.log_dir, \"test_log\", args.architecture_setting, args.experiment)\n    FileSystemUtil().make_dirs(test_log_dir)\n\n    print(\"Get device IP address \\n\")\n    device_ip_addr = NetUtil().get_sock_ip_address()\n\n    print(\"Get the device unique indexes \\n\")\n    node_index, device_index = DeviceUtil().get_device_id(device_type=args.device_type,\n                                                          device_ip_addr=device_ip_addr,\n                                                          client_group_start_index=args.client_group_start_index,\n                                                          n_higher_nodes=args.n_higher_nodes)\n\n    # Dump experiment arguments before running\n    with open(os.path.join(args.log_dir,\n                           'experiment_arguments_{}_{}.json'.format(args.architecture_setting, args.experiment)),\n              'w') as f:\n        json.dump(str(args), f)\n\n    print(\"Initialize logger \\n\")\n    logger = Logger(args.experiment, args.architecture_setting, args.device_type, device_index, info_log_dir,\n                    train_log_dir, test_log_dir)\n\n    logger.log_info(\"Initialize event handler \\n\")\n    event_handler = _init_event_handler(args, device_index, device_ip_addr)\n\n    logger.log_info(\"Load dataset \\n\")\n    X_train, y_train, X_valid, y_valid, test_ds = _init_dataset(dataset=args.dataset, experiment=args.experiment,\n                                                                device_type=args.device_type,\n                                                                dataset_config_file=args.dataset_config_file,\n                                                                node_index=node_index, device_index=device_index,\n                                                                data_dir=args.data_dir, n_nodes=args.n_nodes)\n\n    train_sess = TrainingSession(args.net_config, args.lr, args.device_type, device_index, event_handler, args.parent,\n                                 args.epochs, args.batch_size,\n                                 X_train, y_train, X_valid, y_valid, test_ds, args.interval_steps, device_ip_addr,\n                                 args.published_port, args.n_child_nodes, args.experiment, logger)\n    if mode == \"train\" or mode == \"train_from_checkpoint\":\n        train_sess.train_net(mode)\n    train_sess.test_net()\n\n\nif __name__ == '__main__':\n    run_exp()\n","sub_path":"app/experiment.py","file_name":"experiment.py","file_ext":"py","file_size_in_byte":11087,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"120596701","text":"from tkinter import *\nimport time, copy\nfrom random import randrange, randint\n\ncanvasConfig = {\n\t\"width\": 400,\n\t\"height\": 400,\n\t\"bg\": \"#bbada0\"\n}\n# #cdc1b4\npositionConfig = {\n\t\"marginAround\": 10,\n\t\"marginBetween\": 10,\n\t\"width_count\": 4,\n\t\"height_count\": 4\n}\n\ncell = {\n\t\"map\" : [],\n\t\"bg\": {\n\t\t0: \t\t\"#cdc1b4\",\n\t\t2: \t\t\"#eee4da\",\n\t\t4: \t\t\"#ede0c8\",\n\t\t8: \t\t\"#f2b179\",\n\t\t16: \t\"#f59563\",\n\t\t32: \t\"#f67c5f\",\n\t\t64: \t\"#f65e3b\",\n\t\t128: \t\"#edcf72\",\n\t\t256: \t\"#edcc61\",\n\t\t512: \t\"#edc850\",\n\t\t1024: \t\"#edc53f\",\n\t\t2048: \t\"#edc22e\"\n\t\t# TODO: continue colors\n\t},\n\t\"fontColor\" : {\n\t\t0 : \t\"#776e65\",\n\t\t2 : \t\"#776e65\",\n\t\t4 : \t\"#776e65\",\n\t\t8 : \t\"#f9f6f2\",\n\t\t16 : \t\"#f9f6f2\",\n\t\t32 : \t\"#f9f6f2\",\n\t\t64 : \t\"#f9f6f2\",\n\t\t128 : \t\"#f9f6f2\",\n\t\t256 : \t\"#f9f6f2\",\n\t\t512 : \t\"#f9f6f2\",\n\t\t1024 : \t\"#f9f6f2\",\n\t\t2048 : \t\"#f9f6f2\"\n\t\t# TODO: recreate colors\n\t}\n\t# TODO: add font color\n}\n\n\n\"\"\"\n.--------------------->X\n|  (0,0) (1,0) (2,0)  // (X,Y)\n|  (0,1) (1,1) (2,1)\n|  (0,2) (1,2) (2,2)\n\\/\nY\n\"\"\"\n\n# coord: X or Y value\n# side: \"width\" or \"height\"\ndef coord_calc(coord, side, boxsize):\n\treturn positionConfig[\"marginAround\"] + coord * positionConfig[\"marginBetween\"] + coord * boxsize\n\ndef size_calc(coord, side):\n\treturn ( canvasConfig[side]\n\t\t\t- positionConfig[\"marginAround\"] * 2\n\t\t\t- (positionConfig[side+\"_count\"]-1) * positionConfig[\"marginBetween\"] ) / positionConfig[side+\"_count\"]\n\ndef coords_calc(Xpos, Ypos):\n\twidth = size_calc(Xpos, \"width\")\n\theight = size_calc(Xpos, \"height\")\n\treturn {\n\t\t\"width\" \t: width,\n\t\t\"height\" \t: height,\n\t\t\"x\"\t\t\t: coord_calc(Xpos, \"width\", width),\n\t\t\"y\"\t\t\t: coord_calc(Ypos, \"height\", height)\n\t}\n\n\ndef canvas_init(master):\n\tcanvas = Canvas(master, \n\t\t\t\twidth=canvasConfig[\"width\"],\n\t\t\t\theight=canvasConfig[\"height\"],\n\t\t\t\tbg=canvasConfig[\"bg\"])\n\tcanvas.pack()\n\treturn canvas\n\ndef rectangles_generation():\n\tfor X in range(0,positionConfig[\"width_count\"]):\n\t\tfor Y in range(0,positionConfig[\"height_count\"]):\n\t\t\tcoords = coords_calc(X, Y)\n\t\t\tvalue = cell[\"map\"][X][Y]\n\n\t\t\tcanvas.create_rectangle(coords[\"x\"], \n\t\t\t\tcoords[\"y\"], \n\t\t\t\tcoords[\"x\"]+coords[\"width\"], \n\t\t\t\tcoords[\"y\"]+coords[\"height\"], \n\t\t\t\tfill=cell[\"bg\"][value],\n\t\t\t\toutline=cell[\"bg\"][value])\n\n\t\t\ttext = (value if value else \"\")\n\t\t\tcanvas.create_text(coords[\"x\"]+coords[\"width\"]/2, \n\t\t\t\tcoords[\"y\"]+coords[\"width\"]/2, \n\t\t\t\tfill=cell[\"fontColor\"][value], \n\t\t\t\ttext=text)\n\t\t\t\t# font=tkFont.Font(family='Helvetica', size=36, weight='bold'))\n\ndef map_init():\n\tcell[\"map\"] = []\n\tfor X in range(0, positionConfig[\"width_count\"]):\n\n\t\trow = []\n\t\tfor Y in range(0, positionConfig[\"height_count\"]):\n\t\t\trow.append(0)\n\t\t\n\t\tcell[\"map\"].append(row)\n\n\ndef moveUp():\n\tfor X in range(0, positionConfig[\"width_count\"]):\n\t\tfor Y in range(0, positionConfig[\"height_count\"]-1):\n\n\t\t\t\"\"\"\n\t\t\t\tIf the cell is empthy - find next not empthy cell\n\t\t\t\tand swap values\n\t\t\t\"\"\"\n\t\t\tif cell[\"map\"][X][Y] == 0:\n\t\t\t\tfor curY in range(Y+1, positionConfig[\"height_count\"]):\n\t\t\t\t\tif cell[\"map\"][X][curY] != 0:\n\t\t\t\t\t\tcell[\"map\"][X][Y] = cell[\"map\"][X][curY]\n\t\t\t\t\t\tcell[\"map\"][X][curY] = 0\n\t\t\t\t\t\tbreak\n\n\t\t\t\"\"\"\n\t\t\t\tIf the cell isn't empthy - find next cell with the same value\n\t\t\t\tand double the value\n\t\t\t\"\"\"\n\t\t\tif cell[\"map\"][X][Y] != 0:\n\t\t\t\tfor curY in range(Y+1, positionConfig[\"height_count\"]):\n\t\t\t\t\tif cell[\"map\"][X][curY] == cell[\"map\"][X][Y]:\n\t\t\t\t\t\tcell[\"map\"][X][Y] = cell[\"map\"][X][Y] * 2\n\t\t\t\t\t\tcell[\"map\"][X][curY] = 0\n\t\t\t\t\t\tbreak\n\t\t\t\t\telif cell[\"map\"][X][curY] != 0:\n\t\t\t\t\t\tbreak \n\n\ndef moveDown():\n\tfor X in range(0, positionConfig[\"width_count\"]):\n\t\tfor Y in reversed(range(1, positionConfig[\"height_count\"])):\n\t\t\t\n\t\t\tif cell[\"map\"][X][Y] == 0:\n\t\t\t\tfor curY in reversed(range(0, Y)):\n\t\t\t\t\tif cell[\"map\"][X][curY] != 0:\n\t\t\t\t\t\tcell[\"map\"][X][Y] = cell[\"map\"][X][curY]\n\t\t\t\t\t\tcell[\"map\"][X][curY] = 0\n\t\t\t\t\t\tbreak\n\n\t\t\tif cell[\"map\"][X][Y] != 0:\n\t\t\t\tfor curY in reversed(range(0, Y)):\n\t\t\t\t\tif cell[\"map\"][X][curY] == cell[\"map\"][X][Y]:\n\t\t\t\t\t\tcell[\"map\"][X][Y] = cell[\"map\"][X][Y] * 2\n\t\t\t\t\t\tcell[\"map\"][X][curY] = 0\n\t\t\t\t\t\tbreak\n\t\t\t\t\telif cell[\"map\"][X][curY] != 0:\n\t\t\t\t\t\tbreak \n\ndef moveLeft():\n\tfor Y in range(0, positionConfig[\"height_count\"]):\n\t\tfor X in range(0, positionConfig[\"width_count\"]-1):\n\n\t\t\tif cell[\"map\"][X][Y] == 0:\n\t\t\t\tfor curX in range(X+1, positionConfig[\"height_count\"]):\n\t\t\t\t\tif cell[\"map\"][curX][Y] != 0:\n\t\t\t\t\t\tcell[\"map\"][X][Y] = cell[\"map\"][curX][Y]\n\t\t\t\t\t\tcell[\"map\"][curX][Y] = 0\n\t\t\t\t\t\tbreak\n\n\t\t\tif cell[\"map\"][X][Y] != 0:\n\t\t\t\tfor curX in range(X+1, positionConfig[\"height_count\"]):\n\t\t\t\t\tif cell[\"map\"][curX][Y] == cell[\"map\"][X][Y]:\n\t\t\t\t\t\tcell[\"map\"][X][Y] = cell[\"map\"][X][Y] * 2\n\t\t\t\t\t\tcell[\"map\"][curX][Y] = 0\n\t\t\t\t\t\tbreak\n\t\t\t\t\telif cell[\"map\"][curX][Y] != 0:\n\t\t\t\t\t\tbreak \n\ndef moveRight():\n\tfor Y in range(0, positionConfig[\"height_count\"]):\n\t\tfor X in reversed(range(1, positionConfig[\"width_count\"])):\n\t\t\t\n\t\t\tif cell[\"map\"][X][Y] == 0:\n\t\t\t\tfor curX in reversed(range(0, X)):\n\t\t\t\t\tif cell[\"map\"][curX][Y] != 0:\n\t\t\t\t\t\tcell[\"map\"][X][Y] = cell[\"map\"][curX][Y]\n\t\t\t\t\t\tcell[\"map\"][curX][Y] = 0\n\t\t\t\t\t\tbreak\n\n\t\t\tif cell[\"map\"][X][Y] != 0:\n\t\t\t\tfor curX in reversed(range(0, X)):\n\t\t\t\t\tif cell[\"map\"][curX][Y] == cell[\"map\"][X][Y]:\n\t\t\t\t\t\tcell[\"map\"][X][Y] = cell[\"map\"][X][Y] * 2\n\t\t\t\t\t\tcell[\"map\"][curX][Y] = 0\n\t\t\t\t\t\tbreak\n\t\t\t\t\telif cell[\"map\"][curX][Y] != 0:\n\t\t\t\t\t\tbreak \n\ndef show_table():\n\tprint(\"Table\")\n\tfor Y in range(0, positionConfig[\"height_count\"]):\n\t\tfor X in range(0, positionConfig[\"width_count\"]):\n\t\t\tprint(cell[\"map\"][X][Y], end=\"\")\n\t\tprint()\n\ndef get_rand_coord(side):\n\treturn randint(0, positionConfig[side+\"_count\"]-1)\n\ndef get_rand_empthy_cell():\n\tx = get_rand_coord(\"width\")\n\ty = get_rand_coord(\"height\")\n\twhile cell[\"map\"][x][y] != 0:\n\t\tx = get_rand_coord(\"width\")\n\t\ty = get_rand_coord(\"height\")\n\treturn {\n\t\t\"x\": x,\n\t\t\"y\": y\n\t}\n\ndef is_table_full():\n\tfor X in range(0, positionConfig[\"width_count\"]):\n\t\tfor Y in range(0, positionConfig[\"height_count\"]):\n\t\t\tif cell[\"map\"][X][Y] == 0:\n\t\t\t\treturn False\n\treturn True\n\ndef generete_new():\n\tif is_table_full():\n\t\treturn\n\tcoords = get_rand_empthy_cell()\n\tvalue = 2 ** randint(1, 2)\n\t# print(value)\n\tcell[\"map\"][coords[\"x\"]][coords[\"y\"]] = value\n\ndef repaint():\n\tcanvas.delete(\"all\")\n\trectangles_generation()\n\tcanvas.pack()\n\ndef key_pressed(event):\n\n\toldMap = copy.deepcopy(cell[\"map\"])\n\tprint(oldMap)\n\tif event.char == \"w\":\n\t\tmoveUp()\n\telif event.char == \"a\":\n\t\tmoveLeft()\n\telif event.char == \"s\":\n\t\tmoveDown()\n\telif event.char == \"d\":\n\t\tmoveRight()\n\telse:\n\t\treturn\n\n\tif is_table_full():\n\t\ttime.sleep(3)\n\t\tmap_init()\n\n\tprint(oldMap)\n\tif oldMap != cell[\"map\"]:\n\t\tgenerete_new()\n\t# show_table() # TODO: delete\n\trepaint()\n\ndef event_init(master):\n\tmaster.bind(\"<Key>\", key_pressed)\n\n\n\nmaster = Tk()\ncanvas = canvas_init(master)\n\nmap_init()\nrepaint()\n\nevent_init(master)\n\ngenerete_new()\n\nmaster.mainloop()","sub_path":"tkinterTry.py","file_name":"tkinterTry.py","file_ext":"py","file_size_in_byte":6706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"599125887","text":"from typing import List\n\n\nclass Solution:\n    def maxProfit(self, prices: List[int]) -> int:\n        max = 0\n        if len(prices) == 0: return max\n        buy = prices[0]\n        sell = prices[0]\n        for n in prices:\n            if buy > n:\n                buy = n\n                sell = n\n            if buy < n and sell < n:\n                sell = n\n            if sell > buy and sell - buy > max: max = sell - buy\n        return max\n\n\nprint(Solution.maxProfit(1, [2, 4, 1]))\n","sub_path":"leetcode/maxProfit_121.py","file_name":"maxProfit_121.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"178934285","text":"import logging\n\nimport cupy as cp\n\nfrom tike.linalg import projection, norm, orthogonalize_gs\nfrom tike.opt import randomizer, get_batch, put_batch, adam\n\nfrom ..position import PositionOptions, update_positions_pd, _image_grad\nfrom ..object import positivity_constraint, smoothness_constraint\nfrom ..probe import (orthogonalize_eig, get_varying_probe, update_eigen_probe,\n                     constrain_variable_probe)\n\nlogger = logging.getLogger(__name__)\n\n\ndef lstsq_grad(\n    op,\n    comm,\n    data,\n    batches,\n    *,\n    probe,\n    scan,\n    psi,\n    algorithm_options,\n    eigen_probe=None,\n    eigen_weights=None,\n    probe_options=None,\n    position_options=None,\n    object_options=None,\n):\n    \"\"\"Solve the ptychography problem using Odstrcil et al's approach.\n\n    Object and probe are updated simultaneouly using optimal step sizes\n    computed using a least squares approach.\n\n    Parameters\n    ----------\n    op : :py:class:`tike.operators.Ptycho`\n        A ptychography operator.\n    comm : :py:class:`tike.communicators.Comm`\n        An object which manages communications between GPUs and nodes.\n    data : list((FRAME, WIDE, HIGH) float32, ...)\n        A list of unique CuPy arrays for each device containing\n        the intensity (square of the absolute value) of the propagated\n        wavefront; i.e. what the detector records. FFT-shifted so the\n        diffraction peak is at the corners.\n    batches : list(list((BATCH_SIZE, ) int, ...), ...)\n        A list of list of indices along the FRAME axis of `data` for\n        each device which define the batches of `data` to process\n        simultaneously.\n    probe : list((1, 1, SHARED, WIDE, HIGH) complex64, ...)\n        A list of duplicate CuPy arrays for each device containing\n        the shared complex illumination function amongst all positions.\n    scan : list((POSI, 2) float32, ...)\n        A list of unique CuPy arrays for each device containing\n        coordinates of the minimum corner of the probe grid for each\n        measurement in the coordinate system of psi. Coordinate order\n        consistent with WIDE, HIGH order.\n    psi : list((WIDE, HIGH) complex64, ...)\n        A list of duplicate CuPy arrays for each device containing\n        the wavefront modulation coefficients of the object.\n    algorithm_options : :py:class:`tike.ptycho.IterativeOptions`\n        The options class for this algorithm.\n    position_options : :py:class:`tike.ptycho.PositionOptions`\n        A class containing settings related to position correction.\n    probe_options : :py:class:`tike.ptycho.ProbeOptions`\n        A class containing settings related to probe updates.\n    object_options : :py:class:`tike.ptycho.ObjectOptions`\n        A class containing settings related to object updates.\n\n    Returns\n    -------\n    result : dict\n        A dictionary containing the updated keyword-only arguments passed to\n        this function.\n\n    References\n    ----------\n    Michal Odstrcil, Andreas Menzel, and Manuel Guizar-Sicaros. Iterative\n    least-squares solver for generalized maximum-likelihood ptychography.\n    Optics Express. 2018.\n\n    .. seealso:: :py:mod:`tike.ptycho`\n\n    \"\"\"\n\n    for n in randomizer.permutation(len(batches[0])):\n\n        bdata = comm.pool.map(get_batch, data, batches, n=n)\n        bscan = comm.pool.map(get_batch, scan, batches, n=n)\n\n        if position_options:\n            bposition_options = comm.pool.map(PositionOptions.split,\n                                              position_options,\n                                              [b[n] for b in batches])\n        else:\n            bposition_options = None\n\n        if isinstance(eigen_probe, list):\n            beigen_weights = comm.pool.map(\n                get_batch,\n                eigen_weights,\n                batches,\n                n=n,\n            )\n            beigen_probe = eigen_probe\n        else:\n            beigen_probe = [None] * comm.pool.num_workers\n            beigen_weights = [None] * comm.pool.num_workers\n\n        unique_probe = comm.pool.map(\n            get_varying_probe,\n            probe,\n            beigen_probe,\n            beigen_weights,\n        )\n\n        nearplane, cost = zip(*comm.pool.map(\n            _update_wavefront,\n            bdata,\n            unique_probe,\n            bscan,\n            psi,\n            op=op,\n        ))\n\n        if comm.use_mpi:\n            cost = comm.Allreduce_reduce(cost, 'cpu')\n        else:\n            cost = comm.reduce(cost, 'cpu')\n\n        (\n            psi,\n            probe,\n            beigen_probe,\n            beigen_weights,\n            bscan,\n            bposition_options,\n        ) = _update_nearplane(\n            op,\n            comm,\n            nearplane,\n            psi,\n            bscan,\n            probe,\n            unique_probe,\n            beigen_probe,\n            beigen_weights,\n            object_options is not None,\n            probe_options is not None,\n            bposition_options,\n        )\n\n        if position_options:\n            comm.pool.map(PositionOptions.join, position_options,\n                          bposition_options, [b[n] for b in batches])\n\n        if isinstance(eigen_probe, list):\n            comm.pool.map(\n                put_batch,\n                beigen_weights,\n                eigen_weights,\n                batches,\n                n=n,\n            )\n\n        comm.pool.map(\n            put_batch,\n            bscan,\n            scan,\n            batches,\n            n=n,\n        )\n\n    if probe_options and probe_options.orthogonality_constraint:\n        probe[0] = orthogonalize_gs(probe[0], axis=(-2, -1))\n        probe = comm.pool.bcast([probe[0]])\n\n    if object_options:\n        psi = comm.pool.map(positivity_constraint,\n                            psi,\n                            r=object_options.positivity_constraint)\n\n        psi = comm.pool.map(smoothness_constraint,\n                            psi,\n                            a=object_options.smoothness_constraint)\n\n    if isinstance(eigen_probe, list):\n        eigen_probe, eigen_weights = (list(a) for a in zip(*comm.pool.map(\n            constrain_variable_probe,\n            eigen_probe,\n            eigen_weights,\n        )))\n\n    algorithm_options.costs.append(cost)\n    return {\n        'probe': probe,\n        'psi': psi,\n        'scan': scan,\n        'eigen_probe': eigen_probe,\n        'eigen_weights': eigen_weights,\n        'algorithm_options': algorithm_options,\n        'probe_options': probe_options,\n        'object_options': object_options,\n        'position_options': position_options,\n    }\n\n\ndef _get_nearplane_gradients(nearplane, psi, scan_, probe, unique_probe, op, m,\n                             recover_psi, recover_probe):\n\n    pad, end = op.diffraction.pad, op.diffraction.end\n\n    patches = op.diffraction.patch.fwd(\n        patches=cp.zeros(nearplane[..., [m], pad:end, pad:end].shape,\n                         dtype='complex64')[..., 0, 0, :, :],\n        images=psi,\n        positions=scan_,\n    )[..., None, None, :, :]\n\n    # χ (diff) is the target for the nearplane problem; the difference\n    # between the desired nearplane and the current nearplane that we wish\n    # to minimize.\n    diff = nearplane[..., [m], pad:end,\n                     pad:end] - unique_probe[..., [m], :, :] * patches\n\n    logger.info('%10s cost is %+12.5e', 'nearplane', norm(diff))\n\n    if recover_psi:\n        grad_psi = cp.conj(unique_probe[..., [m], :, :]) * diff\n\n        # (25b) Common object gradient. Use a weighted (normalized) sum\n        # instead of division as described in publication to improve\n        # numerical stability.\n        common_grad_psi = op.diffraction.patch.adj(\n            patches=grad_psi[..., 0, 0, :, :],\n            images=cp.zeros(psi.shape, dtype='complex64'),\n            positions=scan_,\n        )\n\n        dOP = op.diffraction.patch.fwd(\n            patches=cp.zeros(patches.shape, dtype='complex64')[..., 0, 0, :, :],\n            images=common_grad_psi,\n            positions=scan_,\n        )[..., None, None, :, :] * unique_probe[..., [m], :, :]\n        A1 = cp.sum((dOP * dOP.conj()).real + 0.5, axis=(-2, -1))\n    else:\n        common_grad_psi = None\n        dOP = None\n        A1 = None\n\n    if recover_probe:\n        grad_probe = cp.conj(patches) * diff\n\n        # (25a) Common probe gradient. Use simple average instead of\n        # division as described in publication because that's what\n        # ptychoshelves does\n        common_grad_probe = cp.mean(\n            grad_probe,\n            axis=-5,\n            keepdims=True,\n        )\n\n        dPO = common_grad_probe * patches\n        A4 = cp.sum((dPO * dPO.conj()).real + 0.5, axis=(-2, -1))\n    else:\n        grad_probe = None\n        common_grad_probe = None\n        dPO = None\n        A4 = None\n\n    if __debug__:\n        patches = op.diffraction.patch.fwd(\n            patches=cp.zeros(nearplane[..., [m], pad:end, pad:end].shape,\n                             dtype='complex64')[..., 0, 0, :, :],\n            images=psi,\n            positions=scan_,\n        )[..., None, None, :, :]\n        logger.info(\n            '%10s cost is %+12.5e', 'nearplane',\n            norm(probe[..., [m], :, :] * patches -\n                 nearplane[..., [m], pad:end, pad:end]))\n\n    return (patches, diff, grad_probe, common_grad_psi, common_grad_probe, dOP,\n            dPO, A1, A4)\n\n\ndef _get_nearplane_steps(diff, dOP, dPO, A1, A4, recover_psi, recover_probe):\n    # (22) Use least-squares to find the optimal step sizes simultaneously\n    if recover_psi and recover_probe:\n        b1 = cp.sum((dOP.conj() * diff).real, axis=(-2, -1))\n        b2 = cp.sum((dPO.conj() * diff).real, axis=(-2, -1))\n        A2 = cp.sum((dOP * dPO.conj()), axis=(-2, -1))\n        A3 = A2.conj()\n        determinant = A1 * A4 - A2 * A3\n        x1 = -cp.conj(A2 * b2 - A4 * b1) / determinant\n        x2 = cp.conj(A1 * b2 - A3 * b1) / determinant\n    elif recover_psi:\n        b1 = cp.sum((dOP.conj() * diff).real, axis=(-2, -1))\n        x1 = b1 / A1\n    elif recover_probe:\n        b2 = cp.sum((dPO.conj() * diff).real, axis=(-2, -1))\n        x2 = b2 / A4\n\n    if recover_psi:\n        step = x1[..., None, None]\n\n        # (27b) Object update\n        weighted_step_psi = cp.mean(step, keepdims=True, axis=-5)\n\n    if recover_probe:\n        step = x2[..., None, None]\n\n        weighted_step_probe = cp.mean(step, axis=-5, keepdims=True)\n    else:\n        weighted_step_probe = None\n\n    return weighted_step_psi, weighted_step_probe\n\n\ndef _update_A(A, delta):\n    A += 0.5 * delta\n    return A\n\n\ndef _get_residuals(grad_probe, grad_probe_mean):\n    return grad_probe - grad_probe_mean\n\n\ndef _update_residuals(R, eigen_probe, axis, c, m):\n    R -= projection(\n        R,\n        eigen_probe[..., c:c + 1, m:m + 1, :, :],\n        axis=axis,\n    )\n    return R\n\n\ndef _get_coefs_intensity(weights, xi, P, O, m):\n    OP = O * P\n    num = cp.sum(cp.real(cp.conj(OP) * xi), axis=(-1, -2))\n    den = cp.sum(cp.abs(OP)**2, axis=(-1, -2))\n    weights[..., 0:1, m:m + 1] = weights[..., 0:1, m:m + 1] + 0.1 * num / den\n    return weights\n\n\ndef _update_nearplane(op, comm, nearplane, psi, scan_, probe, unique_probe,\n                      eigen_probe, eigen_weights, recover_psi, recover_probe,\n                      position_options):\n\n    for m in range(probe[0].shape[-3]):\n\n        (\n            patches,\n            diff,\n            grad_probe,\n            common_grad_psi,\n            common_grad_probe,\n            dOP,\n            dPO,\n            A1,\n            A4,\n        ) = (list(a) for a in zip(*comm.pool.map(\n            _get_nearplane_gradients,\n            nearplane,\n            psi,\n            scan_,\n            probe,\n            unique_probe,\n            op=op,\n            m=m,\n            recover_psi=recover_psi,\n            recover_probe=recover_probe,\n        )))\n\n        if recover_psi:\n            if comm.use_mpi:\n                delta = comm.Allreduce_mean(A1, axis=-3)\n            else:\n                delta = comm.pool.reduce_mean(A1, axis=-3)\n            A1 = comm.pool.map(_update_A, A1, comm.pool.bcast([delta]))\n\n        if recover_probe:\n            if comm.use_mpi:\n                delta = comm.Allreduce_mean(A4, axis=-3)\n            else:\n                delta = comm.pool.reduce_mean(A4, axis=-3)\n            A4 = comm.pool.map(_update_A, A4, comm.pool.bcast([delta]))\n\n        if recover_probe or recover_psi:\n            (\n                weighted_step_psi,\n                weighted_step_probe,\n            ) = (list(a) for a in zip(*comm.pool.map(\n                _get_nearplane_steps,\n                diff,\n                dOP,\n                dPO,\n                A1,\n                A4,\n                recover_psi=recover_psi,\n                recover_probe=recover_probe,\n            )))\n\n        if recover_probe and eigen_weights[0] is not None:\n            logger.info('Updating eigen probes')\n\n            eigen_weights = comm.pool.map(\n                _get_coefs_intensity,\n                eigen_weights,\n                diff,\n                [p[..., m:m + 1, :, :] for p in probe],\n                patches,\n                m=m,\n            )\n\n            # (30) residual probe updates\n            if eigen_weights[0].shape[-2] > 1:\n                if comm.use_mpi:\n                    grad_probe_mean = comm.Allreduce_mean(\n                        common_grad_probe,\n                        axis=-5,\n                    )\n                    grad_probe_mean = comm.pool.bcast([grad_probe_mean])\n                else:\n                    grad_probe_mean = comm.pool.bcast(\n                        [comm.pool.reduce_mean(\n                            common_grad_probe,\n                            axis=-5,\n                        )])\n                R = comm.pool.map(_get_residuals, grad_probe, grad_probe_mean)\n\n            if m < eigen_probe[0].shape[-3]:\n                assert eigen_weights[0].shape[\n                    -2] == eigen_probe[0].shape[-4] + 1\n                for n in range(1, eigen_probe[0].shape[-4] + 1):\n\n                    eigen_probe, eigen_weights = update_eigen_probe(\n                        comm,\n                        R,\n                        eigen_probe,\n                        eigen_weights,\n                        patches,\n                        diff,\n                        β=0.01,  # TODO: Adjust according to mini-batch size\n                        c=n,\n                        m=m,\n                    )\n\n                    if n + 1 < eigen_weights[0].shape[-2]:\n                        # Subtract projection of R onto new probe from R\n                        R = comm.pool.map(\n                            _update_residuals,\n                            R,\n                            eigen_probe,\n                            axis=(-2, -1),\n                            c=n - 1,\n                            m=m,\n                        )\n\n        # Update each direction\n        if recover_psi:\n            if comm.use_mpi:\n                weighted_step_psi[0] = comm.Allreduce_mean(\n                    weighted_step_psi,\n                    axis=-5,\n                )[..., 0, 0, 0]\n                common_grad_psi[0] = comm.Allreduce_reduce(\n                    common_grad_psi,\n                    dest='gpu',\n                )[0]\n            else:\n                weighted_step_psi[0] = comm.pool.reduce_mean(\n                    weighted_step_psi,\n                    axis=-5,\n                )[..., 0, 0, 0]\n                common_grad_psi[0] = comm.reduce(common_grad_psi, 'gpu')[0]\n\n            psi[0] += weighted_step_psi[0] * common_grad_psi[0]\n            psi = comm.pool.bcast([psi[0]])\n\n        if recover_probe:\n            if comm.use_mpi:\n                weighted_step_probe[0] = comm.Allreduce_mean(\n                    weighted_step_probe,\n                    axis=-5,\n                )\n                common_grad_probe[0] = comm.Allreduce_mean(\n                    common_grad_probe,\n                    axis=-5,\n                )\n            else:\n                weighted_step_probe[0] = comm.pool.reduce_mean(\n                    weighted_step_probe,\n                    axis=-5,\n                )\n                common_grad_probe[0] = comm.pool.reduce_mean(\n                    common_grad_probe,\n                    axis=-5,\n                )\n\n            # (27a) Probe update\n            probe[0][..., [m], :, :] += (weighted_step_probe[0] *\n                                         common_grad_probe[0])\n            probe = comm.pool.bcast([probe[0]])\n\n        if position_options and m == 0:\n            scan_, position_options = zip(*comm.pool.map(\n                _update_position,\n                position_options,\n                diff,\n                patches,\n                scan_,\n                unique_probe,\n                m=m,\n                op=op,\n            ))\n\n    return psi, probe, eigen_probe, eigen_weights, scan_, position_options\n\n\ndef _update_wavefront(data, varying_probe, scan, psi, op):\n\n    farplane = op.fwd(probe=varying_probe, scan=scan, psi=psi)\n    intensity = cp.sum(\n        cp.square(cp.abs(farplane)),\n        axis=list(range(1, farplane.ndim - 2)),\n    )\n    cost = op.propagation.cost(data, intensity)\n    logger.info('%10s cost is %+12.5e', 'farplane', cost)\n    farplane -= 0.5 * op.propagation.grad(data, farplane, intensity)\n\n    if __debug__:\n        intensity = cp.sum(\n            cp.square(cp.abs(farplane)),\n            axis=list(range(1, farplane.ndim - 2)),\n        )\n        cost = op.propagation.cost(data, intensity)\n        logger.info('%10s cost is %+12.5e', 'farplane', cost)\n        # TODO: Only compute cost every 20 iterations or on a log sampling?\n\n    farplane = op.propagation.adj(farplane, overwrite=True)\n\n    return farplane, cost\n\n\ndef _mad(x, **kwargs):\n    \"\"\"Return the mean absolute deviation around the median.\"\"\"\n    return cp.mean(cp.abs(x - cp.median(x, **kwargs)), **kwargs)\n\n\ndef _update_position(\n    position_options,\n    diff,\n    patches,\n    scan,\n    unique_probe,\n    m,\n    op,\n):\n    main_probe = unique_probe[..., m:m + 1, :, :]\n\n    # According to the manuscript, we can either shift the probe or the object\n    # and they are equivalent (in theory). Here we shift the object because\n    # that is what ptychoshelves does.\n    grad_x, grad_y = _image_grad(op, patches)\n\n    numerator = cp.sum(cp.real(diff * cp.conj(grad_x * main_probe)),\n                       axis=(-2, -1))\n    denominator = cp.sum(cp.abs(grad_x * main_probe)**2, axis=(-2, -1))\n    step_x = numerator / (denominator + 1e-6)\n\n    numerator = cp.sum(cp.real(diff * cp.conj(grad_y * main_probe)),\n                       axis=(-2, -1))\n    denominator = cp.sum(cp.abs(grad_y * main_probe)**2, axis=(-2, -1))\n    step_y = numerator / (denominator + 1e-6)\n\n    step_x = step_x[..., 0, 0]\n    step_y = step_y[..., 0, 0]\n\n    # Momentum\n    if position_options.use_adaptive_moment:\n        logger.info(\n            \"position correction with ADAptive Momemtum acceleration enabled.\")\n        step_x, position_options.vx, position_options.mx = adam(\n            step_x,\n            position_options.vx,\n            position_options.mx,\n            vdecay=position_options.vdecay,\n            mdecay=position_options.mdecay)\n        step_y, position_options.vy, position_options.my = adam(\n            step_y,\n            position_options.vy,\n            position_options.my,\n            vdecay=position_options.vdecay,\n            mdecay=position_options.mdecay)\n\n    # Step limit for stability\n    max_shift = patches.shape[-1] * 0.1\n    _max_shift = cp.minimum(\n        max_shift,\n        _mad(\n            cp.concatenate((step_x, step_y), axis=-1),\n            axis=-1,\n            keepdims=True,\n        ),\n    )\n    step_x = cp.maximum(-_max_shift, cp.minimum(step_x, _max_shift))\n    step_y = cp.maximum(-_max_shift, cp.minimum(step_y, _max_shift))\n\n    # SYNCHRONIZE ME?\n    # step -= comm.Allreduce_mean(step)\n    # Ensure net movement is zero\n    step_x -= cp.mean(step_x, axis=-1, keepdims=True)\n    step_y -= cp.mean(step_y, axis=-1, keepdims=True)\n    logger.info('position update norm is %+.3e', norm(step_x))\n\n    # print(cp.abs(step_x) > 0.5)\n    # print(cp.abs(step_y) > 0.5)\n\n    scan[..., 0] -= step_y\n    scan[..., 1] -= step_x\n\n    return scan, position_options\n","sub_path":"src/tike/ptycho/solvers/lstsq.py","file_name":"lstsq.py","file_ext":"py","file_size_in_byte":20315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"147937427","text":"from functools import wraps\nfrom datetime import datetime\nimport pickle\n\ndef instance_cache(timeout=None):\n    \"\"\" Decorator to the result of a function call inside a class\n    Cache value will be store inside __instance_cache__ field of the class or instance\n    \n    Parameters:\n        timeout (timedelta): when the cache value become invalid\n    \n    Returns:\n        wrapped function\n    \"\"\"\n    def __decorate(f):\n        @wraps(f)\n        def __wraps(self, *args, **kwargs):\n            cache_dict_name = '__instance_cache__'\n            if not hasattr(self, cache_dict_name):\n                setattr(self, cache_dict_name, {})\n            cache_dict = self.__instance_cache__\n            f_key = pickle.dumps([f.__name__, args, kwargs])\n            if timeout:\n                expire_ts = datetime.utcnow() + timeout\n            else:\n                expire_ts = None\n            if f_key not in cache_dict:\n                # Cache missing\n                cache_dict[f_key] = {\n                    'expire_ts': expire_ts,\n                    'result': f(self, *args, **kwargs),\n                }\n            cached_data = cache_dict[f_key]\n            if cached_data['expire_ts'] is not None and \\\n                    cached_data['expire_ts'] < datetime.utcnow():\n                # Cache expire\n                cache_dict[f_key] = {\n                    'expire_ts': expire_ts,\n                    'result': f(self, *args, **kwargs),\n                }\n                cached_data = cache_dict[f_key]\n            return cached_data['result']\n        return __wraps\n    return __decorate","sub_path":"cl/utils/cache.py","file_name":"cache.py","file_ext":"py","file_size_in_byte":1592,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"551873428","text":"from django.http import HttpResponse\nfrom django.template import loader\nfrom django.shortcuts import get_object_or_404, render\nfrom django.http import Http404\nfrom .models import Recipe, Ingredient, RecipeIngredient, Track\nfrom django.db.models import Sum\nfrom datetime import date\n\ndef index(request):\n    recipe_list = Recipe.objects.all()[:15]\n    args = {'recipe_list': recipe_list}\n    template = loader.get_template('scal/index.html')\n    return HttpResponse(template.render(args, request))\n\ndef detail(request, recipe_id):\n    recipe = get_object_or_404(Recipe, pk=recipe_id)\n    total_calories = 0\n    for ri in recipe.recipeingredient_set.all():\n        total_calories += (ri.ingredient.calories * ri.quantity)\n    args = {'recipe': recipe, 'total_calories': total_calories}\n    return render(request, 'scal/detail.html', args)\n\ndef track(request):\n    tracks_today = Track.objects.filter(date__year=date.today().year, date__month=date.today().month, date__day=date.today().day)\n    total_calories = tracks_today.aggregate(Sum('calories')).values()[0]\n    args = {'tracks_today': tracks_today, 'total_calories': total_calories}\n    return render(request, 'scal/tracker.html', args)","sub_path":"scal/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1190,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"20740797","text":"# -*- coding:utf-8 -*-\nfrom __future__ import print_function\n\nimport sys\nimport io\nfrom operator import add\n\nfrom pyspark import SparkContext\n\nif __name__ == \"__main__\":\n    if len(sys.argv) != 1:\n        print(\"Usage: examplent\")\n        exit(-1)\n\n    sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')\n\n   # print(sys.getdefaultencoding())\n   # print(sys.stdout.encoding)\n\n    fileOct = \"hdfs:///user/y_tadayasu/data/sales-october.csv\"\n    fileNov = \"hdfs:///user/y_tadayasu/data/sales-november.csv\"\n    fileProducts = \"/home/y_tadayasu/data/products.csv\"\n    fileResult = \"hdfs:///user/y_tadayasu/data/result.csv\"\n\n    sc = SparkContext(appName=\"Example\")\n\n    salesOctRDD = sc.textFile(fileOct).map(lambda x: x.split(\",\")).map(lambda x:(x[2],int(x[3]))).reduceByKey(add).filter(lambda x: x[1] >= 50)\n\n    salesNovRDD = sc.textFile(fileNov).map(lambda x: x.split(\",\")).map(lambda x:(x[2],int(x[3]))).reduceByKey(add).filter(lambda x: x[1] >= 50)\n\n    salesOctNovRDD = salesOctRDD.join(salesNovRDD)\n   \n    # A data of salesOctNovRDD element is like this tupple (8 (60,72)) \n    salesRDD = salesOctNovRDD.map(lambda x: (x[0],x[1][0]+x[1][1]))\n\n    productsMap = {}\n    for line in open(fileProducts, 'r',encoding='utf-8'):\n        splitLine = line.split(',')\n        productId = splitLine[0]\n        productName = splitLine[1]\n        unitPrice = int(splitLine[2])\n        productsMap[productId] = (productName,unitPrice)\n\n    broadcastMap = sc.broadcast(productsMap)\n\n    result_list = []\n    output = salesRDD.collect()\n    for (productid, count) in output:\n        result_list.append((broadcastMap.value[productid][0],count*productsMap[productid][1]))\n\n    resultRDD = sc.parallelize(result_list)\n    resultRDD.saveAsTextFile(fileResult)\n    sc.stop()\n","sub_path":"chap5-1.py","file_name":"chap5-1.py","file_ext":"py","file_size_in_byte":1773,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"477117101","text":"import re\nfrom bsddb3 import db\n\nTE = 'te.idx'\nDA = 'da.idx'\nTW = 'tw.idx'\n\nteDB = db.DB()\ndaDB = db.DB()\ntwDB = db.DB()\nteDB.open(TE, None, db.DB_BTREE, db.DB_DIRTY_READ)\ndaDB.open(DA, None, db.DB_BTREE, db.DB_DIRTY_READ)\ntwDB.open(TW, None, db.DB_HASH, db.DB_DIRTY_READ)\nteCurs = teDB.cursor()\ndaCurs = daDB.cursor()\ntwCurs = twDB.cursor()\n\ndef grammar():\n    tids = []\n    queries = input('Enter a searching query: ').lower()\n    queries = queries.split(' ')\n    queryNum = 0\n\n    for query in queries:\n        if ':' in query:\n            query = query.split(':')\n            if query[0] == 'text':\n                termPrefix = 't-'\n            elif query[0] == 'name':\n                termPrefix = 'n-'\n            elif query[0] == 'location':\n                termPrefix = 'l-'\n            elif query[0] == 'date':\n                termPrefix = ''\n            else:\n                raise RuntimeError('invalid query')\n\n            # this line can add some detection rule for invalid input of term\n            if query[1][-1] == '%':\n                if termPrefix == '':\n                    raise RuntimeError('invalid query')\n                else:\n                    newTids = partialSearch(termPrefix, query[1][:-1])\n            else:\n                newTids = search(termPrefix, query[1])\n\n        elif '>' in query:\n            query = query.split('>')\n            if query[0] != 'date':\n                raise RuntimeError('invalid query')\n            else:\n                newTids = afterDate(query[1])\n\n        elif '<' in query:\n            query = query.split('<')\n            if query[0] != 'date':\n                raise RuntimeError('invalid query')\n            else:\n                newTids = beforeDate(query[1])\n\n        elif query[-1] == '%': # this can be a more specific rule to avoid % is in the term rather than at the end\n            textTids = partialSearch('t-', query[:-1])\n            nameTids = partialSearch('n-', query[:-1])\n            locationTids = partialSearch('l-', query[:-1])\n            newTids = list(set(textTids).union(set(nameTids).union(locationTids)))\n\n        else:\n            textTids = search('t-', query)\n            nameTids = search('n-', query)\n            locationTids = search('l-', query)\n            newTids = list(set(textTids).union(set(nameTids).union(locationTids)))\n\n        # print(newTids)\n\n        # if tids == []: This line is commented b/c it will cause bug when there are multiple queries and one of them is no result. When one query is no result, tids should be empty, but tids will be reset to newTids due to this line.\n        if queryNum == 0:\n            tids = newTids\n        else:\n            tids = list(set(tids).intersection(newTids))\n\n        queryNum += 1\n\n    return tids\n\ndef search(termPrefix, term):\n    if termPrefix == '':\n        curs = daCurs\n    else:\n        curs = teCurs\n\n    key = termPrefix + term\n    key = key.encode()\n    results = []\n\n    result = curs.get(key, db.DB_SET)\n    while result != None: # curs.get(key, db.DB_NEXT_DUP) will return None if it reaches end\n        # print(result)\n        results.append(result[1])\n        result = curs.get(key, db.DB_NEXT_DUP)\n\n    return results\n\ndef partialSearch(termPrefix, term):\n    key = termPrefix + term\n    key = key.encode()\n    results = []\n\n    result = teCurs.get(key, db.DB_SET_RANGE)\n    while result != None: # if there is no result, Nonetype cannot be compared with bytes\n        if result[0].startswith(key):\n            # print(result)\n            results.append(result[1])\n        else:\n            break\n        result = teCurs.get(key, db.DB_NEXT)\n\n    return results\n\ndef afterDate(date):\n    date = date.encode()\n    results = []\n\n    result = daCurs.get(date, db.DB_SET_RANGE)\n    while result != None: # when the date goes through to the end, it will return None\n        if result[0] != date: # we just want to include result after the given date\n            # print(result)\n            results.append(result[1])\n        result = daCurs.get(date, db.DB_NEXT)\n\n    return results\n\ndef beforeDate(date):\n    date = date.encode()\n    results = []\n\n    result = daCurs.get(date, db.DB_SET_RANGE)\n    result = daCurs.get(date, db.DB_PREV) # move the cursor to previous one\n    while result != None: # when the date goes through to the end, it will return None\n        # print(result)\n        results.append(result[1])\n        result = daCurs.get(date, db.DB_PREV) # since it goes through the databse reversely, the result will be from latest date to earliest date\n\n    return results\n\ndef searchTweets(tids):\n    for tid in tids:\n        result = twCurs.get(tid, db.DB_SET)\n        output(result[1])\n\ndef output(line):\n    line = line.decode()\n    # print('debug: ' + line)\n\n    tid = re.findall(r'<id>(.+?)</id>', line)\n    text = re.findall(r'<text>(.+?)</text>', line)\n    tname = re.findall(r'<name>(.+?)</name>', line)\n    tlocation = re.findall(r'<location>(.+?)</location>', line)\n    tdate = re.findall(r'<created_at>(.+?)</created_at>', line)\n    retweet = re.findall(r'<retweet_count>(.+?)</retweet_count>', line)\n    description = re.findall(r'<description>(.+?)</description>', line)\n    url = re.findall(r'<url>(.+?)</url>', line)\n    print(\"tid: %s \\nname: %s \\nlocation: %s \\ndate: %s \\nretweet: %s \\ndescription: %s \\nurl: %s \\ntext: %s\" % (''.join(tid), ''.join(tname), ''.join(tlocation), ''.join(tdate),''.join(retweet),''.join(description),''.join(url), ''.join(text)))\n    print('\\n')\n\ndef main():\n    invalidInput = True\n    while invalidInput:\n        try:\n            tids = grammar()\n            invalidInput = False\n        except RuntimeError:\n            print('invalid query')\n\n    if tids == []:\n        print('No result matches.')\n    else:\n        searchTweets(tids)\n\n    teCurs.close()\n    daCurs.close()\n    twCurs.close()\n    teDB.close()\n    daDB.close()\n    twDB.close()\n\nmain()\n","sub_path":"phase3.py","file_name":"phase3.py","file_ext":"py","file_size_in_byte":5881,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"34882663","text":"def xyz():\n  ip = input()\n  op = ip.count('(')\n  ed = ip.count(')')\n  q = ip.count('?')\n  qop = 0\n  qed = 0\n  if op>ed:\n    qed = op-ed\n    q-= qed\n  elif op<ed:\n    qop = ed-op\n    q-= qop\n  if q&1:\n    return 'NO'\n  else:\n    qop+=q//2\n    qed+=q//2\n  \n  ip = list(ip)\n  v=0\n  for i in range(len(ip)):\n    if ip[i]=='(':\n      v+=1\n    elif ip[i]==')':\n      v-=1\n    elif ip[i]=='?' and qop>0:\n      v+=1\n      qop-=1\n    elif ip[i]=='?' and qed>0:\n      v-=1\n      qed-=1\n    if v<0:\n      return 'NO'\n  \n  return 'YES' if v==0 else 'NO'\n\n\nfor _ in range(int(input())):\n  print(xyz())\n","sub_path":"Regular Bracket Sequence.py","file_name":"Regular Bracket Sequence.py","file_ext":"py","file_size_in_byte":589,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"305100509","text":"import os\nimport time\nimport math\nimport subprocess\nfrom pathlib import Path\nfrom multiprocessing.pool import Pool, ThreadPool\n\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\nfrom tqdm import tqdm\nfrom sklearn import preprocessing\n\n\ndef execute(wav_path, out_dir, smile_path, smile_conf):\n    \"\"\"Execute external openSMILE command to extract features from audio\n    files\"\"\"\n    _, wav_name = os.path.split(wav_path)\n    name, _ = os.path.splitext(wav_name)\n    save_path = os.path.join(out_dir, name + \".arff\")\n    # Execute commands\n    command = \"{} -C {} -I {} -O {}\".format(\n        smile_path, smile_conf, wav_path, save_path)\n\n    process = subprocess.Popen(\n        command, shell=True, stdout=subprocess.PIPE,\n        stderr=subprocess.STDOUT, close_fds=True)\n    if process.stderr is not None:\n        print(\"Error processing {}. message: {}\".format(\n            wav_path, process.stderr.read()))\n        process.stderr.close()\n    else:\n        res = process.stdout.read().decode(\"utf-8\")\n        if res.split().count(\"successfully\") != 2:\n            print(\"Error processing {}\".format(wav_path))\n    process.stdout.close()\n\n\ndef extract(iemocap_dir, out_dir, smile_path, smile_conf,\n            num_processes=32, step_size=32):\n    \"\"\"Extract features from input audio files using openSMILE and save\n    extracted features to files.\n\n    Parameters\n    ----------\n    iemocap_dir : str\n        Directory to the \"IEMOCAP_full_release\" folder.\n    out_dir : str\n        Output directory.\n    num_processes : int\n        Number of threads in the thread pool.\n    step_size : int\n        Size of each step (like batch size).\n\n    \"\"\"\n    wav_paths = list(Path(iemocap_dir).rglob(\"sentences/wav/**/*.wav\"))\n    num_steps = math.ceil(len(wav_paths) / step_size)\n    pool = ThreadPool(processes=num_processes)\n\n    for i in tqdm(range(num_steps)):\n        start = i * step_size\n        end = min(start + step_size, len(wav_paths))\n\n        fn = lambda path: execute(path, out_dir, smile_path, smile_conf)\n        pool.map(fn, wav_paths[start:end])\n\n\ndef get_feature_idxs():\n    \"\"\"Get feature indices for files extracted using `extract` function\"\"\"\n    mfcc = np.arange(4, 16)\n    mfcc_del = np.arange(30, 42)\n    mfcc_del_del = np.arange(56, 68)\n\n    loudness = np.array([3])\n    loudness_del = np.array([29])\n    loudness_del_del = np.array([55])\n\n    f0 = np.array([26])\n    f0_del = np.array([52])\n\n    voice_prob = np.array([25])\n    voice_prob_del = np.array([51])\n\n    zcr = np.array([24])\n    zcr_del = np.array([50])\n\n    idxs = np.concatenate(\n        [f0, f0_del, loudness, loudness_del, loudness_del_del, voice_prob,\n         voice_prob_del, zcr, zcr_del, mfcc, mfcc_del, mfcc_del_del]\n    )\n\n    return idxs\n\n\ndef get_count_and_name(filepath):\n    \"\"\"Helper function to get the number of datapoints and file name without\n    extension for a feature file extracted using `extract` function\"\"\"\n    # First 86 lines is irrelevant\n    count = len(open(filepath, \"r\").readlines()) - 86\n    _, filename = os.path.split(filepath)\n    name, _ = os.path.splitext(filename)\n    return count, name\n\n\ndef get_feature(filepath):\n    \"\"\"Read features from a feature file extracted using `extract` function.\n\n    Parameters\n    ----------\n    filepath : str\n        Path to the feature file.\n\n    Returns\n    -------\n    ndarray\n        Array of shape (N, F), where N is sequence length and F is the number\n        of features (45 by default).\n\n    \"\"\"\n    feature_idxs = get_feature_idxs()\n    with open(filepath, \"r\") as fin:\n        for i in range(86):\n            fin.readline()\n        features = pd.read_csv(fin, header=None, usecols=feature_idxs)\n    features = features[feature_idxs].to_numpy()\n    return features\n\n\ndef get_feature_wrapper(i, filepaths, counts_cum, feats):\n    \"\"\"Helper function that wraps function `get_feature` to be used in\n    multithreading\"\"\"\n    feat = get_feature(filepaths[i])\n\n    start = 0 if i == 0 else counts_cum[i - 1]\n    end = start + len(feat)\n    feats[start:end] = feat\n\n\ndef normalize(features_dir, num_processes=32):\n    \"\"\"Normalize features with respect to speakers.\n\n    Parameters\n    ----------\n    features_dir : str\n        Folder containing feature files extracted using `extract` function.\n    num_processes : type\n        Number of threads in the thread pool.\n\n    Returns\n    -------\n    dict\n        Dictionary of pairs \"name:features\", where \"name\" is the utterance file\n        name without extension (e.g., Ses01F_impro01_F000) and features are\n        respective features extracted and normalized.\n\n    \"\"\"\n    speakers = {}\n    pool = ThreadPool(processes=num_processes)\n\n    # Read files\n    for filename in os.listdir(features_dir):\n        speaker = filename.split(\"_\")[0]\n        if speaker not in speakers:\n            speakers[speaker] = list()\n\n        filepath = os.path.join(features_dir, filename)\n        speakers[speaker].append(filepath)\n\n    # Loop over each speaker and do normalization\n    all_feat = {}\n    for speaker, filepaths in tqdm(speakers.items()):\n        # Get sequence lengths and file names\n        count_and_name = pool.map(get_count_and_name, filepaths)\n        counts, names = tuple(zip(*count_and_name))\n        counts_cum = np.cumsum(counts)\n\n        # Get all features for this speaker\n        feats = np.empty((counts_cum[-1], 45), dtype=\"float64\")\n        fn = lambda i: get_feature_wrapper(i, filepaths, counts_cum, feats)\n        pool.map(fn, range(len(filepaths)))\n\n        # Normalize\n        feats_zscore = stats.zscore(feats)\n        start = 0\n        for name, count_cum in zip(names, counts_cum):\n            end = count_cum\n            all_feat[name] = feats_zscore[start:end]\n            start = end\n    return all_feat\n\n\ndef get_sequence(arr, step_size, overlap):\n    seq = list()\n    start = 0\n    end = step_size\n    cont = True\n\n    while cont:\n        if end >= len(arr):\n            end = len(arr)\n            cont = False\n        seq.append(np.arange(start, end))\n        start += step_size - overlap\n        end = start + step_size\n    return np.concatenate(seq)\n\n\ndef average_pool_single(arr, step_size=5, overlap=0, pad=True, max_len=2500):\n    \"\"\"Average pooling over the time axis of a single time series data.\n\n    Parameters\n    ----------\n    arr : ndarray\n        Array of shape [N, F], where N is the sequence length and F is the\n        feature dimension.\n    step_size : int\n        Step size (width of pool).\n    max_len : int\n        Maximum sequence length.\n        If `max_len=0` or negative, the array is kept as is.\n        Otherwise, any data beyond this threshold is removed.\n    overlap : int\n        Overlapping length between two subsequences. This is set to 0 (no\n        overlapping) in the original implementation.\n    pad : bool\n        Whether to pad zeros to the end of the sequence. This is set to True in\n        the original implementation. However, it is fairly obvious that it is\n        better set to False.\n\n    Returns\n    -------\n    ndarray\n        Array after average pooling.\n\n    \"\"\"\n    # Truncate\n    if max_len > 0 and len(arr) > max_len:\n        arr = arr[:max_len]\n    # Overlapping\n    if overlap >= step_size:\n        raise ValueError(\"Invalid overlap value: {}. It should be smaller \"\n                         \"than `step_size` ({})\".format(overlap, step_size))\n    idxs = get_sequence(arr, step_size=step_size, overlap=overlap)\n    arr = arr[idxs]\n    # Pad\n    excess = len(arr) % step_size\n    if excess != 0 and pad:\n        pad_len = step_size - excess\n        arr = np.pad(arr, [(0, pad_len), (0, 0)])\n    # Trim at the end\n    excess = len(arr) % step_size\n    split_point = len(arr) - excess\n    last = arr[split_point:]\n    arr = arr[:split_point]\n    # Average pooling\n    num_features = arr.shape[1]\n    arr = arr.reshape(-1, step_size, num_features).mean(axis=1)\n    if len(last) > 0:\n        last = last.mean(axis=0)\n        # Append the last cut\n        arr = np.vstack([arr, last])\n    return arr\n\n\ndef average_pool(features, num_processes=32, pool_size=32, step_size=5,\n                 overlap=0, pad=True, max_len=2500):\n    \"\"\"Average pooling over the time axis of a single time series data.\n\n    Parameters\n    ----------\n    features : ndarray\n        Dictionary of pairs \"name:features\", where \"name\" is the utterance file\n        name without extension (e.g., Ses01F_impro01_F000) and features are\n        respective features extracted and normalized. This should be the\n        results of executing the `extract_and_normalize.py`\n    num_processes : int\n        Number of threads in the thread pool.\n    pool_size : int\n        Thread pool size.\n    step_size : int\n        Step size (width of pool).\n    max_len : int\n        Maximum sequence length.\n        If `max_len=0` or negative, the array is kept as is.\n        Otherwise, any data beyond this threshold is removed.\n    overlap : int\n        Overlapping length between two subsequences. This is set to 0 (no\n        overlapping) in the original implementation.\n    pad : bool\n        Whether to pad zeros to the end of the sequence. This is set to True in\n        the original implementation. However, it is fairly obvious that it is\n        better set to False.\n\n    Returns\n    -------\n    ndarray\n        Array after average pooling.\n\n    \"\"\"\n    names = list(features.keys())\n    feats = list(features.values())\n    features_pooled = list()\n    # Parameters for multithreading\n    num_steps = math.ceil(len(features) / pool_size)\n    pool = ThreadPool(processes=num_processes)\n    fn = lambda arr: average_pool_single(arr, step_size, overlap, pad, max_len)\n    # Loop over arrays and do average pooling\n    for i in tqdm(range(num_steps)):\n        start = i * pool_size\n        end = min(start + pool_size, len(features))\n\n        feat = feats[start:end]\n        feats_pooled = pool.map(fn, feat)\n        features_pooled.extend(feats_pooled)\n\n    assert len(names) == len(features_pooled)\n    return dict(zip(names, features_pooled))\n\n\ndef get_label(iemocap_dir):\n    \"\"\"Get labels for every utterance under the form \"utt_name:label\".\n\n    Parameters\n    ----------\n    iemocap_dir : str\n        Path to the `IEMOCAP_full_release` directory.\n\n    Returns\n    -------\n    dict\n        Dictionary of pairs utt_name:label\n        (e.g., 'Ses01F_impro01_F000': 'neu').\n\n    \"\"\"\n    emo_dict = dict()\n\n    def get_info(line):\n        line = line.split()\n        name = line[3]\n        label = line[4]\n        # Convert \"exc\" (excited) to \"hap\" (happy)\n        label = \"hap\" if label == \"exc\" else label\n        return name, label\n\n    for filepath in Path(iemocap_dir).rglob(\"EmoEvaluation/*.txt\"):\n        with open(filepath, \"r\") as fin:\n            lines = fin.readlines()\n\n        lines = filter(lambda x: x[0] == \"[\", lines)  # filter lines with label\n        emo_dict.update(dict(map(get_info, lines)))\n    return emo_dict\n\n\ndef get_dialog_order(iemocap_dir):\n    \"\"\"Get order of speakers for every dialog. For session 2, the order is read\n    from the label files *.lab (not from the transcription files *.txt as in\n    other sessions). This differs from the original implementation that the\n    order is sorted based on utterances' start time AND end time.\n\n    Parameters\n    ----------\n    iemocap_dir : str\n        Path to the `IEMOCAP_full_release` directory.\n\n    Returns\n    -------\n    dict\n        Dictionary of pairs \"scene:utt_orders\".\n\n    \"\"\"\n    def filt(utt_name):\n        return utt_name[:3] == \"Ses\" and \"XX\" not in utt_name\n\n    order_dict = dict()\n    # All sessions, except session 2\n    for filepath in Path(iemocap_dir).rglob(\"dialog/transcriptions/*.txt\"):\n        _, filename = os.path.split(filepath)\n        # Session 2 needs to be handled differently\n        if int(filename[3:5]) == 2:\n            continue\n        scene, _ = os.path.splitext(filename)\n        with open(filepath, \"r\") as fin:\n            lines = fin.readlines()\n        utt_orders = list(map(lambda x: x.split()[0], lines))\n        # Filter unnecessary utterances\n        utt_orders = list(filter(filt, utt_orders))\n        order_dict[scene] = utt_orders\n    # Session 2\n    for filepath in Path(iemocap_dir).rglob(\n            \"Session2/dialog/lab/Ses02_F/*.lab\"):\n        _, filename = os.path.split(filepath)\n        scene, _ = os.path.splitext(filename)\n\n        # Read *.lab files as csv\n        f_path = str(filepath)\n        f_csv = pd.read_csv(\n            f_path, delim_whitespace=True, header=None, skiprows=[0])\n        m_path = f_path.replace(\"Ses02_F\", \"Ses02_M\")\n        m_csv = pd.read_csv(\n            m_path, delim_whitespace=True, header=None, skiprows=[0])\n\n        # Sort by start and end time\n        tot_csv = pd.concat([f_csv, m_csv], ignore_index=True)\n        tot_csv = tot_csv.sort_values([0, 1]).reset_index(drop=True)\n        utt_orders = tot_csv[2].to_list()\n        order_dict[scene] = utt_orders\n\n    return order_dict\n\n\ndef generate_interaction_sample(index_words, emo_dict,\n                                emo=['ang', 'hap', 'neu', 'sad']):\n    \"\"\"Generate interaction training samples of pairs \"center - target -\n    opposite\", where \"center\" is the current utterance, \"target\" is the\n    previous utterance of the current speaker, and \"opposite\" is the previous\n    utterance of the interlocutor.\n\n    Parameters\n    ----------\n    index_words : list\n        Utterance order of the current scene generated by `get_dialog_order`\n        function.\n    emo_dict : dict\n        Dictionary of pairs \"utt_name:label\" generated by `get_label` function.\n    emo : list\n        List of emotions to use. Defaults to ['ang', 'hap', 'neu', 'sad'],\n        which is used in the original implementation.\n\n    Returns\n    -------\n    tuple\n        Tuple of lists: center utterance names, target utterance names,\n        opposite utterance names, center utterance labels, target utterance\n        labels, opposite utterance labels, distance between center utterance\n        and target utterance, distance between center utterance and opposite\n        utterance.\n\n    \"\"\"\n    # Utterance names\n    centers, targets, opposites = list(), list(), list()\n    # Utterance labels\n    center_labels, target_labels, opposite_labels = list(), list(), list()\n    # Distances\n    target_dists, opposite_dists = list(), list()\n    # Compatibility\n    emo_dict[None] = None\n\n    for i, center in enumerate(index_words):\n        if emo_dict[center] not in emo:\n            continue\n\n        centers.append(center)\n        center_labels.append(emo_dict[center])\n        target = None\n        target_dist = None\n        opposite = None\n        opposite_dist = None\n\n        # Traverse up to previous 8 utterances\n        for j in range(min(i, 8)):\n            #  Stop if both are found\n            if target is not None and opposite is not None:\n                break\n\n            dist = j + 1\n            curr_word = index_words[i - dist]\n            # If it is the same speaker\n            if curr_word[-4] == center[-4]:\n                if target is None:\n                    target = curr_word\n                    target_dist = dist\n            elif opposite is None:\n                opposite = curr_word\n                opposite_dist = dist\n\n        # Update\n        targets.append(target)\n        target_labels.append(emo_dict[target])\n        target_dists.append(target_dist)\n\n        opposites.append(opposite)\n        opposite_labels.append(emo_dict[opposite])\n        opposite_dists.append(opposite_dist)\n\n    return (centers, targets, opposites, center_labels, target_labels,\n            opposite_labels, target_dists, opposite_dists)\n\n\ndef generate_random_sample(index_words, emo_dict, keep_speakers=False,\n                           emo=['ang', 'hap', 'neu', 'sad']):\n    \"\"\"Generate random training samples of pairs \"center - target -\n    opposite\".\n\n    Parameters\n    ----------\n    index_words : list\n        Utterance order of the current scene.\n    emo_dict : dict\n        Dictionary of pairs \"utt_name:label\" generated by `get_label` function.\n    keep_speakers : bool\n        Whether to use the speaker type to sample.\n            If True, \"target\" is the still the utterance of the current\n            speaker, and \"opposite\" is still the utterance of the interlocutor.\n            Otherwise, \"target\" and \"opposite\" are chosen completely randomly,\n            i.e, \"target\" may now be the interlocutor's utterance, and vice\n            versa. This is equivalent to the original implementation.\n    emo : list\n        List of emotions to use. Defaults to ['ang', 'hap', 'neu', 'sad'],\n        which is used in the original implementation.\n\n    Returns\n    -------\n    tuple\n        Same as `generate_interaction_data`.\n\n    \"\"\"\n    # Utterance names\n    centers, targets, opposites = list(), list(), list()\n    # Utterance labels\n    center_labels, target_labels, opposite_labels = list(), list(), list()\n    # Distances are always None (??)\n    target_dists, opposite_dists = list(), list()\n\n    for i, center in enumerate(index_words):\n        if emo_dict[center] not in emo:\n            continue\n\n        centers.append(center)\n        center_labels.append(emo_dict[center])\n\n        if keep_speakers:\n            # Get target and opposite indices\n            target_idxs, opposite_idxs = list(), list()\n            for j in range(len(index_words)):\n                if j == i:\n                    continue\n                if index_words[i][-4] == index_words[j][-4]:\n                    target_idxs.append(j)\n                else:\n                    opposite_idxs.append(j)\n            # Sample\n            target_i = np.random.choice(target_idxs)\n            opposite_i = np.random.choice(opposite_idxs)\n\n        # Sample completely randomly\n        else:\n            idxs = [j for j in range(len(index_words)) if j != i]\n            target_i, opposite_i = np.random.choice(\n                idxs, size=(2,), replace=False)\n\n        targets.append(index_words[target_i])\n        target_labels.append(emo_dict[index_words[target_i]])\n\n        opposites.append(index_words[opposite_i])\n        opposite_labels.append(emo_dict[index_words[opposite_i]])\n\n        target_dists.append(None)\n        opposite_dists.append(None)\n\n    return (centers, targets, opposites, center_labels, target_labels,\n            opposite_labels, target_dists, opposite_dists)\n\n\ndef generate_interaction_data(order_dict, emo_dict, val_prefix=None,\n                              test_prefix=None, mode=\"context\",\n                              emo=['ang', 'hap', 'neu', 'sad'],\n                              num_processes=32, **kwargs):\n    \"\"\"Generate train, validation and test data.\n\n    Parameters\n    ----------\n    order_dict : dict\n        Dictionary of pairs \"scene:utt_orders\" generated by the\n        `get_dialog_order` function, where \"utt_orders\" is the order of the\n        utterances in that particular scene.\n    emo_dict : dict\n        Dictionary of pairs \"utt_name:label\" generated by `get_label` function.\n    val_prefix : str\n        Scenes with this as prefix will be used as validation data, e.g.,\n        \"Ses01F\".\n    test_prefix ; str\n        Scenes with this as prefix will be used as test data, e.g., \"Ses01M\".\n    mode : str\n        One of [\"context\", \"random\"]. Refer to `generate_interaction_sample`\n        and `generate_random_sample`, respectively, for more details.\n    emo : list\n        List of emotions to use. Defaults to ['ang', 'hap', 'neu', 'sad'],\n        which is used in the original implementation.\n    num_processes : int\n        Number of threads in the thread pool.\n    **kwargs\n        Keyword arguments to be passed to the sample generator (either\n        `generate_interaction_sample` or `generate_random_sample`).\n\n    Returns\n    -------\n    pd.DataFrame, pd.DataFrame, pd.DataFrame\n        Dataframe of train, validation and test data, respectively.\n\n    \"\"\"\n    if mode == \"context\":\n        generator = generate_interaction_sample\n    elif mode == \"random\":\n        generator = generate_random_sample\n    else:\n        raise ValueError(\"Invalid mode value. Expected one of \"\n                         \"['context', 'random'], got {} instead.\".format(mode))\n\n    train_scenes, val_scenes, test_scenes = list(), list(), list()\n    train_utt_orders, val_utt_orders, test_utt_orders = list(), list(), list()\n    for scene, utt_orders in order_dict.items():\n        if val_prefix is not None and val_prefix in scene:\n            val_scenes.append(scene)\n            val_utt_orders.append(utt_orders)\n        elif test_prefix is not None and test_prefix in scene:\n            test_scenes.append(scene)\n            test_utt_orders.append(utt_orders)\n        else:\n            train_scenes.append(scene)\n            train_utt_orders.append(utt_orders)\n\n    fn = lambda order: generator(order, emo_dict, emo=emo, **kwargs)\n\n    pool = ThreadPool(processes=num_processes)\n\n    train_data = list(zip(*pool.map(fn, train_utt_orders)))\n    train_data = [sum(data, []) for data in train_data]\n\n    val_data = list(zip(*pool.map(fn, val_utt_orders)))\n    val_data = [sum(data, []) for data in val_data]\n\n    test_data = list(zip(*pool.map(fn, test_utt_orders)))\n    test_data = [sum(data, []) for data in test_data]\n\n    # 8 is length of tuple returned by the generator\n    assert len(train_data) in [0, 8]\n    assert len(val_data) in [0, 8]\n    assert len(test_data) in [0, 8]\n\n    # Save results to a dataframe\n    cols = [\"center\", \"target\", \"opposite\", \"center_label\", \"target_label\",\n            \"opposite_label\", \"target_dist\", \"opposite_dist\"]\n    df_train = pd.DataFrame(dict(zip(cols, train_data)))\n    df_val = pd.DataFrame(dict(zip(cols, val_data)))\n    df_test = pd.DataFrame(dict(zip(cols, test_data)))\n    return df_train, df_val, df_test\n\n\nclass InteractionDataGenerator:\n    \"\"\"\n    IEMOCAP interaction data generator, which returns a tuple of four arrays:\n    center (the current utterances), target (previous utterance of the current\n    speakers) and opposite (previous utterance of the interlocutors), as well\n    as labels for the center utterances.\n    \"\"\"\n    def __init__(self, df, batch_size, features_dict, sort_by_len=True,\n                 original=False):\n        \"\"\"Initialize.\n\n        Parameters\n        ----------\n        df : pd.DataFrame\n            Dataframe containing data information.\n        batch_size : int\n            Batch size.\n        features_dict : dict\n            Dictionary of pairs \"utt_name:features\".\n        sort_by_len : bool\n            Whether to sort the data by length (maximum length of three:\n            center, target and opposite across all samples). `sort_by_len=True`\n            is equivalent to `mode=\"bucketing\"` in the original implementation.\n                If True, sort the data by length as described.\n                Otherwise, shuffle the data randomly.\n        original : bool\n            Whether to truncate the data as in the original implementation. The\n            data will be truncated to the maximum length of 3 arrays (center,\n            target and opposite) of the LAST SAMPLE in the batch. This is a\n            really strange behavior and could potentially be an unintentional\n            mistake. This option is provided for the ONLY purpose of comparing\n            with the original implementation. You don't want to set this to\n            True in any other cases.\n\n        \"\"\"\n        # Cache data\n        self.df = df\n        self.features_dict = features_dict\n        self.sort_by_len = sort_by_len\n        self.original = original\n\n        # Info inferred from features\n        self.dtype = list(features_dict.values())[0].dtype\n        self.features_dim = list(features_dict.values())[0].shape[1]\n\n        # Iterator\n        self.num_samples = len(df)\n        self.batch_size = batch_size\n        self.num_batches = math.ceil(len(df) / batch_size)\n\n        # Rearrange data\n        self._rearrange()\n\n        # One-hot encoding\n        self.encoder = preprocessing.LabelEncoder()\n        self.label = self.encoder.fit_transform(self.df[\"center_label\"])\n\n        # For compatibility\n        self.features_dict[None] = np.zeros(shape=(0, self.features_dim))\n        self.features_dict[np.NaN] = np.zeros(shape=(0, self.features_dim))\n\n    def _rearrange(self):\n        \"\"\"\n        If `self.sort_by_len=True`, sort by the maximum length of data\n        across center, target and opposite features data.\n        Otherwise, shuffle the dataframe randomly.\n        \"\"\"\n        def transform(row):\n            \"\"\"Function to be applied to every row in the dataframe\"\"\"\n            center_len = len(self.features_dict[row[\"center\"]])\n            target_len = opposite_len = 0\n            if row[\"target\"] not in [None, np.NaN]:\n                target_len = len(self.features_dict[row[\"target\"]])\n            if row[\"opposite\"] not in [None, np.NaN]:\n                opposite_len = len(self.features_dict[row[\"opposite\"]])\n            return max(center_len, target_len, opposite_len)\n        self.df[\"seq_len\"] = self.df.apply(transform, axis=1)\n        # Sort by len\n        if self.sort_by_len:\n            self.df = self.df.sort_values(\"seq_len\")\n            self.idxs = self.df.index.to_list()\n            self.df = self.df.reset_index(drop=True)\n        # Shuffle randomly\n        else:\n            self.idxs = np.random.permutation(len(self))\n            self.df = self.df.iloc[self.idxs].reset_index(drop=True)\n\n    def __iter__(self):\n        \"\"\"Initialize generator\"\"\"\n        self.batch_idx = 0\n        return self\n\n    def __next__(self):\n        if self.batch_idx >= self.num_batches:\n            raise StopIteration\n\n        start_idx = self.batch_idx * self.batch_size\n        end_idx = min(start_idx + self.batch_size, self.num_samples)\n        batch_size = end_idx - start_idx\n\n        # Initialize\n        max_len = max(self.df.iloc[start_idx:end_idx][\"seq_len\"])\n        center_features = np.zeros(\n            shape=(batch_size, max_len, self.features_dim), dtype=self.dtype)\n        target_features = np.zeros(\n            shape=(batch_size, max_len, self.features_dim), dtype=self.dtype)\n        opposite_features = np.zeros(\n            shape=(batch_size, max_len, self.features_dim), dtype=self.dtype)\n        labels = np.zeros(shape=(batch_size,), dtype=\"int\")\n\n        # Read data\n        df = self.df.loc[:, [\"center\", \"target\", \"opposite\",\n                             \"center_label\", \"seq_len\"]]\n        for i, j in enumerate(range(start_idx, end_idx)):\n            center_f = self.features_dict[df.iloc[j, 0]]\n            center_features[i, :len(center_f)] = center_f\n\n            target_f = self.features_dict[df.iloc[j, 1]]\n            target_features[i, :len(target_f)] = target_f\n\n            opposite_f = self.features_dict[df.iloc[j, 2]]\n            opposite_features[i, :len(opposite_f)] = opposite_f\n\n            labels[i] = self.label[j]\n\n        # In the original implementation, the features data is truncated to\n        # the length of the LAST SAMPLE in the batch. This is a really strange\n        # behavior and could be an unintentional mistake.\n        if self.original:\n            max_len = df.iloc[j][\"seq_len\"]\n            center_features = center_features[:, :max_len]\n            target_features = target_features[:, :max_len]\n            opposite_features = opposite_features[:, :max_len]\n\n        self.batch_idx += 1\n\n        return center_features, target_features, opposite_features, labels\n\n    def __len__(self):\n        return self.num_batches\n","sub_path":"speech_utils/IAAN/data_utils.py","file_name":"data_utils.py","file_ext":"py","file_size_in_byte":27507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"590759679","text":"import os\nimport aylien_news_api\nfrom aylien_news_api.rest import ApiException\nfrom pprint import pprint\n\nconfiguration = aylien_news_api.Configuration()\nconfiguration.api_key['X-AYLIEN-NewsAPI-Application-ID'] = os.environ.get(\n    'NEWSAPI_APP_ID')\nconfiguration.api_key['X-AYLIEN-NewsAPI-Application-Key'] = os.environ.get(\n    'NEWSAPI_APP_KEY')\n\nclient = aylien_news_api.ApiClient(configuration)\napi_instance = aylien_news_api.DefaultApi(client)\n\n\ndef get_cluster_from_trends():\n    response = api_instance.list_trends(\n        field='clusters',\n        categories_taxonomy='iptc-subjectcode',\n        categories_id=['11000000'],\n        published_at_end='NOW-12HOURS',\n        entities_body_links_dbpedia=[\n            'http://dbpedia.org/resource/United_States_Congress']\n    )\n\n    return [item.value for item in response.trends]\n\n\n\"\"\"\nReturns the representative story, number of stories, and time value for a given cluster\n\"\"\"\n\n\ndef get_cluster_metadata(cluster_id):\n    response = api_instance.list_clusters(\n        id=[cluster_id]\n    )\n\n    clusters = response.clusters\n\n    if clusters is None or len(clusters) == 0:\n        return None\n\n    first_cluster = clusters[0]\n\n    return {\n        \"cluster\": first_cluster.id,\n        \"representative_story\": first_cluster.representative_story,\n        \"story_count\": first_cluster.story_count,\n        \"time\": first_cluster.time\n    }\n\n\ndef get_top_stories(cluster_id):\n    \"\"\"\n    Returns 3 stories associated with the cluster from the highest-ranking publishers\n    \"\"\"\n    response = api_instance.list_stories(\n        clusters=[cluster_id],\n        sort_by=\"source.rankings.alexa.rank.US\",\n        per_page=3\n    )\n\n    return response.stories\n\n\nclusters = {}\ncluster_ids = get_cluster_from_trends()\n\nfor cluster_id in cluster_ids:\n    metadata = get_cluster_metadata(cluster_id)\n    if metadata is not None:\n        stories = get_top_stories(cluster_id)\n        metadata[\"stories\"] = stories\n        pprint(metadata)\n    else:\n        print(\"{} empty\".format(cluster_id))\n","sub_path":"samples/python/trends_clusters.py","file_name":"trends_clusters.py","file_ext":"py","file_size_in_byte":2036,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"374119101","text":"\"\"\"\nThis script scrapes the food offered in the Mensa of U Konstanz.\n\"\"\"\n\nfrom bs4 import BeautifulSoup\nimport requests\nimport pandas as pd\nimport re\n\n# Initialize beautifulsoup.\nsource_code = requests.get('https://www.seezeit.com/essen/speiseplaene/menseria-giessberg/')\nsoup = BeautifulSoup(source_code.content)\n\n# Get day and date of each tab.\nlist_of_tabs = ['tab1', 'tab2', 'tab3', 'tab4', 'tab5', 'tab6', 'tab7', 'tab8', 'tab9', 'tab10']\ndict_of_tabs = {'tab1': None, 'tab2': None, 'tab3': None, 'tab4': None, 'tab5': None, 'tab6': None, 'tab7': None, 'tab8': None, 'tab9': None, 'tab10': None}\n\ntabs = soup.find('div', {'class': 'tabs'})\n\nfor key in dict_of_tabs:\n    print(tabs.find_all('a', {'class': key})[0])\n\n    dates = tabs.find('a', {'class': key})\n    for span in dates.find_all('span'):\n        dict_of_tabs[key] =span.get_text()\n\n\n\n# Iterate over each tab (day).\nfor key in dict_of_tabs:\n\n    # Get soup for day.\n    day = soup.find('div', {'id': key})\n\n    print(\"-----------------------------------------------------------------\")\n    print(\"On\", dict_of_tabs.get(key), \"the following meals will be served: \")\n\n    # Find section of soup that gives the Speiseplan.\n    speiseplan = day.find_all('div', {'class': 'speiseplanTagKat'})\n\n    # Iterate over each meal option in Speiseplan.\n    for i in speiseplan:\n\n        # Extract type/category of meal. E.g. Stammessen, Vegetarisch, etc.\n        cat = i.find('div', {'class': 'category'}).get_text()\n        cat_clean = re.sub(' +', ' ', re.sub(r'\\([^)]*\\)', '', cat))\n        print(\"Category:\\t\\t\", cat_clean)\n\n        # Extract meal name.\n        title = i.find('div', {'class': 'title'}).get_text()\n        title_clean = re.sub(' +', ' ', re.sub(r'\\([^)]*\\)', '', title))\n        title_exclude_price = re.sub(r'(100g /)* \\d,\\d\\d Euro', '', title_clean)\n        alternative_price = re.search(r'(100g /)* \\d,\\d\\d Euro', title_clean)\n        title_list = [x.strip() for x in re.split(', | und ', title_exclude_price)]\n\n\n        if alternative_price is not None:\n            alt_price = alternative_price.group(0)\n        else:\n            alt_price = None\n\n        print(\"Meal:\\t\\t\\t\", title_list)\n\n\n\n        # Extract meal pricing.\n        if i.find('div', {'class': 'preise'}) is not None:\n            price = re.sub(' +', ' ', i.find('div', {'class': 'preise'}).get_text()).split(\" | \")\n            for p in price:\n                if \"Studierende\" in p:\n                    student_price = float(re.sub(r'€ Studierende', '', p).replace(',', '.'))\n                if \"Schüler\" in p:\n                    schueler_price = float(re.sub(r'€ Schüler', '', p).replace(',', '.'))\n                if \"Mitarbeiter\" in p:\n                    mitarbeiter_price = float(re.sub(r'€ Mitarbeiter', '', p).replace(',', '.'))\n                if \"Gäste\" in p:\n                    gaeste_price = float(re.sub(r'€ Gäste', '', p).replace(',', '.'))\n\n        else:\n            student_price = alt_price\n            schueler_price = alt_price\n            mitarbeiter_price = alt_price\n            gaeste_price = alt_price\n\n        print(\"Studierende:\\t\", student_price)\n        print(\"Schüler:\\t\\t\", schueler_price)\n        print(\"Mitarbeiter:\\t\", mitarbeiter_price)\n        print(\"Gäste:\\t\\t\\t\", gaeste_price)\n        print(\"\")\n\n    print(\" \")\n\n","sub_path":"scrape_mensa_ukn.py","file_name":"scrape_mensa_ukn.py","file_ext":"py","file_size_in_byte":3308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"250127542","text":"user = {\n    \"user\": {\n        \"name\": \"Артем\",\n        \"surname\": \"Багдасарян\",\n        \"memberships\": {\n            \"membership1\": {\n                \"project\": {\n                    \"id\": 123,\n                    \"name\": \"project1\"\n                },\n                \"roles\": {\n                    \"name\": \"Не участвует\",\n                    \"name\": \"Заказчик\"\n                }\n            },\n            \"membership2\": {\n                \"project\": {\n                    \"id\": 456,\n                    \"name\": \"project2\"\n                },\n                \"roles\": {\n                    \"name\": \"Участник\"\n                }\n            },\n            \"membership3\": {\n                \"project\": {\n                    \"id\": 789,\n                    \"name\": \"someproject1\"\n                },\n                \"roles\": {\n                    \"name\": \"Не участвует\",\n                    \"name\": \"Заказчик\"\n                }\n            },\n            \"membership4\": {\n                \"project\": {\n                    \"id\": 1011,\n                    \"name\": \"someproject2\"\n                },\n                \"roles\": {\n                    \"name\": \"Участник\"\n                }\n            }\n        }\n    }\n}\n\ndef fixed_code():\n    projects_ids = {}\n    for membership in user.get('user', {}).get('memberships', {}).values():\n        if list(filter(lambda role_name: role_name != 'Участник', membership.get('roles', {}).values())):\n            project_id = membership.get('project', {}).get('id', None)\n            projects_ids[project_id] = 1\n    \n    return projects_ids\n\ndef optimized_code():\n    projects_ids = {}\n\n    memberships = user.get('user', {}).get('memberships', {}).values()\n    project_id = lambda membership: membership.get('project', {}).get('id', None)\n\n    def get_role_names(membership, predicate):\n        return [role for role in membership.get('roles', {}).values() if predicate(role)]\n\n    projects_ids = {project_id(membership):1 for membership in memberships\n                    if get_role_names(membership, lambda role: role != 'Участник')}\n\n    return projects_ids\n\ntry:\n    assert fixed_code() == {123:1,789:1}, \"Test fixed_code failed\"\n    print(\"Test fixed_code passed!!!\")\n    \n    assert optimized_code() == {123:1,789:1}, \"Test optimized_code failed\"\n    print(\"Test optimized_code passed!!!\")\nexcept AssertionError as e:\n    print(e)","sub_path":"fixed_and_optimized_code.py","file_name":"fixed_and_optimized_code.py","file_ext":"py","file_size_in_byte":2450,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"434480888","text":"#!/usr/bin/env python3\n# coding: utf-8\n\nimport json\nimport copy\nimport time\nimport threading\nimport functools\nfrom queue import Queue\nimport websocket\n\ntry:\n    import gevent\n    from gevent import monkey\n    monkey.patch_all()\nexcept:\n    pass\n\nmessage_queue = Queue()\n\nclass UArmWebSocketClient(threading.Thread):\n    def __init__(self, parent=None):\n        super(UArmWebSocketClient, self).__init__(parent)\n        websocket.enableTrace(False)\n        self._connected = False\n        self.connect()\n\n    @property\n    def connected(self):\n        return self._connected\n    @connected.setter\n    def connected(self, state):\n        self._connected = state\n\n    def connect(self):\n        url = 'ws://localhost:18321/ws'\n        self.ws = websocket.WebSocketApp(url,\n            on_open=self.on_open,\n            on_close=self.on_close,\n            on_error=self.on_error,\n            on_message=self.on_message)\n\n    def on_open(self, ws):\n        self._connected = True\n        print(\"======open=========\")\n\n    def on_close(self, ws):\n        self._connected = False\n\n    def on_error(self, ws, error):\n        print('======error: {}'.format(error))\n        self._connected = False\n\n    def on_message(self, ws, message):\n        try:\n            message = json.loads(message, encoding='utf-8')\n            if isinstance(message, str):\n                message = json.loads(message, encoding='utf-8')\n            message_queue.put(message)\n        except Exception as e:\n            pass\n\n    def send(self, message):\n        if self.connected:\n            try:\n                self.ws.send(json.dumps(message))\n            except:\n                pass\n\n    def run(self):\n        self.ws.run_forever()\n        self._connected = False\n\ndef auto_new_single_instance(func):\n    @functools.wraps(func)\n    def decorator(*args, **kwargs):\n        cls = args[0]\n        cls = cls.__new__(cls, *args[1:], **kwargs)\n        return func(*args, **kwargs)\n    return decorator\n\nclass UArm(object):\n    message_thread = None\n    connect_thread = None\n    callback_thread = None\n    ws_thread = None\n\n    uarm_connected = False\n    power_connection_state = False\n    port_name = None\n    port_serial_number = None\n    firmware_version = None\n    hardware_version = None\n    speed = 1\n    report_position = False\n    current_x = 0\n    current_y = 0\n    current_z = 0\n    current_r = 0\n    pump_status = False\n    gripper_status = False\n    camera_connected = False\n\n    msg_id = 0\n    msg_buffer = {}\n    callbacks = []\n\n    def __init__(self):\n        super(UArm, self).__init__()\n\n    def __new__(cls, *args, **kwargs):\n        if not hasattr(cls, '_instance'):\n            cls._instance = super(UArm, cls).__new__(cls)\n            print(\"========new single instance========\")\n            cls.message_thread = threading.Thread(target=cls.process_message_thread, name='process-message-thread')\n            cls.message_thread.setDaemon(True)\n            cls.message_thread.start()\n\n            cls.connect_thread = threading.Thread(target=cls.auto_connect_thread, name='auto-connect-thread')\n            cls.connect_thread.setDaemon(True)\n            cls.connect_thread.start()\n\n            cls.callback_thread = threading.Thread(target=cls.process_callback_thread, name='process-callback-thread')\n            cls.callback_thread.setDaemon(True)\n            cls.callback_thread.start()\n\n            time.sleep(1)\n            start_time = time.time()\n            while time.time() - start_time < 10:\n                if cls.ws_thread and (not cls.ws_thread.is_alive() or cls.ws_thread.connected):\n                    break\n                time.sleep(0.2)\n            if not cls.ws_thread.connected:\n                raise Exception('can not connect uarmcore')\n        return cls._instance\n\n    def init_uarm_info(cls):\n        cls.uarm_connected = False\n        cls.power_connection_state = False\n        cls.port_name = None\n        cls.port_serial_number = None\n        cls.firmware_version = None\n        cls.hardware_version = None\n        cls.speed = 1\n        cls.report_position = False\n        cls.current_x = 0\n        cls.current_y = 0\n        cls.current_z = 0\n        cls.current_r = 0\n        cls.pump_status = False\n        cls.gripper_status = False\n        cls.camera_connected = False\n\n    @classmethod\n    @auto_new_single_instance\n    def test(cls, *args, **kwargs):\n        print(cls.uarm_connected)\n\n    @classmethod\n    def set_variable(cls, variable, value):\n        exec('cls.' + variable + ' = ' + str(value))\n\n    @classmethod\n    def auto_connect_thread(cls):\n        while True:\n            if cls.ws_thread is None or not cls.ws_thread.is_alive():\n                print(\"==========start websocket thread============\")\n                cls.uarm_connected = False\n                cls.camera_connected = False\n                cls.ws_thread = UArmWebSocketClient()\n                cls.ws_thread.setDaemon(True)\n                cls.ws_thread.start()\n            time.sleep(2)\n\n    @classmethod\n    def process_message_thread(cls):\n        print(\"==========start process message thread==========\")\n        while True:\n            try:\n                message = message_queue.get(timeout=5)\n                if message.get('type', '') == 'broadcast':\n                    cls.broadcast_message_process(message)\n                else:\n                    # cls.msg_buffer[str(message['id'])] = message\n                    for callback in cls.callbacks:\n                        if message['id'] == callback['id']:\n                            cls.msg_buffer[message['id']] = message\n\n                cmd = message.get('cmd', None)\n                if cmd:\n                    data = message.get('data')\n                    if cmd == 'uarm_connection':\n                        if 'connection_state' in data:\n                            cls.uarm_connected = data.get('connection_state')\n            except TimeoutError:\n                pass\n            except Exception as e:\n                # print(e)\n                pass\n\n    @classmethod\n    def process_callback_thread(cls):\n        print(\"==========start process callback thread==========\")\n        while True:\n            try:\n                if len(cls.callbacks) > 0:\n                    callbacks = copy.deepcopy(cls.callbacks)\n                    for i, callback in enumerate(callbacks):\n                        if callback['id'] in cls.msg_buffer.keys():\n                            func = callback['callback']\n                            try:\n                                gevent.spawn(func, cls.msg_buffer[callback['id']])\n                            except:\n                                t = threading.Thread(target=func, args=(cls.msg_buffer[callback['id']],))\n                                t.setDaemon(True)\n                                t.start()\n                                # func(cls.msg_buffer[callback['id']])\n\n                            cls.callbacks.remove(callback)\n                            cls.msg_buffer.pop(callback['id'])\n\n                        elif time.time() - callback['time'] > 10:\n                            cls.callbacks.remove(callback)\n            except:\n                pass\n            time.sleep(0.01)\n\n    @classmethod\n    def broadcast_message_process(cls, message):\n        cmd = message.get('cmd', '')\n\n        if not cmd or not isinstance(message.get('data'), dict):\n            return\n        data = message.get('data')\n        if cmd == 'greeting':\n            pass\n        elif cmd == 'uarm_connection':\n            if 'connection_state' in data:\n                cls.uarm_connected = data.get('connection_state')\n            if cls.uarm_connected:\n                if 'port_serial_number' in data:\n                    cls.port_serial_number = data.get('port_serial_number')\n                if 'port_name' in data:\n                    cls.port_name = data.get('port_name')\n                if 'firmware_version' in data:\n                    cls.firmware_version = data.get('firmware_version')\n                if 'hardware_version' in data:\n                    cls.hardware_version = data.get('hardware_version')\n        elif cmd == 'uarm_connecting':\n            pass\n        elif cmd == 'uarm_play_progress':\n            pass\n        elif cmd == 'uarm_record_progress':\n            pass\n        elif cmd == 'uarm_stop_playing':\n            pass\n        elif cmd == 'uarm_report_position':\n            if 'position' in data and len(data.get('position')) >= 4:\n                cls.current_x, cls.current_y, cls.current_z, cls.current_r = data.get('position')\n                print('current position: {}'.format(data.get('position')))\n        elif cmd == 'vision_report_face_detected':\n            pass\n        elif cmd == 'uarm_button_report':\n            pass\n        elif cmd == 'uarm_teach_status_report':\n            pass\n        elif cmd == 'uarm_power_connection_report':\n            cls.power_connection_state = data.get('status')\n        elif cmd == 'uarm_tip_sensor_state_report':\n            pass\n        elif cmd == 'uarm_stop_printing':\n            pass\n        elif cmd == 'uarm_printing_progress':\n            print('progress: {}'.format(data.get('progress')))\n\n\n    @classmethod\n    def send_msg(cls, message):\n        # if cls.ws_thread and cls.ws_thread.connected and cls.uarm_connected:\n        if cls.ws_thread and cls.ws_thread.connected:\n            if cls.msg_id > 100000:\n                cls.msg_id = 0\n            cls.msg_id += 1\n            message.update({'id': cls.msg_id})\n            # print(message)\n            cls.ws_thread.send(message)\n            return cls.msg_id\n\n    @classmethod\n    def send_and_callback(cls, message, callback=None):\n        id = cls.send_msg(message)\n        if callback is not None and callable(callback):\n            cls.callbacks.append({'id': id, 'callback': callback, 'time': time.time()})\n\n    @classmethod\n    @auto_new_single_instance\n    def set_position(cls, *args, **kwargs):\n        wait = kwargs.get('wait', True)\n        msg = {\n            'cmd': 'uarm_set_position',\n            'data': {\n                'position': [kwargs.get('x', 150), kwargs.get('y', 0), kwargs.get('z', 150)],\n                'speed': kwargs.get('speed', 6000),\n                'relative': kwargs.get('relative', False),\n                'wait': wait\n            }\n        }\n        if wait:\n            cls.send_and_callback(msg, cls.test_callback)\n        else:\n            cls.send_msg(msg)\n\n    @classmethod\n    def test_callback(cls, message):\n        cls.set_buzzer(frequency=1000, duration=2)\n\n    @classmethod\n    @auto_new_single_instance\n    def wait_connect_uarm(cls, timeout=10):\n        start_time = time.time()\n        while time.time() -start_time < timeout and not cls.uarm_connected:\n            time.sleep(0.01)\n        if not cls.uarm_connected:\n            raise Exception('Can not connect uArm')\n\n    @classmethod\n    @auto_new_single_instance\n    def set_buzzer(cls, *args, **kwargs):\n        msg = {\n            'cmd': 'uarm_set_buzzer',\n            'data': {\n                'frequency': kwargs.get('frequency', 1000),\n                'duration': kwargs.get('duration', 1)\n            }\n        }\n        if kwargs.get('wait', False):\n            cls.send_and_callback(msg)\n        else:\n            cls.send_msg(msg)\n\n    @classmethod\n    @auto_new_single_instance\n    def set_servo_angle(cls, *args, **kwargs):\n        angle = kwargs.get('angle', 90)\n        angle = angle if angle >= 0 else 0\n        angle = angle if angle <= 180 else 180\n        wait = kwargs.get('wait', True)\n        speed = kwargs.get('speed', cls.speed * 6000 if wait else 0)\n        msg = {\n            'cmd': 'uarm_set_servo_angle',\n            'data': {\n                'angle': angle,\n                'servo_num': kwargs.get('servoNumber', 0),\n                'speed': speed,\n                'wait': wait\n            }\n        }\n        cls.send_and_callback(msg)\n\n    @classmethod\n    @auto_new_single_instance\n    def base_turn(cls, *args, **kwargs):\n        angle = kwargs.get('angle', 90)\n        wait = kwargs.get('wait', True)\n        speed = kwargs.get('speed', cls.speed * 3000 if wait else cls.speed * 10)\n\n        kwargs = {\n            'angle': angle,\n            'servoNumber': 0,\n            'speed': speed,\n            'relative': True,\n            'wait': True\n        }\n        cls.set_servo_angle(**kwargs)\n\n    @classmethod\n    @auto_new_single_instance\n    def set_pump(cls, *args, **kwargs):\n        on = kwargs.get('ON', False)\n        wait = kwargs.get('wait', True)\n        msg = {\n            'cmd': 'uarm_set_pump',\n            'data': {\n                'on': on,\n                'wait': wait\n            }\n        }\n        if wait:\n            cls.send_and_callback(msg, lambda x: cls.set_variable('pump_status', on))\n        else:\n            cls.send_msg(msg)\n\n    @classmethod\n    @auto_new_single_instance\n    def set_gripper(cls, *args, **kwargs):\n        on = kwargs.get('ON', False)\n        wait = kwargs.get('wait', True)\n        msg = {\n            'cmd': 'uarm_set_gripper',\n            'data': {\n                'on': on,\n                'wait': wait\n            }\n        }\n        if wait:\n            cls.send_and_callback(msg, lambda x: cls.set_variable('gripper_status', on))\n        else:\n            cls.send_msg(msg)\n\n    @classmethod\n    @auto_new_single_instance\n    def wrist_turn(cls, *args, **kwargs):\n        angle = kwargs.get('angle', 90)\n        wait = kwargs.get('wait', True)\n        cls.current_r = angle\n\n        kwargs = {\n            'angle': 180 - angle,\n            'servoNumber': 3,\n            'wait': wait\n        }\n        cls.set_servo_angle(**kwargs)\n\n    @classmethod\n    @auto_new_single_instance\n    def set_servo_detach(cls, *args, **kwargs):\n        wait = kwargs.get('wait', True)\n        msg = {\n            'cmd': 'uarm_set_servo_detach',\n            'data': {\n                'servo_num': kwargs.get('servoNumber', 0),\n                'detach_all': kwargs.get('detachAll', False),\n                'wait': wait\n            }\n        }\n        if wait:\n            cls.send_and_callback(msg)\n        else:\n            cls.send_msg(msg)\n\n    @classmethod\n    @auto_new_single_instance\n    def set_servo_attach(cls, *args, **kwargs):\n        wait = kwargs.get('wait', True)\n        msg = {\n            'cmd': 'uarm_set_servo_attach',\n            'data': {\n                'servo_num': kwargs.get('servoNumber', 0),\n                'attach_all': kwargs.get('attachAll', False),\n                'wait': wait\n            }\n        }\n        if wait:\n            cls.send_and_callback(msg)\n        else:\n            cls.send_msg(msg)\n\n    @classmethod\n    @auto_new_single_instance\n    def start_report_position(cls, *args, **kwargs):\n        msg = {\n            'cmd': 'uarm_start_report_position',\n            'data': {\n                'interval': kwargs.get('interval', 0.5)\n            }\n        }\n        cls.send_msg(msg)\n        cls.report_position = True\n\n    @classmethod\n    @auto_new_single_instance\n    def stop_report_position(cls, *args, **kwargs):\n        msg = {\n            'cmd': 'uarm_stop_report_position',\n            'data': ''\n        }\n        cls.send_msg(msg)\n        cls.report_position = False\n\n    @classmethod\n    @auto_new_single_instance\n    def startFaceDetectionReport(cls, *args, **kwargs):\n        msg = {\n            'cmd': 'vision_start_report_face_detected',\n            'data': ''\n        }\n        cls.send_msg(msg)\n\n    @classmethod\n    @auto_new_single_instance\n    def stopFaceDetectionReport(cls, *args, **kwargs):\n        msg = {\n            'cmd': 'vision_stop_report_face_detected',\n            'data': ''\n        }\n        cls.send_msg(msg)\n\n    @classmethod\n    @auto_new_single_instance\n    def switch_camera(cls, *args, **kwargs):\n        is_on = kwargs.get('on', False)\n        msg = {\n            'cmd': 'switch_camera',\n            'data': {\n                'on': kwargs.get('on', is_on)\n            }\n        }\n        cls.send_msg(msg)\n        cls.camera_connected = is_on\n        if is_on:\n            cls.startFaceDetectionReport()\n        else:\n            cls.stopFaceDetectionReport()\n\n    @classmethod\n    def set_speed(cls, speed):\n        cls.speed = speed if speed >= 0 else 1\n\n    @classmethod\n    @auto_new_single_instance\n    def set_acceleration(cls, *args, **kwargs):\n        msg = {\n            'cmd': 'uarm_set_acceleration',\n            'data': {\n                'printing_moves': kwargs.get('printingMoves', 200),\n                'retract_moves': kwargs.get('retractMoves', 200),\n                'travel_moves': kwargs.get('travelMoves', 200),\n            }\n        }\n        cls.send_msg(msg)\n\n    @classmethod\n    @auto_new_single_instance\n    def server_exit(cls, *args, **kwargs):\n        msg = {\n            'cmd': 'stop_core',\n            'data': ''\n        }\n        cls.send_msg(msg)\n\n    @classmethod\n    @auto_new_single_instance\n    def list_ports(cls, *args, **kwargs):\n        msg = {\n            'cmd': 'uarm_list_ports',\n            'data': ''\n        }\n        cls.send_msg(msg)\n\n    @classmethod\n    @auto_new_single_instance\n    def set_polar(cls, *args, **kwargs):\n        wait = kwargs.get('wait', True)\n        rotation = kwargs.get('rotation', 0.0)\n        stretch = kwargs.get('stretch', 0.0)\n        height = kwargs.get('height', 0.0)\n        msg = {\n            'cmd': 'uarm_set_polar',\n            'data': {\n                'position': [float(rotation), float(stretch), float(height)],\n                'speed': int(kwargs.get('speed', cls.speed * 6000 if wait else cls.speed * 10)),\n                'relative': kwargs.get('relative', False),\n                'wait': wait\n            }\n        }\n        if wait:\n            cls.send_and_callback(msg)\n        else:\n            cls.send_msg(msg)\n\n    @classmethod\n    @auto_new_single_instance\n    def get_position(cls, callback=None):\n        msg = {\n            'cmd': 'uarm_get_position',\n            'data': ''\n        }\n        cls.send_and_callback(msg, callback)\n\n    @classmethod\n    @auto_new_single_instance\n    def start_standby_mode(cls, *args, **kwargs):\n        msg = {\n            'cmd': 'uarm_start_teach_standby_mode',\n            'data': ''\n        }\n        cls.send_msg(msg)\n\n    @classmethod\n    @auto_new_single_instance\n    def stop_standby_mode(cls, *args, **kwargs):\n        msg = {\n            'cmd': 'uarm_stop_teach_standby_mode',\n            'data': ''\n        }\n        cls.send_msg(msg)\n\n    @classmethod\n    @auto_new_single_instance\n    def start_recording(cls, *args, **kwargs):\n        msg = {\n            'cmd': 'uarm_start_recording',\n            'data': ''\n        }\n        cls.send_msg(msg)\n\n    @classmethod\n    @auto_new_single_instance\n    def stop_recording(cls, *args, **kwargs):\n        msg = {\n            'cmd': 'uarm_stop_recording',\n            'data': ''\n        }\n        cls.send_msg(msg)\n\n    @classmethod\n    @auto_new_single_instance\n    def start_playing(cls, *args, **kwargs):\n        msg = {\n            'cmd': 'uarm_start_playing',\n            'data': {\n                'speed': kwargs.get('speed', 1),\n                'times': kwargs.get('times', 1)\n            }\n        }\n        cls.send_msg(msg)\n\n    @classmethod\n    @auto_new_single_instance\n    def stop_playing(cls, *args, **kwargs):\n        msg = {\n            'cmd': 'uarm_stop_playing',\n            'data': ''\n        }\n        cls.send_msg(msg)\n\n    @classmethod\n    @auto_new_single_instance\n    def set_play_speed(cls, *args, **kwargs):\n        msg = {\n            'cmd': 'uarm_set_play_speed',\n            'data': {\n                'speed': kwargs.get('speed')\n            }\n        }\n        cls.send_msg(msg)\n\n\n\n\nif __name__ == '__main__':\n    # UArm.test()\n    # UArm.test()\n    # UArm.test()\n    # uarm = UArm()\n    # uarm.test()\n\n\n    UArm.set_position(x=250, y=0, z=90, wait=False)\n    UArm.set_position(x=180, y=0, z=150, wait=True)\n\n    # UArm.set_buzzer(frequency=500, duration=2)\n\n    # UArm.set_pump(ON=True, wait=True)\n    # time.sleep(2)\n    # print('>>>>>>>', UArm.pump_status)\n    # UArm.set_pump(ON=False, wait=True)\n    # time.sleep(1)\n    # print('>>>>>>>', UArm.pump_status)\n\n    # UArm.set_gripper(ON=True, wait=True)\n    # time.sleep(2)\n    # UArm.set_gripper(ON=False, wait=True)\n\n    # UArm.set_servo_angle(angle=120, servoNumber=0, speed=6000, wait=True)\n    # UArm.set_servo_angle(angle=90, servoNumber=0, speed=6000, wait=True)\n\n    # UArm.base_turn(angle=120, speed=6000, wait=True)\n    # UArm.base_turn(angle=90, speed=6000, wait=True)\n\n    # UArm.wrist_turn(angle=120, wait=True)\n    # UArm.wrist_turn(angle=90, wait=True)\n\n    # UArm.set_servo_detach(servoNumber=0, detachAll=False, wait=True)\n    # time.sleep(1)\n    # UArm.set_servo_attach(servoNumber=0, attachAll=False, wait=True)\n    #\n    # print('start_report_position')\n    # UArm.start_report_position()\n    # time.sleep(10)\n    # print('stop_report_position')\n    # UArm.stop_report_position()\n\n    # UArm.start_recording()\n    # time.sleep(10)\n    # UArm.stop_recording()\n    # time.sleep(5)\n    # UArm.start_playing()\n    # time.sleep(15)\n    # UArm.stop_playing()\n\n    # UArm.get_position(print)\n\n    # UArm.server_exit()\n\n\n    time.sleep(10)\n\n    # import time\n    # while True:\n    #     time.sleep(10)\n\n","sub_path":"uarmcore/blockly/blockly - 副本.py","file_name":"blockly - 副本.py","file_ext":"py","file_size_in_byte":21311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"144957947","text":"# coding=utf-8\nfrom keras import Input, Model\nfrom keras.layers import Conv2D, BatchNormalization, MaxPooling2D, UpSampling2D, Add, Dropout, concatenate,Flatten,Dense\nfrom keras.layers import Lambda,Reshape,LocallyConnected2D,SeparableConv1D,LocallyConnected1D,LeakyReLU\n# import os\n# os.environ['CUDA_VISIBLE_DEVICES'] ='-1'\nfrom keras.utils import plot_model\nimport keras.backend as K\nfrom keras.layers import Input,Dense,Conv2D,MaxPooling2D,UpSampling2D,Conv2DTranspose\nfrom keras.layers.convolutional import Conv3D,Conv3DTranspose\nfrom keras.layers.wrappers import TimeDistributed\nfrom keras.layers.convolutional_recurrent import ConvLSTM2D\nfrom keras.models import Model,load_model\nfrom keras.layers.core import Reshape,Flatten,Dense\nfrom keras.layers.merge import Concatenate\n#from init import *\nimport numpy as np\nimport  matplotlib.pyplot as plt\nimport keras.backend as K\nimport sys\nfrom  scipy.misc import  imsave,imread\nfrom keras.layers.core import Lambda\nimport keras\nimport cv2\n#from generator import *\nfrom keras_contrib.losses import DSSIMObjective\nfrom image_warp import *\n#from image_warp_keras import *\nfrom scipy import misc\nimport keras_contrib.backend as KC\nfrom keras.utils import multi_gpu_model\nimport tensorflow as tf\n# from init import *\nfrom model_utils import *\n\nbatch_size=4\n\nSHAPE_Y=436\nSHAPE_X=1024\n\ndef cconv(image, g_kernel):\n    g_kernel=g_kernel[:,:,np.newaxis,np.newaxis]\n    d_kernel=tf.Variable(g_kernel)\n    #image=tf.Variable(image)\n    out=tf.nn.conv2d(image,d_kernel,strides=[1, 1, 1, 1], padding='VALID')\n    #conv = K.function(inputs=[M],outputs=[out])\n    return out\n\n# u shape=(?, 240, 368, 1)\ndef grad_xy(y):\n    pw_x = np.array([[-1,0,1],[-1,0,1],[-1,0,1]]).astype(np.float32)\n    y_x = cconv(y, pw_x)\n    pw_y = np.array([[-1,-1,-1],[0,0,0],[1,1,1]]).astype(np.float32)\n    y_y = cconv(y, pw_y)\n   \n    return (y_x,y_y)\n\ndef unet_down_1(filter_count, inputs, activation='linear', pool=(2, 2), n_layers=3):\n    down = inputs\n    for i in range(n_layers):\n        down = Conv2D(filter_count, (3, 3), padding='same', activation=activation)(down)\n        down = BatchNormalization()(down)\n        if pool is not None:\n            x = MaxPooling2D(pool, strides=pool)(down)\n        else:\n            x = down\n    return (x, down)\n\ndef unet_up_1(filter_count, inputs, down_link, activation='linear', n_layers=3):\n    reduced = Conv2D(filter_count, (1, 1), padding='same', activation=activation)(inputs)\n    up = UpSampling2D((1, 1))(reduced)\n    up = BatchNormalization()(up)\n    link = Conv2D(filter_count, (1, 1), padding='same', activation=activation)(down_link)\n    link = BatchNormalization()(link)\n    up = Add()([up,link])\n    for i in range(n_layers):\n        up = Conv2D(filter_count, (3, 3), padding='same', activation=activation)(up)\n        up = BatchNormalization()(up)\n    return up\n\ndef div_into_patch_back(x):\n    p_y=p_x=8\n    patch_size = [1,p_y,p_x,1]\n    stride_size=[1,p_y,p_x,1]\n    patches = tf.extract_image_patches(x,patch_size, stride_size, [1, 1, 1, 1], 'VALID')\n\n\n    x2=tf.depth_to_space(patches,p_x)\n    return x2\n\ndef create_model(input_shape=(SHAPE_Y,SHAPE_X,3)):\n    n_layers_down = [2,2,2]\n    n_layers_up = [2,2,2]\n    n_filters_down = [5,8,10]\n    n_filters_up = [4,8,10]\n    kernels_up=[(3,3),(3,3),(3,3),(3,3)]\n    n_filters_center=6\n    n_layers_center=4\n\n\n    print('n_filters_down:%s  n_layers_down:%s'%(str(n_filters_down),str(n_layers_down)))\n    print('n_filters_center:%d  n_layers_center:%d'%(n_filters_center, n_layers_center))\n    print('n_filters_up:%s  n_layers_up:%s'%(str(n_filters_up),str(n_layers_up)))\n    activation='relu'\n    input1 = Input(shape=input_shape)\n    input2 = Input(shape=input_shape)\n    inputs=Concatenate()([input1,input2])   \n\n    x = inputs\n    x = BatchNormalization()(x)\n    xbn = x\n    depth = 0\n    back_links = []\n\n    for n_filters in n_filters_down:\n        n_layers = n_layers_down[depth]\n        x, down_link = unet_down_1(n_filters, x, activation=activation,pool=None, n_layers=n_layers)\n        back_links.append(down_link)\n        depth += 1\n\n    \n    #x2=Lambda(div_into_patch_back,output_shape=K.get_variable_shape(x)[1:])(x)\n    x2=x\n\n\n    center, _ = unet_down_1(n_filters_center, x2, activation='relu', pool=None, n_layers=n_layers_center)\n\n    # center\n    x3 = center\n    while depth > 0:\n        depth -= 1\n        link = back_links.pop()\n        n_filters = n_filters_up[depth]\n        n_layers = n_layers_up[depth]\n        x3 = unet_up_1(n_filters, x3, link, activation='relu', n_layers=n_layers)\n        #if depth <= 1:\n            #x1 = Dropout(0.25)(x1)\n    \n    #x1 = concatenate([x1,xbn])\n    x3 = Conv2D(4, (3, 3), padding='same', activation=None)(x3)\n    x3 = BatchNormalization()(x3)\n    F = Conv2D(2, (1, 1), activation=None)(x3)\n\n    model = Model(inputs=[input1,input2], outputs=[F])\n\n    # model.compile(loss=c_recMse_grad,optimizer=\"Adam\")\n\n    return model\n\n#compile with new loss\ndef compile_model_new(model,b_size,lambda1=0.002):\n    \"\"\"\n    session=tf.Session()\n    session.run(tf.global_variables_initializer())\n    \n    var = [v for v in tf.trainable_variables() if v.name == \"sig1:0\"][0]\n    session.run(var)\n    \"\"\"\n    #s1 = tf.get_variable(\"sig1\", shape=(1,), trainable=True,initializer=tf.constant([0.3]))\n    #s2 = tf.get_variable(\"sig2\", shape=(1,), trainable=True,initializer=tf.constant([0.7]))\n    #s1 = tf.get_variable(\"sig1\",  trainable=True,initializer=tf.constant([0.3]))\n    #s2 = tf.get_variable(\"sig2\",  trainable=True,initializer=tf.constant([0.7]))\n    #s1_2=s1*s1\n    #s2_2=s1*s1\n    \n    input1_rec=image_warp(model.inputs[0],model.outputs[0],num_batch=b_size)\n    input0_rec=image_warp(model.inputs[1],-model.outputs[0],num_batch=b_size)\n\n   \n    ux,uy=grad_xy(model.outputs[0][:,:,:,:1])\n    vx,vy=grad_xy(model.outputs[0][:,:,:,1:2])\n    sm_loss=lambda1*(K.mean(K.abs(ux*ux)+ K.abs(uy*uy)+ K.abs(vx*vx)+ K.abs(vy*vy)))\n\n    re_loss=DSSIMObjective(kernel_size=50)(model.inputs[1],input1_rec)\n\n    loss_mse = K.mean(K.square(model.outputs[0] - model.inputs[1]))\n    \n    #total_loss=(1/s1_2)*re_loss+(1/s2_2)*sm_loss+K.log(s1_2)+K.log(s2_2)\n    total_loss=lambda1*sm_loss+re_loss + loss_mse\n\n    model.add_loss(total_loss)\n\n    model.compile(optimizer='rmsprop')\n   \t\n    return model\n\n#input_image shape  (sample,SHAPE_Y,SHAPE_X,1)    F->(sample,SHAPE_Y,SHAPE_X,F)\ndef recons_img(input_image,F):\n\n    inp1 = K.placeholder(shape=input_image.shape)\n    F_in = K.placeholder(shape=F.shape)\n    input1_rec=image_warp(inp1,F,num_batch=F.shape[0])\n    f1=K.function(inputs=[inp1,F_in],outputs=[input1_rec])\n   \n    out=f1([input_image,F])\n    return out\n\n\n# model_base=create_model()\n\n# model=compile_model_new(model_base,batch_size,lambda1=0.1)\n\nmodel = create_model()","sub_path":"dump/unsupervised/dump/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":6745,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"100667194","text":"import pygame\nimport random\nimport time\n\npygame.init()\nfont = pygame.font.SysFont('Arial', 15)\n\nwidth = 400\nheight = 150\nwhite = (255, 255, 255)\nred = (255, 0, 0)\nblue = (0, 0, 255)\nblack = (0, 0, 0)\ncolor = 0\nLEFT_BUTTON = 1\nrects = list()\nrect_colors = list()\nscreen = pygame.display.set_mode((width, height))\n\n# position for boxes\nx = 10\ny = 10\nsize = 30\n\nfor a in xrange(0, 7):\n    rects.append(pygame.Rect(x, y, size, size))\n    x = x + (size + 5)\n\n# position for boxes\nx = 10\ny = 45\nfor a in xrange(0, 7):\n    rects.append(pygame.Rect(x, y, size, size))\n    x = x + (size + 5)\n\n# nullstillir\ncatsHit = 0\nmiceHit = 0\nnullHit = 0\n\n# red = cat\n# blue = mouse\nrunning = True\nwhile running:\n    # makes screen white\n    screen.fill(white)\n    event = pygame.event.poll()\n\n    # Writes text on app\n    screen.blit(font.render('Click on the blue boxes', True, (0, 0, 0)), (10, 80))\n    screen.blit(font.render('Red = cat. Don\\'t click them', True, (0, 0, 0,)), (10, 95))\n    screen.blit(font.render('Blue = mouse.', True, (0, 0, 0)), (10, 110))\n\n    # when game is quit stats prints out\n    if event.type == pygame.QUIT:\n        print('Mice hit = ' + str(miceHit))\n        print('Cats hit = ' + str(catsHit))\n        print('Other hit= ' + str(nullHit))\n        running = False\n    # Check what color user clicked on\n    elif event.type == pygame.MOUSEBUTTONDOWN and event.button == LEFT_BUTTON:\n        for x in xrange(0, len(rects)):\n            if rects[x].collidepoint(event.pos):\n                if rect_colors[x] == 'Red':\n                    catsHit = catsHit = 1\n                elif rect_colors[x] == 'Blue':\n                    miceHit = miceHit + 1\n                else:\n                    nullHit = nullHit + 1\n\n    rect_colors = list()\n\n    # draws random colors in App\n    for x in xrange(0, len(rects)):\n        color = random.randint(1, 3)\n        if color == 2:\n            pygame.draw.rect(screen, red, rects[x])\n            rect_colors.append('Red')\n        elif color == 1:\n            pygame.draw.rect(screen, blue, rects[x])\n            rect_colors.append('Blue')\n        else:\n            rect_colors.append('None')\n    time.sleep(0.2)  # Wait time after one round\n\n    # Update screen\n    pygame.display.update()\n","sub_path":"Programming/PythonLeikjaforritun/Verkefni_2/MouseHunt.py","file_name":"MouseHunt.py","file_ext":"py","file_size_in_byte":2236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"498236937","text":"\"\"\"\n    (扩展)方阵(行数列数相同)转置\n\"\"\"\n\nlist01 = [\n    [1, 2, 3, 4],\n    [5, 6, 7, 8],\n    [9, 10, 11, 12],\n    [13, 14, 15, 16],\n]\n\"\"\"\n# list01[1][0] list01[0][1]\n# list01[2][0] list01[0][2]\n# list01[3][0] list01[0][3]\nfor r in range(1, 4):\n    # list01[r][0]  <-->  list01[0][r]\n    pass\n# list01[2][1] list01[1][2]\n# list01[3][1] list01[1][3]\nfor r in range(2, 4):\n    # list01[r][1]  <-->  list01[1][r]\n    pass\n# list01[3][2] list01[2][3]\nfor r in range(3, 4):\n    # list01[r][2]  <-->  list01[2][r]\n    pass\n\nfor c in range(1, 4):  # 1 2 3\n    for r in range(c, 4):\n        # list01[r][c-1]  <-->  list01[c-1][r]\n        pass\n\"\"\"\n\n\ndef square_matrix_transposition(list_matrix):\n    \"\"\"\n        方阵转置（列转换为行）\n    :param list_matrix: 需要转置的方阵\n    :return:\n    \"\"\"\n    for c in range(1, len(list_matrix)):  # 1 2 3\n        for r in range(c, len(list_matrix)):\n            list_matrix[r][c - 1], list_matrix[c - 1][r] = list_matrix[c - 1][r], list_matrix[r][c - 1]\n\n# 方阵转置的转置，等于原方阵\nsquare_matrix_transposition(list01)\nprint(list01)\n\nsquare_matrix_transposition(list01)\nprint(list01)","sub_path":"01-python基础/code/day08/day07_exercise/exercise01.py","file_name":"exercise01.py","file_ext":"py","file_size_in_byte":1157,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"151137763","text":"#\r\n# copyright (c) 2009 Ralf Habacker <ralf.habacker@freenet.de>\r\n#\r\n# definitions for the qmake build system\r\n\r\nimport os\r\nimport utils\r\nimport compiler\r\n\r\nfrom BuildSystem.BuildSystemBase import *\r\n\r\nclass QMakeBuildSystem(BuildSystemBase):\r\n    def __init__( self):\r\n        BuildSystemBase.__init__(self, \"qmake\")\r\n        self.platform = \"\"\r\n        if compiler.isMSVC():\r\n            self.platform = \"win32-%s\" % self.compiler()\r\n        elif compiler.isMinGW():\r\n            self.platform = \"win32-g++\"\r\n        elif compiler.isIntel():\r\n            self.platform = \"win32-icc\"\r\n        else:\r\n            utils.die( \"QMakeBuildSystem: unsupported compiler platform %s\" % self.compiler() )\r\n\r\n    def setPathes( self ):\r\n            # for building qt with qmake\r\n        utils.putenv( \"PATH\", os.path.join( self.buildDir(), \"bin\" ) + \";\" + os.getenv(\"PATH\") )\r\n\r\n        # so that the mkspecs can be found, when -prefix is set\r\n        utils.putenv( \"QMAKEPATH\", self.sourceDir() )\r\n        # to be sure\r\n        utils.putenv( \"QMAKESPEC\", os.path.join(self.sourceDir(), 'mkspecs', self.platform ))\r\n\r\n    def configure( self, configureDefines=\"\" ):\r\n        \"\"\"inplements configure step for Qt projects\"\"\"\r\n\r\n        self.enterBuildDir()\r\n\r\n        # here follows some automatic configure tool detection\r\n        # 1. search for configure.exe in the order\r\n        #      a. provided by method call\r\n        #      b. in source directory\r\n        # 2. if qmake is available search for a pro-file named as the package\r\n        # 3. if a pro-file is available through configureOptions, run it with qmake\r\n        # 4. otherwise run qmake without any pro file given\r\n        configTool = os.path.join(self.configureSourceDir(), \"configure.exe\")\r\n        qmakeTool = os.path.join(self.mergeDestinationDir(), \"bin\", \"qmake.exe\")\r\n        topLevelProFilesFound = 0\r\n        topLevelProFile = \"\"\r\n        for fileListed in os.listdir(self.configureSourceDir()):\r\n            if fileListed.endswith(\".pro\"):\r\n                if topLevelProFilesFound == 0:\r\n                    topLevelProFile = os.path.join(self.configureSourceDir(), fileListed)\r\n                topLevelProFilesFound += 1\r\n        if self.subinfo.options.configure.tool != None and self.subinfo.options.configure.tool != False:\r\n            command = \"%s %s\" % (self.subinfo.options.configure.tool, self.configureOptions(configureDefines))\r\n        elif os.path.exists(configTool):\r\n            command = \"%s %s\" % (configTool, self.configureOptions(configureDefines))\r\n        elif os.path.exists(qmakeTool):\r\n            if self.buildType() == \"Release\":\r\n                configureDefines += ' \"CONFIG -= debug\"'\r\n                configureDefines += ' \"CONFIG += release\"'\r\n                configureDefines += ' \"CONFIG -= debug_and_release\"'\r\n            elif self.buildType() == \"Debug\":\r\n                configureDefines += ' \"CONFIG += debug\"'\r\n                configureDefines += ' \"CONFIG -= release\"'\r\n                configureDefines += ' \"CONFIG -= debug_and_release\"'\r\n            elif self.buildType() == \"RelWithDebInfo\":\r\n                configureDefines += ' \"CONFIG -= debug\"'\r\n                configureDefines += ' \"CONFIG -= release\"'\r\n                configureDefines += ' \"CONFIG += debug_and_release\"'\r\n            if os.path.exists(topLevelProFile) and topLevelProFilesFound == 1:\r\n                command = \"qmake -makefile %s %s\" % (topLevelProFile, self.configureOptions(configureDefines))\r\n            else:\r\n                command = \"qmake %s\" % self.configureOptions(configureDefines)\r\n        else:\r\n            utils.die(\"could not find configure.exe or top level pro-file, please take a look into the source and setup the config process.\")\r\n\r\n        return self.system( command, \"configure\" )\r\n\r\n    def make( self, options=\"\"):\r\n        \"\"\"implements the make step for Qt projects\"\"\"\r\n        self.enterBuildDir()\r\n\r\n        command = ' '.join([self.makeProgramm, self.makeOptions(options, maybeVerbose=False)])\r\n\r\n        return self.system( command, \"make\" )\r\n\r\n    def install( self, options=None ):\r\n        \"\"\"implements the make step for Qt projects\"\"\"\r\n        if not BuildSystemBase.install(self):\r\n            return False\r\n\r\n        # There is a bug in jom that parallel installation of qmake projects\r\n        # does not work. So just use the usual make programs. It's hacky but\r\n        # this was decided on the 2012 Windows sprint.\r\n        if compiler.isMSVC() or compiler.isIntel():\r\n            installmake=\"nmake /NOLOGO\"\r\n        elif compiler.isMinGW():\r\n            installmake=\"mingw32-make\"\r\n\r\n        self.enterBuildDir()\r\n        if options != None:\r\n            command = \"%s %s\" % ( installmake, options )\r\n        else:\r\n            command = \"%s install\" % ( installmake )\r\n\r\n        return self.system( command )\r\n\r\n    def runTest( self ):\r\n        \"\"\"running qmake based unittests\"\"\"\r\n        return True\r\n        \r\n    def ccacheOptions(self):\r\n        return ' \"QMAKE_CC=ccache gcc\" \"QMAKE_CXX=ccache g++\" '\r\n","sub_path":"BuildSystem/QMakeBuildSystem.py","file_name":"QMakeBuildSystem.py","file_ext":"py","file_size_in_byte":5047,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"533581416","text":"from multiprocessing import Process\nfrom threading import Thread\nimport random\nfrom contextlib import redirect_stdout\nimport logging\nimport time\n\n\nclass generatorexample:\n    def __init__(self):\n        pass\n        \n        \n    def dataGeneration(self,filename):\n         \n        IP=['11.192.16.2','12.11.176.1','10.123.15.5','1.134.56.7','12.145.1.15']\n         \n        with open(filename,'a') as f:\n            with redirect_stdout(f):\n                for i in range(10000):\n                    ttms=(random.randint(5,12))\n                    crc=(random.choice(IP))\n                    phi=(random.choice(['hit','miss']))\n                    psql=(random.randint(10,25))\n                    print('ttms {},crc {},phi {},psql {}'.format(ttms,crc,phi,psql))\n                    \n                    \n                    \nobj= generatorexample()\n# for i in range(2):\n#     p=Thread(target=obj.dataGeneration)\n#     p.start()\n#obj.dataGeneration() \n\nif __name__ == '__main__' :\n    p = Process(target=obj.dataGeneration,args=('test1.txt',))\n    p1 = Process(target=obj.dataGeneration,args=('test2.txt',))\n    time.sleep(5)\n    p.start()\n    p1.start()             \n                    \n                                      \n            \n        \n    \n ","sub_path":"practise/writingparallel.py","file_name":"writingparallel.py","file_ext":"py","file_size_in_byte":1256,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"343922320","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nN = int(input())\ncount = 0\nwhile N > 0:\n    N -= 1;\n    s = input();\n    a, b, c = [int(x) for x in s.split(' ')]\n    count += 1\n    print(\"Case #\" + str(count) + \": \", end = \"\")\n    if(int(a) + int(b) > int(c)):\n        print(\"true\" if(N) else \"true\")\n    else:\n        print(\"false\" if(N) else \"false\")","sub_path":"PTA/PAT(Advanced Level)/python/A1065.py","file_name":"A1065.py","file_ext":"py","file_size_in_byte":351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"368405711","text":"import os\nimport errno\nimport numpy as np\nimport deepcell\nfrom sklearn.model_selection import train_test_split\n\ntest_size = 0.1 # % of data saved as test\nseed = 0 # seed for random train-test split\nfilename = 'mousebrain.npz'\nDATA_DIR = 'data/'\nDATA_FILE = os.path.join(DATA_DIR, filename)\n\ntraining_data = np.load(DATA_FILE)\nX = training_data['X']\ny = training_data['y']\n\nfilename = 'mousebrain_reduced.npz'\nDATA_DIR = 'data/'\nDATA_FILE = os.path.join(DATA_DIR, filename)\n\nnp.savez(DATA_FILE, X = X[:100], y = y[:100])\nprint(DATA_FILE)\n# confirm the data file is available\nassert os.path.isfile(DATA_FILE)\n\n\ntraining_data = np.load(DATA_FILE)\nX = training_data['X']\ny = training_data['y']\nprint(y.shape)\nX_train, X_test, y_train, y_test = train_test_split(\n        X, y, test_size=test_size, random_state=seed)\ndel X, y\nprint('X.shape: {}\\ny.shape: {}'.format(X_train.shape, y_train.shape))\nprint('X.shape: {}\\ny.shape: {}'.format(X_test.shape, y_test.shape))\n\n\n# Set up other required filepaths\n\n# If the data file is in a subdirectory, mirror it in MODEL_DIR and LOG_DIR\nPREFIX = os.path.relpath(os.path.dirname(DATA_FILE), DATA_DIR)\n\n# ROOT_DIR = '/data'  # TODO: Change this! Usually a mounted volume\nROOT_DIR = '3dsamplebased_seg/'\nMODEL_DIR = os.path.abspath(os.path.join(ROOT_DIR, 'models', PREFIX))\nLOG_DIR = os.path.abspath(os.path.join(ROOT_DIR, 'logs', PREFIX))\n\n# create directories if they do not exist\nfor d in (MODEL_DIR, LOG_DIR):\n    try:\n        os.makedirs(d)\n    except OSError as exc:  # Guard against race condition\n        if exc.errno != errno.EEXIST:\n            raise\n#-----------------------------------------------------------------------------------------\nfrom tensorflow.keras.optimizers import SGD\nfrom deepcell.utils.train_utils import rate_scheduler\n\nfgbg_model_name = 'sample_fgbg_3d_model'\nsample_model_name = 'sample_edgeseg_3d_model'\n\nn_epoch = 2  # Number of training epochs\nreceptive_field = 61  # should be adjusted for the scale of the data\n\noptimizer = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n\nlr_sched = rate_scheduler(lr=0.01, decay=0.99)\n\n# Transformation settings\ntransform = 'pixelwise'\ndilation_radius = 1  # change dilation radius for edge dilation\nseparate_edge_classes = True  # break edges into cell-background edge, cell-cell edge\nn_features = 4 if separate_edge_classes else 3\n\n# 3D Settings\nframes_per_batch = 3\nnorm_method = 'whole_image'  # data normalization - `whole_image` for 3d conv\n\n# Sample mode settings\nbatch_size = 8  # number of images per batch (should be 2 ^ n)\nwin = (receptive_field - 1) // 2  # sample window size\nwin_z = (frames_per_batch - 1) // 2 # z window size\nbalance_classes = True  # sample each class equally\nmax_class_samples = 1e6  # max number of samples per class.\n\nfrom deepcell import model_zoo\n\nfgbg_model = model_zoo.bn_feature_net_3D(\n    receptive_field=receptive_field,\n    n_features=2,\n    norm_method=norm_method,\n    n_frames=frames_per_batch,\n    n_channels=X_train.shape[-1])\n\nfrom deepcell.training import train_model_sample\n\nfgbg_model = train_model_sample(\n    model=fgbg_model,\n    dataset=DATA_FILE,  # full path to npz file\n    model_name=fgbg_model_name,\n    test_size=test_size,\n    seed=seed,\n    window_size=(win, win, (frames_per_batch - 1) // 2),\n    optimizer=optimizer,\n    batch_size=batch_size,\n    balance_classes=balance_classes,\n    max_class_samples=max_class_samples,\n    transform='fgbg',\n    n_epoch=n_epoch,\n    model_dir=MODEL_DIR,\n    lr_sched=lr_sched,\n    rotation_range=180,\n    flip=True,\n    shear=False)\n\n\nfrom deepcell import model_zoo\n\nsample_model = model_zoo.bn_feature_net_3D(\n    receptive_field=receptive_field,\n    n_features=4,  # (background edge, interior edge, cell interior, background)\n    n_frames=frames_per_batch,\n    norm_method=norm_method,\n    n_channels=X_train.shape[-1])\n\nfrom deepcell.training import train_model_sample\n\nsample_model = train_model_sample(\n    model=sample_model,\n    dataset=DATA_FILE,  # full path to npz file\n    window_size=(win, win, (frames_per_batch - 1) // 2),\n    model_name=sample_model_name,\n    test_size=test_size,\n    seed=seed,\n    transform=transform,\n    separate_edge_classes=separate_edge_classes,\n    dilation_radius=dilation_radius,\n    optimizer=optimizer,\n    batch_size=batch_size,\n    balance_classes=balance_classes,\n    max_class_samples=max_class_samples,\n    n_epoch=n_epoch,\n    log_dir=LOG_DIR,\n    model_dir=MODEL_DIR,\n    lr_sched=lr_sched,\n    rotation_range=180,\n    flip=True,\n    shear=False,\n    zoom_range=(0.8, 1.2))\n\n\nfgbg_weights_file = os.path.join(MODEL_DIR, '{}.h5'.format(fgbg_model_name))\nfgbg_model.save_weights(fgbg_weights_file)\n\nsample_weights_file = os.path.join(MODEL_DIR, '{}.h5'.format(sample_model_name))\nsample_model.save_weights(sample_weights_file)\n\n\n\n\n\n\n","sub_path":"scripts/PanOpticFPN.py","file_name":"PanOpticFPN.py","file_ext":"py","file_size_in_byte":4800,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"7069845","text":"# coding=utf-8\n\nfrom skimage import data, filters, img_as_ubyte\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nimport cv2\nimport numpy as np\n# 图像读取\n# img = data.camera()\n# plt.imshow(img, plt.cm.gray)\n\nimg = Image.open('edge.png').convert('L')\n\n'''**********skimage*************'''\n# sobel边缘检测\nedges = filters.sobel(img)\n# 浮点型转成uint8型\nedges = img_as_ubyte(edges)\n# 显示图像\nplt.figure()\nplt.imshow(edges, plt.cm.gray)\n\n# sobel 水平方向边缘检测\nedgesh = filters.sobel_h(img)\nedgesh = img_as_ubyte(edgesh)\nplt.figure()\nplt.imshow(edgesh, plt.cm.gray)\n\n# sobel 竖直方向边缘检测\nedgesv = filters.sobel_v(img)\nedgesv = img_as_ubyte(edgesv)\nplt.figure()\nplt.imshow(edgesv, plt.cm.gray)\n\n'''**********opencv*************'''\n# sobel边缘检测\nedges = cv2.Sobel(np.array(img), cv2.CV_16S, 1, 1)\n# 浮点型转成uint8型\nedges = cv2.convertScaleAbs(edges)\nplt.figure()\nplt.imshow(edges, plt.cm.gray)\n\n# sobel 水平方向边缘检测\nedges = cv2.Sobel(np.array(img), cv2.CV_16S, 1, 0)\nedgesh = cv2.convertScaleAbs(edgesh)\nplt.figure()\nplt.imshow(edgesh, plt.cm.gray)\n\n# sobel 竖直方向边缘检测\nedges = cv2.Sobel(np.array(img), cv2.CV_16S, 0, 1)\nedgesv = cv2.convertScaleAbs(edgesv)\nplt.figure()\nplt.imshow(edgesv, plt.cm.gray)\npass","sub_path":"image_detection/edge_detect_sobel3.py","file_name":"edge_detect_sobel3.py","file_ext":"py","file_size_in_byte":1286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"345611715","text":"# -*- coding: utf-8 -*-\n#\n# This file is part of Kwek Metrics.\n\n\"\"\"Hawkular API calls.\"\"\"\n\n\nimport json\n\nimport requests\nfrom requests import ConnectionError\n\nfrom api import DISABLE_WARNINGS\n\nif DISABLE_WARNINGS:\n    from requests.packages.urllib3.exceptions import InsecureRequestWarning\n    requests.packages.urllib3.disable_warnings(InsecureRequestWarning)\n\n\nclass HawkularAPIError(Exception):\n    def __init__(self, message):\n\n        # Call the base class constructor with the parameters it needs\n        super(HawkularAPIError, self).__init__(message)\n\n\ndef _build_hawkular_headers(tentant=\"\", auth=\"\"):\n    \"\"\"Generate the headers to access the API with successful auth.\n\n    {'Hawkular-Tenant': '_tenant_name',\n     'Accept': 'application/json',\n     'Authorization': 'Bearer XXXX'}\n\n    Args:\n        tentant (str): The tenant which to query\n        auth (str): The authorization token\n\n    Returns:\n        dict: A dict with formatted headers\n    \"\"\"\n    return{\n        'Hawkular-Tenant': \"{0}\".format(tentant),\n        'Accept': 'application/json',\n        'Authorization': 'Bearer {0}'.format(auth)\n    }\n\n\ndef _build_hawkular_payload(metric):\n    \"\"\"Generate the payload for the current select metric.\n\n    {'tags': 'descriptor_name:memory%2Fusage',\n     'bucketDuration': '60000ms',\n     'start': '-15mn',\n     'stacked': 'true'}\n\n    Args:\n        metric (str): The string describing the metric on the API\n\n    Returns:\n        dict: A dict with formatted payload\n    \"\"\"\n    return{\n        'tags': 'descriptor_name:{0}'.format(metric),\n        'bucketDuration': '60000ms',\n        'start': '-15mn',\n        'stacked': 'true'\n    }\n\n\ndef get_metric(url, tenant, auth, metric):\n    headers = _build_hawkular_headers(tenant, auth)\n    payload = _build_hawkular_payload(metric)\n    try:\n        r = query_api(url, headers, payload)\n        return json.loads(r.text)\n    except (ValueError, ConnectionError) as err:\n        raise err\n\n\ndef _build_os_headers(auth=\"\"):\n    \"\"\"Generate the headers to access the API with successful auth.\n\n    {'Hawkular-Tenant': '_tenant_name',\n     'Accept': 'application/json',\n     'Authorization': 'Bearer XXXX'}\n\n    Args:\n        tentant (str): The tenant which to query\n        auth (str): The authorization token\n\n    Returns:\n        dict: A dict with formatted headers\n    \"\"\"\n    return{\n        'Authorization': 'Bearer {0}'.format(auth)\n    }\n\n\ndef get_os_projects(url, auth):\n    headers = _build_os_headers(auth)\n    try:\n        r = query_api(url, headers)\n        return json.loads(r.text)['items']\n    except (ValueError, ConnectionError) as err:\n        raise err\n\n\ndef query_api(url, headers={}, payload={}):\n    \"\"\"Perfoms a GET request.\n\n    Args:\n        url (str): The url to perfom the GET\n        headers (dict, optional): The headers for the request\n        payload (dict, optional): The payload for the request\n\n    Returns:\n        Request: The Request object\n    \"\"\"\n    try:\n        r = requests.get(url, headers=headers, verify=False, params=payload)\n        return r\n    except ConnectionError as err:\n        raise err\n","sub_path":"api/hawkular.py","file_name":"hawkular.py","file_ext":"py","file_size_in_byte":3099,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"365036901","text":"import itchat\nimport sys\n\nclass WeiChat(object):\n    def main(self):\n        itchat.auto_login()\n        itchat.run()\n\n    @itchat.msg_register(itchat.content.TEXT)\n    def reply(self, msg):\n        return msg.text\n\n\nif __name__ == '__main__':\n    weichat = WeiChat()\n    weichat.main()\n\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":288,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"33712677","text":"import math\r\nfrom collections import deque\r\nfrom time import time\r\n\r\ndef fractionToDecimal(numerator, denominator):\r\n    \"\"\"\r\n    :type numerator: int\r\n    :type denominator: int\r\n    :rtype: str\r\n    \"\"\"\r\n    sign = '-' if numerator * denominator < 0 else ''\r\n    quotient, remainder = divmod(abs(numerator), abs(denominator))\r\n    result_list = [sign, str(quotient), '.']\r\n    remainders = []\r\n    while remainder not in remainders:\r\n        remainders.append(remainder)\r\n        quotient, remainder = divmod(remainder * 10, abs(denominator))\r\n        result_list.append(str(quotient))\r\n\r\n    idx = remainders.index(remainder)\r\n    result_list.insert(idx + 3, '(')\r\n    result_list.append(')')\r\n    result = ''.join(result_list).replace('(0)', '').rstrip('.')\r\n    print(result)\r\n    return result\r\n\r\nif __name__ == \"__main__\":\r\n    assert fractionToDecimal(1, 2) == '0.5'\r\n    assert fractionToDecimal(2, 1) == '2'\r\n    assert fractionToDecimal(2, 3) == '0.(6)'\r\n    #56789*1234, 99999*9999\r\n    fractionToDecimal(70077626, 999890001)\r\n    #23456789*1234567, 99999999*9999999\r\n    fractionToDecimal(28958977625363, 999999890000001)\r\n    \r\n\r\n\r\n","sub_path":"samples-labs-exercises/samples/neurons/yhy_repeat_digits/test3.py","file_name":"test3.py","file_ext":"py","file_size_in_byte":1146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"453693760","text":"from LogisticRegression import LogisticRegression,featureScale\nimport numpy as np,pandas as pd,matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.linear_model import LogisticRegression as LR\n\n#loading data\ndata = pd.read_csv('data.csv')\nX = data.iloc[:, :-1].values\ny = data.iloc[:, -1].values\n\n#scaling X\nX=featureScale(X)\n\n#splitting data to test and train sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)\n\n#initializing model\nmodel=LogisticRegression()\n\n#training the model\nJ,theta=model.train(X_train,y_train)\n\n#predicting using X_test data\ny_pred=model.predict(X_test)\ny_pred=np.round(y_pred)\n\n#visualizing confusion matrix\ncm=confusion_matrix(y_test,y_pred)\nprint(cm)\n\n#visualizing cost function per iteration\nplt.plot(J)\nplt.show()\n\n#visualizing mean square error and root mean square error\nMSE=J[-1]\nRMSE=np.sqrt(MSE)\nprint(\"mean square error: \",MSE)\nprint(\"root mean square error: \",RMSE)","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1027,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"101165866","text":"import abc\nimport argparse\nimport collections\nimport functools\nimport importlib\nimport inspect as ins\nimport logging\nimport os\nimport pathlib\nimport shutil\nimport sys\n\nimport toml\n\nfrom .exceptions import NotConfiguredError\nfrom .common import Module, Workspace\n# pylint: disable=redefined-builtin\n# noinspection PyShadowingBuiltins\nfrom .util import classproperty, Configuration, colored, _dict as dict\n\n\nclass Command(abc.ABC):\n    \"\"\"Command interface.\"\"\"\n    __slots__ = []\n\n    @classproperty\n    def help(cls):  # pylint: disable=no-self-argument\n        # pylint: disable=no-member\n        return 'command ' + cls.__name__.lower()\n\n    def _run(self, args):\n        # pylint: disable=protected-access\n        ws = get_app().workspace(args.workspace)\n        del args.command, args.func, args.workspace\n        args = {name: value for (name, value) in args._get_kwargs()}\n        args = Configuration(args)\n        return self.run(ws, args)\n\n    @abc.abstractmethod\n    def run(self, ws, args):\n        raise NotImplementedError\n\n\nclass config(Command):\n    \"\"\"Command ``config``,\n\n    Configure a module and its parameters for a workspace.\n\n    Example:\n        .. code-block:: bash\n\n            $ fret config Simple -foo=5\n            In [ws/_default]: configured \"main\" as \"Simple\" with: foo=5\n    \"\"\"\n\n    help = 'configure module for workspace'\n\n    def __init__(self, app, parser):\n        # pylint: disable=protected-access\n        parser.add_argument('name', default='main', nargs='?',\n                            help='module name')\n        if sys.version_info < (3, 7):\n            subs = parser.add_subparsers(title='modules available',\n                                         dest='module')\n        else:\n            subs = parser.add_subparsers(title='modules available',\n                                         dest='module', required=False)\n        group_options = collections.defaultdict(set)\n        try:\n            _modules = app._modules\n        except ImportError:\n            _modules = dict()\n\n        for module, module_cls in _modules.items():\n            _parser_formatter = argparse.ArgumentDefaultsHelpFormatter\n            sub = subs.add_parser(module, help=module_cls.help,\n                                  formatter_class=_parser_formatter)\n            group = sub.add_argument_group('config')\n            module_cls._add_arguments(group)\n            for action in group._group_actions:\n                group_options[module].add(action.dest)\n\n            def save(args):\n                with get_app().workspace(args.workspace) as ws:\n                    m = args.module\n                    cfg = [(name, value)\n                           for (name, value) in args._get_kwargs()\n                           if name in group_options[m]]\n                    cfg = Configuration(cfg)\n                    msg = '[%s] configured \"%s\" as \"%s\"' % \\\n                        (ws, args.name, m)\n                    if cfg._config:\n                        msg += ' with: ' + str(cfg)\n                    print(msg, file=sys.stderr)\n                    ws.register(args.name, get_app().load_module(m),\n                                **cfg._dict())\n\n            sub.set_defaults(func=save)\n\n    def run(self, ws, args):\n        cfg = ws.config_path\n        if cfg.exists():\n            cfg = cfg.open().read().strip()\n            return cfg\n        else:\n            raise NotConfiguredError('no configuration in this workspace')\n\n\nclass clean(Command):\n    \"\"\"Command ``clean``.\n\n    Remove all checkpoints in specific workspace. If ``--all`` is specified,\n    clean the entire workspace\n    \"\"\"\n\n    help = 'clean workspace'\n\n    def __init__(self, _, parser):\n        parser.add_argument('--all', action='store_true',\n                            help='clean the entire workspace')\n        parser.add_argument('-c', dest='config', action='store_true',\n                            help='remove workspace configuration')\n        parser.add_argument('-l', dest='log', action='store_true',\n                            help='clear workspace logs')\n        parser.add_argument('-s', dest='snapshot', action='store_true',\n                            help='clear snapshots')\n\n    def run(self, ws, args):\n        if args.all:\n            shutil.rmtree(str(ws))\n        else:\n            if (not args.config and not args.log) or args.snapshot:\n                shutil.rmtree(str(ws.snapshot()))\n            if args.snapshot:\n                shutil.rmtree(str(ws.snapshot()))\n            if args.config:\n                try:\n                    (ws.path / 'config.toml').unlink()\n                except FileNotFoundError:\n                    pass\n            if args.log:\n                shutil.rmtree(str(ws.log()))\n\n\nclass App:\n    \"\"\"Application object. In charge of locating suitable app, importing\n    modules, and run commands.\"\"\"\n\n    __slots__ = ['_root', '_config', '_commands', '_modules', '_cwd', '_imp']\n\n    def __init__(self, path=None):\n        self._cwd = None\n        if path is None:\n            p = pathlib.Path().absolute()\n            if p.joinpath('fret.toml').exists():\n                path = str(p)\n            else:\n                while p != pathlib.Path(p.root):\n                    if p.joinpath('fret.toml').exists():\n                        path = str(p)\n                        self._cwd = os.getcwd()\n                        os.chdir(path)\n                        break\n                    p = p.parent\n                else:\n                    path = pathlib.Path().absolute()\n\n        sys.path.insert(0, str(path))\n        self._root = pathlib.Path(path)\n        self._imp = None\n\n        self._config = None\n        self._commands = dict()\n        self._modules = dict()\n\n        if self._root.joinpath('fret.toml').exists():\n            cfg = toml.load(self._root.joinpath('fret.toml').open())\n        else:\n            cfg = dict()\n        self._config = Configuration(cfg)\n\n    def import_modules(self):\n        appname = os.environ.get('FRETAPP')\n        if appname is not None:\n            self._imp = importlib.import_module(appname)\n        elif 'appname' in self._config:\n            self._imp = importlib.import_module(self._config.appname)\n        else:\n            for appname in ['main', 'app']:\n                try:\n                    self._imp = importlib.import_module(appname)\n                    break\n                except ImportError as e:\n                    if e.name != appname:\n                        raise\n            else:\n                logging.warning('no app found')\n        return self._imp\n\n    def register_module(self, cls, name=None):\n        if name is None:\n            name = cls.__name__\n        self._modules[name] = cls\n\n    def register_command(self, cls, name=None):\n        if name is None:\n            name = cls.__name__\n        self._commands[name] = cls\n\n    def load_module(self, name):\n        if self._config is None:\n            self.load()\n        return self._modules[name]\n\n    @property\n    def config(self):\n        if self._config is None:\n            self.load()\n        return self._config\n\n    @property\n    def argument_style(self):\n        style = self.config._get('argument_style')\n        if style not in ['java', 'gnu']:\n            return 'java'\n        return style\n\n    def workspace(self, path=None):\n        if path is None:\n            path = pathlib.Path(self._cwd).relative_to(self._root) if \\\n                self._cwd is not None else 'ws/_default'\n        return Workspace(self, path)\n\n    def __getattr__(self, key):\n        return getattr(self.config, key)\n\n    def main(self, args=None):\n        self.import_modules()\n\n        if self._modules:\n            self.register_command(config)\n            self.register_command(clean)\n\n        main_parser = _ArgumentParser(\n            formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n            prog='fret')\n\n        main_parser.add_argument('-q', action='store_true', help='quiet')\n        main_parser.add_argument('-v', action='store_true', help='verbose')\n        main_parser.add_argument('-w', '--workspace', help='workspace dir')\n\n        _subparsers = main_parser.add_subparsers(title='supported commands',\n                                                 dest='command')\n        _subparsers.required = True\n\n        subparsers = dict()\n        for _cmd, _cls in self._commands.items():\n            _sub = _subparsers.add_parser(\n                _cmd, help=_cls.help,\n                formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n            subparsers[_cmd] = _sub\n            # pylint: disable=protected-access\n            _sub.set_defaults(func=_cls(self, _sub)._run)\n\n        args = main_parser.parse_args() if args is None \\\n            else main_parser.parse_args(args)\n\n        # configure logger\n        _logger = logging.getLogger()\n        if args.q:\n            _logger.setLevel(logging.WARNING)\n        elif args.v:\n            _logger.setLevel(logging.DEBUG)\n        else:\n            _logger.setLevel(logging.INFO)\n\n        # remove logging related options\n        del args.q\n        del args.v\n\n        logger = logging.getLogger(args.command)\n        try:\n            return args.func(args)\n        except KeyboardInterrupt:\n            # print traceback info to screen only\n            import traceback\n            sys.stderr.write(traceback.format_exc())\n            logger.warning('cancelled by user')\n        except NotConfiguredError as e:\n            print('error:', e)\n            subparsers['config'].print_usage()\n            sys.exit(1)\n        except Exception as e:  # pylint: disable=broad-except\n            # print traceback info to screen only\n            import traceback\n            sys.stderr.write(traceback.format_exc())\n            logger.error('exception occurred: %s: %s',\n                         e.__class__.__name__, e)\n\n    def configurable(self, wraps=None, submodules=None, states=None,\n                     build_subs=True):\n        def wrapper(cls):\n            orig_init = cls.__init__\n            spec = funcspec(orig_init)\n\n            if submodules is not None:\n                setattr(cls, 'submodules', submodules)\n\n            orig_state_dict = getattr(cls, 'state_dict', lambda _: dict())\n            orig_load_state_dict = getattr(cls, 'load_state_dict',\n                                           lambda *_: None)\n\n            def state_dict(sf):\n                if states:\n                    d = {s: getattr(sf, s) for s in states}\n                else:\n                    d = dict()\n                d.update(orig_state_dict(sf))\n                return d\n\n            def load_state_dict(sf, state):\n                if states:\n                    for s in states:\n                        setattr(sf, s, state[s])\n                        del state[s]\n                orig_load_state_dict(sf, state)\n\n            @classmethod\n            def add_arguments(_, parser):\n                with ParserBuilder(parser, self.argument_style) as builder:\n                    for name, opt in spec.kw:\n                        builder.add_opt(name, opt)\n\n            def new_init(sf, *args, **kwargs):\n                # get config from signature\n                args, kwargs, cfg = spec.get_call_args(sf, *args, **kwargs)\n                if not hasattr(sf, 'config'):\n                    global_config = self.config._get(cls.__name__)\n                    if global_config:\n                        d = global_config.copy()\n                        d.update(dict(cfg[1:]))\n                    else:\n                        d = dict(cfg[1:])\n                    Module.__init__(sf, **d)\n                orig_init(*args, **kwargs)\n\n            # inherit Module methods\n            for k, v in Module.__dict__.items():\n                if k != '__dict__' and k not in cls.__dict__:\n                    setattr(cls, k, v)\n            setattr(cls, '__init__', new_init)\n            setattr(cls, 'add_arguments', add_arguments)\n            setattr(cls, 'state_dict', state_dict)\n            setattr(cls, 'load_state_dict', load_state_dict)\n            setattr(cls, '_build_subs', build_subs)\n\n            if not cls.__name__.startswith('_'):\n                self.register_module(cls)\n            return cls\n\n        if wraps is None:\n            return wrapper\n        else:\n            return wrapper(wraps)\n\n    def command(self, f):\n        name = f.__name__\n        spec = funcspec(f)\n        if spec.pos and spec.pos[0] == 'ws':\n            static = False\n        else:\n            static = True\n\n        @functools.wraps(f)\n        def new_f(*args, **kwargs):\n            args, kwargs, cfg = spec.get_call_args(*args, **kwargs)\n            global_config = self.config._get(name)\n            if global_config:\n                d = global_config.copy()\n                d.update(dict(cfg[int(not static):]))\n                cfg = d\n            else:\n                cfg = cfg[int(not static):]\n            new_f.config = Configuration(cfg)\n            return f(*args, **kwargs)\n\n        argument_style = self.argument_style\n\n        class _Command(Command):\n            def __init__(self, _, parser):\n                with ParserBuilder(parser, argument_style) as builder:\n                    for arg in spec.pos[int(not static):]:\n                        builder.add_opt(arg, argspec())\n                    for k, v in spec.kw:\n                        builder.add_opt(k, v)\n\n            def run(self, ws, args):\n                if static:\n                    return new_f(**args._dict())\n                else:\n                    return new_f(ws, **args._dict())\n\n        _Command.__name__ = name\n        self.register_command(_Command)\n\n        return new_f\n\n\nclass funcspec:\n    \"\"\"Utility to generate argument specification from function signature.\"\"\"\n\n    __slots__ = ['pos', 'kw', 'kw_only']\n\n    def __init__(self, f):\n        spec = ins.getfullargspec(f)\n        if spec.defaults:\n            self.kw_only = False\n            n_config = len(spec.defaults)\n            self.pos = spec.args[:-n_config]\n            opts = [v if isinstance(v, argspec) else argspec.from_param(v)\n                    for v in spec.defaults]\n            self.kw = [] if n_config == 0 else \\\n                list(zip(spec.args[-n_config:], opts))\n        else:\n            self.kw_only = True\n            self.pos = spec.args\n            if spec.kwonlydefaults:\n                self.kw = list(spec.kwonlydefaults.items())\n            else:\n                self.kw = []\n\n    def get_call_args(self, *args, **kwargs):\n        defaults = dict((k, v.default()) for k, v in self.kw)\n        if not self.kw_only:\n            if len(args) > len(self.pos):\n                n_other = len(args) - len(self.pos)\n                defaults.update(dict([(self.kw[i][0], args[i-n_other])\n                                      for i in range(n_other)]))\n                args = args[:-n_other]\n        defaults.update(dict([(k, v.default()) for k, v in self.kw]))\n        defaults.update(kwargs)\n        cfg = list(zip(self.pos, args)) + list(defaults.items())\n        return args, defaults, cfg\n\n\nclass argspec:\n    \"\"\"In control of the behavior of commands. Represents arguments for\n    :meth:`argparse.ArgumentParser.add_argument`.\"\"\"\n\n    __slots__ = ['_args', '_kwargs', '_params']\n\n    def __init__(self, *args, **kwargs):\n        self._args = args\n        self._kwargs = kwargs\n        self._params = None\n\n    @classmethod\n    def from_param(cls, param):\n        kwargs = dict()\n\n        if isinstance(param, tuple):\n            assert len(param) == 2 or len(param) == 3, \\\n                'should be default, help[, choices]'\n            kwargs['default'] = param[0]\n            kwargs['help'] = param[1]\n            if len(param) == 3:\n                kwargs['choices'] = param[2]\n        elif param is not None:\n            kwargs['default'] = param\n\n        if isinstance(kwargs['default'], list):\n            if kwargs['default']:\n                kwargs['nargs'] = '+'\n                kwargs['type'] = type(kwargs['default'][0])\n            else:\n                kwargs['nargs'] = '*'\n        elif isinstance(kwargs['default'], bool):\n            if kwargs['default']:\n                kwargs['action'] = 'store_false'\n            else:\n                kwargs['action'] = 'store_true'\n        elif kwargs['default'] is not None:\n            kwargs['type'] = type(kwargs['default'])\n\n        obj = cls(**kwargs)\n        obj._params = param  # pylint: disable=protected-access\n        return obj\n\n    def default(self):\n        return self._kwargs.get('default')\n\n    def spec(self):\n        return self._args, self._kwargs\n\n\nclass ParserBuilder:\n    \"\"\"Utility to generate CLI arguments in different styles.\"\"\"\n    def __init__(self, parser, style='java'):\n        self._parser = parser\n        self._style = style\n        self._names = []\n        self._spec = []\n\n    def add_opt(self, name, spec):\n        \"\"\"Add option with specification.\n\n        Args:\n            name (str) : option name\n            spec (argspec): argument specification\"\"\"\n\n        if spec.default() is True:\n            # change name for better bool support\n            spec._kwargs['dest'] = name  # pylint: disable=protected-access\n            name = 'no_' + name\n        self._names.append(name)\n        self._spec.append(spec)\n\n    def __enter__(self):\n        return self\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        prefix = '-' if self._style == 'java' else '--'\n        seen = set(self._names)\n        for name, spec in zip(self._names, self._spec):\n            args, kwargs = spec.spec()\n            if not args:\n                args = [prefix + name]\n                short = ''.join(seg[0] for seg in name.split('_'))\n                if short not in seen:\n                    args.append('-' + short)\n                    seen.add(short)\n            else:\n                kwargs['dest'] = name\n            if 'help' not in kwargs:\n                kwargs['help'] = 'parameter ' + name\n            self._parser.add_argument(*args, **kwargs)\n\n\nclass _ArgumentParser(argparse.ArgumentParser):\n    def error(self, message):\n        # customize error message\n        self.print_usage(sys.stderr)\n        err = colored('error:', 'r', style='b')\n        self.exit(2, '%s %s\\n' % (err, message))\n\n\n_app = App()\n\n\ndef get_app():\n    \"\"\"Get current global app.\"\"\"\n    return _app\n\n\ndef set_global_app(app):\n    \"\"\"Set current global app.\"\"\"\n    global _app  # pylint: disable=global-statement\n    _app = app\n\n\ndef workspace(path=None):\n    \"\"\"Build workspace within current app.\n\n    Args:\n        path (str) : workspace path (default: ``None``)\n    \"\"\"\n    return get_app().workspace(path)\n\n\ndef configurable(wraps=None, submodules=None, build_subs=True, states=None):\n    \"\"\"Class decorator that registers configurable module under current app.\n\n    Args:\n        wraps (class or None) : object to be decorated; could be given later\n        submodules (list) : submodules of this module\n        build_subs (bool) : whether submodules are built before building this\n                            module (default: ``True``)\n        states (list) : members that would appear in state_dict\n    \"\"\"\n    return get_app().configurable(wraps, submodules, states, build_subs)\n\n\ndef command(f):\n    \"\"\"Function decorator that would turn a function into a fret command.\"\"\"\n    return get_app().command(f)\n\n\n__all__ = ['get_app', 'set_global_app', 'argspec', 'App',\n           'workspace', 'configurable', 'command']\n","sub_path":"fret/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":19513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"221558711","text":"import os\nimport sys\nimport subprocess\nimport uuid\nimport re\nimport hashlib\nimport shutil\nimport mmap\nimport tempfile\n\nAUTHORITY_MARKER = 'Authority='\n\n\nclass BuckCodesign(object):\n    def __init__(self, codesign_tool_path, app_bundle_path):\n        self.codesign_tool_path = codesign_tool_path\n        self.app_bundle_path = app_bundle_path\n\n    def get_output_for_command(self, command_as_array, fail_automatically=True, merge_stderr=True):\n        err_output = None\n        if merge_stderr:\n            err_output = subprocess.STDOUT\n        process = subprocess.Popen(command_as_array, stdout=subprocess.PIPE, stderr=err_output)\n        out, err = process.communicate()\n        exitCode = process.wait()\n        if exitCode != 0 and fail_automatically:\n            print('Command `' + ' '.join(command_as_array) + '` returned ' + str(exitCode))\n            print('Output:\\n' + out)\n            sys.exit(exitCode)\n        return out\n\n    def check_bundle_was_previously_signed(self):\n        print(\"Checking previous signature of the bundle\")\n        out = self.get_output_for_command([self.codesign_tool_path,\n                                          '--verify',\n                                          self.app_bundle_path],\n                                          fail_automatically=False)\n        if 'code object is not signed at all' in out:\n            print(\"Bundle was not signed, doing nothing\")\n            sys.exit(0)\n\n    def prepare_entitlements(self):\n        print(\"Getting entitlements from \" + self.app_bundle_path)\n        entitlements = self.get_output_for_command([self.codesign_tool_path,\n                                                   '-d',\n                                                   '--entitlements',\n                                                   '-',\n                                                   self.app_bundle_path],\n                                                   merge_stderr=False)\n        self.entitlements_file = tempfile.NamedTemporaryFile(mode='w+b')\n        self.entitlements_file.write(entitlements)\n        self.entitlements_file.seek(0)\n\n    def prepare_identity(self):\n        signature = self.get_output_for_command([self.codesign_tool_path,\n                                                 '-vvvv',\n                                                 '-d',\n                                                 self.app_bundle_path])\n        for line in signature.splitlines():\n            if line.startswith(AUTHORITY_MARKER):\n                self.identity = line[len(AUTHORITY_MARKER):].strip()\n                print(\"Found identity: \" + self.identity)\n                break\n        if not self.identity:\n            print(\"Unable to find identity information from signature:\\n\" + signature)\n            sys.exit(1)\n\n    def prepare_identity_sha(self):\n        available_identities = self.get_output_for_command(['security',\n                                                            'find-identity',\n                                                            '-v',\n                                                            '-p',\n                                                            'codesigning'])\n        print(\"Available codesigning identities:\\n\" + available_identities)\n        for line in available_identities.splitlines():\n            if self.identity in line:\n                identity_sha = re.search(r'[0-9A-F]{40}', line)\n                if identity_sha:\n                    self.identity_sha = identity_sha.group(0)\n                    print(\"Found SHA for identity: \" + self.identity_sha)\n                    break\n        if not self.identity_sha:\n            print(\"Unable to find SHA for identity: \" + self.identity)\n            sys.exit(1)\n\n    def invoke_sign(self):\n        out = self.get_output_for_command([self.codesign_tool_path,\n                                           '-s',\n                                           self.identity_sha,\n                                           '-f',\n                                           '--entitlements',\n                                           self.entitlements_file.name,\n                                           self.app_bundle_path])\n        print(\"Codesign complete: \" + out)\n\n    def sign(self):\n        self.check_bundle_was_previously_signed()\n        self.prepare_entitlements()\n        self.prepare_identity()\n        self.prepare_identity_sha()\n        self.invoke_sign()\n\n\ndef parse_args():\n    import argparse\n    description = \"\"\"Performs re-signing of the bundle. Uses original\n    entitlements from the bundle as well as embedded provisioning profile.\"\"\"\n    parser = argparse.ArgumentParser(description=description)\n    parser.add_argument(\n        'codesign_tool_path',\n        help='Path to `codesign` tool, e.g. /usr/bin/codesign')\n    parser.add_argument(\n        'app_bundle_path',\n        help='Path to .app bundle to re-sign, e.g. /path/to/appname.app')\n    return parser.parse_args()\n\n\ndef main():\n    args = parse_args()\n    if not os.path.exists(args.app_bundle_path):\n        sys.exit('Cannot find app bundle at ' + args.app_bundle_path)\n    if not os.path.exists(args.app_bundle_path):\n        sys.exit('Cannot find codesign tool at ' + args.codesign_tool_path)\n    signer = BuckCodesign(args.codesign_tool_path, args.app_bundle_path)\n    signer.sign()\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"src/com/facebook/buck/apple/codesign.py","file_name":"codesign.py","file_ext":"py","file_size_in_byte":5374,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"426970843","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('Cadastros', '0006_fatura_valor'),\n        ('Remessa', '0001_initial'),\n    ]\n\n    operations = [\n        migrations.AddField(\n            model_name='lote',\n            name='cliente',\n            field=models.ForeignKey(to='Cadastros.Cliente', null=True),\n        ),\n    ]\n","sub_path":"Remessa/migrations/0002_lote_cliente.py","file_name":"0002_lote_cliente.py","file_ext":"py","file_size_in_byte":453,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"102271031","text":"from openerp.osv import osv\r\nfrom openerp.osv import fields\r\nfrom openerp.tools import  DEFAULT_SERVER_DATETIME_FORMAT\r\nfrom datetime import date\r\nimport time\r\nfrom openerp.tools.translate import _\r\n\r\n\r\nclass bj_assessment_window(osv.osv):\r\n    _name = 'bj.assessment.window'\r\n    _order = \"id desc\"\r\n    def on_change_confirm_bj(self, cr, uid, ids, context=None):\r\n        invoice_ids = []\r\n        for key in self.browse(cr,uid,ids,context=context):\r\n            reg_no = key.reg_no\r\n            output = key.wakf_id.id\r\n            bj_line_id = key.bj_line_id\r\n            for bj_line in key.bj_line_id:\r\n                product_id = 3\r\n                name = \"BJ\"\r\n                quantity = 1\r\n                price_subtotal = bj_line.net_income1\r\n                price_unit = bj_line.net_income1\r\n                new_amount = bj_line.net_income1\r\n                acc_year = bj_line.account_year_line.id\r\n                invoice_ids.append((0,0,{'product_id':product_id,'name':name,'quantity':quantity,'price_subtotal':price_subtotal,'price_unit':price_unit,'new_amount':new_amount}))\r\n                self.pool.get('account.invoice').create(cr,uid,{'is_assessment':True,'registration_no':reg_no,'account_id':1,'journal_id':'1','partner_id':output,'invoice_line':invoice_ids,'assess_year_saleorder':acc_year,'assessment_type':'bj'})\r\n                invoice_ids = []\r\n            self.write(cr, uid, ids, {'state':'approved'})\r\n        return False\r\n    \r\n    \r\n    def _total_amount_net_bj(self, cr, uid, ids, field_name, arg, context=None):\r\n        val1 = 0\r\n        res = {}\r\n        for order in self.browse(cr, uid, ids, context=context):\r\n            res[order.id] = {\r\n            'total_income' : 0,\r\n            }\r\n            for line in order.bj_line_id:\r\n                val1 += line.net_income1\r\n            res[order.id]['net_income']=val1          \r\n        return res\r\n    def _total_amount_net_Assess(self, cr, uid, ids, field_name, arg, context=None):\r\n        val1 = 0\r\n        res = {}\r\n        for order in self.browse(cr, uid, ids, context=context):\r\n            res[order.id] = {\r\n            'total_income' : 0,\r\n            }\r\n            for line in order.bj_line_id:\r\n                val1 += line.assess_amount\r\n            res[order.id]['net_income_assess']=val1          \r\n        return res\r\n    def _total_amount_contribution_bj(self, cr, uid, ids, field_name, arg, context=None):\r\n        val1 = 0\r\n        res = {}\r\n        for order in self.browse(cr, uid, ids, context=context):\r\n            res[order.id] = {\r\n            'total_income' : 0,\r\n            }\r\n            for line in order.bj_line_id:\r\n                val1 += line.contri_amount1\r\n            res[order.id]['contri_amount']=val1          \r\n        return res\r\n    def _deflt_ass_year(self, cr, uid, ids, context=None):\r\n        today = date.today()\r\n        month_of = today.month\r\n        if month_of <= 3:\r\n            date_of = '%d-%d'%(today.year-1,today.year)\r\n        if month_of >= 4:\r\n            date_of = '%d-%d'%(today.year,today.year+1)\r\n        company_id = self.pool['res.company']._company_default_get(cr,uid,object='case.registration'),\r\n        search_condition = [('name', '=', date_of),('company_id','=',company_id)]\r\n        search_ids = self.pool.get('account.fiscalyear').search(cr, uid, search_condition, context=context)\r\n        if search_ids:\r\n            similar_objs = self.pool.get('account.fiscalyear').browse(cr, uid, search_ids, context=context)\r\n        if similar_objs:\r\n            return similar_objs[0].id\r\n        return False\r\n   \r\n    def action_approve(self, cr, uid, ids, context=None):\r\n        follow_list = []\r\n        dicto = {}\r\n        for rec in self.browse(cr, uid, ids, context=context):\r\n            dicto = {'name':\"Approved\",'who':uid,'when':time.strftime(DEFAULT_SERVER_DATETIME_FORMAT)}\r\n            follow_list.append((0,0,dicto))\r\n            self.write(cr, uid, ids, {'state':'approved','follow_line_id':follow_list})\r\n        return True\r\n    def action_send(self, cr, uid, ids, context=None):\r\n        follow_list = []\r\n        dicto = {}\r\n        invoice_ids = []\r\n        for rec in self.browse(cr, uid, ids, context=context):\r\n            dicto = {'name':\"BJ Send\",'who':uid,'when':time.strftime(DEFAULT_SERVER_DATETIME_FORMAT)}\r\n            follow_list.append((0,0,dicto))\r\n            price_unit_income = rec.net_bj or False\r\n            new_amount_income = rec.net_bj or False\r\n            output = rec.wakf_id.id or False\r\n            acc_year = rec.account_year.id or False\r\n            ass_year = rec.assessment_year.id or False\r\n            reg_no = rec.reg_no or False\r\n            ##############################################################################################################\r\n            search_ids = self.pool.get('product.product').search(cr,uid,[('name','=',\"BJ\")])\r\n            if not search_ids:\r\n                raise osv.except_osv(_('Warning!'), _('Please create a \"BJ\" property first'))\r\n            income_id = self.pool.get('product.product').browse(cr,uid,search_ids)[0].id\r\n            \r\n            search_ids = self.pool.get('account.account').search(cr,uid,[('name','=',\"Accounts Receivable\")])\r\n            if not search_ids:\r\n                raise osv.except_osv(_('Warning!'), _('Please create an account \"Accounts Receivable\" first'))\r\n            account_id = self.pool.get('account.account').browse(cr,uid,search_ids)[0].id\r\n            \r\n            search_ids = self.pool.get('account.journal').search(cr,uid,[('name','=',\"Assessment Journal\")])\r\n            if not search_ids:\r\n                raise osv.except_osv(_('Warning!'), _('Please create \"Assessment Journal\" First'))\r\n            journal_id = self.pool.get('account.journal').browse(cr,uid,search_ids)[0].id\r\n            ##############################################################################################################\r\n            invoice_ids.append((0,0,{'product_id':income_id,'name':\"BJ Assessment\",'quantity':1,'price_unit':price_unit_income,'new_amount':new_amount_income,'sws':False}))   # sws =True, 7% calculation disabled\r\n            self.pool.get('account.invoice').create(cr,uid,{'registration_no':reg_no,'assess_year_saleorder':acc_year,'account_year_saleorder':ass_year,'is_assessment':True,'appli_no':False,'account_id':account_id,'journal_id':journal_id,'partner_id':output,'invoice_line':invoice_ids,'total_income_saleorder':False,'total_expense_saleorder':False,'assessment_type':'bj'})\r\n        self.write(cr, uid, ids, {'state':'send','follow_line_id':follow_list})\r\n        return True\r\n    def action_cancel(self, cr, uid, ids, context=None):\r\n        follow_list = []\r\n        dicto = {}\r\n        for rec in self.browse(cr, uid, ids, context=context):\r\n            dicto = {'name':\"BJ Cancelled\",'who':uid,'when':time.strftime(DEFAULT_SERVER_DATETIME_FORMAT)}\r\n            follow_list.append((0,0,dicto))\r\n            self.write(cr, uid, ids, {'state':'cancelled','follow_line_id':follow_list})\r\n        return True\r\n    \r\n    def action_re_assessment(self, cr, uid, ids, context=None):\r\n        follow_list = []\r\n        dicto = {}\r\n        for rec in self.browse(cr, uid, ids, context=context):\r\n            dicto = {'name':\"Cancelled and Re-Assessment\",'who':uid,'when':time.strftime(DEFAULT_SERVER_DATETIME_FORMAT)}\r\n            follow_list.append((0,0,dicto))\r\n            output = rec.wakf_id.id or False\r\n            acc_year = rec.account_year.id or False\r\n            ass_year = rec.assessment_year.id or False\r\n            self.write(cr, uid, ids, {'state':'re_assessment','follow_line_id':follow_list})\r\n            search_invoice = self.pool.get('account.invoice').search(cr,uid,[('partner_id','=',output),('assess_year_saleorder','=',acc_year),('account_year_saleorder','=',ass_year),('is_assessment','=',True),('appli_no','=',False),('assessment_type','=','bj'),('state','=','draft')])\r\n            list_unlink = [ invoice.id for invoice in self.pool.get('account.invoice').browse(cr,uid,search_invoice)]\r\n            self.pool.get('account.invoice').unlink(cr,uid,list_unlink,context=context)\r\n        return True\r\n    \r\n    def action_rr(self, cr, uid, ids, context=None):\r\n        follow_list = []\r\n        dicto = {}\r\n        browse_invoice_draft = 0\r\n        browse_invoice_open = 0\r\n        for rec in self.browse(cr, uid, ids, context=context):\r\n            dicto = {'name':\"Send RR\",'who':uid,'when':time.strftime(DEFAULT_SERVER_DATETIME_FORMAT)}\r\n            follow_list.append((0,0,dicto))\r\n            reg_no = rec.reg_no\r\n            output = rec.wakf_id.id or False\r\n            acc_year = rec.account_year.id or False\r\n            ass_year = rec.assessment_year.id or False\r\n            #############################################\r\n            search_invoice_draft = self.pool.get('account.invoice').search(cr,uid,[('partner_id','=',output),('assess_year_saleorder','=',acc_year),('account_year_saleorder','=',ass_year),('is_assessment','=',True),('appli_no','=',False),('assessment_type','=','bj'),('state','=','draft')])\r\n            search_invoice_open = self.pool.get('account.invoice').search(cr,uid,[('partner_id','=',output),('assess_year_saleorder','=',acc_year),('account_year_saleorder','=',ass_year),('is_assessment','=',True),('appli_no','=',False),('assessment_type','=','bj'),('state','=','open')])\r\n            search_invoice_paid = self.pool.get('account.invoice').search(cr,uid,[('partner_id','=',output),('assess_year_saleorder','=',acc_year),('account_year_saleorder','=',ass_year),('is_assessment','=',True),('appli_no','=',False),('assessment_type','=','bj'),('state','=','paid')])\r\n            if search_invoice_draft:\r\n                browse_invoice_draft = self.pool.get('account.invoice').browse(cr, uid, search_invoice_draft)\r\n            if search_invoice_open:\r\n                browse_invoice_open = self.pool.get('account.invoice').browse(cr, uid, search_invoice_open)\r\n            if browse_invoice_open:\r\n                self.pool.get('account.invoice').action_cancel(cr,uid,[browse_invoice_open[0].id],context=context)\r\n            if browse_invoice_draft:\r\n                list_unlink = [ invoice.id for invoice in self.pool.get('account.invoice').browse(cr,uid,search_invoice_draft)]\r\n                self.pool.get('account.invoice').unlink(cr,uid,list_unlink,context=context)\r\n            self.write(cr, uid, ids, {'state':'rr','follow_line_id':follow_list})\r\n            \r\n            ##############################################################################################\r\n            ##########################################  RR Created  ##########################################\r\n            id_create = self.pool.get('revenue.recovery').create(cr,uid,{'from_a':'bj','reg_no':reg_no,'partner_id':output,'assess_year':ass_year,'account_year':acc_year})\r\n            return {\r\n                    'type': 'ir.actions.act_window',\r\n                    'name': \"Revenue Recovery form\",\r\n                    'view_type': 'form',\r\n                    'view_mode': 'form',\r\n                    'context': context,\r\n                    'res_id':id_create,\r\n                    #'domain' : [('order_id','in',sale_ids)],\r\n                    'res_model': 'revenue.recovery',\r\n                    'target': 'new',\r\n                    'nodestroy': True,}\r\n        return True\r\n    \r\n    def showcause(self, cr, uid, ids, context=None):\r\n        acc_year_1 = False\r\n        acc_year = False\r\n        for key in self.browse(cr,uid,ids,context=context):\r\n            reg_no = key.reg_no\r\n            output = key.wakf_id.id\r\n            amount = key.contri_amount\r\n            assessment_year=key.assessment_year.id\r\n            count = 0\r\n            for bj_line in key.bj_line_id:\r\n                if count == 0:\r\n                    acc_year_1 = bj_line.account_year_line.id\r\n                acc_year = bj_line.account_year_line.id\r\n                count += 1          \r\n            self.pool.get('show.cause').create(cr,uid,{'reg_no':reg_no,'assessment_year':assessment_year,'acc_year_from':acc_year_1,'partner_id':output,'acc_year_to':acc_year,'amount':amount})\r\n            self.write(cr, uid, ids, {'state':'showcause'})\r\n        return False\r\n    \r\n    ##################################################################################################\r\n    #                                    CANNOT DELETE BJ ASSESSMENT RECORDS                         #\r\n    #                                                TESTED\r\n    ##################################################################################################\r\n    \r\n    #def unlink(self, cr, uid, ids, context=None):\r\n    #    if context is None:\r\n    #        context = {}\r\n    #    \"\"\"Allows to delete assessment lines in other states\"\"\"\r\n    #    for rec in self.browse(cr, uid, ids, context=context):\r\n    #        if rec.state not in ['draft']:\r\n    #            raise osv.except_osv(_('Invalid Action!'), _('Cannot delete an Assessment Record which is in state \\'%s\\'.') %(rec.state,))\r\n    #    return super(bj_assessment_window, self).unlink(cr, uid, ids, context=context)\r\n    \r\n\r\n    \r\n    _columns = {\r\n         'reg_no':fields.integer('Registration No:',size=16),\r\n         'wakf_id':fields.many2one('res.partner','Wakf Name'),\r\n         'ass_date':fields.date('Assessment Date',size=16),\r\n         'net_assess':fields.float('Net Income(Assessment)'),\r\n         'net_bj':fields.float('Net Income(BJ)'),\r\n         'contribution':fields.float('Contribution'),                \r\n         'assessment_year':fields.many2one('account.fiscalyear','Assessment Year',ondelete='set null'),\r\n         'account_year':fields.many2one('account.fiscalyear','Account Year',ondelete='set null'),\r\n         'follow_line_id':fields.one2many('follow.up.bj','follow_id','FOLLOW UPs'),\r\n         'state':fields.selection((('draft','Draft'), ('approved','BJ Confirmed'),('send','BJ Send'),('re_assessment','Re-Assessment'),('rr','RR Send'),('cancelled','BJ Cancelled'),('completed','BJ Completed')),'BJ State',required=False), \r\n         'user_id':fields.char('user id',size=16), \r\n         'company_id': fields.many2one('res.company', 'Company', required=False)                              \r\n                }\r\n    _defaults ={\r\n                'assessment_year':_deflt_ass_year,\r\n                'state':'approved',\r\n                'user_id': lambda obj, cr, uid, context: uid,\r\n                'company_id': lambda self,cr,uid,ctx: self.pool['res.company']._company_default_get(cr,uid,object='bj.assessment.window',context=ctx)\r\n                }\r\nbj_assessment_window()\r\n\r\nclass follow_up_bj(osv.osv):\r\n    \r\n    _name = 'follow.up.bj'\r\n    \r\n    _columns = {\r\n               'name':fields.char('Action',readonly=True),\r\n               'who':fields.many2one('res.users','Who',readonly=True),\r\n               'when':fields.char('When',readonly=True), \r\n               'follow_id':fields.many2one('bj.assessment.window','FOLLOW UP'),\r\n                \r\n                \r\n                }\r\nfollow_up_bj()\r\n\r\n\r\n\r\n","sub_path":"wakf_assessment/BJ_assessment.py","file_name":"BJ_assessment.py","file_ext":"py","file_size_in_byte":15058,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"629933224","text":"from segmed.models import unet\nfrom segmed.metrics import metrics as mts\nimport tensorflow as tf\n\n\ndef train_segmed(\n    model,\n    img_path,\n    mask_path,\n    batch_size=16,\n    epochs=25,\n    steps_per_epoch=3125,\n    val_split=0.2,\n    optimizer=tf.keras.optimizers.Adam(),\n    monitor=\"val_jaccard_index\",\n    model_file=\"unet_simple.h5\",\n    seed=1,\n    show=False,\n):\n\n    \"\"\"A simple utility function for training the any `segmed` model.\n\n    Takes two paths for images and segmentation maps and rescales them, splits\n    them into training and validation, while saving the best model trained.\n    The paths must comply to the Keras API convention as specified in their\n    documentation.\n\n    Args:\n        model (tf.keras.Model): A keras model instance.\n        img_path (str): The relative path were the images are located.\n        mask_path (str): The relative path were the maps are located.\n        batch_size (int): Size of the batch to be processed.\n        epochs (int): Number of epochs to train the model.\n        steps_per_epoch (int): Total number of steps (batches of samples) to yield \n            before declaring one epoch finished and starting the next epoch.\n        val_split (float): Value between 0.0 and 1.0 representing the size in percentage\n            to split the dataset.\n        optimizer (`Optimizer`): A Keras optimizer instance with a valid syntax from TensorFlow.\n        monitor (str): Quantity to monitor during training; follow the Keras convention.\n        model_file (str): File to create for the ModelCheckpoint callback from Keras.\n        seed (int): Value to seed the ImageDataGenerator and always retrieve the same batch\n            of pairs of images.\n        show (bool): If true, plots a pair of size `batch_size` to see if the image and its\n            segmentation maps are consistent. For debugging purposes only.\n\n    Returns:\n        history (`History`): A `History` object. Its `History.history` attribute is a record of\n            training loss values and metrics values at successive epochs, as well as validation\n            loss values and validation metrics values (if applicable).\n    \"\"\"\n\n    # Rescale and convert to float32 both subsets\n    data_gen_args = {\n        \"rescale\": 1.0 / 255.0,\n        \"validation_split\": val_split,\n        \"dtype\": tf.float32,\n    }\n\n    # Crea the training generators with the defined transformations\n    image_datagen = tf.keras.preprocessing.image.ImageDataGenerator(**data_gen_args)\n    mask_datagen = tf.keras.preprocessing.image.ImageDataGenerator(**data_gen_args)\n    # Take images from directories\n    image_generator_train = image_datagen.flow_from_directory(\n        img_path,\n        class_mode=None,\n        color_mode=\"rgb\",\n        batch_size=batch_size,\n        seed=seed,\n        subset=\"training\",\n    )\n    mask_generator_train = mask_datagen.flow_from_directory(\n        mask_path,\n        class_mode=None,\n        color_mode=\"grayscale\",\n        batch_size=batch_size,\n        seed=seed,\n        subset=\"training\",\n    )\n    # Combine both generators\n    # This need to be a generator of generators, see the following\n    # https://github.com/tensorflow/tensorflow/issues/32357\n    train_generator = (\n        pair for pair in zip(image_generator_train, mask_generator_train)\n    )\n\n    # And now the validation generators\n    image_generator_val = image_datagen.flow_from_directory(\n        img_path,\n        class_mode=None,\n        color_mode=\"rgb\",\n        batch_size=batch_size,\n        seed=seed,\n        subset=\"validation\",\n    )\n    mask_generator_val = mask_datagen.flow_from_directory(\n        mask_path,\n        class_mode=None,\n        color_mode=\"grayscale\",\n        batch_size=batch_size,\n        seed=seed,\n        subset=\"validation\",\n    )\n    # Combine both generators, with same issue as before\n    val_generator = (pair for pair in zip(image_generator_val, mask_generator_val))\n\n    # DEBUGGING ONLY, for checking that both image and maps are batched together\n    if show:\n        import matplotlib.pyplot as plt\n\n        for i, j in train_generator:\n            plt.figure(0)\n            plt.imshow(i[0, ...])\n            plt.figure(1)\n            plt.imshow(j[0, ..., 0])\n            plt.show()\n\n    # Define the checkpoint callback, always maximum mode for custom metrics\n    checkpoint = tf.keras.callbacks.ModelCheckpoint(\n        model_file, monitor=monitor, verbose=1, save_best_only=True, mode=\"max\"\n    )\n\n    # Compile the model with an and custom metrics\n    model.compile(\n        loss=tf.keras.losses.binary_crossentropy,\n        optimizer=optimizer,\n        metrics=[mts.jaccard_index, mts.dice_coef],\n    )\n\n    # Create history of model and return it\n    history = model.fit_generator(\n        train_generator,\n        steps_per_epoch=steps_per_epoch,\n        epochs=epochs,\n        callbacks=[checkpoint],\n        verbose=1,\n        validation_data=val_generator,\n        validation_steps=steps_per_epoch,\n    )\n\n    return history\n","sub_path":"segmed/train/train_segnet.py","file_name":"train_segnet.py","file_ext":"py","file_size_in_byte":4993,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"116288578","text":"def uname(kv, pwd):\n    if uid not in list(kv.keys()):\n        return False\n    if uid in list(kv.keys()):\n        return True\n\n\ndef upwd(kv, uid):\n    if pwd == kv[uid]:\n        return True\n    if pwd != kv[uid]:\n        return False\n\n\ndef logn(kv):\n    while True:\n        userid = input(\"输入注册的用户名:\")\n        userpwd_1 = input(\"输入密码:\")\n        userpwd_2 = input(\"请再次确认密码:\")\n        if userpwd_1 == userpwd_2:\n            kv[userid] = userpwd_1\n            break\n        if userpwd_1 != userpwd_2:\n            r = input(\"两次密码不一致，是否继续注册？(yes or no)\")\n            if r == 'no':\n                break\n            logn(kv)\n            break\n\n\nif __name__ == '__main__':\n    print(\"^^ Welcome to test logn web ! ^^\")\n    kv = {'zhang': '123', 'wang': '234', 'zhu': '345'}\n    n = 0\n    while True:\n        if n:\n             break\n        s = input('我已有账密(yes or no):')\n        if s == 'no':\n            t = input('是否需要注册账密(yes or no)？:')\n            if t == 'no':\n                print('那下次再来注册哦！')\n                break\n        if s == 'no':\n            logn(kv)\n        uid = input('请输入您的登录账户名:')\n        pwd = input('请输入您的账户名对应的密码:')\n        count = 0\n\n        while True:\n            if uname(kv, pwd) and upwd(kv, uid):\n                print('登陆成功')\n                n = 1\n                break\n            if count == 2:\n                print(\"抱歉，次数已达上限，禁止登陆\")\n                break\n            else:\n                count = count + 1\n                print(\"账户名或密码错误,请重新登录，错误三次将锁定账户\")\n                p = input('是否需要注册账户(yes or no)')\n                if p == 'yes':\n                    logn(kv)\n                uid = input('请输入您的用户名:')\n                pwd = input('请输入登录密码:')\n        count = 0\n\n","sub_path":"GaoMingYue/House_Forecast.py","file_name":"House_Forecast.py","file_ext":"py","file_size_in_byte":1984,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"389338669","text":"#!/usr/bin/python\n\nfrom __future__ import print_function\n\nimport logging\nfrom logging.handlers import RotatingFileHandler\nfrom logging import handlers\n\nimport argparse\nimport os\nimport sys\n\nPREFIX_FORMAT = \"%(asctime)s %(levelname)s %(message)s\"\nDATE_FORMAT = \"%a %m/%d/%Y %I:%M:%S %p\"\nMESSAGE_FORMAT = \"{}: {}\"\n\n\nclass Journal(object):\n\n    def __init__(self,\n                 logger_name,\n                 log_file_name,\n                 program_name,\n                 log_debug=True,\n                 log_information=True,\n                 log_warning=True,\n                 log_error=True,\n                 log_critical=True,\n                 max_bytes=10000,\n                 backup_count=1,\n                 ):\n\n        # Save internal variables\n        self._logger_name = logger_name\n        self._log_file_name = log_file_name\n        self._program_name = program_name\n        self._max_bytes = max_bytes\n        self._backup_count = backup_count\n\n        # Save which types of messages to be sent to the console\n        # and logged. These can be changed during execution.\n        # Default is everything is printed and logged.\n\n        self.log_debug = log_debug\n        self.log_information = log_information\n        self.log_warning = log_warning\n        self.log_error = log_error\n        self.log_critical = log_critical\n\n        # Setup the logging service.\n\n        self._log = logging.getLogger(self._logger_name)\n        self._log.setLevel(logging.DEBUG)\n        self._format = logging.Formatter(PREFIX_FORMAT, DATE_FORMAT)\n\n        self._ch = logging.StreamHandler(sys.stdout)\n        self._ch.setFormatter(self._format)\n        self._log.addHandler(self._ch)\n\n        self._fh = handlers.RotatingFileHandler(self._log_file_name,\n                                                maxBytes=self._max_bytes,\n                                                backupCount=self._backup_count,\n                                                )\n        self._fh.setFormatter(self._format)\n        self._log.addHandler(self._fh)\n\n    def debug(self, message):\n        \"\"\"\n        Log a debug message.\n        \"\"\"\n        if self.log_debug:\n            msg = MESSAGE_FORMAT.format(self._program_name, message)\n            self._log.debug(msg)\n\n    def information(self, message):\n        \"\"\"\n        Log a information message.\n        \"\"\"\n        if self.log_information:\n            msg = MESSAGE_FORMAT.format(self._program_name, message)\n            self._log.info(msg)\n\n    def warning(self, message):\n        \"\"\"\n        Log a warning message.\n        \"\"\"\n        if self.log_warning:\n            msg = MESSAGE_FORMAT.format(self._program_name, message)\n            self._log.warn(msg)\n\n    def error(self, message):\n        \"\"\"\n        Log an error message.\n        \"\"\"\n        if self.log_error:\n            msg = MESSAGE_FORMAT.format(self._program_name, message)\n            self._log.error(msg)\n\n    def critical(self, message):\n        \"\"\"\n        Log a critical message.\n        \"\"\"\n        if self.log_critical:\n            msg = MESSAGE_FORMAT.format(self._program_name, message)\n            self._log.critical(msg)\n\ndef test():\n\n    my_journal = Journal(\"test\", \"log-test.log\", os.path.basename(__file__))\n\n    print(\"Log messages with default settings.\")\n\n    my_journal.debug(\"internally speaking, this might be interesting\")\n    my_journal.information(\"you might want to know this\")\n    my_journal.warning(\"this might be serious\")\n    my_journal.error(\"we have a problem Houston\")\n    my_journal.critical(\"it's all over\")\n\n    my_journal.log_debug = False\n    my_journal.log_information = False\n    my_journal.log_warning = False\n    my_journal.log_error = False\n    my_journal.log_critical = False\n\n    print(\"Log messages with all logging set to False.\")\n\n    # None of these should be logged.\n    my_journal.debug(\"internally speaking, this might be interesting\")\n    my_journal.information(\"you might want to know this\")\n    my_journal.warning(\"this might be serious\")\n    my_journal.error(\"we have a problem Houston\")\n    my_journal.critical(\"it's all over\")\n\n    my_journal.log_debug = True\n    my_journal.log_information = True\n    my_journal.log_warning = True\n    my_journal.log_error = True\n    my_journal.log_critical = True\n\n    print(\"Log messages with all logging set to True.\")\n\n    # All of these should be logged.\n    my_journal.debug(\"internally speaking, this might be interesting\")\n    my_journal.information(\"you might want to know this\")\n    my_journal.warning(\"this might be serious\")\n    my_journal.error(\"we have a problem Houston\")\n    my_journal.critical(\"it's all over\")\n\ndef main():\n    parser = argparse.ArgumentParser(description=\"send a message to a log file\")\n    parser.add_argument(\"message\")\n    level_group = parser.add_mutually_exclusive_group()\n    level_group.add_argument(\"-d\",\n                        \"--debug\",\n                        action='store_true',\n                        help=\"send a debug message to the specified log file\",\n                        )\n    level_group.add_argument(\"-i\",\n                        \"--info\",\n                        action='store_true',\n                        help=\"send an information message to the specified log file\",\n                        )\n    level_group.add_argument(\"-w\",\n                        \"--warn\",\n                        action='store_true',\n                        help=\"send a warning message to the specified log file\",\n                        )\n    level_group.add_argument(\"-e\",\n                        \"--error\",\n                        action='store_true',\n                        help=\"send an error message to the specified log file\",\n                        )\n    level_group.add_argument(\"-c\",\n                        \"--crit\",\n                        action='store_true',\n                        help=\"send a critical message to the specified log file\",\n                        )\n    parser.add_argument(\"-l\",\n                        \"--logfile\",\n                        required=True,\n                        help=\"send a critical message to the specified log file\",\n                        )\n    parser.add_argument(\"-t\",\n                        \"--test\",\n                        action='store_true',\n                        help=\"runs test cases\",\n                        )\n\n    args = parser.parse_args()\n\n    if args.test:\n        test()\n    else:\n        program_name = os.path.basename(__file__)\n\n        my_journal = Journal(\"command\", args.logfile, program_name)\n\n        if args.debug:\n            my_journal.debug(args.message)\n        if args.info:\n            my_journal.information(args.message)\n        if args.warn:\n            my_journal.warning(args.message)\n        if args.error:\n            my_journal.error(args.message)\n        if args.crit:\n            my_journal.critical(args.message)\n       \n\nif __name__ == \"__main__\":\n    main()\n\n","sub_path":"journal.py","file_name":"journal.py","file_ext":"py","file_size_in_byte":6898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"203985117","text":"from FetchData import FetchData\nimport datetime\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\n\nclass LSTM(nn.Module):\n    def __init__(self, input_dim=1, hidden_dim=15, batch_size=1, output_dim=1, num_layers=2):\n        super(LSTM, self).__init__()\n        self.input_dim = input_dim\n        self.hidden_dim = hidden_dim\n        self.batch_size = batch_size\n        self.num_layers = num_layers\n\n        # Define the LSTM layer\n        self.lstm = nn.LSTM(self.input_dim, self.hidden_dim, self.num_layers)\n\n        # Define the output layer\n        self.linear = nn.Linear(self.hidden_dim, output_dim)\n\n    def forward(self, inputs):\n        outputs = []\n\n        for seq in inputs:\n            lstm_out, self.hidden = self.lstm(seq.view(len(seq), self.batch_size, -1))\n            y_pred = self.linear(lstm_out[-1].view(self.batch_size, -1))\n            outputs.append(y_pred)\n\n        return torch.stack(outputs).squeeze(1).squeeze(1)\n\nclass History_analysis(object):\n    def __init__(self, history_price, shares):\n        \"\"\"\n        history_price数据类型是ndarray,资产历史价格 m*n维 m是资产个数 n是天数\n        该类可以整合个股和组合的功能 个股只需m=1即可 之后调用时name均为None\n        shares数据类型是ndarray，资产份额 长度为m\n        能用numpy作计算就numpy 提高速度\n        \"\"\"\n        self.history_price = history_price\n        self.shares = shares\n        self.SEQ_LENGTH = 8\n        #未计算 先设为None\n        self.daily_return = None\n        self.total_asset = None\n        self.vol = None\n        self.xtrain = None\n        self.ytrain = None\n        self.xtest = None\n        self.ytest = None\n        self.model = None\n\n    def fill_nan(self):\n        \"填充nan（停牌或未上市）数值 用前一天的价格表示当天价格 如果第一天就是nan 用第一个不是nan的数值填充第一天\"\n        m,n=self.history_price.shape\n        for i in range(m):\n            local = self.history_price[i]\n            nan = np.isnan(local)\n            if nan[0]==True:\n                for j in range(1,n):\n                    if nan[j]==False:\n                        local[0]=local[j]\n                        break\n            for j in range(1,n):\n                if nan[j] == True:\n                    local[j]=local[j-1]\n\n    def cal_history_return(self):\n        \"先填充nan 再做计算每日收益率\"\n        num,length=self.history_price.shape\n        daily_return = np.zeros(length - 1)\n\n        self.total_asset=self.shares.dot(self.history_price)\n\n        for i in range(1,length):\n            daily_return[i] = (self.total_asset[i]-self.total_asset[i-1])/self.total_asset[i-1]\n\n        self.daily_return = daily_return\n        return daily_return\n\n    #计算波动率 以年为单位\n    def volatility(sequence):\n        yield_rates = [(sequence[i + 1] - sequence[i]) / sequence[i] for i in range(len(sequence) - 1)]\n        return np.std(yield_rates) * np.sqrt(250)\n\n    #计算收益的波动率\n    def cal_volatility(self):\n        length = self.total_asset.shape[0]\n        self.vol=np.zeros(length-self.SEQ_LENGTH+1)\n        for i in range(length-self.SEQ_LENGTH+1):\n            self.vol = self.volatility(self.total_asset[i:i+self.SEQ_LENGTH])\n        return self.vol\n\n\n    def train_test_split(self,data, test_prop=0.0):  # N=7, SEQ_LENGTH=N+1\n        \"\"\"\n            data type:   np.ndarray\n            return type: np.ndarray\n            test_prop设为0.0 暂时不需要测试集 全部过拟合即可\n        \"\"\"\n        xtrain, ytrain = [], []\n        xtest, ytest = [], []\n        for i in range(0, int(len(data) * (1 - test_prop)) - self.SEQ_LENGTH):\n            #用SEQ_LENGTH-1的历史 预测后一天\n            seq = data[i:i + self.SEQ_LENGTH]\n            xtrain.append(seq[:-1])\n            ytrain.append(seq[-1])\n\n        for i in range(int(len(data) * (1 - test_prop)) - self.SEQ_LENGTH, len(data) - self.SEQ_LENGTH):\n            seq = data[i:i + self.SEQ_LENGTH]\n            xtest.append(seq[:-1])\n            ytest.append(seq[-1])\n\n        self.xtrain=np.array(xtrain)\n        self.ytrain=np.array(ytrain)\n        self.xtest=np.array(xtest)\n        self.ytest=np.array(ytest)\n\n    def train_LSTM(self):\n        \"训练LSTM模型\"\n        self.train_test_split(self.total_asset)\n        self.model = LSTM().double()\n        criterion = nn.MSELoss()\n        # 优化器\n        optimizer = optim.LBFGS(self.model.parameters(), lr=0.1)\n        # 开始训练 40可以改\n        for i in range(40):\n            def closure():\n                optimizer.zero_grad()\n                out = self.model(self.xtrain)\n                loss = criterion(out, self.ytrain)\n                print('loss: %5.3f  ' % (loss.item()))\n                loss.backward()\n                return loss\n            optimizer.step(closure)\n\n\n    def predict_return(self):\n        last_N_x=self.total_asset[-(self.SEQ_LENGTH-1):]\n        y_pred = self.model(last_N_x)[0].item()\n        pred_return = (y_pred - self.total_asset[-1])/self.total_asset[-1]\n        return pred_return\n\n    def predict_vol(self):\n        last_N_x = self.total_asset[-(self.SEQ_LENGTH - 1):]\n        y_pred = self.model(last_N_x)[0].item()\n        pred_vol = self.volatility(np.append(last_N_x,y_pred))\n        return pred_vol\n\n\n    def VaR_calculate(self):\n        last_N_x = self.total_asset[-(self.SEQ_LENGTH - 1):]\n        pred_return = self.predict_return()\n        all = np.append(last_N_x,pred_return)\n        log_return = np.zeros(self.SEQ_LENGTH-1)\n        for i in range(len(log_return)):\n            log_return[i]=np.log(all[i+1]/all[i])\n        log_vol=np.std(log_return)\n        log_pred_return = np.log(pred_return)\n        percentile95 = 1.6499\n        var95 = - np.exp(log_pred_return-percentile95*log_vol)\n        return var95*self.total_asset[-1]\n\nif __name__=='__main__':\n    #从fetchdata里获得股票组合的historydata\n    #history ndarray\n    #shares ndarray\n    history=None\n    shares=None\n\n    analysis = History_analysis(history,shares)\n    analysis.fill_nan() #填充nan值 规则见函数\n\n    daily_return = analysis.cal_history_return() #计算每日收益率 （和每日总资产 不返回）\n    volatility = analysis.cal_volatility() #计算每日波动率 长度比history短SEQ_LENGTH-1\n\n    analysis.train_test_split()\n    analysis.train_LSTM() #预测总资产数量的模型\n\n    pred_return = analysis.predict_return()\n    pred_vol = analysis.predict_vol()\n\n    var95 = analysis.VaR_calculate() #95%的VaR","sub_path":"graph/stock_calculation(untested).py","file_name":"stock_calculation(untested).py","file_ext":"py","file_size_in_byte":6622,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"387566361","text":"#!/usr/bin/python3\nimport sys, os, time, builtins, shutil\nimport subprocess as sbp\nimport socket\n\ndef print(*args, **kwargs):\n    builtins.print(\"main:\", *args, **kwargs)\n\ndef main():\n# create an INET, STREAMing socket\n    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n# now connect to the web server on port 80 - the normal http port\n    s.connect((\"www.python.org\", 80))\n\nif __name__ == '__main__':\n    main()\n","sub_path":"sm20-4/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"605023590","text":"#Este código é baseado na figura 1.8 do livro do Spong\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import axes3d\nfrom math import cos, sin, sqrt, pi, atan2 \nimport numpy as np\nimport math\nimport random \n\n#transforma uma matriz A m x n em uma matriz  3 x 3\ndef M_3x3(A):\n    L1 = np.array([A[0,0],A[0,1],A[0,2]])\n    L2 = np.array([A[1,0],A[1,1],A[1,2]])\n    L3 = np.array([A[2,0],A[2,1],A[2,2]])\n    H = np.array([L1,L2,L3])\n    return H\n                    \n#transforma um vetor v n x 1 em um vetor 3 x 1\ndef v_3d(v):\n    return np.array([v[0],v[1],v[2]])\n\n#Retorna a Matriz de transformacao Homogeneadados  usando como entrada os parametros de DH\ndef matrix_homogenea(d,a,alfa,theta):\n    L1 = np.array([round(cos(theta),4), round(-sin(theta)*cos(alfa),4), round(sin(theta)*sin(alfa),4),round(a*cos(theta),4)])\n    L2 = np.array([round(sin(theta),4), round(cos(theta)*cos(alfa),4),round(-cos(theta)*sin(alfa),4),round(a*sin(theta),4)])\n    L3 = np.array([0, round(sin(alfa),4), round(cos(alfa),4), d])\n    L4 = np.array([0,0,0,1])\n    A = np.array([L1,L2,L3,L4])\n    return A\n\n#Calcula a distancia Euclidiana entre dois pontos no R^n\ndef distancia(a,b,n):\n    d = 0\n    for i in range(n):\n        d = d + (a[i] - b[i])**2      \n    return sqrt(d)\n\n#Calcula os angulos de Euler a partir de uma matriz de Rotacao\ndef orientacao(A):\n    #Z-Y-Z Euler Angles\n    if(A[0,2] != 0 or A[1,2] !=0):\n        theta = atan2(sqrt(1 - A[2,2]**2),A[2,2])\n        phi = atan2(A[1,2],A[0,2])\n        psi = atan2(A[2,1],-A[2,0])\n    else:\n        if(A[2,2] == 1):\n            theta = 0\n            phi = 0\n            psi = atan2(A[0,0],A[1,0])\n        else:\n            theta = pi\n            phi = 0\n            psi = - atan2(-A[0,0],-A[0,1]) \n    result = np.array([theta,phi,psi])\n    return result\n\ndef matriz_B(v): #Matriz B(alpha) da equação 4.108 do livro Spong-RobotmodelingandControl\n    L1 = np.array([round(cos(v[2])*sin(v[0]),4),round(-sin(v[2]),4),0])\n    L2 = np.array([round(sin(v[2])*sin(v[0]),4),round(cos(v[2]),4),0])\n    L3 = np.array([round(cos(v[0]),4),0,1])\n    A = np.array([L1,L2,L3])\n    return A\n\ndef Ja(J,B):#Usando como entrada a matriz B(alpha) e a Jacobiana analitica eh calculada a matriz geometrica\n    L1 = np.array([1,0,0,0,0,0])\n    L2 = np.array([0,1,0,0,0,0])\n    L3 = np.array([0,0,1,0,0,0])\n    C = np.linalg.inv(B)\n    L4 = np.array([0,0,0,C[0,0],C[0,1],C[0,2]])\n    L5 = np.array([0,0,0,C[1,0],C[1,1],C[1,2]])\n    L6 = np.array([0,0,0,C[2,0],C[2,1],C[2,2]])\n    A = np.array([L1,L2,L3,L4,L5,L6])\n    return A@J\n\n#Função que plota o manipulador, quando recebe os pontos de interesse\ndef plotagem(a,b,c,d,t):\n    #Plotando Elos\n    plt.plot([a[0,0],b[0,0]],[a[1,0],b[1,0]],[a[2,0],b[2,0]])\n    plt.plot([b[0,0],c[0,0]],[b[1,0],c[1,0]],[b[2,0],c[2,0]])\n    plt.plot([c[0,0],d[0,0]],[c[1,0],d[1,0]],[c[2,0],d[2,0]])\n\n    #Plotando Juntas\n    ax.scatter(a[0,0],a[1,0],a[2,0])\n    ax.scatter(b[0,0],b[1,0],b[2,0])\n    ax.scatter(c[0,0],c[1,0],c[2,0])\n\n    #Plotando efetuador\n    ax.scatter(d[0,0],d[1,0],d[2,0])\n\n    ax.scatter(t[0],t[1],t[2])\n    #Legendas\n    plt.legend(['Elo 1','Elo 2','Elo 3','Junta 1 (Revolucao)','Junta 2 (Revolucao)'\\\n                ,'Junta 3 (Revolucao)','Efetuador','objetivo'])\n\n    ax.set_xlabel('Eixo x')\n    ax.set_ylabel('Eixo y')\n    ax.set_zlabel('Eixo z')\n\n    #titulo\n    plt.title('Manipulador RRR')\n    ax.scatter(2,2,2) #ponto 1 para melhor visualização do gráfico\n    ax.scatter(-2,-2,2) #ponto 2 para melhor visualização do gráfico\n    fig.canvas.draw() #mostra o plot\n\n\n#configurando o plot\nplt.ion()\nfig = plt.figure()\nax =  fig.add_subplot(111, projection = '3d')\n#plt.pause(0.01)\n\n#variaveis\ncont = 0\nc = 0.1 #tamanho do passo\ndestino = np.array([[1.2,1,1.2]]).T\n\n#vetores colunas do sistema de coordenadas global\ni = np.array([[1,0,0,1]]).T\nj = np.array([[0,1,0,1]]).T\nk = np.array([[0,0,1,1]]).T\no = np.array([[0,0,0,1]]).T #origem\n\n##Parametros dos robos\nb1 = 1\nb2 = 1\nb3 = 1\n\n#angulos de juntas iniciais\nq1 = 2*pi*random.random()\nq2 = 2*pi*random.random()\nq3 = 2*pi*random.random()\nq = np.array([[q1,q2,q3]]).T\nfor v in range(1000):\n    \n    # parametros de DH\n    d1 = b1 \n    d2 = 0\n    d3 = 0\n    a1 = 0\n    a2 = b2\n    a3 = b3\n    alfa1 = math.pi/2\n    alfa2 = 0\n    alfa3 = 0\n    theta1 = math.pi/2 + q[0]\n    theta2 = q[1] \n    theta3 = q[2]\n\n    #Matrizes homogêneas\n    A1 = matrix_homogenea(d1,a1,alfa1,theta1)\n    A2 = matrix_homogenea(d2,a2,alfa2,theta2)\n    A3 = matrix_homogenea(d3,a3,alfa3,theta3)\n    A = (A1@A2@A3)\n    #Encontrando os pontos de interesse no sistema Global\n    o1_1 = o #origem do SC 1\n    o2_2 = o #origem do SC 2\n    o3_3 = o #origem do SC 3\n    o1_0 = A1@o1_1\n    o2_0 = A1@(A2@o2_2)\n    o3_0 = A1@(A2@(A3@o3_3))\n    \n    #os vetores z serao transformados em vetores linhas no R^3, para poder ser usado a funcao np.cross\n    z0_0 = v_3d(k).T\n    z1_0 = v_3d(A1@k).T\n    z2_0 = v_3d(A1@(A2@k)).T\n    z3_0 = v_3d(A1@(A2@(A3@k))).T\n\n    aux = (v_3d(o3_0) - v_3d(o)).T\n    aux = np.cross(z0_0,aux)\n    J = np.array([np.concatenate((aux,z0_0),axis = None)])\n\n    aux = (v_3d(o3_0) - v_3d(o1_0)).T\n    aux = np.cross(z1_0,aux)\n    J2 = np.array([np.concatenate((aux,z1_0),axis = None)])\n    J = np.concatenate((J,J2),axis = 0)\n\n    aux = (v_3d(o3_0) - v_3d(o2_0)).T\n    aux = np.cross(z2_0,aux)\n    J3 = np.array([np.concatenate((aux,z2_0),axis = None)])\n    J = np.concatenate((J,J3),axis = 0)\n    J = J.T \n    f = (v_3d(o3_0) - destino)\n    J = M_3x3(J) #foi descartado a parte da velocidade angular, porque ela nao esta sendo usada\n    #e caso estivesse sendo, teria que ser convertida em taxa de angulo.\n    #c = 1/(distancia(v_3d(o3_0),destino,3))\n    q = q - c*(J.T@f)\n    erro = distancia(o3_0,destino,3)\n    print('erro: ',erro,'\\n','iteracao: ',v,'\\n') \n    if(erro < 0.001): break\n    ax.clear()\n    plotagem(o,o1_0,o2_0,o3_0,destino)\n    plt.pause(0.001)\n    \n\n\n\n\n    \n","sub_path":"Simulacoes_Matplotlib/Jacobian/GradienteDescendente_Janalitica_RRRV2.py","file_name":"GradienteDescendente_Janalitica_RRRV2.py","file_ext":"py","file_size_in_byte":5906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"116848045","text":"import torch\nimport torch.nn as nn\n\nclass SupervisedLoss(nn.Module):\n\n    def __init__(self, args, n_features, n_classes):\n        super(SupervisedLoss, self).__init__()\n        self.args = args\n        self.n_features = n_features\n        self.n_classes = n_classes\n\n        # create linear classifier\n        self.model = nn.Sequential(\n            nn.Linear(n_features, n_classes)\n        ).to(args.device)\n\n        self.num_labels = 1\n        self.criterion = nn.BCEWithLogitsLoss()\n    \n    def predict(self, x):\n        x = x.permute(0, 2, 1)\n        pooled = nn.functional.adaptive_avg_pool1d(x, 1) # one label\n        pooled = pooled.permute(0, 2, 1).reshape(-1, self.n_features)\n        output = self.model(pooled)\n        predicted = output.argmax(1)\n        return predicted\n\n    def get_pooled(self, x):\n        x = x.permute(0, 2, 1)\n        pooled = nn.functional.adaptive_avg_pool1d(x, 1) # one label\n        pooled = pooled.permute(0, 2, 1).reshape(-1, self.n_features) \n        return pooled\n\n    def forward(self, x, y):\n        x = self.get_pooled(x)\n        output = self.model(x)\n        loss = self.criterion(output, y)\n        return loss, output\n\n    def get(self, x, z, c, y=None):\n        return self.forward(c, y) # use context vector","sub_path":"modules/cpc/supervised_loss.py","file_name":"supervised_loss.py","file_ext":"py","file_size_in_byte":1261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"419990978","text":"from constants import *\nimport constants\nfrom pygame.locals import *\nimport pygame\nimport pickle\n\n\nclass Setting(object):\n\n    def __init__(self, SCREEN):\n        self.screen = SCREEN\n        self.messagex = int((SCREENWIDTH - IMAGES['board'].get_width()) / 2)\n        self.messagey = int(SCREENHEIGHT * 0.09)\n        self.buttonx = int(\n            (SCREENWIDTH - IMAGES['playButton'].get_width()) / 2)\n        self.buttony = int(SCREENHEIGHT * 0.85)\n        self.levelx = int((SCREENWIDTH - IMAGES['easy'].get_width()) / 2)\n        self.basex = 0\n        self.baseShift = IMAGES['base'].get_width(\n        ) - IMAGES['background'].get_width()\n        self.offsety = 105\n        self.PIPEGAP = 120\n\n    def run(self):\n        playing = True\n        while playing:\n            for event in pygame.event.get():\n                if event.type == QUIT or (event.type == KEYDOWN and\n                                          event.key == K_ESCAPE):\n                    return 'quit'\n                if event.type == pygame.MOUSEBUTTONDOWN:\n                    (x, y) = event.pos\n                    if self.isInBound(x, y) == 'intro':\n                        return ('intro', self.PIPEGAP)\n                    if self.isInEasy(x, y) == True:\n                        self.PIPEGAP = 150\n                    elif self.isInMedium(x, y) == True:\n                        self.PIPEGAP = 120\n                    elif self.isInHard(x, y) == True:\n                        self.PIPEGAP = 90\n\n            self.basex = -((-self.basex + 4) % self.baseShift)\n\n            self.screen.blit(IMAGES['background'], (0, 0))\n            self.screen.blit(IMAGES['base'], (self.basex, BASEY))\n            self.screen.blit(IMAGES['playButton'],\n                             (self.buttonx, self.buttony))\n            self.screen.blit(IMAGES['easy'], (self.levelx, 50))\n            self.screen.blit(IMAGES['medium'],\n                             (self.levelx, 50 + self.offsety))\n            self.screen.blit(\n                IMAGES['hard'], (self.levelx, 50 + self.offsety * 2))\n            pygame.display.update()\n            FPSCLOCK.tick(FPS)\n\n    def isInBound(self, x, y):\n        if (x > self.buttonx and\n            x < self.buttonx + IMAGES['playButton'].get_width() and\n                y > self.buttony and\n                y < self.buttony + IMAGES['playButton'].get_height()):\n            return 'intro'\n\n    def isInEasy(self, x, y):\n        if (x > self.levelx and\n            x < self.levelx + IMAGES['easy'].get_width() and\n                y > 50 and y < 50 + IMAGES['easy'].get_height()):\n            return True\n\n    def isInMedium(self, x, y):\n        if (x > self.levelx and x < self.levelx + IMAGES['medium'].get_width()\n            and y > 50 + self.offsety\n                and y < 50 + self.offsety + IMAGES['medium'].get_height()):\n            return True\n\n    def isInHard(self, x, y):\n        if (x > self.levelx and x < self.levelx + IMAGES['hard'].get_width()\n            and y > 50 + self.offsety * 2 and\n                y < 50 + self.offsety * 2 + IMAGES['hard'].get_height()):\n            return True\n","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":3100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"319163927","text":"import numpy as np\nfrom pathlib import Path\nimport lazy_ops\nfrom ...imagingextractor import ImagingExtractor\nfrom ...extraction_tools import check_get_frames_args, get_video_shape\n\ntry:\n    import h5py\n    HAVE_H5 = True\nexcept ImportError:\n    HAVE_H5 = False\n\n\nclass Hdf5ImagingExtractor(ImagingExtractor):\n    extractor_name = 'Hdf5Imaging'\n    installed = HAVE_H5  # check at class level if installed or not\n    is_writable = True\n    mode = 'file'\n    installation_mesg = \"To use the Hdf5 Extractor run:\\n\\n pip install h5py\\n\\n\"  # error message when not installed\n\n    def __init__(self, file_path, mov_field='mov', sampling_frequency=None, start_time=None, metadata=None,\n                 channel_names=None):\n        assert HAVE_H5, self.installation_mesg\n        ImagingExtractor.__init__(self)\n        self.filepath = Path(file_path)\n        self._sampling_frequency = sampling_frequency\n        self._mov_field = mov_field\n        assert self.filepath.suffix in ['.h5', '.hdf5'], \"\"\n        self._channel_names = channel_names\n\n        with h5py.File(file_path, \"r\") as f:\n            if 'mov' in f.keys():\n                self._video = f[self._mov_field]\n            else:\n                raise Exception(f\"{file_path} does not contain the 'mov' dataset\")\n\n        if sampling_frequency is None:\n            assert 'fr' in f.keys(), \"sampling frequency information is unavailable!\"\n            self._sampling_frequency = self._video.attrs[\"fr\"]\n        else:\n            self._sampling_frequency = sampling_frequency\n\n        if start_time is None:\n            if 'start_time' in f.keys():\n                self._start_time = self._video.attrs[\"start_time\"]\n        else:\n            self._start_time = start_time\n\n        if metadata is None:\n            if 'metadata' in f.keys():\n                self.metadata = self._video.attrs[\"metadata\"]\n        else:\n            self.metadata = metadata\n\n        self._num_channels, self._num_frames, self._size_x, self._size_y = get_video_shape(self._video)\n\n        if len(self._video.shape) == 3:\n            # check if this converts to np.ndarray\n            self._video = self._video[np.newaxis, :]\n\n        if self._channel_names is not None:\n            assert len(self._channel_names) == self._num_channels, \"'channel_names' length is different than number \" \\\n                                                                   \"of channels\"\n        else:\n            self._channel_names = [f'channel_{ch}' for ch in range(self._num_channels)]\n\n        self._kwargs = {'file_path': str(Path(file_path).absolute()), 'mov_field': mov_field,\n                        'sampling_frequency': sampling_frequency, 'channel_names': channel_names}\n\n    @check_get_frames_args\n    def get_frames(self, frame_idxs, channel=0):\n        if frame_idxs.size > 1 and np.all(np.diff(frame_idxs) > 0):\n            return lazy_ops.DatasetView(self._video).lazy_slice[channel, frame_idxs]\n        else:\n            sorted_idxs = np.sort(frame_idxs)\n            argsorted_idxs = np.argsort(frame_idxs)\n            return lazy_ops.DatasetView(self._video).lazy_slice[channel, sorted_idxs].lazy_slice[:, argsorted_idxs]\n\n    def get_image_size(self):\n        return [self._size_x, self._size_y]\n\n    def get_num_frames(self):\n        return self._num_frames\n\n    def get_sampling_frequency(self):\n        return self._sampling_frequency\n\n    def get_channel_names(self):\n        return self._channel_names\n\n    def get_num_channels(self):\n        return self._num_channels\n\n    @staticmethod\n    def write_imaging(imaging, savepath):\n        pass\n","sub_path":"roiextractors/extractors/hdf5imagingextractor/hdf5imagingextractor.py","file_name":"hdf5imagingextractor.py","file_ext":"py","file_size_in_byte":3584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"339327029","text":"from django.forms import ModelForm, Textarea, CheckboxSelectMultiple\nfrom .models import Comment, Post\n\n\nclass NewCommentForm(ModelForm):\n    class Meta:\n        model = Comment\n        fields = ['content']\n        widgets = {\n            'content': Textarea(attrs={'rows': 3, 'cols': 100, 'class': 'bg-light'}),\n        }\n\n\nclass NewPostForm(ModelForm):\n    class Meta:\n        model = Post\n        fields = ['title', 'content', 'categories']\n        widgets = {\n            'categories': CheckboxSelectMultiple()\n        }\n","sub_path":"blog/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":525,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"251440083","text":"# Front matter\nimport pandas as pd\nimport numpy as np\n\n# Take selection of valid compositions from EOS paper and convert to atomic \n# percentages, because all of the compositions in this study are in terms of \n# atomic percentages\n\n# The compositions to look at:\n# Panel a: 5 wt% Ni, ~5 wt% Si, and up to 2% O\n# Panel c: 5 wt% Ni, 1 wt% C, 6 wt% Si\n# Panel e: 5 wt% Ni, 4 wt% Si, 0.5 wt% C, 3.0 wt% S\n# Panel g: 5 wt% Ni and up to 7.5 wt% S\n\n# Element molecular weights\nFe_mw = 55.845\nNi_mw = 58.693\nSi_mw = 28.085\nO_mw = 15.999\nC_mw = 12.01\nS_mw = 32.07\n\n# # Convert to a list in atomic fraction (at%/100) composition space\n# # a_i = (w_i/M_i) / sum_j(w_j/M_j); where a_i = at frac for element i,\n# #                                         w_i = wt frac for element i,\n# #                                         M = mol. wt. for element i\n# element_mw = np.fromiter(element_mw_dict.values(),\n# \t dtype=float) # Convert from ordereddict to np.array\n# # Note: Very important to specificy axis on np.sum(),\n# # as this can operate a variety of ways\n# atfrac_all = np.divide(np.divide(wtfrac_all,element_mw), \n# \tnp.sum(np.divide(wtfrac_all,element_mw),axis=1))\n\ndef wtper_2_atper(wtfrac_array,element_mw_array):\n\tatfrac_array = np.divide(np.divide(wtfrac_array,element_mw_array), \n\t\tnp.sum(np.divide(wtfrac_array,element_mw_array),axis=0))\n\treturn atfrac_array\n\n# Panel a: 5 wt% Ni, ~5 wt% Si, and up to 2% O\nwtfrac_array = [0.88, 0.05, 0.05, 0.02]\nelement_mw_array = [Fe_mw, Ni_mw, Si_mw, O_mw]\natfrac_array = wtper_2_atper(wtfrac_array,element_mw_array)\nprint(atfrac_array)\n# [ 0.8023298   0.04337488  0.09064631  0.06364902]\n\n# Panel c: 5 wt% Ni,  6 wt% Si, 1 wt% C\nwtfrac_array = [0.88, 0.05, 0.06, 0.01]\nelement_mw_array = [Fe_mw, Ni_mw, Si_mw, C_mw]\natfrac_array = wtper_2_atper(wtfrac_array,element_mw_array)\n# print(atfrac_array)\n# [ 0.80484499  0.04351085  0.10911657  0.0425276 ]\n\n# Panel e: 5 wt% Ni, 4 wt% Si, 3.0 wt% S, 0.5 wt% C\nwtfrac_array = [0.875, 0.05, 0.04, 0.03, 0.005]\nelement_mw_array = [Fe_mw, Ni_mw, Si_mw, S_mw, C_mw]\natfrac_array = wtper_2_atper(wtfrac_array,element_mw_array)\n# print(atfrac_array)\n# [ 0.81198906  0.04414791  0.07380945  0.04847845  0.02157513]\n\n# Panel g: 5 wt% Ni and up to 7.5 wt% S\nwtfrac_array = [0.875, 0.05, 0.075]\nelement_mw_array = [Fe_mw, Ni_mw, S_mw]\natfrac_array = wtper_2_atper(wtfrac_array,element_mw_array)\n# print(atfrac_array)\n# [ 0.83082121  0.04517181  0.12400698]\n","sub_path":"140_ComparisonToEOSPaper/wtper_2_atper.py","file_name":"wtper_2_atper.py","file_ext":"py","file_size_in_byte":2429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"304753649","text":"import unittest\n\nfrom mlpipe.utils.datautils import LabelSelector\n\n\nclass TestLabelSelector(unittest.TestCase):\n    def test_value_not_in_list(self):\n        selector = LabelSelector(elements=['ApfelBaum', 'BirnenBaum', 'AprikosenBaum', 'Katze', 'Hund'])\n        self.assertRaises(ValueError, lambda: selector.select(selection=['REGEX:.*Baum'], enable_regex=False))\n\n    def test_regex(self):\n        selector = LabelSelector(elements=['ApfelBaum', 'BirnenBaum', 'AprikosenBaum', 'Katze', 'Hund'])\n        result = selector.select(selection=['REGEX:.*Baum'], enable_regex=True)\n        self.assertListEqual([0, 1, 2], result.indexes)\n        self.assertListEqual([3, 4], result.indexes_unselected)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"mlpipe/test/utils/test_label_selector.py","file_name":"test_label_selector.py","file_ext":"py","file_size_in_byte":747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"192992355","text":"from sanascan_backend.word import TagWord\nfrom sanascan_backend.key import Key\n\nfrom tests.use_lang_model import UseLangModel\n\n\nclass TestVocabulary(UseLangModel):\n    def setUp(self) -> None:\n        self.lm = self.__class__.LM\n        self.vocab = self.lm.create_vocabrary()\n        self.key = Key.from_int([3, 1, 4, TagWord('<num>')])\n        self.keys = [\n            Key.from_int([TagWord('<num>')]),\n            Key.from_int([4, TagWord('<num>')]),\n            Key.from_int([1, 4, TagWord('<num>')]),\n            Key.from_int([3, 1, 4, TagWord('<num>')]),\n        ]\n\n    def test_have_num_tagword(self) -> None:\n        with self.subTest():\n            self.assertIn(TagWord('<num>'), self.lm._get_word_set())\n\n        self.assertIn(\n            Key.from_int([TagWord('<num>')]),\n            self.vocab._datum.keys()\n        )\n\n    def test_get_by_key(self) -> None:\n        self.assertNotEqual(\n            list(self.vocab.get_by_key(self.key)),\n            []\n        )\n        for word, key in self.vocab.get_by_key(self.key):\n            self.assertIn(key, self.keys)\n","sub_path":"sanascan_backend/tests/test_vocablary.py","file_name":"test_vocablary.py","file_ext":"py","file_size_in_byte":1078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"2032554","text":"from webweb import Web\n\n# make a network from an adjacency matrix\nadjacency_matrix = [\n    [0, 1, 0, 0],\n    [1, 0, 1, 0],\n    [0, 1, 0, 1],\n    [0, 0, 1, 0],\n]\n\n# webweb'll guess whether the adjacency you've given it is a list or a matrix,\n# but if you're pass it a matrix with fewer than 4 nodes, it'll think it's\n# an adjacency list unless you put it in it's place like so: \n# web = Web(adjacency_matrix, adjacency_type='matrix')\nweb = Web(adjacency_matrix)\n\n# create the web\nweb = Web(adjacency_matrix)\n\n# open the browser with the result\nweb.draw()\n","sub_path":"examples/python/3-adjacency_matrix_web.py","file_name":"3-adjacency_matrix_web.py","file_ext":"py","file_size_in_byte":554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"148574268","text":"# 1258. [S/W 문제해결 응용] 7일차 - 행렬찾기 D4\n# https://www.swexpertacademy.com/main/code/problem/problemDetail.do?contestProbId=AV18LoAqItcCFAZN&categoryId=AV18LoAqItcCFAZN&categoryType=CODE\n\ndef check(r,c):\n    global board\n    tr=r\n    tc=c\n    while(tr<n and board[tr][tc]!='0'):\n        tr+=1\n    rr=tr-r\n    tr=r\n    while(tc<n and board[tr][tc]!='0'):\n        tc+=1\n    rc=tc-c\n    for i in range(rr):\n        for j in range(rc):\n            board[r+i][c+j]='0'\n    return (rr,rc)\n\nfor tc in range(1,int(input())+1):\n    n=int(input())\n    board=[input().split() for _ in range(n)]\n    result=[]\n    for r in range(n):\n        for c in range(n):\n            if board[r][c]!='0':\n                result.append(check(r,c))\n    result.sort(key=lambda x:x[0])\n    result.sort(key=lambda x:x[0]*x[1])\n    print(f\"#{tc} {len(result)} \",end=\"\")\n    for i in result:\n        print(f\"{i[0]} {i[1]} \",end=\"\")\n    print()","sub_path":"SWEA/sol/1258.py","file_name":"1258.py","file_ext":"py","file_size_in_byte":931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"609300819","text":"# -*- coding:utf-8 -*-\nimport time\n\nfrom medvision.aug_cuda import CudaPatches, CudaDisplay\nfrom medvision.visulaize import volume2tiled\n\nfrom load_utils import *\n\n\ndef test2d():\n    result, img_filename, seg_filename = load_2d_image_with_seg()\n\n    transform = CudaPatches(patch_size=(300, 300))\n    tic = time.time()\n    transformed_result = transform(result)\n    toc = time.time()\n    print(transform)\n    print(toc - tic)\n    print(transform.params)\n    print(transformed_result.keys())\n\n    volume2tiled(transformed_result['img'][:, 0].cpu().numpy(), f'{save_to}/{img_filename}.png', 1)\n    volume2tiled(transformed_result['gt_seg'][:, 0].cpu().numpy(), f'{save_to}/{seg_filename}.png', 1)\n\n    print('\\n')\n    CudaDisplay()(transformed_result)\n\n\ndef test3d():\n    result, img_filename, seg_filename = load_3d_image_with_seg()\n\n    transform = CudaPatches(patch_size=(256, 256, 256))\n    tic = time.time()\n    transformed_result = transform(result)\n    toc = time.time()\n    print(transform)\n    print(toc - tic)\n    print(transform.params)\n    print(transformed_result.keys())\n\n    volume2tiled(transformed_result['img'][0, 0].cpu().numpy(), f'{save_to}/{img_filename}.png', 10)\n    volume2tiled(transformed_result['gt_seg'][0, 0].cpu().numpy(), f'{save_to}/{seg_filename}.png', 10)\n    print('\\n')\n    CudaDisplay()(transformed_result)\n\n\nif __name__ == \"__main__\":\n    __dir__ = os.path.dirname(os.path.realpath(__file__))\n    save_to = os.path.join(__dir__, 'CudaPatches')\n    os.makedirs(save_to, exist_ok=True)\n    os.chdir(__dir__)\n\n    test2d()\n    test3d()\n","sub_path":"test/aug_cuda_test_Patches.py","file_name":"aug_cuda_test_Patches.py","file_ext":"py","file_size_in_byte":1570,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"133432692","text":"import pyreadr\nresult = pyreadr.read_r('D:\\LEARNING\\Applied-Predictive-Modeling-with-Python\\data\\cars.Rdata')\nr = pyreadr.read_r('BloodBrain.RData')\ntype(result)\n\nprint(result.keys())\ndata = result['cars']\ntype(result['cars'])\ndata.head()\n\nimport os\nos.chdir('D:\\LEARNING\\Applied-Predictive-Modeling-with-Python\\data')\nfor file in os.listdir():\n    print(file)\n    result = pyreadr.read_r(file)\n    print(result.keys())\n\n","sub_path":"pyread.py","file_name":"pyread.py","file_ext":"py","file_size_in_byte":421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"118369964","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Apr 24 10:41:08 2017\n\n@author: dkilanga\n\"\"\"\n\nfrom __future__ import print_function\nimport os, sys\nimport numpy as np\nimport numpy.linalg as LA\nfrom math import pow\nfrom math import floor\nfrom math import sqrt, exp\nnp.random.seed(2017)\n\ndef read_data_from_file(input_file):\n    data = []\n    if not os.path.isfile(input_file):\n        print (\"Could not open file: \", input_file)\n        return None\n    with open(input_file, 'r') as f:\n        for line in f:\n            float_casted_data = []\n            line = (' '.join(line.strip().split())).split()\n            if '#' in line:\n                continue\n            elif not line:\n                continue\n            else:\n                for value in enumerate([float(j) for j in line]):\n                    float_casted_data.append(value[1])\n                data.append(float_casted_data)\n    data = np.array(data)\n    return data\n\ndef get_binary_class_data(file_name):\n    parsed_data = read_data_from_file(file_name)\n    X = parsed_data[:,5:]\n    y = parsed_data[:,4]\n    # Find indices where data correspond to Ground or Facade\n    index0 = y==1200\n    index1 = y==1004\n    X_binary = np.concatenate((X[index0,:], X[index1,:]), axis = 0)\n    y_binary = np.concatenate((-np.ones(((y[index0]).shape[0], 1 )), np.ones(((y[index1]).shape[0], 1))), axis = 0)\n    # Suffle all the data so that they are random\n    data_binary = np.random.permutation(np.concatenate((X_binary, y_binary), axis = 1))\n    X_binary = data_binary[:, 0:10]\n    y_binary = (data_binary[:,-1]).reshape(-1,1)\n    return X_binary, y_binary\n\ndef rbf_kernel(x_i, X_train, l):\n    diff = np.subtract(x_i, X_train)\n    norm_diff = LA.norm(diff, axis=1)\n    k = np.exp(-np.divide(np.square(norm_diff), pow(l,2)))\n    return k\n\ndef generate_k(x_s, X, kernel, l):\n    number_data = X.shape[0]\n#    k = np.zeros((number_data,1))\n#    for i in range(number_data):\n#        k[i] = rbf_kernel(x_s.T, X, l)\n    k = rbf_kernel(x_s.T, X, l)\n    k = k.reshape(-1,1)\n    return k\n\n\ndef f(x_i,X_train, alpha):\n    func_val = np.dot(alpha.T, generate_k(x_i, X_train, 'rbf', 4))\n    return func_val\n\ndef gamma(y_train, X_train, alpha):\n    gamma = np.empty(alpha.shape)\n    for i in range(gamma.shape[0]):\n        gamma[i] = y_train[i]/(1+exp(-y_train[i]*f(X_train[i,:].T, X_train, alpha)))\n    return gamma\n\n\n\n\nfile_name = \"oakland_part3_an_rf.node_features\"\nfile_name1 = \"oakland_part3_am_rf.node_features\"\n\n\nX,y = get_binary_class_data(file_name)\ntotal_number_data = X.shape[0]\nnumber_train_data = int(floor(total_number_data*0.75))\nnumber_tain_data_used = int(floor(number_train_data*0.01))\nX_train = X[0:number_train_data,:]\ny_train = y[0:number_train_data]\nX_test = X[number_train_data:,:]\ny_test = y[number_train_data:]\nalpha = np.zeros((number_tain_data_used, 1))\ny_predict = np.empty(y_test.shape)\nLambda = 0.1\neta = 0.1\nground_success = 0\nground_fail = 0\nveg_success = 0\nveg_fail = 0\ntot_ground = 0\ntot_veg = 0\ncount = 0\nfor i in range(y_test.shape[0]):\n    idx = np.random.randint(number_train_data, size=number_tain_data_used)\n    X_train_used = X_train[idx,:]\n    print(X_train_used.shape)\n    y_train_used = y_train[idx]\n    y_predict[i] = f(X_test[i,:].T,X_train_used, alpha)\n    alpha = alpha - eta*(Lambda*alpha + gamma(y_train_used, X_train_used, alpha))\n    if (y_predict[i] >= 0) and (y_test[i] == 1):\n        ground_success += 1\n    elif (y_predict[i] < 0) and (y_test[i] == -1):\n        veg_success += 1\n    elif (y_predict[i] >= 0) and (y_test[i] == -1):\n        veg_fail += 1\n    elif (y_predict[i] < 0) and (y_test[i] == 1):\n        ground_fail += 1\n    tot_ground = ground_success + ground_fail\n    tot_veg = veg_success + veg_fail\n#    print(\"ground success \", ground_success, \" out of \", tot_ground, \" veg success\", veg_success, \"out of \", tot_veg)\n    print(\"ground success \", ground_success/(tot_ground+0.00000000001), \" veg success\", veg_success/(tot_veg+0.00000000001))\n\nX,y = get_binary_class_data(file_name1)\nX_test = X[0:10000,:]\ny_test = y[0:10000]\n\nground_success = 0\nground_fail = 0\nveg_success = 0\nveg_fail = 0\ntot_ground = 0\ntot_veg = 0\n\nR_X = generate_R(X_train, 2, 0.15)\nR = generate_R(X_test, 2, 0.15)\n\ninv_part = LA.inv(np.dot(R_X, R_X.T) + 0.02*np.identity(R_X.shape[0]))\ny_predict = np.dot(np.dot(np.dot(y_train.T, R_X.T), inv_part), R)\ny_predict = y_predict.T        \n\nfor i in range(y_predict.shape[0]):\n    if (y_predict[i] >= 0) and (y_test[i] == 1):\n        ground_success += 1\n    elif (y_predict[i] < 0) and (y_test[i] == -1):\n        veg_success += 1\n    elif (y_predict[i] >= 0) and (y_test[i] == -1):\n        veg_fail += 1\n    elif (y_predict[i] < 0) and (y_test[i] == 1):\n        ground_fail += 1\n    tot_ground = ground_success + ground_fail\n    tot_veg = veg_success + veg_fail\n    print(\"ground success \", ground_success/(tot_ground+0.00001), \" veg success \", veg_success/(tot_veg+0.00001))\n\n\n\n#k = generate_k(X_test[1,:].T, X_train, 'rbf', 4)\n#print(k.shape)","sub_path":"Lab4/kern_log_reg.py","file_name":"kern_log_reg.py","file_ext":"py","file_size_in_byte":4991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"376699099","text":"import itertools\nimport time\n\nimport flopy\nimport numpy as np\nfrom scipy import interpolate as spint\nfrom scipy.spatial import qhull as qhull\n\n\ndef get_source_dest_model_xys(source_model, dest_model,\n                              source_mask=None):\n    \"\"\"Get the xyz and uvw inputs to the interp_weights function.\n\n    Parameters\n    ----------\n    source_model : flopy.modeflow.Modflow, flopy.mf6.MFModel, or MFsetupGrid instance\n    dest_model : mfsetup.MFnwtModel, mfsetup.MF6model instance\n    \"\"\"\n    source_modelgrid = source_model\n    if isinstance(source_model, flopy.mbase.ModelInterface):\n        source_modelgrid = source_model.modelgrid\n    dest_modelgrid = dest_model.modelgrid\n\n    if source_mask is None:\n        if dest_model.parent_mask.shape == source_modelgrid.xcellcenters.shape:\n            source_mask = dest_model.parent_mask\n        else:\n            source_mask = np.ones(source_modelgrid.xcellcenters.shape, dtype=bool)\n    else:\n        if source_mask.shape != source_modelgrid.xcellcenters.shape:\n            msg = 'source mask of shape {} incompatible with source grid of shape {}'\n            raise ValueError(msg.format(source_mask.shape,\n                                        source_modelgrid.xcellcenters.shape))\n    x = source_modelgrid.xcellcenters[source_mask].flatten()\n    y = source_modelgrid.ycellcenters[source_mask].flatten()\n    x2, y2 = dest_modelgrid.xcellcenters.ravel(), \\\n             dest_modelgrid.ycellcenters.ravel()\n    source_model_xy = np.array([x, y]).transpose()\n    dest_model_xy = np.array([x2, y2]).transpose()\n    return source_model_xy, dest_model_xy\n\n\ndef interp_weights(xyz, uvw, d=2):\n    \"\"\"Speed up interpolation vs scipy.interpolate.griddata (method='linear'),\n    by only computing the weights once:\n    https://stackoverflow.com/questions/20915502/speedup-scipy-griddata-for-multiple-interpolations-between-two-irregular-grids\n\n    Parameters\n    ----------\n    xyz : ndarray of shape n source points x ndims\n        x, y, (z) locations of source data.\n    uvw : ndarray of shape n destination points x ndims\n        x, y, (z) locations of where source data will be interpolated\n\n    Returns\n    -------\n    indices : ndarray of shape n destination points x 3\n        Index positions in flattened (1D) xyz array\n    weights : ndarray of shape n destination points x 3\n        Fractional weights for each row position\n        in indices. Weights in each row sum to 1\n        across the 3 columns.\n    \"\"\"\n    print('Calculating interpolation weights...')\n    t0 = time.time()\n    tri = qhull.Delaunay(xyz)\n    simplex = tri.find_simplex(uvw)\n    vertices = np.take(tri.simplices, simplex, axis=0)\n    temp = np.take(tri.transform, simplex, axis=0)\n    delta = uvw - temp[:, d]\n    bary = np.einsum('njk,nk->nj', temp[:, :d, :], delta)\n    print(\"finished in {:.2f}s\\n\".format(time.time() - t0))\n    return vertices, np.hstack((bary, 1 - bary.sum(axis=1, keepdims=True)))\n\n\ndef interpolate(values, vtx, wts, fill_value='mean'):\n    \"\"\"Apply the interpolation weights to a set of values.\n\n    Parameters\n    ----------\n    values : 1D array of length n source points (same as xyz in interp_weights)\n    vtx : indices returned by interp_weights\n    wts : weights returned by interp_weights\n    fill_value : float\n        Value used to fill in for requested points outside of the convex hull\n        of the input points (i.e., those with at least one negative weight).\n        If not provided, then the default is nan.\n    Returns\n    -------\n    interpolated values\n    \"\"\"\n    result = np.einsum('nj,nj->n', np.take(values, vtx), wts)\n\n    # fill nans that might result from\n    # child grid points that are outside of the convex hull of the parent grid\n    # and for an unknown reason on the Appveyor Windows environment\n    if fill_value == 'mean':\n        fill_value = np.nanmean(result)\n    result[np.any(wts < 0, axis=1)] = fill_value\n    return result\n\n\ndef regrid(arr, grid, grid2, mask1=None, mask2=None, method='linear'):\n    \"\"\"Interpolate array values from one model grid to another,\n    using scipy.interpolate.griddata.\n\n    Parameters\n    ----------\n    arr : 2D numpy array\n        Source data\n    grid : flopy.discretization.StructuredGrid instance\n        Source grid\n    grid2 : flopy.discretization.StructuredGrid instance\n        Destination grid (to interpolate onto)\n    mask1 : boolean array\n        mask for source grid. Areas that are masked will be converted to\n        nans, and not included in the interpolation.\n    mask2 : boolean array\n        mask denoting active area for destination grid.\n        The mean value will be applied to inactive areas if linear interpolation\n        is used (not for integer/categorical arrays).\n    method : str\n        interpolation method ('nearest', 'linear', or 'cubic')\n    \"\"\"\n    try:\n        from scipy.interpolate import griddata\n    except:\n        print('scipy not installed\\ntry pip install scipy')\n        return None\n\n    arr = arr.copy()\n    # only include points specified by mask\n    x, y = grid.xcellcenters, grid.ycellcenters\n    if mask1 is not None:\n        mask1 = mask1.astype(bool)\n        arr = arr[mask1]\n        x = x[mask1]\n        y = y[mask1]\n\n    points = np.array([x.ravel(), y.ravel()]).transpose()\n\n    arr2 = griddata(points, arr.flatten(),\n                   (grid2.xcellcenters, grid2.ycellcenters),\n                   method=method, fill_value=np.nan)\n\n    # fill any areas that are nan\n    # (new active area includes some areas not in uwsp model)\n    fill = np.isnan(arr2)\n\n    # if new active area is supplied, fill areas outside of that too\n    if mask2 is not None:\n        mask2 = mask2.astype(bool)\n        fill = ~mask2 | fill\n\n    # only fill with mean value if linear interpolation used\n    # (floating point arrays)\n    if method == 'linear':\n        fill_value = np.nanmean(arr2)\n        arr2[fill] = np.nanmean(arr2[~fill])\n    #else:\n    #    arr2[fill] = nodataval\n    if arr2.min() < 0:\n        j=2\n    return arr2\n\n\nif __name__ == '__main__':\n    \"\"\"Exampmle from stack overflow. In this example, both\n    xyz and uvw have points in 3 dimensions. (npoints x ndim)\"\"\"\n    m, n, d = int(3.5e4), int(3e3), 3\n    # make sure no new grid point is extrapolated\n    bounding_cube = np.array(list(itertools.product([0, 1], repeat=d)))\n    xyz = np.vstack((bounding_cube,\n                     np.random.rand(int(m - len(bounding_cube)), d)))\n    f = np.random.rand(m)\n    g = np.random.rand(m)\n    uvw = np.random.rand(n, d)\n\n    vtx, wts = interp_weights(xyz, uvw)\n\n    np.allclose(interpolate(f, vtx, wts), spint.griddata(xyz, f, uvw))\n","sub_path":"mfsetup/interpolate.py","file_name":"interpolate.py","file_ext":"py","file_size_in_byte":6617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"204438631","text":"# -*- coding: utf-8 -*-\n\nimport logging\nlogger = logging.getLogger('smartHome.heatingmodule')\n\nlogger.info('Django setup')\n\nimport os, sys\n\ndjango_path = \"../smarthome_django/\"\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"smarthome_django.settings\")\nsys.path.append(django_path)\nos.chdir(django_path)\n\nfrom django.core.wsgi import get_wsgi_application\napplication = get_wsgi_application()\n\nimport smarthome.models\n\nimport socket\nimport time\nfrom threading import Thread\nfrom collections import OrderedDict\n\n\nclass Sensor:\n    def __init__(self, name, address):\n        self.name = name\n        self.address = address\n        self.temp = -100.\n\n    def get_temperature(self):\n        return self.temp\n\n\nclass LocalSensor(Sensor):\n    def update_temperature(self):\n        try:\n            path = '/sys/bus/w1/devices/{}/w1_slave'.format(self.address)\n            sfile = open(path, 'r')\n            lines = sfile.readlines()\n            sfile.close()\n\n            if lines[0].find('YES') is -1:\n                raise Exception('{} sensor read failed.'.format(self.address))\n\n            temp = lines[1][(lines[1].find('t=') + 2):]\n            self.temp = float(temp) / 1000\n        except Exception as e:\n            self.temp = -100.\n            logger.error(str(e))\n\n\nclass RemoteSensor(Sensor):\n    def update_temperature(self):\n        try:\n            sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n            sock.connect((self.address, 909))\n            msg = self._receive(sock)\n            self.temp = float(msg)\n        except Exception as e:\n            self.temp = -100.\n            logger.error(str(e))\n\n    def _receive(self, sock):\n        chunks = []\n\n        size = ''\n        allowed = '0123456789 '\n        while len(size) < 10:\n            character = sock.recv(1).decode('utf-8')\n            if character == '':\n                raise Exception('Connection closed')\n            if character in allowed:\n                size += character\n        size = int(size)\n\n        bytes_recd = 0\n\n        while bytes_recd < size:\n            chunk = sock.recv(min(size - bytes_recd, 2048)).decode('utf-8')\n            if chunk == b'':\n                raise Exception('Connection closed')\n            chunks.append(chunk)\n            bytes_recd += len(chunk)\n\n        if bytes_recd < size:\n            raise Exception('Received message too short')\n\n        msg = ''.join(chunks)\n        return msg\n\n\ndef sensor_from_model(model):\n    if model.type is smarthome.models.Sensor.LOCAL:\n        return LocalSensor(model.name, model.address)\n    else:\n        return RemoteSensor(model.name, model.address)\n\n\nclass HeatingModule:\n    def __init__(self):\n        self._load_sensors()\n\n        self.thread = Thread(target=self._run)\n        self.thread.start()\n\n    def _load_sensors(self):\n        db_entries = smarthome.models.Sensor.objects.all()\n        self.sensors = OrderedDict()\n        for sensor in db_entries:\n            self.sensors[sensor.id] = sensor_from_model(sensor)\n\n    def _update_sensors(self):\n        for id, sensor in self.sensors.items():\n            sensor.update_temperature()\n\n    def _run(self):\n        while True:\n            try:\n                self._update_sensors()\n            except Exception as e:\n                logger.error(str(e))\n\n            time.sleep(3)\n\n    def query(self, args):\n        try:\n            command = args.get('command')\n            args = args.get('args')\n\n            if command == 'get_temperature':\n                temp = self.sensors[args.get('id')].get_temperature()\n                if temp != -100.:\n                    return {'response': 'ok', 'temperature': temp}\n                else:\n                    return {'response': 'error'}\n            elif command == 'get_all_temperatures':\n                ids = []\n                names = []\n                temps = []\n\n                for id, sensor in self.sensors.items():\n                    ids.append(id)\n                    names.append(sensor.name)\n                    temps.append(sensor.get_temperature())\n\n                return {'response': 'ok',\n                        'sensors_count': len(self.sensors),\n                        'ids': ids,\n                        'names': names,\n                        'temps': temps}\n\n        except Exception as e:\n            logger.error(str(e))\n\n        return {'response': 'error'}\n","sub_path":"smarthome_master/heatingmodule.py","file_name":"heatingmodule.py","file_ext":"py","file_size_in_byte":4373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"195800014","text":"#!/usr/bin/env python3\n\nimport faiss\nimport numpy as np\n\n\n# @build:deps [\n# @/deeplearning/projects/faiss:pyfaiss\n# ]\n# also in fbsource/fbcode/fblearner/flow/projects/rl/TARGETS\n# but no need in fbsource/fbcode/fblearner/flow/facebook/canary/TARGETS\n# if need to include faiss_gpu:\n# # @/deeplearning/projects/faiss:pyfaiss_gpu\n\n\n# Note: this is a simple replacement for np.unique(a, axis=0) since\n#  axis is supported after numpy 1.3.7 where ours is 1.3.1\ndef unique_rows(data):\n    sorted_data = data[np.lexsort(data.T), :]\n    row_mask = np.append([True], np.any(np.diff(sorted_data, axis=0), 1))\n    return sorted_data[row_mask]\n\n\n# https://stackoverflow.com/questions/14766194/\ndef contain_row(data, row):\n    return any(np.equal(data, row).all(1))\n\n\ndef lookup_row(data, row):\n    return np.where((data == row).all(axis=1))[0].tolist()\n\n\ndef fetch_new_rows_toappend(data, newrows):\n    def not_contained_in_data(row):\n        return not contain_row(data, row)\n\n    row_mask = np.apply_along_axis(not_contained_in_data, 1, newrows)\n    return newrows[row_mask]\n\n\ndef act_hash(act_vector):\n    # temporarily using the array itself\n    return str(act_vector)\n\n\nclass KnnEngine(object):\n    def __init__(self, data_dim, data_size_limit, k=1, index_type=\"IP\"):\n        self._knn_indextype = index_type\n        # 'L2' or 'IP' # for inner product\n        self._data_dim = data_dim\n        self._data_size_limit = data_size_limit\n        self._knn_search_index = None\n        self._knn_search_index_quatizer = None\n        self._knn_search_k = k\n        self._knn_default_n_centroid = 100\n        self._knn_datasize_threshold_faster = 500000\n        self._knn_subsampling_training = 0.05  # subsampling only if positive\n\n    # benchmark:\n    # full: https://github.com/facebookresearch/faiss/blob/master/benchs/README.md\n    # 1m https://github.com/facebookresearch/faiss/wiki/Indexing-1M-vectors\n    # 1mgpu https://github.com/facebookresearch/faiss/blob/master/benchs/bench_gpu_sift1m.py\n\n    def build_index_knn(self, all_data):\n        all_data = all_data.astype(np.float32)\n\n        if all_data.shape[0] <= self._knn_datasize_threshold_faster:\n            self._knn_search_index = (\n                faiss.IndexFlatIP(self._data_dim)\n                if self._knn_indextype == \"IP\"\n                else faiss.IndexFlatL2(self._data_dim)\n            )\n        # check if data count worth training of cluster\n        if all_data.shape[0] > self._knn_datasize_threshold_faster:\n            # try:\n            # index_type = faiss.METRIC_IP if self._knn_indextype == 'IP' else \\\n            #     faiss.METRIC_L2\n            target_dim = self._data_dim\n            self._knn_search_index_quatizer = (\n                faiss.IndexFlatIP(target_dim)\n                if self._knn_indextype == \"IP\"\n                else faiss.IndexFlatL2(target_dim)\n            )\n            self._knn_search_index = faiss.IndexIVFPQ(\n                self._knn_search_index_quatizer, target_dim, 16, 8, 8\n            )\n            # faiss.IndexIVFFlat(coarseQuantizer, self._data_dim, nlist, index_type)\n            self._knn_search_index.do_polysemous_training = True\n            self._knn_search_index.verbose = True\n            training_dataset = None\n            if self._knn_subsampling_training > 0:\n                trained_w10per = np.random.choice(\n                    len(all_data),\n                    int(round(len(all_data) * self._knn_subsampling_training)),\n                )\n                training_dataset = all_data[trained_w10per, :]\n            else:\n                training_dataset = all_data\n            print(\"training vectors to index\")\n            self._knn_search_index.train(training_dataset)\n            # finally:\n            #     print(\n            #         \"Warning: training tree failed, but still with regular knn tree\"\n            #     )\n\n        self._knn_search_index.add(all_data)\n        return True\n\n    def append_index_knn(self, append_more_data):\n        assert self._knn_search_index.is_trained\n        self._knn_search_index.add(append_more_data.astype(np.float32))\n\n    def find_knn_dist_ind(self, data_query):\n        assert self._knn_search_index.is_trained\n        dist, indx = self._knn_search_index.search(\n            data_query.astype(np.float32), k=self._knn_search_k\n        )\n        # return k for each data in query, if q special case only need 1\n        return dist, indx\n\n    def find_knn_best_dist_ind(self, data_query):\n        dist, indx = self.find_knn_dist_ind(data_query)\n        if self._knn_search_k == 1:\n            return dist.flatten(), indx.flatten()\n        dist_slicebyk = dist.reshape((self._knn_search_k, -1))\n        indx_slicebyk = indx.reshape((self._knn_search_k, -1))\n        best_slice = np.argmax(dist_slicebyk, axis=0)\n        best_dist = dist_slicebyk[best_slice, range(best_slice.shape[0])]\n        best_indx = indx_slicebyk[best_slice, range(best_slice.shape[0])]\n        return best_dist, best_indx\n\n    def check_engine_build(self):\n        return self._knn_search_index is not None\n","sub_path":"ml/rl/training/knn_engine.py","file_name":"knn_engine.py","file_ext":"py","file_size_in_byte":5053,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"446580487","text":"#!/usr/bin/env python2\n# Se importa modulos de ROS\nimport rospy\n# Se importa modulos de dependencias de mensajes de tipo String\nfrom std_msgs.msg import String\n\n\n# Se inicia el nodo publicador donde envia cadenas de texto\npub = rospy.Publisher('emocion_topic', String, queue_size=10)\n# Se inicia el nodo actual \nrospy.init_node('emocion_usuario', anonymous=True)\n\n# Se realiza un bucle para publicar los datos respectivos mientras no se cierre o muera el nodo\nwhile not rospy.is_shutdown():\n    # Se imprime un mensaje por consola para indicar que ingrese cadena de texto con una palabra emocion\n    emocion = raw_input(\"Ingrese su emocion: \")\n    # Notificamos y se publican los datos\n    rospy.loginfo(\"Emocion de usuario publicado!\")\n    pub.publish(emocion)\n","sub_path":"build/trabajo/catkin_generated/installspace/emocion_usuario_nodo.py","file_name":"emocion_usuario_nodo.py","file_ext":"py","file_size_in_byte":762,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"376625971","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Mar 15 09:44:48 2015\n\n@author: kberry\n\"\"\"\n\nimport re\nimport eqn_helper\nfrom vector_class import *\noperator = re.compile(\\\n'(e\\-|e|\\*\\*\\-|\\*\\-|\\/\\-|\\*+|\\/|\\+|(?<!(?:\\*|\\/))\\-|\\%|cross|dot)')\n\nclass Evaluator:\n    def __init__(self, phrase):\n        self.phrase = re.sub('(?<!\\*\\*)(?<!e)\\-', '-1*', phrase)\n        #fix this to better handle exponential terms\n        self.phrase = eqn_helper.make_friendly(self.phrase)\n        self.operators = self.init_operators()\n        self.terms = self.init_terms()\n        \n    def init_operators(self):\n        \"\"\"gets all of the operators outside of parentheses in a phrase\"\"\"\n        operators = []\n        i = 0\n        if len(self.phrase) > 0 and self.phrase[0] == '-':\n            i += 1\n        while i < len(self.phrase):\n            #tacks on phrases within parentheses to be dealt\n            #with at the evaluation stage\n            if self.phrase[i] == '(':\n                parens = 1\n                i += 1\n                while parens > 0:\n                    if self.phrase[i] == '(':\n                        parens += 1\n                    if self.phrase[i] == ')':\n                        parens -= 1\n                    i += 1\n                i -= 1\n            if self.phrase[i] == '<':\n                parens = 1\n                i += 1\n                while parens > 0:\n                    if self.phrase[i] == '<':\n                        parens += 1\n                    if self.phrase[i] == '>':\n                        parens -= 1\n                    i += 1\n                i -= 1\n            else:\n                match = operator.match(self.phrase[i:])\n                if match:\n                    operators.append(match.groups(0)[0])\n                    i += len(match.groups(0)[0]) - 1\n            i += 1\n        return operators\n    \n    def evaluate(self):\n        \"\"\"recursively iterates through all parentheses, evaluating the \\\n        innermost parenthetical phrase first and working its way back \\\n        to the outermost\"\"\"\n        try:\n            return float(self.phrase)\n        except ValueError:\n            while self.has_parenthesis():\n                index = self.get_nested_index()\n                if str(self.terms[index][0]) == \"-\":\n                    #there may be some issues with the type casting here\n                    try:\n                        self.terms[index] = -1 * \\\n                        (float(Evaluator(eqn_helper.unshell(\\\n                        self.terms[index])).evaluate()))\n                    except ValueError:\n                        self.terms[index] = -1 * \\\n                        (complex(Evaluator(eqn_helper.unshell(\\\n                        self.terms[index])).evaluate()))\n                else:\n                    self.terms[index] = \\\n                    Evaluator(eqn_helper.unshell(self.terms[index])).evaluate()\n            index = 0\n            terms = self.terms.copy()\n            operators = self.operators.copy()\n            #iterates through the term and operator equations,\n            #trimming them as it goes\n            while len(terms) > 1 and len(operators) > 0:\n                index = self.get_op_index(operators)\n                terms[index] = self.operate(operators[index], terms[index],\\\n                terms[index+1])\n                if index < len(terms):\n                    del(terms[index+1])\n                del(operators[index])\n            #returns the last remaining dreg of the term equation\n            return terms[0]\n    \n    def init_terms(self):\n        \"\"\"gets all of the terms in an evaluator\"\"\"\n        #makes sure that the first term is added as a negative if it's negative\n        #this is more difficult to handle than makes reasonable sense\n        if self.phrase[0] == '-':\n            temp = '-'\n            i = 1\n        else:\n            temp = ''\n            i = 0\n        terms = []\n        parens = 0\n        while i < len(self.phrase):\n            if re.match(eqn_helper.symbol_pattern, self.phrase[i]):\n                #adds on complex unit if complex, keeps searching otherwise\n                if self.phrase[i] == 'j':\n                    temp = temp + 'j'\n                if self.phrase[i] == 'e':\n                    terms.append(temp)\n                    temp = ''\n                else:\n                    pass\n            elif self.phrase[i] == '(':\n                temp, parens = temp + '(', 1\n                i += 1\n                while parens > 0:\n                    if self.phrase[i] == '(':\n                        parens += 1\n                    if self.phrase[i] == ')':\n                        parens -= 1\n                    temp = temp + self.phrase[i]\n                    i += 1\n                i -= 1\n                terms.append(temp)\n                temp = ''\n            elif self.phrase[i] == '<':\n                parens = 1\n                i += 1\n                while parens > 0:\n                    if self.phrase[i] == '<':\n                        parens += 1\n                    if self.phrase[i] == '>':\n                        parens -= 1\n                    temp = temp + self.phrase[i]\n                    i += 1\n                temp = temp[:len(temp)-1]\n                i -= 1\n                terms.append(Vector(eval(temp)))\n                temp = ''\n            #keeps adding non-operators to temp\n            elif not operator.match(self.phrase[i]):\n                temp = temp + self.phrase[i]\n            #adds temp to terms upon finding an operator, then repeats\n            else:\n                if len(temp) > 0:\n                    terms.append(temp)\n                temp = ''\n            i += 1\n        if len(temp) > 0:\n            terms.append(temp)\n        return terms\n    \n    def operate(self, operator, t1, t2):\n        \"\"\"returns the result of an operation performed on two terms\"\"\"\n        #casts strings to the appropriate type\n        if type(t1) == type(''):\n            try:\n                t1 = float(t1)\n            except ValueError:\n                try:\n                    t1 = complex(eval(t1))\n                #I wrote my own method for complex casting because Python's\n                #native type cast relies on very specific pre-formatting\n                except ValueError:\n                    t1 = eqn_helper.make_complex(t1)\n        if type(t2) == type(''):\n            try:\n                t2 = float(t2)\n            except ValueError:\n                try:\n                    t2 = complex(eval(t2))\n                except ValueError:\n                    t2 = eqn_helper.make_complex(t2)\n        try:\n        #makes sure that if one term is a vector, both terms become vectors\n            if type(t2) == type(Vector()) and type(t1) != type(Vector()):\n                t1 = Vector(t1)\n            if operator == '+':\n                return t1 + t2\n            if operator == '-':\n                return t1 - t2\n            if operator == '*':\n                return t1 * t2\n            if operator == '/':\n                return t1 / t2\n            if operator == '**':\n                return t1 ** t2\n            if operator == '*-':\n                return t1 * -t2\n            if operator == '/-':\n                return t1 / -t2\n            if operator == '**-':\n                return 1 / t1 ** t2\n            if operator == 'e':\n                return t1 * 10 ** t2\n            if operator == 'e-':\n                return t1 * 10 ** -t2\n            if operator == 'dot':\n                return t1.dot(t2)\n            if operator == 'cross':\n                return t1.cross(t2)\n            if operator == '%':\n                return t1 % t2\n        except OverflowError:\n            print('OverflowError. Value too large.')\n            return 0\n        #no default return because not returning signifies an error elsewhere\n            \n    def get_op_index(self, operators):\n        \"\"\"gets the index of the next operator to be evaluated.\"\"\"\n        for i in range(len(operators)):\n            if operators[i] == \"**\" or operators[i] == \"**-\"\\\n            or operators[i] == \"e\" or operators[i] == \"e-\":\n                return i\n        for i in range(len(operators)):\n            if operators[i] == \"*\" or operators[i] == \"/\"\\\n            or operators[i] == \"%\" or operators[i] == \"/-\"\\\n            or operators[i] == \"*-\" or operators[i] == 'dot'\\\n            or operators[i] == \"cross\":\n                return i\n        for i in range(len(self.operators)):\n            if operators[i] == \"+\" or operators[i] == \"-\":\n                return i\n    \n    def has_parenthesis(self):\n        \"\"\"Returns true if there is a parenthesis in the evaluator's terms\"\"\"\n        for i in self.terms:\n            i = str(i)\n            if re.match('\\(', str(i)):\n                if i.count('(') == 1 and i.count('j') > 0 \\\n                and i.count(')') == 1:\n                    pass\n                else:\n                    return True\n        return False\n    \n    def get_nested_index(self):\n        \"\"\"Gets the index of the first parenthetical item to be evaluated\"\"\"\n        for i in range(len(self.terms)):\n            if re.match('\\(', str(self.terms[i])):\n                if str(self.terms[i]).count('(') == 1 and \\\n                str(self.terms[i]).count('j') > 0 \\\n                and str(self.terms[i]).count(')') == 1:\n                    pass\n                else:\n                    return i\n        return -1\n                \n    def fp(self):\n        \"\"\"full print\"\"\"\n        print('Phrase:', self.phrase)\n        print('Operators:', self.operators)\n        print('Terms:', self.terms)\n    \n    def __repr__(self):\n        return self.phrase\n    \n    def __str__(self):\n        return self.phrase\n\ndef evaluate_numbers(phrase):\n    \"\"\"creates an evaluator for a phrase and automatically evaluates it.\"\"\"\n    temp = Evaluator(phrase).evaluate()\n    if type(temp) != type(''):\n        return temp\n    else:\n        try:\n            #FIX THIS\n            return eval(temp)\n        except (ValueError, TypeError):\n            try:\n                return complex(temp)\n            except (TypeError, ValueError):\n                try:\n                    return eqn_helper.make_complex(temp)\n                except (TypeError, ValueError):\n                    return Vector(temp)\n\ndef eval_test():\n    evaluators = []\n    evaluators.append(Evaluator('-11 + 2*-3 -4 -((4/5 + 5)/6 + 1/9*6)'))\n    evaluators.append(Evaluator('<2, 3(3*0)> + 2 - (2<2, 2>2)'))\n    evaluators.append(Evaluator('2<2, 2>'))\n    evaluators.append(Evaluator('<2, 3(3*0)> + (2<2, 2>2)'))\n    evaluators.append(Evaluator('2j+2'))\n    evaluators.append(Evaluator('<2, 3, 4> / 1j / 21**-2'))\n    evaluators.append(Evaluator('2(2+1j)^3+1/((2+1j)^2)'))\n    return evaluators\n","sub_path":"v3.0/evaluator.py","file_name":"evaluator.py","file_ext":"py","file_size_in_byte":10745,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"118330002","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.5 (62131)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-i686/egg/tw/prototype/js.py\n# Compiled at: 2008-06-02 11:15:04\nfrom tw.api import JSLink, js_function\n__all__ = ['prototype_js', 'S', 'Event']\nprototype_js = JSLink(modname=__name__, filename='static/prototype.js')\nS = js_function('$')\n\nclass Event:\n    observe = js_function('Event.observe')","sub_path":"pycfiles/tw.prototype-0.8-py2.5/js.py","file_name":"js.py","file_ext":"py","file_size_in_byte":469,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"402955324","text":"# -*- coding: utf-8 -*-\n\nfrom openerp import api, exceptions, fields, models, _\nfrom odoo.report import report_sxw\nfrom datetime import date\nimport time\nfrom itertools import groupby\nfrom dateutil.relativedelta import relativedelta\n\n\nclass report_contract_wizard(models.AbstractModel):\n    _name = 'report.biocare_reports_modifier.report_contract_wizard'\n\n    @api.model\n    def render_html(self, docids, data=None):\n        data['context'].update({'active_ids': data['ids']})\n        if not docids:\n            docids = data['ids']\n\n        model = self.env.context.get('active_model')\n        docs = self.env[model].browse(data['ids'])\n        ctx = self.env.context.copy()\n        ctx.update({'active_ids': data['ids']})\n\n        if not data.get('form'):\n            raise exceptions.UserError(_(\"Form content is missing, this report cannot be printed.\"))\n        obj_analytic = self.env['account.analytic.account'].browse(data['ids'])\n\n        docargs = {\n            'doc_ids': docids,\n            'doc_model': self.env['account.analytic.account'],\n            'data': data,\n            'docs': self.env['account.analytic.account'].browse(data['ids']),\n            'time': time,\n            'doc_model': model,\n        }\n\n        return self.env['report'].render('biocare_reports_modifier.report_contract_wizard', docargs)\n\n    def _print_report(self, data):\n        data['form'].update(self.read(['start_date', 'end_date'])[0])\n        return self.env['report'].get_action(self, 'biocare_reports_modifier.report_contract_wizard', data=data)\n\n\n# vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4:\n","sub_path":"beta-dev1/biocare_reports_modifier/report/report_contract_wizard.py","file_name":"report_contract_wizard.py","file_ext":"py","file_size_in_byte":1615,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"108538345","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-x86_64/egg/assimulo/examples/cvode_basic_backward.py\n# Compiled at: 2017-12-28 04:09:42\nimport numpy as N, pylab as P, nose\nfrom assimulo.solvers import CVode\nfrom assimulo.problem import Explicit_Problem\n\ndef run_example(with_plots=True):\n    \"\"\"\n    The same as example :doc:`EXAMPLE_cvode_basic`  but now integrated backwards in time.\n    \n    on return:\n    \n       - :dfn:`exp_mod`    problem instance\n    \n       - :dfn:`exp_sim`    solver instance\n       \n    \"\"\"\n\n    def f(t, y):\n        ydot = -y[0]\n        return N.array([ydot])\n\n    exp_mod = Explicit_Problem(f, t0=5, y0=0.02695, name='CVode Test Example (reverse time): $\\\\dot y = - y$ ')\n    exp_sim = CVode(exp_mod)\n    exp_sim.iter = 'Newton'\n    exp_sim.discr = 'BDF'\n    exp_sim.atol = [1e-08]\n    exp_sim.rtol = 1e-08\n    exp_sim.backward = True\n    t, y = exp_sim.simulate(0)\n    nose.tools.assert_almost_equal(float(y[(-1)]), 4.0, 3)\n    if with_plots:\n        P.plot(t, y, color='b')\n        P.title(exp_mod.name)\n        P.ylabel('y')\n        P.xlabel('Time')\n        P.show()\n    return (exp_mod, exp_sim)\n\n\nif __name__ == '__main__':\n    mod, sim = run_example()","sub_path":"pycfiles/Assimulo-3.0-py2.7-linux-x86_64/cvode_basic_backward.py","file_name":"cvode_basic_backward.py","file_ext":"py","file_size_in_byte":1316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"336684522","text":"import base64\nimport json\nimport os\nimport requests\n\nimport hca\nfrom matrix.common.config import MatrixInfraConfig\n\nif __name__ == '__main__':\n    gcp_service_acct_creds = json.loads(base64.b64decode(MatrixInfraConfig().gcp_service_acct_creds).decode())\n    with open('gcp_creds.json', 'w') as outfile:\n        json.dump(gcp_service_acct_creds, outfile)\n    try:\n        os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = os.getcwd() + \"/gcp_creds.json\"\n        deployment_stage = os.environ['DEPLOYMENT_STAGE']\n        replica = \"aws\"\n        jmespath_query = \"(event_type==`CREATE` || event_type==`TOMBSTONE` || event_type==`DELETE`) \\\n            && (files.library_preparation_protocol[].library_construction_method.ontology==`EFO:0008931` \\\n            || files.library_preparation_protocol[].library_construction_method.ontology_label==`10X v2 sequencing`) \\\n            && files.analysis_process[].type.text==`analysis`\"\n\n        if deployment_stage == \"prod\":\n            swagger_url = \"https://dss.data.humancellatlas.org/v1/swagger.json\"\n            matrix_callback = \"https://matrix.data.humancellatlas.org/v0/dss/notifications\"\n        else:\n            swagger_url = f\"https://dss.{deployment_stage}.data.humancellatlas.org/v1/swagger.json\"\n            matrix_callback = f\"https://matrix.{deployment_stage}.data.humancellatlas.org/v0/dss/notifications\"\n\n        dss_client = hca.dss.DSSClient(swagger_url=swagger_url)\n\n        for subscription in dss_client.get_subscriptions(replica=replica, subscription_type=\"jmespath\")[\"subscriptions\"]:\n            if matrix_callback == subscription[\"callback_url\"]:\n                res = dss_client.delete_subscription(replica=replica, subscription_type=\"jmespath\", uuid=subscription[\"uuid\"])\n                print(\"Deleted subscription {}: {}\".format(subscription[\"uuid\"], res))\n\n        print(\"Not resubscribing to dss for the time being.\")\n        # resp = dss_client.put_subscription(callback_url=matrix_callback,\n        #                                    jmespath_query=jmespath_query,\n        #                                    replica=\"aws\")\n        # print(\"Created subscription {}: {}\".format(resp[\"uuid\"], resp))\n\n    except Exception as e:\n        print(e)\n    print(\"deleted creds file\")\n    os.remove(os.getcwd() + \"/gcp_creds.json\")\n","sub_path":"scripts/dss_subscription.py","file_name":"dss_subscription.py","file_ext":"py","file_size_in_byte":2299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"482107925","text":"\n__author__ = 'victor'\n\nfrom theano import tensor as T\nimport numpy as np\n\nfrom common import Layer, Dropout\nfrom ..initializer import Xavier, Orthogonal, Zero\n\nclass MemoryLayer(Layer):\n\n    count = 0\n\n    def forward(self, a, h_tm1, train=True):\n        raise NotImplementedError\n\n\nclass RNNMemoryLayer(MemoryLayer):\n\n    def __init__(self, n_in, n_out, **param_attributes):\n        super(RNNMemoryLayer, self).__init__()\n        self.n_in, self.n_out = n_in, n_out\n        params = {\n            'W_in_initializer': Xavier(),\n            'W_h_initializer': Orthogonal(),\n            'b_h_initializer': Zero(),\n            'h0_initializer': Zero(),\n        }\n        params.update(param_attributes)\n        self.W_in = self.add_param('W_in', (n_in, n_out), **params)\n        self.W_h = self.add_param('W_h', (n_out, n_out), **params)\n        self.b_h = self.add_param('b_h', n_out, **params)\n        self.h0 = self.add_param('h0', n_out, **params)\n        self.mem0 = [self.h0]\n\n    def forward(self, a, train, h_tm1):\n        r\"\"\" Computes the forward function\n\n        .. math::\n            h_t = sigmoid( a W_{in} + h_{t-1} W_h + b_h)\n\n        \"\"\"\n        h_t = T.nnet.sigmoid(T.dot(a, self.W_in) + T.dot(h_tm1, self.W_h) + self.b_h)\n        mem_t = [h_t]\n        return h_t, mem_t\n\n\nclass GRUMemoryLayer(MemoryLayer):\n\n    def __init__(self, n_in, n_out, **param_attributes):\n        super(GRUMemoryLayer, self).__init__()\n        self.n_in, self.n_out = n_in, n_out\n\n        params = {}\n        for gate in ['_reset', '_update', '']:\n            params['W_in' + gate + '_initializer'] = Xavier()\n            params['W_h' + gate + '_initializer'] = Orthogonal()\n            params['b' + gate + '_initializer'] = Zero()\n        params['h0_initializer'] = Zero()\n        params.update(param_attributes)\n\n        # reset gate\n        self.W_in_reset = self.add_param('W_in_reset', (n_in, n_out), **params)\n        self.W_h_reset = self.add_param('W_h_reset', (n_out, n_out), **params)\n        self.b_reset = self.add_param('b_reset', n_out, **params)\n\n        # update gate\n        self.W_in_update = self.add_param('W_in_update', (n_in, n_out), **params)\n        self.W_h_update = self.add_param('W_h_update', (n_out, n_out), **params)\n        self.b_update = self.add_param('b_update', n_out, **params)\n\n        # activation\n        self.W_in = self.add_param('W_in', (n_in, n_out), **params)\n        self.W_h = self.add_param('W_h', (n_out, n_out), **params)\n        self.b = self.add_param('b', n_out, **params)\n\n        self.h0 = self.add_param('h0', n_out, **params)\n        self.mem0 = [self.h0]\n\n    def forward(self, a, train, h_tm1):\n        r\"\"\" Computes the forward function\n\n        Note that to be consistent with other layers, we rename the update variable to ``u`` instead.\n\n        .. math::\n            u_t = sigmoid( a W_{in\\_update} + h_{t-1} W_{h\\_update} )\n\n            r_t = sigmoid( a W_{in\\_reset} + h_{t-1} W_{h\\_reset} )\n\n            g_t = tanh( a W_{in} + r_t * h_{t-1} W_h )\n\n            h_t = u_t * h_{t-1} + (1-u_t) * g_t\n\n        \"\"\"\n        u_t = T.nnet.sigmoid(T.dot(a, self.W_in_update) + T.dot(h_tm1, self.W_h_update) + self.b_update)\n        r_t = T.nnet.sigmoid(T.dot(a, self.W_in_reset) + T.dot(h_tm1, self.W_h_reset) + self.b_reset)\n        g_t = T.tanh(T.dot(a, self.W_in) + r_t * T.dot(h_tm1, self.W_h) + self.b)\n        h_t = u_t * h_tm1 + (1-u_t) * g_t\n        mem_t = [h_t]\n        return h_t, mem_t\n\n\nclass LSTMMemoryLayer(MemoryLayer):\n    \"\"\" Gated Recurrent Units\n    \"\"\"\n\n    def __init__(self, n_in, n_out, dropout=None, **param_attributes):\n        super(LSTMMemoryLayer, self).__init__()\n        self.n_in, self.n_out, self.dropout = n_in, n_out, dropout\n        if self.dropout:\n            assert isinstance(self.dropout, Dropout), 'dropout argument must be an instance of Dropout object'\n\n        params = {}\n        for gate in ['_input', '_forget', '_output', '']:\n            params['W_in' + gate + '_initializer'] = Xavier()\n            params['W_h' + gate + '_initializer'] = Orthogonal()\n            params['b' + gate + '_initializer'] = Zero()\n        params['h0_initializer'] = Zero()\n        params['c0_initializer'] = Zero()\n        params.update(param_attributes)\n\n        # input gate\n        self.W_in_input = self.add_param('W_in_input', (n_in, n_out), **params)\n        self.W_h_input = self.add_param('W_h_input', (n_out, n_out), **params)\n        self.b_input = self.add_param('b_input', n_out, **params)\n\n        # forget gate\n        self.W_in_forget = self.add_param('W_in_forget', (n_in, n_out), **params)\n        self.W_h_forget = self.add_param('W_h_forget', (n_out, n_out), **params)\n        self.b_forget = self.add_param('b_forget', n_out, **params)\n\n        # output gate\n        self.W_in_output = self.add_param('W_in_output', (n_in, n_out), **params)\n        self.W_h_output = self.add_param('W_h_output', (n_out, n_out), **params)\n        self.b_output = self.add_param('b_output', n_out, **params)\n\n        # activation\n        self.W_in = self.add_param('W_in', (n_in, n_out), **params)\n        self.W_h = self.add_param('W_h', (n_out, n_out), **params)\n        self.b = self.add_param('b', n_out, **params)\n\n        self.h0 = self.add_param('h0', n_out, **params)\n        self.c0 = self.add_param('c0', n_out, **params)\n\n        self.mem0 = [self.h0, self.c0]\n\n    def forward(self, a, train, h_tm1, c_tm1):\n        r\"\"\" Computes the forward function\n\n        Note that to be consistent with other layers, we rename the update variable to ``u`` instead.\n\n        .. math::\n            i_t = sigmoid( a W_{in\\_input} + h_{t-1} W_{h\\_input} )\n\n            f_t = sigmoid( a W_{in\\_forget} + h_{t-1} W_{h\\_forget} )\n\n            o_t = sigmoid( a W_{in\\_output} + h_{t-1} W_{h\\_output} )\n\n            g_t = tanh( a W_{in} + h_{t-1} W_h )\n\n            c_t = f_t * c_{t-1} + i_t * g_t\n\n            h_t = o_t * tanh(c_t)\n\n        \"\"\"\n\n        i_t = T.nnet.sigmoid(T.dot(a, self.W_in_input) + T.dot(h_tm1, self.W_h_input) + self.b_input)\n        if self.dropout:\n            i_t = self.dropout.forward(i_t, train)\n        f_t = T.nnet.sigmoid(T.dot(a, self.W_in_forget) + T.dot(h_tm1, self.W_h_forget) + self.b_forget)\n        if self.dropout:\n            f_t = self.dropout.forward(f_t, train)\n        o_t = T.nnet.sigmoid(T.dot(a, self.W_in_output) + T.dot(h_tm1, self.W_h_output) + self.b_output)\n\n        g_t = T.tanh(T.dot(a, self.W_in) + T.dot(h_tm1, self.W_h) + self.b)\n        c_t = f_t * c_tm1 + i_t * g_t\n        h_t = o_t * T.tanh(c_t)\n\n        mem_t = [h_t, c_t]\n        return h_t, mem_t\n","sub_path":"pystacks/layers/memory.py","file_name":"memory.py","file_ext":"py","file_size_in_byte":6585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"515247391","text":"def interface():\n    print (\"Blood Test Analysis\")\n    while True:\n        print (\"\\nOptions\")\n        print (\"1 - HDL\")\n        print (\"2 - LDL\")\n        print (\"9 - Quit\")\n        choice = input (\"Enter an option: \")\n        if choice == \"9\":\n            return \n        elif choice == \"1\":\n            HDL_driver()\n        elif choice == \"2\":\n            LDL_driver()\n\n\ndef HDL_driver():\n    HDL = get_HDL_input()\n    analysis1 = analyze_HDL(HDL)\n    output_HDL(HDL, analysis1)\n\ndef get_HDL_input():\n    HDL = input(\"Enter HDL Level: \")\n    return int(HDL)\n\ndef analyze_HDL(HDL):\n    if HDL >= 60:\n        return (\"Normal\")\n    elif HDL < 40:\n        return (\"Low\")\n    else:\n        return (\"Borderline Low\")\n\ndef output_HDL(HDL, analysis1):\n    print(\"The HDL entered was {}\".format(HDL))\n    print(\"The level is {}\".format(analysis1))\n\n\ndef LDL_driver():\n    LDL = get_LDL_input()\n    analysis2 = analyze_LDL(LDL)\n    output_LDL(LDL, analysis2)\n\ndef get_LDL_input():\n    LDL = input(\"Enter LDL Level: \")\n    return int(LDL)\n\ndef analyze_LDL(LDL):\n    if LDL < 130:\n        return (\"Normal\")\n    elif 130 <= LDL <= 159:\n        return (\"Borderline Hight\")\n    elif 160 <= LDL <= 189:\n        return (\"High\")\n    else:\n        return (\"Very High\")\n\ndef output_LDL(LDL, analysis2):\n    print(\"The LDL entered was {}\".format(LDL))\n    print(\"The level is {}\".format(analysis2))\n\nif __name__ == \"__main__\":\n    interface()","sub_path":"blood_tests.py","file_name":"blood_tests.py","file_ext":"py","file_size_in_byte":1423,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"172478698","text":"from rest_framework import serializers\n\nfrom .models import District, Campus\n\n#########################\n#  SUMMARY SERIALIZERS  #\n#########################\n\n# Summary serializers are just for list and reference behavior\n\n# Putting summary serializers up top so there are no conflicts\n# with reference accross models due to script running order\n#\nclass DistrictSerializer(serializers.ModelSerializer):\n\n    class Meta:\n        model = District\n        fields = ('id', 'name', 'datecreated')\n\nclass CampusSerializer(serializers.ModelSerializer):\n    districtid = DistrictSerializer()\n\n    class Meta:\n        model = Campus\n        fields = ('id', 'name', 'districtid', 'datecreated')","sub_path":"api/api/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"652283077","text":"#!/usr/bin/env python\n\"\"\"\nShape related functions.\n\"\"\"\n__author__ = \"Song Huang\"\n\nimport numpy as np\n\nfrom scipy.spatial import Delaunay\nfrom scipy.spatial import ConvexHull\n\n__all__ = ['alpha_shape', 'convex_hull', 'concave_hull']\n\n\ndef alpha_shape(points, alpha, only_outer=True):\n    '''Compute the alpha shape (concave hull) of a set of points.\n    Parameters\n    ----------\n    points: np.array of shape (n,2) points\n         Coordinates of the points.\n    alpha: float\n         Alpha parameter to control the behaviour of the concave hull.\n    only_outer: bool, optional\n         If we keep only the outer border or also the inner edges.\n         Default: True\n    Returns\n    -------\n    boundary_lst: list\n        List of indices for boundary points.\n    Notes\n    -----\n        From a StackOverflow answer by Iddo Hanniel:\n        https://stackoverflow.com/questions/23073170/calculate-bounding-polygon-of-alpha-shape-from-the-delaunay-triangulation\n    '''\n    assert points.shape[0] > 3, \"Need at least four points\"\n\n    def add_edge(edges, i, j):\n        \"\"\"\n        Add a line between the i-th and j-th points,\n        if not in the list already\n        \"\"\"\n        if (i, j) in edges or (j, i) in edges:\n            # Already added\n            assert (j, i) in edges, \"Can't go twice over same directed edge right?\"\n            if only_outer:\n                # If both neighboring triangles are in shape, it's not a boundary edge\n                edges.remove((j, i))\n            return\n        edges.add((i, j))\n\n    tri = Delaunay(points)\n    edges = set()\n    # Loop over triangles:\n    # ia, ib, ic = indices of corner points of the triangle\n    for ia, ib, ic in tri.vertices:\n        pa = points[ia]\n        pb = points[ib]\n        pc = points[ic]\n        # Computing radius of triangle circumcircle\n        a = np.sqrt((pa[0] - pb[0]) ** 2 + (pa[1] - pb[1]) ** 2)\n        b = np.sqrt((pb[0] - pc[0]) ** 2 + (pb[1] - pc[1]) ** 2)\n        c = np.sqrt((pc[0] - pa[0]) ** 2 + (pc[1] - pa[1]) ** 2)\n        s = (a + b + c) / 2.0\n        area = np.sqrt(s * (s - a) * (s - b) * (s - c))\n        circum_r = a * b * c / (4.0 * area)\n        if circum_r < alpha:\n            add_edge(edges, ia, ib)\n            add_edge(edges, ib, ic)\n            add_edge(edges, ic, ia)\n\n    return _stitch_boundaries(edges)\n\n\ndef concave_hull(points, **kwargs):\n    ''' Get the concave hull edges for a set of points.\n    Parameters\n    ----------\n    points: `np.array` of shape (n,2) points\n         Coordinates of the points.\n    Returns\n    -------\n    edges: `np.array` of shape (n,2) points\n        List of indices for boundary points.\n    '''\n    edges = np.asarray(alpha_shape(points, **kwargs)[0])\n    return np.vstack([points[edges[:, 0], 0], points[edges[:, 1], 1]]).T\n\n\ndef convex_hull(points):\n    ''' Get the convex hull edges for a set of points.\n    Parameters\n    ----------\n    points: `np.array` of shape (n,2) points\n         Coordinates of the points.\n    Returns\n    -------\n    edges: `np.array` of shape (n,2) points\n        List of indices for boundary points.\n    '''\n    hull = ConvexHull(points)\n    return np.vstack([points[hull.vertices, 0], points[hull.vertices, 1]]).T\n\n\ndef _stitch_boundaries(edges):\n    \"\"\"Stitches the output edge set into sequences of consecutive edges.\n    Parameters\n    ----------\n    edges: set\n         Set of (i,j) pairs representing edges of the alpha-shape. (i,j) are\n         the indices in the points array.\n    Returns\n    -------\n    boundary_lst: list\n        List of indices for boundary points.\n    Notes\n    -----\n        From a StackOverflow answer by Iddo Hanniel:\n        https://stackoverflow.com/questions/50549128/boundary-enclosing-a-given-set-of-points\n    \"\"\"\n    edge_set = edges.copy()\n    boundary_lst = []\n    while edge_set:\n        boundary = []\n        edge0 = edge_set.pop()\n        boundary.append(edge0)\n        last_edge = edge0\n        while edge_set:\n            _, j = last_edge\n            j_first, j_second = _find_edges_with(j, edge_set)\n            if j_first:\n                edge_set.remove((j, j_first[0]))\n                edge_with_j = (j, j_first[0])\n                boundary.append(edge_with_j)\n                last_edge = edge_with_j\n            elif j_second:\n                edge_set.remove((j_second[0], j))\n                edge_with_j = (j, j_second[0])  # flip edge rep\n                boundary.append(edge_with_j)\n                last_edge = edge_with_j\n            if edge0[0] == last_edge[1]:\n                break\n        boundary_lst.append(boundary)\n\n    return boundary_lst\n\n\ndef _find_edges_with(i, edge_set):\n    i_first = [j for (x, j) in edge_set if x==i]\n    i_second = [j for (j, x) in edge_set if x==i]\n    return i_first, i_second\n\n","sub_path":"python/skipper/shape.py","file_name":"shape.py","file_ext":"py","file_size_in_byte":4750,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"160055135","text":"\"\"\"webapp_athenea_django URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/1.11/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  url(r'^$', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.conf.urls import url, include\n    2. Add a URL to urlpatterns:  url(r'^blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls import url, include\nfrom django.contrib import admin\nfrom django.urls import path\nimport home\n\nurlpatterns = [\n    url(r'', include('login.urls')),\n    url(r'^home', include('home.urls')),\n    url(r'^login/', include('login.urls')),\n    url(r'^admin/', admin.site.urls),\n    path('course/<str:course_id>', home.views.course_detail),\n    path('user/<str:username>', home.views.user_detail),\n    path('map', home.views.map),\n    url('course/submit/comment', home.views.submit_comment),\n    path('search', home.views.search)\n]\n\n\n","sub_path":"webapp_athenea_django/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1203,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"249441626","text":"import math\n\n\ndef binarySearch(a, l, r, x):\n    length = len(a)\n\n    if r >= l:\n        mid = math.ceil(l + (r - l) / 2)\n\n        if a[mid] == x:\n            return mid\n\n        if x <= a[0]:\n            return 0\n        elif x >= a[length - 2]:\n            return length - 1\n\n        if a[mid] <= x <= a[mid + 1]:\n            return mid + 1\n\n        elif a[mid] > x:\n            return binarySearch(a, l, mid - 1, x)\n        else:\n            return binarySearch(a, mid + 1, r, x)\n","sub_path":"RandomizedAlgorithms/modules/misc.py","file_name":"misc.py","file_ext":"py","file_size_in_byte":482,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"338534608","text":"import urllib2,json, ast, datetime\n\nurl = 'http://challenge.code2040.org/api/dating'\npostdata = {'token':'3bcc66432859f116f85e48a7bce43961'}\n\nreq = urllib2.Request(url)\nreq.add_header('Content-Type','application/json')\ndata = json.dumps(postdata)\n\nresponse = urllib2.urlopen(req,data)\ndictionary = ast.literal_eval(response.read())\n\ndate_stamp = dictionary[\"datestamp\"]\nprint(date_stamp)\ninterval = dictionary[\"interval\"]\n\n\ndef add_seconds(date_stamp, interval):\n\tISO_8601_FORMAT = '%Y-%m-%dT%H:%M:%SZ' \n\tstamp = datetime.datetime.strptime(date_stamp, ISO_8601_FORMAT)\n\tincrement = datetime.timedelta(seconds=interval)\n\tstamp = stamp + increment\n\tstamp = stamp.strftime('%Y-%m-%dT%H:%M:%SZ')\n\tprint(stamp)\n\treturn stamp\n\nupdated_stamp = add_seconds(date_stamp, interval)\n\t\t\nurl = 'http://challenge.code2040.org/api/dating/validate'\npostdata = {'token':'3bcc66432859f116f85e48a7bce43961', 'datestamp': updated_stamp}\n\nreq = urllib2.Request(url)\nreq.add_header('Content-Type','application/json')\ndata = json.dumps(postdata)\nresponse = urllib2.urlopen(req,data)","sub_path":"step5.py","file_name":"step5.py","file_ext":"py","file_size_in_byte":1058,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"526973109","text":"from django.contrib import admin\n\nfrom ietf.review.models import (ReviewerSettings, ReviewSecretarySettings, UnavailablePeriod,\n    ReviewWish, NextReviewerInTeam, ReviewRequest, ReviewTeamSettings )\n\nclass ReviewerSettingsAdmin(admin.ModelAdmin):\n    def acronym(self, obj):\n        return obj.team.acronym\n    list_display = ['id', 'person', 'acronym', 'min_interval', 'filter_re', 'remind_days_before_deadline', ]\n    list_filter = [\"team\"]\n    search_fields = [\"person__name\"]\n    ordering = [\"-id\"]\n    raw_id_fields = [\"team\", \"person\"]\n\nadmin.site.register(ReviewerSettings, ReviewerSettingsAdmin)\n\nclass ReviewSecretarySettingsAdmin(admin.ModelAdmin):\n    list_display = [u'id', 'team', 'person', 'remind_days_before_deadline']\n    raw_id_fields = ['team', 'person']\nadmin.site.register(ReviewSecretarySettings, ReviewSecretarySettingsAdmin)\n\nclass UnavailablePeriodAdmin(admin.ModelAdmin):\n    list_display = [\"person\", \"team\", \"start_date\", \"end_date\", \"availability\", \"reason\"]\n    list_display_links = [\"person\"]\n    list_filter = [\"team\"]\n    date_hierarchy = \"start_date\"\n    search_fields = [\"person__name\"]\n    ordering = [\"-id\"]\n    raw_id_fields = [\"team\", \"person\"]\n\nadmin.site.register(UnavailablePeriod, UnavailablePeriodAdmin)\n\nclass ReviewWishAdmin(admin.ModelAdmin):\n    list_display = [\"person\", \"team\", \"doc\"]\n    list_display_links = [\"person\"]\n    list_filter = [\"team\"]\n    search_fields = [\"person__name\"]\n    ordering = [\"-id\"]\n    raw_id_fields = [\"team\", \"person\", \"doc\"]\n\nadmin.site.register(ReviewWish, ReviewWishAdmin)\n\nclass NextReviewerInTeamAdmin(admin.ModelAdmin):\n    list_display = [\"team\", \"next_reviewer\"]\n    list_display_links = [\"team\"]\n    ordering = [\"team\"]\n    raw_id_fields = [\"team\", \"next_reviewer\"]\n\nadmin.site.register(NextReviewerInTeam, NextReviewerInTeamAdmin)\n\nclass ReviewRequestAdmin(admin.ModelAdmin):\n    list_display = [\"doc\", \"time\", \"type\", \"team\", \"deadline\"]\n    list_display_links = [\"doc\"]\n    list_filter = [\"team\", \"type\", \"state\", \"result\"]\n    ordering = [\"-id\"]\n    raw_id_fields = [\"doc\", \"team\", \"requested_by\", \"reviewer\", \"review\"]\n    date_hierarchy = \"time\"\n    search_fields = [\"doc__name\", \"reviewer__person__name\"]\n\nadmin.site.register(ReviewRequest, ReviewRequestAdmin)\n\nclass ReviewTeamSettingsAdmin(admin.ModelAdmin):\n    list_display = [\"group\", ] \n    search_fields = [\"group__acronym\", ]\n    raw_id_fields = [\"group\", ]\n    filter_horizontal = [\"review_types\", \"review_results\", \"notify_ad_when\"]\n\nadmin.site.register(ReviewTeamSettings, ReviewTeamSettingsAdmin)\n","sub_path":"ietf/review/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":2554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"473005272","text":"import simulationRunningFunctions as srf\nimport simulationResultEvaluatingFunctions as sref\nimport numpy as np\nfrom math import fabs\nfrom matplotlib import pyplot\n\n\ndef initializeGridTest(dimensions):\n\t\"\"\"tests if intial grid is random and displays intial grid\"\"\"\n\tgridLength, alloyFraction = 20, 50\n\tgrid = srf.initializeGrid(gridLength, alloyFraction, dimensions)\n\t# checks order of inital grid\n\tresult, actualList, binList = sref.getOrder(grid, alloyFraction, dimensions)\n\tif result > gridLength * 0.1:\n\t\tprint(\"CORRECT: initializeGrid produces random grid\")\n\telse:\n\t\tprint(\n\t\t\t\"\"\"ERROR: initializeGrid most likely does NOT produce a random grid. Run 1\n\t\t\tmore time to make sure.\"\"\")\n\tif dimensions == 2:\n\t\t# displays intial grid, if 2D\n\t\tpyplot.figure(num=\"Initial Grid Test\")\n\t\timg = pyplot.imshow(grid, interpolation='nearest')\n\t\tpyplot.colorbar(img)\n\t\tpyplot.show()\n\n\ndef cValidateTest(dimensions=2):\n\tgridLength, alloyFraction = 50, 50\n\tgrid = srf.initializeGrid(gridLength, alloyFraction, dimensions)\n\t# xCase (+1)\n\tresult = srf.cValidate(gridLength + 1, grid, gridLength, 'x', dimensions)\n\tif result != 0:\n\t\tprint(\"cValidate does NOT work for x case\")\n\t# xCase (-1)\n\tresult = srf.cValidate(-1, grid, gridLength, 'x', dimensions)\n\tif result == 0:\n\t\tprint(\"ERROR: xCase - 1 doesn't work\") \t# i.e. would produce index error\n\n\ndef performSwapTest(dimensions=2):\n\t# specific case\n\tgrid = np.zeros((3, 3))\n\tgrid[0, 0] = 1\n\tgrid[1, 2] = 1\n\tgrid[2, 0] = 1\n\txA, yA = 1, 1\n\txB, yB = 1, 2\n\tgrid = srf.performSwap(grid, xA, yA, None, xB, yB, None, dimensions)\n\tif grid[1, 1] == 1 and grid[1, 2] == 0:\n\t\tprint(\"CORRECT: performSwap function correctly swapped atoms.\")\n\telse:\n\t\tprint(\"ERROR: performSwap function did NOT correctly swap atoms.\")\n\n\ndef getTotalEnergyTest(dimensions=2):\n\tlocalEam = 0.1\n\t# specific case\n\tgrid = np.zeros((3, 3))\n\tgrid[0, 0] = 1\n\tgrid[1, 2] = 1\n\tgrid[2, 0] = 1\n\tresult = sref.getTotalEnergy(grid, localEam, dimensions)\n\tif result == 10 * localEam:\n\t\tprint(\"CORRECT: getTotalEnergy result matches expected result.\")\n\telse:\n\t\tprint(\"ERROR: getTotalEnergy result does NOT match expected result.\")\n\t\tprint(result)\n\n\ndef localEnergyCountTest(dimensions=2):\n\tlocalEam = 0.1\n\t# specific case; test using simple case that can be evaluated manually\n\tgrid = np.zeros((10, 10))\n\t# set up grid according to specific case:\n\tgrid[4, 4] = 1 \t# U1\n\tgrid[5, 5] = 1 \t# B\n\tgrid[5, 6] = 1 \t# M2\n\tgrid[6, 5] = 1 \t# L2\n\n\tresultBeforeSwap, grid = srf.getLocalEnergyCounts(\n\t\tgrid, 5, 4, None, 5, 5, None, localEam, dimensions)\n\tresultAfterSwap, grid = srf.getLocalEnergyCounts(\n\t\tgrid, 5, 4, None, 5, 5, None, localEam, dimensions)\n\n\tdeltaE_1 = resultAfterSwap - resultBeforeSwap\n\n\t# test by comparing results of localEnergyCount to getTotalEnergy\n\teBefore = sref.getTotalEnergy(grid, localEam, dimensions)\n\tgrid = srf.performSwap(grid, 5, 4, None, 5, 5, None, dimensions)\n\teAfter = sref.getTotalEnergy(grid, localEam, dimensions)\n\tdeltaE_2 = eAfter - eBefore\n\tif fabs(deltaE_1 - deltaE_2) < 0.0001:\n\t\tprint(\n\t\t\t\"\"\"CORRECT: Both localEnergyCount & getTotalEnergy methods give the same\n\t\t\tchange in energy.\"\"\")\n\telse:\n\t\tprint(\n\t\t\t\"ERROR: localEnergyCount & getTotalEnergy methods give different answers.\")\n\t\tprint(\"localEnergyCount method gives {}\".format(str(deltaE_1)))\n\t\tprint(\"getTotalEnergy method gives {}\".format(str(deltaE_2)))\n\n\ndef randomAtomPairChooserTest(dimensions=2):\n\tlengthOfGrid = 10\n\tgrid = np.zeros((lengthOfGrid, lengthOfGrid))\n\tx1, y1, z1, x2, y2, z2 = srf.randomAtomPairChooser(grid, lengthOfGrid, dimensions)\n\tgrid[x1, y1] = 1\n\tgrid[x2, y2] = 1\n\tprint(\n\t\t\"\"\"If following matrix (randomAtomPairDisplay) shows an apparently random\n\t\tpair of nearest\tneighbour atoms then randomAtomPairChooser function works\n\t\tfine.\"\"\")\n\tpyplot.figure(num=\"randomAtomPairDisplay\")\n\timg = pyplot.imshow(grid, interpolation='nearest')\n\tpyplot.colorbar(img)\n\tpyplot.show()\n\n\ndef getOrderTest(dimensions):\n\tgridLength = 20\n\tcomp = 40\n\tranGrid = srf.initializeGrid(gridLength, comp, dimensions)\n\tnForeignAtoms = int(comp * 0.01 * gridLength**dimensions)\n\torderedGrid = np.zeros([gridLength] * dimensions)\n\tnumAdded = 0\n\ttry:  # try, except statement creates ordered grid (2 blocks)\n\t\tfor i in range(gridLength):\n\t\t\tfor j in range(gridLength):\n\t\t\t\tif dimensions == 2:\n\t\t\t\t\torderedGrid[i, j] = 1\n\t\t\t\t\tnumAdded += 1\n\t\t\t\telif dimensions == 3:\n\t\t\t\t\tfor k in range(gridLength):\n\t\t\t\t\t\torderedGrid[i, j, k] = 1\n\t\t\t\t\t\tnumAdded += 1\n\t\t\t\tassert(numAdded < nForeignAtoms)\n\texcept(AssertionError):  # exception raised simply means we should stop adding\n\t\t# atoms, not that there is a problem\n\t\tpass\n\n\tif dimensions == 2:\n\t\t# shows ordered grid to show that we are testing an ordered grid\n\t\tpyplot.figure(num=\"orderedGrid\")\n\t\ttempImg = pyplot.imshow(orderedGrid, interpolation='nearest')\n\t\tpyplot.colorbar(tempImg)\n\t\tpyplot.show()\n\tranRan, actualList, binList = sref.getOrder(ranGrid, comp, dimensions)\n\tranOrd, actualList, binList = sref.getOrder(orderedGrid, comp, dimensions)\n\t# ranRan should be much lower than ranOrd\n\tif ranRan < 0.2 * ranOrd:\n\t\tprint(\"CORRECT: random and ordered grids produces correct orderValues.\")\n\tprint(\"The order value for the random grid is: {}\".format(str(ranRan)))\n\tprint(\"The order value for the random grid is: {}\".format(str(ranOrd)))\n\n\n\ndef runAllTests():\n\t\"\"\"runs all the tests when called\"\"\"\n\tinitializeGridTest(2)\n\tprint(\"\\n\")\n\tinitializeGridTest(3)\n\tprint(\"\\n\")\n\tcValidateTest()\n\tprint(\"\\n\")\n\tperformSwapTest()\n\tprint(\"\\n\")\n\tgetTotalEnergyTest()\n\tprint(\"\\n\")\n\tlocalEnergyCountTest()\n\tprint(\"\\n\")\n\trandomAtomPairChooserTest()\n\tprint(\"\\n\")\n\tgetOrderTest(2)\n\tprint(\"\\n\")\n\tgetOrderTest(3)\n\nif __name__ == '__main__':\n\trunAllTests()\n","sub_path":"tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":5655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"601622600","text":"# -*- coding: utf-8 -*-\n##############################################################################\n#\n#    Copyright (C)\n#        2004-2011: Pexego Sistemas Informáticos. (http://pexego.es)\n#        2013:      Top Consultant Software Creations S.L.\n#                   (http://www.topconsultant.es/)\n#        2014:      Serv. Tecnol. Avanzados (http://www.serviciosbaeza.com)\n#                   Pedro M. Baeza <pedro.baeza@serviciosbaeza.com>\n#\n#    Autores originales: Luis Manuel Angueira Blanco (Pexego)\n#                        Omar Castiñeira Saavedra(omar@pexego.es)\n#    Migración OpenERP 7.0: Ignacio Martínez y Miguel López.\n#\n#    This program is free software: you can redistribute it and/or modify\n#    it under the terms of the GNU Affero General Public License as\n#    published by the Free Software Foundation, either version 3 of the\n#    License, or (at your option) any later version.\n#\n#    This program is distributed in the hope that it will be useful,\n#    but WITHOUT ANY WARRANTY; without even the implied warranty of\n#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n#    GNU Affero General Public License for more details.\n#\n#    You should have received a copy of the GNU Affero General Public License\n#    along with this program.  If not, see <http://www.gnu.org/licenses/>.\n#\n##############################################################################\nimport re\nfrom openerp import models, fields, api, exceptions, _\nfrom openerp.addons.l10n_es_aeat_mod349.models.account_invoice \\\n    import OPERATION_KEYS\n\nMONTH_MAPPING = [\n    ('01', 'January'),\n    ('02', 'February'),\n    ('03', 'March'),\n    ('04', 'April'),\n    ('05', 'May'),\n    ('06', 'June'),\n    ('07', 'July'),\n    ('08', 'August'),\n    ('09', 'September'),\n    ('10', 'October'),\n    ('11', 'November'),\n    ('12', 'December'),\n]\n\n# TODO: Quitarlo de aquí y pasarlo a l10n_es_aeat con sustituciones\nNAME_RESTRICTIVE_REGEXP = re.compile(\n    r\"^[a-zA-Z0-9\\sáÁéÉíÍóÓúÚñÑçÇäÄëËïÏüÜöÖ\"\n    r\"àÀèÈìÌòÒùÙâÂêÊîÎôÔûÛ\\.,-_&'´\\\\:;:/]*$\", re.UNICODE | re.X)\n\n\ndef _check_valid_string(text_to_check):\n    \"\"\"Checks if string fits with RegExp\"\"\"\n    if text_to_check and NAME_RESTRICTIVE_REGEXP.match(text_to_check):\n        return True\n    return False\n\n\ndef _format_partner_vat(partner_vat=None, country=None):\n    \"\"\"Formats VAT to match XXVATNUMBER (where XX is country code).\"\"\"\n    if country:\n        country_pattern = \"[\" + country.code + country.code.lower() + \"]{2}.*\"\n        vat_regex = re.compile(country_pattern, re.UNICODE | re.X)\n        if partner_vat and not vat_regex.match(partner_vat):\n            partner_vat = country.code + partner_vat\n    return partner_vat\n\n\nclass Mod349(models.Model):\n    _inherit = \"l10n.es.aeat.report\"\n    _name = \"l10n.es.aeat.mod349.report\"\n    _description = \"AEAT Model 349 Report\"\n\n    @api.one\n    @api.depends('partner_record_ids', 'partner_refund_ids',\n                 'partner_record_ids.total_operation_amount',\n                 'partner_refund_ids.total_operation_amount')\n    def _get_report_totals(self):\n        self.total_partner_records = len(self.partner_record_ids)\n        self.total_partner_records_amount = sum([\n            record.total_operation_amount for record in\n            self.partner_record_ids])\n        self.total_partner_refunds = len(self.partner_refund_ids)\n        self.total_partner_refunds_amount = sum([\n            refund.total_operation_amount for refund in\n            self.partner_refund_ids])\n\n    @api.one\n    @api.depends()\n    def _get_report_alias(self):\n        \"\"\"Returns an alias as name for the report.\"\"\"\n        self.name = '%s - %s/%s' % (\n            self.company_id.name or '', self.fiscalyear_id.name or '',\n            self.period_selection or '')\n\n    def _create_349_partner_records(self, invoices, partner, operation_key):\n        \"\"\"creates partner records in 349\"\"\"\n        rec_obj = self.env['l10n.es.aeat.mod349.partner_record']\n        partner_country = partner.country_id\n        sum_credit = sum([invoice.cc_amount_untaxed for invoice in invoices\n                          if invoice.type not in ('in_refund', 'out_refund')])\n        sum_debit = sum([invoice.cc_amount_untaxed for invoice in invoices\n                         if invoice.type in ('in_refund', 'out_refund')])\n        invoice_created = rec_obj.create(\n            {'report_id': self.id,\n             'partner_id': partner.id,\n             'partner_vat': _format_partner_vat(partner_vat=partner.vat,\n                                                country=partner_country),\n             'operation_key': operation_key,\n             'country_id': partner_country.id or False,\n             'total_operation_amount': sum_credit - sum_debit\n             })\n        # Creation of partner detail lines\n        for invoice in invoices:\n            detail_obj = self.env['l10n.es.aeat.mod349.partner_record_detail']\n            detail_obj.create({'partner_record_id': invoice_created.id,\n                               'invoice_id': invoice.id,\n                               'amount_untaxed': invoice.cc_amount_untaxed})\n        return invoice_created\n\n    def _create_349_refund_records(self, refunds, partner, operation_key):\n        \"\"\"Creates restitution records in 349\"\"\"\n        partner_detail_obj = self.env[\n            'l10n.es.aeat.mod349.partner_record_detail']\n        obj = self.env['l10n.es.aeat.mod349.partner_refund']\n        obj_detail = self.env['l10n.es.aeat.mod349.partner_refund_detail']\n        partner_country = partner.country_id\n        record = {}\n        for refund in refunds:\n            # goes around all refunded invoices\n            for origin_inv in refund.origin_invoices_ids:\n                if origin_inv.state in ('open', 'paid'):\n                    # searches for details of another 349s to restore\n                    refund_details = partner_detail_obj.search(\n                        [('invoice_id', '=', origin_inv.id)])\n                    if refund_details:\n                        # creates a dictionary key with partner_record id to\n                        # after recover it\n                        key = refund_details.partner_record_id\n                        record[key] = record.get(key, []).append(refund)\n                        break\n        # recorremos nuestro diccionario y vamos creando registros\n        for partner_rec in record:\n            record_created = obj.create(\n                {'report_id': self.id,\n                 'partner_id': partner.id,\n                 'partner_vat': _format_partner_vat(\n                     partner_vat=partner.vat, country=partner_country),\n                 'operation_key': operation_key,\n                 'country_id': partner_country.id,\n                 'total_operation_amount': partner_rec.total_operation_amount -\n                    sum([x.cc_amount_untaxed for x in record[partner_rec]]),\n                 'total_origin_amount': partner_rec.total_operation_amount,\n                 'period_selection': partner_rec.report_id.period_selection,\n                 'month_selection': partner_rec.report_id.month_selection,\n                 'fiscalyear_id': partner_rec.report_id.fiscalyear_id.id})\n            # Creation of partner detail lines\n            for refund in record[partner_rec]:\n                obj_detail.create(\n                    {'refund_id': record_created.id,\n                     'invoice_id': refund.id,\n                     'amount_untaxed': refund.cc_amount_untaxed})\n        return True\n\n    @api.multi\n    def calculate(self):\n        \"\"\"Computes the records in report.\"\"\"\n        partner_obj = self.env['res.partner']\n        invoice_obj = self.env['account.invoice']\n        for mod349 in self:\n            # Remove previous partner records and partner refunds in report\n            mod349.partner_record_ids.unlink()\n            mod349.partner_refund_ids.unlink()\n            # Returns all commercial partners\n            partners = partner_obj.with_context(active_test=False).search(\n                [('parent_id', '=', False)])\n            for partner in partners:\n                for op_key in [x[0] for x in OPERATION_KEYS]:\n                    # Invoices\n                    invoices_total = invoice_obj._get_invoices_by_type(\n                        partner, operation_key=op_key,\n                        period_selection=mod349.period_selection,\n                        fiscalyear=mod349.fiscalyear_id,\n                        period_id=[x.id for x in mod349.period_ids],\n                        month=mod349.month_selection)\n                    # Separates normal invoices from restitution\n                    invoices, refunds = \\\n                        invoices_total.clean_refund_invoices(\n                            partner, fiscalyear=mod349.fiscalyear_id,\n                            periods=mod349.period_ids,\n                            month=mod349.month_selection,\n                            period_selection=mod349.period_selection)\n                    if invoices:\n                        mod349._create_349_partner_records(invoices, partner,\n                                                           op_key)\n                    if refunds:\n                        mod349._create_349_refund_records(refunds, partner,\n                                                          op_key)\n        return True\n\n    @api.multi\n    def _check_report_lines(self):\n        \"\"\"Checks if all the fields of all the report lines\n        (partner records and partner refund) are filled\n        \"\"\"\n        for item in self:\n            # Browse partner record lines to check if\n            # all are correct (all fields filled)\n            for partner_record in item.partner_record_ids:\n                if not partner_record.partner_record_ok:\n                    raise exceptions.Warning(\n                        _(\"All partner records fields (country, VAT number) \"\n                          \"must be filled.\"))\n                if partner_record.total_operation_amount < 0:\n                    raise exceptions.Warning(\n                        _(\"All amounts must be positives\"))\n            for partner_record in item.partner_refund_ids:\n                if not partner_record.partner_refund_ok:\n                    raise exceptions.Warning(\n                        _(\"All partner refunds fields (country, VAT number) \"\n                          \"must be filled.\"))\n                if (partner_record.total_operation_amount < 0 or\n                        partner_record.total_origin_amount < 0):\n                    raise exceptions.Warning(\n                        _(\"All amounts must be positives\"))\n\n    @api.multi\n    def _check_names(self):\n        \"\"\"Checks that names are correct (not formed by only one string)\"\"\"\n        for item in self:\n            # Check Full name (contact_name)\n            if (not item.contact_name or\n                    len(item.contact_name.split(' ')) < 2):\n                raise exceptions.Warning(\n                    _('Contact name (Full name) must have name and surname'))\n\n    @api.multi\n    def _check_restrictive_names(self):\n        \"\"\"Checks if names have not allowed characters and returns a message\"\"\"\n        for item in self:\n            if not _check_valid_string(item.contact_name):\n                raise exceptions.Warning(\n                    _(\"Name '%s' have not allowed characters.\\nPlease, fix it \"\n                      \"before confirm the report\") % item.contact_name)\n            # Check partner record partner names\n            for partner_record in item.partner_record_ids:\n                if not _check_valid_string(partner_record.partner_id.name):\n                    raise exceptions.Warning(\n                        _(\"Partner name '%s' in partner records is not valid \"\n                          \"due to incorrect characters\") %\n                        partner_record.partner_id.name)\n            # Check partner refund partner names\n            for partner_refund in item.partner_refund_ids:\n                if not _check_valid_string(partner_refund.partner_id.name):\n                    raise exceptions.Warning(\n                        _(\"Partner name '%s' in refund lines is not valid due \"\n                          \"to incorrect characters\") %\n                        partner_refund.partner_id.name)\n\n    @api.multi\n    def button_confirm(self):\n        \"\"\"Checks if all the fields of the report are correctly filled\"\"\"\n        self._check_names()\n        self._check_report_lines()\n        self._check_restrictive_names()\n        return super(Mod349, self).button_confirm()\n\n    @api.multi\n    def onchange_period_selection(self, period_selection, fiscalyear_id):\n        period = False\n        if period_selection:\n            if period_selection in ['1T', '2T', '3T', '4T']:\n                period = self.env['account.period'].search(\n                    [('name', 'like', period_selection),\n                     ('fiscalyear_id', '=', fiscalyear_id)])\n        return {'value': {'period_id': period and period.id}}\n\n    name = fields.Char(compute=\"_get_report_alias\", string=\"Name\")\n    period_ids = fields.Many2many(\n        comodel_name='account.period', relation='mod349_mod349_period_rel',\n        column1='mod349_id', column2='period_ids', string='Periods')\n    period_selection = fields.Selection(\n        [('0A', '0A - Annual'),\n         ('MO', 'MO - Monthly'),\n         ('1T', '1T - First Quarter'),\n         ('2T', '2T - Second Quarter'),\n         ('3T', '3T - Third Quarter'),\n         ('4T', '4T - Fourth Quarter')],\n        string='Period', required=True, select=1, default='0A',\n        states={'confirmed': [('readonly', True)]})\n    month_selection = fields.Selection(\n        selection=MONTH_MAPPING, string='Month',\n        states={'confirmed': [('readonly', True)]})\n    frequency_change = fields.Boolean(\n        string='Frequency change', states={'confirmed': [('readonly', True)]})\n    contact_name = fields.Char(\n        string=\"Full Name\", size=40, help=\"Must have name and surname.\",\n        states={'calculated': [('required', True)],\n                'confirmed': [('readonly', True)]})\n    contact_phone = fields.Char(\n        \"Phone\", size=9, states={'calculated': [('required', True)],\n                                 'confirmed': [('readonly', True)]})\n    total_partner_records = fields.Integer(\n        compute=\"_get_report_totals\", string=\"Partners records\")\n    total_partner_records_amount = fields.Float(\n        compute=\"_get_report_totals\", string=\"Partners records amount\")\n    total_partner_refunds = fields.Integer(\n        compute=\"_get_report_totals\", string=\"Partners refunds\")\n    total_partner_refunds_amount = fields.Float(\n        compute=\"_get_report_totals\", string=\"Partners refunds amount\")\n    partner_record_ids = fields.One2many(\n        comodel_name='l10n.es.aeat.mod349.partner_record',\n        inverse_name='report_id', string='Partner records', ondelete='cascade',\n        states={'confirmed': [('readonly', True)]})\n    partner_refund_ids = fields.One2many(\n        comodel_name='l10n.es.aeat.mod349.partner_refund',\n        inverse_name='report_id', string='Partner refund IDS',\n        ondelete='cascade', states={'confirmed': [('readonly', True)]})\n    number = fields.Char(default='349')\n\n\nclass Mod349PartnerRecord(models.Model):\n    \"\"\"AEAT 349 Model - Partner record\n    Shows total amount per operation key (grouped) for each partner\n    \"\"\"\n    _name = 'l10n.es.aeat.mod349.partner_record'\n    _description = 'AEAT 349 Model - Partner record'\n    _order = 'operation_key asc'\n\n    @api.one\n    @api.depends('partner_vat')\n    def get_record_name(self):\n        \"\"\"Returns the record name.\"\"\"\n        self.name = self.partner_vat\n\n    @api.one\n    @api.depends('partner_vat', 'country_id', 'total_operation_amount')\n    def _check_partner_record_line(self):\n        \"\"\"Checks if all line fields are filled.\"\"\"\n        self.partner_record_ok = bool(self.partner_vat and self.country_id and\n                                      self.total_operation_amount)\n\n    @api.multi\n    def onchange_format_partner_vat(self, partner_vat, country_id):\n        \"\"\"Formats VAT to match XXVATNUMBER (where XX is country code)\"\"\"\n        if country_id:\n            country = self.env['res.country'].browse(country_id)\n            partner_vat = _format_partner_vat(partner_vat=partner_vat,\n                                              country=country)\n        return {'value': {'partner_vat': partner_vat}}\n\n    report_id = fields.Many2one(\n        comodel_name='l10n.es.aeat.mod349.report',\n        string='AEAT 349 Report ID')\n    name = fields.Char(compute=\"get_record_name\")\n    partner_id = fields.Many2one(\n        comodel_name='res.partner', string='Partner', required=True)\n    partner_vat = fields.Char(string='VAT', size=15, select=1)\n    country_id = fields.Many2one(comodel_name='res.country', string='Country')\n    operation_key = fields.Selection(\n        selection=OPERATION_KEYS, string='Operation key', required=True)\n    total_operation_amount = fields.Float(string='Total operation amount')\n    partner_record_ok = fields.Boolean(\n        compute=\"_check_partner_record_line\", string='Partner Record OK',\n        help='Checked if partner record is OK')\n    record_detail_ids = fields.One2many(\n        comodel_name='l10n.es.aeat.mod349.partner_record_detail',\n        inverse_name='partner_record_id', string='Partner record detail IDS',\n        ondelete='cascade')\n\n\nclass Mod349PartnerRecordDetail(models.Model):\n    \"\"\"AEAT 349 Model - Partner record detail\n    Shows detail lines for each partner record.\n    \"\"\"\n    _name = 'l10n.es.aeat.mod349.partner_record_detail'\n    _description = 'AEAT 349 Model - Partner record detail'\n\n    partner_record_id = fields.Many2one(\n        comodel_name='l10n.es.aeat.mod349.partner_record',\n        default=lambda self: self.env.context.get('partner_record_id'),\n        string='Partner record', required=True, ondelete='cascade', select=1)\n    invoice_id = fields.Many2one(\n        comodel_name='account.invoice', string='Invoice')\n    amount_untaxed = fields.Float(string='Amount untaxed')\n    date = fields.Date(related='invoice_id.date_invoice', string=\"Date\",\n                       readonly=True)\n\n\nclass Mod349PartnerRefund(models.Model):\n    _name = 'l10n.es.aeat.mod349.partner_refund'\n    _description = 'AEAT 349 Model - Partner refund'\n    _order = 'operation_key asc'\n\n    @api.one\n    @api.depends('partner_vat', 'country_id', 'total_operation_amount',\n                 'total_origin_amount', 'period_selection', 'fiscalyear_id')\n    def _check_partner_refund_line(self):\n        \"\"\"Checks if partner refund line have all fields filled.\"\"\"\n        self.partner_refund_ok = bool(\n            self.partner_vat and self.country_id and\n            self.total_operation_amount >= 0.0 and\n            self.total_origin_amount >= 0.0 and\n            self.period_selection and self.fiscalyear_id)\n\n    report_id = fields.Many2one(\n        comodel_name='l10n.es.aeat.mod349.report', string='AEAT 349 Report ID')\n    partner_id = fields.Many2one(\n        comodel_name='res.partner', string='Partner', required=1, select=1)\n    partner_vat = fields.Char(string='VAT', size=15)\n    operation_key = fields.Selection(\n        selection=OPERATION_KEYS, string='Operation key', required=True)\n    country_id = fields.Many2one(comodel_name='res.country', string='Country')\n    fiscalyear_id = fields.Many2one(\n        comodel_name='account.fiscalyear', string='Fiscal year')\n    total_operation_amount = fields.Float(string='Total operation amount')\n    total_origin_amount = fields.Float(\n        string='Original amount', help=\"Refund original amount\")\n    partner_refund_ok = fields.Boolean(\n        compute=\"_check_partner_refund_line\", string='Partner refund OK',\n        help='Checked if refund record is OK')\n    period_selection = fields.Selection(\n        [('0A', '0A - Annual'),\n         ('MO', 'MO - Monthly'),\n         ('1T', '1T - First Quarter'),\n         ('2T', '2T - Second Quarter'),\n         ('3T', '3T - Third Quarter'),\n         ('4T', '4T - Fourth Quarter')], 'Period')\n    month_selection = fields.Selection(selection=MONTH_MAPPING, string='Month')\n    refund_detail_ids = fields.One2many(\n        comodel_name='l10n.es.aeat.mod349.partner_refund_detail',\n        inverse_name='refund_id', string='Partner refund detail IDS',\n        ondelete='cascade')\n\n    @api.multi\n    def onchange_format_partner_vat(self, partner_vat, country_id):\n        \"\"\"Formats VAT to match XXVATNUMBER (where XX is country code)\"\"\"\n        if country_id:\n            country = self.env['res.country'].browse(country_id)\n            partner_vat = _format_partner_vat(partner_vat=partner_vat,\n                                              country=country)\n        return {'value': {'partner_vat': partner_vat}}\n\n\nclass Mod349PartnerRefundDetail(models.Model):\n    _name = 'l10n.es.aeat.mod349.partner_refund_detail'\n    _description = 'AEAT 349 Model - Partner refund detail'\n\n    refund_id = fields.Many2one(\n        comodel_name='l10n.es.aeat.mod349.partner_refund',\n        string='Partner refund ID')\n    invoice_id = fields.Many2one(\n        comodel_name='account.invoice', string='Invoice ID')\n    amount_untaxed = fields.Float(string='Amount untaxed')\n    date = fields.Date(related='invoice_id.date_invoice', string=\"Date\",\n                       readonly=True)\n","sub_path":"l10n_es_aeat_mod349/models/mod349.py","file_name":"mod349.py","file_ext":"py","file_size_in_byte":21431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"283722882","text":"#!/usr/bin/python\nimport os\nimport re\n\nfp = open(\"log.txt\")\nlines = fp.readlines()\nlist_len = len(lines)\n#print \"<html><body><h5>\"\nfor elem in lines:\n\tprint(elem)\n#print \"</h5></body></html>\"\nfp.close()\n","sub_path":"src/readlog.py","file_name":"readlog.py","file_ext":"py","file_size_in_byte":203,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"443581330","text":"import sqlite3\r\nimport pandas as pd\r\n\r\nconnection = sqlite3.connect('chatdata.db')\r\nc = connection.cursor()\r\nprint(\"Successfully connected to SQLite\")\r\nlimit = 5000\r\nlastUnix = 0\r\ncurrLength = limit\r\ncounter = 0\r\ntestDone = False\r\n\r\nwhile currLength == limit:\r\n    df = pd.read_sql(\"SELECT * FROM parent_reply WHERE unix > {} ORDER BY unix ASC LIMIT {}\".format(lastUnix, limit), connection)\r\n    lastUnix = df.tail(1)['unix'].values[0]\r\n    currLength = len(df)\r\n    if not testDone:\r\n        with open(\"test.from\", 'a', encoding='utf8') as f:\r\n            for content in df['parent_comment'].values:\r\n                f.write(content+'\\n')\r\n        with open(\"test.to\", 'a', encoding='utf8') as f:\r\n            for content in df['reply_comment'].values:\r\n                f.write(content+'\\n')\r\n        testDone = True\r\n    else:\r\n        with open(\"train.from\", 'a', encoding='utf8') as f:\r\n            for content in df['parent_comment'].values:\r\n                f.write(content+'\\n')\r\n        with open(\"train.to\", 'a', encoding='utf8') as f:\r\n            for content in df['reply_comment'].values:\r\n                f.write(content+'\\n')\r\n        testDone = True\r\n    counter += 1\r\n    if counter % 20 == 0:\r\n        print(counter * limit, 'rows completed so far')","sub_path":"TrainingData.py","file_name":"TrainingData.py","file_ext":"py","file_size_in_byte":1266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"612811752","text":"import pandas as pd\r\nimport csv\r\n\r\nwith open('pokemon.csv') as csvfile:\r\n    readCSV = csv.reader(csvfile, delimiter=',')\r\n    dex = []\r\n    name = []\r\n    hp = []\r\n    attack = []\r\n    defense = []\r\n    sp_atk = []\r\n    sp_def = []\r\n    speed = []\r\n    bst = []\r\n    type1 = []\r\n    type2 = []\r\n    gen = []\r\n    gender_ratio = []\r\n    for row in readCSV:\r\n        dex.append(row[0])\r\n        name.append(row[1])\r\n        hp.append(row[2])\r\n        attack.append(row[3])\r\n        defense.append(row[4])\r\n        sp_atk.append(row[5])\r\n        sp_def.append(row[6])\r\n        speed.append(row[7])\r\n        bst.append(row[8])\r\n        type1.append(row[9])\r\n        type2.append(row[10])\r\n        gen.append(row[11])\r\n        gender_ratio.append(row[12])\r\n\r\nteam = []\r\n\r\ndef compare_pkmn(pkmn):\r\n    try:\r\n        index_num = name.index(pkmn)\r\n        print(name[index_num] + \" stats\")\r\n        print(\"Base Stat Total: \" + bst[index_num])\r\n        if type2[index_num] == \"\":\r\n            print(\"Type: \" + type1[index_num])\r\n        else:\r\n            print(\"Type: \" + type1[index_num] + \", \" + type2[index_num])\r\n\r\n    except ValueError:\r\n        print(\"Please enter a valid Pokemon\")\r\n    return\r\n\r\ndef search_by_name(pkmn):\r\n    try:\r\n        index_num = name.index(pkmn)\r\n        print(name[index_num] + \" stats\")\r\n        print(\"Base Stat Total: \" + bst[index_num])\r\n        if type2[index_num] == \"\":\r\n            print(\"Type: \" + type1[index_num])\r\n        else:\r\n            print(\"Type: \" + type1[index_num] + \", \" + type2[index_num])\r\n        option = input(\"Select an option: \\n1. Add to team \\n2. See more stats \\n3. Compare with another Pokemon\")\r\n        if option == '1':\r\n            team.append(pkmn)\r\n            print(\"Your team: \")\r\n            print(team)\r\n        elif option == '2':\r\n            print(\"HP: \" + hp[index_num])\r\n            print(\"Attack: \" + attack[index_num])\r\n            print(\"Defense: \" + defense[index_num])\r\n            print(\"Sp. Atk.: \" + sp_atk[index_num])\r\n            print(\"Sp. Def.: \" + sp_def[index_num])\r\n            print(\"Speed: \" + speed[index_num])\r\n            if input(\"Add to team? \") == \"Yes\":\r\n                team.append(pkmn)\r\n\r\n        elif option == '3':\r\n            compare_with = input(\"Compare with? \")\r\n            compare_pkmn(compare_with)\r\n\r\n        else:\r\n            print(\"That was not a valid option. Enter 1, 2, or 3.\")\r\n\r\n    except ValueError:\r\n        print(\"Please enter a valid Pokemon\")\r\n\r\n    return\r\n\r\nwhile len(team) < 6:\r\n    search_by_name(input(\"Search for a Pokemon: \"))\r\n    if len(team) < 6:\r\n        print(\"You have \" + str(len(team)) + \" Pokemon in your team.\")\r\nprint(\"You finally have a complete team!\")\r\n","sub_path":"simulator.py","file_name":"simulator.py","file_ext":"py","file_size_in_byte":2701,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"574721897","text":"from selenium import webdriver\nfrom selenium.common.exceptions import NoSuchElementException\nimport operator\nfrom functools import reduce\nimport pandas as pd\nimport csv\n\noptions = webdriver.ChromeOptions()\noptions.binary_location = '/Applications/Google Chrome Canary.app/Contents/MacOS/Google Chrome Canary'\noptions.add_argument('window-size=800x841')\n# options.add_argument('headless')\n\ndriver = webdriver.Chrome(chrome_options=options)\ndriver.get('http://sportowa.warszawa.pl/ksiazka-adresowa/obiekty_sportowe-258/sale_gimnastyczne-274')\n\nsale = []\n\nwhile True:\n    try:\n        ### arrays of selenium objects ###\n        sale_elements_xpath = (\"//div[@class='Label ui-accordion-header']\")\n        sale_elements = driver.find_elements_by_xpath(sale_elements_xpath)\n        sale.append([x.text for x in sale_elements])\n\n        #### pagination ####\n        nextsite = driver.find_element_by_xpath(\"//span[@class='Next']//a\")\n        driver.execute_script(\"arguments[0].click();\", nextsite)\n\n    except NoSuchElementException:\n        break\ndriver.quit()\n\n### creating one list out of list of lists ###\nall_gyms = reduce(operator.concat, sale)\ndframe = pd.DataFrame(all_gyms)\ndframe.to_csv(\"./gyms_WS.csv\", encoding='utf-8', index=False, quoting=csv.QUOTE_ALL)\n","sub_path":"Python-master-2/webscr/webscrap_sale.py","file_name":"webscrap_sale.py","file_ext":"py","file_size_in_byte":1262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"540058395","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# # Statistical Forecasting of Closing Price\n# - Work in Progress\n# - AR/ARMA/ARIMA modelling\n# - Based off of several good online articles\n\n# In[32]:\n\n\nget_ipython().run_line_magic('load_ext', 'autoreload')\nget_ipython().run_line_magic('autoreload', '2')\np = print\n\nimport os\nimport datetime\n\nimport pandas as pd\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nget_ipython().run_line_magic('matplotlib', 'inline')\n\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.model_selection import train_test_split\n\nfrom crypr.util import get_project_path\n\nimport statsmodels\nimport statsmodels.formula.api as smf\nimport statsmodels.tsa.api as smt\nimport statsmodels.api as sm\nimport scipy.stats as scs\n\nimport arch\n\n\n# In[98]:\n\n\n\"\"\"\nImport Data.\n\"\"\"\nSYM = 'BTC'\ndata_path = os.path.join(get_project_path(), 'data', 'raw', SYM + '.csv')\ndata = pd.read_csv(os.path.join(data_path), index_col=-1)\ndata.head()\n\n\n# In[99]:\n\n\n# # log returns\n# lrets = np.log(df.close/df.close.shift(1)).dropna()\n# lrets.plot()\n# plt.show()\n\n# percent change\npchange = data['close'].pct_change()\npchange.plot()\nplt.show()\n\n\n# In[195]:\n\n\nY = data['close'].pct_change().dropna()\nY.index = list(map(lambda ix: datetime.datetime.strptime(ix, '%Y-%m-%d %H:%M:%S').timestamp(), Y.index))\nmax_lag = 30\nfreq = 'H'\nforecast_steps = 21\nY.head()\n\n\n# In[196]:\n\n\ndef tsplot(y, lags=None, figsize=(10, 8), style='bmh'):\n    if not isinstance(y, pd.Series):\n        y = pd.Series(y)\n    with plt.style.context(style):    \n        fig = plt.figure(figsize=figsize)\n        #mpl.rcParams['font.family'] = 'Ubuntu Mono'\n        layout = (3, 2)\n        ts_ax = plt.subplot2grid(layout, (0, 0), colspan=2)\n        acf_ax = plt.subplot2grid(layout, (1, 0))\n        pacf_ax = plt.subplot2grid(layout, (1, 1))\n        qq_ax = plt.subplot2grid(layout, (2, 0))\n        pp_ax = plt.subplot2grid(layout, (2, 1))\n        \n        y.plot(ax=ts_ax)\n        ts_ax.set_title('Time Series Analysis Plots')\n        smt.graphics.plot_acf(y, lags=lags, ax=acf_ax, alpha=0.5)\n        smt.graphics.plot_pacf(y, lags=lags, ax=pacf_ax, alpha=0.5)\n        sm.qqplot(y, line='s', ax=qq_ax)\n        qq_ax.set_title('QQ Plot')        \n        scs.probplot(y, sparams=(y.mean(), y.std()), plot=pp_ax)\n\n        plt.tight_layout()\n    return \n\n\n# In[197]:\n\n\n_ = tsplot(Y, lags=max_lag)\n\n\n# In[272]:\n\n\n# Select best lag order for BTC returns using Autoregressive Model\nar_est_order = smt.AR(Y).select_order(maxlag=max_lag, ic='aic', trend='nc')\np('best estimated lag order = {}'.format(ar_est_order))\n\n\n# In[158]:\n\n\n# Fit MA(3) to BTC returns\narma_mdl = smt.ARMA(Y, order=(0, 3)).fit(maxlag=max_lag, method='mle', trend='nc', freq=freq)\np(arma_mdl.summary())\n\n\n# # ARMA\n\n# In[199]:\n\n\n# Fit ARMA model to BTC returns\ndef _get_best_arma(TS):\n    best_aic = np.inf \n    best_order = None\n    best_mdl = None\n\n    for i in range(5):\n        for j in range(5):\n            try:\n                tmp_mdl = smt.ARMA(TS, order=(i, j)).fit(method='mle', trend='nc', freq=freq)\n                tmp_aic = tmp_mdl.aic\n                if tmp_aic < best_aic:\n                    best_aic = tmp_aic\n                    best_order = (i, j)\n                    best_mdl = tmp_mdl\n            except: continue\n    p('aic: {:6.5f} | order: {}'.format(best_aic, best_order))\n    return best_aic, best_order, best_mdl\n\narma_aic, arma_order, arma_mdl = _get_best_arma(Y)\n\n\n# # ARIMA\n\n# In[200]:\n\n\ndef _get_best_arima(TS):\n    best_aic = np.inf \n    best_order = None\n    best_mdl = None\n\n    pq_rng = range(5)\n    d_rng = range(2)\n    for i in pq_rng:\n        for d in d_rng:\n            for j in pq_rng:\n                try:\n                    tmp_mdl = smt.ARIMA(TS, order=(i, d, j)).fit(method='mle', trend='nc', freq=freq)\n                    tmp_aic = tmp_mdl.aic\n                    if tmp_aic < best_aic:\n                        best_aic = tmp_aic\n                        best_order = (i, d, j)\n                        best_mdl = tmp_mdl\n                except: continue\n    p('aic: {:6.5f} | order: {}'.format(best_aic, best_order))                    \n    return best_aic, best_order, best_mdl\n\n\n# In[201]:\n\n\narima_aic, arima_order, arima_mdl = _get_best_arima(Y)\n\n\n# # Predict Volatility with ARIMA\n\n# In[202]:\n\n\n# Create a XXX hour forecast of BTC returns with 95%, 99% CI\nf, err95, ci95 = arima_mdl.forecast(steps=forecast_steps) # 95% CI\n_, err99, ci99 = arima_mdl.forecast(steps=forecast_steps, alpha=0.01) # 99% CI\n\nidx = pd.date_range(Y.index[-1], periods=forecast_steps, freq=freq)\nfc_95 = pd.DataFrame(np.column_stack([f, ci95]), \n                     index=idx, columns=['forecast', 'lower_ci_95', 'upper_ci_95'])\nfc_99 = pd.DataFrame(np.column_stack([ci99]), \n                     index=idx, columns=['lower_ci_99', 'upper_ci_99'])\nfc_all = fc_95.combine_first(fc_99)\nfc_all.head()\n\n\n# In[271]:\n\n\n# Plot XXX hour forecast for crypto returns\nplt.style.use('bmh')\nfig = plt.figure(figsize=(9, 7))\nax = plt.gca()\nstyles = ['b-', '0.2', '0.75', '0.2', '0.75']\n\nfc_all.plot(style=styles, ax=ax)\nplt.fill_between(fc_all.lower_ci_95, fc_all.upper_ci_95, color='gray', alpha=0.7)\nplt.fill_between(fc_all.lower_ci_99, fc_all.upper_ci_99, color='gray', alpha=0.2)\nplt.title('{} Hour {} Return Forecast\\nARIMA {}'.format(forecast_steps, arima_order, SYM))\nplt.legend(loc='best', fontsize=10)\nplt.show()\n\n\n# # GARCH based on ARIMA\n\n# In[204]:\n\n\n# Now we can fit the arch model using the best fit arima model parameters\n\np_ = arima_order[0]\no_ = arima_order[1]\nq_ = arima_order[2]\n\n# Using student T distribution usually provides better fit\narch_mdl = arch.arch_model(arima_mdl.resid, p=p_, o=o_, q=q_, vol='GARCH', dist='StudentsT')\narch_res = arch_mdl.fit(update_freq=5, disp='off')\np(arch_res.summary())\n\n\n# In[205]:\n\n\nidx = pd.date_range(Y.index[-1], periods=forecast_steps, freq=freq)\n\n\n# In[206]:\n\n\n# Create a 21 hour forecast of BTC returns\narch_f = arch_res.forecast(horizon=forecast_steps, start=Y.index[-1], method='simulation', simulations=1000) # 95% CI\n\n\n# In[207]:\n\n\n\nsims = arch_f.simulations\nfig, ax = plt.subplots()\nlines = plt.plot(idx, sims.values[-1, :, :].T, color='blue', alpha=0.1)\nlines[0].set_label('Simulated paths')\nfig.autofmt_xdate()\nplt.show()\n\np(np.percentile(sims.values[-1, :, -1].T, 5))\nplt.hist(sims.values[-1, :, -1], bins=50)\nplt.title('Distribution of Returns')\nplt.show()\n\n\n# In[208]:\n\n\nfig = arch_res.hedgehog_plot(horizon=forecast_steps)\n\n\n# In[209]:\n\n\narch_res.plot(annualize='D', scale=365)\n\n\n# # From Medium\n# https://medium.com/auquan/time-series-analysis-for-finance-arch-garch-models-822f87f1d755\n\n# In[210]:\n\n\ntsplot(arch_res.resid**2, lags=30)\n\n\n# In[259]:\n\n\nY_ = Y.iloc[-1000:]\nwindow = 300\nforeLength = len(Y_) - window\nsignal = Y_[-foreLength:]\nsignal.head()\n\n\n# In[260]:\n\n\ndef backtest(Y, foreLength, window, signal, order=''):\n    for d in range(foreLength):\n\n        # create a rolling window by selecting \n        # values between d+1 and d+T of S&P500 returns\n\n        TS = Y[(1+d):(window+d)] \n        \n        if order:\n            best_order=order\n            best_mdl=smt.ARIMA(TS, order=(best_order[0], best_order[1], best_order[2]), freq=freq).fit(\n                        method='mle', trend='nc'\n                    )\n        else:\n            # Find the best ARIMA fit \n            # set d = 0 since we've already taken log return of the series\n            _, best_order, best_mdl = _get_best_arima(TS)\n        \n        \n        #now that we have our ARIMA fit, we feed this to GARCH model\n        p_ = best_order[0]\n        o_ = best_order[1]\n        q_ = best_order[2]\n        \n        if p_ == o_ == 0:\n            p_ = 1\n        \n        am = arch.arch_model(best_mdl.resid, p=p_, o=o_, q=q_, dist='StudentsT')\n        res = am.fit(update_freq=5, disp='off')\n\n        # Generate a forecast of next day return using our fitted model\n        out = res.forecast(horizon=1, start=None, align='origin')\n\n        #Set trading signal equal to the sign of forecasted return\n        # Buy if we expect positive returns, sell if negative\n\n        signal.iloc[d] = np.sign(out.mean['h.1'].iloc[-1])\n    return signal\n\n\n# In[261]:\n\n\nsignal = backtest(Y_, foreLength, window, signal)\n\n\n# In[262]:\n\n\nreturns = pd.DataFrame(index=signal.index, \n                       columns=['Buy and Hold', 'Strategy'])\nreturns['Buy and Hold'] = Y_.iloc[-foreLength:]\nreturns['Strategy'] = signal*returns['Buy and Hold']\n\neqCurves = pd.DataFrame(index=signal.index, \n                        columns=['Buy and Hold', 'Strategy'])\neqCurves['Buy and Hold'] = returns['Buy and Hold'].cumsum() + 1\neqCurves['Strategy'] = returns['Strategy'].cumsum() + 1\n\neqCurves['Strategy'].plot(figsize=(10, 8))\neqCurves['Buy and Hold'].plot()\nplt.legend()\nplt.show()\n\n\n# # From Arch website\n\n# In[273]:\n\n\nfrom arch.univariate import ARX\nar = ARX(Y, lags=30)\nprint(ar.fit().summary())\n\n\n# In[270]:\n\n\nfrom arch.univariate import ARCH, GARCH\nar.volatility = GARCH(p=3, o=0, q=3)\nres = ar.fit(update_freq=0, disp='off')\np(res.summary())\n\n\n# In[265]:\n\n\nfrom arch.univariate import StudentsT\nar.distribution = StudentsT()\nres = ar.fit(update_freq=0, disp='off')\np(res.summary())\n\n\n# In[266]:\n\n\narf = ar.forecast(horizon=forecast_steps, start=Y.index[-1], \n                  params=res.params, method='simulation')\n\n\n# In[267]:\n\n\nplt.plot(idx, arf.simulations.values[-1].T, \n         color='blue', alpha=0.1)\nplt.show()\n\n\n# In[269]:\n\n\nplt.style.use('bmh')\nfig, ax = plt.subplots()\nplt.plot(idx, arf.simulations.variances[-1,::].T, \n         color='blue', alpha=0.1, label='simulated var')\nplt.plot(idx, arf.variance.iloc[-1], \n         color='red', label='expected var')\nfig.autofmt_xdate()\nplt.show()\n\n\n# In[225]:\n\n\n# arp = ar.simulate(params=res.params, nobs=res.nobs)\n# arp[['data', 'errors']].plot(alpha=0.3)\n\n","sub_path":"notebooks/statistical_model_dev.py","file_name":"statistical_model_dev.py","file_ext":"py","file_size_in_byte":9789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"393630721","text":"from preprocess import *\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D\nfrom keras.utils import to_categorical\nimport wandb\nfrom wandb.keras import WandbCallback\n\nwandb.init()\nconfig = wandb.config\n\nconfig.max_len = 11\nconfig.buckets = 20\n\n\n# Save data to array file first\nsave_data_to_array(max_len=config.max_len, n_mfcc=config.buckets)\n\nlabels=[\"bed\", \"happy\", \"cat\"]\n\n# # Loading train set and test set\nX_train, X_test, y_train, y_test = get_train_test()\n\n# # Feature dimension\nchannels = 1\nconfig.epochs = 300#200#50#49#50\nconfig.batch_size = 50#100#200#100#50#100\nconfig.cnn_dropout = 0.2\n\nnum_classes = 3\n\nX_train = X_train.reshape(X_train.shape[0], config.buckets, config.max_len, channels)\nX_test = X_test.reshape(X_test.shape[0], config.buckets, config.max_len, channels)\nprint(X_test.shape)\n\ny_train_hot = to_categorical(y_train)\ny_test_hot = to_categorical(y_test)\nprint(y_train_hot.shape)\n\nmodel = Sequential()\n#model.add(Reshape((28,28,1), input_shape=(28,28))) # change from 28x28 into 28x28x1\nmodel.add(Dropout(config.cnn_dropout))\n#model.add(Conv2D(32, (3,3), padding='same', activation='relu')) #32\nmodel.add(Conv2D(64, (3,3), padding='same', activation='relu')) #32\nmodel.add(MaxPooling2D(pool_size=(2,2)))\nmodel.add(Dropout(config.cnn_dropout))\n#model.add(Conv2D(12, (3,3), padding='same', activation='relu')) #32\nmodel.add(Conv2D(32, (3,3), padding='same', activation='relu')) #32\nmodel.add(MaxPooling2D(pool_size=(2,2)))\nmodel.add(Dropout(config.cnn_dropout))\n#model.add(Conv2D(12, (3,3), padding='same', activation='relu')) #32\nmodel.add(Conv2D(12, (3,3), padding='same', activation='relu')) #32\nmodel.add(MaxPooling2D(pool_size=(2,2)))\nmodel.add(Dropout(config.cnn_dropout))\n#model.add(Conv2D(12, (3,3), padding='same', activation='relu')) #32\n#model.add(Conv2D(4, (3,3), padding='same', activation='relu')) #32\n#model.add(MaxPooling2D(pool_size=(2,2)))\n#model.add(Dropout(config.cnn_dropout))\n#model.add(Conv2D(4, (3,3), padding='same', activation='relu')) #32\n#model.add(MaxPooling2D(pool_size=(2,2)))\nmodel.add(Flatten(input_shape=(config.buckets, config.max_len, channels)))\n#model.add(Dense(num_classes*10))\nmodel.add(Dense(num_classes*5, activation='relu'))\n#model.add(Dense(num_classes*3))\nmodel.add(Dense(num_classes, activation='softmax'))\nmodel.compile(loss=\"categorical_crossentropy\",\n                  optimizer=\"adam\",\n                  metrics=['accuracy'])\nmodel.fit(X_train, y_train_hot, batch_size=config.batch_size, epochs=config.epochs, validation_data=(X_test, y_test_hot), callbacks=[WandbCallback(data_type=\"image\", labels=labels)])","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":2651,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"318398896","text":"# Coding: UTF-8\n#################### EVM ####################\nimport pandas as pd\n\n# Ease of Movement\ndef EVM(data, ndays=14):\n    dm = ((data['High'] + data['Low']) / 2) - (\n        (data['High'].shift(1) + data['Low'].shift(1)) / 2)\n    br = (data['Volume'] / 100000000) / ((data['High'] - data['Low']))\n    EVM = dm / br\n    EVM_MA = pd.Series(EVM.rolling(ndays).mean(), name=str(ndays)+'_EVM')\n    data = data.join(EVM_MA)\n    return data\n\n#################### Test ####################\n# from pandas_datareader import data as web\n# import matplotlib.pyplot as plt\n#\n# # Retrieve the AAPL data from Yahoo finance:\n# data = web.DataReader('AAPL', data_source='yahoo', start='1/1/2015', end='1/1/2016')\n# data = pd.DataFrame(data)\n#\n# # Compute the 14-day Ease of Movement for AAPL\n# n = 14\n# AAPL_EVM = EVM(data, n)\n# EVM = AAPL_EVM[str(n) + '_EVM']\n#\n# # Plotting the Price Series chart and the Ease Of Movement below\n# fig = plt.figure(figsize=(7, 5))\n# ax = fig.add_subplot(2, 1, 1)\n# ax.set_xticklabels([])\n# plt.plot(data['Close'], lw=1)\n# plt.title('AAPL Price Chart')\n# plt.ylabel('Close Price')\n# plt.grid(True)\n# bx = fig.add_subplot(2, 1, 2)\n# plt.plot(EVM, 'k', lw=0.75, linestyle='-', label='EVM(14)')\n# plt.legend(loc=2, prop={'size': 9})\n# plt.ylabel('EVM values')\n# plt.grid(True)\n# plt.setp(plt.gca().get_xticklabels(), rotation=30)\n# plt.show()","sub_path":"ml_q/features/EVM.py","file_name":"EVM.py","file_ext":"py","file_size_in_byte":1364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"77226700","text":"# write a program to print a given string is palindrome or not.\n# i/p: \"hello\"  o/p: Not a palindrome.\n# i/p: \"madam\"  o/p : It's a palindrome.\n\n# One can check for blank output and improve the code for mixed characters\n\n\ndef palindrome(param):\n    inverse_word = \"\"\n\n    for char in param:\n        # print(char)\n        inverse_word = char + inverse_word\n\n    print(\"Inverse Word{0}\".format(inverse_word))\n    # ideally write the condition in different line and then return back\n    return inverse_word == param\n\nword_to_check = \"madam\"\nbool_ans = palindrome(word_to_check)\nprint(\"Is {0} Palindrome? Answer: {1}\".format(word_to_check, bool_ans))\n","sub_path":"10Palindrome.py","file_name":"10Palindrome.py","file_ext":"py","file_size_in_byte":647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"188603980","text":"from PIL import Image\nimport os\n\ninp_d = r'/home/mseals1/Documents/CNN Sensitivity/COCO DATA/Enhanced/'\nd = []\n\nfor dirs, subdirs, files in os.walk(inp_d):\n    for f in files:\n        d.append(os.path.join(dirs, f))\n\nfor im in d:\n    img = Image.open(im)\n\n    img.save(im[:-4] + \".jpg\", 'jpeg')\n","sub_path":"convert_bmp2jpg.py","file_name":"convert_bmp2jpg.py","file_ext":"py","file_size_in_byte":295,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"611051794","text":"from conans.client.generators.virtualrunenv import VirtualRunEnvGenerator\nfrom conans import ConanFile, CMake\nfrom conans.tools import os_info\nimport os\n\nclass ubitrack_virtualenv_generator(VirtualRunEnvGenerator):\n\n    def __init__(self, conanfile):\n        super(ubitrack_virtualenv_generator, self).__init__(conanfile)\n        self.venv_name = \"ubitrackrunenv\"\n\n    def ubitrack_env_items(self):\n        items = {}\n        if os_info.is_windows and not os_info.is_posix:\n            items['UBITRACK_COMPONENTS_PATH'] = os.path.join(self.output_path, \"bin\", \"ubitrack\")\n        else:\n            items['UBITRACK_COMPONENTS_PATH'] = os.path.join(self.output_path, \"lib\", \"ubitrack\")\n\n        deps_env_vars = self.conanfile.deps_env_info.vars\n        if \"TRACKMAN_BIN_PATH\" in deps_env_vars:\n          items[\"CLASSPATH\"] = deps_env_vars[\"TRACKMAN_LIB_PATH\"][0]\n        return items.items()\n\n    def trackman_config_items(self):\n        items = {}\n        items[\"UbitrackComponentDirectory\"] = os.path.join(self.output_path, \"lib\", \"ubitrack\")\n        items[\"LastDirectory\"] = \".\"\n        items[\"UbitrackWrapperDirectory\"] = os.path.join(self.output_path, \"lib\")\n        items[\"AutoCompletePatterns\"] = \"\"\n        items[\"PatternTemplateDirectory\"] = os.path.join(self.output_path, \"share\", \"Ubitrack\", \"utql\")\n        items[\"UbitrackLibraryDirectory\"] = os.path.join(self.output_path, \"lib\")\n        return items.items()\n\n    def _sh_lines(self):\n        activate_lines, deactivate_lines = super(ubitrack_virtualenv_generator, self)._sh_lines()\n\n        variables = self.ubitrack_env_items()\n        \n        for name, activate, deactivate in self.format_values(\"sh\", variables):\n            activate_lines.append(\"%s=%s\" % (name, activate))\n            activate_lines.append(\"export %s\" % name)\n            if deactivate == '\"\"':\n                deactivate_lines.append(\"unset %s\" % name)\n            else:\n                deactivate_lines.append(\"%s=%s\" % (name, deactivate))\n                deactivate_lines.append(\"export %s\" % name)            \n        activate_lines.append('')\n        deactivate_lines.append('')\n\n        return activate_lines, deactivate_lines\n\n    def _cmd_lines(self):\n        activate_lines, deactivate_lines = super(ubitrack_virtualenv_generator, self)._cmd_lines()\n\n        variables = self.ubitrack_env_items()\n\n        for name, activate, deactivate in self.format_values(\"cmd\", variables):\n            activate_lines.append(\"SET %s=%s\" % (name, activate))\n            deactivate_lines.append(\"SET %s=%s\" % (name, deactivate))\n        activate_lines.append('')\n        deactivate_lines.append('')\n\n        return activate_lines, deactivate_lines\n\n    def _ps1_lines(self):\n        activate_lines, deactivate_lines = super(ubitrack_virtualenv_generator, self)._ps1_lines()\n\n        variables = self.ubitrack_env_items()\n\n        for name, activate, deactivate in self.format_values(\"ps1\", variables):\n            activate_lines.append('$env:%s = %s' % (name,activate))\n            deactivate_lines.append('$env:%s = %s' % (name,deactivate))\n        activate_lines.append('')\n\n        return activate_lines, deactivate_lines\n\n    def _trackman_config_lines(self):\n        config_lines = []\n        for key, value in self.trackman_config_items():\n            # need to escape backslashes for windows\n            if os_info.is_windows and not os_info.is_posix:\n                value = value.replace(\"\\\\\", \"\\\\\\\\\")\n            config_lines.append('%s=%s' % (key, value))\n        return config_lines\n\n\n    def _add_trackman_files(self, result):\n        deps_env_vars = self.conanfile.deps_env_info.vars\n        if \"TRACKMAN_BIN_PATH\" in deps_env_vars:\n            trackman_items = {}\n            trackman_items[\"CLASSPATH\"] = deps_env_vars[\"TRACKMAN_LIB_PATH\"][0]\n\n            # add trackman config file\n            trackman_config_lines = self._trackman_config_lines()\n            result[\"trackman.conf\"] = os.linesep.join(trackman_config_lines)\n\n\n            if os_info.is_windows and not os_info.is_posix:\n                script_lines, _ = self._cmd_lines()\n                for name, activate, deactivate in self.format_values(\"cmd\", trackman_items.items()):\n                    script_lines.append(\"SET %s=%s\" % (name, activate))\n                script_lines.append(\"java -jar %s\" % os.path.join(deps_env_vars[\"TRACKMAN_BIN_PATH\"][0], \"trackman.jar\"))\n                result[\"startTrackman.bat\"] = os.linesep.join(script_lines)\n\n                script_lines, _ = self._ps1_lines()\n                for name, activate, deactivate in self.format_values(\"cmd\", trackman_items.items()):\n                    script_lines.append(\"SET %s=%s\" % (name, activate))\n                script_lines.append(\"java -jar %s\" % os.path.join(deps_env_vars[\"TRACKMAN_BIN_PATH\"][0], \"trackman.jar\"))\n                result[\"startTrackman.ps1\"] = os.linesep.join(script_lines)\n\n            if os_info.is_posix:\n                script_lines, _ = self._sh_lines()\n                for name, activate, deactivate in self.format_values(\"sh\", trackman_items.items()):\n                    script_lines.append(\"%s=%s\" % (name, activate))\n                    script_lines.append(\"export %s\" % name)\n                script_lines.append(\"java -jar %s\" % os.path.join(deps_env_vars[\"TRACKMAN_BIN_PATH\"][0], \"trackman.jar\"))\n                result[\"startTrackman.sh\"] = os.linesep.join(script_lines)\n\n        return result\n\n    @property\n    def content(self):\n        ret = super(ubitrack_virtualenv_generator, self).content\n        return self._add_trackman_files(ret)\n\n\nclass MyCustomGeneratorPackage(ConanFile):\n    name = \"ubitrack_virtualenv_generator\"\n    version = \"1.3.0\"\n    url = \"https://github.com/Ubitrack/ubitrack_virtualenv_generator\"\n    license = \"GPL\"\n\n    def build(self):\n      pass\n\n    def package_info(self):\n      self.cpp_info.includedirs = []\n      self.cpp_info.libdirs = []\n      self.cpp_info.bindirs = []","sub_path":"conanfile.py","file_name":"conanfile.py","file_ext":"py","file_size_in_byte":5925,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"439556035","text":"# -*- coding: utf-8 -*-\n\nimport subprocess\n\n# Retorna 0 se Sucesso e 1 se Falha. Cada comando entra como se fosse uma lista\nprocess = subprocess.run(['python','--version']) \n\n# Irá executar o comando dir no windows\na = subprocess.run('dir',shell=True)\n\n\n\n# Popen()\n# Redireciona os valores de saída para uma variável\n\nprocess = subprocess.Popen('python --version',shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE)\nresult =process.communicate()\nprint(result)","sub_path":"pythonicos/sub_process.py","file_name":"sub_process.py","file_ext":"py","file_size_in_byte":468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"365148971","text":"import time\nimport rustpylib\n\ndef testInt():\n    t0 = time.time()\n    result = rustpylib.integer_test(5)\n    t1 = time.time()\n    print(\"Time required: {}, output: {}, output type: {}\".format(t1-t0, result, type(result)))\n\ndef testString():\n    t0 = time.time()\n    result = rustpylib.string_test(\"My python string \")\n    t1 = time.time()\n    print(\"Time required: {}, output: {}, output type: {}\".format(t1-t0, result, type(result)))\n\ndef testList():\n    t0 = time.time()\n    result = rustpylib.list_test([1, 2, 7])\n    t1 = time.time()\n    print(\"Time required: {}, output: {}, output type: {}\".format(t1-t0, result, type(result)))\n\nclass Point:\n    def __init__(self, x, y):\n        self.x = x\n        self.y = y\n    def distance_to(self, other):\n        return ((other.x - self.x)**2 + (other.y - self.y)**2)**0.5\n\ndef testClass():\n    t0 = time.time()\n    p1 = Point(2, 4)\n    p2 = Point(5, 8)\n    result = rustpylib.class_test(p1, p2)\n    t1 = time.time()\n    print(\"Time required: {}, output: {}, output type: {}\".format(t1-t0, result, type(result)))\n\nif __name__ == \"__main__\":\n    testInt()\n    testString()\n    testList()\n    testClass()\n","sub_path":"step6/rustpylib/target/release/pythontest.py","file_name":"pythontest.py","file_ext":"py","file_size_in_byte":1148,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"395701543","text":"import cv2\nfrom matplotlib import pyplot as plt\n\nimg_bgr = cv2.imread('S__26132483.jpg')\n\ncv2.imshow(\"img_bgr\", img_bgr)\n# pause\nkeycode = cv2.waitKey(0)\n# ESC key to exit\nif keycode == 27:\n    cv2.destroyAllWindows()\n\nimg_bitwise_not_bgr = cv2.bitwise_not(img_bgr)\n\ncv2.imshow(\"img_bitwise_not_bgr\", img_bitwise_not_bgr)\n# pause\nkeycode = cv2.waitKey(0)\n# ESC key to exit\nif keycode == 27:\n    cv2.destroyAllWindows()\n\nimg_bitwise_not_bgr2gray = cv2.cvtColor(img_bitwise_not_bgr, cv2.COLOR_BGR2GRAY)\n\ncv2.imshow(\"img_bitwise_not_bgr2gray\", img_bitwise_not_bgr2gray)\n# pause\nkeycode = cv2.waitKey(0)\n# ESC key to exit\nif keycode == 27:\n    cv2.destroyAllWindows()\n\nret, img_binary = cv2.threshold(img_bitwise_not_bgr2gray, 150, 255, cv2.THRESH_BINARY)\n\ncv2.imshow(\"img_binary\", img_binary)\n# pause\nkeycode = cv2.waitKey(0)\n# ESC key to exit\nif keycode == 27:\n    cv2.destroyAllWindows()\n\ncontours, hierarchy = cv2.findContours(img_binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\nimg_contour = cv2.drawContours(img_bgr, contours, -1, (0, 255, 0), 2)\n\ncv2.imshow(\"img_contour\", img_contour)\n# pause\nkeycode = cv2.waitKey(0)\n# ESC key to exit\nif keycode == 27:\n    cv2.destroyAllWindows()\n","sub_path":"image_test/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1188,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"384582377","text":"import click\n\nimport os\n\nimport quesadiya\nimport quesadiya.commands.create\nimport quesadiya.commands.run\nimport quesadiya.commands.inspect\nimport quesadiya.commands.delete\nimport quesadiya.commands.export\nimport quesadiya.commands.modify\n\n\n@click.group()\n@click.version_option()\ndef cli():\n    \"\"\"A delicious mexican dish.\"\"\"\n    pass\n\n\n@cli.command()\ndef path():\n    \"\"\"Print path to this package.\"\"\"\n    click.echo(\"quesadiya resides @ {}\".format(\n        quesadiya.get_base_path()\n    ))\n\n\n@cli.command()\n@click.argument(\n    \"project_name\",\n    metavar=\"PROJECT\"\n)\n@click.argument(\n    \"admin_name\",\n    metavar=\"ADMIN\",\n)\n@click.argument(\n    \"input_data_path\",\n    metavar=\"DATAPATH\"\n)\n@click.option(\n    \"-d\",\n    \"--project-description\",\n    type=click.STRING,\n    default=\"No description\",\n    help=\"Description of a project to create.\"\n)\n@click.option(\n    \"-c\",\n    \"--contact\",\n    type=click.STRING,\n    default=\"No contact\",\n    help=\"Contact to admin user.\"\n)\n@click.option(\n    \"-a\",\n    \"--add-collaborators\",\n    type=click.STRING,\n    help=\"Add collaboratos from a jsonl file. Each row in the file must \"\n         \"follow the following format: \"\n         \"{'name': str, 'password': str, 'contact': str}. \"\n         \"Note that `contact` field can be empty.\"\n)\ndef create(\n    project_name,\n    admin_name,\n    input_data_path,\n    project_description,\n    contact,\n    add_collaborators\n):\n    \"\"\"Create a data annotation project.\"\"\"\n    quesadiya.commands.create.operator(\n        project_name=project_name,\n        project_description=project_description,\n        input_data_path=input_data_path,\n        admin_name=admin_name,\n        admin_contact=contact,\n        collaborator_input_path=add_collaborators\n    )\n\n\n@cli.command()\n@click.option(\n    \"-p\",\n    \"--port\",\n    default=1133,\n    help=\"Select the port number to run quesadiya server. The default port for \"\n         \"quesadiya is 1133.\"\n)\ndef run(port):\n    \"\"\"Run annotation project.\"\"\"\n    quesadiya.commands.run.operator(port)\n\n\n@cli.command()\n@click.argument(\n    \"project_name\",\n    metavar=\"PROJECT\"\n)\n@click.option(\n    \"-s\",\n    \"--show-collaborators\",\n    is_flag=True,\n    default=False,\n    help=\"Show all collaborators associated with a project. \"\n         \"This operation requires admin authentication.\"\n)\ndef inspect(project_name, show_collaborators):\n    \"\"\"Show project information indicated by project name.\"\"\"\n    quesadiya.commands.inspect.operator(\n        project_name=project_name,\n        show_collaborators=show_collaborators\n    )\n\n\n@cli.command()\n@click.argument(\n    \"project_name\",\n    metavar=\"PROJECT\"\n)\n@click.option(\n    \"-e\",\n    \"--edit\",\n    type=click.Choice([\"contact\", \"description\"], case_sensitive=True),\n    help=\"Edit `admin contact` or `project description` of a project.\"\n)\n@click.option(\n    \"-t\",\n    \"--transfer-ownership\",\n    is_flag=True,\n    default=False,\n    help=\"Change `admin name` and `admin password` of a project.\"\n)\n@click.option(\n    \"-a\",\n    \"--add-collaborators\",\n    type=click.STRING,\n    help=\"Add collaboratos from a jsonl file. Each row in the file must \"\n         \"follow the following format: \"\n         \"{'name': str, 'password': str, 'contact': str}. \"\n         \"Note that `contact` field can be empty.\"\n)\ndef modify(\n    project_name,\n    edit,\n    transfer_ownership,\n    add_collaborators\n):\n    \"\"\"Modify a project indicated by project name. Note that it exectues one\n    operation per run.\n    \"\"\"\n    quesadiya.commands.modify.operator(\n        project_name=project_name,\n        edit=edit,\n        transfer_ownership=transfer_ownership,\n        collaborator_input_path=add_collaborators\n    )\n\n\n@cli.command()\n@click.argument(\n    \"project_name\",\n    metavar=\"PROJECT\"\n)\ndef delete(project_name):\n    \"\"\"Delete project indicated by project name. Note that this operation\n    will delete all data associated with a project.\"\"\"\n    quesadiya.commands.delete.operator(project_name=project_name)\n\n\n@cli.command()\n@click.argument(\n    \"project_name\",\n    metavar=\"PROJECT\"\n)\n@click.argument(\"output_path\")\n@click.option(\n    \"-i\",\n    \"--include-text\",\n    is_flag=True,\n    default=False,\n    help=\"Include text field assocaited with sample ids into output file.\"\n)\ndef export(project_name, output_path, include_text):\n    \"\"\"Export data associated with a project indicated by project name.\"\"\"\n    quesadiya.commands.export.operator(\n        project_name=project_name,\n        output_path=output_path,\n        include_text=include_text\n    )\n","sub_path":"quesadiya/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":4501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"629554056","text":"from nltk import FreqDist\nimport numpy as np\n\n\ndef word_frequency_vs_rank(text,\n                           as_probability=False):\n    frequency_distribution = FreqDist(text)\n    frequency = np.asarray(sorted(frequency_distribution.values(),\n                                  reverse=True))\n    if as_probability:\n        frequency / len(text)\n\n    rank = np.arange(1, len(frequency) + 1)\n\n    return rank, frequency\n","sub_path":"wojciech/nlp/word_frequency_vs_rank.py","file_name":"word_frequency_vs_rank.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"538460259","text":"import joblib\n\nimport pandas as pd\nimport optuna\nfrom optuna.integration import XGBoostPruningCallback\n\nimport numpy as np\nimport xgboost as xgb\nfrom xgboost import XGBRegressor\nfrom optuna import Trial\nfrom sklearn.metrics import mean_squared_error\n\n\nfrom . import tscv\nfrom ..data import df_to_X_y\n\n\nMAX_EVALS = 50\n\n\nDEFAULT_PARAMS = {\"n_jobs\": -1,\n                  \"objective\": \"reg:squarederror\"}\n\n\ndef _xgb_feval(y_pred, dtrain):\n    try:\n        result = mean_squared_error(\n            dtrain.get_label(), np.clip(y_pred, 0, 20),\n            squared=False)\n    except ValueError:\n        result = np.nan\n    return 'clipped-rmse', result\n\n\ndef _trial_to_params(trial: Trial):\n    params = {**DEFAULT_PARAMS,\n              # 'gblinear' and 'dart' boosters are too slow\n              \"booster\": trial.suggest_categorical(\"booster\", ['gbtree']),\n              \"seed\": trial.suggest_int('seed', 0, 999999),\n              \"learning_rate\": trial.suggest_loguniform(\n                  'learning_rate', 0.005, 0.5),\n              \"lambda\": trial.suggest_loguniform(\"lambda\", 1e-8, 1.0),\n              \"alpha\": trial.suggest_loguniform(\"alpha\", 1e-8, 1.0)}\n\n    if params['booster'] == 'gbtree' or params['booster'] == 'dart':\n        sampling_method = trial.suggest_categorical(\n            \"sampling_method\", [\"uniform\", \"gradient_based\"])\n        if sampling_method == 'uniform':\n            subsample = trial.suggest_discrete_uniform('subsample',\n                                                       .5, 1, .05)\n        else:\n            subsample = trial.suggest_discrete_uniform('subsample',\n                                                       .1, 1, .05)\n        params.update({\n            \"max_depth\": trial.suggest_int('max_depth', 2, 25),\n            \"sampling_method\": sampling_method,\n            \"subsample\": subsample,\n            \"colsample_bytree\": trial.suggest_discrete_uniform(\n                'colsample_bytree', .20, 1., .01),\n            \"colsample_bylevel\": trial.suggest_discrete_uniform(\n                'colsample_bylevel', .20, 1., .01),\n            \"colsample_bynode\": trial.suggest_discrete_uniform(\n                'colsample_bynode', .20, 1., .01),\n            \"gamma\": trial.suggest_categorical(\"gamma\", [0, 0, 0, 0, 0, 0.01,\n                                                         0.1, 0.2, 0.3, 0.5,\n                                                         1., 10., 100.]),\n            \"min_child_weight\": trial.suggest_categorical('min_child_weight',\n                                                          [1, 1, 1, 1, 2, 3,\n                                                           4, 5, 1, 6, 7, 8, 9,\n                                                           10, 11, 15, 30, 60,\n                                                           100, 1, 1, 1]),\n            \"max_delta_step\": trial.suggest_categorical(\"max_delta_step\",\n                                                        [0, 0, 0, 0, 0,\n                                                         1, 2, 5, 8]),\n            \"grow_policy\": trial.suggest_categorical(\n                \"grow_policy\", [\"depthwise\", \"lossguide\"]),\n            \"tree_method\": \"gpu_hist\",\n            \"gpu_id\": 0})\n    if params[\"booster\"] == \"dart\":\n        params.update({\n            \"sample_type\": trial.suggest_categorical(\n                \"sample_type\", [\"uniform\", \"weighted\"]),\n            \"normalize_type\": trial.suggest_categorical(\n                \"normalize_type\", [\"tree\", \"forest\"]),\n            \"rate_drop\": trial.suggest_loguniform(\"rate_drop\", 1e-8, 1.0),\n            \"skip_drop\": trial.suggest_loguniform(\"skip_drop\", 1e-8, 1.0)})\n    return params\n\n\ndef make_xgb_loss(dtrain, cv_splits, verbose=True):\n    def loss(params, callbacks=[]):\n        return xgb.cv(\n            params, dtrain,\n            callbacks=callbacks,\n            folds=cv_splits, verbose_eval=verbose,\n            feval=_xgb_feval, maximize=False, num_boost_round=500,\n            early_stopping_rounds=10\n        )['test-clipped-rmse-mean'].min()\n\n    return loss\n\n\ndef make_xgb_objective(xgb_loss):\n    return lambda trial: xgb_loss(\n        _trial_to_params(trial), callbacks=[\n            XGBoostPruningCallback(\n                trial, observation_key='test-clipped-rmse')])\n\n\ndef _complete_params(params):\n    params = {**DEFAULT_PARAMS, **params}\n    if params['booster'] == 'gbtree':\n        params.update({\"tree_method\": \"gpu_hist\",\n                       \"gpu_id\": 0})\n    return params\n\n\ndef best_num_round(params, dall: xgb.DMatrix, cv_splits, verbose=True):\n    params = _complete_params(params)\n    train_idx, test_idx = cv_splits[-1]\n    dtrain = dall.slice(train_idx)\n    dtest = dall.slice(test_idx)\n    bst = xgb.train(params, dtrain, early_stopping_rounds=50,\n                    num_boost_round=500, evals=[(dtrain, 'dtrain'),\n                                                (dtest, 'dtest')],\n                    feval=_xgb_feval, verbose_eval=verbose)\n    return bst.best_ntree_limit\n\n\ndef sklearn_regressor(booster, params, num_round):\n    reg = XGBRegressor(n_estimators=num_round, missing=-999,\n                       **_complete_params(params))\n    reg._Booster = booster\n    return reg\n\n\nif __name__ == '__main__':\n    import sys\n    train_set_path = sys.argv[1]\n    trials_db_path = sys.argv[2]\n    output_path = sys.argv[3]\n\n    train_set = pd.read_parquet(train_set_path)\n    cv_splits = tscv.split(train_set['date_block_num'].values, n=1, window=16)\n    dtrain = xgb.DMatrix(*df_to_X_y(train_set), missing=-999)\n    del train_set\n\n    trials_db = 'sqlite:///%s' % trials_db_path\n    study = optuna.create_study(\n        direction='minimize', load_if_exists=True, study_name=output_path,\n        storage=trials_db, pruner=optuna.pruners.HyperbandPruner())\n\n    n_trials = MAX_EVALS - len(study.trials)\n    if n_trials > 0:\n        objective = make_xgb_objective(make_xgb_loss(dtrain, cv_splits))\n        try:\n            study.optimize(objective, n_trials=n_trials, n_jobs=1,\n                           gc_after_trial=True, catch=(xgb.core.XGBoostError,))\n        except KeyboardInterrupt:\n            print(\"Canceling optimization step before it finishes\")\n\n    print('Best parameters so far: %s' % study.best_params)\n\n    best_ntree_limit = best_num_round(study.best_params, dtrain, cv_splits)\n\n    print('Best n estimators: %d' % best_ntree_limit)\n\n    print('Fitting final regressor')\n    booster = xgb.train(_complete_params(study.best_params), dtrain,\n                        num_boost_round=best_ntree_limit)\n\n    reg = sklearn_regressor(booster, study.best_params,\n                            best_ntree_limit)\n    print(reg)\n    joblib.dump(reg, output_path)\n","sub_path":"src/model/make_xgb.py","file_name":"make_xgb.py","file_ext":"py","file_size_in_byte":6671,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"524762590","text":"from django import forms\n\nfrom .models import Allergy, Asthma, Diabetes, Seizure, Generic\n\n\nclass AllergyForm(forms.ModelForm):\n    def __init__(self, *args, **kwargs):\n        self.base_fields['asthma'].required = True\n        self.base_fields['allow_carry'].required = True\n        self.base_fields['self_carry_admin'].required = True\n        super(AllergyForm, self).__init__(*args, **kwargs)\n\n    class Meta:\n        model = Allergy\n        widgets = {\n            'asthma': forms.Select,\n            'allow_carry': forms.Select,\n            'self_carry_admin': forms.Select\n        }\n        fields = ('physician_name', 'physician_number', 'nurse', 'asthma', 'allow_carry', 'self_carry_admin',\n                  'hc_provide_sign', 'par_provide_sign', 'allergens', 'other_allergy', 'insect_stings')\n\n\nclass AsthmaForm(forms.ModelForm):\n    def __init__(self, *args, **kwargs):\n        self.base_fields['history_of_anaphylaxis'].required = True\n        self.base_fields['student_self_carry'].required = True\n        super(AsthmaForm, self).__init__(*args, **kwargs)\n\n    class Meta:\n        model = Asthma\n        widgets = {\n            'history_of_anaphylaxis': forms.Select\n        }\n        fields = ('physician_name', 'physician_number', 'history_of_anaphylaxis',\n                  'triggers', 'nurse', 'allergy_to', 'green_other_trigger', 'green_quick_relief',\n                  'green_quick_relief_other', 'green_when_before_trigger', 'green_when_other',\n                  'yellow_possible_side_effects', 'red_repeat_quick_relief', 'red_other_medication',\n                  'hc_provide_sign', 'par_provide_sign', 'student_self_carry')\n\n\nclass DiabetesForm(forms.ModelForm):\n    class Meta:\n        model = Diabetes\n        widgets = {\n            'diabetes_type': forms.Select,\n            'student_typical_lunch': forms.Select,\n        }\n        fields = (\n            'physician_name', 'physician_number', 'nurse', 'diabetes_type', 'age_at_diagnosis', 'hc_provide_sign',\n            'par_provide_sign', 'dmmo', 'blood_glucose_monitoring', 'method_of_insuline_delivery',\n            'insulin_delivery_ability', 'glucose_correction_dose_threshold', 'student_typical_lunch',\n            'blood_glucose_below_at_lunch', 'do_this_blood_glucose', 'decrease_insulin_dose_amount', 'do_this_other',\n            'hypoglycemia_other_symptom', 'needs_treatment_when_glucose_below', 'needs_treatment_glucose_below_amount',\n            'treated_outside_classroom', 'blood_glucose_below', 'blood_glucose_below_dose_amount',\n            'blood_glucose_below_give', 'recheck_blood_sugar', 'repeat_until_blood_glucose',\n            'repeat_until_blood_glucose_over_amount', 'disconnect_or_suspend_pump', 'hyperglycemia_other_symptom',\n            'needs_treatment_when_glucose_over', 'needs_treatment_when_glucose_over_amount',\n            'contact_parent_blood_sugar_over', 'contact_parent_blood_sugar_over_amount', 'allow_unrestricted_bathroom',\n            'drink_water_sugar_free_drinks', 'notify_parents_when_blood_glucose_below',\n            'notify_parents_when_blood_glucose_over', 'special_considerations_pe', 'special_considerations_pe_below',\n            'special_considerations_pe_above', 'special_considerations_school_parties',\n            'special_considerations_school_parties_other', 'special_considerations_field_trips', 'academic_testing',\n            'academic_testing_blood_glucose_below', 'academic_testing_blood_glucose_over', 'person_to_give_glucagon',\n            'person_to_give_glucagon_volunteer', 'location_of_glucagon')\n\n\nclass SeizureForm(forms.ModelForm):\n    class Meta:\n        model = Seizure\n        fields = (\n            'physician_name', 'physician_number', 'nurse', 'background', 'triggers_warning_signs',\n            'reaction_to_seizure', 'smmo', 'hc_provide_sign',\n            'par_provide_sign', 'action_see_this', 'seizure_action_eye_movement', 'seizure_action_other',\n            'emergency_protocol', 'emergency_protocol_911_minutes', 'emergency_protocol_911_transport_to',\n            'expected_behavior_after_seizure_other', 'vagus_nerve_stimulator',\n            'describe_magnet_use', 'special_considerations_precautions',\n            'person_to_give_medication', 'person_to_give_medication_volunteers', 'person_to_give_medication_other',\n            'location_of_medication'\n        )\n\n\nclass GenericForm(forms.ModelForm):\n    class Meta:\n        model = Generic\n        fields = (\n            'physician_name', 'physician_number', 'nurse',\n            'brief_medical_history', 'hc_provide_sign', 'follow_up_other',\n            'par_provide_sign', 'baseline_status', 'if_you_see_this', 'do_this', 'emergency_protocol',\n            'emergency_protocol_transport', 'emergency_protocol_other', 'expected_behavior_after_event',\n            'expected_behavior_after_event_other',\n            'special_health_care_needs', 'special_considerations_precautions', 'transportation_special_care',\n            'transportation_special_care_comments', 'has_emergency_medications', 'give_rescue_medication',\n            'rescue_medication_volunteer', 'location_of_rescue_medication', 'has_routine_medications',\n            'give_routine_medication_at_school', 'routine_medication_school_staff', 'location_of_routine_medication'\n        )\n        widgets = {\n            'brief_medical_history': forms.Textarea(attrs={'class': 'materialize-textarea'}),\n            'baseline_status': forms.Textarea(attrs={'class': 'materialize-textarea'}),\n            'if_you_see_this': forms.Textarea(attrs={'class': 'materialize-textarea'}),\n            'do_this': forms.Textarea(attrs={'class': 'materialize-textarea'})\n        }\n","sub_path":"powerschool_apps/health_care_plan/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":5646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"596030409","text":"from MotionGPT import *\nfrom Params import *\nfrom VRNN import *\n\nfrom datetime import datetime\nfrom torch.utils.tensorboard import SummaryWriter\n\n\nif __name__ == \"__main__\":\n    now = datetime.now()\n    date_time = now.strftime(\"%Y_%m_%d_%H_%M_%S\" + \"_nr\")\n    print(\"date and time:\", date_time)\n\n    writer = SummaryWriter(\"runs/\" + date_time)\n\n    loader = FBXDataLoader(data_file, radian=radian, has_translate=consider_root_translate) #has_finger=consider_finger\n    loader.LoadData()\n    loader.PrepareTrainingData(training_joint_index=[i for i in range(len(G_MIXAMO_JOINTS))], frame_gap=frame_gap)\n\n    params_dict = {\n        \"data file\": data_file,\n        \"radian or degree\": \"radian\" if radian else \"degree\",\n        \"frame interval\": frame_gap,\n        \"all data num\": len(loader),\n        \"hidden dim\": h_dim,\n        \"z dim\": z_dim,\n        \"n layers\": n_layers,\n        \"batch size\": batch_size,\n        \"n_epochs\": n_epochs,\n        \"gradient clip\": clip,\n        \"learning_rate\": learning_rate,\n        \"print_every\": print_every,\n        \"consider_root_translate\": consider_root_translate,\n        \"consider_finger\": consider_finger\n    }\n\n    writer.add_hparams(params_dict, {})\n\n    input_dim = len(loader.train_data[0][1])\n    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")  # device\n\n    print(\"Training data dim:\", input_dim)\n    print(\"Training on device: \", device)\n    #Build model\n    model = VRNN(input_dim, h_dim, z_dim, n_layers, device)\n    model.to(device)\n    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n\n    kld_loss_list = []\n    mse_loss_list = []\n\n    #Train\n    for epoch in range(n_epochs):\n        batch_idx = 0\n        model.train()\n        for batch_data, pad_data in loader.next_batch(batch_size, batch_first=False):\n            batch_idx += 1\n\n            batch_data = batch_data.to(device)\n            pad_data = pad_data.to(device)\n\n            optimizer.zero_grad()\n\n            kld_loss, mse_loss, _, _ = model(batch_data, pad_data)\n            kld_loss = kld_loss / len(pad_data)\n            mse_loss = mse_loss / len(pad_data)\n\n            #print(torch.sum(pad_data))\n            #print(torch.sum(pad_data))\n\n            loss = kld_loss + mse_loss\n            loss.backward()\n\n            # grad norm clipping, only in pytorch version >= 1.10\n            torch.nn.utils.clip_grad_norm_(model.parameters(), clip)\n            optimizer.step()\n\n            kld_loss_list.append(kld_loss.data)\n            mse_loss_list.append(mse_loss.data)\n\n            if len(kld_loss_list) > 10:\n                kld_loss_list.pop(0)\n\n            if len(mse_loss_list) > 10:\n                mse_loss_list.pop(0)\n\n\n            # printing\n            if batch_idx % print_every == 0:\n                print('Train Epoch: {} [{}/{} ({:.0f}%)]\\t KLD Loss: {:.6f} \\t NLL Loss: {:.6f}'.format(\n                    epoch, batch_idx * len(batch_data), len(loader.train_data),\n                       100. * batch_idx / (len(loader.train_data) // batch_size),\n                       kld_loss.data,\n                       mse_loss.data))\n\n                #print(torch.sum(pad_data).data)\n\n        writer.add_scalar('kld_loss loss',\n                          sum(kld_loss_list) / len(kld_loss_list),\n                          epoch) #* len(loader.train_data) // batch_size + batch_idx\n\n        writer.add_scalar('mse_loss loss',\n                          sum(mse_loss_list) / len(mse_loss_list),\n                          epoch)\n\n\n        #test\n        model.eval()\n        test_kld_loss_list = []\n        test_mse_loss_list = []\n\n        for batch_data, pad_data in loader.next_batch(batch_size, train_mode=False):\n            batch_data = batch_data.to(device)\n            pad_data = pad_data.to(device)\n\n            kld_loss, mse_loss, _, _ = model(batch_data, pad_data)\n            kld_loss = kld_loss / torch.sum(pad_data)\n            mse_loss = mse_loss / torch.sum(pad_data)\n\n            test_kld_loss_list.append(kld_loss.data)\n            test_mse_loss_list.append(mse_loss.data)\n\n        # for i in range(len(loader.test_data)):\n        #     test_sample = torch.FloatTensor(loader.test_data[i])\n        #     test_sample = test_sample.unsqueeze(1)\n        #     test_sample_mask = torch.LongTensor([[1]]*test_sample.size(0))\n        #\n        #     batch_data = test_sample.to(device)\n        #     batch_mask = test_sample_mask.to(device)\n        #\n        #\n        #     kld_loss, mse_loss, _, _ = model(batch_data, batch_mask)\n        #\n        #     test_kld_loss_list.append(kld_loss.data / len(test_sample))\n        #     test_mse_loss_list.append(mse_loss.data / len(test_sample))\n\n\n        writer.add_scalar('test kld_loss loss',\n                          sum(test_kld_loss_list) / len(test_kld_loss_list),\n                          epoch)\n\n        writer.add_scalar('test mse_loss loss',\n                          sum(test_mse_loss_list) / len(test_mse_loss_list),\n                          epoch)\n\n    #save model\n    writer.close()\n    torch.save(model.state_dict(), \"runs/\" + date_time + \".pth\")\n\n    date_time = now.strftime(\"%Y_%m_%d_%H_%M_%S\" + \"_nr\")\n    print(\"date and time:\", date_time)","sub_path":"SocialAffordance/TrainVRNN.py","file_name":"TrainVRNN.py","file_ext":"py","file_size_in_byte":5180,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"79202640","text":"import asyncio\nimport contextlib\nimport datetime\nimport functools\nimport os\nimport pathlib\nimport re\nfrom typing import (\n    Any,\n    AsyncContextManager,\n    Callable,\n    Coroutine,\n    Dict,\n    List,\n    Optional,\n    Protocol,\n    Type,\n    Union,\n    runtime_checkable,\n)\n\nimport devtools\nimport httpx\nimport kubernetes_asyncio\nimport pydantic\nimport pytest\nimport pytz\nimport respx\n\nimport servo\nimport servo.cli\nimport servo.connectors.kubernetes\nimport servo.connectors.opsani_dev\nimport servo.connectors.prometheus\n\n\n@pytest.fixture\ndef config(kube) -> servo.connectors.opsani_dev.OpsaniDevConfiguration:\n    return servo.connectors.opsani_dev.OpsaniDevConfiguration(\n        namespace=kube.namespace,\n        deployment=\"fiber-http\",\n        container=\"fiber-http\",\n        service=\"fiber-http\",\n        cpu=servo.connectors.kubernetes.CPU(min=\"125m\", max=\"4000m\", step=\"125m\"),\n        memory=servo.connectors.kubernetes.Memory(min=\"128 MiB\", max=\"4.0 GiB\", step=\"128 MiB\"),\n        __optimizer__=servo.configuration.Optimizer(id=\"test.com/foo\", token=\"12345\")\n    )\n\n\n@pytest.fixture\ndef checks(config: servo.connectors.opsani_dev.OpsaniDevConfiguration) -> servo.connectors.opsani_dev.OpsaniDevChecks:\n    return servo.connectors.opsani_dev.OpsaniDevChecks(config=config)\n\nclass TestConfig:\n    def test_generate(self) -> None:\n        config = servo.connectors.opsani_dev.OpsaniDevConfiguration.generate()\n        assert list(config.dict().keys()) == ['description', 'namespace', 'deployment', 'container', 'service', 'port', 'cpu', 'memory', 'prometheus_base_url']\n\n    def test_generate_yaml(self) -> None:\n        config = servo.connectors.opsani_dev.OpsaniDevConfiguration.generate()\n        assert config.yaml(exclude_unset=True) == (\n            \"namespace: default\\n\"\n            \"deployment: app-deployment\\n\"\n            \"container: main\\n\"\n            \"service: app\\n\"\n            \"cpu:\\n\"\n            \"  min: 250m\\n\"\n            \"  max: '4'\\n\"\n            \"memory:\\n\"\n            \"  min: 256.0MiB\\n\"\n            \"  max: 4.0GiB\\n\"\n        )\n\n    def test_assign_optimizer(self) -> None:\n        config = servo.connectors.opsani_dev.OpsaniDevConfiguration.generate()\n        config.__optimizer__ = None\n\n\n@pytest.mark.applymanifests(\n    \"opsani_dev\",\n    files=[\n        \"deployment.yaml\",\n        \"service.yaml\",\n        \"prometheus.yaml\",\n    ],\n)\n@pytest.mark.integration\n@pytest.mark.usefixtures(\"kubeconfig\", \"kubernetes_asyncio_config\")\nclass TestIntegration:\n    class TestChecksOriginalState:\n        @pytest.fixture(autouse=True)\n        async def load_manifests(\n            self, kube, checks: servo.connectors.opsani_dev.OpsaniDevChecks, kubeconfig\n        ) -> None:\n            kube.wait_for_registered()\n            checks.config.namespace = kube.namespace\n\n            # Fake out the servo metadata in the environment\n            # These env vars are set by our manifests\n            pods = kube.get_pods(labels={ \"app.kubernetes.io/name\": \"servo\"})\n            assert pods, \"servo is not deployed\"\n            try:\n                os.environ['POD_NAME'] = list(pods.keys())[0]\n                os.environ[\"POD_NAMESPACE\"] = kube.namespace\n\n                yield\n\n            finally:\n                os.environ.pop('POD_NAME', None)\n                os.environ.pop('POD_NAMESPACE', None)\n\n\n        @pytest.mark.parametrize(\n            \"resource\", [\"namespace\", \"deployment\", \"container\", \"service\"]\n        )\n        async def test_resource_exists(\n            self, resource: str, checks: servo.connectors.opsani_dev.OpsaniDevChecks\n        ) -> None:\n            result = await checks.run_one(id=f\"check_kubernetes_{resource}\")\n            assert result.success\n\n        async def test_target_container_resources_within_limits(\n            self, kube, checks: servo.connectors.opsani_dev.OpsaniDevChecks, config: servo.connectors.opsani_dev.OpsaniDevConfiguration\n        ) -> None:\n            config.cpu.min = \"125m\"\n            config.cpu.max = \"2000m\"\n            config.memory.min = \"128MiB\"\n            config.memory.max = \"4GiB\"\n            result = await checks.run_one(id=f\"check_target_container_resources_within_limits\")\n            assert result.success, f\"Expected success but got: {result}\"\n\n        async def test_target_container_resources_outside_of_limits(\n            self, kube, checks: servo.connectors.opsani_dev.OpsaniDevChecks, config: servo.connectors.opsani_dev.OpsaniDevConfiguration\n        ) -> None:\n            config.cpu.min = \"4000m\"\n            config.cpu.max = \"5000m\"\n            config.memory.min = \"2GiB\"\n            config.memory.min = \"4GiB\"\n            result = await checks.run_one(id=f\"check_target_container_resources_within_limits\")\n            assert result.exception\n\n        async def test_service_routes_traffic_to_deployment(\n            self, kube, checks: servo.connectors.opsani_dev.OpsaniDevChecks\n        ) -> None:\n            result = await checks.run_one(id=f\"check_service_routes_traffic_to_deployment\")\n            assert result.success, f\"Failed with message: {result.message}\"\n\n        async def test_prometheus_sidecar_exists(\n            self, kube, checks: servo.connectors.opsani_dev.OpsaniDevChecks\n        ) -> None:\n            result = await checks.run_one(id=f\"check_prometheus_sidecar_exists\")\n            assert result.success\n\n        async def test_prometheus_sidecar_is_ready(\n            self, kube, checks: servo.connectors.opsani_dev.OpsaniDevChecks\n        ) -> None:\n            result = await checks.run_one(id=f\"check_prometheus_sidecar_is_ready\")\n            assert result.success\n\n        async def test_check_prometheus_restart_count(\n            self, kube, checks: servo.connectors.opsani_dev.OpsaniDevChecks\n        ) -> None:\n            result = await checks.run_one(id=f\"check_prometheus_restart_count\")\n            assert result.success\n\n        async def test_check_prometheus_container_port(\n            self, kube, checks: servo.connectors.opsani_dev.OpsaniDevChecks\n        ) -> None:\n            result = await checks.run_one(id=f\"check_prometheus_container_port\")\n            assert result.success\n\n        @pytest.fixture\n        def go_memstats_gc_sys_bytes(self) -> dict:\n            return {\n                \"status\": \"success\",\n                \"data\": {\n                    \"resultType\": \"matrix\",\n                    \"result\": [\n                        {\n                            \"metric\": {\n                                \"__name__\": \"go_memstats_gc_sys_bytes\",\n                                \"instance\": \"localhost:9090\",\n                                \"job\": \"prometheus\",\n                            },\n                            \"values\": [\n                                [1595142421.024, \"3594504\"],\n                                [1595142481.024, \"3594504\"],\n                            ],\n                        }\n                    ],\n                },\n            }\n\n        @pytest.fixture\n        def mocked_api(self, go_memstats_gc_sys_bytes):\n            with respx.mock(\n                base_url=servo.connectors.opsani_dev.PROMETHEUS_SIDECAR_BASE_URL,\n                assert_all_called=False\n            ) as respx_mock:\n                respx_mock.get(\n                    \"/ap/v1/targets\",\n                    name=\"targets\"\n                ).mock(return_value=httpx.Response(200, json=[]))\n\n                respx_mock.get(\n                    re.compile(r\"/ap/v1/query_range.+\"),\n                    name=\"query\",\n                ).mock(return_value=httpx.Response(200, json=go_memstats_gc_sys_bytes))\n                yield respx_mock\n\n        async def test_check_prometheus_is_accessible(\n            self, kube, checks: servo.connectors.opsani_dev.OpsaniDevChecks\n        ) -> None:\n            with respx.mock(base_url=servo.connectors.opsani_dev.PROMETHEUS_SIDECAR_BASE_URL) as respx_mock:\n                request = respx_mock.get(\"/api/v1/targets\").mock(return_value=httpx.Response(status_code=503))\n                check = await checks.run_one(id=f\"check_prometheus_is_accessible\")\n                assert request.called\n                assert check\n                assert check.name == 'Prometheus is accessible'\n                assert check.id == \"check_prometheus_is_accessible\"\n                assert check.critical\n                assert not check.success\n                assert check.message is not None\n                assert isinstance(check.exception, httpx.HTTPStatusError)\n\n@pytest.mark.applymanifests(\n    \"opsani_dev\",\n    files=[\n        \"deployment.yaml\",\n        \"service.yaml\",\n        \"prometheus.yaml\",\n    ],\n)\n@pytest.mark.integration\n@pytest.mark.usefixtures(\"kubeconfig\", \"kubernetes_asyncio_config\")\n@pytest.mark.clusterrolebinding('cluster-admin')\nclass TestServiceMultiport:\n    @pytest.fixture\n    async def multiport_service(self, kube, checks: servo.connectors.opsani_dev.OpsaniDevChecks) -> None:\n        kube.wait_for_registered()\n        service = await servo.connectors.kubernetes.Service.read(checks.config.service, checks.config.namespace)\n        assert service\n        assert len(service.ports) == 1\n        assert service.find_port('http')\n\n        # Add a port\n        port = kubernetes_asyncio.client.V1ServicePort(name=\"elite\", port=31337)\n        service.obj.spec.ports.append(port)\n\n        await service.patch()\n\n        assert len(service.ports) == 2\n        assert service.find_port('http')\n        assert service.find_port('elite')\n\n        return service\n\n    async def test_requires_port_config_when_multiple_exist(\n        self, kube, checks: servo.connectors.opsani_dev.OpsaniDevChecks, multiport_service\n    ) -> None:\n        result = await checks.run_one(id=f\"check_kubernetes_service_port\")\n        assert not result.success\n        assert result.exception\n        assert result.message == 'caught exception (ValueError): service defines more than one port: a `port` (name or number) must be specified in the configuration'\n\n    async def test_resolve_port_by_name(\n        self, kube, checks: servo.connectors.opsani_dev.OpsaniDevChecks, multiport_service\n    ) -> None:\n        checks.config.port = 'elite'\n        result = await checks.run_one(id=f\"check_kubernetes_service_port\")\n        assert result.success\n        assert result.message == 'Service Port: elite 31337:31337/TCP'\n\n    async def test_resolve_port_by_number(\n        self, kube, checks: servo.connectors.opsani_dev.OpsaniDevChecks, multiport_service\n    ) -> None:\n        checks.config.port = 80\n        result = await checks.run_one(id=f\"check_kubernetes_service_port\")\n        assert result.success\n        assert result.message == 'Service Port: http 80:8480/TCP'\n\n    async def test_cannot_resolve_port_by_name(\n        self, kube, checks: servo.connectors.opsani_dev.OpsaniDevChecks, multiport_service\n    ) -> None:\n        kube.wait_for_registered()\n        pass\n        # checks.config.port = 'invalid'\n        # result = await checks.run_one(id=f\"check_kubernetes_service_port\")\n        # assert not result.success\n        # assert result.message == 'caught exception (LookupError): could not find a port named: invalid'\n\n    async def test_cannot_resolve_port_by_number(\n        self, kube, checks: servo.connectors.opsani_dev.OpsaniDevChecks, multiport_service\n    ) -> None:\n        checks.config.port = 187\n        result = await checks.run_one(id=f\"check_kubernetes_service_port\")\n        assert not result.success\n        assert result.message == 'caught exception (LookupError): could not find a port numbered: 187'\n    # Errors:\n    # Permissions, (Namespace, Deployment, Container, Service -> doesn't Exist, Cant Read), ports don't match\n\n    # Warnings:\n    # 9980 port conflict\n\n    # init containers are ignored\n\n    # TODO: Test deployment, pod with init container, test nginx not match,\n    # TODO: check namespace affinity only scrapes in current namespace\n\n    # async def test_permissions(self) -> None:\n    #     ...\n\n    # async def test_cannot_read_namespace(self) -> None:\n    #     ...\n\n    # async def test_cannot_read_deployment(self) -> None:\n    #     ...\n\n    # async def test_cannot_find_container(self) -> None:\n    #     ...\n\n    # async def test_resource_requirements(self) -> None:\n    #     ...\n\n    # async def test_deployment_ready(self) -> None:\n    #     ...\n    class TestInstall:\n        @pytest.fixture(autouse=True)\n        async def load_manifests(\n            self, kube, kubeconfig, kubernetes_asyncio_config, checks: servo.connectors.opsani_dev.OpsaniDevChecks\n        ) -> None:\n            kube.wait_for_registered()\n            checks.config.namespace = kube.namespace\n\n            # Fake out the servo metadata in the environment\n            # These env vars are set by our manifests\n            deployment = kube.get_deployments()[\"servo\"]\n            pod = deployment.get_pods()[0]\n            os.environ['POD_NAME'] = pod.name\n            os.environ[\"POD_NAMESPACE\"] = kube.namespace\n\n        async def test_process(\n            self, kube, checks: servo.connectors.opsani_dev.OpsaniDevChecks,\n            kube_port_forward: Callable[[str, int], AsyncContextManager[str]],\n            load_generator: Callable[[], 'LoadGenerator'],\n        ) -> None:\n            # Deploy fiber-http with annotations and Prometheus will start scraping it\n            envoy_proxy_port = servo.connectors.opsani_dev.ENVOY_SIDECAR_DEFAULT_PORT\n            async with kube_port_forward(\"deploy/servo\", 9090) as prometheus_base_url:\n                # Connect the checks to our port forward interface\n                checks.config.prometheus_base_url = prometheus_base_url\n\n                deployment = await servo.connectors.kubernetes.Deployment.read(checks.config.deployment, checks.config.namespace)\n                assert deployment, f\"failed loading deployment '{checks.config.deployment}' in namespace '{checks.config.namespace}'\"\n\n                prometheus_config = servo.connectors.prometheus.PrometheusConfiguration.generate(base_url=prometheus_base_url)\n                prometheus_connector = servo.connectors.prometheus.PrometheusConnector(config=prometheus_config)\n\n                ## Step 1\n                servo.logger.critical(\"Step 1 - Annotate the Deployment PodSpec\")\n                async with assert_check_raises_in_context(\n                    servo.checks.CheckError,\n                    match=\"deployment 'fiber-http' is missing annotations\"\n                ) as assertion:\n                    assertion.set(checks.run_one(id=f\"check_deployment_annotations\"))\n\n                # Add a subset of the required annotations to catch partial setup cases\n                async with change_to_resource(deployment):\n                    await add_annotations_to_podspec_of_deployment(deployment,\n                        {\n                            \"prometheus.opsani.com/path\": \"/stats/prometheus\",\n                            \"prometheus.opsani.com/port\": \"9901\",\n                            \"servo.opsani.com/optimizer\": checks.config.optimizer.id,\n                        }\n                    )\n                await assert_check_raises(\n                    checks.run_one(id=f\"check_deployment_annotations\"),\n                    servo.checks.CheckError,\n                    re.escape(\"deployment 'fiber-http' is missing annotations: prometheus.opsani.com/scheme, prometheus.opsani.com/scrape\")\n                )\n\n                # Fill in the missing annotations\n                async with change_to_resource(deployment):\n                    await add_annotations_to_podspec_of_deployment(deployment,\n                        {\n                            \"prometheus.opsani.com/scrape\": \"true\",\n                            \"prometheus.opsani.com/scheme\": \"http\",\n                        }\n                    )\n                await assert_check(checks.run_one(id=f\"check_deployment_annotations\"))\n\n                # Step 2: Verify the labels are set on the Deployment pod spec\n                servo.logger.critical(\"Step 2 - Label the Deployment PodSpec\")\n                await assert_check_raises(\n                    checks.run_one(id=f\"check_deployment_labels\"),\n                    servo.checks.CheckError,\n                    re.escape(\"deployment 'fiber-http' is missing labels: servo.opsani.com/optimizer=test.com_foo, sidecar.opsani.com/type=envoy\")\n                )\n\n                async with change_to_resource(deployment):\n                    await add_labels_to_podspec_of_deployment(deployment,\n                        {\n                            \"sidecar.opsani.com/type\": \"envoy\",\n                            \"servo.opsani.com/optimizer\": servo.connectors.kubernetes.labelize(checks.config.optimizer.id),\n                        }\n                    )\n                await assert_check(checks.run_one(id=f\"check_deployment_labels\"))\n\n                # Step 3\n                servo.logger.critical(\"Step 3 - Inject Envoy sidecar container\")\n                await assert_check_raises(\n                    checks.run_one(id=f\"check_deployment_envoy_sidecars\"),\n                    servo.checks.CheckError,\n                    re.escape(\"deployment 'fiber-http' pod template spec does not include envoy sidecar container ('opsani-envoy')\")\n                )\n\n                # servo.logging.set_level(\"DEBUG\")\n                async with change_to_resource(deployment):\n                    servo.logger.info(f\"injecting Envoy sidecar to Deployment {deployment.name} PodSpec\")\n                    await deployment.inject_sidecar('opsani-envoy', 'opsani/envoy-proxy:latest', service=\"fiber-http\")\n\n                await wait_for_check_to_pass(functools.partial(checks.run_one, id=f\"check_deployment_envoy_sidecars\"))\n                await wait_for_check_to_pass(functools.partial(checks.run_one, id=f\"check_pod_envoy_sidecars\"))\n\n                # Step 4\n                servo.logger.critical(\"Step 4 - Check that Prometheus is discovering and scraping annotated Pods\")\n                servo.logger.info(\"waiting for Prometheus to scrape our Pods\")\n\n                async def wait_for_targets_to_be_scraped() -> List[servo.connectors.prometheus.ActiveTarget]:\n                    servo.logger.info(f\"Waiting for Prometheus scrape Pod targets...\")\n                    # NOTE: Prometheus is on a 5s scrape interval\n                    scraped_since = pytz.utc.localize(datetime.datetime.now())\n                    while True:\n                        targets = await prometheus_connector.targets()\n                        if targets:\n                            if not any(filter(lambda t: t.last_scraped_at is None or t.last_scraped_at < scraped_since, targets.active)):\n                                # NOTE: filter targets to match our namespace in\n                                # case there are other things running in the cluster\n                                return list(filter(lambda t: t.labels[\"kubernetes_namespace\"] == kube.namespace, targets.active))\n\n                await wait_for_targets_to_be_scraped()\n                await assert_check(checks.run_one(id=f\"check_prometheus_targets\"))\n\n                # Step 5\n                servo.logger.critical(\"Step 5 - Check that traffic metrics are coming in from Envoy\")\n                await assert_check_raises(\n                    checks.run_one(id=f\"check_envoy_sidecar_metrics\"),\n                    servo.checks.CheckError,\n                    re.escape(\"Envoy is not reporting any traffic to Prometheus\")\n                )\n\n                servo.logger.info(f\"Sending test traffic to Envoy through deploy/fiber-http\")\n                async with kube_port_forward(\"deploy/fiber-http\", envoy_proxy_port) as envoy_url:\n                    await load_generator(envoy_url).run_until(\n                        wait_for_check_to_pass(\n                            functools.partial(checks.run_one, id=f\"check_envoy_sidecar_metrics\")\n                        )\n                    )\n\n                # Let Prometheus scrape to see the traffic\n                await wait_for_check_to_pass(functools.partial(checks.run_one, id=f\"check_prometheus_targets\"))\n\n                # Step 6\n                servo.logger.critical(\"Step 6 - Proxy Service traffic through Envoy\")\n                await assert_check_raises(\n                    checks.run_one(id=f\"check_service_proxy\"),\n                    servo.checks.CheckError,\n                    re.escape(f\"service 'fiber-http' is not routing traffic through Envoy sidecar on port {envoy_proxy_port}\")\n                )\n\n                # Update the port to point to the sidecar\n                service = await servo.connectors.kubernetes.Service.read(\"fiber-http\", checks.config.namespace)\n                service.ports[0].target_port = envoy_proxy_port\n                async with change_to_resource(service):\n                    await service.patch()\n                await wait_for_check_to_pass(functools.partial(checks.run_one, id=f\"check_service_proxy\"))\n\n                # Send traffic through the service and verify it shows up in Envoy\n                port = service.ports[0].port\n                servo.logger.info(f\"Sending test traffic through proxied Service fiber-http on port {port}\")\n\n                async with kube_port_forward(f\"service/fiber-http\", port) as service_url:\n                    await load_generator(envoy_url).run_until(wait_for_targets_to_be_scraped())\n\n                # Let Prometheus scrape to see the traffic\n                await assert_check(checks.run_one(id=f\"check_prometheus_targets\"))\n\n                # Step 7\n                servo.logger.critical(\"Step 7 - Bring tuning Pod online\")\n                async with change_to_resource(deployment):\n                    await deployment.ensure_tuning_pod()\n                await assert_check(checks.run_one(id=f\"check_tuning_is_running\"))\n\n                # Step 8\n                servo.logger.critical(\"Step 8 - Verify Service traffic makes it through Envoy and gets aggregated by Prometheus\")\n                async with kube_port_forward(f\"service/fiber-http\", port) as service_url:\n                    await load_generator(service_url).run_until(wait_for_targets_to_be_scraped())\n\n                targets = await wait_for_targets_to_be_scraped()\n                assert len(targets) == 2\n                main = next(filter(lambda t: \"opsani_role\" not in t.labels, targets))\n                tuning = next(filter(lambda t: \"opsani_role\" in t.labels, targets))\n                assert main.pool == \"opsani-envoy-sidecars\"\n                assert main.health == \"up\"\n                assert main.labels[\"app_kubernetes_io_name\"] == \"fiber-http\"\n\n                assert tuning.pool == \"opsani-envoy-sidecars\"\n                assert tuning.health == \"up\"\n                assert tuning.labels[\"opsani_role\"] == \"tuning\"\n                assert tuning.discovered_labels[\"__meta_kubernetes_pod_name\"] == \"fiber-http-tuning\"\n                assert tuning.discovered_labels[\"__meta_kubernetes_pod_label_opsani_role\"] == \"tuning\"\n\n                async with kube_port_forward(f\"service/fiber-http\", port) as service_url:\n                    await load_generator(service_url).run_until(\n                        wait_for_check_to_pass(\n                            functools.partial(checks.run_one, id=f\"check_traffic_metrics\")\n                        )\n                    )\n\n                servo.logger.success(\"🥷 Opsani Dev is now deployed.\")\n                servo.logger.critical(\"🔥 Now witness the firepower of this fully ARMED and OPERATIONAL battle station!\")\n\n                # Cancel outstanding tasks\n                tasks = [t for t in asyncio.all_tasks() if t is not asyncio.current_task()]\n                [task.cancel() for task in tasks]\n\n                await asyncio.gather(*tasks, return_exceptions=True)\n\n        async def test_install_wait(\n            self, kube, checks: servo.connectors.opsani_dev.OpsaniDevChecks,\n            kube_port_forward: Callable[[str, int], AsyncContextManager[str]],\n            load_generator: Callable[[], 'LoadGenerator'],\n            tmp_path: pathlib.Path\n        ) -> None:\n            servo.logging.set_level(\"TRACE\")\n\n            async with kube_port_forward(\"deploy/servo\", 9090) as prometheus_base_url:\n                # Connect the checks to our port forward interface\n                checks.config.prometheus_base_url = prometheus_base_url\n\n                deployment = await servo.connectors.kubernetes.Deployment.read(checks.config.deployment, checks.config.namespace)\n                assert deployment, f\"failed loading deployment '{checks.config.deployment}' in namespace '{checks.config.namespace}'\"\n\n                async def loop_checks() -> None:\n                    while True:\n                        results = await checks.run_all()\n                        next_failure = next(filter(lambda r: r.success is False, results), None)\n                        if next_failure:\n                            servo.logger.critical(f\"Attempting to remedy failing check: {devtools.pformat(next_failure)}\")#, exception=next_failure.exception)\n                            await _remedy_check(\n                                next_failure.id,\n                                config=checks.config,\n                                deployment=deployment,\n                                kube_port_forward=kube_port_forward,\n                                load_generator=load_generator,\n                                checks=checks\n                            )\n                        else:\n                            break\n\n                await asyncio.wait_for(loop_checks(), timeout=420.0)\n\n            servo.logger.success(\"🥷 Opsani Dev is now deployed.\")\n            servo.logger.critical(\"🔥 Now witness the firepower of this fully ARMED and OPERATIONAL battle station!\")\n\n\n\n##\n# FIXME: Migrate these assertions into a better home and fix the line number mess\n\nasync def assert_check_fails(\n    check: servo.checks.Check,\n    message: Optional[str] = None\n) -> None:\n    \"\"\"Assert that a check fails.\n\n    The check provided can be a previously executed Check object or a coroutine that returns a Check.\n    This assertion does not differentiate between boolean and exceptional failures. If you want to test\n    a particular exceptional failure condition, take a look at `assert_check_raises`.\n    \"\"\"\n    return await assert_check(check, message, _success=False)\n\n@runtime_checkable\nclass Assertable(Protocol):\n    \"\"\"An object that can set an assertable value.\"\"\"\n\n    def set(value: Any) -> None:\n        \"\"\"Set the value of the assertion.\"\"\"\n        ...\n\n# TODO: doesn't have to be async\n@contextlib.asynccontextmanager\nasync def assert_check_raises_in_context(\n    type_: Type[Exception],\n    match: Optional[str] = None,\n    *,\n    message: Optional[str] = None,\n) -> AsyncContextManager[Assertable]:\n    \"\"\"Assert that a check fails due to a specific exception being raised within an execution context.\n\n    The check provided can be a previously executed Check object or a coroutine that returns a Check.\n    The exception type is evaluated and the `match` parameter is matched against the underlying exception\n    via `pytest.assert_raises` and supports strings and regex objects.\n\n    This method is an asynchronous context manager for use via the `async with ..` syntax:\n        ```\n        async with assert_check_raises_in_context(AttributeError) as assertion:\n            assertion.set(check.whatever(True))\n        ```\n\n    The opaque `Assertable` object yielded exposes a single method `set` that accepts a `Check` object value or\n    a callable that returns a Check object. The callable can be asynchronous and will be awaited.\n    This syntax can be more readable and enables setup/teardown and debugging logic that would otherwise\n    be rather unergonomic.\n\n    Args:\n        type_: The type of exception expected.\n        match: A string or regular expression for matching against the error raised.\n        message: An optional override for the error message returned on failure.\n    \"\"\"\n\n    class _Assertion(Assertable):\n        def set(self, value) -> None:\n            self._value = value\n\n        async def get(self) -> servo.checks.Check:\n            if asyncio.iscoroutine(self._value):\n                return await self._value\n            else:\n                return self._value\n\n    assertion = _Assertion()\n    yield assertion\n    value = await assertion.get()\n\n    assert value is not None, f\"invalid use as context manager: must return a Check\"\n    await assert_check(value, message, _success=False, _exception_type=type_, _exception_match=match)\n\nasync def assert_check_raises(\n    check,\n    type_: Type[Exception] = Exception,\n    match: Optional[str] = None,\n    *,\n    message: Optional[str] = None,\n) -> None:\n    \"\"\"Assert that a check fails due to a specific exception being raised.\n\n    The check provided can be a previously executed Check object or a coroutine that returns a Check.\n    The exception type is evaluated and the `match` parameter is matched against the underlying exception\n    via `pytest.assert_raises` and supports strings and regex objects.\n\n    Args:\n        check: A check object or callable that returns a check object to be evaluated.\n        type_: The type of exception expected to fail the check.\n        match: A string or regular expression to be evaluated against the message of the exception that triggered the failure.\n    \"\"\"\n    async with assert_check_raises_in_context(type_, match, message=message) as assertion:\n        assertion.set(check)\n\nasync def assert_check(\n    check: Union[servo.checks.Check, Callable],\n    message: Optional[str] = None,\n    *,\n    _success: bool = True,\n    _exception_type: Optional[Type[Exception]] = None,\n    _exception_match: Optional[str] = None\n) -> None:\n    \"\"\"Assert the outcome of a check matches expectations.\n\n    The check provided can be a previously executed Check object or a coroutine that returns a Check.\n    In the event of an exceptional failure, underlying exceptions are chained to facilitate debugging.\n    This method underlies other higher level semantically named assertions prefixed with `assert_check_`.\n\n    Args:\n        check: The check object or coroutine that returns a check object to be evaluated.\n        message: An optional message to annotate a failing assertion. When omitted, a message is synthesized.\n    \"\"\"\n    if asyncio.iscoroutine(check):\n        result = await check\n        if callable(result):\n            result = await result\n    elif isinstance(check, servo.checks.Check):\n        result = check\n    else:\n        raise TypeError(f\"unknown check: {check}\")\n\n    # NOTE: Take care to chain the exceptions rescued by the Check for attribution\n    if _success is False and result.success is False:\n        # Make sure we failed in the right way\n        if result.exception:\n            if _exception_type or _exception_match:\n                with pytest.raises(_exception_type, match=_exception_match):\n                    raise result.exception\n        elif _exception_type:\n            raise AssertionError(\n                f\"Check(id='{result.id}') '{result.name}' was expected to raise a {_exception_type.__name__} but it did not: {message}\"\n            ) from result.exception\n\n    if result.success != _success:\n        if result.success:\n            raise AssertionError(f\"Check(id='{result.id}') '{result.name}' succeeded when you were expecting a failure\")\n        else:\n            raise AssertionError(\n                f\"Check(id='{result.id}') '{result.name}' failed: {message or result.message}\"\n            ) from result.exception\n\n\n# TODO: Move these into library functions. Do we want replace/merge versions?\nasync def add_annotations_to_podspec_of_deployment(deployment, annotations: Dict[str, str]) -> None:\n    servo.logger.info(f\"adding annotations {annotations} to PodSpec of Deployment '{deployment.name}'\")\n    existing_annotations = deployment.pod_template_spec.metadata.annotations or {}\n    existing_annotations.update(annotations)\n    deployment.pod_template_spec.metadata.annotations = existing_annotations\n    await deployment.patch()\n\n\nasync def add_labels_to_podspec_of_deployment(deployment, labels: List[str]) -> None:\n    servo.logger.info(f\"adding labels {labels} to PodSpec of Deployment '{deployment.name}'\")\n    existing_labels = deployment.pod_template_spec.metadata.labels or {}\n    existing_labels.update(labels)\n    deployment.pod_template_spec.metadata.labels = existing_labels\n    await deployment.patch()\n\n@contextlib.asynccontextmanager\nasync def change_to_resource(resource: servo.connectors.kubernetes.KubernetesModel):\n    status = resource.status\n    metadata = resource.obj.metadata\n\n    if isinstance(resource, servo.connectors.kubernetes.Deployment):\n        async with resource.rollout():\n            yield\n    else:\n        # allow the resource to be changed\n        yield\n\n    await resource.refresh()\n\n    # early exit if nothing changed\n    if (resource.obj.metadata.resource_version == metadata.resource_version):\n        servo.logger.debug(f\"exiting early: metadata resource version has not changed\")\n        return\n\n    if hasattr(status, \"observed_generation\"):\n        while status.observed_generation == resource.status.observed_generation:\n            await resource.refresh()\n\n    # wait for the change to roll out\n    if isinstance(resource, servo.connectors.kubernetes.Deployment):\n        await resource.wait_until_ready()\n    elif isinstance(resource, servo.connectors.kubernetes.Pod):\n        await resource.wait_until_ready()\n    elif isinstance(resource, servo.connectors.kubernetes.Service):\n        pass\n    else:\n        servo.logger.warning(f\"no change observation strategy for Kubernetes resource of type `{resource.__class__.__name__}`\")\n\nclass LoadGenerator(pydantic.BaseModel):\n    request_count: int = 0\n    _request: httpx.Request = pydantic.PrivateAttr()\n    _event: asyncio.Event = pydantic.PrivateAttr(default_factory=asyncio.Event)\n    _task: Optional[asyncio.Task] = pydantic.PrivateAttr(None)\n\n    def __init__(self, target: Union[str, httpx.Request]) -> None:\n        super().__init__()\n        if isinstance(target, httpx.Request):\n            self._request = target\n        elif isinstance(target, str):\n            self._request = httpx.Request(\"GET\", target)\n        else:\n            raise TypeError(f\"unknown target type '{target.__class__.__name__}': expected str or httpx.Request\")\n\n    @property\n    def request(self) -> httpx.Request:\n        return self._request\n\n    @property\n    def url(self) -> str:\n        return self._request.url\n\n    def start(self) -> None:\n        \"\"\"Start sending traffic.\"\"\"\n        if self.is_running:\n            raise RuntimeError(\"Cannot start a load generator that is already running\")\n\n        async def _send_requests() -> None:\n            started_at = datetime.datetime.now()\n            async with httpx.AsyncClient() as client:\n                while not self._event.is_set():\n                    servo.logger.trace(f\"Sending traffic to {self.url}...\")\n                    try:\n                        await client.send(self.request, timeout=1.0)\n                    except httpx.TimeoutException as err:\n                        servo.logger.warning(f\"httpx.TimeoutException encountered sending request {self.request}: {err}\")\n                        pass\n                    self.request_count += 1\n\n            duration = servo.Duration(datetime.datetime.now() - started_at)\n            servo.logger.success(f\"Sent {self.request_count} requests to {self.url} over {duration} seconds.\")\n\n        self._event.clear()\n        self._task = asyncio.create_task(_send_requests())\n\n    @property\n    def is_running(self) -> bool:\n        \"\"\"Return True if traffic is being sent.\"\"\"\n        return self._task is not None\n\n    def stop(self) -> None:\n        \"\"\"Stop sending traffic.\"\"\"\n        self._event.set()\n\n        if self._task:\n            self._task.cancel()\n\n    async def run_until(\n        self,\n        condition: Union[servo.Futuristic, servo.DurationDescriptor],\n        *,\n        timeout: servo.DurationDescriptor = servo.Duration(\"1m\")\n    ) -> None:\n        \"\"\"Send traffic until a condition is met or a timeout expires.\n\n        If the load generator is not already running, it is started.\n\n        Args:\n            condition: A futuristic object (async Task, coroutine, or awaitable)\n                to monitor for completion or a time duration descriptor (e.g. \"30s\",\n                15, 2.5, or a servo.Duration object) to send for a fixed time\n                interval.\n            timeout: A time duration descriptor describing the timeout interval.\n\n        Raises:\n            asyncio.TimeoutError: Raised if the timeout expires before the\n                condition is met.\n        \"\"\"\n        if servo.isfuturistic(condition):\n            future = asyncio.ensure_future(condition)\n        else:\n            # create a sleeping coroutine for the desired duration\n            duration = servo.Duration(condition)\n            future = asyncio.create_task(asyncio.sleep(duration.total_seconds()))\n\n        if not self.is_running:\n            self.start()\n\n        try:\n            duration = servo.Duration(timeout)\n            await asyncio.wait_for(\n                future,\n                timeout=duration.total_seconds()\n            )\n        except asyncio.TimeoutError:\n            servo.logger.error(f\"Timed out after {duration} waiting for condition: {condition}\")\n        finally:\n            self.stop()\n\n            with contextlib.suppress(asyncio.CancelledError):\n                await self._task\n\n@pytest.fixture\ndef load_generator() -> Callable[[Union[str, httpx.Request]], LoadGenerator]:\n    return LoadGenerator\n\nasync def wait_for_check_to_pass(\n    check: Coroutine[None, None, servo.Check],\n    *,\n    timeout: servo.Duration = servo.Duration(\"15s\")\n) -> servo.Check:\n    async def _loop_check() -> servo.Check:\n        while True:\n            result = await check()\n            if result.success:\n                break\n\n        return result\n\n    try:\n        check = await asyncio.wait_for(\n            _loop_check(),\n            timeout=timeout.total_seconds()\n        )\n    except asyncio.TimeoutError as err:\n        servo.logger.error(f\"Check timed out after {timeout}. Final state: {devtools.pformat(check)}\")\n        raise err\n\n    return check\n\nasync def _remedy_check(id: str, *, config, deployment, kube_port_forward, load_generator, checks) -> None:\n    envoy_proxy_port = servo.connectors.opsani_dev.ENVOY_SIDECAR_DEFAULT_PORT\n    servo.logger.warning(f\"Remedying failing check '{id}'...\")\n    if id == 'check_deployment_annotations':\n        ## Step 1\n        servo.logger.critical(\"Step 1 - Annotate the Deployment PodSpec\")\n        async with change_to_resource(deployment):\n            await add_annotations_to_podspec_of_deployment(deployment,\n                {\n                    \"prometheus.opsani.com/path\": \"/stats/prometheus\",\n                    \"prometheus.opsani.com/port\": \"9901\",\n                    \"prometheus.opsani.com/scrape\": \"true\",\n                    \"prometheus.opsani.com/scheme\": \"http\",\n                    \"servo.opsani.com/optimizer\": config.optimizer.id,\n                }\n            )\n\n    elif id == 'check_deployment_labels':\n        # Step 2: Verify the labels are set on the Deployment pod spec\n        servo.logger.critical(\"Step 2 - Label the Deployment PodSpec\")\n        async with change_to_resource(deployment):\n            await add_labels_to_podspec_of_deployment(deployment,\n                {\n                    \"sidecar.opsani.com/type\": \"envoy\",\n                    \"servo.opsani.com/optimizer\": servo.connectors.kubernetes.labelize(config.optimizer.id),\n                }\n            )\n\n    elif id == 'check_deployment_envoy_sidecars':\n        # Step 3\n        servo.logger.critical(\"Step 3 - Inject Envoy sidecar container\")\n        async with change_to_resource(deployment):\n            servo.logger.info(f\"injecting Envoy sidecar to Deployment {deployment.name} PodSpec\")\n            await deployment.inject_sidecar('opsani-envoy', 'opsani/envoy-proxy:latest', service=\"fiber-http\")\n\n    elif id in {'check_prometheus_sidecar_exists', 'check_pod_envoy_sidecars', 'check_prometheus_is_accessible'}:\n        servo.logger.warning(f\"check failed: {id}\")\n        pass\n\n    elif id == 'check_prometheus_targets':\n        # Step 4\n        servo.logger.critical(\"Step 4 - Check that Prometheus is discovering and scraping annotated Pods\")\n        servo.logger.info(\"waiting for Prometheus to scrape our Pods\")\n\n    elif id == 'check_envoy_sidecar_metrics':\n        # Step 5\n        servo.logger.critical(\"Step 5 - Check that traffic metrics are coming in from Envoy\")\n        servo.logger.info(f\"Sending test traffic to Envoy through deploy/fiber-http\")\n        async with kube_port_forward(\"deploy/fiber-http\", envoy_proxy_port) as envoy_url:\n            await load_generator(envoy_url).run_until(\n                wait_for_check_to_pass(\n                    functools.partial(checks.run_one, id=f\"check_envoy_sidecar_metrics\")\n                )\n            )\n\n\n    elif id == 'check_service_proxy':\n        # Step 6\n        servo.logger.critical(\"Step 6 - Proxy Service traffic through Envoy\")\n\n        # Update the port to point to the sidecar\n        service = await servo.connectors.kubernetes.Service.read(\"fiber-http\", config.namespace)\n        service.ports[0].target_port = envoy_proxy_port\n        async with change_to_resource(service):\n            await service.patch()\n\n    elif id == 'check_tuning_is_running':\n        servo.logger.critical(\"Step 7 - Bring tuning Pod online\")\n        async with change_to_resource(deployment):\n            await deployment.ensure_tuning_pod()\n\n    elif id == 'check_traffic_metrics':\n        # Step 8\n        servo.logger.critical(\"Step 8 - Verify Service traffic makes it through Envoy and gets aggregated by Prometheus\")\n        async with kube_port_forward(f\"service/fiber-http\", 80) as service_url:\n            await load_generator(service_url).run_until(\n                wait_for_check_to_pass(\n                    functools.partial(checks.run_one, id=f\"check_traffic_metrics\")\n                )\n            )\n\n    else:\n        raise AssertionError(f\"unhandled check: '{id}'\")\n","sub_path":"tests/connectors/opsani_dev_test.py","file_name":"opsani_dev_test.py","file_ext":"py","file_size_in_byte":42366,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"183949671","text":"import boto3\nimport botocore\nfrom config import S3_BUCKET_NAME, S3_ACCESS_KEY, S3_SECRET_KEY\nimport time\n\ns3 = boto3.client(\n    \"s3\",\n    aws_access_key_id=S3_ACCESS_KEY,\n    aws_secret_access_key=S3_SECRET_KEY\n)\n\n\ndef upload_file_to_s3(file, bucket_name, acl='public-read'):\n    try:\n        s3.upload_fileobj(\n            file,\n            bucket_name,\n            file.filename,\n            ExtraArgs={\n                \"ACL\": acl,\n                \"ContentType\": file.content_type\n            }\n        )\n\n    except Exception as e:\n        return False\n\n    return True\n\n\ndef unique_filename(file):\n    return str(int(time.time())) + file.filename\n","sub_path":"instagram_web/util/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"452400867","text":"import csv\nimport random\n\n##file = open(\"Books.csv\", 'w')\n##header = \"Book,Author,Year Released\\n\"\n##file.write(str(header))\n##book1 = \"To Kill a Mockingbird,Harper Lee,1960\\n\"\n##book2 = \"A Brief History of Time,Stephen Hwaking,1988\\n\"\n##book3 = \"The Great Gatsby,F.Scott Fitzgerald,1922\\n\"\n##book4 = \"The Man Who Mistook His Wife for a Hat,Oliver Sacks,1985\\n\"\n##book5 = \"Pride and Prejudice,Jane Austein,1813\\n\"\n##file.write(book1)\n##file.write(book2)\n##file.write(book3)\n##file.write(book4)\n##file.write(book5)\n##file.close()\n\n\n##file = open(\"Books.csv\", \"a\")\n##title = input(\"Enter a title: \")\n##author = input(\"Enter author: \")\n##year = input(\"Enter the year it was released: \")\n##new_record = title + ',' + author + ',' + year + '\\n'\n##file.write(str(new_record))\n##file.close()\n##\n##file = open(\"Books.csv\", \"r\")\n##ignore = file.readline()\n##for row in file:\n##    print(row)\n##file.close()\n\n\n##file = open(\"Books.csv\", \"a\")\n##count = int(input(\"How many records do you want to add: \"))\n##for i in range(count):\n##    title = input(\"Enter a title: \")\n##    author = input(\"Enter author: \")\n##    year = input(\"Enter the year it was released: \")\n##    record = title + ',' + author + ',' + year + '\\n'\n##    file.write(str(record))\n##file.close()\n##\n##file = open(\"Books.csv\", \"r\")\n##search = input(\"Enter the author youra are searching for: \")\n###reader = csv.reader(file)\n##count = 0\n##for row in file:\n##    if search in str(row):\n##        print(row)\n##        count = count + 1\n##if count == 0:\n##    print(\"There are no books by that author in this list.\")\n##file.close()\n\n\n##file = open(\"Books.csv\", 'r')\n##start_year = int(input(\"Enter a starting year: \"))\n##end_year = int(input(\"Enter an end year: \"))\n##book_list = list(csv.reader(file))\n##\n##for row in book_list[1:]:\n##    if int(row[2]) >= start_year and int(row[2]) <= end_year:\n##        print(row)\n##file.close()\n#---------------------------------------------\n##start = int(input(\"Start year: \"))\n##end = int(input(\"End year: \"))\n##\n##file = list(csv.reader(open(\"Books.csv\")))\n##tmp = []\n###   should be file[1:] to skip first line that contain columns names\n##for row in file[1:]:\n##    tmp.append(row)\n##\n##x = 0\n##for row in tmp:\n##    if int(tmp[x][2]) >= start and int(tmp[x][2]) <= end:\n##        print(tmp[x])\n##    x = x + 1\n    \n\n##file = open(\"Books.csv\", \"r\")\n##x = 1\n##for row in file:\n##    print(x,\"): \", end='')\n##    print(row, end='')\n##    x = x + 1\n\n\n##file = list(csv.reader(open(\"Books.csv\")))\n##tmp = []\n##header = ['Title', 'Author', 'Year\\n']\n##for row in file[1:]:\n##    tmp.append(row)\n##\n##x = 0\n##for row in tmp:\n##    display = x, row\n##    print(display)\n##    x = x + 1\n##\n##delete = int(input(\"Which row would you remove?: \"))\n##del tmp[delete]\n##\n##x = 0\n##for row in tmp:\n##    display = x, row\n##    print(display)\n##    x = x + 1\n##\n##change_ind = int(input(\"Which row would you change?: \"))\n##x = 0\n##for row in tmp[change_ind]:\n##    display = x, tmp[change_ind][x]\n##    x = x + 1\n##\n##part = int(input(\"Which part do you want to change?: \"))\n##new_data = input(\"Enter a new data: \")\n##tmp[change_ind][part] = new_data\n##print(tmp[change_ind])\n##    \n####new_data = []\n####print(\"Input new: \")\n####title = input(\"Title: \")\n####author = input(\"Author: \")\n####year = input(\"Year: \")\n####new_data.append(title)\n####new_data.append(author)\n####new_data.append(year)\n####print(\"New data: \", new_data)\n####\n####tmp[change_ind] = new_data\n####tmp.insert(0,header)\n##\n##x = 0\n##for row in tmp:\n##    display = x, row\n##    print(display)\n##    x = x + 1 \n##\n##file = open(\"Books2.csv\", 'w')\n##for row in tmp:\n##    new_rec = row[0] + ',' + row[1] + ',' + row[2] + '\\n'\n##    file.write(new_rec)\n##file.close()\n\n\n\nscore = 0\nname = input(\"Enter your name: \")\na = random.randint(0,10)\nb = random.randint(0,10)\nquestion1 = str(a) + \" + \" + str(b)\nans1 = int(input(question1 + ' = '))\nif ans1 == (a+b):\n    score = score + 1\na = random.randint(0,10)\nb = random.randint(0,10)\nquestion2 = str(a) + \" + \" + str(b)\nans2 = int(input(question2 + ' = '))\nif ans2 == (a+b):\n    score = score + 1\n\nfile = open(\"Quiz.csv\", 'a')\ndata = name + ',' + question1 + ',' + str(ans1) + ',' + question2 + ',' + str(ans2) + ',' + str(score) + '\\n'\nfile.write(str(data))\nfile.close()\n\n\n\n\n\n\n","sub_path":"Python_By_Examples/ch_14_csv_files.py","file_name":"ch_14_csv_files.py","file_ext":"py","file_size_in_byte":4269,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"178345677","text":"import bisect\nclass Solution:\n    def findRightInterval(self, intervals):\n        \n        if len(intervals)<=1:\n            return [-1]\n        \n        l = len(intervals)\n        ints = sorted([[x,y,i] for i,[x,y] in enumerate(intervals)])\n        begs = [i for i,_,_ in ints]\n        res = [-1]*l\n        \n        for _,y,i in ints:\n            \n            t = bisect.bisect_left(begs, y)\n            if t<l:\n                res[i] = ints[t][2]\n                      \n        return res\n            \nprint(Solution().findRightInterval([ [3,4], [2,3], [1,2] ]))","sub_path":"findRightInterval.py","file_name":"findRightInterval.py","file_ext":"py","file_size_in_byte":564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"238765461","text":"import cv2\r\n\r\n#Our Image\r\nimg_file='CarImage3.jpg'\r\n\r\n#video = cv2.VideoCapture('')\r\nvideo = cv2.VideoCapture('video1.mp4')\r\nvideo1 = cv2.VideoCapture('ped.mp4')\r\n\r\n\r\n#Our pre-trained car classifier\r\nclassifier_file = 'car_detector.xml'\r\n\r\n\r\n# create car classifier\r\ncar_tracker = cv2.CascadeClassifier(classifier_file) #brain\r\npedestrian_tracker = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_fullbody.xml')\r\n\r\n#run forever until car stops or crashes\r\nwhile True:\r\n    #read the current frame\r\n    read_succesful,frame = video1.read() # reads one frame of video\r\n    #read_successful says if the read was successful or not, and frame is what we are interested in, and they are a tuple\r\n    \r\n    \r\n    \r\n    #safe coding\r\n    if read_succesful:\r\n        #must convert to grayscale\r\n        grayscaled_frame = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)\r\n    else:\r\n        break  \r\n\r\n    #detect cars\r\n    cars = car_tracker.detectMultiScale(grayscaled_frame) \r\n    pedestrians = pedestrian_tracker.detectMultiScale(grayscaled_frame) \r\n\r\n\r\n    # Draw rectangles around the cars\r\n    for (x,y,w,h) in cars:\r\n        cv2.rectangle(frame,(x,y),(x+w,y+h),(0,0,255),2)\r\n\r\n    # Draw rectangles around the pedestrians\r\n    for (x,y,w,h) in pedestrians:\r\n        cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,255),2)    \r\n\r\n\r\n\r\n    # Display the image with the cars spotted\r\n    cv2.imshow('video',frame) #pops a window with the image \r\n\r\n    #Don't autoclose\r\n    cv2.waitKey(1) # waits for a key to be pressed to close window\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n#create opencv image\r\nimg = cv2.imread(img_file)\r\n\r\n#convert to grayscale (needed for haarcascade)\r\nblack_n_white = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\r\n\r\n\r\n#detect cars\r\ncars = car_tracker.detectMultiScale(black_n_white)  #multiscale means detect cars of any scale\r\n\r\n\r\n# print(cars)\r\n\r\n# output [top,left,width,height]\r\n# [[178 720  54  54]\r\n#  [814 294  73  73]\r\n#  [881 759  94  94]\r\n#  [190 733  34  34]]\r\n#[x,y,w,h]\r\n\r\n\r\n# Draw rectangles around the cars\r\nfor (x,y,w,h) in cars:\r\n    cv2.rectangle(img,(x,y),(x+w,y+h),(0,40,255),2)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n# Display the image with the cars spotted\r\ncv2.imshow('Car_detector',img) #pops a window with the image \r\n\r\n#Don't autoclose\r\ncv2.waitKey() # waits for a key to be pressed to close window\r\n\r\n\"\"\"\r\n\r\n\r\nprint(\"cc\")","sub_path":"PedestrianTracker.py","file_name":"PedestrianTracker.py","file_ext":"py","file_size_in_byte":2347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"263567859","text":"\"\"\" Convert LaTeX Sentence to SymPy Expression \"\"\"\n# Author: Ken Sible\n# Email:  ksible *at* outlook *dot* com\n\nfrom sympy import Function, Derivative, Symbol, Integer, Rational, Float, Pow, Add\nfrom sympy import sin, cos, tan, sinh, cosh, tanh, asin, acos, atan, asinh, acosh, atanh\nfrom sympy import pi, exp, log, sqrt, expand, diff\nfrom functional import chain, uniquify\nfrom inspect import currentframe\nfrom expr_tree import ExprTree\nimport indexedexp as ixp\nimport re, sys, warnings\n\n# pylint: disable = attribute-defined-outside-init, protected-access, exec-used\nsympy_env = (('sin', sin), ('cos', cos), ('tan', tan), ('sinh', sinh), ('cosh', cosh), ('tanh', tanh),\n    ('asin', asin), ('acos', acos), ('atan', atan), ('asinh', asinh), ('acosh', acosh), ('atanh', atanh),\n    ('pi', pi), ('exp', exp), ('log', log), ('sqrt', sqrt), ('diff', diff))\n\nclass Lexer:\n    \"\"\" LaTeX Lexer\n\n        The following class will tokenize a LaTeX sentence for parsing.\n    \"\"\"\n\n    def __init__(self):\n        greek_pattern = '|'.join(letter for letter in (r'\\\\[aA]lpha', r'\\\\[bB]eta', r'\\\\[gG]amma', r'\\\\[dD]elta',\n            r'\\\\[eE]psilon', r'\\\\[zZ]eta', r'\\\\[eE]ta', r'\\\\[tT]heta', r'\\\\[iI]ota', r'\\\\[kK]appa', r'\\\\[lL]ambda',\n            r'\\\\[mM]u', r'\\\\[nN]u', r'\\\\[xX]i', r'\\\\[oO]mikron', r'\\\\[pP]i', r'\\\\[Rr]ho', r'\\\\[sS]igma', r'\\\\[tT]au',\n            r'\\\\[uU]psilon', r'\\\\[pP]hi', r'\\\\[cC]hi', r'\\\\[pP]si', r'\\\\[oO]mega'))\n        symmetry = r'nosym|(?:sym|anti)[0-9]+(?:_(?:sym|anti)[0-9]+)*'\n        # define a regex pattern for every token, create a named capture group for\n        # every pattern, join together the resulting pattern list using a pipe symbol\n        # for regex alternation, and compile the generated regular expression\n        self.regex = re.compile('|'.join(['(?P<%s>%s)' % pattern for pattern in\n            [ ('SPACE_DELIM',    r'(?:\\s|\\\\,|\\{\\})+|\\&'),\n              ('DIMENSION',      r'[2-4]D'),\n              ('RATIONAL',       r'[0-9]+\\/[1-9]+|\\\\frac{[0-9]+}{[1-9]+}'),\n              ('DECIMAL',        r'[0-9]+\\.[0-9]+'),\n              ('INTEGER',        r'\\-?[0-9]+'),\n              ('NABLA',          r'\\\\nabla'),\n              ('PI',             r'\\\\pi'),\n              ('EULER',          r'e'),\n              ('PLUS',           r'\\+'),\n              ('MINUS',          r'\\-'),\n              ('DIVIDE',         r'\\/'),\n              ('EQUAL',          r'\\='),\n              ('CARET',          r'\\^'),\n              ('COMMA',          r'\\,'),\n              ('COLON',          r'\\:'),\n              ('COMMENT',        r'\\%'),\n              ('LEFT_PAREN',     r'\\('),\n              ('RIGHT_PAREN',    r'\\)'),\n              ('LEFT_BRACE',     r'\\{'),\n              ('RIGHT_BRACE',    r'\\}'),\n              ('LEFT_BRACKET',   r'\\['),\n              ('RIGHT_BRACKET',  r'\\]'),\n              ('BIGL_DELIM',     r'\\\\[bB]igl'),\n              ('BIGR_DELIM',     r'\\\\[bB]igr'),\n              ('LEFT_DELIM',     r'\\\\left'),\n              ('RIGHT_DELIM',    r'\\\\right'),\n              ('LINE_BREAK',     r'(?:\\;|\\\\\\\\|\\\\cr)'),\n              ('BEGIN_ALIGN',    r'\\\\begin{align\\*?}'),\n              ('END_ALIGN',      r'\\\\end{align\\*?}'),\n              ('PARTIAL',        r'\\\\partial'),\n              ('SQRT_CMD',       r'\\\\sqrt'),\n              ('FRAC_CMD',       r'\\\\frac'),\n              ('TRIG_CMD',       r'\\\\sinh|\\\\cosh|\\\\tanh|\\\\sin|\\\\cos|\\\\tan'),\n              ('NLOG_CMD',       r'\\\\ln|\\\\log'),\n              ('VPHANTOM',       r'\\\\vphantom'),\n              ('DEFINE_MACRO',   r'define'),\n              ('ASSIGN_MACRO',   r'assign'),\n              ('PARSE_MACRO',    r'parse'),\n              ('INDEX_KWRD',     r'index'),\n              ('BASIS_KWRD',     r'basis'),\n              ('DERIV_KWRD',     r'deriv'),\n              ('DERIV_TYPE',     r'symbolic|_d'),\n              ('UNDERSCORE',     r'\\_'),\n              ('DIACRITIC',      r'\\\\hat|\\\\tilde|\\\\bar'),\n              ('SYMMETRY',       r'const|metric|permutation|kronecker|' + symmetry),\n              ('MATHOP',         r'\\\\mathop'),\n              ('LETTER',         r'[a-zA-Z]|' + greek_pattern),\n              ('COMMAND',        r'\\\\[a-z]+')]]))\n\n    def initialize(self, sentence, position=0):\n        \"\"\" Initialize Lexer\n\n            :arg: sentence (raw string)\n            :arg: position [default: 0]\n        \"\"\"\n        self.sentence = sentence\n        self.token    = None\n        self.lexeme   = None\n        self.marker   = None\n        self.index    = position\n\n    def tokenize(self):\n        \"\"\" Tokenize Sentence\n\n            :return: token iterator\n        \"\"\"\n        while self.index < len(self.sentence):\n            token = self.regex.match(self.sentence, self.index)\n            if token is None:\n                raise ParseError('unexpected \\'%s\\' at position %d' %\n                    (self.sentence[self.index], self.index), self.sentence, self.index)\n            self.index = token.end()\n            if token.lastgroup not in ('SPACE_DELIM', 'BIGL_DELIM', 'BIGR_DELIM', 'LEFT_DELIM', 'RIGHT_DELIM'):\n                self.lexeme = token.group()\n                yield token.lastgroup\n\n    def lex(self):\n        \"\"\" Retrieve Next Token\n\n            :return: next token\n        \"\"\"\n        try:\n            self.token = next(self.tokenize())\n        except StopIteration:\n            self.token  = None\n            self.lexeme = ''\n        return self.token\n\n    def mark(self):\n        \"\"\" Mark Iterator Position\n\n            :return: previous position\n        \"\"\"\n        self.marker = self.index - len(self.lexeme)\n        return self.marker\n\n    def reset(self):\n        \"\"\" Reset Token Iterator \"\"\"\n        if not self.sentence:\n            raise RuntimeError('cannot reset uninitialized lexer')\n        self.initialize(self.sentence, self.marker)\n        self.lex()\n\nclass Parser:\n    \"\"\" LaTeX Parser\n\n        The following class will parse a tokenized LaTeX sentence.\n\n        LaTeX Extended BNF Grammar:\n        <ROOT>          -> <STRUCTURE> { <LINE_BREAK> <STRUCTURE> }*\n        <STRUCTURE>     -> <CONFIG> | <ENVIRONMENT> | <ASSIGNMENT>\n        <CONFIG>        -> '%' ( <DEFINE> | <ASSIGN> | <PARSE> )\n        <PARSE>         -> <PARSE_MACRO> <ASSIGNMENT>\n        <ENVIRONMENT>   -> <BEGIN_ALIGN> <ASSIGNMENT> { <LINE_BREAK> <ASSIGNMENT> }* <END_ALIGN>\n        <ASSIGNMENT>    -> ( <TENSOR> | <COVDRV> ) = <EXPRESSION>\n        <EXPRESSION>    -> <TERM> { ( '+' | '-' ) <TERM> }*\n        <TERM>          -> <FACTOR> { [ '/' ] <FACTOR> }*\n        <FACTOR>        -> ( <BASE> | <EULER> ) { '^' <EXPONENT> }*\n        <BASE>          -> [ '-' ] ( <ATOM> | '(' <EXPRESSION> ')' )\n        <EXPONENT>      -> <BASE> | '{' <BASE> '}' | '{{' <BASE> '}}'\n        <ATOM>          -> <NUMBER> | <TENSOR> | <COMMAND> | <OPERATOR>\n        <NUMBER>        -> <RATIONAL> | <DECIMAL> | <INTEGER> | <PI>\n        <COMMAND>       -> <SQRT> | <FRAC> | <NLOG> | <TRIG>\n        <OPERATOR>      -> <PARDRV> | <COVDRV>\n        <SQRT>          -> <SQRT_CMD> [ '[' <INTEGER> ']' ] '{' <EXPRESSION> '}'\n        <FRAC>          -> <FRAC_CMD> '{' <EXPRESSION> '}' '{' <EXPRESSION> '}'\n        <NLOG>          -> <NLOG_CMD> [ '_' ( <INTEGER> | { <INTEGER> } ) ] ( <TENSOR> | <INTEGER> | '(' <EXPRESSION> ')' )\n        <TRIG>          -> <TRIG_CMD> [ '^' ( <INTEGER> | { <INTEGER> } ) ] ( <TENSOR> | <INTEGER> | '(' <EXPRESSION> ')' )\n        <PARDRV>        -> [ <VPHANTOM> '{' <DERIV_TYPE> '}' ] { <PARTIAL> [ '^' <INTEGER> ] '_' <LETTER> }+ ( <TENSOR> | '(' <EXPRESSION> ')' )\n        <COVDRV>        -> [ <VPHANTOM> '{' <DERIV_TYPE> '}' ] { ( <NABLA> | <DIACRITIC> '{' <NABLA> '}' ) ( '^' | '_' ) <LETTER> }+ ( <TENSOR> | '(' <EXPRESSION> ')' )\n        <DEFINE>        -> <DEFINE_MACRO> ( <GLOBAL> | [ <SYMMETRY> ] { <SYMBOL> }+ [ '(' <DIMENSION> ')' ] ) { ',' ( <GLOBAL> | [ <SYMMETRY> ] { <SYMBOL> }+ [ '(' <DIMENSION> ')' ] ) }*\n        <GLOBAL>        -> <BASIS_KWRD> <BASIS> | <DERIV_KWRD> <DERIV_TYPE> | <INDEX_KWRD> <RANGE>\n        <BASIS>         -> <BASIS_KWRD> <LEFT_BRACKET> <LETTER> [ ',' <LETTER> ]* <RIGHT_BRACKET>\n        <RANGE>         -> ( <LETTER> | '[' <LETTER> '-' <LETTER> ']' ) '=' <INTEGER> ':' <INTEGER>\n        <SYMBOL>        -> <LETTER> | <DIACRITIC> '{' <LETTER> '}' | <MATHOP> '{' <LETTER> { <LETTER> | <INTEGER> | <UNDERSCORE> }* '}'\n        <TENSOR>        -> <SYMBOL> [ ( '_' <LOWER_INDEX> ) | ( '^' <UPPER_INDEX> [ '_' <LOWER_INDEX> ] ) ]\n        <LOWER_INDEX>   -> <LETTER> | <INTEGER> | '{' { <LETTER> | <INTEGER> }* [ ',' { <LETTER> }+ ] '}'\n        <UPPER_INDEX>   -> <LETTER> | <INTEGER> | '{' { <LETTER> | <INTEGER> }+ '}'\n    \"\"\"\n\n    _namespace = {}\n    continue_parsing = True\n\n    def __init__(self, debug=False):\n        self.lexer = Lexer()\n        if 'basis' not in self._namespace:\n            self._namespace['basis'] = []\n        if 'deriv' not in self._namespace:\n            self._namespace['deriv'] = 'symbolic'\n        if 'index' not in self._namespace:\n            self._namespace['index'] = {chr(105 + n): (0, 3) for n in range(4)}\n        def excepthook(exception_type, exception, traceback):\n            if not debug:\n                print('%s: %s' % (exception_type.__name__, exception))\n            else: sys.__excepthook__(exception_type, exception, traceback)\n        sys.excepthook = excepthook\n\n    def parse(self, sentence, expression=False):\n        \"\"\" Parse LaTeX Sentence\n\n            :arg:    latex sentence (raw string)\n            :arg:    expression mode [default: disabled]\n            :return: namespace or expression\n        \"\"\"\n        stack = []; i_1 = i_2 = i_3 = 0\n        for i, lexeme in enumerate(sentence):\n            if   lexeme == '(': stack.append(i)\n            elif lexeme == ')': i_1, i_2 = stack.pop(), i + 1\n            elif lexeme == ',' and sentence[i - 1] == '{':\n                i_3 = sentence.find('}', i) + 1\n                subexpr, indexing = sentence[i_1:i_2], sentence[i_2:i_3][3:-1]\n                operator = ''.join('\\\\partial_' + index for index in indexing)\n                sentence = sentence.replace(sentence[i_1:i_3], operator + ' ' + subexpr)\n            elif lexeme == ';' and sentence[i - 1] == '{':\n                i_3 = sentence.find('}', i) + 1\n                subexpr, indexing = sentence[i_1:i_2], sentence[i_2:i_3][3:-1]\n                operator = ''.join('\\\\nabla_' + index for index in indexing)\n                sentence = sentence.replace(sentence[i_1:i_3], operator + ' ' + subexpr)\n        self.lexer.initialize(sentence)\n        self.lexer.lex()\n        if expression:\n            tree = ExprTree(self._expression())\n            for subtree in tree.preorder():\n                subexpr, rank = subtree.expr, len(subtree.expr.args)\n                if subexpr.func == Function('Tensor') and rank == 1:\n                    subtree.expr = subexpr.args[0]\n                    del subtree.children[:]\n            return tree.reconstruct()\n        self._root()\n        return self._namespace\n\n    # <ROOT> -> <STRUCTURE> { <LINE_BREAK> <STRUCTURE> }*\n    def _root(self):\n        self._structure()\n        while self.accept('LINE_BREAK') or self.peek('COMMENT'):\n            self._structure()\n\n    # <STRUCTURE> -> <CONFIG> | <ENVIRONMENT> | <ASSIGNMENT>\n    def _structure(self):\n        if self.peek('COMMENT'):\n            self._config()\n        elif self.peek('BEGIN_ALIGN'):\n            self._environment()\n        else: self._assignment()\n\n    # <CONFIG> -> '%' ( <DEFINE> | <ASSIGN> | <PARSE> )\n    def _config(self):\n        self.expect('COMMENT')\n        if self.peek('PARSE_MACRO'):\n            self._parse()\n        elif self.peek('DEFINE_MACRO'):\n            self._define()\n        elif self.peek('ASSIGN_MACRO'):\n            self._assign()\n        else:\n            sentence, position = self.lexer.sentence, self.lexer.mark()\n            raise ParseError('unsupported macro at position %d' %\n                position, sentence, position)\n\n    # <PARSE> -> <PARSE_MACRO> <ASSIGNMENT>\n    def _parse(self):\n        self.expect('PARSE_MACRO')\n        self._assignment()\n\n    # <ENVIRONMENT> -> <BEGIN_ALIGN> <ASSIGNMENT> { <LINE_BREAK> <ASSIGNMENT> }* <END_ALIGN>\n    def _environment(self):\n        self.expect('BEGIN_ALIGN')\n        self._assignment()\n        while self.accept('LINE_BREAK'):\n            self._assignment()\n        self.expect('END_ALIGN')\n\n    # <ASSIGNMENT> -> ( <TENSOR> | <COVDRV> ) = <EXPRESSION>\n    def _assignment(self):\n        covdrv = self.peek('NABLA')\n        self.lexer.mark()\n        if self.accept('DIACRITIC'):\n            self.expect('LEFT_BRACE')\n            covdrv = self.peek('NABLA')\n            self.lexer.reset()\n        LHS = self._covdrv('LHS') if covdrv else self._tensor()\n        indexed = LHS.func == Function('Tensor') and len(LHS.args) > 1\n        self.expect('EQUAL')\n        tree = ExprTree(self._expression())\n        if not indexed:\n            for subtree in tree.preorder():\n                subexpr, rank = subtree.expr, len(subtree.expr.args)\n                if subexpr.func == Function('Tensor') and rank > 1:\n                    indexed = True\n        if indexed:\n            function, RHS = LHS, expand(tree.root.expr)\n            # perform implied summation on indexed expression\n            (LHS, RHS), dimension = self._summation(LHS, RHS)\n            global_env = dict(sympy_env)\n            global_env.update(self._namespace)\n            for key in global_env:\n                if isinstance(global_env[key], Tensor):\n                    global_env[key] = global_env[key].structure\n                if isinstance(global_env[key], Function('Constant')):\n                    global_env[key] = global_env[key].args[0]\n            # evaluate each implied summation and update namespace\n            exec('%s = %s' % (LHS, RHS), global_env)\n            symbol = LHS.split('[')[0]\n            tensor = Tensor(function, dimension, global_env[symbol])\n            self._namespace.update({symbol: tensor})\n        else:\n            # replace each tensor function with symbol whenever scalar\n            for subtree in tree.preorder():\n                subexpr, rank = subtree.expr, len(subtree.expr.args) - 1\n                if subexpr.func in (Function('Tensor'), Function('Constant')):\n                    if rank == 0:\n                        subtree.expr = subexpr.args[0]\n                        del subtree.children[:]\n            self._namespace.update({str(LHS.args[0]): tree.reconstruct()})\n\n    # <EXPRESSION> -> <TERM> { ( '+' | '-' ) <TERM> }*\n    def _expression(self):\n        expr = self._term()\n        while self.peek('PLUS') or self.peek('MINUS'):\n            if self.accept('PLUS'):\n                expr += self._term()\n            elif self.accept('MINUS'):\n                expr -= self._term()\n        return expr\n\n    # <TERM> -> <FACTOR> { [ '/' ] <FACTOR> }*\n    def _term(self):\n        expr = self._factor()\n        while any(self.peek(token) for token in ('LEFT_PAREN', 'PARTIAL',\n                'LETTER', 'RATIONAL', 'DECIMAL', 'INTEGER', 'NABLA', 'PI', 'EULER', 'DIACRITIC',\n                'DIVIDE', 'COMMAND', 'SQRT_CMD', 'FRAC_CMD', 'TRIG_CMD', 'NLOG_CMD', 'VPHANTOM')):\n            if self.accept('DIVIDE'):\n                expr /= self._factor()\n            else: expr *= self._factor()\n        return expr\n\n    # <FACTOR> -> ( <BASE> | <EULER> ) { '^' <EXPONENT> }*\n    def _factor(self):\n        stack = ['e'] if self.accept('EULER') else [self._base()]\n        while self.accept('CARET'):\n            stack.append(self._exponent())\n        expr = stack.pop()\n        for subexpr in reversed(stack):\n            expr = exp(expr) if subexpr == 'e' else subexpr ** expr\n        return expr\n\n    # <BASE> -> [ '-' ] ( <ATOM> | '(' <EXPRESSION> ')' )\n    def _base(self):\n        sign = -1 if self.accept('MINUS') else 1\n        if self.accept('LEFT_PAREN'):\n            expr = sign * self._expression()\n            self.expect('RIGHT_PAREN')\n            return expr\n        return sign * self._atom()\n\n    # <EXPONENT> -> <BASE> | '{' <BASE> '}' | '{{' <BASE> '}}'\n    def _exponent(self):\n        if self.accept('LEFT_BRACE'):\n            if self.accept('LEFT_BRACE'):\n                base = self._base()\n                self.expect('RIGHT_BRACE')\n            else: base = self._base()\n            self.expect('RIGHT_BRACE')\n            return base\n        return self._base()\n\n    # <ATOM> -> <NUMBER> | <TENSOR> | <COMMAND> | <OPERATOR>\n    def _atom(self):\n        self.lexer.mark()\n        if self.accept('DIACRITIC'):\n            self.expect('LEFT_BRACE')\n            if self.peek('NABLA'):\n                self.lexer.reset()\n                return self._operator()\n            self.lexer.reset()\n        if any(self.peek(token) for token in\n                ('RATIONAL', 'DECIMAL', 'INTEGER', 'PI')):\n            return self._number()\n        if any(self.peek(token) for token in\n                ('LETTER', 'DIACRITIC', 'MATHOP')):\n            sentence, position = self.lexer.sentence, self.lexer.mark()\n            tensor = Tensor(self._tensor(), None)\n            symbol, indexing = tensor.symbol, tensor.indexing\n            if symbol[:5] == 'Gamma': # reserved keyword for christoffel symbol\n                metric = 'gamma' if symbol[5:] == 'tilde' else 'g'\n                if (metric + symbol[5:-3] + 'DD') not in self._namespace:\n                    raise ParseError('cannot generate christoffel symbol without defined metric \\'%s\\'' %\n                        (metric + symbol[5:-3]), sentence, position)\n                sentence, position = self.lexer.sentence, self.lexer.mark()\n                self.parse(self._generate_christoffel(tensor.function))\n                self.lexer.initialize(sentence, position)\n                self.lexer.lex()\n            if symbol not in self._namespace:\n                if indexing:\n                    raise ParseError('cannot index undefined tensor \\'%s\\' at position %d' %\n                        (symbol, position), sentence, position)\n                self._define_tensor(Tensor(tensor.function, 0))\n            return tensor.function\n        if any(self.peek(token) for token in\n                ('COMMAND', 'SQRT_CMD', 'FRAC_CMD', 'NLOG_CMD', 'TRIG_CMD')):\n            return self._command()\n        if any(self.peek(token) for token in\n                ('VPHANTOM', 'PARTIAL', 'NABLA')):\n            return self._operator()\n        sentence, position = self.lexer.sentence, self.lexer.mark()\n        raise ParseError('unexpected \\'%s\\' at position %d' %\n            (sentence[position], position), sentence, position)\n\n    # <NUMBER> -> <RATIONAL> | <DECIMAL> | <INTEGER> | <PI>\n    def _number(self):\n        number = self.lexer.lexeme\n        if self.accept('RATIONAL'):\n            rational = re.match(r'([1-9][0-9]*)\\/([1-9][0-9]*)', number)\n            if not rational:\n                rational = re.match(r'\\\\frac{([0-9]+)}{([1-9]+)}', number)\n            return Rational(rational.group(1), rational.group(2))\n        if self.accept('DECIMAL'):\n            return Float(number)\n        if self.accept('INTEGER'):\n            return Integer(number)\n        if self.accept('PI'):\n            return pi\n        sentence, position = self.lexer.sentence, self.lexer.mark()\n        raise ParseError('unexpected \\'%s\\' at position %d' %\n            (sentence[position], position), sentence, position)\n\n    # <COMMAND> -> <SQRT> | <FRAC> | <NLOG> | <TRIG>\n    def _command(self):\n        command = self.lexer.lexeme\n        if self.peek('SQRT_CMD'):\n            return self._sqrt()\n        if self.peek('FRAC_CMD'):\n            return self._frac()\n        if self.peek('NLOG_CMD'):\n            return self._nlog()\n        if self.peek('TRIG_CMD'):\n            return self._trig()\n        sentence, position = self.lexer.sentence, self.lexer.mark()\n        raise ParseError('unsupported command \\'%s\\' at position %d' %\n            (command, position), sentence, position)\n\n    # <OPERATOR> -> <PARDRV> | <COVDRV>\n    def _operator(self):\n        operator = self.lexer.lexeme\n        self.lexer.mark()\n        if self.accept('VPHANTOM'):\n            self.lexer.lex()\n            self.lexer.lex()\n            self.lexer.lex()\n            operator = self.lexer.lexeme\n            if self.peek('PARTIAL'):\n                self.lexer.reset()\n                return self._pardrv()\n            if self.peek('NABLA') or self.peek('DIACRITIC'):\n                self.lexer.reset()\n                return self._covdrv('RHS')\n            self.lexer.reset()\n        if self.peek('PARTIAL'):\n            return self._pardrv()\n        if self.peek('NABLA') or self.peek('DIACRITIC'):\n            return self._covdrv('RHS')\n        sentence, position = self.lexer.sentence, self.lexer.mark()\n        raise ParseError('unsupported operator \\'%s\\' at position %d' %\n            (operator, position), sentence, position)\n\n    # <SQRT> -> <SQRT_CMD> [ '[' <INTEGER> ']' ] '{' <EXPRESSION> '}'\n    def _sqrt(self):\n        self.expect('SQRT_CMD')\n        if self.accept('LEFT_BRACKET'):\n            integer = self.lexer.lexeme\n            self.expect('INTEGER')\n            root = Rational(1, integer)\n            self.expect('RIGHT_BRACKET')\n        else: root = Rational(1, 2)\n        self.expect('LEFT_BRACE')\n        expr = self._expression()\n        self.expect('RIGHT_BRACE')\n        if root == Rational(1, 2):\n            return sqrt(expr)\n        return Pow(expr, root)\n\n    # <FRAC> -> <FRAC_CMD> '{' <EXPRESSION> '}' '{' <EXPRESSION> '}'\n    def _frac(self):\n        self.expect('FRAC_CMD')\n        self.expect('LEFT_BRACE')\n        numerator = self._expression()\n        self.expect('RIGHT_BRACE')\n        self.expect('LEFT_BRACE')\n        denominator = self._expression()\n        self.expect('RIGHT_BRACE')\n        return numerator / denominator\n\n    # <NLOG> -> <NLOG_CMD> [ '_' ( <INTEGER> | { <INTEGER> } ) ] ( <TENSOR> | <INTEGER> | '(' <EXPRESSION> ')' )\n    def _nlog(self):\n        func = self._strip(self.lexer.lexeme)\n        self.expect('NLOG_CMD')\n        if func == 'log':\n            if self.accept('UNDERSCORE'):\n                if self.accept('LEFT_BRACE'):\n                    base = self.lexer.lexeme\n                    self.expect('INTEGER')\n                    self.expect('RIGHT_BRACE')\n                else:\n                    base = self.lexer.lexeme\n                    self.expect('INTEGER')\n                base = int(base)\n            else: base = 10\n        if self.peek('LETTER'):\n            sentence, position = self.lexer.sentence, self.lexer.mark()\n            tensor = Tensor(self._tensor(), None)\n            symbol, indexing = tensor.symbol, tensor.indexing\n            if symbol not in self._namespace:\n                if indexing:\n                    raise ParseError('cannot index undefined tensor \\'%s\\' at position %d' %\n                        (symbol, position), sentence, position)\n                self._define_tensor(Tensor(tensor.function, 0))\n            expr = tensor.function\n        elif self.peek('INTEGER'):\n            expr = self.lexer.lexeme\n            self.expect('INTEGER')\n        elif self.accept('LEFT_PAREN'):\n            expr = self._expression()\n            self.expect('RIGHT_PAREN')\n        else:\n            sentence, position = self.lexer.sentence, self.lexer.mark()\n            raise ParseError('unexpected \\'%s\\' at position %d' %\n                (sentence[position], position), sentence, position)\n        if func == 'ln': return log(expr)\n        return log(expr, base)\n\n    # <TRIG> -> <TRIG_CMD> [ '^' ( <INTEGER> | { <INTEGER> } ) ] ( <TENSOR> | <INTEGER> | '(' <EXPRESSION> ')' )\n    def _trig(self):\n        func = self._strip(self.lexer.lexeme)\n        self.expect('TRIG_CMD')\n        if self.accept('CARET'):\n            if self.accept('LEFT_BRACE'):\n                exponent = self.lexer.lexeme\n                self.expect('INTEGER')\n                self.expect('RIGHT_BRACE')\n            else:\n                exponent = self.lexer.lexeme\n                self.expect('INTEGER')\n            exponent = int(exponent)\n        else: exponent = 1\n        if   func == 'cosh': trig = acosh if exponent == -1 else cosh\n        elif func == 'sinh': trig = asinh if exponent == -1 else sinh\n        elif func == 'tanh': trig = atanh if exponent == -1 else tanh\n        elif func == 'cos':  trig = acos  if exponent == -1 else cos\n        elif func == 'sin':  trig = asin  if exponent == -1 else sin\n        elif func == 'tan':  trig = atan  if exponent == -1 else tan\n        if self.peek('LETTER'):\n            sentence, position = self.lexer.sentence, self.lexer.mark()\n            tensor = Tensor(self._tensor(), None)\n            symbol, indexing = tensor.symbol, tensor.indexing\n            if symbol not in self._namespace:\n                if indexing:\n                    raise ParseError('cannot index undefined tensor \\'%s\\' at position %d' %\n                        (symbol, position), sentence, position)\n                self._define_tensor(Tensor(tensor.function, 0))\n            expr = tensor.function\n        elif self.peek('INTEGER'):\n            expr = self.lexer.lexeme\n            self.expect('INTEGER')\n        elif self.accept('LEFT_PAREN'):\n            expr = self._expression()\n            self.expect('RIGHT_PAREN')\n        else:\n            sentence, position = self.lexer.sentence, self.lexer.mark()\n            raise ParseError('unexpected \\'%s\\' at position %d' %\n                (sentence[position], position), sentence, position)\n        if exponent == -1: return trig(expr)\n        return trig(expr) ** exponent\n\n    # <PARDRV> -> [ <VPHANTOM> '{' <DERIV_TYPE> '}' ] { <PARTIAL> [ '^' <INTEGER> ] '_' <LETTER> }+ ( <TENSOR> | '(' <EXPRESSION> ')' )\n    def _pardrv(self):\n        deriv_type = self._namespace['deriv']\n        if self.accept('VPHANTOM'):\n            self.expect('LEFT_BRACE')\n            deriv_type = self.lexer.lexeme\n            self.expect('DERIV_TYPE')\n            self.expect('RIGHT_BRACE')\n        indexing, order = [], 1\n        while self.accept('PARTIAL'):\n            if self.accept('CARET'):\n                order = self.lexer.lexeme\n                self.expect('INTEGER')\n            self.expect('UNDERSCORE')\n            index = self._strip(self.lexer.lexeme)\n            self.expect('LETTER')\n            indexing.extend(int(order)*[Symbol(index)])\n        if all(index in self._namespace['basis'] for index in indexing):\n            deriv_type = 'symbolic'\n        if self.accept('LEFT_PAREN'):\n            if deriv_type == 'symbolic':\n                derivative = Derivative(self._expression(), *indexing)\n                self.expect('RIGHT_PAREN')\n                return derivative\n            if deriv_type == '_d':\n                tree = ExprTree(self._expression())\n                self.expect('RIGHT_PAREN')\n                for subtree in tree.preorder():\n                    subexpr = subtree.expr\n                    if subexpr.func == Function('Tensor'):\n                        # insert temporary symbol '_x' for symbolic differentiation\n                        subtree.expr = Function('_Tensor')(subexpr, Symbol('_x'))\n                        del subtree.children[:]\n                expr = tree.reconstruct()\n                # differentiate the expression, including product rule expansion\n                tree = ExprTree(diff(expr, Symbol('_x')))\n                for subtree in tree.preorder():\n                    subexpr = subtree.expr\n                    if subexpr.func == Derivative:\n                        # remove temporary symbol '_x' from tensor function\n                        symbol, order = str(subexpr.args[0].args[0].args[0]), len(indexing)\n                        _indexing = list(subexpr.args[0].args[0].args[1:]) + indexing\n                        if symbol not in self._namespace:\n                            raise ParseError('cannot differentiate undefined tensor \\'%s\\'' %\n                                symbol, self.lexer.sentence)\n                        tensor = self._namespace[symbol]\n                        # instantiate partial derivative and update namespace\n                        symbol = symbol + ('' if '_d' in symbol else '_d') + order * 'D'\n                        function, rank = Function('Tensor')(symbol, *_indexing), len(_indexing) - order\n                        symmetry = 'sym%d%d' % (rank, rank + order - 1) if order == 2 else 'nosym'\n                        self._define_tensor(Tensor(function, tensor.dimension), symmetry)\n                        subtree.expr = function\n                        del subtree.children[:]\n                    elif subexpr.func == Function('_Tensor'):\n                        # remove temporary symbol '_x' from tensor function\n                        subtree.expr = subexpr.args[0]\n                        del subtree.children[:]\n                return tree.reconstruct()\n        if deriv_type == 'symbolic':\n            return Derivative(self._tensor(), *indexing)\n        sentence, position = self.lexer.sentence, self.lexer.mark()\n        function = self._tensor()\n        symbol, order = str(function.args[0]), len(indexing)\n        indexing = list(function.args[1:]) + indexing\n        if symbol not in self._namespace:\n            raise ParseError('cannot differentiate undefined tensor \\'%s\\' at position %d' %\n                (symbol, position), sentence, position)\n        tensor = self._namespace[symbol]\n        # instantiate partial derivative and update namespace\n        symbol = symbol + ('' if '_d' in symbol else '_d') + order * 'D'\n        function, rank = Function('Tensor')(symbol, *indexing), len(indexing) - order\n        symmetry = 'sym%d%d' % (rank, rank + order - 1) if order == 2 else 'nosym'\n        self._define_tensor(Tensor(function, tensor.dimension), symmetry)\n        return function\n\n    # <COVDRV> -> [ <VPHANTOM> '{' <DERIV_TYPE> '}' ] { ( <NABLA> | <DIACRITIC> '{' <NABLA> '}' ) ( '^' | '_' ) <LETTER> }+ ( <TENSOR> | '(' <EXPRESSION> ')' )\n    def _covdrv(self, location):\n        deriv_type = self._namespace['deriv']\n        if self.accept('VPHANTOM'):\n            self.expect('LEFT_BRACE')\n            deriv_type = self.lexer.lexeme\n            self.expect('DERIV_TYPE')\n            self.expect('RIGHT_BRACE')\n        indexing, equation, diacritic = [], ['', ' = ', '', ''], ''\n        sentence, position = self.lexer.sentence, self.lexer.mark()\n        while self.peek('NABLA') or self.peek('DIACRITIC'):\n            lexeme = self._strip(self.lexer.lexeme)\n            operator = '\\\\nabla'\n            if self.accept('DIACRITIC'):\n                diacritic = lexeme\n                operator = '\\\\%s{\\\\nabla}' % diacritic\n                self.expect('LEFT_BRACE')\n                self.expect('NABLA')\n                self.expect('RIGHT_BRACE')\n            else: self.expect('NABLA')\n            metric = 'gamma' if diacritic == 'tilde' else 'g'\n            if (metric + diacritic + 'DD') not in self._namespace:\n                raise ParseError('cannot generate covariant derivative without defined metric \\'%s\\'' %\n                    (metric + diacritic), sentence, position)\n            equation[0] += operator\n            if deriv_type != 'symbolic':\n                operator = '\\\\vphantom{%s} \\\\nabla' % deriv_type\n            equation[3] += operator\n            if self.accept('CARET'):\n                index = self.lexer.lexeme\n                equation[0] += '^' + index + ' '\n                bound_index = next(x for x in (chr(97 + n) for n in range(26)) if x != index)\n                metric = '\\\\%s{g}' % diacritic if diacritic in ('bar', 'hat') \\\n                    else '\\\\%s{\\\\gamma}' % diacritic if diacritic == 'tilde' \\\n                    else 'g'\n                equation[2] += '%s^{%s %s} ' % (metric, index, bound_index)\n                equation[3] += '_' + bound_index + ' '\n                index = self._strip(index)\n                self.expect('LETTER')\n                indexing.append((Symbol(index), 'U'))\n            elif self.accept('UNDERSCORE'):\n                index = self.lexer.lexeme\n                equation[0] += '_' + index + ' '\n                equation[3] += '_' + index + ' '\n                index = self._strip(index)\n                self.expect('LETTER')\n                indexing.append((Symbol(index), 'D'))\n            else:\n                sentence, position = self.lexer.sentence, self.lexer.mark()\n                raise ParseError('unexpected \\'%s\\' at position %d' %\n                    (sentence[position], position), sentence, position)\n        # instantiate covariant derivative and update namespace\n        marker_1 = self.lexer.mark()\n        function = self._tensor()\n        marker_2 = self.lexer.index\n        equation[0] += self.lexer.sentence[marker_1:marker_2].strip()\n        equation[3] += self.lexer.sentence[marker_1:marker_2].strip()\n        if location == 'RHS':\n            global_deriv = self._namespace['deriv']\n            if equation[2]:\n                sentence, position = self.lexer.sentence, self.lexer.mark()\n                self.parse(''.join(equation))\n                self.lexer.initialize(sentence, position)\n                self.lexer.lex()\n            else:\n                sentence, position = self.lexer.sentence, self.lexer.mark()\n                symbol = str(function.args[0])\n                if symbol not in self._namespace:\n                    sentence, position = self.lexer.sentence, self.lexer.mark()\n                    raise ParseError('cannot differentiate undefined tensor \\'%s\\'' %\n                        (symbol), sentence, position)\n                tensor = Tensor(function, self._namespace[symbol].dimension)\n                self.parse(self._generate_covdrv(tensor, indexing, deriv_type, diacritic))\n                self.lexer.initialize(sentence, position)\n                self.lexer.lex()\n            self._namespace['deriv'] = global_deriv\n        symbol, suffix = str(function.args[0]), ''.join([index[1] for index in indexing])\n        symbol = symbol + ('' if '_cd' in symbol else '_cd' + diacritic) + suffix\n        indexing = list(function.args[1:]) + [index[0] for index in indexing]\n        return Function('Tensor')(symbol, *indexing)\n\n    # <DEFINE> -> <DEFINE_MACRO> ( <GLOBAL> | [ <SYMMETRY> ] { <SYMBOL> }+ [ '(' <INTEGER> ')' ] ) { ',' ( <GLOBAL> | [ <SYMMETRY> ] { <SYMBOL> }+ [ '(' <INTEGER> ')' ] ) }*\n    def _define(self):\n        self.expect('DEFINE_MACRO')\n        while True:\n            if any(self.peek(kwrd) for kwrd in ('BASIS_KWRD', 'DERIV_KWRD', 'INDEX_KWRD')):\n                self._global()\n            else:\n                symmetry = self.lexer.lexeme\n                if self.peek('SYMMETRY'):\n                    self.lexer.lex()\n                else: symmetry = None\n                symbol = []\n                if self.peek('EULER'):\n                    self.lexer.token = 'LETTER'\n                while any(self.peek(token) for token in ('LETTER', 'DIACRITIC', 'MATHOP')):\n                    symbol.append(self._symbol())\n                    if self.peek('EULER'):\n                        self.lexer.token = 'LETTER'\n                symbol = ''.join(symbol)\n                if self.accept('LEFT_PAREN'):\n                    dimension = self.lexer.lexeme[:-1]\n                    self.expect('DIMENSION')\n                    dimension = int(dimension)\n                    self.expect('RIGHT_PAREN')\n                else: dimension = None\n                if symmetry != 'const' and not dimension:\n                    raise TensorError('dimension only omittable for constant')\n                if symmetry == 'const':\n                    self._namespace[symbol] = Function('Constant')(Symbol(symbol))\n                else:\n                    tensor = Tensor(Function('Tensor')(symbol), dimension)\n                    self._define_tensor(tensor, symmetry, invertible=(symmetry == 'metric'),\n                        permutation=(symmetry == 'permutation'), kronecker=(symmetry == 'kronecker'))\n            if not self.accept('COMMA'): break\n\n    # <GLOBAL> -> <BASIS_KWRD> <BASIS> | <DERIV_KWRD> <DERIV_TYPE> | <INDEX_KWRD> <RANGE>\n    def _global(self):\n        if self.accept('BASIS_KWRD'):\n            self._basis()\n        elif self.accept('DERIV_KWRD'):\n            deriv_type = self.lexer.lexeme\n            self.expect('DERIV_TYPE')\n            self._namespace['deriv'] = deriv_type\n        elif self.accept('INDEX_KWRD'):\n            self._range()\n        else:\n            sentence, position = self.lexer.sentence, self.lexer.mark()\n            raise ParseError('unexpected keyword at position %d' %\n                position, sentence, position)\n\n    # <BASIS> -> <BASIS_KWRD> <LEFT_BRACKET> <LETTER> [ ',' <LETTER> ]* <RIGHT_BRACKET>\n    def _basis(self):\n        self.expect('LEFT_BRACKET')\n        while True:\n            symbol = self._strip(self.lexer.lexeme)\n            self.expect('LETTER')\n            if symbol in self._namespace['basis']:\n                sentence, position = self.lexer.sentence, self.lexer.mark()\n                raise ParseError('duplicate basis symbol \\'%s\\' at position %d' %\n                    (sentence[position], position), sentence, position)\n            self._namespace['basis'].append(Symbol(symbol))\n            if not self.accept('COMMA'): break\n        self.expect('RIGHT_BRACKET')\n\n    # <RANGE> -> ( <LETTER> | '[' <LETTER> '-' <LETTER> ']' ) '=' <INTEGER> ':' <INTEGER>\n    def _range(self):\n        if self.accept('LEFT_BRACKET'):\n            index_1 = self._strip(self.lexer.lexeme)\n            self.expect('LETTER')\n            self.expect('MINUS')\n            index_2 = self._strip(self.lexer.lexeme)\n            self.expect('LETTER')\n            index = [chr(i) for i in range(ord(index_1), ord(index_2) + 1)]\n            self.expect('RIGHT_BRACKET')\n        else:\n            index = [self._strip(self.lexer.lexeme)]\n            self.expect('LETTER')\n        self.expect('EQUAL')\n        lower = self.lexer.lexeme\n        self.expect('INTEGER')\n        self.expect('COLON')\n        upper = self.lexer.lexeme\n        self.expect('INTEGER')\n        self._namespace['index'].update({i: (int(lower), int(upper) + 1) for i in index})\n\n    # <ASSIGN> -> <ASSIGN_MACRO> [ <SYMMETRY> ] { <SYMBOL> }+\n    def _assign(self):\n        self.expect('ASSIGN_MACRO')\n        symmetry = self.lexer.lexeme\n        if self.peek('SYMMETRY'):\n            self.lexer.lex()\n        symbol = []\n        if self.peek('EULER'):\n            self.lexer.token = 'LETTER'\n        while any(self.peek(token) for token in ('LETTER', 'DIACRITIC', 'MATHOP')):\n            symbol.append(self._symbol())\n            if self.peek('EULER'):\n                self.lexer.token = 'LETTER'\n        symbol = ''.join(symbol)\n        if symbol not in self._namespace:\n            raise TensorError('cannot update undefined tensor \\'%s\\'' % symbol)\n        tensor = self._namespace[symbol]\n        structure, dimension = tensor.structure, tensor.dimension\n        if symmetry == 'metric':\n            inverse_symbol = symbol.replace('U', 'D') if 'U' in symbol else symbol.replace('D', 'U')\n            if dimension == 2:\n                inverse, determinant = ixp.symm_matrix_inverter2x2(structure)\n            elif dimension == 3:\n                inverse, determinant = ixp.symm_matrix_inverter3x3(structure)\n            elif dimension == 4:\n                inverse, determinant = ixp.symm_matrix_inverter4x4(structure)\n            _symbol = symbol.replace('U', 'D') if 'U' in symbol else symbol.replace('D', 'U')\n            function = Function('Tensor')(_symbol, *tensor.function.args[1:])\n            self._namespace[_symbol] = Tensor(function, dimension, inverse)\n            _symbol = symbol[:-2] + 'det'\n            function = Function('Tensor')(_symbol)\n            self._namespace[_symbol] = Tensor(function, 0, determinant \\\n                if symbol[-2:] == 'DD' else (determinant)**(-1))\n            self._namespace.update(self._namespace)\n\n    # <TENSOR> -> <SYMBOL> [ ( '_' <LOWER_INDEX> ) | ( '^' <UPPER_INDEX> [ '_' <LOWER_INDEX> ] ) ]\n    def _tensor(self):\n        indexing = []\n        symbol = list(self._strip(self._symbol()))\n        if self.accept('UNDERSCORE'):\n            index, order, _ = self._lower_index()\n            indexing.extend(index)\n            symbol.extend((len(index) - order) * ['D'])\n            if order > 0:\n                if symbol not in self._namespace:\n                    sentence, position = self.lexer.sentence, self.lexer.mark()\n                    raise ParseError('cannot differentiate undefined tensor \\'%s\\'' %\n                        (symbol), sentence, position)\n                tensor = self._namespace[symbol]\n                symbol = ''.join(symbol) + ('' if '_d' in ''.join(symbol) else '_d') + order * 'D'\n                function, rank = Function('Tensor')(symbol, *indexing), len(indexing) - order\n                symmetry = 'sym%d%d' % (rank, rank + order - 1) if order == 2 else 'nosym'\n                self._define_tensor(Tensor(function, tensor.dimension), symmetry)\n                return function\n        self.lexer.mark()\n        if self.accept('CARET'):\n            if self.accept('LEFT_BRACE'):\n                if self.accept('LEFT_BRACE'):\n                    self.lexer.reset()\n                    symbol = ''.join(symbol)\n                    function = Function('Tensor')(symbol)\n                    if symbol in self._namespace:\n                        if isinstance(self._namespace[symbol], Function('Constant')):\n                            return self._namespace[symbol]\n                    return function\n                self.lexer.reset()\n                self.lexer.lex()\n            index = self._upper_index()\n            indexing.extend(index)\n            symbol.extend(len(index) * ['U'])\n            if self.accept('UNDERSCORE'):\n                index, order, _ = self._lower_index()\n                indexing.extend(index)\n                symbol.extend((len(index) - order) * ['D'])\n                if order > 0:\n                    if symbol not in self._namespace:\n                        sentence, position = self.lexer.sentence, self.lexer.mark()\n                        raise ParseError('cannot differentiate undefined tensor \\'%s\\'' %\n                            symbol, sentence, position)\n                    tensor = self._namespace[symbol]\n                    symbol = ''.join(symbol) + ('' if '_d' in ''.join(symbol) else '_d') + order * 'D'\n                    function, rank = Function('Tensor')(symbol, *indexing), len(indexing) - order\n                    symmetry = 'sym%d%d' % (rank, rank + order - 1) if order == 2 else 'nosym'\n                    self._define_tensor(Tensor(function, tensor.dimension), symmetry)\n                    return function\n        scalar, symbol = len(symbol) == 1, ''.join(symbol)\n        if scalar and symbol in self._namespace:\n            if isinstance(self._namespace[symbol], Function('Constant')):\n                return self._namespace[symbol]\n        return Function('Tensor')(symbol, *indexing)\n\n    # <LOWER_INDEX> -> <LETTER> | <INTEGER> | '{' { <LETTER> | <INTEGER> }* [ ',' { <LETTER> }+ ] '}'\n    def _lower_index(self):\n        indexing, covariant = [], False\n        def append_index():\n            index = self._strip(self.lexer.lexeme)\n            self.lexer.lex()\n            indexing.append(Symbol(index))\n        order = 0\n        if self.peek('LETTER') or self.peek('INTEGER'):\n            append_index()\n            return indexing, order, covariant\n        if self.accept('LEFT_BRACE'):\n            while self.peek('LETTER') or self.peek('INTEGER'):\n                append_index()\n            # TODO: ADD SEMICOLON NOTATION FOR COVARIANT DERIVATIVE\n            if self.accept('COMMA'):\n                while self.peek('LETTER'):\n                    order += 1\n                    append_index()\n            elif self.accept('SEMICOLON'):\n                covariant = True\n                while self.peek('LETTER'):\n                    order += 1\n                    append_index()\n            self.expect('RIGHT_BRACE')\n            return indexing, order, covariant\n        sentence, position = self.lexer.sentence, self.lexer.mark()\n        raise ParseError('unexpected \\'%s\\' at position %d' %\n            (sentence[position], position), sentence, position)\n\n    # <UPPER_INDEX> -> <LETTER> | <INTEGER> | '{' { <LETTER> | <INTEGER> }+ '}'\n    def _upper_index(self):\n        indexing = []\n        def append_index():\n            index = self._strip(self.lexer.lexeme)\n            self.lexer.lex()\n            indexing.append(Symbol(index))\n        if self.peek('LETTER') or self.peek('INTEGER'):\n            append_index()\n            return indexing\n        if self.accept('LEFT_BRACE'):\n            while self.peek('LETTER') or self.peek('INTEGER'):\n                append_index()\n            self.expect('RIGHT_BRACE')\n            return indexing\n        sentence, position = self.lexer.sentence, self.lexer.mark()\n        raise ParseError('unexpected \\'%s\\' at position %d' %\n            (sentence[position], position), sentence, position)\n\n    # <SYMBOL> -> <LETTER> | <DIACRITIC> '{' <LETTER> '}' | <MATHOP> '{' <LETTER> { <LETTER> | <INTEGER> | <UNDERSCORE> }* '}'\n    def _symbol(self):\n        lexeme = self.lexer.lexeme\n        if self.accept('LETTER'):\n            return lexeme\n        if self.accept('DIACRITIC'):\n            self.expect('LEFT_BRACE')\n            symbol = self.lexer.lexeme + lexeme[1:]\n            self.expect('LETTER')\n            self.expect('RIGHT_BRACE')\n            return symbol\n        if self.peek('MATHOP'):\n            self.expect('MATHOP')\n            self.expect('LEFT_BRACE')\n            symbol = [self.lexer.lexeme]\n            if not (self.accept('LETTER') or self.accept('EULER')):\n                sentence, position = self.lexer.sentence, self.lexer.mark()\n                raise ParseError('unexpected \\'%s\\' at position %d' %\n                    (sentence[position], position), sentence, position)\n            while any(self.peek(token) for token in\n                    ('LETTER', 'EULER', 'INTEGER', 'UNDERSCORE')):\n                symbol.extend([self.lexer.lexeme])\n                self.lexer.lex()\n            self.expect('RIGHT_BRACE')\n            return ''.join(symbol).replace('\\\\', '')\n        sentence, position = self.lexer.sentence, self.lexer.mark()\n        raise ParseError('unexpected \\'%s\\' at position %d' %\n            (sentence[position], position), sentence, position)\n\n    def _define_tensor(self, tensor, symmetry=None, invertible=False, permutation=False, kronecker=False):\n        def sgn(sequence):\n            \"\"\" Permutation Signature (Parity)\"\"\"\n            cycle_length = 0\n            for n, i in enumerate(sequence[:-1]):\n                for j in sequence[(n + 1):]:\n                    if i == j: return 0\n                    cycle_length += i > j\n            return (-1)**cycle_length\n        symbol, rank = tensor.symbol, tensor.rank\n        if symbol not in self._namespace:\n            symmetry = 'sym01' if symmetry == 'metric' \\\n                  else None    if symmetry == 'nosym' \\\n                  else symmetry\n            dimension = tensor.dimension\n            if permutation:\n                # instantiate permutation (Levi-Civita) symbol using parity\n                index  = [chr(105 + n) for n in range(rank)]\n                prefix = '[' * rank + 'sgn([' + ', '.join(index) + '])'\n                suffix = ''.join(' for %s in range(%d)]' % (index[rank - i], dimension) for i in range(1, rank + 1))\n                tensor.structure  = eval(prefix + suffix, {'sgn': sgn})\n            elif kronecker:\n                if rank != 2:\n                    raise TensorError('cannot instantiate kronecker delta of rank ' + str(rank))\n                tensor.structure = ixp.declare_indexedexp(rank=rank, dimension=dimension)\n                for i in range(dimension): tensor.structure[i][i] = 1\n            elif rank == 0:\n                tensor.structure = Symbol(symbol)\n            else:\n                tensor.structure = ixp.declare_indexedexp(rank, symbol, symmetry, dimension)\n            if invertible:\n                if rank != 2:\n                    raise TensorError('cannot invert tensor of rank ' + str(rank))\n                if dimension == 2:\n                    inverse, determinant = ixp.symm_matrix_inverter2x2(tensor.structure)\n                elif dimension == 3:\n                    inverse, determinant = ixp.symm_matrix_inverter3x3(tensor.structure)\n                elif dimension == 4:\n                    inverse, determinant = ixp.symm_matrix_inverter4x4(tensor.structure)\n                _symbol = symbol.replace('U', 'D') if 'U' in symbol else symbol.replace('D', 'U')\n                function = Function('Tensor')(_symbol, *tensor.function.args[1:])\n                self._namespace[_symbol] = Tensor(function, dimension, inverse)\n                _symbol = symbol[:-2] + 'det'\n                function = Function('Tensor')(_symbol)\n                self._namespace[_symbol] = Tensor(function, 0, determinant \\\n                    if symbol[-2:] == 'DD' else (determinant)**(-1))\n            if symbol in self._namespace:\n                # pylint: disable=unused-argument\n                def formatwarning(message, category, filename=None, lineno=None, file=None, line=None):\n                    return '%s: %s\\n' % (category.__name__, message)\n                warnings.formatwarning = formatwarning\n                # throw warning whenever duplicate namespace variable\n                warnings.warn(symbol, OverrideWarning)\n            self._namespace[symbol] = tensor\n\n    def _summation(self, LHS, RHS):\n        def replace_function(sentence, subexpr, idx_map):\n            # replace every tensor function with array notation\n            tree = ExprTree(subexpr)\n            for subtree in tree.preorder():\n                subexpr = subtree.expr\n                if subexpr.func == Function('Tensor'):\n                    symbol = str(subexpr.args[0])\n                    dimension = self._namespace[symbol].dimension\n                    tensor = Tensor(subexpr, dimension)\n                    for index in subexpr.args[1:]:\n                        if str(index) in self._namespace['index']:\n                            lower, upper = self._namespace['index'][str(index)]\n                        else: lower, upper = (0, dimension)\n                        if str(index) in idx_map and (lower, upper) != idx_map[str(index)]:\n                            raise ParseError('inconsistent indexing range for index \\'%s\\'' %\n                                index, self.lexer.sentence)\n                        idx_map[str(index)] = (lower, upper)\n                    sentence = sentence.replace(str(subexpr), tensor.array_format())\n                elif subexpr.func == Function('Constant'):\n                    symbol = str(subexpr.args[0])\n                    sentence = sentence.replace(str(subexpr), symbol)\n            return sentence\n        def separate_indexing(subexpr):\n            # extract every index present in the subexpression\n            idx_list = re.findall(r'\\[([a-zA-Z]+)\\]', subexpr)\n            # extract every index position (ex: U or D)\n            pos_list = re.findall(r'[UD]', subexpr)\n            if len(idx_list) != len(pos_list):\n                pos_list.extend((len(idx_list) - len(pos_list)) * ['D'])\n            free_index, bound_index = [], []\n            # iterate over every unique index in the subexpression\n            for idx in uniquify((idx_list)):\n                count = U = D = 0; index_tuple = []\n                # count index occurrence and position occurrence\n                for idx_, pos_ in zip(idx_list, pos_list):\n                    if idx_ == idx:\n                        index_tuple.append((idx_, pos_))\n                        if pos_ == 'U': U += 1\n                        if pos_ == 'D': D += 1\n                        count += 1\n                # identify every bound index on the RHS\n                if count > 1:\n                    if count != 2 or U != D:\n                        # raise exception upon violation of the following rule:\n                        # a bound index must appear exactly once as a superscript\n                        # and exactly once as a subscript in any single term\n                        raise TensorError('illegal bound index')\n                    bound_index.append(idx)\n                # identify every free index on the RHS\n                else: free_index.extend(index_tuple)\n            return uniquify(free_index), bound_index\n        iterable = RHS.args if RHS.func == Add else [RHS]\n        LHS, RHS = Tensor(LHS, None).array_format(), str(RHS)\n        # count every index on LHS to determine the rank\n        rank = len(re.findall(r'\\[[^\\]]+\\]', LHS))\n        # construct a tuple list of every LHS free index\n        free_index_LHS, _ = separate_indexing(LHS)\n        # construct a tuple list of every RHS free index\n        free_index_RHS = []\n        for element in iterable:\n            original, idx_map = str(element), {}\n            if original[0] == '-':\n                original = original[1:]\n            modified = original\n            tree = ExprTree(element)\n            for subtree in tree.preorder():\n                subexpr = subtree.expr\n                if subexpr.func == Derivative:\n                    argument = subexpr.args[0]\n                    derivative = 'diff(' + str(argument)\n                    argument = replace_function(str(argument), argument, idx_map)\n                    free_index, _ = separate_indexing(argument)\n                    for idx, _ in reversed(free_index):\n                        lower, upper = idx_map[idx]\n                        dimension = upper - lower\n                    if not free_index: dimension = 0\n                    for index, order in subexpr.args[1:]:\n                        if str(index) in self._namespace['index']:\n                            lower, upper = self._namespace['index'][str(index)]\n                        else: lower, upper = (0, dimension)\n                        if str(index) in idx_map and (lower, upper) != idx_map[str(index)]:\n                            raise ParseError('inconsistent indexing range for index \\'%s\\'' %\n                                index, self.lexer.sentence)\n                        idx_map[str(index)] = (lower, upper)\n                        if index not in self._namespace['basis']:\n                            if not self._namespace['basis']:\n                                message = 'cannot differentiate symbolically without specifying a basis'\n                                raise ParseError(message, self.lexer.sentence)\n                            derivative += ', (basis[%s], %s)' % (index, order)\n                        else: derivative += ', (%s, %s)' % (index, order)\n                    derivative += ')'\n                    modified = modified.replace(str(subexpr), derivative)\n            modified = replace_function(modified, element, idx_map)\n            free_index, bound_index = separate_indexing(modified)\n            free_index_RHS.append(free_index)\n            # generate implied summation over every bound index\n            for idx in bound_index:\n                lower, upper = idx_map[idx]\n                modified = 'sum(%s for %s in range(%d, %d))' % (modified, idx, lower, upper)\n            RHS = RHS.replace(original, modified)\n        for i in range(len(free_index_RHS)):\n            if sorted(free_index_LHS) != sorted(free_index_RHS[i]):\n                # raise exception upon violation of the following rule:\n                # a free index must appear in every term with the same\n                # position and cannot be summed over in any term\n                raise TensorError('unbalanced free index')\n        # generate tensor instantiation with implied summation\n        for idx, _ in reversed(free_index_LHS):\n            lower, upper = idx_map[idx]\n            RHS = '[%s for %s in range(%d, %d)]' % (RHS, idx, lower, upper)\n            LHS_dimension = upper - lower\n        if not free_index_LHS: LHS_dimension = 0\n        # shift tensor indexing forward whenever dimension > upper bound\n        for subtree in tree.preorder():\n            subexpr = subtree.expr\n            if subexpr.func == Function('Tensor'):\n                symbol = str(subexpr.args[0])\n                dimension = self._namespace[symbol].dimension\n                tensor = Tensor(subexpr, dimension)\n                array_format = tensor.array_format()\n                for index in subexpr.args[1:]:\n                    if str(index) in self._namespace['index']:\n                        _, upper = self._namespace['index'][str(index)]\n                        if dimension > upper:\n                            shift = dimension - upper\n                            for i, (idx, pos) in enumerate(tensor.indexing):\n                                if str(idx) == str(index):\n                                    tensor.indexing[i] = ('%s + %s' % (idx, shift), pos)\n                RHS = RHS.replace(array_format, tensor.array_format())\n        if rank == len(re.findall(r'\\[[^0-9\\]]+\\]', LHS)):\n            return (LHS.split('[')[0], RHS), LHS_dimension\n        LHS_dimension = self._namespace[LHS.split('[')[0]].dimension\n        return (re.sub(r'\\[[^0-9\\]]+\\]', '[:]', LHS), RHS), LHS_dimension\n\n    @staticmethod\n    def _generate_christoffel(function):\n        symbol, indexing = '\\\\' + str(function.args[0])[:-3], function.args[1:]\n        diacritic = 'bar'   if 'bar'   in symbol \\\n               else 'hat'   if 'hat'   in symbol \\\n               else 'tilde' if 'tilde' in symbol \\\n               else None\n        metric = '\\\\%s{g}' % diacritic if diacritic in ('bar', 'hat') \\\n            else '\\\\%s{\\\\gamma}' % diacritic if diacritic == 'tilde' \\\n            else 'g'\n        if diacritic: symbol = '\\\\%s{%s}' % (diacritic, symbol[:-len(diacritic)])\n        indexing = [('\\\\' if len(str(index)) > 1 else '') + str(index) for index in indexing]\n        bound_index = next(x for x in (chr(97 + n) for n in range(26)) if x not in indexing)\n        return (('{symbol}^{i1}_{{{i2}{i3}}} = \\\\frac{{1}}{{2}} {metric}^{{{i1} {bound_index}}}(\\\\partial_{i2} {metric}_{{{i3} {bound_index}}} + \\\\partial_{i3} {metric}_{{{bound_index} {i2}}} - \\\\partial_{bound_index} {metric}_{{{i2} {i3}}})')\n                .format(i1 = indexing[0], i2 = indexing[1], i3 = indexing[2], symbol = symbol, metric = metric, bound_index = bound_index))\n\n    @staticmethod\n    def _generate_covdrv(tensor, deriv_index, deriv_type, diacritic=''):\n        indexing = [str(index[0]) for index in chain(tensor.indexing, deriv_index)]\n        alphabet, order, LHS = (chr(97 + n) for n in range(26)), len(deriv_index), ''\n        for i, index in enumerate(indexing):\n            if index in indexing[:i]:\n                indexing[i] = next(x for x in alphabet if x not in indexing)\n        for diff_index in indexing[-order:]:\n            if len(diff_index) > 1:\n                diff_index = '\\\\' + diff_index\n            LHS += ('\\\\%s{\\\\nabla}' % diacritic if diacritic else '\\\\nabla') + ('_%s ' % diff_index)\n        LHS += tensor.latex_format()\n        def generate_RHS(symbol, order, indexing):\n            if order == 0:\n                _tensor = Tensor(tensor.function, tensor.dimension)\n                _tensor.indexing = [(index, position)\n                    for index, (_, position) in zip(indexing, tensor.indexing)]\n                return _tensor.latex_format()\n            diff_index, RHS = indexing[len(indexing) - order], ''\n            if len(diff_index) > 1:\n                diff_index = '\\\\' + diff_index\n            latex = generate_RHS(symbol, order - 1, indexing)\n            RHS += '\\\\partial_%s (%s)' % (diff_index, latex)\n            for index, (_, position) in zip(indexing, tensor.indexing):\n                alphabet = (chr(97 + n) for n in range(26))\n                bound_index = next(x for x in alphabet if x not in indexing)\n                latex = generate_RHS(symbol, order - 1,\n                    [bound_index if i == str(index) else i for i in indexing])\n                if len(str(index)) > 1:\n                    index = '\\\\' + str(index)\n                RHS += ' + ' if position == 'U' else ' - '\n                RHS += '\\\\%s{\\\\Gamma}' % diacritic if diacritic else '\\\\Gamma'\n                if position == 'U':\n                    RHS += '^%s_{%s %s} (%s)' % (index, bound_index, diff_index, latex)\n                else:\n                    RHS += '^%s_{%s %s} (%s)' % (bound_index, index, diff_index, latex)\n            return RHS\n        if deriv_type != 'symbolic':\n            LHS = '% define deriv ' + deriv_type + ';\\n' + LHS\n        return LHS + ' = ' + generate_RHS(tensor.symbol, order, indexing)\n\n    @staticmethod\n    def ignore_override():\n        warnings.filterwarnings('ignore', category=OverrideWarning)\n\n    @staticmethod\n    def clear_namespace():\n        Parser._namespace.clear()\n\n    @staticmethod\n    def _strip(symbol):\n        return symbol[1:] if symbol[0] == '\\\\' else symbol\n\n    def peek(self, token):\n        return self.lexer.token == token\n\n    def accept(self, token):\n        if self.peek(token):\n            self.lexer.lex()\n            return True\n        return False\n\n    def expect(self, token):\n        if not self.accept(token):\n            sentence, position = self.lexer.sentence, self.lexer.mark()\n            raise ParseError('expected token %s at position %d' %\n                (token, position), sentence, position)\n\nclass ParseError(Exception):\n    \"\"\" Invalid LaTeX Sentence \"\"\"\n\n    def __init__(self, message, sentence, position=None):\n        if position is not None:\n            length = 0\n            for i, substring in enumerate(sentence.split('\\n')):\n                if position - length <= len(substring):\n                    sentence = substring.strip()\n                    position -= length + i\n                    position += len(sentence) - len(substring)\n                    break\n                length += len(substring)\n            super(ParseError, self).__init__('%s\\n%s^\\n' % (sentence, (12 + position) * ' ') + message)\n        else: super(ParseError, self).__init__(message)\n\nclass Tensor:\n    \"\"\" Tensor Structure \"\"\"\n\n    def __init__(self, function, dimension, structure=None):\n        self.function  = function\n        self.dimension = dimension\n        self.structure = structure\n        self.symbol    = str(function.args[0])\n        location = re.findall(r'[UD]', self.symbol)\n        self.rank      = len(location)\n        self.indexing  = list(zip(function.args[1:], location))\n\n    def array_format(self):\n        \"\"\" Tensor Notation for Array Formatting \"\"\"\n        if not self.indexing:\n            return self.symbol\n        return self.symbol + ''.join(['[' + str(index) + ']' for index, _ in self.indexing])\n\n    def latex_format(self):\n        \"\"\" Tensor Notation for LaTeX Formatting \"\"\"\n        latex = [re.split('[UD]', self.symbol)[0], [], []]\n        U_count, D_count = 0, 0\n        for index, position in self.indexing:\n            index = str(index)\n            if len(index) > 1:\n                index = '\\\\' + index\n            if position == 'U':\n                latex[1].append(index)\n                U_count += 1\n            else:\n                latex[2].append(index)\n                D_count += 1\n        latex[1] = ' '.join(latex[1])\n        latex[2] = ' '.join(latex[2])\n        if U_count > 0:\n            if U_count > 1:\n                latex[1] = '^{' + latex[1] + '}'\n            else: latex[1] = '^' + latex[1]\n        if D_count > 0:\n            if D_count > 1:\n                latex[2] = '_{' + latex[2] + '}'\n            else: latex[2] = '_' + latex[2]\n        return ''.join(latex)\n\n    def __repr__(self):\n        if self.rank == 0 and self.dimension == 0:\n            return 'Scalar(%s)' % self.symbol\n        return 'Tensor(%s, %dD)' % (self.symbol, self.dimension)\n\n    __str__ = __repr__\n\nclass TensorError(Exception):\n    \"\"\" Invalid Tensor Indexing or Dimension \"\"\"\nclass OverrideWarning(UserWarning):\n    \"\"\" Overridden Namespace Variable \"\"\"\n\ndef parse_expr(sentence, verbose=False):\n    \"\"\" Convert LaTeX Sentence to SymPy Expression (Expression Mode)\n\n        :arg: latex sentence (raw string)\n        :arg: verbose mode [default: disabled]\n        :return: expression\n    \"\"\"\n    return Parser(verbose).parse(sentence, expression=True)\n\ndef parse(sentence, verbose=False):\n    \"\"\" Convert LaTeX Sentence to SymPy Expression\n\n        :arg: latex sentence (raw string)\n        :arg: verbose mode [default: disabled]\n        :return: namespace\n    \"\"\"\n    if not Parser.continue_parsing:\n        Parser.clear_namespace()\n    _namespace = Parser._namespace.copy()\n    namespace = Parser(verbose).parse(sentence)\n    kwrd_dict = {}\n    for kwrd in ('basis', 'deriv', 'index'):\n        kwrd_dict[kwrd] = namespace[kwrd]\n        del namespace[kwrd]\n    key_diff = tuple(key for key in namespace if key not in _namespace)\n    # inject updated namespace into the previous stack frame\n    frame = currentframe().f_back\n    for key in namespace:\n        if isinstance(namespace[key], Tensor):\n            frame.f_globals[key] = namespace[key].structure\n        elif isinstance(namespace[key], Function('Constant')):\n            frame.f_globals[key] = namespace[key].args[0]\n        else:\n            frame.f_globals[key] = namespace[key]\n    namespace.update(kwrd_dict)\n    if verbose:\n        return tuple(namespace[key] for key in key_diff)\n    return key_diff\n","sub_path":"latex_parser.py","file_name":"latex_parser.py","file_ext":"py","file_size_in_byte":65451,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"362077358","text":"import unittest\nimport time\nfrom datetime import datetime, timedelta\n\nfrom influxdb import InfluxDBClient\nfrom incursion.client import INDBClient\nfrom incursion.query import InfluxQuery, datetime_to_secs\n\n\ndef geneate_points(from_date, to_date, interval, amount):\n    data = []\n    now = from_date\n    while now <= to_date:\n        data += [[datetime_to_secs(now), 'value'] for x in xrange(0, amount)]\n        now = now + interval\n\n    return data\n\n\n@unittest.skip(\"skipping untill we get influxdb on travis ci\")\nclass TestResponseParser(unittest.TestCase):\n    def setUp(self):\n        self.client = InfluxDBClient('127.0.0.1', 8086, 'root', 'root')\n        self.database_name = 'test_%s' % (time.time())\n        self.client.create_database(self.database_name)\n        self.client.switch_db(self.database_name)\n        now = datetime.utcnow()\n        then = now - timedelta(hours=4)\n        self.points = geneate_points(then, now, timedelta(seconds=10), 10)\n\n        test_data = [{\n            \"name\": \"test\",\n            \"columns\": [\"time\", \"key\"],\n            \"points\": self.points\n        }]\n        self.client.write_points(data=test_data, batch_size=2000)\n\n    def tearDown(self):\n        self.client.delete_database(self.database_name)\n\n    def test_simple(self):\n        query = InfluxQuery.for_series('test').limit(None)\n\n        client = INDBClient(conn=self.client)\n\n        resp = client.result_for_query(query)\n\n        series = resp.get('test')\n\n        self.assertEqual(len(series), len(self.points))\n\n        query = query.limit(10)\n        resp = client.result_for_query(query)\n\n        series = resp.get('test')\n        assert len(series) == 10\n\n        query = InfluxQuery.for_series('test').columns(InfluxQuery.count('key')).limit(None)\n        resp = client.result_for_query(query)\n        series = resp.get('test')\n\n        self.assertEqual(series[0].count, len(self.points))\n\n    def test_groups(self):\n        q = InfluxQuery.for_series('test').columns(InfluxQuery.count('key'))\n        q = q.limit(None)\n        q = q.group_by(InfluxQuery.time('1h'))\n        client = INDBClient(conn=self.client)\n        resp = client.result_for_query(q)\n        series = resp.get('test')\n        assert sum(map(lambda x: x.count, series)) == len(self.points)\n","sub_path":"tests/test_client.py","file_name":"test_client.py","file_ext":"py","file_size_in_byte":2269,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"433808733","text":"#SVC doesent work on the dataset. There are to many samples. LinearSVC works\n#and perfomrs slighly better than naive bayes\nfrom sklearn.svm import SVC\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.model_selection import StratifiedKFold\nimport time\nimport numpy as np\nimport pickle\n\nimport get_data\nimport metrics\n\n\nclass SVC_:\n    def __init__(self):\n        self.data = get_data.Data()\n        self.train_data,\\\n        self.train_target,\\\n        self.test_data,\\\n        self.test_target\\\n        = self.data.fetch_train_data()\n\n\n    def create_model(self, para):\n        '''\n        0 - c\n        1 - gamma\n        2 - shrinking\n        3 - coef0\n        '''\n        vectorizer = TfidfVectorizer()\n\n        clf = SVC(C=para[0], gamma=para[1], kernel='poly', coef0=para[3],\n                degree=2, shrinking=para[2])\n        pipeline = Pipeline([\n            ('vec', vectorizer),\n            ('clf', clf),\n        ])\n\n        return pipeline\n\n\n    def train_model(self, parameters):\n        '''\n        0 - c\n        1 - gamma\n        2 - shrinking\n        3 - coef0\n        '''\n\n        all_para = ('tfidf', *parameters, 1e-3, 'poly', 2)\n        metric = metrics.MetricsAndParameters('svc', all_para)\n\n        if(metric.duplicate()):\n            return\n\n        skf = StratifiedKFold(n_splits=10, shuffle=True)\n        for train_index, test_index in skf.split(self.train_data, self.train_target):\n            #Data\n            train_data = self.train_data[train_index]\n            train_target = self.train_target[train_index]\n            test_data = self.train_data[test_index]\n            test_target = self.train_target[test_index]\n\n            #Training\n            train_time = time.time()\n            model = self.create_model(parameters)\n            model.fit(train_data, train_target)\n            train_time =  time.time() - train_time\n\n            #Prediction\n            prediction_time = time.time()\n            test_results = model.predict(test_data)\n            prediction_time =  time.time() - prediction_time\n\n            #Metric calculation\n            metric.calculate_metrics(test_target, test_results, train_time, prediction_time)\n\n        #save to database\n        metric.store_metrics()\n        metric.store_parameters()\n\n\n    def find_optimal_parameters(self, parameters):\n        for a in parameters[0]:\n            for b in parameters[1]:\n                for c in parameters[2]:\n                    for d in parameters[3]:\n                        self.train_model((a,b,c,d))\n\n\n    def create_and_save_model(self, parameters):\n        train_data = self.train_data\n        train_target = self.train_target\n\n        model = self.create_model(parameters)\n        model.fit(train_data, train_target)\n\n        with open(\"model_svc_poly.pickle\", 'wb') as handle:\n            pickle.dump(model, handle, protocol=pickle.HIGHEST_PROTOCOL)\n\n\ndef main():\n    svc = SVC_()\n\n    #Finding optimal c value\n    c = [i for i in np.arange(0.5,1.5,0.0125)]\n    gamma = [1]\n    shrinking = [True, False]\n    coef0 = [0.0]\n    parameters = \\\n            (c,\n            gamma,\n            shrinking,\n            coef0)\n\n    svc.find_optimal_parameters(parameters)\n\n    #Finding optimal gamma value\n    c = [0.96]\n    gamma = [i for i in np.arange(0.5,1.5,0.0125)]\n    shrinking = [True, False]\n    coef0 = [0.0]\n    parameters = \\\n            (c,\n            gamma,\n            shrinking,\n            coef0)\n\n    svc.find_optimal_parameters(parameters)\n\n    #Finding optimal Coef0 value\n    c = [0.96]\n    gamma = [1.2]\n    shrinking = [True, False]\n    coef0 = [i for i in np.arange(0.00,3,0.0375)]\n    parameters = \\\n            (c,\n            gamma,\n            shrinking,\n            coef0)\n\n    svc.find_optimal_parameters(parameters)\n\n    #para = (0.96, 1.2, False, 0.63)\n    #svc.create_and_save_model(para)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"Model Training/SVC_Poly.py","file_name":"SVC_Poly.py","file_ext":"py","file_size_in_byte":3989,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"18854062","text":"# -*- coding: utf-8 -*-\n\nfrom twisted.internet import reactor\nimport scrapy\nfrom scrapy.crawler import CrawlerRunner\nfrom scrapy.utils.log import configure_logging\nfrom scrapy.settings import Settings\nfrom scrapy.utils.project import get_project_settings\nfrom scrapy.crawler import CrawlerProcess\n\nimport scrapy, json, re, os\nimport urllib.parse\nfrom pyquery import PyQuery as pq\nimport datetime\nimport time\n\nfrom scrapy_redis.spiders import RedisSpider\n\n\nclass CompanySpider(RedisSpider):\n    name = 'company_data'\n    allowed_domains = ['xin.baidu.com']\n\n    def __init__(self,  *args, **kwargs):\n        super(CompanySpider, self).__init__(*args, **kwargs)\n        self.redis_key = \"yellow_page:start_urls\"\n        self.crawler_table_name = \"baidu_directory\"\n\n    def parse(self, response):\n        \"\"\"\n        进入百度信用关键词搜索页\n        :param response:\n        :return:\n        \"\"\"\n        # 获取用户uid，防止后面返回的url错误,开始获取第一页微博\n        # uid = response.meta.get(\"uid\")\n        company_list = response.xpath('//div[@class=\"zx-list-item\"]')\n        if company_list:\n            company_href = response.xpath(\n                '//div[@class=\"zx-list-item\"][1]//a[@class=\"zx-list-item-url\"]/@href').extract_first()\n            # print(company_href)\n            pid_ = company_href.split(\"?\")[1]  # 拿出pid，请求json\n            # print(pid_)\n            url = 'https://xin.baidu.com/detail/basicAjax?{pid}&_={times}&fl=1&castk=LTE%3D'.format(pid=pid_, times=int(\n                time.time() * 1000))\n            # print(url)\n            yield scrapy.Request(url, callback=self.parse_data)\n\n    def parse_data(self, response):\n        res_json = json.loads(response.text)\n        data = res_json['data']\n        item = {}\n        item[\"公司名字\"] = data['entName']\n        item[\"电话\"] = data[\"telephone\"]\n        item[\"邮箱\"] = data['email']\n        item[\"官网\"] = data['claimUrl']\n        item[\"地址\"] = data[\"regAddr\"]\n        item[\"简介\"] = data[\"describe\"]\n        item[\"注册资本\"] = data['regCapital']\n        item[\"实缴资本\"] = data[\"paidinCapital\"]\n        item[\"法定代表人\"] = data[\"legalPerson\"]\n        item[\"经营状态\"] = data[\"openStatus\"]\n        item[\"曾用名\"] = data[\"prevEntName\"]\n        item[\"所属行业\"] = data[\"industry\"]\n        item[\"统一社会信用代码\"] = data[\"unifiedCode\"]\n        item[\"纳税人识别号\"] = data[\"taxNo\"]\n        try:\n            item[\"工商注册号\"] = data[\"licenseNumber\"]\n        except:\n            item[\"工商注册号\"] = ''\n        item[\"组织机构代码\"] = data[\"orgNo\"]\n        item[\"���记机关\"] = data[\"authority\"]\n        item[\"成立日期\"] = data[\"startDate\"]\n        item[\"企业类型\"] = data[\"entType\"]\n        item[\"营业期限\"] = data[\"openTime\"]\n        item[\"行政区划\"] = data[\"district\"]\n        item[\"审核/年检日期\"] = data[\"annualDate\"]\n        item[\"注册地址\"] = data[\"regAddr\"]\n        item[\"经营范围\"] = data[\"scope\"]\n        yield item\n        # company_name = response.xpath('//span[@class=\"entName\"]/text()').extract_first(default='')  # 公司名字\n","sub_path":"项目模块/scarpy-redis 例子/baidu/baidu/spiders/company_data.py","file_name":"company_data.py","file_ext":"py","file_size_in_byte":3163,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"221210431","text":"import gc\nimport os\n\nimport joblib\nimport numpy as np\nimport pytest\nimport qcodes as qc\nfrom qcodes import new_experiment\nfrom qcodes.dataset.data_set import DataSet\nfrom qcodes.dataset.experiment_container import experiments, load_by_id\nfrom qcodes.dataset.measurements import Measurement\nfrom qcodes.instrument.base import Instrument\nfrom qcodes.tests.instrument_mocks import (DummyChannel,\n                                           DummyChannelInstrument,\n                                           DummyInstrument,\n                                           DummyInstrumentWithMeasurement)\nfrom sklearn.dummy import DummyClassifier\n\nimport nanotune as nt\nimport nanotune.tests.data_generator_methods as gm\nfrom nanotune.classification.classifier import Classifier\nfrom nanotune.device.device import Device\nfrom nanotune.device.gate import Gate\nfrom nanotune.tests.data_generator_methods import (\n    DotCurrent, DotSensor, PinchoffCurrent, PinchoffSensor,\n    doubledot_triple_points, generate_coloumboscillation_metadata,\n    generate_coulomboscillations, generate_default_metadata,\n    generate_doubledot_data, generate_doubledot_metadata,\n    generate_pinchoff_data, generate_pinchoff_metadata,\n    populate_db_coulomboscillations, populate_db_doubledots,\n    populate_db_pinchoffs)\n\nfrom .dac_mocks import DummyDAC, DummyDACChannel\nfrom .data_savers import save_1Ddata_with_qcodes, save_2Ddata_with_qcodes\nfrom .mock_classifier import MockClassifer\n\nideal_run_labels = {\n    0: \"pinchoff\",\n    1: \"pinchoff\",\n    2: \"pinchoff\",\n    3: \"pinchoff\",\n    4: \"pinchoff\",\n    5: \"doubledot\",\n}\n\n\n@pytest.fixture(scope=\"function\")\ndef empty_temp_db(tmp_path):\n    global n_experiments\n    n_experiments = 0\n    try:\n        nt.new_database(\"temp.db\", tmp_path)\n        yield\n    finally:\n        gc.collect()\n\n\n@pytest.fixture(scope=\"function\")\ndef second_empty_temp_db(tmp_path):\n    global n_experiments\n    n_experiments = 0\n    try:\n        nt.new_database(\"temp2.db\", tmp_path)\n        yield\n    finally:\n        gc.collect()\n\n\n@pytest.fixture(scope=\"function\")\ndef empty_db_different_folder(tmp_path):\n    global n_experiments\n    n_experiments = 0\n    try:\n        path = os.path.join(str(tmp_path), \"test\")\n        os.mkdir(path)\n        nt.new_database(\"temp2.db\", path)\n        yield\n    finally:\n        gc.collect()\n\n\n@pytest.fixture(scope=\"function\")\ndef experiment(empty_temp_db):\n    e = new_experiment(\"test-experiment\", sample_name=\"test_sample\")\n    try:\n        yield e\n    finally:\n        e.conn.close()\n\n\n@pytest.fixture(scope=\"function\")\ndef second_experiment_second_db(second_empty_temp_db):\n    e = new_experiment(\"test-experiment2\", sample_name=\"test_sample\")\n    try:\n        yield e\n    finally:\n        e.conn.close()\n\n\n@pytest.fixture(scope=\"function\")\ndef experiment_different_db_folder(empty_db_different_folder):\n    e = new_experiment(\"test-experiment-duh\", sample_name=\"test_sample\")\n    try:\n        yield e\n    finally:\n        e.conn.close()\n\n\n@pytest.fixture(name=\"dummy_dmm\", scope=\"function\")\ndef _make_dummy_inst():\n    inst = DummyInstrument(\"dummy_dmm\")\n    inst.dac1.get = lambda: 0.001\n    inst.dac2.get = lambda: 1.4\n    try:\n        yield inst\n    finally:\n        inst.close()\n\n\n@pytest.fixture(name=\"dummy_dac\", scope=\"function\")\ndef _make_dummy_dac():\n    inst = DummyDAC(\"dummy_dac\", DummyDACChannel)\n    try:\n        yield inst\n    finally:\n        inst.close()\n\n\n@pytest.fixture(name=\"dummy_device\", scope=\"function\")\ndef _make_dummy_device(experiment, tmp_path):\n    device = Device(\n        \"test_sample\",\n        \"doubledot_2D\",\n    )\n    try:\n        yield device\n    finally:\n        device.close()\n\n\n@pytest.fixture(scope=\"function\", params=[\"numeric\"])\ndef qc_dataset_pinchoff(experiment, request):\n    \"\"\"\"\"\"\n    datasaver = save_1Ddata_with_qcodes(generate_pinchoff_data, None)\n\n    try:\n        yield datasaver.dataset\n    finally:\n        datasaver.dataset.conn.close()\n\n\n@pytest.fixture(scope=\"function\", params=[\"numeric\"])\ndef nt_dataset_pinchoff(experiment, request):\n    \"\"\"\"\"\"\n    datasaver = save_1Ddata_with_qcodes(\n        generate_pinchoff_data, generate_pinchoff_metadata\n    )\n    try:\n        yield datasaver.dataset\n    finally:\n        datasaver.dataset.conn.close()\n\n\n@pytest.fixture(scope=\"function\", params=[\"numeric\"])\ndef qc_dataset_doubledot(experiment, request):\n    \"\"\"\"\"\"\n    datasaver = save_2Ddata_with_qcodes(generate_doubledot_data, None)\n\n    try:\n        yield datasaver.dataset\n    finally:\n        datasaver.dataset.conn.close()\n\n\n@pytest.fixture(scope=\"function\")\ndef nt_dataset_doubledot(experiment, tmp_path):\n    datasaver = save_2Ddata_with_qcodes(\n        generate_doubledot_data, generate_doubledot_metadata\n    )\n\n    try:\n        yield datasaver.dataset\n    finally:\n        datasaver.dataset.conn.close()\n\n\n@pytest.fixture(scope=\"function\")\ndef experiment_pinchoffs(empty_temp_db):\n    e = new_experiment(\"test_experiment\", sample_name=\"test_sample\")\n    populate_db_pinchoffs()\n    try:\n        yield e\n    finally:\n        e.conn.close()\n\n\n@pytest.fixture(scope=\"function\")\ndef experiment_doubledots(empty_temp_db):\n    e = new_experiment(\"test_experiment\", sample_name=\"test_sample\")\n    populate_db_doubledots()\n    try:\n        yield e\n    finally:\n        e.conn.close()\n\n\n@pytest.fixture(scope=\"function\", params=[\"numeric\"])\ndef nt_dataset_coulomboscillation(experiment, request):\n    \"\"\"\"\"\"\n    datasaver = save_1Ddata_with_qcodes(\n        generate_coulomboscillations, generate_coloumboscillation_metadata\n    )\n    try:\n        yield datasaver.dataset\n    finally:\n        datasaver.dataset.conn.close()\n\n\n@pytest.fixture(scope=\"function\")\ndef experiment_ideal_run(empty_temp_db):\n    e = new_experiment(\"test_tuning_run\", sample_name=\"test_sample\")\n    for run_id in range(len(ideal_run_labels)):\n        label = ideal_run_labels[run_id]\n        if label == \"pinchoff\":\n            _ = save_1Ddata_with_qcodes(\n                generate_pinchoff_data,\n                generate_pinchoff_metadata,\n            )\n        elif label == \"doubledot\":\n            _ = save_2Ddata_with_qcodes(\n                generate_doubledot_data,\n                generate_doubledot_metadata,\n            )\n        else:\n            raise NotImplementedError\n    try:\n        yield e\n    finally:\n        e.conn.close()\n\n\n@pytest.fixture(scope=\"function\")\ndef pinchoff_dmm(dummy_dmm):\n    dummy_dmm.add_parameter(\n        \"po_current\",\n        parameter_class=PinchoffCurrent,\n        initial_value=0,\n        label=\"pinchoff dc current\",\n        unit=\"A\",\n        get_cmd=None,\n        set_cmd=None,\n    )\n\n    dummy_dmm.add_parameter(\n        \"po_sensor\",\n        parameter_class=PinchoffSensor,\n        initial_value=0,\n        label=\"pinchoff sensor\",\n        unit=\"A\",\n        get_cmd=None,\n        set_cmd=None,\n    )\n    try:\n        yield dummy_dmm\n    finally:\n        dummy_dmm.close()\n\n\n@pytest.fixture(scope=\"function\")\ndef dot_dmm(dummy_dmm):\n    dummy_dmm.add_parameter(\n        \"dot_current\",\n        parameter_class=DotCurrent,\n        label=\"dot current\",\n        unit=\"A\",\n        get_cmd=None,\n        set_cmd=None,\n    )\n\n    dummy_dmm.add_parameter(\n        \"dot_sensor\",\n        parameter_class=DotSensor,\n        label=\"dot sensor\",\n        unit=\"A\",\n        get_cmd=None,\n        set_cmd=None,\n    )\n    try:\n        yield dummy_dmm\n    finally:\n        dummy_dmm.close()\n\n\n@pytest.fixture(scope=\"function\")\ndef gate_1(dummy_dac, dummy_device):\n    gate = Gate(\n        dummy_device,\n        dummy_dac,\n        channel_id=1,\n        layout_id=1,\n        name=\"test_gate\",\n        label=\"test_label\",\n        delay=0,\n        max_jump=0.01,\n        ramp_rate=0.2,\n    )\n    yield gate\n\n\n@pytest.fixture(scope=\"function\")\ndef gate_2(dummy_dac, dummy_device):\n    gate = Gate(\n        dummy_device,\n        dummy_dac,\n        channel_id=2,\n        layout_id=2,\n        name=\"test_gate_y\",\n        label=\"test_label_y\",\n        delay=0,\n        max_jump=0.01,\n        ramp_rate=0.2,\n    )\n    yield gate\n\n\n@pytest.fixture(scope=\"function\")\ndef gatecharacterization1D_settings(pinchoff_dmm, gate_1, tmp_path):\n    gate_1.current_valid_range([-0.1, 0])\n    pinchoff_dmm.po_current.gate = gate_1\n    pinchoff_dmm.po_sensor.gate = gate_1\n\n    readout_methods = {\n        \"dc_current\": pinchoff_dmm.po_current,\n        \"dc_sensor\": pinchoff_dmm.po_sensor,\n    }\n    setpoint_settings = {\n        \"voltage_precision\": 0.001,\n        \"parameters_to_sweep\": [gate_1.dc_voltage],\n        \"current_valid_ranges\": [gate_1.current_valid_range()],\n        \"safety_voltage_ranges\": [(-3, 0)],\n    }\n    data_settings = {\n        \"db_name\": \"temp.db\",\n        \"normalization_constants\": {\n            \"dc_current\": [0, 1.2],\n            \"dc_sensor\": [-0.13, 1.1],\n            \"rf\": [0, 1],\n        },\n        \"db_folder\": tmp_path,\n    }\n    tuningstage_settings = {\n        \"readout_methods\": readout_methods,\n        \"setpoint_settings\": setpoint_settings,\n        \"data_settings\": data_settings,\n    }\n    yield tuningstage_settings\n\n\n@pytest.fixture(scope=\"function\")\ndef chargediagram_settings(dot_dmm, tmp_path, gate_1, gate_2):\n    gate_1.current_valid_range([-0.2, -0.1])\n    gate_2.current_valid_range([-0.3, -0.2])\n\n    dot_dmm.dot_current.gate_x = gate_1\n    dot_dmm.dot_current.gate_y = gate_2\n\n    dot_dmm.dot_sensor.gate_x = gate_1\n    dot_dmm.dot_sensor.gate_y = gate_2\n\n    readout_methods = {\n        \"dc_current\": dot_dmm.dot_current,\n        \"dc_sensor\": dot_dmm.dot_sensor,\n    }\n    setpoint_settings = {\n        \"voltage_precision\": 0.001,\n        \"parameters_to_sweep\": [gate_1.dc_voltage, gate_2.dc_voltage],\n        \"current_valid_ranges\": [\n            gate_1.current_valid_range(),\n            gate_2.current_valid_range(),\n        ],\n        \"safety_voltage_ranges\": [(-3, 0), (-3, 0)],\n    }\n    data_settings = {\n        \"db_name\": \"temp.db\",\n        \"segment_db_name\": \"temp_dots_seg.db\",\n        \"segment_db_folder\": tmp_path,\n        \"normalization_constants\": {\n            \"dc_current\": [0, 2],\n            \"dc_sensor\": [-0.32, 3],\n            \"rf\": [0, 1],\n        },\n        \"db_folder\": tmp_path,\n        \"segment_size\": 0.05,\n    }\n\n    tuningstage_settings = {\n        \"readout_methods\": readout_methods,\n        \"setpoint_settings\": setpoint_settings,\n        \"data_settings\": data_settings,\n    }\n    yield tuningstage_settings\n\n\n@pytest.fixture(scope=\"function\")\ndef device_gate_inputs(dummy_dac):\n    gate_parameters = {}\n    for gate_id in range(5):\n        gate_parameters[gate_id] = {\n            \"channel_id\": gate_id,\n            \"dac_instrument\": dummy_dac,\n            \"label\": f\"test_gate{gate_id}\",\n            \"safety_range\": (-2, 0),\n        }\n    ohmic_parameters = {\n        0: {\n            \"dac_instrument\": dummy_dac,\n            \"channel_id\": 10,\n            \"label\": \"test_ohmic\",\n        }\n    }\n    gate_inputs = {\n        \"gate_parameters\": gate_parameters,\n        \"ohmic_parameters\": ohmic_parameters,\n    }\n    yield gate_inputs\n\n\n@pytest.fixture(scope=\"function\")\ndef dot_readout_methods(dot_dmm):\n    readout_methods = {\n        \"dc_current\": dot_dmm.dot_current,\n        \"dc_sensor\": dot_dmm.dot_sensor,\n    }\n    yield readout_methods\n\n\n@pytest.fixture(scope=\"function\")\ndef device_pinchoff(pinchoff_dmm, device_gate_inputs):\n    readout_methods = {\n        \"dc_current\": pinchoff_dmm.po_current,\n        \"dc_sensor\": pinchoff_dmm.po_sensor,\n    }\n\n    device = nt.Device(\n        name=\"test_doubledot\",\n        device_type=\"doubledot_2D\",\n        readout_methods=readout_methods,\n        **device_gate_inputs,\n    )\n\n    pinchoff_dmm.po_current.gate = device.left_barrier\n    pinchoff_dmm.po_sensor.gate = device.left_barrier\n    try:\n        yield device\n    finally:\n        device.close()\n","sub_path":"nanotune/tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":11769,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"410207694","text":"#\n# Insert Node at Specific Position in linked List\n#\n# Insert a node at a specific position in a linked list. The given\n# list may be null / empty.\n#\n\nclass Node(object):\n    def __init(self, data=None, next_node=None):\n        self.data = data\n        self.next = next_node\n\n\ndef InsertNth(head, data, position):\n    current = head\n\n    # Handle NULL list\n    if not head: return Node(data)\n\n    # Handle inserting at head of list\n    if position == 0:\n        return Node(data, head)\n\n    # Determine insertion position\n    for i in range(position - 1):\n        current = current.next\n\n    new_node = Node(data, current.next)\n    current.next = new_node\n    return head\n","sub_path":"Linked List/linkedlist_insert.py","file_name":"linkedlist_insert.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"409642455","text":"from selenium import webdriver\n\n\nclass Driver:\n    \"\"\"\n    Initialized driver.\n    \"\"\"\n    def get_driver(self):\n        \"\"\"\n        Gets driver.\n        :return: created driver.\n        \"\"\"\n        URL = 'http://localhost:4444/wd/hub'\n\n        capabilities = {\n            \"browserName\": \"firefox\",\n            \"version\": \"\",\n            \"enableVNC\": True,\n            \"enableVideo\": True,\n            \"enableLog\": True\n        }\n\n        driver = webdriver.Remote(\n            command_executor=URL,\n            desired_capabilities=capabilities)\n\n        return driver","sub_path":"SelTests/DriverInitializer.py","file_name":"DriverInitializer.py","file_ext":"py","file_size_in_byte":570,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"615634996","text":"def mono_window_emmissivity(ndvi: np.ndarray)->np.ndarray:\n    \"\"\"\n    compute emmissivity from ndvi image \n\n    # Reference:\n\n    Args:\n        ndvi (np.ndarray): normalised difference vegetation index (cloud and water masked). Use getemp.compute_ndvi(...) to obtain \n        ndvi image that is masked \n\n    Returns:\n        np.ndarray: Emissivity image\n    \"\"\"\n    emissivity = np.empty(ndvi.shape)\n    # Reference: Avdan, Ugur, and Gordana Jovanovska. \"Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data.\" Journal of Sensors 2016 (2016).\n    nan_mask = np.isnan(ndvi)\n    \n    # Set values in emissivity matrix based on NDVI matrix value with same index\n    mask1 = (ndvi >= -1) & (ndvi < 0.2)\n    mask2 = (ndvi > 0.5) & (ndvi <= 1)\n    mask3 = (ndvi >= 0.2) & (ndvi <= 0.5)\n    \n\n    i, j = np.where(mask1)\n    emissivity[i, j] = 0.97\n    i, j =np.where(mask2)\n    emissivity[i, j] = 0.99\n    i, j = np.where(mask3)\n    emissivity[i, j] = (0.004*(((ndvi[i, j] - 0.2)/(0.5 - 0.2))**2)) + 0.986\n    i, j = np.where(nan_mask)\n    emissivity[i, j] = np.nan\n    return emissivity\n\n\n    \n# Method 2 for LSE\ndef compute_LSE_NBEM(NDVI, red_band) :\n    #Reference: https://www.sciencedirect.com/science/article/pii/S0169204618306480#b0240\n    #reference : https://www.mdpi.com/2072-4292/6/10/9829 # Xiaolei Yu ,Xulin Guo andZhaocong Wu \n    #NDVI : NDVI image\n    #Red_band: Red band of image (0.63-0.69 micrometers)\n\n    assert NDVI.shape == red_band.shape , ValueError(\"Input images (NDVI and Red band) must be of equal dimension\")\n    \n    emiss_matrix_10 = np.zeros(NDVI.shape) #Emissivity matrix for band 10 landsat 8 data (applies also to LST bands in other landsat data)\n\n\n    # Assign different emissivity values to different NDVI ranges\n    # 1st build a mask based on iNDVI values to assign emissivity values based on NDVI ranges\n\n    # Define variables given in algorithm\n    emissivity_soil_10 = 0.9668 # Baresoil emissivity value\n    emissivity_veg_10 = 0.9863 # Vegetation emmisivity value\n  \n\n    #p = FRACTIONAL VEGETATION COVER = (NDVI - NDVI_min) / (NDVI_max - NDVI_min)^2\n    p = ((NDVI - 0.2)/(0.5 - 0.2))**2\n\n    #Cavity effect = C (Defined in reference literature)\n    c_10 = 0.018009838 *(1 - p) #For Band 10\n  \n\n    #Create mask to compute LSE based on NDVIO value ranges\n    mask_ndvi_baresoil = (NDVI < 0.2) #Baresoil class is defined with NDVI less than 0.2\n    mask_ndvi_veg = (NDVI > 0.5)      #veg pixels are defined with NDVI greater than 0.5\n    mask_ndvi_mixed = (NDVI >= 0.2) & (NDVI <= 0.5) #Mixed pixels are defined with NDVI less than or equal to 0.5 and Greater than of equal to 0.2\n\n    i, j = np.where(mask_ndvi_baresoil)\n    emiss_matrix_10[i, j] = 0.973 - (0.047 * red_band[i, j])\n\n    k, l = np.where(mask_ndvi_mixed)\n    emiss_matrix_10[k, l] = (emissivity_veg_10 * p[k, l]) + (emissivity_soil_10 *(1 - p[k, l])) + c_10[k, l]\n\n    m, n = np.where(mask_ndvi_veg)\n    emiss_matrix_10[m, n] = emissivity_veg_10 + c_10[m, n]\n\n\n    return emiss_matrix_10\n\n# Method 3 for LSE\ndef compute_LSE_using_fvc(NDVI):\n    #FVC mean fractional vegetation cover. This method uses fractional vegetation cover to estimate LSE\n    \n    #Reference: Split window Algorithm for Retrieval of Land surface temperature using Landsat 8 Thermal infrared data\n    #by Gopinadh Rongali et al (2018)\n\n    # Assign different emissivity values to different NDVI ranges\n    # 1st build a mask based on iNDVI values to assign emissivity values based on NDVI ranges\n\n\n\n    # Define variables given in algorithm (See publication)\n    emissivity_soil_10 = 0.9668\n    emissivity_veg_10 = 0.9747\n   \n\n    # p = FRACTIONAL VEGETATION COVER = (NDVI - NDVI_min) / (NDVI_max - NDVI_min)^2\n    p = ((NDVI - 0.2) / (0.5 - 0.2)) ** 2\n\n    emiss_matrix_10 = (emissivity_soil_10 * (1 - p)) + (emissivity_veg_10 * p)\n   \n    return emiss_matrix_10","sub_path":"boiler_plate_fns/emissivity.py","file_name":"emissivity.py","file_ext":"py","file_size_in_byte":3876,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"3929812","text":"# Copyright 2018 Peter Wild\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#Lightcycles\n#By Peter Wild\n#Improved and fixed version\n\n### Game settings     ###\nplayer_count  = 4       # Normally 2 but could be 4. 3 is unfair, and 1 is snake without food.\nstyle_name    = \"dark\"  # Can be either \"dark\" or \"lght\".\ndestroy_trail = False   # Can be either True or False.\nscale         = 8       # Default = 8    NOTE: scale * grid should be smaller than the monitor resolution.\ngrid          = 100     # Default = 100  NOTE: Ideally an even number.\nmidway        = grid/2  # Default = 50   NOTE: Should be the midpoint of grid.\ntick_speed    = 30      # Default = 30   NOTE: Larger grids should have higher tick_speed.\n### Game settings END ###\n\nimport pygame\n\n\npygame.init()\n\nstyle         = ([0,1] if (style_name == \"dark\") else ([1,0] if (style_name == \"lght\") else [0,0]))\ndestroy_trail = (False if (destroy_trail == False) else (False if (player_count <= 2) else True))\nmidway        = int(midway)\ngame_time     = pygame.time.Clock()\nwindow        = pygame.display.set_mode((scale*(grid+1),scale*(grid+1)))\n\npygame.display.set_caption(\"Lightcycles\")\n\n\nclass C:  # for constants\n    start     = [[     0, grid - midway, \"r\"],\n                 [  grid, midway, \"l\"],\n                 [midway,      0, \"d\"],\n                 [grid - midway,   grid, \"u\"]]\n\n    colours   = [pygame.Color(  0,  0,  0),\n                 pygame.Color(255,255,255)]\n\n    players   = [[\"Blue\"  , pygame.Color(  0, 191, 255)],\n                 [\"Orange\", pygame.Color(255, 127,   0)],\n                 [\"Green\" , pygame.Color(  0, 191,   0)],\n                 [\"Purple\", pygame.Color(191,   0, 255)]]\n\n    controls  = [[100 , 97, 119, 115], #WASD        - Blue\n                 [275, 276, 273, 274], #ARROW KEYS  - Orange\n                 [262, 260, 264, 261], #NUMPAD 8456 - Green\n                 [107, 104, 117, 106]] #UHJK        - Purple\n\n\n\nclass Player:\n    def __init__(self, player_no, startx, starty, startdir):\n        self.player_no = player_no\n        self.location  =  [startx,starty]\n        self.trail     = [[startx,starty]]\n        self.direction = startdir\n        self.alive     = True\n        return\n    \n    def move(self, cd):\n        if self.alive == True:\n            cd = cd[self.player_no]\n            \n            if cd == \"r\" and not self.direction == \"l\":\n                self.direction = cd\n            if cd == \"l\" and not self.direction == \"r\":\n                self.direction = cd\n            if cd == \"u\" and not self.direction == \"d\":\n                self.direction = cd\n            if cd == \"d\" and not self.direction == \"u\":\n                self.direction = cd\n\n            if self.direction == \"r\":\n                self.location[0] += 1\n            if self.direction == \"l\":\n                self.location[0] -= 1\n            if self.direction == \"u\":\n                self.location[1] -= 1\n            if self.direction == \"d\":\n                self.location[1] += 1\n\n            self.trail.insert(0, [self.location[0], self.location[1]])\n        return\n\n    def collide(self, global_trails): #collison detction help needed\n        if self.alive == True:\n            for i in range(len(global_trails)):\n                for j in (global_trails[i][1:] if (i == self.player_no) else global_trails[i]):\n                    if self.location[0] == j[0] and self.location[1] == j[1]:\n                        if C.players[self.player_no][0] == C.players[i][0]:\n                            print(\"Player\",C.players[self.player_no][0],\"has committed suicide.\")\n                        else:\n                            print(\"Player\",C.players[self.player_no][0],\"was killed by Player\",str(C.players[i][0])+\".\")\n                        self.die(global_trails)\n                        return\n            \n            for k in range(2):\n                if self.location[k] > grid or self.location[k] < 0:\n                    print(\"Player\",C.players[self.player_no][0],\"has died.\")\n                    self.die(global_trails)\n                    return\n        elif self.alive == None: self.die(global_trails)\n        return\n\n    def die(self, global_trails):\n        if self.alive == None:\n            self.alive = False\n            if destroy_trail == True:\n                self.trail.clear()\n            else:\n                status = 0\n                for i in range(len(global_trails)):\n                    if i != self.player_no:\n                        if self.location[0] == global_trails[i][0][0] and self.location[1] == global_trails[i][0][1]:\n                            status = 1\n                if status == 0:\n                    del self.trail[0]\n        else:\n            self.alive = None\n        return\n\n\n\ndef determine_win_text(a):\n    if a.count(False) == (player_count - 1):\n        for i in range(player_count):\n            if a[i] == True:\n                x =  (str(C.players[i][0]) + \" Wins\")\n                print(\"Player\",C.players[i][0],\"has won the game.\\n\")\n    elif a.count(False) == player_count:\n        x = \"No One Wins\"\n        print(\"No one has won the game.\")\n    else:\n        x = None\n    return x\n\ndef render_text(w_text, alive):\n    if alive.count(False) >= (player_count - 1):\n\n        if w_text == None:\n            print(\"\\nGAME OVER\")\n            w_text = determine_win_text(alive)\n        \n        w_font = pygame.font.Font(\"freesansbold.ttf\", int(10*scale*(grid/100)))\n        w_surf = w_font.render(w_text, True, C.colours[style[1]])\n        w_rect = w_surf.get_rect()\n        w_rect.midtop = (midway * scale, (int(grid/10)+1)* scale)\n        window.blit(w_surf, w_rect)\n        return w_text\n\ndef update(obj):\n    trails = []\n    alive  = []\n    for o in obj:\n        trails.append(o.trail)\n        alive.append(o.alive)\n    return [trails, alive]\n\ndef main():\n\n    p        = []\n    cd       = []\n    updates  = [[], []]\n    win_text = None\n    close    = False\n    print(\"GAME STARTED\\n\")\n\n    for i in range(player_count):\n        cd.append(C.start[i][2])\n        p.append(Player(i, C.start[i][0], C.start[i][1], C.start[i][2]))\n    \n    while True:\n        \n        for event in pygame.event.get():\n            if event.type == 12:\n                close = True\n            \n            elif event.type == 2:\n                if event.key == 27:\n                    close = True\n                for i in range(player_count):\n                    if event.key == C.controls[i][0]:\n                        cd[i] = \"r\"\n                    if event.key == C.controls[i][1]:\n                        cd[i] = \"l\"\n                    if event.key == C.controls[i][2]:\n                        cd[i] = \"u\"\n                    if event.key == C.controls[i][3]:\n                        cd[i] = \"d\"\n\n        for players in p:\n            players.move(cd)\n        \n        updates = update(p)\n\n        for players in p:\n            players.collide(updates[0])\n        \n        updates = update(p)\n\n        #DRAWING SECTION\n        window.fill(C.colours[style[0]])\n        for i in range(len(updates[0])):\n            for j in updates[0][i]:\n                pygame.draw.rect(window, C.players[i][1], pygame.Rect(j[0]*scale, j[1]*scale, scale, scale))\n            \n        win_text = render_text(win_text, updates[1])\n        \n        pygame.display.flip()\n        game_time.tick(tick_speed)\n\n        if close == True:\n            if __name__ == \"__main__\":\n                pygame.quit()\n                quit()\n            return\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"lightcycles/Lightcycles.py","file_name":"Lightcycles.py","file_ext":"py","file_size_in_byte":7998,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"501962739","text":"#!/usr/bin/env python3\n\n\n# comentário de uma linha\n\n\n\"\"\"Comentario de multiplas linhas,\n   essa parte do código será ignorada\n   pelo interpretador Python, sendo\n   apenas texto simples/comum.\"\"\"\n\n\nx = 42\ny = 73\n\n\nif x < y:\n    print('x < y: x is {}  and y is {}'.format(x, y))\nelif x > y:\n    print(('x > y: x is {} and y is {}'.format(x, y)))\nelif x == y:\n    print('x = y: x is {} and y is {}'.format(x, y))\nelse:\n    print('Something else')\n\n# exemplo 2\n# count = 0\n#\n# while count < 5:\n#     print(count, \" is less than 5\")\n#     count = count + 1\n# else:\n#     print(count, \" is not less than 5\")\n\n# exemplo 3\n# no = int(input('any number: '))\n#\n# numbers = [11,33,55,39,55,75,37,21,23,41,13]\n#\n# for num in numbers:\n#     if num == no:\n#         print ('number found in list')\n#         break\n# else:\n#     print('number not found in list')\n\n","sub_path":"01_Sintaxe_Basica/02_Blocos_comentarios/blocks.py","file_name":"blocks.py","file_ext":"py","file_size_in_byte":852,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"625653432","text":"from django.contrib import admin\nfrom django.contrib.admin.options import csrf_protect_m\nfrom django.core.cache import cache\nfrom django.conf.urls import url\nfrom django.http import HttpResponse, JsonResponse\nfrom django.contrib.admin import register\nfrom django.shortcuts import render_to_response\nfrom django.template import RequestContext\n\nimport pytz\n\nfrom .models import (EmailTemplate, EmailWidget, EmailContentItem,\n                     EmailButton, EmailTheme, UserEmail, EmailStats)\nfrom .forms import EmailWidgetFormSet, EmailWidgetForm\n\n\nclass EmailModelAdmin(admin.ModelAdmin):\n    readonly_fields = ('created_by', 'created_at', 'last_edited_by', 'last_edited_at')\n\n    def save_model(self, request, obj, form, change):\n        if not change:\n            obj.created_by = request.user.uuid\n\n        obj.last_edited_by = request.user.uuid\n        obj.save()\n\n\n@register(EmailButton)\nclass EmailButtonAdmin(admin.ModelAdmin):\n    list_display = ('description', 'text', 'url_link', 'theme')\n    search_fields = ('description', 'text', 'url_link', 'theme_description')\n\n\n@register(EmailTheme)\nclass EmailThemeAdmin(EmailModelAdmin):\n    list_display = ('description', 'background_color', 'color', 'created_by', 'created_at')\n\n\nclass EmailContentItemInline(admin.TabularInline):\n    model = EmailContentItem\n    can_delete = True\n    extra = 0\n    min_num = 3\n    verbose_name = 'Email Widget'\n    verbose_name_plural = 'Email Widgets'\n\n    def save_model(self, request, obj, form, change):\n        if not change:\n            obj.created_by = request.user.uuid\n\n        obj.last_edited_by = request.user.uuid\n        obj.save()\n\n\nclass EmailWidgetInline(admin.StackedInline):\n    model = EmailWidget\n    form = EmailWidgetForm\n    formset = EmailWidgetFormSet\n\n    fields = (\n        'order',\n        ('widget_type', 'direction_ltr'),\n        ('image', 'image_alt_text', 'alt_text_size'),\n        ('font_family', 'bg_color'),\n        ('header_color', 'header_size', 'header_line_height'),\n        'header',\n        ('font_color', 'font_size', 'font_line_height'),\n        'body',\n        'button'\n    )\n\n\n@register(EmailWidget)\nclass EmailWidgetAdmin(EmailModelAdmin):\n    save_as = True\n    list_display = ('description', 'widget_type', 'header', 'body')\n    list_filter = ('widget_type',)\n    fields = (\n        'description',\n        ('widget_type', 'direction_ltr', 'theme'),\n        ('image', 'image_alt_text', 'alt_text_size'),\n        'header_theme',\n        'header',\n        'body_theme',\n        'body',\n        'button'\n    )\n\n\n@register(EmailTemplate)\nclass EmailTemplateAdmin(EmailModelAdmin):\n    list_display = ('email_type', 'subject', 'default_template', 'release', 'created_at', 'created_by')\n    change_form_template = 'admin/django_dodo/email_template_change_form.html'\n\n    fields = (\n        'base_template',\n        ('email_type', 'release', 'default_template'),\n        'base_theme',\n        'subject', 'title', 'pre_header',\n        ('created_by', 'created_at'),\n        ('last_edited_by', 'last_edited_at'),\n        'widgets'\n    )\n\n    def get_urls(self):\n        info = self.model._meta.app_label, self.model._meta.model_name\n        urlpatterns = super(EmailTemplateAdmin, self).get_urls()\n\n        extra_patterns = [\n            url(r'^preview/$', self.admin_site.admin_view(self.preview_view), name='%s_%s_preview' % info),\n        ]\n        return extra_patterns + urlpatterns\n\n    def preview_view(self, request):\n        if request.method == 'POST':\n            obj = None\n            object_id = request.GET.get('object_id', None)\n            if object_id:\n                obj = self.model.objects.get(pk=object_id)\n\n            ModelForm = self.get_form(request, obj)\n            form = ModelForm(request.POST, request.FILES)\n            instance = form.save(commit=False)\n\n            widgets_list = form.data['widgets'].split(',')\n            widgets = []\n            for widget in widgets_list:\n                widgets.append(EmailWidget.objects.get(pk=widget))\n\n            context = {'email_template': instance, 'widgets': widgets}\n            # rendered_email = instance.render_preview(settings.PREVIEW_EMAIL, context=context)\n            rendered_email = instance.render_preview(context=context)\n            return JsonResponse(rendered_email)\n\n        return HttpResponse('Unable to process.', content_type='text/plain')\n\n\ndef resend_email(modeladmin, request, queryset):\n    for obj in queryset:\n        obj.resend(request.user.uuid)\n\n\n@register(UserEmail)\nclass UserEmailAdmin(admin.ModelAdmin):\n    list_display = ('primary_to', 'timestamp', 'timestamp_sent', 'bounced', 'timestamp_resend')\n    actions = [resend_email]\n\n    def has_add_permission(self, request):\n        return False\n\n\n@register(EmailStats)\nclass EmailStatsAdmin(admin.ModelAdmin):\n    list_display = ('timestamp', 'delivery_attempts', 'bounces', 'complaints', 'rejects')\n    change_list_template = 'services/send_stats.html'\n\n    # def get_urls(self):\n    #     extra_urls = [\n    #         url(r'^ses/(?P<pk>[-\\w]+)/$', self.admin_site.admin_view(self.ses_dashboard), name='ses-stats'),\n    #     ]\n    #     return extra_urls + super(StatsAdmin, self).get_urls()\n\n    @csrf_protect_m\n    def changelist_view(self, request, extra_context=None):\n        \"\"\"\n        Graph SES send statistics over time.\n        \"\"\"\n        template = self.change_list_template\n        # cache_key = 'vhash:ses_stats'\n        cache_key = 'django_dodo:ses_stats'\n        cached_view = cache.get(cache_key)\n        if cached_view:\n            return cached_view\n\n        self.model.update_send_stats()\n\n        send_stats = self.model.get_send_stats()\n\n        extra_context = {\n            'title': 'SES Statistics',\n            'data_points': send_stats,\n            # '24hour_quota': quota['Max24HourSend'],\n            # '24hour_sent': quota['SentLast24Hours'],\n            # '24hour_remaining': float(quota['Max24HourSend']) - float(quota['SentLast24Hours']),\n            # 'per_second_rate': quota['MaxSendRate'],\n            # 'verified_emails': verified_emails,\n            # 'summary': summary,\n            # 'access_key': connection.gs_access_key_id,\n            'local_time': True if pytz else False,\n        }\n\n        response = render_to_response(template, extra_context, context_instance=RequestContext(request))\n\n        cache.set(cache_key, response, 60 * 15)  # Cache for 15 minutes\n        return response\n","sub_path":"django_dodo/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":6428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"565249329","text":"import gym\nimport shannon_switching\nfrom baselines import deepq\nfrom baselines.common import models\nimport pickle\nfrom gym.envs.registration import register\nimport tensorflow as tf\nimport sys\n\ndef callback(lcl, _glb):\n\tprint(lcl)\n\treturn False\n\t\ndef main():\n\t# setup environment\n\tishumanFirstPlayer = int(sys.argv[1])\n\tishumanCut = int(sys.argv[2])\n\titerNo = int(sys.argv[3])\n\tenv = gym.make('shannon_switching-v0')\n\tenv.configureEnvironment(computerType=\"selfPlayZero\", ishumanFirstPlayer=ishumanFirstPlayer, \n\t\tishumanCut=ishumanCut, iterNo=iterNo)\n\tprint(\"Computer Type: \", \"selfPlayZero\")\n\tprint(\"ishumanFirstPlayer \", ishumanFirstPlayer)\n\tprint(\"ishumanCut\", ishumanCut)\n\tprint(\"iterNo\", iterNo)\n\t# input(\"Press Enter to continue...\")\n\n\t# train network\n\tact = deepq.learn(\n\t\tenv,\n\t\tnetwork=models.mlp(num_hidden=25, num_layers=8),\n\t\tlr=5e-4,\n\t\ttotal_timesteps=20,\n\t\tbuffer_size=50000,\n\t\texploration_fraction=0.1,\n\t\texploration_final_eps=0.02,\n\t\tprint_freq=1,\n\t\tparam_noise=False,\n\t\tprioritized_replay=True,\n\t\tload_path='model/selfPlayZero/shannon_switching_{}_{}_{}.pkl'.format(ishumanFirstPlayer, ishumanCut, iterNo-1) if iterNo > 0 else None\t\t\n\t)\n\tprint(\"Saving model to model/selfPlayZero/shannon_switching_{}_{}_{}.pkl\".format(ishumanFirstPlayer, ishumanCut, iterNo))\n\t#act.save(\"model/selfPlayZero/shannon_switching_{}_{}_{}.pkl\".format(ishumanFirstPlayer, ishumanCut, iterNo))\n\t#act.save_act(\"model/selfPlayZero/shannon_switching_train_{}_{}_{}.pkl\".format(ishumanFirstPlayer, ishumanCut, iterNo))\n\nif __name__ == '__main__':\n\tmain()\n\n\n\n","sub_path":"trainSelfPlayZero.py","file_name":"trainSelfPlayZero.py","file_ext":"py","file_size_in_byte":1548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"84373293","text":"'''\nIterative:\nTime Complexity: O(n)\nSpace Complexity: O(1)\nDid this code successfully run on Leetcode : Yes\nExplanation: Find the last occurance of each character in a frequency array. For every character we see set the end\npointer to the index of the last occurance of the character. If our cursor reaches this end then we reached one partition\nend and the size is end-start+1, increase the start now to the cursor +1\n'''\nclass Solution:\n    def partitionLabels(self, S: str) -> List[int]:\n        res = []\n\n        lastIndex = [0 for i in range(26)]\n\n        for i in range(len(S)):\n            ch = S[i]\n            lastIndex[ord(ch) - ord('a')] = i\n\n        start = 0\n        end = 0\n\n        for cursor in range(len(S)):\n            ch = S[cursor]\n\n            end = max(end, lastIndex[ord(ch) - ord('a')])\n\n            # end of partition\n            if cursor == end:\n                # record partition\n                partition = end - start + 1\n                res.append(partition)\n                start = cursor + 1\n\n        return res\n\n","sub_path":"partitionLabel.py","file_name":"partitionLabel.py","file_ext":"py","file_size_in_byte":1048,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"183173102","text":"\nimport sys\nimport tensorflow as tf\n\nfrom params import FLAGS\n\n\nclass LRModel():\n    def __init__(self):\n        self.W = tf.get_variable(name='Weights', shape=[FLAGS.feature_size], dtype=tf.float32, initializer=tf.constant_initializer(0))\n        self.b = tf.get_variable(name='Bisas', shape=[1], dtype=tf.float32, initializer=tf.constant_initializer(0)) \n        pass\n\n    def inference(self, input_fields, mode):\n        if mode == tf.contrib.learn.ModeKeys.TRAIN:\n            str_features, str_labels = input_fields[2], input_fields[3]\n        elif mode == tf.contrib.learn.ModeKeys.INFER:\n            str_features, str_labels = input_fields[2], \"1\"\n        features = tf.string_split(str_features, ';')\n        features = tf.SparseTensor(\n            indices = features.indices,\n            values=tf.string_to_number(features.values, out_type=tf.int32),\n            dense_shape=features.dense_shape)\n        labels = tf.reshape(str_labels, [-1])\n        \n        product = tf.nn.embedding_lookup_sparse(self.W, features, None, combiner='sum')\n        pred = tf.nn.sigmoid(product + self.b)\n\n        return input_fields[0], input_fields[1], pred, labels\n\n    def calc_loss(self, inference_res):\n        a, b, pred, labels = inference_res\n        batch_size = tf.shape(pred)[0]\n        \n        cost = tf.losses.log_loss(labels=labels, predictions=pred)\n        loss = tf.reduce_sum(cost)\n        weight = batch_size\n\n        return [loss], weight\n\n    def predict(self, inference_res):\n        a, b, pred, labels = inference_res\n        return a, b, pred\n\n    def get_optimizer(self, optimizer_mode = \"Grad\"):\n        if optimizer_mode == \"Adam\":\n            return [tf.train.AdadeltaOptimizer(FLAGS.learning_rate)]\n        elif optimizer_mode == \"Grad\":\n            return [tf.train.GradientDescentOptimizer(FLAGS.learning_rate)]\n        elif optimizer_mode == \"FTRL\":\n            return [tf.train.FtrlOptimizer(FLAGS.learning_rate)]","sub_path":"LR.py","file_name":"LR.py","file_ext":"py","file_size_in_byte":1939,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"649779739","text":"#!/usr/bin/env python\n\n\"\"\"\nTass PythonFundamentals Course\nSolution to: \"Prime, Armstrong and Perfect number tester\"\n\"\"\"\n\nimport unittest\n\ndef is_armstrong(number):\n    \"\"\" Tests if a number is an armstrong number \"\"\"\n    sum_of_digits = 0\n    number_string = str(number)\n    power = len(number_string)\n    for char in number_string:\n        sum_of_digits += int(char) ** power\n    return sum_of_digits == number\n    \n\ndef is_prime(number):\n    \"\"\" Tests if a number is a prime \"\"\"\n    prime = True\n    for divisor in range(2, number-1):\n        if number % divisor == 0:\n            prime = False\n            break\n    return prime\n\n\ndef is_perfect(number):\n    \"\"\" Tests if a number is a perfect number \"\"\"\n    sum_of_divisors = 0\n    for divisor in range(1, number//2+1):\n        if number % divisor == 0:\n            sum_of_divisors += divisor\n    return sum_of_divisors == number\n\n\n\nclass TestAll(unittest.TestCase):\n    \"\"\"\n    Test class for all 3 methods\n    \"\"\"\n    def test_is_prime(self):\n        \"\"\" Tests the is_prime function \"\"\"\n        primes = (2, 3, 5, 7, 11, 13, 17, 19, 23, 29)\n        for number in range(2, 30):\n            prime = is_prime(number)\n            if number in primes:\n                self.assertTrue(prime)\n            else:\n                self.assertFalse(prime)\n\n    def test_is_armstrong(self):\n        \"\"\" Test the is_armstrong function \"\"\"\n        for number in (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 153,\n                       370, 371, 407, 1634, 8208, 9474):\n            self.assertTrue(is_armstrong(number))\n\n        for number in range(10, 152):\n            self.assertFalse(is_armstrong(number))\n\n    def test_is_perfect(self):\n        \"\"\" Test the is_perfect function \"\"\"\n        perfects = (6, 28, 496, 8128)\n        for number in perfects:\n            self.assertTrue(is_perfect(number))\n\n        for number in range(1, 40):\n            if number not in perfects:\n                self.assertFalse(is_perfect(number))\n\nunittest.main()\n","sub_path":"python fundamentals/PythonFundamentals_exercises_solutions/functions/number_tester.py","file_name":"number_tester.py","file_ext":"py","file_size_in_byte":1977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"392018111","text":"# -*-* coding:utf-8 -*-*\nimport math\nimport multiprocessing\nfrom Module import Command\nfrom Module import Function\nimport asyncio\n\n\nclass GeneralMain(object):\n    def __init__(self):\n        self.parameter_list = {}\n\n    def words_count(self, current_length):\n        line_sum = 1\n        prefix_len = suffix_len = 0\n        if Command.find_value('prefix'):\n            prefix_len = len(Command.get_value('prefix'))\n        if Command.find_value('suffix'):\n            suffix_len = len(Command.get_value('suffix'))\n        for value in Command.get_value('element_list')[:current_length]:\n            line_sum *= len(value)\n        current_total_lines = line_sum\n        current_total_size = current_total_lines * (prefix_len + current_length + suffix_len + 2)\n        if Command.find_value('total_lines'):\n            current_total_lines = Command.get_value('total_lines')\n            current_total_size = current_total_lines * (current_length + 2)\n        if Command.find_value('total_size'):\n            current_total_size = Command.get_value('total_size')\n            current_total_lines = Command.get_value('total_size') / (current_length + 2)\n        return current_total_lines, current_total_size\n\n    def center_control(self):\n        process_pool = []\n        total_size = 0\n        total_lines = 0\n        for current_length in range(Command.get_value('min'), Command.get_value('max') + 1):\n            for n in range(len(Command.get_value('element_list')[0])):\n                for s in range(len(Command.get_value('element_list')[1])):\n                    tmp_list = []\n                    for m in range(current_length):\n                        if m == 0:\n                            tmp_list.append([n, n])\n                        elif m == 1:\n                            tmp_list.append([s, s])\n                        else:\n                            tmp_list.append([0, len(Command.get_value('element_list')[m]) - 1])\n                    process_pool.append(tmp_list)\n            current_total_lines, current_total_size = self.words_count(current_length)\n            total_lines += current_total_lines\n            total_size += current_total_size\n        Command.set_value('total_lines', total_lines)\n        Command.set_value('total_size', total_size)\n        Command.set_value('current_lines', 0)\n        self.parameter_list = Command.get_dict().copy()\n        Function.check_file_exist(Command.get_value('output'))\n        Function.show_file_info(total_lines, total_size)\n        m = len(process_pool)\n        n = int(math.ceil(m / multiprocessing.cpu_count()))\n        process_pool = [process_pool[i:i + n] for i in range(0, m, n)]\n        Function.show_info('please wait a moment')\n        self.multi_process(process_pool)\n\n    def multi_process(self, process_pool):\n        lock = multiprocessing.Lock()\n        process_list = [multiprocessing.Process(target=self.micro_thread, args=(one_process_seqence, lock,)) for\n                        one_process_seqence in process_pool]\n        for index, process in enumerate(process_list):\n            process.start()\n        for process in process_list:\n            process.join()\n\n    def micro_thread(self, one_process_seqence, lock):\n        loop = asyncio.get_event_loop()  # 获取一个event_loop\n        thread_list = [self.thread_task(one_thread_seqence, lock) for one_thread_seqence in one_process_seqence]\n        loop.run_until_complete(asyncio.gather(*thread_list))  # \"阻塞\"直到所有的tasks完成\n        loop.close()\n\n    @asyncio.coroutine\n    def thread_task(self, start_seqence, lock):\n        try:\n            current_length = len(start_seqence)\n            self.create_words(0, current_length, start_seqence, [], lock)\n        except TypeError:\n            pass\n\n    def create_words(self, current_width, current_length, start_seqence, result_list, lock):\n        tmp_width = current_length\n        if current_length == current_width:\n            return start_seqence\n        else:\n            pre_seqence = self.create_words(current_width + 1, current_length, start_seqence, result_list, lock)\n            if pre_seqence == 'end':\n                if len(result_list) == 0:\n                    return\n                with lock:\n                    Function.write_file(result_list,\n                                        self.parameter_list['output'][:-4] + self.parameter_list['output'][-4:])\n                return\n            while tmp_width >= current_width:\n                while 1:\n                    if pre_seqence[tmp_width - 1][0] > pre_seqence[tmp_width - 1][1]:\n                        pre_seqence[tmp_width - 2][0] += 1\n                        pre_seqence[tmp_width - 1][0] = 0\n                        break\n                    elif pre_seqence[1][0] > pre_seqence[1][1]:\n                        return 'end'\n                    else:\n                        tmp_width = current_length\n                        last_result = []\n                        for index, value in enumerate(pre_seqence):\n                            last_result.append(self.parameter_list['element_list'][index][value[0]])\n                        result = ''.join(last_result)\n                        if 'regex' in self.parameter_list.keys():\n                            result = Function.screen_regex_words(result, self.parameter_list['regex'])\n                        if 'repeat' in self.parameter_list.keys():\n                            result = Function.screen_repeat_words(result, self.parameter_list['repeat'])\n                        if result is None:\n                            pre_seqence[current_length - 1][0] += 1\n                            continue\n                        if 'prefix' in self.parameter_list.keys():\n                            result = self.parameter_list['prefix'] + result\n                        if 'suffix' in self.parameter_list.keys():\n                            result = result + self.parameter_list['suffix']\n                        if 'hash' in self.parameter_list.keys():\n                            result = Function.change_words(result, self.parameter_list['hash'])\n                        if 'lines' in self.parameter_list.keys() or 'size' in self.parameter_list.keys():\n                            if self.parameter_list['current_lines'] >= self.parameter_list['total_lines']:\n                                return 'end'\n                        result_list.append(result)\n                        pre_seqence[current_length - 1][0] += 1\n                tmp_width -= 1\n                if tmp_width == 1:\n                    return 'end'\n        return pre_seqence\n\n    def main(self):\n        self.center_control()\n","sub_path":"Module/General.py","file_name":"General.py","file_ext":"py","file_size_in_byte":6640,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"571840649","text":"# -*- coding: utf-8 -*-\nfrom odoo import api, fields, models, tools\nfrom dateutil.relativedelta import relativedelta\nfrom datetime import datetime\nimport json\n\nimport logging\n_logger = logging.getLogger(__name__)\n\nimport boto3\nfrom botocore.exceptions import ClientError\n\nclass OniadTransaction(models.Model):\n    _name = 'oniad.transaction'\n    _description = 'Oniad Transaction'\n    \n    account_payment_id = fields.Many2one(\n        comodel_name='account.payment',\n        string='Pago'\n    )\n    currency_id = fields.Many2one(\n        comodel_name='res.currency',\n        string='Moneda'\n    )\n    amount = fields.Monetary(\n        string='Importe'\n    )\n    tax = fields.Monetary(\n        string='Tax'\n    )\n    total = fields.Monetary(\n        string='Total'\n    )\n    date = fields.Date(\n        string='Fecha'\n    )\n    oniad_user_id = fields.Many2one(\n        comodel_name='oniad.user',\n        string='Oniad User'\n    )\n    oniad_address_id = fields.Many2one(\n        comodel_name='oniad.address',\n        string='Oniad Address'\n    )\n    type = fields.Selection(\n        selection=[\n            ('TYPE_CREDIT','Credito'), \n            ('TYPE_COMMISSION','Comision'),\n            ('TYPE_SERVICE','Servicio')                          \n        ],\n        string='Tipo'\n    )\n    state = fields.Selection(\n        selection=[\n            ('STATUS_PENDING','Pendiente'), \n            ('STATUS_COMPLETED','Completada'),\n            ('STATUS_CANCELLED','Cancelada')                          \n        ],\n        string='Estado'\n    )\n    actor = fields.Selection(\n        selection=[\n            ('ACTOR_ONIAD','Oniad'), \n            ('ACTOR_USER','Usuario'),\n            ('ACTOR_AGENCY','Agencia'),\n            ('ACTOR_ACCOUNT','Account')                          \n        ],\n        string='Actor'\n    )\n    medium = fields.Selection(\n        selection=[\n            ('MEDIUM_STRIPE','Stripe'), \n            ('MEDIUM_BRAINTREE','Braintree'),\n            ('MEDIUM_INTERNAL','Interno'),\n            ('MEDIUM_OFFLINE','Offline')                          \n        ],\n        string='Medium'\n    )\n    subject = fields.Selection(\n        selection=[\n            ('SUBJECT_CHARGE','Pago ONiAD'), \n            ('SUBJECT_VOUCHER','Cup�n aplicado'),\n            ('SUBJECT_BANNERS','Dise�o de creatividades'),\n            ('SUBJECT_COMPENSATION','Compensaci�n comercial'),\n            ('SUBJECT_GIFT','Promoci�n'),\n            ('SUBJECT_REFUND','Reembolso'),\n            ('SUBJECT_CONVERT_COMMISSION_TO_CREDIT','Comisi�n Afiliado a cr�dito'),\n            ('SUBJECT_CONVERT_AGENCYCOMMISSION_TO_CREDIT','Comisi�n Partner a cr�dito'),\n            ('SUBJECT_SETTLEMENT','Liquidaci�n'),\n            ('SUBJECT_TRANSFER','Transferencia'),\n            ('SUBJECT_COMMISSION_RECOMMENDED','Programa de recomendados'),\n            ('SUBJECT_COMMISSION_AGENCY','Programa de Partners'),\n            ('SUBJECT_COMMISSION','Programa de Afiliados')                          \n        ],\n        string='Asunto'\n    )\n    \n    @api.one\n    def check_account_payment(self):\n        #define\n        stranger_ids_need_skip = [1743, 52076, 52270, 52271, 52281]#Fix transacciones devueltas por stripe 'raras'                \n        #need_create_account_payment\n        need_create_account_payment = False        \n        if self.id>94:#Fix eliminar los de 2017\n            if self.id not in stranger_ids_need_skip:\n                if self.type!='TYPE_COMMISSION':\n                    if self.subject in ['SUBJECT_CHARGE', 'SUBJECT_REFUND']:\n                        if self.medium=='MEDIUM_STRIPE':\n                            if self.create_date>'2020-01-01':\n                                need_create_account_payment = True\n        #need_create_account_invoice\n        need_create_account_invoice = False\n        if self.id>94:#Fix eliminar los de 2017\n            if self.id not in stranger_ids_need_skip:\n                if self.type!='TYPE_COMMISSION':\n                    if self.subject in ['SUBJECT_CHARGE', 'SUBJECT_REFUND']:\n                        if self.medium=='MEDIUM_OFFLINE':\n                            if self.create_date>'2020-02-12':\n                                need_create_account_invoice = True\n        #operations need_create_account_payment\n        if need_create_account_payment==True:\n            if self.account_payment_id.id==0:\n                #account.payment                                            \n                account_payment_vals = {\n                    'payment_type': 'inbound',\n                    'partner_type': 'customer',\n                    'payment_method_id': 1,\n                    'state': 'draft',\n                    'currency_id': self.currency_id.id,\n                    'partner_id': self.oniad_address_id.partner_id.id,\n                    'oniad_user_id': self.oniad_user_id.id,\n                    'journal_id': int(self.env['ir.config_parameter'].sudo().get_param('oniad_stripe_journal_id')),\n                    'amount': self.total,\n                    'payment_date': self.date,\n                    'oniad_purchase_price': 0,\n                    'communication': dict(self.fields_get(allfields=['subject'])['subject']['selection'])[self.subject],\n                    'oniad_transaction_id': self.id,                                                                                                 \n                }                     \n                #oniad_product_id\n                if self.type=='TYPE_CREDIT':\n                    account_payment_vals['oniad_product_id'] = int(self.env['ir.config_parameter'].sudo().get_param('oniad_credit_product_id'))                \n                elif self.type=='TYPE_SERVICE':\n                    account_payment_vals['oniad_product_id'] = int(self.env['ir.config_parameter'].sudo().get_param('oniad_service_product_id'))                            \n                #oniad_purchase_price\n                if self.type=='TYPE_CREDIT':\n                    account_payment_vals['oniad_purchase_price'] = self.total*0.5\n                #communication                \n                    subjects_with_date = ['SUBJECT_CHARGE', 'SUBJECT_REFUND']\n                    if self.subject in subjects_with_date:\n                        date_explode = self.date.split('-')\n                        account_payment_vals['communication'] += ' '+str(date_explode[2])+'/'+str(date_explode[1])+'/'+str(date_explode[0])           \n                #SUBJECT_REFUND            \n                if self.subject=='SUBJECT_REFUND':\n                    account_payment_vals['payment_type'] = 'outbound'\n                    account_payment_vals['oniad_purchase_price'] = 0\n                    #fix negative amounts\n                    if self.total<0:\n                        account_payment_vals['amount'] = self.total*-1\n                #create                                                                \n                account_payment_obj = self.env['account.payment'].sudo().create(account_payment_vals)\n                if account_payment_obj.id>0:\n                    self.account_payment_id = account_payment_obj.id\n                    if self.account_payment_id.state=='draft': \n                        return_post = self.account_payment_id.post()\n            else:\n                if self.account_payment_id.state=='draft':\n                    self.account_payment_id.post()\n        #operations need_create_account_invoice\n        if need_create_account_invoice==True:\n            #check_if_need_create\n            account_invoice_line_ids = self.env['account.invoice.line'].search([('oniad_transaction_id', '=', self.id)])\n            if len(account_invoice_line_ids)==0:\n                #define\n                oniad_account_invoice_journal_id = int(self.env['ir.config_parameter'].sudo().get_param('oniad_account_invoice_journal_id'))\n                oniad_product_id = int(self.env['ir.config_parameter'].sudo().get_param('oniad_credit_product_id'))\n                product = self.env['product.product'].search([('id','=',oniad_product_id)])\n                communication = dict(self.fields_get(allfields=['subject'])['subject']['selection'])[self.subject]\n                #creamos una factura con la linea de esta transaccion\n                account_invoice_vals = {\n                    'partner_id': self.oniad_address_id.partner_id.id,\n                    'partner_shipping_id': self.oniad_address_id.partner_id.id,\n                    'account_id': self.oniad_address_id.partner_id.property_account_receivable_id.id,\n                    'journal_id': oniad_account_invoice_journal_id,#Facturas cliente OniAd\n                    'date': self.date,\n                    'date_invoice': self.date,\n                    'date_due': self.date,\n                    'state': 'draft',\n                    'comment': ' ',\n                    'currency_id': self.currency_id.id,\n                    'oniad_address_id': self.oniad_address_id.id,                                         \n                }\n                #user_id\n                if self.oniad_user_id.partner_id.id>0:\n                    if self.oniad_user_id.partner_id.user_id.id>0:\n                        account_invoice_vals['user_id'] = self.oniad_user_id.partner_id.user_id.id                 \n                #create\n                account_invoice_obj = self.env['account.invoice'].sudo().create(account_invoice_vals)\n                #lines\n                account_invoice_line_vals = {\n                    'invoice_id': account_invoice_obj.id,\n                    'oniad_transaction_id': self.id,\n                    'name': communication,\n                    'quantity': 1,\n                    'price_unit': self.total,\n                    'account_id': product.property_account_income_id.id,\n                    'purchase_price': self.total*0.5,\n                    'currency_id': self.currency_id.id,\n                    'product_id': oniad_product_id                      \n                }                 \n                account_invoice_line_obj = self.env['account.invoice.line'].sudo().create(account_invoice_line_vals)\n                account_invoice_line_obj._onchange_product_id()\n                account_invoice_line_obj._onchange_account_id()                \n                #price\n                price_unit = self.total/((account_invoice_line_obj.invoice_line_tax_ids.amount/100)+1)\n                account_invoice_line_obj.update({\n                    'price_unit': round(price_unit,4),\n                    'name': communication,\n                    'price_subtotal': price_unit\n                })\n                account_invoice_obj.compute_taxes()\n                #valid\n                account_invoice_obj.action_invoice_open()\n                #el sns se realizaria cuando la factura se validara (generando el PDF y guardandolo en un bucket de S3 y enviando el SNS correspondiente con toda la informacion)                                     \n    \n    @api.model\n    def create(self, values):\n        return_item = super(OniadTransaction, self).create(values)\n        #operations\n        return_item.check_account_payment()\n        #return\n        return return_item\n    \n    @api.one\n    def write(self, vals):                        \n        return_write = super(OniadTransaction, self).write(vals)\n        #operations\n        self.check_account_payment()\n        #return    \n        return return_write            \n    \n    @api.multi    \n    def cron_sqs_oniad_transaction(self, cr=None, uid=False, context=None):\n        _logger.info('cron_sqs_oniad_transaction')\n        \n        sqs_oniad_transaction_url = tools.config.get('sqs_oniad_transaction_url')\n        AWS_ACCESS_KEY_ID = tools.config.get('aws_access_key_id')        \n        AWS_SECRET_ACCESS_KEY = tools.config.get('aws_secret_key_id')\n        AWS_SMS_REGION_NAME = tools.config.get('aws_region_name')                        \n        #boto3\n        sqs = boto3.client(\n            'sqs',\n            region_name=AWS_SMS_REGION_NAME, \n            aws_access_key_id=AWS_ACCESS_KEY_ID,\n            aws_secret_access_key= AWS_SECRET_ACCESS_KEY\n        )        \n        # Receive message from SQS queue\n        total_messages = 10\n        while total_messages>0:\n            response = sqs.receive_message(\n                QueueUrl=sqs_oniad_transaction_url,\n                AttributeNames=['All'],\n                MaxNumberOfMessages=10,\n                MessageAttributeNames=['All']\n            )\n            if 'Messages' in response:\n                total_messages = len(response['Messages'])\n            else:\n                total_messages = 0\n            #continue\n            if 'Messages' in response:\n                for message in response['Messages']:\n                    #message_body           \n                    message_body = json.loads(message['Body'])\n                    #fix message\n                    if 'Message' in message_body:\n                        message_body = json.loads(message_body['Message'])\n                    #result_message\n                    result_message = {\n                        'statusCode': 200,\n                        'return_body': 'OK',\n                        'message': message_body\n                    }\n                    #fields_need_check\n                    fields_need_check = ['id']\n                    for field_need_check in fields_need_check:\n                        if field_need_check not in message_body:\n                            result_message['statusCode'] = 500\n                            result_message['return_body'] = 'No existe el campo '+str(field_need_check)\n                    #operations\n                    if result_message['statusCode']==200:\n                        previously_found = False\n                        id_item = int(message_body['id'])                        \n                        oniad_transaction_ids = self.env['oniad.transaction'].search([('id', '=', id_item)])\n                        if len(oniad_transaction_ids)>0:\n                            previously_found = True\n                        #params\n                        data_oniad_transaction = {\n                            'currency_id': 1,\n                            'amount': str(message_body['amount']),\n                            'tax': str(message_body['tax']),\n                            'total': str(message_body['total']),\n                            'oniad_user_id': int(message_body['actor_destination_id']),\n                            'oniad_address_id': int(message_body['address_id']),\n                            'type': str(message_body['type']),\n                            'state': str(message_body['status']),\n                            'actor': str(message_body['actor_origin']),\n                            'medium': str(message_body['medium_type']),\n                            'subject': str(message_body['subject']),\n                            'date': str(message_body['completed_at']),\n                            'create_date': str(message_body['completed_at'])\n                        }\n                        #fix prevent error oniad_user_id\n                        if data_oniad_transaction['oniad_user_id']=='0':\n                            del data_oniad_transaction['oniad_user_id']\n                            #result\n                            result_message['statusCode'] = 500\n                            result_message['return_body'] = 'El campo oniad_user_id no puede ser 0'\n                        #fields_type_date                                                                            \n                        fields_type_date = ['date', 'create_date']\n                        for field_type_date in fields_type_date:\n                            if field_type_date in data_oniad_transaction:\n                                if 'T' in data_oniad_transaction[field_type_date]:\n                                    field_item_split = data_oniad_transaction[field_type_date].split('T')                            \n                                    field_item_split2 = field_item_split[1].split('+')\n                                    field_new = field_item_split[0]+' '+field_item_split2[0]\n                                    data_oniad_transaction[field_type_date] = field_new\n                        #add_id\n                        if previously_found==False:\n                            data_oniad_transaction['id'] = int(message_body['id'])\n                        #search oniad_user_id (prevent errors)\n                        if 'oniad_user_id' in data_oniad_transaction:\n                            if data_oniad_transaction['oniad_user_id']>0:\n                                oniad_user_ids = self.env['oniad.user'].search([('id', '=', int(data_oniad_transaction['oniad_user_id']))])\n                                if len(oniad_user_ids)==0:\n                                    result_message['statusCode'] = 500\n                                    result_message['return_body'] = 'No existe el oniad_user_id='+str(data_oniad_transaction['oniad_user_id'])\n                        #search oniad_address_id (prevent errors)\n                        if 'oniad_address_id' in data_oniad_transaction:\n                            if data_oniad_transaction['oniad_address_id']==0:\n                                result_message['statusCode'] = 500\n                                result_message['return_body'] = 'El campo oniad_address_id no puede ser 0'\n                            else:\n                                oniad_address_ids = self.env['oniad.address'].search([('id', '=', int(data_oniad_transaction['oniad_address_id']))])\n                                if len(oniad_address_ids)==0:\n                                    result_message['statusCode'] = 500\n                                    result_message['return_body'] = 'No existe el oniad_address_id='+str(data_oniad_transaction['oniad_address_id'])                                                                                           \n                        #final_operations\n                        result_message['data'] = data_oniad_transaction\n                        _logger.info(result_message)\n                        #create-write\n                        if result_message['statusCode']==200:#error, data not exists\n                            if previously_found==False:\n                                oniad_transaction_obj = self.env['oniad.transaction'].sudo().create(data_oniad_transaction)\n                            else:\n                                oniad_transaction_id = oniad_transaction_ids[0]\n                                #write\n                                oniad_transaction_id.write(data_oniad_transaction)                                                    \n                    #remove_message                \n                    if result_message['statusCode']==200:                \n                        response_delete_message = sqs.delete_message(\n                            QueueUrl=sqs_oniad_transaction_url,\n                            ReceiptHandle=message['ReceiptHandle']\n                        )\n            \n    @api.multi    \n    def cron_action_account_invoices_generate(self, cr=None, uid=False, context=None):\n        _logger.info('cron_action_account_invoices_generate')   \n        #define\n        oniad_stripe_journal_id = int(self.env['ir.config_parameter'].sudo().get_param('oniad_stripe_journal_id'))\n        oniad_account_invoice_journal_id = int(self.env['ir.config_parameter'].sudo().get_param('oniad_account_invoice_journal_id'))\n        oniad_account_invoice_product = int(self.env['ir.config_parameter'].sudo().get_param('oniad_account_invoice_product'))        \n        product = self.env['product.product'].search([('id','=',oniad_account_invoice_product)])\n        #dates      \n        current_date = datetime.today()        \n        start_date = current_date + relativedelta(months=-1, day=1)\n        end_date = datetime(start_date.year, start_date.month, 1) + relativedelta(months=1, days=-1)        \n        \n        date_invoice = end_date\n        if end_date.day==31 and end_date.month==12:\n            date_invoice = date_invoice + relativedelta(days=-1)        \n        #oniad_transaction_ids\n        oniad_transaction_ids = self.env['oniad.transaction'].search(\n            [\n                ('id', '>', 94),#Fix eliminar los de 2017\n                ('id', 'not in', (1743, 52076, 52270, 52271, 52281)),#Fix transacciones devueltas por stripe 'raras'\n                ('type', '=', 'TYPE_CREDIT'),\n                ('state', '=', 'STATUS_COMPLETED'),\n                ('actor', '=', 'ACTOR_ONIAD'),\n                ('medium', '=', 'MEDIUM_STRIPE'),\n                ('subject', 'in', ('SUBJECT_CHARGE', 'SUBJECT_REFUND')),                \n                ('account_payment_id', '!=', False),\n                ('account_payment_id.journal_id', '=', oniad_stripe_journal_id),\n                ('account_payment_id.state', 'in', ('posted', 'sent')),\n                ('account_payment_id.payment_type', 'in', ('inbound', 'outbound')),\n                ('account_payment_id.payment_date', '<=', end_date.strftime(\"%Y-%m-%d\")),\n                ('date', '>=', '2020-01-01')#Fix que quitaremos cuando queramos facturas las negativas 'viejas'\n            ]\n        )\n        if len(oniad_transaction_ids)>0:\n            partner_payments = {}\n            for oniad_transaction_id in oniad_transaction_ids:\n                payment_with_invoice = False \n                for account_invoice in oniad_transaction_id.account_payment_id._get_invoices():\n                    payment_with_invoice = True\n                    \n                if payment_with_invoice==False:\n                    if oniad_transaction_id.account_payment_id.partner_id.id not in partner_payments:\n                        partner_payments[oniad_transaction_id.account_payment_id.partner_id.id] = []\n                    #append\n                    partner_payments[oniad_transaction_id.account_payment_id.partner_id.id].append(oniad_transaction_id.account_payment_id)\n            #operations            \n            _logger.info('Facturas a crear: '+str(len(partner_payments)))            \n            if len(partner_payments)>0:\n                count = 0                \n                #for\n                for partner_id, partner_payments_item in partner_payments.items():                    \n                    count += 1\n                    #types\n                    partner_payments_by_type = {'inbound': [],'outbound': []}\n                    payment_types_item_amount = {'inbound': 0, 'outbound': 0}\n                    #calculate_total and by_type\n                    for partner_payment_item in partner_payments_item:\n                        #amount\n                        payment_types_item_amount[str(partner_payment_item.payment_type)] += partner_payment_item.amount\n                        #add_items\n                        partner_payments_by_type[str(partner_payment_item.payment_type)].append(partner_payment_item)\n                    #operations\n                    #inbound\n                    if payment_types_item_amount['inbound']>0:\n                        #partner_payment_by_type_item_0\n                        partner_payment_by_type_item_0 = partner_payments_by_type['inbound'][0]\n                        #partner\n                        partner = partner_payment_by_type_item_0.partner_id\n                        #percent                \n                        percent = (float(count)/float(len(partner_payments)))*100\n                        percent = \"{0:.2f}\".format(percent)                                    \n                        #account.invoice\n                        account_invoice_vals = {\n                            'oniad_address_id': partner_payment_by_type_item_0.oniad_transaction_id.oniad_address_id.id,\n                            'partner_id': partner.id,\n                            'partner_shipping_id': partner.id,\n                            'account_id': partner.property_account_receivable_id.id,\n                            'journal_id': oniad_account_invoice_journal_id,#Facturas cliente OniAd\n                            'date': date_invoice,\n                            'date_invoice': date_invoice,\n                            'date_due': date_invoice,\n                            'state': 'draft',\n                            'comment': ' ',\n                            'currency_id': partner_payment_by_type_item_0.currency_id.id                                         \n                        }\n                        #user_id (el del partner_payment_by_type_item_0 > oniad_user_id > partner_id > user_id)\n                        if partner_payment_by_type_item_0.oniad_transaction_id.id>0:\n                            if partner_payment_by_type_item_0.oniad_transaction_id.oniad_user_id.id>0:\n                                if partner_payment_by_type_item_0.oniad_transaction_id.oniad_user_id.partner_id.id>0:\n                                    if partner_payment_by_type_item_0.oniad_transaction_id.oniad_user_id.partner_id.user_id.id>0:\n                                        account_invoice_vals['user_id'] = partner_payment_by_type_item_0.oniad_transaction_id.oniad_user_id.partner_id.user_id.id\n                        #continue\n                        _logger.info('Prepararmos para generar al partner_id '+str(account_invoice_vals['partner_id'])+\" y al partner_shipping_id \"+str(partner.id))            \n                        account_invoice_obj = self.env['account.invoice'].sudo().create(account_invoice_vals)\n                        _logger.info('Factura '+str(account_invoice_obj.id)+' creada correctamente')                        \n                        #oniad_root_complete_data\n                        account_invoice_obj.oniad_root_complete_data(product, partner_payments_by_type['inbound'])                                                \n                        #logger_percent\n                        _logger.info(str(percent)+'% ('+str(count)+'/'+str(len(partner_payments))+')')\n                    #outbound\n                    if payment_types_item_amount['outbound']>0:\n                        #partner_payment_by_type_item_0\n                        partner_payment_by_type_item_0 = partner_payments_by_type['outbound'][0]\n                        #search out_invoice\n                        account_invoice_ids_out_invoice = self.env['account.invoice'].search(\n                            [\n                                ('type', '=', 'out_invoice'),\n                                ('partner_id', '=', partner_payment_by_type_item_0.partner_id.id),\n                                ('amount_total', '>=', payment_types_item_amount['outbound'])\n                            ], order=\"date_invoice desc\"\n                        )\n                        if len(account_invoice_ids_out_invoice)>0:\n                            account_invoice_id_out_invoice = account_invoice_ids_out_invoice[0]\n                            _logger.info('Creamos la negativa respecto a la encontrada '+str(account_invoice_id_out_invoice.id))\n                            #percent                \n                            percent = (float(count)/float(len(partner_payments)))*100\n                            percent = \"{0:.2f}\".format(percent)                                    \n                            #account_invoice_vals\n                            account_invoice_vals = {\n                                'oniad_address_id': account_invoice_id_out_invoice.oniad_address_id.id,\n                                'partner_id': account_invoice_id_out_invoice.partner_id.id,\n                                'partner_shipping_id': account_invoice_id_out_invoice.partner_id.id,\n                                'account_id': account_invoice_id_out_invoice.partner_id.property_account_receivable_id.id,\n                                'journal_id': account_invoice_id_out_invoice.journal_id.id,\n                                'date': date_invoice,\n                                'date_invoice': date_invoice,\n                                'date_due': date_invoice,\n                                'state': 'draft',\n                                'type': 'out_refund',\n                                'origin': account_invoice_id_out_invoice.number,\n                                'name': 'Devolucion',\n                                'comment': ' ',\n                                'currency_id': account_invoice_id_out_invoice.currency_id.id                                         \n                            }\n                            #user_id\n                            if account_invoice_id_out_invoice.user_id.id>0:\n                                account_invoice_vals['user_id'] = account_invoice_id_out_invoice.user_id.id                                            \n                            #continue\n                            _logger.info('Prepararmos para generar al partner_id '+str(account_invoice_vals['partner_id'])+\" y al partner_shipping_id \"+str(account_invoice_vals['partner_id']))            \n                            account_invoice_obj = self.env['account.invoice'].sudo().create(account_invoice_vals)\n                            _logger.info('Factura '+str(account_invoice_obj.id)+' creada correctamente')\n                            #oniad_root_complete_data\n                            account_invoice_obj.oniad_root_complete_data(product, partner_payments_by_type['outbound'])                            \n                            #logger_percent\n                            _logger.info(str(percent)+'% ('+str(count)+'/'+str(len(partner_payments))+')')                                \n                        else:\n                            _logger.info('NO se ha encontrado ninguna factura positiva de importe superior - DEBERIA SER TOTALMENTE IMPOSIBLE')","sub_path":"oniad_root/models/oniad_transaction.py","file_name":"oniad_transaction.py","file_ext":"py","file_size_in_byte":29863,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"231320325","text":"import pyembroidery.EmbPattern as EmbPattern\r\nimport math\r\n\r\n\r\ndef distance_squared(x0: float, y0: float, x1: float, y1: float) -> float:\r\n    dx = x1 - x0\r\n    dy = y1 - y0\r\n    dx *= dx\r\n    dy *= dy\r\n    return dx + dy\r\n\r\n\r\ndef distance(x0: float, y0: float, x1: float, y1: float) -> float:\r\n    return math.sqrt(distance_squared(x0, y0, x1, y1))\r\n\r\n\r\ndef towards(a: float, b: float, amount: float) -> float:\r\n    return (amount * (b - a)) + a\r\n\r\n\r\ndef angleR(x0: float, y0: float, x1: float, y1: float) -> float:\r\n    return math.atan2(y1 - y0, x1 - x0)\r\n\r\n\r\ndef oriented(x0: float, y0: float, x1: float, y1: float, r: float):\r\n    radians = angleR(x0, y0, x1, y1)\r\n    return (x0 + (r * math.cos(radians)), y0 + (r * math.sin(radians)))\r\n\r\nclass WriteEncoder():\r\n    def __init__(self):\r\n        self.max_jump_length = float('inf')  # type: float\r\n        self.max_stitch_length = float('inf')  # type: float\r\n        self.tie_on = False  # type: bool\r\n        self.tie_off = False  # type: bool\r\n        self.needle_x = 0  # type: float\r\n        self.needle_y = 0  # type: float\r\n        self.translate_X = 0  # type: float\r\n        self.translate_Y = 0  # type: float\r\n\r\n    def set_translation(self, x: float, y: float):\r\n        self.translate_X = x\r\n        self.translate_Y = y\r\n\r\n    def jumpTo(self, transcode, x: float, y: float):\r\n        self.step_to_range(transcode, x, y, self.max_jump_length, EmbPattern.JUMP)\r\n        transcode.append([x, y, EmbPattern.JUMP])\r\n\r\n    def stitchTo(self, transcode, x: float, y: float):\r\n        self.step_to_range(\r\n            transcode,\r\n            x,\r\n            y,\r\n            self.max_stitch_length,\r\n            EmbPattern.STITCH)\r\n        transcode.append([x, y, EmbPattern.STITCH])\r\n\r\n    def step_to_range(\r\n            self,\r\n            transcode,\r\n            x: float,\r\n            y: float,\r\n            length: float,\r\n            data: int):\r\n        distance_x = x - self.needle_x\r\n        distance_y = y - self.needle_y\r\n        if abs(distance_x) > length or abs(distance_y) > length:\r\n            stepsX = math.ceil(abs(distance_x / length))\r\n            stepsY = math.ceil(abs(distance_y / length))\r\n            if (stepsX > stepsY):\r\n                steps = stepsX\r\n            else:\r\n                steps = stepsY\r\n            stepSizeX = distance_x / steps\r\n            stepSizeY = distance_y / steps\r\n\r\n            q = 0\r\n            qe = steps\r\n            qx = self.needle_x\r\n            qy = self.needle_y\r\n            while q < qe:\r\n                transcode.append([round(qx), round(qy), data])\r\n                q += 1\r\n                qx += stepSizeX\r\n                qy += stepSizeY\r\n\r\n    def lock_stitch(\r\n            self,\r\n            transcode,\r\n            lockposition_x: float,\r\n            lockposition_y: float,\r\n            towards_x: float,\r\n            towards_y: float):\r\n        if distance(\r\n                lockposition_x,\r\n                lockposition_y,\r\n                towards_x,\r\n                towards_y) > self.max_stitch_length:\r\n            polar = oriented(\r\n                lockposition_x,\r\n                lockposition_y,\r\n                towards_x,\r\n                towards_y,\r\n                self.max_stitch_length)\r\n            towards_x = polar[0]\r\n            towards_y = polar[1]\r\n        self.stitchTo(transcode, lockposition_x, lockposition_y)\r\n        self.stitchTo(\r\n            transcode, towards(\r\n                lockposition_x, towards_x, .33), towards(\r\n                lockposition_y, towards_y, .33))\r\n        self.stitchTo(\r\n            transcode, towards(\r\n                lockposition_x, towards_x, .66), towards(\r\n                lockposition_y, towards_y, .66))\r\n        self.stitchTo(\r\n            transcode, towards(\r\n                lockposition_x, towards_x, .33), towards(\r\n                lockposition_y, towards_y, .33))\r\n\r\n    def process(self, p: EmbPattern) -> EmbPattern:\r\n        copy = EmbPattern.EmbPattern()\r\n        EmbPattern.set(p, copy)\r\n        layer = copy.stitches\r\n        for stitch in layer:\r\n            stitch[0] = round(stitch[0] - self.translate_X)\r\n            stitch[1] = round(stitch[1] - self.translate_Y)\r\n        p.stitches = []\r\n        p.threadlist = []\r\n        self.write_code(copy, p)\r\n        self.write_thread(copy, p)\r\n        return p\r\n\r\n    def write_thread(self, pattern_from, pattern_to):\r\n        threads_to = pattern_to.threadlist\r\n        threads_to.extend(pattern_to.threadlist)\r\n\r\n    def write_code(self, pattern_from, pattern_to):\r\n        from_stitches = pattern_from.stitches\r\n        to_stitches = pattern_to.stitches\r\n        current_index_end = len(from_stitches)\r\n        current_index = 0\r\n        while current_index < current_index_end:\r\n            current = from_stitches[current_index]\r\n            current_x = current[0]\r\n            current_y = current[1]\r\n            current_command = current[2]\r\n            if current_command is EmbPattern.STITCH:\r\n                self.stitchTo(to_stitches, current_x, current_y)\r\n            elif current_command is EmbPattern.STITCH_FINAL_LOCATION:\r\n                if self.tie_off:\r\n                    bi = current_index - 1\r\n                    b = from_stitches[bi]\r\n                    bx = b[0]\r\n                    by = b[1]\r\n                    self.lock_stitch(to_stitches, current_x, current_y, bx, by)\r\n                self.stitchTo(to_stitches, current_x, current_y)\r\n                to_stitches.append([current_x, current_y, EmbPattern.TRIM])\r\n            elif current_command is EmbPattern.STITCH_FINAL_COLOR:\r\n                if self.tie_off:\r\n                    bi = current_index - 1\r\n                    b = from_stitches[bi]\r\n                    bx = b[0]\r\n                    by = b[1]\r\n                    self.lock_stitch(to_stitches, current_x, current_y, bx, by)\r\n                self.stitchTo(to_stitches, current_x, current_y)\r\n                to_stitches.append([current_x, current_y, EmbPattern.TRIM])\r\n                to_stitches.append(\r\n                    [current_x, current_y, EmbPattern.COLOR_CHANGE])\r\n            elif current_command is EmbPattern.STITCH_NEW_LOCATION or current_command is EmbPattern.STITCH_NEW_COLOR:\r\n                self.jumpTo(to_stitches, current_x, current_y)\r\n                self.needle_x = current_x\r\n                self.needle_y = current_y\r\n                if self.tie_on:\r\n                    bi = current_index + 1\r\n                    b = from_stitches[bi]\r\n                    bx = b[0]\r\n                    by = b[1]\r\n                    self.lock_stitch(to_stitches, current_x, current_y, bx, by)\r\n                to_stitches.append([current_x, current_y, EmbPattern.STITCH])\r\n            else:\r\n                to_stitches.append(current)\r\n            self.needle_x = current_x\r\n            self.needle_y = current_y\r\n            current_index += 1\r\n        to_stitches.append([self.needle_x, self.needle_y, EmbPattern.END])\r\n","sub_path":"pyembroidery/WriteEncoder.py","file_name":"WriteEncoder.py","file_ext":"py","file_size_in_byte":6949,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"523611529","text":"#Reference - http://selenium-python.readthedocs.io/page-objects.html#\n\nimport unittest\nfrom selenium import webdriver\nimport selenium.webdriver.support.ui as ui\nimport pages\nimport HTMLTestRunner\nimport time\n\n\nclass PythonOrgSearch(unittest.TestCase):\n    \"\"\"A sample test class to show how page object works\"\"\"\n\n    def setUp(self):\n        self.driver = webdriver.Chrome()\n        self.driver.get(\"http://www.python.org\")\n       \n\n    def test_search_in_python(self):\n        \"\"\"\n        Tests python.org search feature. Searches for the word \"pycon\" then verified that some results show up.\n        Note that it does not look for any particular text in search results page. This test verifies that\n        the results were not empty.\n        \"\"\"\n\n        #Load the main page. In this case the home page of Python.org.\n        main_page = pages.MainPage(self.driver)\n        #Checks if the word \"Python\" is in title\n        assert main_page.is_title_matches()\n         #Sets the text of search textbox to \"pycon\"\n        main_page.search_text_element = \"pycon\"\n        time.sleep(5)\n        main_page.click_go_button()\n        search_results_page = pages.SearchResults(self.driver)\n        time.sleep(5)\n        #Verifies that the results page is not empty\n        assert search_results_page.is_results_found(), \"No Results found\"\n\n    def tearDown(self):\n        time.sleep(5)\n        self.driver.close()\n\nif __name__ == \"__main__\":\n    HTMLTestRunner.main()    ","sub_path":"PageObjectModel/pythongOrgSearch.py","file_name":"pythongOrgSearch.py","file_ext":"py","file_size_in_byte":1465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"547356539","text":"# pylint:disable=missing-docstring, invalid-name\n\nfrom json import dumps, loads\nfrom os import getenv\n\nfrom tests.base import BaseCase\n\n\nclass TestConversation(BaseCase):\n    \"\"\"\n    Convesation resource tests.\n    \"\"\"\n\n    def test_create_conversation_correctly(self):\n        self.user1.save()\n        self.user2.save()\n        response = self.client.post(\n            '/api/v1/conversations/',\n            content_type='application/json',\n            data=dumps(self.conversation3_dict),\n            headers=self.headers)\n        expected = sorted(['participants', 'messages',\n                           'board_id', 'id', 'timestamp'])\n        actual = sorted(\n            [i for i in loads(response.data)['data']['conversation']])\n        self.assertEqual(201, response.status_code)\n        self.assertEqual(expected, actual)\n\n    def test_create_conversation_no_participants(self):\n        self.user1.save()\n        self.user2.save()\n        response = self.client.post(\n            '/api/v1/conversations/',\n            content_type='application/json',\n            data=dumps({}),\n            headers=self.headers)\n        expected = {\n            'status': 'fail',\n            'message': 'Participants list required.',\n            'help': 'It can be empty if conversing with oneself.'\n        }\n        actual = loads(response.data)\n        self.assertEqual(400, response.status_code)\n        self.assertEqual(expected, actual)\n\n    def test_create_conversation_nonexistent_participant(self):\n        self.user1.save()\n        response = self.client.post(\n            '/api/v1/conversations/',\n            content_type='application/json',\n            data=dumps(self.conversation3_dict),\n            headers=self.headers)\n        expected = {\n            'status': 'fail',\n            'message': 'The user does not exist.',\n            'missing_user': 2\n        }\n        actual = loads(response.data)\n        self.assertEqual(404, response.status_code)\n        self.assertEqual(expected, actual)\n","sub_path":"tests/test_views/test_conversation.py","file_name":"test_conversation.py","file_ext":"py","file_size_in_byte":2004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"110874537","text":"#!/usr/bin/env python\n\nimport subprocess,time\n\n#++++++++++++++++++++++++++++ simulate_work: internal Function ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n#\n# This function simulates the processing of images\n\n# Functionality : \n# The function performs floating point operations that take roughly 0.02 seconds\n# \n# Usage: thread.start_new_thread(process_thread)\n# \n# Inputs: \n#         None\n#\n#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n\ndef simulate_work(num):\n\n\ti = 0\n\ttemp = 1\n\twhile(i < num):\n\t\ttemp = temp * 0.9\n\t\ti += 1\n\t\nif __name__ == \"__main__\":\n\n\tstart_time = time.time()\n\n\tfor i in range(1000):\n\n\t\tsimulate_work(300000)\n\n\ttime.sleep(0.5)\n\t\n\ttotal_time = time.time() - start_time\n\tavg_time = total_time/1000\n \t\n\tprint(\"---average time %s seconds ---\" % (avg_time))\n\n","sub_path":"test post speed/testfunc.py","file_name":"testfunc.py","file_ext":"py","file_size_in_byte":868,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"81072021","text":"from adlib27.optimize import optimize\nfrom adlib27.elem_function import exp, sin, cos, tan, arcsin, arccos, arctan, sinh, cosh, tanh, logistic, log, log2, log10, logb, sqrt\nfrom adlib27.autodiff import AutoDiff as AD\nimport numpy as np\n\n#initialize the program and ask for variables\ndef getvars():\n    yeslist = [\"y\", \"Yes\", \"Y\", \"yes\"]\n    more = True\n    variables = []\n\n    if input(\"Welcome to AdLib27. Does your function contain any variables? [y/n]: \") in yeslist:\n         while more == True:\n            #if there are variables, must enter a variable that is composed of letters\n            a = input(\"Please enter your variable: \")\n            if a.isalpha() == True and a not in variables:\n                variables.append(a)\n            #if variable already exists or is not a letter, retry\n            else:\n                print(\"Invalid or duplicate variable name. Please retry.\")\n            #ask if user wants to enter another function, then execute loop again if true.\n            if input(\"Would you like to enter another variable? [y/n]: \") in yeslist:\n                more = True\n            else:\n                more = False\n    #return collected variables\n    print(\"Your variables are: \", variables)             \n    return variables\n\n#function to get the function that will be evaulated\ndef getfunc(variables):\n    #ask for a formatted function (using the formatting in out documentation, and standard to python)\n    func = input(\"Enter a Python-formatted function for evaluation: \")\n    dummyfunc = func\n    x=1\n\n    #check if function contains all variables\n    for var in variables:\n        if not var in dummyfunc:\n            print(\"This function is invalid or does not contain all variables, please retry.\")\n            return False\n        else:\n            dummyfunc = dummyfunc.replace(var,\"x\")\n    try:\n        #check if function is executable, using a dummy variable x\n        exec(dummyfunc)\n    except:\n        #if not valid, retry entry\n        print(\"This is not a valid function, please retry.\")\n        return False\n    else: \n        return func\n\n#function to get the mode: evaluating at a given point, or optimizing over a range\ndef getmode():\n    mode = input(\"Would you like to \\n(a) evaluate at a point, or \\n(b) optimize over a range? [a/b] \")\n    if mode.lower() == 'a':\n        return 0\n    elif mode.lower() == 'b': \n        return 1\n    else: \n        return 2\n\n#function to get values for optimization\ndef getoptvals(variables):\n    #ask for some step size that must be a float\n    step = input(\"What step size would you like to take while finding values? Please enter a float. Note that small step sizes will increase program runtime. \")\n    try: \n        step = abs(float(step))\n    except:\n        #if not a float, retry\n        print(\"Not a valid step size\")\n        return False\n    #ask for start of range that must be float\n    start = input(\"At what value would you like to start searching? \")\n    try:\n        start = float(start)\n    except:\n        print(\"Not a valid start point\")\n        return False \n    #ask for end of range that must be floats\n    end = input(\"At what value would you like to stop searching? \")\n    try:\n        end = float(end)\n        assert end > start\n    except:\n        print(\"Not a valid end point\")\n        return False\n    #create list of values to test using numpy linspace\n    optval = list(np.linspace(start, end, ((end-start)/step)+1))\n    \n    return optval\n\n#for point evaluation mode, find number of points being evaluated\ndef getpointnum():\n    x = input(\"How many points would you like to evaluate at? \")\n    try:\n         x = abs(int(x))\n    except:\n        print(\"This is not a valid number of evaluation points, please retry\")\n        return False\n    else: \n        return x\n\n#for point evaluation mode, ask for places to evaluate for each variables\ndef getvalues(variables, pointnum):\n    i = 0\n    values =[]\n    j = 0\n    while i < len(variables):\n        print(\"Enter an evaluation value for the variable '\", variables[i], \"' as a python-formatted list of length\", pointnum, \"of floats or integers:\")\n        values.append(input().strip('][').split(', '))\n        i += 1\n    #map to floats, must be valid\n    while j < len(values):\n        try: \n            values[j] = list(map(float, values[j]))\n            j += 1\n        except: \n            print(\"Not all evaluation values are valid, please re-start entries.\")\n            return False\n    return values\n\n#use ADlist to evaluate value and derivative for a function \ndef getresult(values,variables,func):\n    funcdict = {\"exp\": exp, \"sin\": sin, \"cos\": cos, \"tan\": tan, \"arcsin\": arcsin, \"arccos\": arccos, \"arctan\": arctan, \"sinh\": sinh, \"cosh\": cosh, \"tanh\": tanh, \"logistic\": logistic, \"log\": log, \"log2\": log2, \"log10\": log10, \"logb\": logb, \"sqrt\": sqrt}\n    i = 0\n    ADlist = []\n    vardict = {}\n    while i < len(variables):\n        ADlist.append(AD(values[i],i,len(variables)))\n        vardict[variables[i]] = ADlist[i]\n        i += 1\n    return eval(func,funcdict,vardict)\n\n#represent result for point evaluation mode\ndef reprresult(values, variables, result, pointnum, func):\n    #represent values after evaluation\n    def reprvals(values, variables, pointnum):\n        valrep = []\n        a = 0\n        while a < pointnum:\n            b = 0\n            pointsrep = [] \n            while b < len(variables):\n                pointrep = []\n                pointrep.append(str(variables[b]))\n                pointrep.append(\" = \")\n                pointrep.append(\"{:.5f}\".format(values[b][a]))\n                pointsrep.append(''.join(pointrep))\n                b += 1\n            valrep.append(', '.join(pointsrep))\n            a += 1\n        return valrep\n\n    #represent derivatives after evaluation\n    def reprders(result, variables, pointnum):\n        derrep = []\n        a = 0\n        while a < pointnum:\n            b = 0\n            drep = [] \n            while b < len(variables):\n                ddrep = []\n                ddrep.append(\"df/d\")\n                ddrep.append(str(variables[b]))\n                ddrep.append(\" = \")\n                ddrep.append(\"{:.5f}\".format(result.der[b][a]))\n                drep.append(''.join(ddrep))\n                b += 1\n            derrep.append(', '.join(drep))\n            a += 1\n        return derrep\n\n    vals = reprvals(values, variables, pointnum)\n    ders = reprders(result, variables, pointnum)\n    c = 0  \n    reslist = []\n\n    #put all together into string for output, for each point\n    while c < pointnum:\n        resmini = []\n        resmini.append(\"For evaluation values (\")\n        resmini.append(vals[c])\n        resmini.append(\"), the value of the function f=\")\n        resmini.append(str(func))\n        resmini.append(\" is: \")\n        resmini.append(\"{:.5f}\".format(result.val[c]))\n        resmini.append(\" and the the derivatives are: \")\n        resmini.append(ders[c])\n        reslist.append(''.join(resmini))\n        c += 1\n\n    return '\\n\\n'.join(reslist)\n\n#represent results for optimization\ndef repropt(optimized, vals, variables, func):\n\n    #within a domain, represent local min and max for function\n    optout = []\n    optout.append(\"\\nIn the domain \")\n    optout.append(\"{:.5f}\".format(vals[0]))\n    optout.append(\" to \")\n    optout.append(\"{:.5f}\".format(vals[-1]))\n    optout.append(\", the extrema for \")\n    optout.append(str(func))\n    optout.append(\" represented as \") \n    optout.append(str(variables))\n    optout.append(\" are:\")\n    optout.append(\"\\nThe local minimum is the \")\n    optout.append((str(optimized.get(\"global minimum\").get(\"inflection type\"))))\n    optout.append(\" located in the range \")\n    optout.append(str(optimized.get(\"global minimum\").get(\"input range\")))\n    optout.append(\" valued near \")\n    optout.append(str(optimized.get(\"global minimum\").get(\"value range\")))\n    optout.append(\"\\nThe local maximum is the \")\n    optout.append((str(optimized.get(\"global maximum\").get(\"inflection type\"))))\n    optout.append(\" located in the range \")\n    optout.append(str(optimized.get(\"global maximum\").get(\"input range\")))\n    optout.append(\" valued near \")\n    optout.append(str(optimized.get(\"global maximum\").get(\"value range\")))\n    \n    return ''.join(optout)\n        \n#ask if cotinued optimizing after output\ndef contopt():\n    yeslist = [\"y\", \"Yes\", \"Y\", \"yes\"]\n    if input(\"Would you like to continue optimizing this function? [y/n] \") in yeslist:\n        return True\n    else:\n        return False\n\ndef main():\n    gv = getvars()\n    gf = getfunc(gv)\n    while gf == False: gf = getfunc(gv)\n    gm = getmode()\n    while gm == 2: gm = getmode()\n    if gm == 0:\n        gp= getpointnum()\n        while gp == False: gp = getpointnum()\n        gx = getvalues(gv, gp)\n        while gx == False: gx = getvalues(gv, gp)\n        print(reprresult(gx,gv,getresult(gx,gv,gf),gp,gf))\n    else:\n        searching = True\n        while searching == True: \n            go = getoptvals(gv)\n            while go == False: go = getoptvals(gv)\n            print(repropt(optimize(go,gv,gf), go, gv, gf))\n            searching = contopt()      \n\nif __name__ == '__main__':\n    main()","sub_path":"adlib27/interact.py","file_name":"interact.py","file_ext":"py","file_size_in_byte":9143,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"634532119","text":"\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.dates as mdates\nfrom datetime import datetime, timedelta\nplt.style.use('bmh')\n\nt0 = datetime.now()\ntime = np.array([t0 - timedelta(minutes=x) for x in range(60*24)])\ny = np.random.rand(time.size)\n\nfig, ax = plt.subplots(figsize=(14, 6))\nax.plot(time, y, 'x-')\nax.set_ylim(-10, 10)\n\nylim = ax.get_ylim()\na, b = 200, 350\nxy = np.array([\n    [mdates.date2num(time[a]), ylim[0]],\n    [mdates.date2num(time[b]), ylim[0]],\n    [mdates.date2num(time[b]), ylim[1]],\n    [mdates.date2num(time[a]), ylim[1]]])\npoly = plt.Polygon(xy, color='salmon', alpha=0.2)\nax.add_patch(poly)\n\nfig.tight_layout()\nplt.show()\n","sub_path":"mpld3/tmp.py","file_name":"tmp.py","file_ext":"py","file_size_in_byte":670,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"615853528","text":"# -*- coding: UTF-8 -*-\n# -----------------------------------------------------------------------------\n#\n#     P A G E B O T\n#\n#     Copyright (c) 2016+ Buro Petr van Blokland + Claudia Mens\n#     www.pagebot.io\n#     Licensed under MIT conditions\n#\n#     Supporting DrawBot, www.drawbot.com\n#     Supporting Flat, xxyxyz.org/flat\n# -----------------------------------------------------------------------------\n#\n#     basecontext.py\n#\n\nimport os\nfrom pagebot.constants import (DISPLAY_BLOCK, DEFAULT_FRAME_DURATION)\nfrom pagebot.toolbox.units import upt, pt, point2D\nfrom pagebot.contexts.abstractdrawbotcontext import AbstractDrawBotContext\n\nclass BaseContext(AbstractDrawBotContext):\n    \"\"\"Extends the DrawBot interface.\n    \"\"\"\n\n    # In case of specific builder addressing, callers can check here.\n    isDrawBot = False\n    isFlat = False\n    isSvg = False\n    isInDesign = False\n\n    # Indication to Typesetter that by default tags should not be included in\n    # output.\n    useTags = False\n\n    # To be redefined by inheriting context classes.\n    STRING_CLASS = None\n    EXPORT_TYPES = None\n\n    def __repr__(self):\n        return '<%s>' % self.name\n\n    def __init__(self):\n        self._path = None # Hold current open DrawBot path.\n\n    def newDocument(self, w, h):\n        raise NotImplementedError\n\n    def saveDocument(self, path, multiPage=None):\n        raise NotImplementedError\n\n    def newString(self, s, e=None, style=None, w=None, h=None, pixelFit=True):\n        \"\"\"Creates a new styles BabelString instance of self.STRING_CLASS from\n        `s` (converted to plain unicode string), using e or style as\n        typographic parameters. Ignore and just answer `s` if it is already a\n        self.STRING_CLASS instance and no style is forced.\"\"\"\n        if not isinstance(s, self.STRING_CLASS):\n            # Otherwise convert s into plain string, from whatever it is now.\n            s = self.STRING_CLASS.newString(str(s), context=self, e=e,\n                    style=style, w=w, h=h, pixelFit=pixelFit)\n        assert isinstance(s, self.STRING_CLASS)\n        return s\n\n    def fitString(self, s, e=None, style=None, w=None, h=None, pixelFit=True):\n        \"\"\"Creates a new styles BabelString instance of self.STRING_CLASS from\n        s assuming that style['font'] is a Variable Font instnace, or a path\n        pointing to one. If the for is not a VF, then behavior is the same as\n        newString. (converted to plain unicode string), using e or style as\n        typographic parameters. Ignore and just answer s if it is already a\n        `self.STRING_CLASS` instance.\n        \"\"\"\n        if not isinstance(s, self.STRING_CLASS):\n            # Otherwise convert s into plain string, from whatever it is now.\n            s = self.STRING_CLASS.fitString(str(s), context=self, e=e, style=style,\n                w=w, h=h, pixelFit=pixelFit)\n        assert isinstance(s, self.STRING_CLASS)\n        return s\n\n    def newText(self, textStyles, e=None, w=None, h=None, newLine=False):\n        \"\"\"Answers a BabelString as a combination of all text and styles in\n        textStyles, which is should have format\n\n        [(baseString, style), (baseString, style), ...]\n\n        Add return \\n to the string is the newLine attribute is True or if a\n        style has\n\n        style.get('display') == DISPLAY_BLOCK\n\n        \"\"\"\n        assert isinstance(textStyles, (tuple, list))\n        s = None\n\n        for t, style in textStyles:\n            if newLine or (style and style.get('display') == DISPLAY_BLOCK):\n                t += '\\n'\n            bs = self.newString(t, style=style, e=e, w=w, h=h)\n            if s is None:\n                s = bs\n            else:\n                s += bs\n        return s\n\n    #   G L Y P H\n\n    def intersectWithLine(self, glyph, line):\n        \"\"\"Answers the sorted set of intersecting points between the straight\n        line and the flatteded glyph path.\"\"\"\n        intersections = set() # As set,  make sure to remove any doubles.\n\n        def det(a, b):\n            return a[0] * b[1] - a[1] * b[0]\n\n        (lx0, ly0), (lx1, ly1) = line\n        maxX = max(lx0, lx1)\n        minX = min(lx0, lx1)\n        maxY = max(ly0, ly1)\n        minY = min(ly0, ly1)\n        glyphPath = self.getGlyphPath(glyph)\n        contours = self.getFlattenedContours(glyphPath)\n\n        if not contours:\n            # Could not generate path or flattenedPath. Or there are no\n            # contours. Give up.\n            return None\n\n        for contour in contours:\n            for n in range(len(contour)):\n                pLine = contour[n], contour[n-1]\n                (px0, py0), (px1, py1) = pLine\n                if minY > max(py0, py1) or maxY < min(py0, py1) or \\\n                        minX > max(px0, px1) or maxX < min(px0, px1):\n                    continue # Skip if boundings boxes don't overlap.\n\n                xdiff = (line[0][0] - line[1][0], pLine[0][0] - pLine[1][0])\n                ydiff = (line[0][1] - line[1][1], pLine[0][1] - pLine[1][1])\n\n                div = det(xdiff, ydiff)\n                if div == 0:\n                   continue # No intersection\n\n                d = (det(*line), det(*pLine))\n                intersections.add((det(d, xdiff) / div, det(d, ydiff) / div))\n\n        # Answer the set as sorted list, increasing x, from left to right.\n        return sorted(intersections)\n\n    def checkExportPath(self, path):\n        \"\"\"If the path starts with \"_export\" make sure it exists, otherwise\n        create it. The _export folders are used to export documents locally,\n        without saving them to git. The _export name is included in the git\n        .gitignore file.\n\n        >>> context = BaseContext()\n        >>> context.checkExportPath('_export/myFile.pdf')\n        >>> os.path.exists('_export')\n        True\n        \"\"\"\n        if path.startswith('_export'):\n            dirPath = '/'.join(path.split('/')[:-1])\n            if not os.path.exists(dirPath):\n                os.makedirs(dirPath)\n\n    #   S C R E E N\n\n    def screenSize(self):\n        \"\"\"Answers the current screen size in DrawBot. Otherwise default is to\n        do nothing.\n\n        >>> from pagebot.contexts.drawbotcontext import DrawBotContext\n        >>> context = DrawBotContext()\n        >>> size = context.screenSize()\n        >>> size[0] > 100 and size[1] > 100\n        True\n        \"\"\"\n        return pt(self.b.sizes().get('screen', None))\n\n    def circle(self, x, y, r):\n        \"\"\"Circle draws a DrawBot oval with (x,y) as middle point and radius r.\n        This method is using the core BezierPath as path to draw on. For a more rich\n        environment use PageBotPath(context) instead.\n\n        >>> from pagebot.contexts.drawbotcontext import DrawBotContext\n        >>> context = DrawBotContext()\n        >>> context.circle(pt(100), pt(200), pt(50))\n        >>> context.circle(100, 200, 50)\n        \"\"\"\n        xpt, ypt, rpt = upt(x, y, r)\n        self.b.oval(xpt-rpt, ypt-rpt, rpt*2, rpt*2) # Render the unit values\n\n\nif __name__ == '__main__':\n    import doctest\n    import sys\n    sys.exit(doctest.testmod()[0])\n","sub_path":"Lib/pagebot/contexts/basecontext.py","file_name":"basecontext.py","file_ext":"py","file_size_in_byte":7080,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"12302226","text":"from django.template import RequestContext\nfrom JuEq.forms import loginForm, SignUpForm, JugadorForm, EquipoForm\nfrom django.contrib.auth import authenticate,login, logout\nfrom django.shortcuts import render, HttpResponse, loader, get_object_or_404, redirect\nfrom JuEq.models import Equipo, Jugador\nfrom django.http import HttpResponseRedirect\nfrom django.contrib.auth.decorators import login_required\n# Create your views here.\n\n#@login_required(login_url = '/login')\ndef equipos(request, equipo_id, auth_form=None, user_form=None):\n\tlista_equipo = Equipo.objects.order_by('anio_creacion')\n\tequipo=get_object_or_404(Equipo,id = equipo_id)\n\ttemplate=loader.get_template('JuEq/Equipos.html')\n\tdatosJugador=Jugador.objects.filter(equipo_jugador=equipo)\n\tcontext={\n\t\t'equipo': equipo,\n\t\t'datosJugador':datosJugador,\n\t\t'lista_equipo': lista_equipo,\n\t}\n\treturn HttpResponse(template.render(context, request))\n\ndef jugador(request, jugador_id, auth_form=None, user_form=None):\n\tlista_equipo = Equipo.objects.order_by('anio_creacion')\n\tjugador=get_object_or_404(Jugador, id=jugador_id)\n\ttemplate = loader.get_template('JuEq/jugador.html')\n\tcontext={\n\t\t'jugador':jugador,\n\t\t'lista_equipo': lista_equipo,\n\t}\n\treturn HttpResponse(template.render(context, request))\n\ndef jugadores(request):\n\tlista_equipo = Equipo.objects.order_by('anio_creacion')\n\tlista_jugador=Jugador.objects.order_by('nombre_jugador')\n\ttemplate = loader.get_template('JuEq/jugadores.html')\n\tcontext={ 'lista_jugador':lista_jugador, 'lista_equipo': lista_equipo,}\n\treturn HttpResponse(template.render(context, request))\n\ndef login_view(request):\n\tmessage = None\n\tif request.method ==\"POST\":\n\t\tform = loginForm(request.POST)\n\t\tif form.is_valid:\n\t\t\tusername = request.POST['username']\n\t\t\tpassword = request.POST['password']\n\t\t\tuser = authenticate(username=username, password=password)\n\t\t\tif user is not None:\n\t\t\t\tif user.is_active:\n\t\t\t\t\tlogin(request,user)\n\t\t\t\t\treturn HttpResponseRedirect('/')\n\t\t\t\telse:\n\t\t\t\t\tmessage = \"Login incorrecto\"\n\t\t\telse:\n\t\t\t\tmessage = \"Nombre y/o password incorrectos\"\n\telse:\n\t\tform = loginForm()\t\t\n\treturn render(request, 'JuEq/login.html', {'message':message, 'form':form})\t\t\t\t\n\ndef home(request):\n\tlista_equipo = Equipo.objects.order_by('anio_creacion')\n\ttemplate = loader.get_template('JuEq/home.html')\n\tcontext={\n\t\t'lista_equipo': lista_equipo,\n\t}\n\treturn HttpResponse(template.render(context, request))\n\ndef nuevoUsuario(request):\n\tif not request.user.is_anonymous():\n\t\treturn HttpResponseRedirect('/')\n\tif request.method=='POST':\n\t\tform = SignUpForm(request.POST)\n\t\tif form.is_valid:\n\t\t\tusername = request.POST['username']\n\t\t\tpassword1 = request.POST['password1']\n\t\t\tpassword2 = request.POST['password2']\n\t\t\tform.save()\n\t\t\treturn HttpResponseRedirect('/')\n\telse:\n\t\tform = SignUpForm()\n\treturn render(request, 'JuEq/register.html', {'form':form})\n\n@login_required\ndef logout_view(request):\n\tlogout(request)\n\treturn HttpResponseRedirect('/')\n\ndef nuevoJugador(request):\n\tif request.method== \"POST\":\n\t\tform = JugadorForm(request.POST)\n\t\tif form.is_valid():\n\t\t\tpost = form.save(commit=False)\n\t\t\tpost.save()\n\t\t\treturn redirect('/jugadores')\n\telse:\n\t\tform=JugadorForm()\n\n\tcontext={\n\t\t'form':form\n\t}\n\ttemplate= loader.get_template('JuEq/crearJugador.html')\n\treturn HttpResponse(template.render(context, request))\n\ndef nuevoEquipo(request):\n\tif request.method== \"POST\":\n\t\tform = EquipoForm(request.POST)\n\t\tif form.is_valid():\n\t\t\tpost = form.save(commit=False)\n\t\t\tpost.save()\n\t\t\treturn redirect('/')\n\telse:\n\t\tform=EquipoForm()\n\n\tcontext={\n\t\t'form':form\n\t}\n\ttemplate= loader.get_template('JuEq/crearEquipo.html')\n\treturn HttpResponse(template.render(context, request))\n\ndef editarJugador(request, jugador_id):\n\tjugador=get_object_or_404(Jugador, id=jugador_id)\n\tif request.method== \"POST\":\n\t\tform = JugadorForm(request.POST, instance=jugador)\n\t\tif form.is_valid():\n\t\t\tpost = form.save(commit=False)\n\t\t\tpost.save()\n\t\t\treturn redirect('JuEq:jugador', jugador_id)\n\telse:\n\t\tform=JugadorForm()\n\n\tcontext={\n\t\t'form':form,\n\t\t'jugador':jugador,\n\t}\n\ttemplate= loader.get_template('JuEq/editarJugador.html')\n\treturn HttpResponse(template.render(context, request))\n\ndef editarEquipo(request, equipo_id):\n\tequipo=get_object_or_404(Equipo,id = equipo_id)\n\tif request.method== \"POST\":\n\t\tform = EquipoForm(request.POST, instance=equipo)\n\t\tif form.is_valid():\n\t\t\tpost = form.save(commit=False)\n\t\t\tpost.save()\n\t\t\treturn redirect('JuEq:equipos', equipo_id)\n\telse:\n\t\tform=EquipoForm()\n\n\tcontext={\n\t\t'form':form,\n\t\t'equipo':equipo,\n\t}\n\ttemplate= loader.get_template('JuEq/editarEquipo.html')\n\treturn HttpResponse(template.render(context, request))\n\ndef borrarJugador(request, jugador_id):\n\tjugador=get_object_or_404(Jugador, id=jugador_id)\n\tjugador.delete()\n\treturn redirect('/jugadores')\n\tcontext={\n\n\t\t'jugador':jugador,\n\t}\n\ttemplate= loader.get_template('JuEq/borrarJugador.html')\n\treturn HttpResponse(template.render(context, request))\n\ndef borrarEquipo(request, equipo_id):\n\tequipo=get_object_or_404(Equipo,id = equipo_id)\n\tequipo.delete()\n\treturn redirect('/')\n\tcontext={\n\n\t\t'equipo':equipo,\n\t}\n\ttemplate= loader.get_template('JuEq/borrarEquipo.html')\n\treturn HttpResponse(template.render(context, request))\n\ndef instrucciones(request):\n\ttemplate = loader.get_template('JuEq/instrucciones.html')\n\treturn HttpResponse(template.render(request))\n","sub_path":"Comunio/JuEq/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"141844757","text":"#!/usr/bin/python\nimport _thread, time, sys\nimport datetime    \nfrom time import sleep\n\ntry:\n    import Weblog.Send as Websend\nexcept Exception as ex:\n\tprint(\"No WEB LOG Module loaded!\", ex)\n\n\nclass Weblog():\n    def __init__(self):\n        self.first = \"1\"\n        self.time = datetime.datetime.now()\n        print(\"#Weblog initialized!\")\n\n    def log(self, temp, zieltemp, first = 0):\n        try:\n            if first == \"1\" or first == 1:\n                self.first = 1\n                \n            if (datetime.datetime.now() - self.time).total_seconds() < 1:\n                return\n                \n            temp = \"%.2f\" % temp\n            time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n            zieltemp = \"%.2f\" % zieltemp\n            \n            \n            _thread.start_new_thread( Websend.SendToDB, (temp, zieltemp, time, str(self.first)) )\n            # Websend.SendToDB(temp, zieltemp, time, self.first)\n            \n            self.time = datetime.datetime.now()\n            self.first = \"0\"\n        except Exception as ex:\n            print(\"could not log to website!\", ex)\n\n\n","sub_path":"Weblog/Weblog.py","file_name":"Weblog.py","file_ext":"py","file_size_in_byte":1116,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"226910789","text":"\"\"\"\nCheck and execute commands\n\"\"\"\n\ndef proc_cmd(robot):\n    command_str = input(\"Command: \").upper()\n    command_list = command_str.split(\" \")\n    command = command_list[0]\n    # print(command, command_list)\n    if not command in [\"PLACE\", \"MOVE\", \"LEFT\", \"RIGHT\", \"REPORT\"]:\n        print(\"Unknown command.\")\n        return\n\n    if (command != \"PLACE\" and len(command_list) > 1) or \\\n        (command == \"PLACE\" and len(command_list) < 2):\n        print(\"Invalid arguments.\")\n        return\n\n    if command == \"PLACE\":\n        try:\n            x, y, facing = \"\".join(command_list[1:]).split(\",\")\n            x = int(x)\n            y = int(y)\n        except ValueError:\n            print(\"Invalid arguments.\")\n            return\n        info = robot.place(x, y, facing)\n    elif command == \"MOVE\":\n        info = robot.move()\n    elif command == \"LEFT\":\n        info = robot.left()\n    elif command == \"RIGHT\":\n        info = robot.right()\n    elif command == \"REPORT\":\n        info = robot.report()\n\n    print(info)\n    return\n\n\n","sub_path":"cmd.py","file_name":"cmd.py","file_ext":"py","file_size_in_byte":1031,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"288024245","text":"# -*- coding: utf-8 -*-\r\n\r\nfrom db_client import *\r\nfrom datetime import timedelta\r\nimport simplejson as json\r\n\r\nclass EventRecord(object):\r\n\r\n\tdef __init__(self, cr, dt_start, dt_end):\r\n\t\tself.cr = cr\r\n\t\tself.dt_start = dt_start\r\n\t\tself.dt_end = dt_end\r\n\t\r\n\tdef _parseParaValue(self, line):\r\n\t\tlog_id = line['LogId']\r\n\t\tevent_name = line['EventName']\r\n\t\tevent_time = line['EventTime']\r\n\t\tevent_count = line['EventCount']\r\n\t\tevent_tag = line['EventTag'] if line['EventTag'] else \"{}\"\r\n\t\tevent_tag = json.loads( event_tag.replace(\"\\\\n\", \"\").replace(\" \", \"\") )\r\n\t\treturn log_id, event_name, event_time, event_count, event_tag\r\n\r\n\tdef _filterEventVal(self, event_name, event_key, event_val):\r\n\t\tif event_name == u\"car-param-comp-detail\" and event_key == u'carIds':\r\n\t\t\tevent_val = event_val.split(',')\r\n\t\tif event_key == u'userId':\r\n\t\t\tevent_val = None if event_val == \"-1\" else event_val\r\n\t\telif event_key == u'trackerId':\r\n\t\t\tevent_val = None if event_val == \"\" else event_val\r\n\t\treturn event_val\r\n\t\t\r\n\tdef _getSQLVal(self, log_id, event_name, event_time, event_count, event_key, event_val):\r\n\t\treturn u\"\"\"\r\n\t\t\t(\r\n\t\t\t\t'{log_id}',\r\n\t\t\t\t'{event_name}',\r\n\t\t\t\t'{event_time}',\r\n\t\t\t\t{event_count},\r\n\t\t\t\t{event_key},\r\n\t\t\t\t{event_val},\r\n\t\t\t\tGETDATE()\r\n\t\t\t)\r\n\t\t\"\"\".format(\r\n\t\t\tlog_id = log_id,\r\n\t\t\tevent_name = event_name,\r\n\t\t\tevent_time = event_time,\r\n\t\t\tevent_count = event_count,\r\n\t\t\tevent_key = \"'\" + event_key + \"'\" if event_key else \"NULL\",\r\n\t\t\tevent_val = \"'\" + event_val + \"'\" if event_val else \"NULL\"\r\n\t\t)\r\n\t\r\n\tdef _getSQLHead(self):\r\n\t\treturn u\"\"\"\r\n\t\t\tINSERT INTO [Example].[dbo].[FactEventRecord](\r\n\t\t\t\t[LogID],\r\n\t\t\t\t[EventName],\r\n\t\t\t\t[EventTime],\r\n\t\t\t\t[EventCount],\r\n\t\t\t\t[EventKey],\r\n\t\t\t\t[EventVal],\r\n\t\t\t\t[LoadTime]\r\n\t\t\t) VALUES \r\n\t\t\"\"\"\r\n\t\t\r\n\tdef _getRecord(self):\r\n\t\tsql = \"\"\"\r\n\t\t\tSELECT\r\n\t\t\t\ttb2.LogId,\r\n\t\t\t\ttb2.ParamValue AS EventName,\r\n\t\t\t\tCONVERT(\r\n\t\t\t\t\tDATETIME, \r\n\t\t\t\t\tSUBSTRING(tb3.ParamValue, 1, 4) + '-' + \r\n\t\t\t\t\tSUBSTRING(tb3.ParamValue, 5, 2) + '-' + \r\n\t\t\t\t\tSUBSTRING(tb3.ParamValue, 7, 2) + ' ' + \r\n\t\t\t\t\tSUBSTRING(tb3.ParamValue, 9, 2) + ':' + \r\n\t\t\t\t\tSUBSTRING(tb3.ParamValue, 11, 2) + ':' + \r\n\t\t\t\t\tSUBSTRING(tb3.ParamValue, 13, 2)\r\n\t\t\t\t) AS EventTime,\r\n\t\t\t\ttb4.ParamValue AS EventCount,\r\n\t\t\t\ttb5.ParamValue AS EventTag\r\n\t\t\tFROM [1.1.1.1].[LogDB].[LGC].[AppLog] tb1\r\n\t\t\tLEFT JOIN [1.1.1.1].[LogDB].[HIS].AppLogParam tb2\r\n\t\t\t\tON tb1.LogId = tb2.LogId AND tb2.ParamKey = 'EventName'\r\n\t\t\tLEFT JOIN [1.1.1.1].[LogDB].[HIS].AppLogParam tb3\r\n\t\t\t\tON tb1.LogId = tb3.LogId AND tb3.ParamKey = 'EventTime'\r\n\t\t\tLEFT JOIN [1.1.1.1].[LogDB].[HIS].AppLogParam tb4\r\n\t\t\t\tON tb1.LogId = tb4.LogId AND tb4.ParamKey = 'EventCount'\r\n\t\t\tLEFT JOIN [1.1.1.1].[LogDB].[HIS].AppLogParam tb5\r\n\t\t\t\tON tb1.LogId = tb5.LogId AND tb5.ParamKey = 'EventTag'\r\n\t\t\tWHERE tb1.OpTypeId = 3 \r\n\t\t\t  AND tb1.CreateTime >= '{dt_start}' AND tb1.CreateTime < '{dt_end}'\r\n\t\t\t  AND tb2.ParamValue IS NOT NULL\r\n\t\t\t  AND tb3.ParamValue IS NOT NULL\r\n\t\t\t  AND tb4.ParamValue IS NOT NULL\r\n\t\t\tORDER BY tb1.LogTime\r\n\t\t\"\"\".format(\r\n\t\t\tdt_start = self.dt_start,\r\n\t\t\tdt_end = self.dt_end\r\n\t\t)\r\n\t\tself.cr.execute(sql)\r\n\t\tall = fetch_all(self.cr)\r\n\t\treturn (line for line in all) if all else None\r\n\t\t\t\r\n\tdef insertRecord(self):\r\n\t\tall = self._getRecord()\r\n\t\tif not all:\r\n\t\t\treturn None\r\n\t\tsql = []\r\n\t\tfor line in all:\r\n\t\t\tlog_id, event_name, event_time, event_count, event_tag = self._parseParaValue(line)\r\n\t\t\tif event_tag:\r\n\t\t\t\tfor event_key, event_val in event_tag.items():\r\n\t\t\t\t\tevent_val = self._filterEventVal(event_name, event_key, event_val)\r\n\t\t\t\t\tif type(event_val) == list:\r\n\t\t\t\t\t\tfor each_event_val in event_val:\r\n\t\t\t\t\t\t\tsql.append(self._getSQLVal(log_id, event_name, event_time, event_count, event_key, each_event_val))\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tsql.append(self._getSQLVal(log_id, event_name, event_time, event_count, event_key, event_val))\r\n\t\t\telse:\r\n\t\t\t\tevent_key, event_val = None, None\r\n\t\t\t\tsql.append(self._getSQLVal(log_id, event_name, event_time, event_count, event_key, event_val))\r\n\t\t\tif len(sql) > 1000:\r\n\t\t\t\tself.cr.execute(self._getSQLHead() + \",\".join(sql[0:1000]))\r\n\t\t\t\tself.cr.commit()\r\n\t\t\t\tsql = sql[1000:]\r\n\t\tif sql:\r\n\t\t\tself.cr.execute(self._getSQLHead() + \",\".join(sql))\r\n\t\t\tself.cr.commit()\r\n\t\t\t\t\t\t\t\r\n\r\ndef updateEventRecord(cr, dt_start, dt_end):\r\n\tfor i in range(0, (dt_end - dt_start).days):\r\n\t\tdt_left = dt_start + timedelta(days=i)\r\n\t\tdt_right = dt_left + timedelta(days=1)\r\n\t\tFactEventRecord = EventRecord(cr, dt_left, dt_right)\r\n\t\tFactEventRecord.insertRecord()\r\n\t\t\r\n\t","sub_path":"data-analysis-system/app/prod_device/event.py","file_name":"event.py","file_ext":"py","file_size_in_byte":4457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"221669101","text":"# Definition for singly-linked list.\nclass ListNode(object):\n    def __init__(self, x):\n        self.val = x\n        self.next = None\n\nclass Solution(object):\n    # 注意就是删除target之后，检查下一个是不是也是target， 如果不是就move cur\n    def removeElements(self, head, val):\n        \"\"\"\n        :type head: ListNode\n        :type val: int\n        :rtype: ListNode\n        \"\"\"\n        fake = ListNode(0)\n        fake.next = head\n        cur = fake\n        while cur and cur.next:\n            if cur.next.val == val:\n                cur.next = cur.next.next  # delete the target\n            # check whether next val is also target\n            # if not, move cur one step\n            if cur.next and cur.next.val != val:\n                cur = cur.next\n        return fake.next\n\n\nroot = ListNode(1)\nroot.next = ListNode(2)\nroot.next.next = ListNode(6)\nroot.next.next.next = ListNode(3)\nroot.next.next.next.next = ListNode(4)\nroot.next.next.next.next.next = ListNode(6)\n\nres = Solution().removeElements(root, 6)\n# 1-2-3-4\nwhile res:\n    print(res.val, end='-')\n    res = res.next\nprint()\n\n\n","sub_path":"leet/题目/e203.py","file_name":"e203.py","file_ext":"py","file_size_in_byte":1113,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"586444104","text":"import numpy as np\nimport os\nimport six.moves.urllib as urllib\nimport sys\nimport tarfile\nimport time \nimport tensorflow as tf\nimport zipfile\nfrom argparse import ArgumentParser\n\nfrom collections import defaultdict\nfrom io import StringIO\nfrom PIL import Image\n\nimport cv2\n\n\nimport matplotlib.pyplot as plt\nimport random\n\n######################################################################################\nfolder = \"high/\"\nimage_list = \"time_list.txt\"\n\ndef show_cv2(img):\n    # swap b and r channels\n    b, g, r = cv2.split(img)\n    img = cv2.merge([r,g,b])\n\n#     plt.figure(figsize=(10, 10))\n    plt.imshow(img)\n    plt.show()\n\ndef rand_color():\n    def component():\n        return random.randint(0,255)\n    return (component(), component(), component())\n\ndef compute_line(p1, p2):\n    A = (p1[1] - p2[1])\n    B = (p2[0] - p1[0])\n    C = (p1[0]*p2[1] - p2[0]*p1[1])\n    return A, B, -C\n\ndef line_intersection(p1, p2):\n    L1 = compute_line(p1[:2], p1[2:4])\n    L2 = compute_line(p2[:2], p2[2:4])\n\n    d  = L1[0] * L2[1] - L1[1] * L2[0]\n    dx = L1[2] * L2[1] - L1[1] * L2[2]\n    dy = L1[0] * L2[2] - L1[2] * L2[0]\n    if d != 0:\n        x = dx / d\n        y = dy / d\n    else:\n        # assume negative coordinates are invalid\n        x = -1\n        y = -1\n    return np.asarray((x,y))\n\n\n\ncap = cv2.VideoCapture(0)\n\n# This is needed since the notebook is stored in the object_detection folder.\nsys.path.append(\"..\")\n\n\n# ## Object detection imports\n# Here are the imports from the object detection module.\n\n# In[3]:\n\n# # Model preparation\n\n# ## Variables\n#\n# Any model exported using the `export_inference_graph.py` tool can be loaded here simply by changing `PATH_TO_CKPT` to point to a new .pb file.\n#\n# By default we use an \"SSD with Mobilenet\" model here. See the [detection model zoo](https://github.com/tensorflow/models/blob/master/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies.\n\n# In[4]:\n\n# What model to download.\nMODEL_NAME = 'six_pm_graph'\n\n# Path to frozen detection graph. This is the actual model that is used for the object detection.\nPATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'\n\n# List of the strings that is used to add correct label for each box.\nPATH_TO_LABELS = os.path.join('training', 'object-detection.pbtxt')\n\nNUM_CLASSES = 1\n\n\n# ## Load a (frozen) Tensorflow model into memory.\n\n# In[6]:\n\ndetection_graph = tf.Graph()\nwith detection_graph.as_default():\n  od_graph_def = tf.GraphDef()\n  with tf.gfile.GFile(PATH_TO_CKPT, '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\n# ## Loading label map\n# Label maps map indices to category names, so that when our convolution network predicts `5`, we know that this corresponds to `airplane`.  Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine\n\n# In[7]:\n\n\n# ## Helper code\n\n# In[8]:\n\ndef load_image_into_numpy_array(image):\n  (im_width, im_height) = image.size\n  return np.array(image.getdata()).reshape(\n      (im_height, im_width, 3)).astype(np.uint8)\n\n\n# # Detection\n\n# In[9]:\n\n# For the sake of simplicity we will use only 2 images:\n# image1.jpg\n# image2.jpg\n# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.\nPATH_TO_TEST_IMAGES_DIR = 'test_images'\nTEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ]\n\n# Size, in inches, of the output images.\nIMAGE_SIZE = (12, 8)\n\n\n#################\n\n#################\n# In[10]:\n#####\ndef find_quad(img_in):\n\n    img= cv2.cvtColor(img_in, cv2.COLOR_RGB2BGR)\n    img_height, img_width, _ = img.shape\n    img_diag = int(np.sqrt(img_height ** 2 + img_width ** 2))\n    # show_cv2(img)\n    img_size = np.linalg.norm(img.shape)\n\n    img_blur = img\n    blur_size = 1 + 2 * int(img_size / 100)\n    if blur_size > 1:\n        blur_size = (blur_size, blur_size)\n        img_blur = cv2.GaussianBlur(img, blur_size, 0)\n    img_hsv = cv2.cvtColor(img_blur, cv2.COLOR_BGR2HSV)\n\n    #img = cv2.resize(img,(500,500))\n    thresh = np.zeros(img.shape[:2], dtype=np.uint8)\n    thresh[img_hsv[:,:,2] <= 160] = 255\n    thresh[img_hsv[:,:,1] >= 127] = 0\n\n    # plt.imshow(thresh)\n    # plt.show()\n\n    #Create default parametrization LSD\n    lsd = cv2.createLineSegmentDetector(cv2.LSD_REFINE_NONE)\n\n    #Detect lines in the image\n    lines = lsd.detect(thresh)[0] #Position 0 of the returned tuple are the detected lines\n    #Draw detected lines in the image\n    #img = lsd.drawSegments(img,lines)\n    valid_lines = []\n\n    def get_v(l):\n        return img_hsv[min(max(int(avg_y + m_y * l), 0), img_height - 1), min(max(int(avg_x + m_x * l), 0), img_width - 1), 2]\n\n    def get_s(l):\n        return img_hsv[min(max(int(avg_y + m_y * l), 0), img_height - 1), min(max(int(avg_x + m_x * l), 0), img_width - 1), 1]\n\n    for line in lines:\n        p1 = line[0,:2]\n        p2 = line[0,2:4]\n        length = np.linalg.norm(p2 - p1)\n        if length < img_diag / 8:\n            continue\n        \n        p_center = (p1 + p2) / 2.0\n\n#         diff = p2 - p1\n#         length = np.linalg.norm(diff)\n#         slope = diff / length\n        \n#         # shrink lines a bit\n#         p1 = p_center + slope * length * 0.45\n#         p2 = p_center - slope * length * 0.45\n\n        x1, y1 = p1\n        x2, y2 = p2\n        \n        \n        p3 = np.asarray([img_width / 2, img_height / 2])\n\n#         d = np.abs(np.cross(p2 - p1, p1 - p3)) / np.linalg.norm(p2 - p1)\n#         if d < img_diag / 15.0:\n#             continue\n\n        good_line = True\n        for i in range(4):\n            avg_x = (x1 * (i + 1) + x2 * (4 - i)) / 5\n            avg_y = (y1 * (i + 1) + y2 * (4 - i)) / 5\n\n            m_x = img_width / 2 - avg_x\n            m_y = img_height / 2 - avg_y\n            m_x /= np.linalg.norm([m_x, m_y])\n            m_y /= np.linalg.norm([m_x, m_y])\n\n            if get_v(4) > get_v(-4) + 20 and get_s(3) < 127 and get_s(-3) < 127:\n                good_line = False\n                break\n\n        if good_line:\n            valid_lines.append(np.asarray([x1, y1, x2, y2]))\n\n    valid_lines.sort(key=lambda l: -(l[0] ** 2 + l[1] ** 2))\n\n    points = []\n    def vote_point(new_p, weight):\n        for i in range(len(points)):\n            p = points[i][0]\n            old_weight = points[i][1]\n            if np.linalg.norm(p - new_p) < img_diag / 20:\n                p *= old_weight / (weight + old_weight)\n                p += new_p * weight / (weight + old_weight)\n                points[i][1] += weight\n                return\n        points.append([new_p, weight])\n\n    for line_index, line in enumerate(valid_lines):\n        x1, y1, x2, y2 = line\n        p1 = np.asarray([x1, y1])\n        p2 = np.asarray([x2, y2])\n\n        slope = p2 - p1\n        length = np.linalg.norm(slope)\n        slope /= length\n        #angle = np.arctan2(diff[1], diff[0])\n\n\n        vote_point(p1, length / 5)\n        vote_point(p2, length / 5)\n        # cv2.line(img, tuple(p1), tuple(p2), rand_color(), 10)\n\n        for other_line in valid_lines[line_index + 1:]:\n            if line is other_line:\n                continue\n\n            ox1, oy1, ox2, oy2 = other_line\n            op1 = np.asarray([ox1, oy1])\n            op2 = np.asarray([ox2, oy2])\n\n            oslope = op2 - op1\n            olength = np.linalg.norm(oslope)\n            oslope /= olength\n            #oangle = np.arctan2(odiff[1], odiff[0])\n\n            intersection = line_intersection(line, other_line)\n\n            # intersection pruning\n#             if np.mean((intersection - op1) / oslope) * np.mean((intersection - op2) / oslope) < -0:\n#                 continue\n#             if np.mean((intersection - p1) / slope) * np.mean((intersection - p2) / slope) < -0:\n#                 continue\n            if np.linalg.norm((p1 + p2) / 2 - intersection) < length * 0.35:\n                continue\n            if np.linalg.norm((op1 + op2) / 2 - intersection) < olength * 0.35:\n                continue\n\n            if 0 <= intersection[0] < img_width and 0 <= intersection[1] <= img_height:# and np.abs(slope @ oslope) < 0.5:\n                vote_point(intersection, (length + olength))#max([length, olength]) + np.mean([length, olength]))\n\n    done = False\n\n    # for p in points:\n        # print(p)\n        # p = p[0]\n        # cv2.circle(img, tuple(p), int(img_size / 50) + 3, rand_color(), thickness=5)\n\n    while done == False:\n        points.sort(key=lambda p: -p[1])\n#         print(points)\n        final_points = points[:4]\n        final_points.sort(key=lambda p: np.arctan2(p[0][1] - img_height / 2, p[0][0] - img_width / 2))\n\n        done = True\n        for i in range(4):\n            v1 = (final_points[i][0] - final_points[i - 1][0]).astype(np.float32)\n            v2 = (final_points[i][0] - final_points[(i + 1) % len(final_points)][0]).astype(np.float32)\n            v1 /= np.linalg.norm(v1)\n            v2 /= np.linalg.norm(v2)\n            theta = np.arccos(v1 @ v2)\n    #         print(theta)\n            if theta < np.pi / 25 or theta > np.pi * 24 / 25:\n                print(\"xxx\")\n                done = False\n                # a bit hacky\n                final_points[i][-1] = -1\n\n    final_points = np.asarray([p[0] for p in final_points], dtype=np.int32)\n\n    # cv2.drawContours(img_in, [final_points], 0, (0,0,255), 5)\n#     plt.imshow(thresh)\n#     plt.show()\n    # show_cv2(img)\n\n\n\ncontent = None\nwith open(image_list) as f:\n    content = f.readlines()\n    print(content[0])\nframes = []\nfor i in range(len(content)):\n    if i == 100:\n      break\n    idx = i\n    im = content[idx].split()\n    print(im)\n    image_path = im[0]\n    image = Image.open(image_path)\n    frame = load_image_into_numpy_array(image)\n\n    frames.append(frame)\n\n  # the array based representation of the image will be used later in order to prepare the\n  # result image with boxes and labels on it.\n  # Expand dimensions since the model expects images to have shape: [1, None, None, 3]\n\nidx = 0\niters = 5\nwith detection_graph.as_default():\n  with tf.Session(graph=detection_graph) as sess:\n    time_list = []\n    length_list= []\n    num_in_each= []\n    for f in frames:\n      start = time.time()\n      for it in range(iters):\n        # faster_rcnn\n        # ret, image_np = cap.read()\n        image_np = f\n        # Expand dimensions since the model expects images to have shape: [1, None, None, 3]\n        image_np_expanded = np.expand_dims(image_np, axis=0)\n        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')\n        # Each box represents a part of the image where a particular object was detected.\n        boxes = detection_graph.get_tensor_by_name('detection_boxes:0')\n        # Each score represent how level of confidence for each of the objects.\n        # Score is shown on the result image, together with the class label.\n        scores = detection_graph.get_tensor_by_name('detection_scores:0')\n        classes = detection_graph.get_tensor_by_name('detection_classes:0')\n        num_detections = detection_graph.get_tensor_by_name('num_detections:0')\n        # Actual detection.\n        (boxes, scores, classes, num_detections) = sess.run(\n            [boxes, scores, classes, num_detections],\n            feed_dict={image_tensor: image_np_expanded})\n        # Visualization of the results of a detection.\n        thresh = 0.9\n        num = 0\n        for s in scores[0,:]:\n          if s > 0.8:\n            num += 1\n          else:\n            break\n\n        W, H, C = image_np.shape\n\n        for n in range(num):\n\n          # print(box)\n          # print(boxes[0][n])\n          minx = int(np.floor(H * boxes[0][n][1]))\n          miny = int(np.floor(W * boxes[0][n][0]))\n          maxx = int(np.ceil(H * boxes[0][n][3]))\n          maxy = int(np.ceil(W * boxes[0][n][2]))\n\n          top = (minx, miny)\n          bot = (maxx, maxy)\n          d = 15\n\n          miny_d = max(miny-d, 0)\n          maxy_d = min(maxy+d, W-1)\n          minx_d = max(minx-d, 0)\n          maxx_d = min(maxx+d, H-1)\n\n          crop_img = image_np[miny_d:maxy_d, minx_d:maxx_d]\n          try:\n            find_quad(crop_img)\n          except:\n            pass\n        idx +=1\n\n      end = time.time()\n      diff = end - start\n      fps = iters / diff\n      time_list.append(fps)\n      length_list.append(W)\n      num_in_each.append(num)\n\n\nfile = open(\"nn_times.csv\",\"w\")\nfor n, l, t in zip(num_in_each, length_list, time_list):\n    file.write(str(n) + \",\" + str(l) + \",\" + str(t) + \"\\n\")\n    print(t)\nfile.close()\n","sub_path":"faster_rcnn/time_classify.py","file_name":"time_classify.py","file_ext":"py","file_size_in_byte":12644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"367610499","text":"\"\"\"\nAlgoritmo de Huffman\nYael Chavoya\n\nEste es el archivo principal para comprimir y descomprimir un texto mediante el Algoritmo de compresión de Huffman\n\"\"\"\nfrom typing import Dict, Tuple\nfrom huffman.tree import Tree\nimport huffman.tools as tools\nimport re\n\n\ndef compress(string: str) -> Tuple[str, Dict[str, str]]:\n    \"\"\"\n    Comprime una cadena de texto con el algoritmo de Huffman, generando códigos específicos basados en la frecuencia\n    de los caracteres.\n\n    :param string: La cadena a comprimir\n    :return: Una cadena con el texto encriptado en binario y el diccionario usado para encriptarlo\n    \"\"\"\n    frecuencias = {}\n\n    # Llenar un diccionario con el carácter como clave y la frecuencia como valor\n    for char in string:\n        if char in frecuencias:\n            frecuencias[char] = frecuencias[char] + 1\n        else:\n            frecuencias[char] = 1\n\n    # Apoyarse de la clase Tree (tree.py) para generar un árbol binario, dejando los caracteres más frecuentes más\n    # cerca de la raíz\n    tree = Tree(frecuencias)\n    # Asignar los códigos a cada carácter basándose en su posición en el árbol\n    codes = tree.to_code_dict()\n\n    # Para cada carácter, insertar su código en la cadena resultante\n    compressed = ''\n    for char in string:\n        compressed += codes[char]\n\n    return compressed, codes\n\n\ndef encode_key(codes: Dict[str, str]) -> str:\n    \"\"\"\n    Convierte el diccionario con los códigos de cada carácter en una cadena de texto, para poder almacenar la\n    información usada para comprimir un texto\n\n    :param codes: El diccionario de códigos en formato \"carácter\": \"código\"\n    :return: Una cadena con información de los códigos de cada carácter\n    \"\"\"\n    encoded = ''\n    for char, code in codes.items():\n        # Para cada carácter, convertir el carácter a hexadecimal Unicode y juntarlo con su código\n        char_hex = hex(int(tools.str_to_bin(char), 2))\n        encoded += char_hex[2:] + '.' + code + ':'\n    return encoded[:-1]\n\n\ndef decode_key(code: str) -> Dict[str, str]:\n    \"\"\"\n    Convierte una cadena de texto con la información usada para comprimir un texto en un diccionario con los códigos\n    de cada carácter en una cadena de texto\n\n    :param code: La cadena de texto con la información\n    :return: Un diccionario de códigos en formato \"carácter\": \"código\"\n    \"\"\"\n    decoded = {}\n    codes = code.split(':')\n    for cd in codes:\n        try:\n            # Convertir el Unicode hexadecimal a un carácter y asignarle su código en el diccionario\n            item = cd.split('.')\n            char = chr(int(item[0], 16))\n            decoded[char] = item[1]\n        except Exception:\n            raise ValueError('Formato de código no reconocido')\n    return decoded\n\n\ndef decompress(binstring: str, codes: Dict[str, str]) -> str:\n    \"\"\"\n    Convierte una representación binaria comprimida en una cadena legible usando un diccionario de códigos\n\n    :param binstring: La representación binaria de la cadena\n    :param codes: El diccionario de códigos en formato \"carácter\": \"código\"\n    :return: La cadena restaurada\n    \"\"\"\n    full_str = ''\n    capture = ''\n    matches = []\n\n    # Verificar que la cadena contenga solamente unos y ceros\n    if not re.search('^[01]+$', binstring):\n        raise ValueError('La cadena proporcionada no es una representación binaria')\n    \n    for bit in binstring:\n        # Para cada bit, ir agregándolo a una cadena de captura y buscar esa captura en el diccionario hasta que sólo\n        # haya una coincidencia, entonces cambiar la cadena capturada hasta el momento por su carácter\n        capture += bit\n\n        # Agregar a matches todos los códigos que empiecen por lo que va en capture\n        matches.clear()\n        for char, code in codes.items():\n            if capture == code[0:len(capture)]:\n                matches.append(char)\n\n        # Si sólo hay una coincidencia, es el carácter que coincide\n        if len(matches) == 1:\n            full_str += matches[0]\n            capture = ''\n\n    return full_str\n","sub_path":"huffman/algorithm.py","file_name":"algorithm.py","file_ext":"py","file_size_in_byte":4079,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"562514266","text":"from inspect import unwrap\nimport random\n\nimport deap\n\nfrom . import optimizer\nimport mock\nfrom unittest import TestCase\n\n\nclass TestOptimizer(TestCase):\n    def setUp(self):\n        random.seed(0)\n\n    def test_bounds_from_config(self):\n        config = {'bounds': {'a': {'mean': 1, 'pct_range': 0.5}}}\n        self.assertEqual(\n            optimizer.RCOptimizer._bounds_from_config(config)['a'],\n            (0.75, 1.25)\n        )\n        self.assertEqual(optimizer.RCOptimizer._bounds_from_config({}), {})\n\n    @mock.patch.object(deap.tools, 'selectselect', mock.MagicMock())\n    @mock.patch.dict(optimizer.crossover_functions,\n                     {'matemate': mock.MagicMock()})\n    @mock.patch.dict(optimizer.mutation_functions,\n                     {'mutatemutate': mock.MagicMock()})\n    def test_registration(self, _, __, select_func):\n        # Test that our mutation / crossover / selection functions register and\n        # get called with appropriate kwargs\n        config = {\n            'parameters': {\n                'mate': {'function': 'matemate'}},\n                'mutate': {\n                    'function': 'mutatemutate', 'kwargs': {'foo': 'bar'}},\n                'select': {\n                    'function': {'selectselect': {'kwargs': {}}}\n                }\n        }\n\n\nclass TestDeapWrappers(TestCase):\n    def setUp(self):\n        random.seed(0)\n\n    def test_cxtwopointcopy(self):\n        class Ind:\n            def __init__(self):\n                self.rc_bounds = {}\n\n        ind1 = Ind()\n        ind2 = Ind()\n        ind1.rc_vals = {'a': 1, 'b': 2, 'c': 3}\n        ind2.rc_vals = {'a': 4, 'b': 5, 'c': 6}\n        ind1, ind2 = optimizer.crossover_functions['cxTwoPoint'](ind1, ind2)\n        self.assertEqual(ind1.rc_vals, {'a': 1, 'b': 2, 'c': 6})\n        self.assertEqual(ind2.rc_vals, {'a': 4, 'b': 5, 'c': 3})\n\n    def test_mutgaussianscaled(self):\n        mutGaussianScaled = unwrap(optimizer.mutGaussianScaled)\n        expect = [1.03, 0.48, 0.28, 149.22, 5.4, 45.35, 1025.3, 306.45, 1906.23]\n        individual = [1, 1, 1, 100, 100, 100, 1000, 1000, 1000]\n        individual = mutGaussianScaled(individual, 0, 0.5, indp=1)[0]\n        individual = [round(i, 2) for i in individual]\n        self.assertListEqual(\n            individual, expect\n        )\n","sub_path":"scripts/optimizer/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2285,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"477645179","text":"import os\nfrom setuptools import setup, find_packages\n\nhere = os.path.abspath(os.path.dirname(__file__))\nreadme = open(os.path.join(here, 'README.rst')).read()\nchanges = open(os.path.join(here, 'CHANGES.rst')).read()\n\nrequires = [\n\n    # Pyramid\n    'pyramid >=1.7, <1.8',\n    'pyramid_mako',\n    'pyramid_tm',\n    'pyramid_debugtoolbar',\n    'pyramid_beaker',\n    'waitress',\n\n    # Backend\n    'sqlalchemy >=1.0, <1.1',\n    'alembic >=0.8, <0.9',\n    'celery >=3.1, <3.2',\n    'transaction',\n    'psycopg2',\n    'zope.sqlalchemy',\n    'redis',\n    'hiredis',\n    'dogpile.cache',\n    'python3-memcached',\n    'pytz',\n    'requests',\n    'geoip2',\n\n    # Frontend\n    'MarkupSafe',\n    'isodate',\n    'misaka',\n    'wtforms',\n\n    # Tests\n    'nose',\n    'coverage',\n\n    ]\n\nsetup(name='fanboi2',\n      version='0.10.1',\n      description='fanboi2',\n      long_description=readme + '\\n\\n' + changes,\n      classifiers=[\n        \"programming language :: python\",\n        \"programming language :: python :: 3\",\n        \"framework :: pyramid\",\n        \"topic :: internet :: www/http\",\n        \"topic :: internet :: www/http :: wsgi :: application\",\n        ],\n      author='',\n      author_email='',\n      url='',\n      keywords='web wsgi bfg pylons pyramid',\n      packages=find_packages(),\n      include_package_data=True,\n      zip_safe=False,\n      test_suite='fanboi2.tests',\n      install_requires=requires,\n      entry_points={\n          \"paste.app_factory\": [\"main = fanboi2:main\"],\n          \"console_scripts\": [\n              \"fb2_board_create = fanboi2.scripts.board_create:main\",\n              \"fb2_board_update = fanboi2.scripts.board_update:main\",\n              \"fb2_topic_sync = fanboi2.scripts.topic_sync:main\",\n              \"fb2_celery = fanboi2.scripts.celery:main\",\n          ]\n      })\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1805,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"370593828","text":"# https://pyjwt.readthedocs.io/en/latest/usage.html#reading-headers-without-validation\n# -> we can put the organization/service in the headers itself\nimport logging\nfrom typing import List, Union\n\nfrom django.conf import settings\nfrom django.db import models, transaction\nfrom django.db.models import Q\nfrom django.utils.functional import cached_property\nfrom django.utils.translation import ugettext as _\n\nimport jwt\nfrom djangorestframework_camel_case.util import underscoreize\nfrom rest_framework.exceptions import PermissionDenied\nfrom rest_framework.response import Response\nfrom zds_client.client import ClientError\n\nfrom .authorizations.models import (\n    Applicatie, AuthorizationsConfig, Autorisatie\n)\nfrom .authorizations.serializers import ApplicatieUuidSerializer\nfrom .constants import VERSION_HEADER\nfrom .models import JWTSecret\nfrom .scopes import Scope\nfrom .utils import get_uuid_from_path\n\nlogger = logging.getLogger(__name__)\n\nEMPTY_PAYLOAD = {\n    'scopes': [],\n    'zaaktypes': [],\n}\n\n\nclass JWTPayload:\n\n    def __init__(self, encoded: str=None):\n        self.encoded = encoded\n\n    def __repr__(self):\n        return \"<%s: payload=%r>\" % (self.__class__.__name__, self.payload)\n\n    def __getitem__(self, key):\n        return self.payload[key]\n\n    def get(self, attr, *args, **kwargs):\n        return self.payload.get(attr, *args, **kwargs)\n\n    @cached_property\n    def payload(self) -> dict:\n        if self.encoded is None:\n            return EMPTY_PAYLOAD\n\n        try:\n            header = jwt.get_unverified_header(self.encoded)\n        except jwt.DecodeError:\n            logger.info(\"Invalid JWT encountered\")\n            raise PermissionDenied(\n                _('JWT could not be decoded. Possibly you made a copy-paste mistake.'),\n                code='jwt-decode-error'\n            )\n\n        try:\n            jwt_secret = JWTSecret.objects.get(identifier=header['client_identifier'])\n        except JWTSecret.DoesNotExist:\n            raise PermissionDenied(\n                'Client identifier bestaat niet',\n                code='invalid-client-identifier'\n            )\n        except KeyError:\n            raise PermissionDenied(\n                'Client identifier is niet aanwezig in JWT',\n                code='missing-client-identifier'\n            )\n        else:\n            key = jwt_secret.secret\n\n        # the jwt package does verification against tampering (TODO: unit test)\n        try:\n            payload = jwt.decode(self.encoded, key, algorithms='HS256')\n        except jwt.InvalidSignatureError as exc:\n            logger.exception(\"Invalid signature - possible payload tampering?\")\n            raise PermissionDenied(\n                'Client credentials zijn niet geldig',\n                code='invalid-jwt-signature'\n            )\n\n        res = payload.get('zds', EMPTY_PAYLOAD)\n        res['client_id'] = header['client_identifier']\n\n        return res\n\n    def has_scopes(self, scopes: Union[Scope, None]) -> bool:\n        \"\"\"\n        Check whether all of the expected scopes are present or not.\n        \"\"\"\n        if scopes is None:\n            # TODO: should block instead of allow it - we'll gradually introduce this\n            return True\n\n        scopes_provided = self.payload.get('scopes', [])\n        logger.debug(\"Scopes provided are: %s\", scopes)\n        # simple form - needs a more complex setup if a scope 'bundles'\n        # other scopes\n        return scopes.is_contained_in(scopes_provided)\n\n\nclass JWTAuth:\n    def __init__(self, encoded: str = None):\n        self.encoded = encoded\n\n    @property\n    def applicaties(self) -> Union[list, None]:\n        if self.client_id is None:\n            return []\n\n        applicaties = self._get_auth()\n\n        if not applicaties:\n            auth_data = self._request_auth()\n            applicaties = self._save_auth(auth_data)\n\n        return applicaties\n\n    @property\n    def autorisaties(self) -> models.QuerySet:\n        \"\"\"\n        Retrieve all authorizations relevant to this component.\n        \"\"\"\n        app_ids = [app.id for app in self.applicaties]\n        config = AuthorizationsConfig.get_solo()\n        return (\n            Autorisatie.objects\n            .filter(\n                applicatie_id__in=app_ids,\n                component=config.component\n            )\n        )\n\n    def _request_auth(self) -> list:\n        client = AuthorizationsConfig.get_client()\n        try:\n            response = client.list(\n                'applicatie',\n                query_params={'client_ids': self.client_id}\n            )\n        except ClientError:\n            logger.warn(\"Authorization component can't be accessed\")\n            return []\n\n        return underscoreize(response['results'])\n\n    def _get_auth(self):\n        return Applicatie.objects.filter(client_ids__contains=[self.client_id])\n\n    @transaction.atomic\n    def _save_auth(self, auth_data):\n        applicaties = []\n\n        for applicatie_data in auth_data:\n            applicatie_serializer = ApplicatieUuidSerializer(data=applicatie_data)\n            uuid = get_uuid_from_path(applicatie_data['url'])\n            applicatie_data['uuid'] = uuid\n            applicatie_serializer.is_valid()\n            applicaties.append(applicatie_serializer.save())\n\n        return applicaties\n\n    @cached_property\n    def client_id(self) -> Union[str, None]:\n        if self.encoded is None:\n            return None\n\n        # jwt check\n        try:\n            payload = jwt.decode(self.encoded, verify=False)\n        except jwt.DecodeError:\n            logger.info(\"Invalid JWT encountered\")\n            raise PermissionDenied(\n                _('JWT could not be decoded. Possibly you made a copy-paste mistake.'),\n                code='jwt-decode-error'\n            )\n\n        # get client_id\n        try:\n            client_id = payload['client_id']\n        except KeyError:\n            try:\n                header = jwt.get_unverified_header(self.encoded)\n            except jwt.DecodeError:\n                logger.info(\"Invalid JWT encountered\")\n                raise PermissionDenied(\n                    _('JWT could not be decoded. Possibly you made a copy-paste mistake.'),\n                    code='jwt-decode-error'\n                )\n            else:\n                try:\n                    client_id = header['client_identifier']\n                except KeyError:\n                    raise PermissionDenied(\n                        'Client identifier is niet aanwezig in JWT',\n                        code='missing-client-identifier'\n                    )\n                else:\n                    logger.warning(_('The support of authorization old format will be terminated soon. '\n                                     'Please use a new format with the separate Authorization Component'))\n\n        # find client_id in DB and retrieve it's secret\n        try:\n            jwt_secret = JWTSecret.objects.get(identifier=client_id)\n        except JWTSecret.DoesNotExist:\n            raise PermissionDenied(\n                'Client identifier bestaat niet',\n                code='invalid-client-identifier'\n            )\n        else:\n            key = jwt_secret.secret\n\n        # check signature of the token\n        try:\n            jwt.decode(self.encoded, key, algorithms='HS256')\n        except jwt.InvalidSignatureError as exc:\n            logger.exception(\"Invalid signature - possible payload tampering?\")\n            raise PermissionDenied(\n                'Client credentials zijn niet geldig',\n                code='invalid-jwt-signature'\n            )\n\n        return client_id\n\n    def has_auth(self, scopes: List[str], zaaktype: Union[str, None],\n                 vertrouwelijkheidaanduiding: Union[str, None]) -> bool:\n\n        if scopes is None:\n            return False\n\n        scopes_provided = set()\n        config = AuthorizationsConfig.get_solo()\n\n        for applicatie in self.applicaties:\n            # allow everything\n            if applicatie.heeft_alle_autorisaties is True:\n                return True\n\n            # consider all scopes at all zaaktypes and vertrouwelijkheidaanduiding\n            zaaktype_q = Q(zaaktype=zaaktype) if zaaktype is not None else Q()\n\n            for autorisatie in applicatie.autorisaties.filter(zaaktype_q, component=config.component):\n                vertrouwelijkheidaanduiding_ok = (\n                    vertrouwelijkheidaanduiding is None\n                    or autorisatie.satisfy_vertrouwelijkheid(vertrouwelijkheidaanduiding)  # noqa\n                )\n\n                if vertrouwelijkheidaanduiding_ok:\n                    scopes_provided.update(autorisatie.scopes)\n\n        return scopes.is_contained_in(list(scopes_provided))\n\n\nclass AuthMiddleware:\n\n    header = 'HTTP_AUTHORIZATION'\n    auth_type = 'Bearer'\n\n    def __init__(self, get_response=None):\n        self.get_response = get_response\n\n    def __call__(self, request):\n        self.extract_jwt_payload(request)\n        return self.get_response(request) if self.get_response else None\n\n    def extract_jwt_payload(self, request):\n        authorization = request.META.get(self.header, '')\n        prefix = f\"{self.auth_type} \"\n        if authorization.startswith(prefix):\n            # grab the actual token\n            encoded = authorization[len(prefix):]\n        else:\n            encoded = None\n\n        request.jwt_payload = JWTPayload(encoded)\n        request.jwt_auth = JWTAuth(encoded)\n\n\nclass APIVersionHeaderMiddleware:\n    \"\"\"\n    Include a header specifying the API-version\n    \"\"\"\n\n    def __init__(self, get_response=None):\n        self.get_response = get_response\n\n    def __call__(self, request):\n        if self.get_response is None:\n            return None\n\n        response = self.get_response(request)\n\n        # not an API response, exit early\n        if not isinstance(response, Response):\n            return response\n\n        # set the header\n        response[VERSION_HEADER] = settings.API_VERSION\n\n        return response\n","sub_path":"vng_api_common/middleware.py","file_name":"middleware.py","file_ext":"py","file_size_in_byte":9982,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"337634738","text":"import sqlite3\nimport plotly\nfrom plotly.graph_objs import Scatter, Layout\n\nconn= sqlite3.connect('rg_db.db')\ndbh = conn.cursor()\n\ndbh.execute(\"SELECT orders, month FROM car_sales\")\n\ny_axis = []\nx_axis = []\n\nfor row in dbh:\n    y_axis.append(row[0])\n    x_axis.append(row[1])\n\n# order units per month\nplotly.offline.plot({\n    \"data\": [Scatter(x=x_axis, y=y_axis)],\n    \"layout\": Layout(title=\"Example Car Sales Report of 2017\")\n})\n","sub_path":"generate_report.py","file_name":"generate_report.py","file_ext":"py","file_size_in_byte":432,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"320736888","text":"import os\nimport time\n\ndef ls_files(dir):\n    files = list()\n    for item in os.listdir(dir):\n        abspath = os.path.join(dir, item)\n        try:\n            if os.path.isdir(abspath):\n                files = files + ls_files(abspath)\n            else:\n                files.append(abspath)\n        except FileNotFoundError as err:\n            print('invalid directory\\n', 'Error: ', err)\n    return files\n\n\ndef modified_since(filename, since):\n    statbuf = os.stat(filename)\n    if (since < statbuf.st_mtime):\n        return statbuf.st_mtime\n    else:\n        return False\n\nscript = [ \"./docs/demo.js\" ]\ntailwind = \"docs/makeitlit\"\npackage_directories = [ \"./src\" ]\nlastmodif = 0\nlastmodif2 = 0\nwhile (True):\n    \n    check1 = modified_since(tailwind+\".css\", lastmodif2)\n    if (check1):\n        lastmodif2 = check1\n        os.system(\"npx tailwindcss build \" +tailwind+ \".css -o \" +tailwind+ \".tail.css\")\n\n    for directory in package_directories:\n        for filename in ls_files(directory) + script:\n            check = modified_since(filename, lastmodif)\n            if check:\n                lastmodif = check+10\n                os.system(\"browserify docs/demo.js -t babelify --outfile docs/bundle.js\")\n                print(\"File \" + filename + \" was modifed!\")\n    time.sleep(1)\n","sub_path":".watch.py","file_name":".watch.py","file_ext":"py","file_size_in_byte":1290,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"213466354","text":"#solution of first problem\r\nwords = input().split(\" \")\r\nlength = len(words)\r\nchain = [words[0]]\r\nwords = words[1:]\r\nfor i in range(0,length - 1):\r\n    for word in words:\r\n        break_flag = False\r\n        if word[0] == chain[-1][-1]:\r\n            for temp_word in words:\r\n                if len(words) != 1 and word[-1] == temp_word[0]:\r\n                    chain.append(word)\r\n                    words.remove(word)\r\n                    break_flag = True\r\n                    break\r\n                elif len(words) == 1: chain.append(word)\r\n            if break_flag: break\r\nif len(chain) == length and chain[0][0] == chain[-1][-1]: print(chain)\r\nelse: print(\"This is not possibe to make circle\")","sub_path":"firstprob.py","file_name":"firstprob.py","file_ext":"py","file_size_in_byte":699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"220276362","text":"from collections import deque\n\n\n# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\n\n\nclass Solution:\n    def rightSideView(self, root: TreeNode) -> List[int]:\n        \"\"\"\n        Approach:\n            This problem follows the Binary Tree Level Order Traversal pattern.\n        We can follow the same BFS approach. The only additional thing we will\n        be do is to append the last node of each level to the result array.\n\n        Time Complexity: O(N)\n        Space Complexity O(N)\n        Explanation: LC102_BinaryTreeLevelOrderTraversal algorithm\n        \"\"\"\n\n        if not root:\n            return None\n\n        queue = deque([root])\n        results = []\n\n        while queue:\n            level_size = len(queue)\n\n            for i in range(level_size):\n                node = queue.popleft()\n\n                if i == level_size - 1:\n                    results.append(node.val)\n\n                if node.left:\n                    queue.append(node.left)\n                if node.right:\n                    queue.append(node.right)\n\n        return results\n","sub_path":"Problems/Leetcode/199_BinaryTreeRightSideView.py","file_name":"199_BinaryTreeRightSideView.py","file_ext":"py","file_size_in_byte":1202,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"193051067","text":"#!/usr/bin/env python\n\nfrom prob_interface.srv import *\nfrom xmlrpclib import ServerProxy\nimport rospy\nimport time\nimport sys\n\nrobot_arm = 0\n\n# myP function handles\ndef handle_move_joint(req):\n  print(\"move_joint(%s, %s, %s, %s, %s, %s)\"%(req.axis, req.deg, req.vel, req.acc, req.block, req.relative))\n  robot_arm.move_joint( req.axis, req.deg, req.vel, req.acc, req.block, req.relative)\n  return 1\n\ndef handle_move_tool(req):\n  print(\"move_tool(%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\"%( req.x, req.y, req.z, req.phi, req.theta, req.psi, req.velocity, req.acceleration, req.velocity_rot, req.acceleration_rot, req.relative, req.block))\n  robot_arm.move( req.x, req.y, req.z, req.phi, req.theta, req.psi, req.velocity, req.acceleration, req.velocity_rot, req.acceleration_rot, req.relative, req.block)\n  return 1\n  \ndef handle_move_to_point(req):\n  print(\"move_to_point(%s, %s, %s, %s, %s)\"%(req.name, req.vel, req.acc, req.block, req.endeffector))\n  robot_arm.move_to_point( req.name, req.vel, req.acc, req.block, req.endeffector)\n  return 1\n  \ndef handle_wait_joint(req):\n  print(\"wait_joint(%s)\"%(req.axis))\n  robot_arm.wait_joint( req.axis)\n  return 1\n  \ndef handle_wait(req):\n  print(\"wait(%s)\"%(req.endeffector))\n  robot_arm.wait( req.endeffector)\n  return 1\n\n# initialize server\ndef start_server(ip):\n\tglobal robot_arm\n\t\n\ttry:\n\t\trospy.init_node('MyP')\n\t\t\n\t\t# ROS service\n\t\tsrv_move_joint = rospy.Service('move_joint', MoveJoint, handle_move_joint)\n\t\tsrv_move_tool = rospy.Service('move_tool', MoveTool, handle_move_tool)\n\t\tsrv_move_to_point = rospy.Service('move_to_point', MoveToPoint, handle_move_to_point)\n\t\tsrv_move_wait_joint = rospy.Service('wait_joint', WaitJoint, handle_wait_joint)\n\t\tsrv_move_wait = rospy.Service('wait', Wait, handle_wait)\n\t\t\n\t\t# Connection to myP XMLRPC server\n\t\trobot_arm = ServerProxy(\"http://\"+ip+\":8000\", verbose=False, allow_none=True)\n\t\t\n\t\trobot_arm.initialize(\"PRob1R\", \"no_robot\")\n\t\ttime.sleep(2)\n\t\t# calibrate robot\n\t\trobot_arm.calibrate()\n\t\ttime.sleep(0.5)\n\t\tprint(\"Connected to PRob. Ready...\")\n\t\trospy.spin()\n\t\t\n\texcept Exception as ex:\n\t\tprint(\"ERROR: \", ex)\n\t\tsys.exit(1)\n\ndef usage():\n    return \"%s [PRob ip address]\"%sys.argv[0]\n\nif __name__ == \"__main__\":\n  if len(sys.argv) == 2:\n    ip = sys.argv[1] #'192.168.21.157'\n  else:\n    print( usage() )\n    sys.exit(1)\n  start_server(ip)\n","sub_path":"src/prob_server.py","file_name":"prob_server.py","file_ext":"py","file_size_in_byte":2345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"614494479","text":"import sqlite3\nDATABASE = \"carheaterdatabase.db\"\ndef db_connect():\n    con = sqlite3.connect(DATABASE)\n    return con\n\ndef create_dev_db():\n    schema = open(\"dbschema.sql\", \"r\").read()\n    print(\"moo\")\n    con = db_connect()\n    cur = con.cursor()\n    cur.executescript(schema)\n\nif __name__ == \"__main__\":\n    create_dev_db()","sub_path":"database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"608995871","text":"# flake8: noqa\nimport pytest\n\nasyncio = pytest.importorskip('asyncio')\n\nimport functools\nfrom time import time\nfrom operator import add\nfrom toolz import isdistinct\nfrom concurrent.futures import CancelledError\nfrom distributed.utils_test import slow\nfrom distributed.utils_test import slowinc\n\nfrom tornado.ioloop import IOLoop\nfrom tornado.platform.asyncio import BaseAsyncIOLoop\n\nfrom distributed.client import Future\nfrom distributed.variable import Variable\nfrom distributed.asyncio import AioClient\nfrom distributed.asyncio import as_completed, wait\nfrom distributed.utils_test import inc, div\n\n\ndef coro_test(fn):\n    assert asyncio.iscoroutinefunction(fn)\n\n    @functools.wraps(fn)\n    def wrapper(*args, **kwargs):\n        loop = None\n        try:\n            IOLoop.clear_current()\n            loop = asyncio.new_event_loop()\n            asyncio.set_event_loop(loop)\n            loop.run_until_complete(fn(*args, **kwargs))\n        finally:\n            if loop is not None:\n                loop.close()\n\n            IOLoop.clear_current()\n            asyncio.set_event_loop(None)\n\n    return wrapper\n\n\n@coro_test\nasync def test_coro_test():\n    assert asyncio.get_event_loop().is_running()\n\n\n@coro_test\nasync def test_asyncio_start_close():\n    c = await AioClient(processes=False)\n\n    assert c.status == 'running'\n    # AioClient has installed its AioLoop shim.\n    assert isinstance(IOLoop.current(instance=False), BaseAsyncIOLoop)\n\n    result = await c.submit(inc, 10)\n    assert result == 11\n\n    await c.close()\n    assert c.status == 'closed'\n    # assert IOLoop.current(instance=False) is None\n\n\n@coro_test\nasync def test_asyncio_submit():\n    async with AioClient(processes=False) as c:\n        x = c.submit(inc, 10)\n        assert not x.done()\n\n        assert isinstance(x, Future)\n        assert x.client is c\n\n        result = await x.result()\n        assert result == 11\n        assert x.done()\n\n        y = c.submit(inc, 20)\n        z = c.submit(add, x, y)\n\n        result = await z.result()\n        assert result == 11 + 21\n\n\n@coro_test\nasync def test_asyncio_future_await():\n    async with AioClient(processes=False) as c:\n        x = c.submit(inc, 10)\n        assert not x.done()\n\n        assert isinstance(x, Future)\n        assert x.client is c\n\n        result = await x\n        assert result == 11\n        assert x.done()\n\n        y = c.submit(inc, 20)\n        z = c.submit(add, x, y)\n\n        result = await z\n        assert result == 11 + 21\n\n\n@coro_test\nasync def test_asyncio_map():\n    async with AioClient(processes=False) as c:\n        L1 = c.map(inc, range(5))\n        assert len(L1) == 5\n        assert isdistinct(x.key for x in L1)\n        assert all(isinstance(x, Future) for x in L1)\n\n        result = await L1[0]\n        assert result == inc(0)\n\n        L2 = c.map(inc, L1)\n\n        result = await L2[1]\n        assert result == inc(inc(1))\n\n        total = c.submit(sum, L2)\n        result = await total\n        assert result == sum(map(inc, map(inc, range(5))))\n\n        L3 = c.map(add, L1, L2)\n        result = await L3[1]\n        assert result == inc(1) + inc(inc(1))\n\n        L4 = c.map(add, range(3), range(4))\n        results = await c.gather(L4)\n        assert results == list(map(add, range(3), range(4)))\n\n        def f(x, y=10):\n            return x + y\n\n        L5 = c.map(f, range(5), y=5)\n        results = await c.gather(L5)\n        assert results == list(range(5, 10))\n\n        y = c.submit(f, 10)\n        L6 = c.map(f, range(5), y=y)\n        results = await c.gather(L6)\n        assert results == list(range(20, 25))\n\n\n@coro_test\nasync def test_asyncio_gather():\n    async with AioClient(processes=False) as c:\n        x = c.submit(inc, 10)\n        y = c.submit(inc, x)\n\n        result = await c.gather(x)\n        assert result == 11\n        result = await c.gather([x])\n        assert result == [11]\n        result = await c.gather({'x': x, 'y': [y]})\n        assert result == {'x': 11, 'y': [12]}\n\n\n@coro_test\nasync def test_asyncio_get():\n    async with AioClient(processes=False) as c:\n        result = await c.get({'x': (inc, 1)}, 'x')\n        assert result == 2\n\n        result = await c.get({'x': (inc, 1)}, ['x'])\n        assert result == [2]\n\n        result = await c.get({}, [])\n        assert result == []\n\n        result = await c.get({('x', 1): (inc, 1),\n                              ('x', 2): (inc, ('x', 1))},\n                             ('x', 2))\n        assert result == 3\n\n\n@coro_test\nasync def test_asyncio_exceptions():\n    async with AioClient(processes=False) as c:\n        result = await c.submit(div, 1, 2)\n        assert result == 1 / 2\n\n        with pytest.raises(ZeroDivisionError):\n            result = await c.submit(div, 1, 0)\n\n        result = await c.submit(div, 10, 2)  # continues to operate\n        assert result == 10 / 2\n\n\n@coro_test\nasync def test_asyncio_exception_on_exception():\n    async with AioClient(processes=False) as c:\n        x = c.submit(lambda: 1 / 0)\n        y = c.submit(inc, x)\n\n        with pytest.raises(ZeroDivisionError):\n            await y\n\n        z = c.submit(inc, y)\n        with pytest.raises(ZeroDivisionError):\n            await z\n\n\n@coro_test\nasync def test_asyncio_as_completed():\n    async with AioClient(processes=False) as c:\n        futures = c.map(inc, range(10))\n\n        results = []\n        async for future in as_completed(futures):\n            results.append(await future)\n\n        assert set(results) == set(range(1, 11))\n\n\n@coro_test\nasync def test_asyncio_cancel():\n    async with AioClient(processes=False) as c:\n        s = c.cluster.scheduler\n\n        x = c.submit(slowinc, 1)\n        y = c.submit(slowinc, x)\n\n        while y.key not in s.tasks:\n            await asyncio.sleep(0.01)\n\n        await c.cancel([x])\n\n        assert x.cancelled()\n        assert 'cancel' in str(x)\n        s.validate_state()\n\n        start = time()\n        while not y.cancelled():\n            await asyncio.sleep(0.01)\n            assert time() < start + 5\n\n        assert not s.tasks\n        assert not s.who_has\n        s.validate_state()\n\n\n@coro_test\nasync def test_asyncio_cancel_tuple_key():\n    async with AioClient(processes=False) as c:\n        x = c.submit(inc, 1, key=('x', 0, 1))\n        await x\n        await c.cancel(x)\n        with pytest.raises(CancelledError):\n            await x\n\n\n@coro_test\nasync def test_asyncio_wait():\n    async with AioClient(processes=False) as c:\n        x = c.submit(inc, 1)\n        y = c.submit(inc, 2)\n        z = c.submit(inc, 3)\n\n        await wait(x)\n        assert x.done() is True\n\n        await wait([y, z])\n        assert y.done() is True\n        assert z.done() is True\n\n\n@coro_test\nasync def test_asyncio_run():\n    async with AioClient(processes=False) as c:\n        results = await c.run(inc, 1)\n        assert len(results) > 0\n        assert [value == 2 for value in results.values()]\n\n        results = await c.run(inc, 1, workers=[])\n        assert results == {}\n\n\n@coro_test\nasync def test_asyncio_run_on_scheduler():\n    def f(dask_scheduler=None):\n        return dask_scheduler.address\n\n    async with AioClient(processes=False) as c:\n        address = await c.run_on_scheduler(f)\n        assert address == c.cluster.scheduler.address\n\n        with pytest.raises(ZeroDivisionError):\n            await c.run_on_scheduler(div, 1, 0)\n\n\n@coro_test\nasync def test_asyncio_run_coroutine():\n    async def aioinc(x, delay=0.02):\n        await asyncio.sleep(delay)\n        return x + 1\n\n    async def aiothrows(x, delay=0.02):\n        await asyncio.sleep(delay)\n        raise RuntimeError('hello')\n\n    async with AioClient(processes=False) as c:\n        results = await c.run_coroutine(aioinc, 1, delay=0.05)\n        assert len(results) > 0\n        assert [value == 2 for value in results.values()]\n\n        results = await c.run_coroutine(aioinc, 1, workers=[])\n        assert results == {}\n\n        with pytest.raises(RuntimeError) as exc_info:\n            await c.run_coroutine(aiothrows, 1)\n        assert \"hello\" in str(exc_info)\n\n\n@slow\n@coro_test\nasync def test_asyncio_restart():\n    c = await AioClient(processes=False)\n\n    assert c.status == 'running'\n    x = c.submit(inc, 1)\n    assert x.key in c.refcount\n\n    await c.restart()\n    assert x.key not in c.refcount\n\n    key = x.key\n    del x\n    import gc\n    gc.collect()\n\n    assert key not in c.refcount\n    await c.shutdown()\n\n\n@coro_test\nasync def test_asyncio_nanny_workers():\n    async with AioClient(n_workers=2) as c:\n        assert await c.submit(inc, 1) == 2\n\n\n@coro_test\nasync def test_asyncio_variable():\n    c = await AioClient(processes=False)\n    s = c.cluster.scheduler\n\n    x = Variable('x')\n    xx = Variable('x')\n    assert x.client is c\n\n    future = c.submit(inc, 1)\n\n    await x.set(future)\n    future2 = await xx.get()\n    assert future.key == future2.key\n\n    del future, future2\n\n    await asyncio.sleep(0.1)\n    assert s.task_state  # future still present\n\n    x.delete()\n\n    start = time()\n    while s.task_state:\n        await asyncio.sleep(0.01)\n        assert time() < start + 5\n","sub_path":"lib/python2.7/site-packages/distributed/tests/py3_test_asyncio.py","file_name":"py3_test_asyncio.py","file_ext":"py","file_size_in_byte":9055,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"286419713","text":"import sys\nimport math\n\n\ndef is_even(n):\n    return 1 if n % 2 == 0 else 0\n\n\nif len(sys.argv) <= 1:\n    exit(0)\n\ntest_cases = open(sys.argv[1], 'r')\nl = []\nfor test in test_cases:\n    test = test.strip(\"\\n\")\n    test = test.strip(\" \")\n    if test == \"\":\n        continue\n    \n    l.append(test)\n  \ntest_cases.close()\n\n\n\nfor s in l:\n\n    print(is_even(int(s)))","sub_path":"CodeEval/Challenge Solutions/easy/Python/100_even_numbers.py","file_name":"100_even_numbers.py","file_ext":"py","file_size_in_byte":359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"571937253","text":"'''\n\nTODO:\n - Build own classifier\n - Confirm precision and recall methods are correct\n\n'''\n\nfrom pathlib import Path\nimport pandas as pd\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.naive_bayes import ComplementNB\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.svm import LinearSVC\nfrom sklearn.linear_model import SGDClassifier\n\nimport our_metrics\nimport math\n\n# Added lib\nfrom sklearn.feature_extraction.text import TfidfTransformer\nfrom sklearn.naive_bayes import MultinomialNB\nimport numpy as np\n\nTRAIN_FILE = Path(\"raw_data/GunViolence/train.tsv\")\nDEV_FILE = Path(\"raw_data/GunViolence/dev.tsv\")\nTEST_FILE = Path(\"raw_data/GunViolence/test.tsv\")\n\nLABELS = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n# These frames/labels correspond to\n# 1) Gun or 2nd Amendment rights\n# 2) Gun control/regulation\n# 3) Politics\n# 4) Mental health\n# 5) School or public space safety\n# 6) Race/ethnicity\n# 7) Public opinion\n# 8) Society/culture\n# 9) Economic consequences\n\n\ndef load_data_file(data_file, is_test_file = False):\n    \"\"\"Load newsframing data\n\n    Returns\n    -------\n    tuple\n        First element is a list of strings(headlines)\n        If `data_file` has labels, the second element\n        will be a list of labels for each headline. \n        Otherwise, the second element will be None.\n    \"\"\"\n    print(\"Loading from {} ...\".format(data_file.name), end=\"\")\n    text_col = \"news_title\"\n    theme1_col = \"Q3 Theme1\"\n\n    with open(data_file) as f:\n        df = pd.read_csv(f, sep=\"\\t\")\n        X = df[text_col].tolist()\n\n        y = None\n        if not is_test_file:\n            if theme1_col in df.columns:\n                y = df[theme1_col].tolist()\n\n        print(\n            \"loaded {} lines {} labels ... done\".format(\n                len(X), \"with\" if y is not None else \"without\"\n            )\n        )\n\n    return (X, y)\n\ndef load_balanced_file(data_file):\n    with open(data_file) as f:\n        df = pd.read_csv(f)\n        df_x, df_y = df['x'].tolist(), df['y'].tolist()\n        return df_x, df_y\n\ndef get_balance_class(X, y):\n    df = pd.DataFrame({'x': X, 'y': y})\n    ave = math.floor(np.mean(np.array(list(df.groupby('y').x.count()))))\n\n    # ave = 120\n    ave = 150\n    # ave = 170   # Average of top 5.\n\n    def sampling_k_elements(group, k=ave):\n        if len(group) < k:\n            return group\n        return group.sample(k)\n\n    df_bal = df.groupby('y').apply(sampling_k_elements).reset_index(drop=True)\n\n    # print(df_bal['y'].value_counts())\n    return df_bal['x'].to_list(), df_bal['y'].to_list()\n\ndef build_naive_bayes():\n    \"\"\"\n\n    Returns\n    -------\n        Pipeline\n        An sklearn Pipeline\n    \"\"\"\n    nb_pipeline = None\n    ##### Write code here #######\n\n    nb_pipeline = Pipeline([\n        ('vect', CountVectorizer()),\n        ('tfidf', TfidfTransformer(use_idf=False)),\n        ('clf', MultinomialNB(alpha=0.1)),\n    ])\n\n    ##### End of your work ######\n    return nb_pipeline\n\ndef build_logistic_regr():\n    \"\"\"\n\n    Returns\n    -------\n        Pipeline\n        An sklearn Pipeline\n    \"\"\"\n    logistic_pipeline = None\n    ##### Write code here #######\n\n    logistic_pipeline = Pipeline([\n        ('vect', CountVectorizer()),\n        ('tfidf', TfidfTransformer(use_idf=False)),\n        ('clf', LogisticRegression(multi_class='ovr')),\n    ])\n\n    ##### End of your work ######\n    return logistic_pipeline\n\n\ndef build_random_forest():\n    random_forest_pipeline = Pipeline([\n        ('vect', CountVectorizer()),\n        ('tfidf', TfidfTransformer(use_idf=False)),\n        ('clf', RandomForestClassifier(n_estimators=10, max_depth=10, random_state=0)),\n    ])\n    return random_forest_pipeline\n\ndef build_linear_svc():\n    linear_svc_pipeline = Pipeline([\n        ('vect', CountVectorizer()),\n        ('tfidf', TfidfTransformer(use_idf=True)),\n        ('clf', LinearSVC(multi_class='ovr')),\n    ])\n    return linear_svc_pipeline\n\ndef build_svc_2():\n    sgd = Pipeline([('vect', CountVectorizer()),\n                ('tfidf', TfidfTransformer(use_idf=False)),\n                ('clf', SGDClassifier(loss='hinge', penalty='l2',alpha=0.0001, random_state=42, max_iter=5, tol=None)),\n               ])\n    return sgd\n\ndef build_own_pipeline() -> Pipeline:\n    \"\"\"\n\n    Returns\n    -------\n        Pipeline\n        An sklearn Pipeline\n    \"\"\"\n    pipeline = None\n    ##### Write code here #######\n\n    pipeline = build_linear_svc()  \n\n    ##### End of your work ######\n    return pipeline\n\n\ndef output_predictions(result):\n    \"\"\"Load test data, predict using given pipeline, and write predictions to file.\n\n    The output must be named \"predictions.tsv\" and must have the following format.\n    Here, the first three examples were predicted to be 7,2,3, and the last were \n    predicted to be 6,6, and 2.\n\n    Be sure not to permute the order of the examples in the test file.\n\n        7\n        2\n        3\n        .\n        .\n        .\n        6\n        6\n        2\n\n    \"\"\"\n    ##### Write code here #######\n\n    np.savetxt(\"./predictions.tsv\", np.array(result), fmt='%d')\n    \n    ##### End of your work ######\n\ndef testing(X_train, y_train_true, X_dev, y_dev_true):\n    from sklearn.feature_extraction.text import CountVectorizer\n    from sklearn.feature_extraction.text import TfidfTransformer\n    from sklearn.naive_bayes import MultinomialNB\n\n    # Count Vectorizer\n    train_vectorizer = CountVectorizer()\n    X_cv_train = train_vectorizer.fit_transform(X_train)\n    # print(vectorizer.get_feature_names())\n    # print(X.toarray())\n\n    # Tfid Transformer\n    tf_train_transformer = TfidfTransformer(use_idf=False).fit(X_cv_train)\n    X_train_tfidf = tf_train_transformer.transform(X_cv_train)\n    # print(X_train_tf)\n\n    # Fit model\n    print(\"X_train_tfidf shape\", X_train_tfidf.shape)\n\n    # print(tfidf_transformer.idf_)\n    # print(cv.get_feature_names())\n    clf = MultinomialNB(alpha=0.1).fit(X_train_tfidf, y_train_true)\n\n    # Predict validation set\n    # dev_vectorizer = CountVectorizer()\n    X_cv_dev = train_vectorizer.transform(X_dev)\n    print(\"fname\", len(train_vectorizer.get_feature_names()))\n\n    # tf_dev_transformer = TfidfTransformer(use_idf=False).fit(X_cv_dev)\n    X_dev_tfidf = tf_train_transformer.transform(X_cv_dev)\n    print(tf_train_transformer.get_params())\n\n    dev_pred = clf.predict(X_dev_tfidf)\n\n    # np_pred = np.array(predicted)\n    # np_y = np.array(y_dev_true)\n    # print(\"Predicted:\")\n    # print(np_pred)\n    # print(\"True:\")\n    # print(np_y)\n\n    # print(\"Correct:\", np.sum(np_pred == np_y))\n    # print(\"Wrong\", np.sum(np_pred != np_y))\n\n    labels = list(np.unique(np.array(y_train_true)))\n\n    print(\"Pipe = {}\".format(\"Self\"))\n    for averaging in ['micro', 'macro']:\n        our_recall = our_metrics.recall(y_dev_true, dev_pred, labels=labels, average=averaging)\n        our_precision = our_metrics.precision(y_dev_true, dev_pred, labels=labels, average=averaging)\n        print(\"\\tAveraging = {}\\n\\t\\tRecall = {}\\n\\t\\tPrecision = {}\".format(averaging, our_recall, our_precision))\n\n\ndef main(balance_class=False):\n    # X_train_bal, y_train_bal_true = load_balanced_file(TRAIN_BALANCED_FILE)\n    print(\"balance_class=\", balance_class)\n\n    X_train, y_train_true = load_data_file(TRAIN_FILE)\n    #if balance_class:\n    #    X_train, y_train_true = get_balance_class(X_train, y_train_true)\n\n    X_dev, y_dev_true = load_data_file(DEV_FILE)\n    X_test, _ = load_data_file(TEST_FILE, is_test_file=True)\n\n    bayes_pipeline = build_naive_bayes()\n    logistic_pipeline = build_logistic_regr()\n    # random_forest_pipeline = build_random_forest()\n    # linear_svc_pipeline = build_linear_svc()\n    # svc_2_pipe = build_svc_2()\n    own_pipeline = build_own_pipeline()\n\n    for name, pipeline in (\n        [\"Naive Bayes\", bayes_pipeline,],\n        [\"Logistic Regression\", logistic_pipeline,],\n        [\"Own Pipeline (Linear SVC)\", own_pipeline,],\n    ):\n        if pipeline is not None:\n\n            ##### Write code here #######\n            # Train model\n            model = pipeline.fit(X_train, y_train_true)\n\n            # Predict with Val set\n            dev_pred = model.predict(X_dev)\n\n            # Get Metrics\n            labels = list(np.unique(np.array(y_train_true)))\n            print(\"Pipe = {}\".format(name))\n            for averaging in ['micro', 'macro']:\n                our_recall = our_metrics.recall(y_dev_true, dev_pred, labels=labels, average=averaging)\n                our_precision = our_metrics.precision(y_dev_true, dev_pred, labels=labels, average=averaging)\n                print(\"\\tAveraging = {}\\n\\t\\tRecall = {}\\n\\t\\tPrecision = {}\".format(averaging, our_recall, our_precision))\n\n            if name == \"Own Pipeline (Linear SVC)\":\n                # Run Test Set\n                test_pred = model.predict(X_test)\n                output_predictions(test_pred)\n\n            ##### End of your work ######\n\n    print(\"\\n\\n\")\nif __name__ == \"__main__\":\n    main()\n    # main(balance_class=True)\n","sub_path":"cs505_hw1/code/classify_frames.py","file_name":"classify_frames.py","file_ext":"py","file_size_in_byte":9032,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"197018480","text":"#!/bin/env python\nfrom numbers import app, interesting_numbers\nfrom flask.ext.script import Manager\nimport nose\n\nmanager = Manager(app)\n\n@manager.command\ndef test():\n\tprint('Running tests with nose.')\n\tnose.run(argv=['numbers', '--with-coverage', '--cover-package=numbers'])\n\ndef main():\n\tmanager.run()\n\nif __name__ == '__main__':\n\tmain()\n","sub_path":"manage.py","file_name":"manage.py","file_ext":"py","file_size_in_byte":339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"278345783","text":"# -*- coding: utf-8 -*-\n\nimport numpy as np\nimport keras.utils as kerasutils\nimport keras.layers as keraslay\nfrom keras.models import Sequential\nfrom nwae.lib.math.NumpyUtil import NumpyUtil\n\n#\n# Prepare random data\n#\nn_rows = 10000\ninput_dim = 5\nn_labels = input_dim\n# Random vectors numpy ndarray type\ndata = np.random.random((n_rows, input_dim))\n# Labels are sum of the rows, then floored to the integer\n# Sum >= 0, 1, 2, 3,...\nrow_sums = np.sum(data, axis=1)\nlabels = np.array(np.round(row_sums-0.5,0), dtype=int)\nprint(labels)\n# labels = np.random.randint(n_labels, size=(n_rows, 1))\n\n# Print some data\nfor i in range(10):\n    print(str(i) + '. ' + str(labels[i]) + ': ' + str(data[i]))\n\n#\n# Build our NN\n#\ndef create_general_model(\n        input_data_dim,\n        unique_labels_count,\n        dense_units\n):\n    model = Sequential()\n    # First layer with standard relu or positive activation\n    model.add(\n        keraslay.Dense(\n            # Output dim somewhat ad-hoc\n            units      = dense_units,\n            activation = 'relu',\n            input_dim  = input_data_dim\n        )\n    )\n    # Subsequent layers input dim no longer required to be specified, implied from previous\n    # Last layer always outputs the labels probability/scores\n    model.add(\n        keraslay.Dense(\n            # Output dim\n            units      = unique_labels_count,\n            # Standard last layer activation as positive probability distribution\n            activation = 'softmax'\n        )\n    )\n    model.compile(\n        optimizer = 'rmsprop',\n        loss      = 'categorical_crossentropy',\n        metrics   = ['accuracy']\n    )\n    model.summary()\n    return model\n\nmodel_general = create_general_model(\n    input_data_dim = input_dim,\n    unique_labels_count = n_labels,\n    dense_units  = 512\n)\n# Convert labels to categorical one-hot encoding\nlabels_categorical = kerasutils.to_categorical(labels, num_classes=n_labels)\n\n# Train the model, iterating on the data in batches of 32 samples\nmodel_general.fit(data, labels_categorical, epochs=10, batch_size=32)\n\nloss, accuracy = model_general.evaluate(data, labels_categorical)\nprint('Accuracy: %f' % (accuracy*100))\n\n# Compare some data\ncount_correct = 0\nfor i in range(data.shape[0]):\n    data_i = np.array([data[i]])\n    label_i = labels[i]\n    prob_distribution = model_general.predict(x=data_i)\n    top_x = NumpyUtil.get_top_indexes(\n        data      = prob_distribution[0],\n        ascending = False,\n        top_x     = 5\n    )\n    if top_x[0] == label_i:\n        count_correct += 1\n    print(str(i) + '. ' + str(data_i) + ': Label=' + str(label_i) + ', predicted=' + str(top_x))\nprint('Accuracy = ' + str(100*count_correct/data.shape[0]) + '%.')\n","sub_path":"src/nwae/deprecated/lib/math/ml/examples/ClassifyAnything.py","file_name":"ClassifyAnything.py","file_ext":"py","file_size_in_byte":2716,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"175572069","text":"#!/usr/bin/env python\n\nimport datetime\nimport logging\n\nfrom . import items\nfrom . import mindbody\n\n# Before we can use this Scraper on a MindBody site, we must first get the admin to open:\n# mindbody.get_activation_link(-99)\n\n# scrapy sets verbose logging by default, but we don't want to see suds spam.\nlogger = logging.getLogger('suds')\nlogger.setLevel(logging.INFO)\n\nSPONSOR_MINDBODY = 'MINDBODY'\n\n\nclass MindBodyScraper(items.StudioScraper):\n    site_id = -99\n    filter_style = False\n\n    start_urls = ['https://www.google.com']\n\n    def _get_url(self, response):\n        return 'https://clients.mindbodyonline.com/ASP/home.asp?studioid=%s' % self.site_id\n\n    def parse_classes(self, response):\n        start = datetime.datetime.combine(datetime.date.today(), datetime.time())\n        end = start + datetime.timedelta(days=14)\n\n        # Temporarily disable DEBUG logging, suds is too verbose\n        #logging.root.setLevel(logging.INFO)\n        result = mindbody.get_classes(start_time=start, end_time=end, hide_canceled_classes=True, site_id=self.site_id)\n        #logging.root.setLevel(logging.NOTSET)\n\n        if result.Status != 'Success':\n            raise IOError(\"MindBody gave result: %s: %s\" % (result.Status, result.Message))\n        if result.TotalPageCount != 1:\n            raise IOError(\"Too many results for our query, got %s pages of data back\" % result.TotalPageCount)\n\n        for studio_class in result.Classes.Class:\n            self.address = '%s, %s' % (studio_class.Location.Address, studio_class.Location.Address2)\n            self.latlong = (studio_class.Location.Latitude, studio_class.Location.Longitude)\n            self.name = studio_class.Location.Name\n\n            style = studio_class.ClassDescription.Name\n            if self.filter_style:\n                if not self._street_style(style):\n                    continue\n\n            item = items.StudioClass()\n            item['style'] = style\n            item['start_time'] = studio_class.StartDateTime\n            item['end_time'] = studio_class.EndDateTime\n            item['teacher'] = studio_class.Staff.Name\n            item['sponsor'] = SPONSOR_MINDBODY\n            yield item\n","sub_path":"scrapers/scrapy/classes/mindbody_scraper.py","file_name":"mindbody_scraper.py","file_ext":"py","file_size_in_byte":2173,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"297344604","text":"from requests import get\nfrom bs4 import BeautifulSoup\nfrom urllib.parse import urlencode\nimport downloader\nimport argparse\n\nurl_search_root = \"https://www.gogoanime.se//search.html?\"\nurl_home = \"https://www2.gogoanime.se\"\n\n# print(strings)\n\n\ndef get_html(url):\n    resp = get(url)\n    return BeautifulSoup(resp.content, 'html.parser')\n\n\ndef search(anime_name, option=None, ep_range=None, recent=False, method=None):\n    # Prepare the Search URL\n    query_params = {'keyword': anime_name}\n    search_url = url_search_root + urlencode(query_params)\n\n    # Do gogoanime search and get options\n    options = []\n    search_html = get_html(search_url)\n    found = search_html.select(\".items li\")\n\n    if len(found) == 0:\n        print(\"Sorry, nothing was found!\")\n        return\n\n    if not option:\n        print(f\"Found {len(found)} Options\")\n    for count, item in enumerate(found):\n        basic_info = item.select(\".name a\")[0]\n        year_released = item.select(\".released\")[0].text.split(\":\")[1].strip()\n\n        name = basic_info.text\n        url = basic_info[\"href\"]\n        new_item = {\"name\": name, \"url\": url, \"year\": year_released}\n        options.append(new_item)\n\n        if not option:\n            # Print Menu Option\n            print(f\"[{count+1}] {new_item['name']} ({new_item['year']})\")\n\n    if not option:\n        # Ask for input and do the corresponding\n        option = int(input(\"\\nChoose Your Choice: \"))\n\n    chosen_option = options[option - 1]\n    chosen_option_url = options[option - 1][\"url\"]\n    option_url = url_home + chosen_option_url\n\n    episode_moniker = option_url.split(\"/\")[-1]\n    base_episode_url = url_home + \"/\" + episode_moniker + \"-episode-\"\n\n    episode_page = get_html(url_home + \"/\" + chosen_option_url)\n    available_ep_range = episode_page.find(\"ul\", attrs={\"id\": \"episode_page\"})\n\n    if not (ep_range or recent):\n        print(f\"Anime – [{chosen_option['name']}]\")\n        ep_range = input(f\"Episode [{available_ep_range.text.strip()}]: \")\n\n    # Getting episode range\n    if not recent:\n        episode_spec = ep_range\n        if \"-\" not in episode_spec:\n            start = episode_spec\n            end = episode_spec\n        else:\n            episode_range = episode_spec.split(\"-\")\n            start = episode_range[0]\n            end = episode_range[1]\n    else:\n        start = int(available_ep_range.text.strip().split(\"-\")[-1])\n        end = start\n\n    params = {\n        \"url\": base_episode_url,\n        \"name\": chosen_option[\"name\"].replace(\":\", \"\"),\n        \"episode\": {\n            \"start\": int(start),\n            \"end\": int(end)\n        }\n    }\n\n    download(params, method)\n\n\ndef download(params, method=None):\n    url = params[\"url\"]\n\n    for i in range(params[\"episode\"][\"start\"], params[\"episode\"][\"end\"] + 1):\n        resp = get(f\"{url}{i}\", stream=True)\n        html = BeautifulSoup(resp.content, 'html.parser')\n        download_page_url = html.select(\".download-anime a\")[0]['href']\n        download_page = BeautifulSoup(\n            get(download_page_url).content, 'html.parser')\n        download_link = download_page.select(\".dowload a\")[0]['href']\n        filename = params[\"name\"] + \"-%02d.mp4\" % (i)\n        downloader.download(download_link, filename, method=method)\n\n\nif __name__ == '__main__':\n    # search(sys.argv[1])\n    parser = argparse.ArgumentParser(description=\"A command line program \"\n                                                 \"to download anime from \"\n                                                 \"gogoanime\")\n    parser.add_argument(\"keyword\", type=str,\n                        help=\"Keyword to search for anime with\")\n    parser.add_argument(\"-s\", \"--schoice\", type=int,\n                        help=\"Specify choice from search options\")\n    parser.add_argument(\"-e\", \"--echoice\", type=str,\n                        help=\"Specify episode range or number\")\n    parser.add_argument(\"-r\", \"--recent\", action=\"store_true\",\n                        help=\"Downloads the most recent episode of the anime \"\n                        \"you specify. It requires no arguments\")\n    parser.add_argument(\"--use\", \"-u\", type=str,\n                        help=\"Specify the downloader you want to use. \"\n                        \"Options are [idm, regular, uget]. If none is \"\n                        \"specified, regular is used, which just uses \"\n                        \"the python request library\",\n                        choices=[\"idm\", \"regular\", \"uget\"])\n    args = parser.parse_args()\n    search(args.keyword, args.schoice, args.echoice, args.recent, args.use)\n","sub_path":"goget.py","file_name":"goget.py","file_ext":"py","file_size_in_byte":4550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"201383285","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom __future__ import absolute_import\n\nimport os\n\nfrom fabric.api import local, task\nfrom fabric.colors import red\n\n\nfrom jinja2 import Environment, FileSystemLoader\n\nfrom simplejson import load, dumps\nfrom codecs import open\nimport markdown\n\nimport numpy as np\n\nOUTDIR = 'out/'\nDATADIR = 'data/'\nWEBDIR = 'web/'\n\nSTATIC = [('static', None), ('.nojekyll', None)]\n\nNAV = [{\"slug\": \"http://www.ucl.ac.uk/\",\n        \"title\": u\"UCL\"},\n       {\"slug\": u\"http://www.ucl.ac.uk/slade\",\n        \"title\": \"Slade School Of Fine Art\"},\n       {\"slug\": \"http://blogs.ucl.ac.uk/slade-archive-project/\",\n        \"title\": \"Slade Archive Project Blog\"},\n       {\"slug\": \"/\",\n        \"title\": \"Slade School Photos\"}\n       ]\n\nDEF_YEARS = range(1930, 1980)\nMISSING_YEARS = [1932, 1948, 1958, 1969, 1970]\n\n\nJINJA_ENV = Environment(loader=FileSystemLoader(\n    os.path.join(WEBDIR, 'templates')))\nJINJA_ENV.filters['tojson'] = dumps\n\n\nclass SlapError(Exception):\n\n    \"\"\"Generic SLAP Exception\"\"\"\n\n\nclass InvalidDeploymentDirectory(SlapError):\n\n    \"\"\"Error thrown when directory for deployement does not exist\"\"\"\n\n\nclass MarkdownFileNotFound(SlapError):\n\n    \"\"\"\"Error raised when markdown file is not found\"\"\"\n\n\ndef makedir(out_dir):\n    \"\"\" Creates a directory or a path\n    \"\"\"\n    try:\n        os.makedirs(out_dir)\n    except OSError as exc:  # Python >2.5\n        if exc.errno == 17 and os.path.isdir(out_dir):\n            pass\n        else:\n            raise\n\n    return out_dir\n\n\ndef import_md_content(filename):\n\n    md = markdown.Markdown(extensions=['meta', 'def_list', 'footnotes'])\n    try:\n        with open(filename, 'r', 'utf') as mdfile:\n            content = mdfile.read()\n            content = md.convert(content)\n            return content, md.Meta\n    except IOError:\n        raise MarkdownFileNotFound\n\n\ndef collect_metadata():\n    \"\"\" Goes into each group photo directory and retreives JSON metadata\n    \"\"\"\n    root, dirs, files = os.walk(DATADIR).next()\n\n    for sdir in dirs:\n        this_path = os.path.join(DATADIR, sdir)\n        with open(os.path.join(this_path, 'metadata.json'),\n                  'r', 'utf') as metadatafile:\n            print(\"loading metadata [{}]\".format(sdir))\n            metadata = load(metadatafile)\n\n        blurbfile = 'blurb.md'\n        mainfile = 'main.md'\n\n        try:\n            metadata['blurb'], _ = import_md_content(\n                os.path.join(this_path, blurbfile))\n        except MarkdownFileNotFound:\n            metadata['blurb'] = ''\n\n        try:\n            metadata['main'], _ = import_md_content(\n                os.path.join(this_path, mainfile))\n        except MarkdownFileNotFound:\n            metadata['main'] = ''\n\n        yield metadata\n\n\n@task\ndef render_static_pages():\n    template = JINJA_ENV.get_template('page.html')\n    root, dirs, files = os.walk(DATADIR).next()\n\n    for static_file in files:\n\n        name, ext = os.path.splitext(static_file)\n\n        if ext == '.md':\n\n            static_file_slug = '{}.html'.format(name)\n            content, meta = import_md_content(\n                os.path.join(DATADIR, static_file))\n\n            nav = NAV + [{'slug': static_file_slug,\n                          'title': meta.get('title')[0]}]\n\n            static_html = template.render({'content': content, 'nav': nav})\n\n            with open(os.path.join(OUTDIR, static_file_slug),\n                      'w',\n                      'utf') as static_filehandle:\n\n                static_filehandle.write(static_html)\n\n\ndef render_group_photo(metadata):\n    template = JINJA_ENV.get_template('group_photo.html')\n    nav = NAV + [{'slug': '/{}/{}'.format('groups',  metadata['group_image']),\n                  'title': metadata.get('title', metadata['group_image'])}]\n\n    metadata['face_locations'] = sorted(metadata['face_locations'],\n                                        key=lambda x: x.get('who', x['name']))\n\n    group_photo_html = template.render(**dict(\n        metadata.items() + {'nav': nav}.items()))\n\n    gp_dir = makedir(os.path.join(OUTDIR,\n                                  'groups',\n                                  metadata['group_image']))\n\n    with open(os.path.join(gp_dir,\n                           'index.html'), 'w', 'utf') as gp_filehandle:\n\n        gp_filehandle.write(group_photo_html)\n\n\ndef render_list(images):\n    template = JINJA_ENV.get_template('list.html')\n    pages = sorted(images, key=lambda x: x.get('title', 'year'))\n\n    image_years = [image.get('year') or 'Undated' for image in images]\n\n    years = []\n    for year in set(DEF_YEARS + image_years):\n        if year in MISSING_YEARS:\n            years.append({'year': year,\n                          'missing': True})\n        elif year in image_years:\n            years.append({'year': year,\n                          'images': [images[i]\n                                     for i, c\n                                     in enumerate(image_years) if c == year]})\n        else:\n            years.append({'year': year,\n                          'processing': True})\n\n    years = sorted(years, key=lambda x: x.get('year'))\n    list_html = template.render(pages=pages, years=years, nav=NAV)\n\n    with open(os.path.join(OUTDIR, 'index.html'),\n              'w',\n              'utf') as list_filehandle:\n\n        list_filehandle.write(list_html)\n\n\ndef render_portrait(metadata, portrait):\n    template = JINJA_ENV.get_template('portrait.html')\n    nav = NAV + [{'slug': '/{}/{}'.format('groups',\n                                          metadata['group_image']),\n                  'title': metadata.get('title',\n                                        metadata['group_image'])},\n\n                 {'slug': '/{}/{}/{}'.format('groups',\n                                             metadata['group_image'],\n                                             portrait['name']),\n                  'title': portrait.get('who',\n                                        portrait['name'])}]\n\n    portrait_html = template.render(\n        group_photo=metadata['group_image'],\n        group_title=metadata.get('title', metadata[\n            'group_image'].replace('_', ' ')),\n        portrait=portrait,\n        nav=nav)\n\n    outfile = os.path.join(\n        OUTDIR,\n        'groups',\n        metadata['group_image'],\n        '{}.html'.format(\n            portrait['name']))\n\n    with open(outfile,\n              'w',\n              'utf') as portrait_filehandle:\n        portrait_filehandle.write(portrait_html)\n\n\ndef calculate_percentage_overlap(locs):\n\n    a = np.array(locs, dtype=np.float)\n\n    x1 = a[:, 0]\n    x2 = x1 + a[:, 2]\n\n    y1 = a[:, 1]\n    y2 = y1 + a[:, 3]\n\n    x_overlap = np.maximum(np.zeros((x1.size, x1.size)), np.minimum(\n        *np.meshgrid(x2, x2)) - np.maximum(*np.meshgrid(x1, x1)))\n    y_overlap = np.maximum(np.zeros((y1.size, y1.size)), np.minimum(\n        *np.meshgrid(y2, y2)) - np.maximum(*np.meshgrid(y1, y1)))\n\n    overlap = x_overlap * y_overlap\n\n    area = a[:, 2] * a[:, 3]\n    average_area = np.add(*np.meshgrid(area, area)) / 2\n\n    percentage_overlap = overlap / average_area\n\n    return percentage_overlap\n\n\ndef overlap_indices(overlaps, index, perc):\n    return np.argwhere(overlaps[:, index] > perc).reshape(-1).tolist()\n\n\ndef check_for_overlaps(face_locations):\n\n    perc = 0.5\n\n    locs = [[loc['locations']['y'],\n             loc['locations']['x'],\n             loc['locations']['h'],\n             loc['locations']['w']] for loc in face_locations]\n\n    overlaps = calculate_percentage_overlap(locs)\n\n    for i, loc in enumerate(face_locations):\n        for j in overlap_indices(overlaps, i, perc):\n            if j is not i:\n                rels = loc.get('related', [])\n                rels.append(face_locations[j]['name'])\n                loc['related'] = rels\n\n                possible_who_list = loc.get('possible_who', [])\n                possible_who = face_locations[j].get('who', None)\n                if possible_who is not None:\n                    possible_who_list.append(possible_who)\n                    possible_who_list = list(set(possible_who_list))\n\n                loc['possible_who'] = possible_who_list\n\n    return face_locations\n\n\ndef render_portraits(m):\n    fl = check_for_overlaps(m.get('face_locations', []))\n    for portrait in fl:\n        render_portrait(m, portrait)\n\n\ndef render_portrait_index(m):\n    template = JINJA_ENV.get_template('nameindex.html')\n    names = []\n    unnamed = []\n    years = {}\n    for group in m:\n        for portrait in group.get('face_locations', []):\n            year = group.get('year', 'undated')\n            name = {\n                'group': group['group_image'],\n                'name': portrait.get('name'),\n                'year': year,\n                'src': '/static/data/{}/{}_face.jpeg'.format(\n                    group['group_image'],\n                    portrait['name']),\n            }\n\n            if year not in years:\n                years[year] = 0\n\n            if portrait.get('possible_who'):\n                name['possible_who'] = portrait.get('possible_who')\n\n            if portrait.get('who'):\n                name['who'] = portrait.get('who')\n                names.append(name)\n                years[year] += 1\n            else:\n                unnamed.append(name)\n\n    nav = NAV + [{'slug': 'groups/nameindex.html',\n                  'title': 'View by name'}]\n\n    names = sorted(names, key=lambda x: x.get('who', 'year'))\n    unnamed = sorted(unnamed, key=lambda x: x.get('year', 'name'))\n\n    index_html = template.render(nav=nav,\n                                 names=names,\n                                 unnamed=unnamed,\n                                 years=years)\n\n    outfile = os.path.join(OUTDIR, 'groups', 'nameindex.html')\n\n    with open(outfile,\n              'w',\n              'utf') as name_index_filehandle:\n        name_index_filehandle.write(index_html)\n\n\n@task\ndef copy_static_dirs():\n    for source, dest in STATIC:\n        source_path = os.path.join(WEBDIR, source)\n        dest_name = dest if dest is not None else os.path.basename(source_path)\n\n        if os.path.isdir(source_path):\n            out_path = makedir(os.path.join(OUTDIR, dest_name))\n            local(('rsync -a '\n                   '-v '\n                   '{source_path}/* '\n                   '{out_path}').format(source_path=source_path,\n                                        out_path=out_path))\n        elif os.path.isfile(source_path):\n            out_path = os.path.join(OUTDIR, dest_name)\n            local(('rsync -a '\n                   '-v '\n                   '{source_path} '\n                   '{out_path}').format(source_path=source_path,\n                                        out_path=out_path))\n\n    local('chmod -R 766 {}'.format(OUTDIR))\n\n\n@task\ndef copy_data_dir():\n    local(('rsync -a -v -L '\n           '{DATADIR} '\n           '{OUTDIR}static/data/').format(DATADIR=DATADIR,\n                                          OUTDIR=OUTDIR))\n\n\n@task\ndef runserver(port=8080):\n\n    import SimpleHTTPServer\n    import SocketServer\n\n    # check for valid port\n    try:\n        int_port = int(port)\n    except ValueError:\n        print(red(\"Invalid port: {}\".format(port)))\n        exit()\n\n    class ReusePortTCPServer(SocketServer.TCPServer):\n\n        \"\"\"Special TCP server class which reuses ports by default\"\"\"\n        allow_reuse_address = True\n\n    os.chdir(OUTDIR)\n\n    Handler = SimpleHTTPServer.SimpleHTTPRequestHandler\n\n    httpd = ReusePortTCPServer((\"\", int_port), Handler)\n\n    print(\"serving at port: {}\".format(int_port))\n    try:\n        httpd.serve_forever()\n    except KeyboardInterrupt:\n        pass\n    finally:\n        httpd.socket.close()\n\n\n@task\ndef render():\n    image_list = []\n    metadata = []\n    for m in collect_metadata():\n        metadata.append(m)\n        render_group_photo(m)\n        image = {\n            \"slug\": m['group_image'],\n            \"title\": m.get('title', m['group_image'].replace(\"_\", \" \")),\n            \"year\": m.get('year', None)}\n        if m.get('blurb', None):\n            image['blurb'] = m.get('blurb')\n        image_list.append(image)\n        render_portraits(m)\n\n    render_list(image_list)\n    render_portrait_index(metadata)\n    render_static_pages()\n\n\n@task\ndef fast_build():\n    render()\n    copy_static_dirs()\n\n\n@task\ndef build():\n    render()\n    copy_static_dirs()\n    copy_data_dir()\n\n\n@task\ndef clean():\n    \"\"\" Empties the output directory\n    \"\"\"\n    local(('rm -rvf {dir_to_clean}').format(\n        dir_to_clean=' '.join([\n            os.path.join(OUTDIR, '*'),\n            os.path.join(OUTDIR, '.[^.]*')])))\n\n\n@task\ndef deploy(out=None):\n    if not out:\n        raise InvalidDeploymentDirectory(\n            'Not a valid directory for deployment')\n        exit\n\n    local(('rsync -a -v --delete '\n           '--exclude \".git\" '\n           '{OUTDIR} '\n           '{out_path}').format(OUTDIR=OUTDIR,\n                                out_path=out))\n","sub_path":"fabfile.py","file_name":"fabfile.py","file_ext":"py","file_size_in_byte":12930,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"77643343","text":"import pymysql.cursors\nfrom sqlalchemy import create_engine\nfrom flask import Flask, render_template, request\nimport csv\n\n# AWS Database Connection\nhost = \"18.116.165.240\"\nport = \"33060\"\nusername = \"root\"\npassword = \"CBykDq08SZ\"\ndatabase = \"mydb\"\nengine = create_engine(f\"mysql+pymysql://{username}:{password}@{host}:{port}/{database}?charset=utf8\")\n\n\nfrom sqlalchemy.orm import sessionmaker\nfrom sqlalchemy.ext.declarative import declarative_base\nBase = declarative_base()\nBase.metadata.create_all(engine)\nDbSession = sessionmaker(bind=engine)\nsession = DbSession()\n\n\n# Flask\napp = Flask(__name__)\n\ndef read_sql(sql):\n    res_list = []\n    for row in sql.fetchall():\n        res_list.append(row)\n    return res_list\n\ndef search(content):\n    search_res = []\n    search_res.append(res_list[0])\n    for item in res_list:\n        if content == item[0]:\n            search_res.append(item)\n    return search_res\n\n@app.route('/')\ndef index():\n    return render_template('index.html')\n\n@app.route('/news', methods=['get', 'post'])\ndef result():\n    content = request.form.get('content')\n    search_res = search(content=content)\n    return render_template('index.html',result=search_res)\n\nif __name__ == '__main__':\n    res_list = read_sql(engine.execute(\"SELECT * from YahooNews\"))\n    app.run(host='0.0.0.0', port=80)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"FE595_Flask.py","file_name":"FE595_Flask.py","file_ext":"py","file_size_in_byte":1331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"363506424","text":"import requests\nimport csv\nfrom bs4 import BeautifulSoup\n\nurl = 'https://www.amazon.in/best-sellers-books-Amazon/zgbs/books/'\n# print(url)\nbookinfo_list = []\nfor i in range(1, 6):\n    url_page = url + \"ref=zg_bs_pg_2?ie=UTF8&pg=\" + str(i)\n    r = requests.get(url_page)\n    html = r.text\n    # parsing into the beautiful soup\n    soup = BeautifulSoup(html, \"html.parser\")\n    # to get the books part of the website\n    name_bestseller = soup.find_all('div', {'class': 'zg_itemImmersion'})\n    # to print each and every book\n    for name in name_bestseller:\n        var = name.contents[3].find_all('a', {'class': 'a-link-normal'})\n        try:\n            b_name = var[0].text.strip()\n        except:\n            b_name = \"Not available\"\n        try:\n            url_book = var[0].get(\"href\")\n            b_addr = \"https://www.amazon.in\" + url_book\n        except:\n            b_addr = \"Not available\"\n        try:\n            b_aut = name.contents[3].find_all(\n                    'div', {\n                            'class': 'a-row a-size-small'\n                           }\n                                             )[0].text.strip()\n\n        except:\n            b_aut = \"Not available\"\n    # var = nam.contents[3].find_all('a',{'class':'a-link-normal'})\n        try:\n            var1 = name.select('.p13n-sc-price')\n            b_price = var1[0].text.strip()\n        except:\n            b_price = \"Not available\"\n        try:\n            var3 = name.select('.a-icon-star')[0].text.strip()\n            b_avgr = var3\n            b_numrat = var[2].text.strip()\n        except:\n            b_avgr = \"Not available\"\n            b_numrat = \"Not available\"\n        bookinfo = [b_name, b_addr, b_aut, b_price, b_numrat, b_avgr]\n        bookinfo_list.append(bookinfo)\nname_list = ['Name', 'URL', 'Author',\n             'Price', 'Number of Ratings', 'Average Rating']\nwith open('./output/in_book.csv', 'w') as csvfile:\n    writer = csv.writer(csvfile)\n    writer.writerow(name_list)\n    for row in bookinfo_list:\n            writer.writerow(row)\n","sub_path":"in_bestseller.py","file_name":"in_bestseller.py","file_ext":"py","file_size_in_byte":2043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"126135830","text":"import requests\nfrom .utils import DO_BASE_URL, DO_HEADERS\n\n\nclass SkiffRegion(object):\n    \"\"\"SkiffRegion\"\"\"\n    def __init__(self, options=None, **kwargs):\n        super(SkiffRegion, self).__init__()\n        if not options:\n            options = kwargs\n\n        self.__dict__.update(options)\n\n    def __repr__(self):\n        return '<' + self.name + ' (' + self.slug + ')>'\n\n\ndef all():\n    r = requests.get(DO_BASE_URL + '/regions', headers=DO_HEADERS)\n    r = r.json()\n    return [SkiffRegion(a) for a in r[\"regions\"]]\n\n\ndef get(r):\n    regions = all()\n    for region in regions:\n        if r in region.slug:\n            return region\n    raise ValueError('No Suitable REgion Found')\n","sub_path":"skiff/Region.py","file_name":"Region.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"191480454","text":"def solution(people, limit):\n    answer = 1\n    people.sort()\n    boat_weight = 0\n    number = 0\n    N = len(people)\n    boat = [0 for i in range(N)]\n    for i in range(N):\n        if(number < 2 and people[i] + boat_weight <= limit):\n            boat_weight += people[i]\n            number += 1\n            boat[i] = answer\n            if(number == 2):\n                number = 0\n                boat_weight = 0\n                answer += 1\n        else:\n            boat_weight = people[i]\n            answer += 1\n            number = 0\n            boat[i] = answer\n    return answer\n\nif __name__ == '__main__':\n    print(solution([70, 80, 50], 100))","sub_path":"programmers/Greedy_4.py","file_name":"Greedy_4.py","file_ext":"py","file_size_in_byte":650,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"590406314","text":"import mysql.connector\nfrom Bill import *\n\n\nclass Database:\n    def __init__(self):\n        self.appDatabase = mysql.connector.connect(\n            host=\"localhost\",\n            user=\"root\",\n            password=\"\"\n        )\n        self.appCursor = self.appDatabase.cursor()\n        self.initDatabase()\n\n    def initDatabase(self):\n        self.appCursor.execute(\"create database if not exists ecodb1\")\n        # self.appCursor.execute(\n        #     \"CREATE TABLE if not exists `ecodb`.`comaplaints` ( `Compalint_No` INT(5) NOT NULL , `Bank_ID` VARCHAR(10) NOT NULL , `Complaint_IssueDate` DATE NOT NULL , PRIMARY KEY (`Compalint_No`));\")\n        # self.appCursor.execute(\"CREATE TABLE if not exists `ecodb`.`complaints` ( `invoice_id` VARCHAR(15) NOT NULL ,  `date` DATE NOT NULL ,  `branch_address` VARCHAR(50) NOT NULL ,  `zone` VARCHAR(10) NOT NULL ,  `complaint_no` INT(10) NOT NULL ,    PRIMARY KEY  (`invoice_id`));\")\n        # self.appCursor.execute(\n        #     \"CREATE TABLE if not exists `ecodb`.`banks` ( `Bank_ID` VARCHAR(10) NOT NULL , `Branch_code` INT(10) NOT NULL , `Zone` VARCHAR(10) NOT NULL , `Bank_Name` VARCHAR(50) NOT NULL , `Bank_Location` VARCHAR(50) NOT NULL , PRIMARY KEY (`Bank_ID`));\")\n        # self.appCursor.execute(\n        #     \"CREATE TABLE if not exists `ecodb`.`services_details` ( `Service_ID` INT NOT NULL , `Service_Description` VARCHAR(100) NOT NULL , `Service_Rate` INT(10) NOT NULL , PRIMARY KEY (`Service_ID`));\")\n        # self.appCursor.execute(\n        #     \"CREATE TABLE if not exists `ecodb`.`quotations` ( `date_of_visit` DATE NOT NULL , `visit_charges` INT(100) NOT NULL DEFAULT '780' , `complaint_no` VARCHAR(30) NOT NULL , `total_price` INT(100) NOT NULL , PRIMARY KEY (`complaint_no`));\")\n        self.appCursor.execute(\"CREATE TABLE IF NOT EXISTS `ecodb1`.`complaints` ( `complaint_number` INT(10) NOT NULL , `bank_name` VARCHAR(100) NOT NULL , `branch_address` VARCHAR(100) NOT NULL , `bank_zone` VARCHAR(15) NOT NULL , `complaint_date` DATE NOT NULL , PRIMARY KEY (`complaint_number`));\")\n        self.appCursor.execute(\"CREATE TABLE IF NOT EXISTS `ecodb1`.`quotations` ( `complaint_number` INT(10) NOT NULL , `quotation_id` INT(10) NOT NULL , `services_amount` FLOAT(10,2) NOT NULL,`tax` FLOAT(10,2) NOT NULL ,`reimbursement_charges` FLOAT(10,2) NOT NULL , `total_amount` FLOAT(10,2) NOT NULL ,  `quotation_date` DATE NOT NULL,`is_done` BOOLEAN NOT NULL DEFAULT FALSE,PRIMARY KEY (`quotation_id`) ) ;\")\n        self.appCursor.execute(\"CREATE TABLE IF NOT EXISTS `ecodb1`.`services` ( `service_id` VARCHAR(32) NOT NULL , `quotation_id` INT(10) NOT NULL , `service_description` VARCHAR(150) NOT NULL , `service_rate` FLOAT(10,2) NOT NULL , `service_qty` FLOAT(10,2) NOT NULL , `service_amount` FLOAT(10,2) NOT NULL , PRIMARY KEY (`service_id`));\")\n        self.appCursor.execute(\n            \"CREATE TABLE IF NOT EXISTS `ecodb1`.`bills` ( `invoice_id` VARCHAR(20) NOT NULL , `quotation_id` INT(10) NOT NULL , `job_completion_date` DATE NULL,PRIMARY KEY (`invoice_id`) ) ;\")\n\n    def getAllServices(self) -> list:\n        self.appCursor.execute(\n            \"SELECT service_description from ecodb.services_details\")\n        services_list = []\n        for row in self.appCursor:\n            services_list.append(row[0])\n        return services_list\n\n    def getServiceRate(self, serice_description) -> float:\n        self.appCursor.execute(\n            \"SELECT service_rate FROM ecodb.services_details WHERE service_description=%s\", (serice_description,))\n\n        for row in self.appCursor:\n            return row[0]\n        return 0\n\n    def getConveyenceCharges(self) -> str:\n        # yahan database sy data uthaya jae ga\n        return \"780\"\n\n    def insertData(self, bill: Bill):\n        self.addComplaint(bill.getComplainInfo())\n        self.addQuotation(bill)\n        self.addServices(bill)\n        self.addBill(bill)\n\n        # def getdata(self):\n        #     self.dbCursor.execute(\"select * from ins.students\")\n        #     for row in self.dbCursor:\n        #         print(row[0])\n        self.appDatabase.commit()\n\n    def addComplaint(self, complaint: ComplaintInfo):\n        # l = (complaint.getComplainNo(), complaint.getBankName(),\n        #      complaint.getAddress(), complaint.getZone(), complaint.getDate())\n        # print(\n        #     \"INSERT INTO  `ecodb1`.`complaints` (`complaint_number`, `bank_name`, `branch_address`, `bank_zone`, `complaint_date`) VALUES (%s,%s,%s,%s,%s)\", l\n        # )\n        self.appCursor.execute(\n            \"INSERT INTO  `ecodb1`.`complaints` (`complaint_number`, `bank_name`, `branch_address`, `bank_zone`, `complaint_date`) VALUES (%s,%s,%s,%s,STR_TO_DATE(%s,'%d/%b/%Y'))\",\n            (complaint.getComplainNo(), complaint.getBankName(),\n             complaint.getAddress(), complaint.getZone(), complaint.getDate(),))\n        self.appDatabase.commit()\n\n    def addQuotation(self, bill: Bill):\n        totalServiceAmount = 0\n        for service in bill.getServices().getServicesList():\n            totalServiceAmount = totalServiceAmount+service[\"amount\"]\n\n        extra_charges = bill.getAdditionalCharges().getPersonQty() * \\\n            bill.getAdditionalCharges().getPerPersonRate()\n        totalAmount = totalServiceAmount + \\\n            (totalServiceAmount*0.16)+extra_charges\n        self.appCursor.execute(\n            \"INSERT INTO `ecodb1`.`quotations` (`complaint_number`, `quotation_id`, `services_amount`, `tax`,`reimbursement_charges`, `total_amount`, `quotation_date`) VALUES (%s,%s,%s,%s,%s,%s,STR_TO_DATE(%s,'%d/%b/%Y'))\",\n            (bill.getComplainInfo().getComplainNo(), bill.getComplainInfo().getComplainNo(), totalServiceAmount, totalServiceAmount*0.16, extra_charges, totalAmount,  bill.getComplainInfo().getDate(),))\n        self.appDatabase.commit()\n\n    def addServices(self, bill: Bill):\n        i = 1\n        for service in bill.getServices().getServicesList():\n            service_id = \"S\"+str(i) + \"-\" + \\\n                str(bill.getComplainInfo().getComplainNo())\n            i = i+1\n            self.appCursor.execute(\"INSERT INTO `ecodb1`.`services` (`service_id`, `quotation_id`, `service_description`, `service_rate`, `service_qty`, `service_amount`) VALUES (%s,%s,%s,%s,%s,%s)\",\n                                   (service_id, bill.getComplainInfo().getComplainNo(), service[\"description\"], service[\"rate\"], service[\"qty\"], service[\"amount\"],))\n        self.appDatabase.commit()\n\n    def addBill(self, bill: Bill):\n        self.appCursor.execute(\n            \"INSERT INTO `ecodb1`.`bills` (`invoice_id`, `quotation_id`) VALUES (%s,%s)\", (bill.getComplainInfo().getInvoiceId(), bill.getComplainInfo().getComplainNo(),))\n        self.appDatabase.commit()\n\n    def getInvoiceId(self, zone: str) -> int:\n        self.appCursor.execute(\n            \"SELECT count(complaint_number) from ecodb1.complaints where bank_zone=upper(%s)\", (zone,))\n        invoiceId = 0\n        for row in self.appCursor:\n            invoiceId = row[0]\n\n        return invoiceId+1\n\n    def fetchTotalRows(self) -> int:\n        self.appCursor.execute(\n            \"SELECT count(complaint_number) from ecodb1.quotations where is_done=0\")\n        for row in self.appCursor:\n            return row[0]\n\n    def fetchRowsByZone(self, zone: str) -> int:\n        self.appCursor.execute(\n            \"SELECT count(complaint_number) from ecodb1.complaints where bank_zone=upper(%s)\", (zone,))\n        for row in self.appCursor:\n            return row[0]\n\n    def fetchRowsByBank(self, bank: str) -> int:\n        self.appCursor.execute(\n            \"SELECT count(complaint_number) from ecodb1.complaints where bank_name=upper(%s)\", (bank,))\n        for row in self.appCursor:\n            return row[0]\n\n    def fetchRowByComplaintNo(self, complaintno: int) -> int:\n        self.appCursor.execute(\n            \"SELECT count(complaint_number) from ecodb1.complaints where complaint_number=%s\", (complaintno,))\n        for row in self.appCursor:\n            return row[0]\n\n    def fetchNotDoneQuotations(self) -> list:\n        self.appCursor.execute(\n            \"SELECT c.complaint_number, c.bank_zone , c.branch_address ,q.quotation_date ,q.total_amount,c.bank_name from ecodb1.complaints c join ecodb1.quotations q on c.complaint_number = q.complaint_number where q.is_done=0\")\n        quotationsList = []\n        for row in self.appCursor:\n            quotationsList.append(row)\n\n        return quotationsList\n\n    def fetchQuotationsByZone(self, zone: str) -> list:\n        self.appCursor.execute(\n            \"SELECT c.complaint_number, c.bank_zone , c.branch_address ,q.quotation_date ,q.total_amount,c.bank_name from ecodb1.complaints c join ecodb1.quotations q on c.complaint_number = q.complaint_number where c.bank_zone=upper(%s)\", (zone,))\n        quotationsList = []\n        for row in self.appCursor:\n            quotationsList.append(row)\n\n        return quotationsList\n\n    def fetchQuotationsByBank(self, bank: str) -> list:\n        self.appCursor.execute(\n            \"SELECT c.complaint_number, c.bank_zone , c.branch_address ,q.quotation_date ,q.total_amount,c.bank_name from ecodb1.complaints c join ecodb1.quotations q on c.complaint_number = q.complaint_number where c.bank_name=upper(%s)\", (bank,))\n        quotationsList = []\n        for row in self.appCursor:\n            quotationsList.append(row)\n\n        return quotationsList\n\n    def fetchQuotationByComplaintNo(self, complaintno: int) -> list:\n        self.appCursor.execute(\n            \"SELECT c.complaint_number, c.bank_zone , c.branch_address ,q.quotation_date ,q.total_amount,c.bank_name from ecodb1.complaints c join ecodb1.quotations q on c.complaint_number = q.complaint_number where c.complaint_number=%s\", (complaintno,))\n        quotationsList = []\n        for row in self.appCursor:\n            quotationsList.append(row)\n\n        return quotationsList\n\n    def deleteQuotation(self, complaintno: int):\n        try:\n            self.appCursor.execute(\n                \"DELETE FROM ecodb1.complaints WHERE complaint_number=%s\", (complaintno,))\n            self.appCursor.execute(\n                \"DELETE FROM ecodb1.bills WHERE quotation_id=%s\", (complaintno,))\n            self.appCursor.execute(\n                \"DELETE FROM ecodb1.quotations WHERE quotation_id=%s\", (complaintno,))\n            self.appCursor.execute(\n                \"DELETE FROM ecodb1.services WHERE quotation_id=%s\", (complaintno,))\n\n            self.appDatabase.commit()\n        except Exception as e:\n            print(e)\n\n    def updateInvoiceId(self, oldinvoiceid, newinvoiceId: str):\n        try:\n            self.appCursor.execute(\n                (\"UPDATE ecodb1.bills set invoice_id=%s where invoice_id=%s\"), (newinvoiceId, oldinvoiceid,))\n            self.appDatabase.commit()\n        except Exception as e:\n            print(e)\n\n    def getInvoiceIdAndZoneFromComplaintNo(self, complaintno: int) -> dict:\n\n        invoiceIDAndZone = {\"invoice_id\": None, \"zone\": None}\n        self.appCursor.execute(\n            \"SELECT b.invoice_id from ecodb1.quotations q join ecodb1.bills b on q.quotation_id = b.quotation_id where q.complaint_number=%s\", (complaintno,))\n        for row in self.appCursor:\n            invoiceIDAndZone[\"invoice_id\"] = row[0]\n        self.appCursor.execute(\n            \"select bank_zone from ecodb1.complaints where complaint_number=%s\", (complaintno,))\n        for row in self.appCursor:\n            invoiceIDAndZone[\"zone\"] = row[0]\n        return invoiceIDAndZone\n\n    def getBranchAddressFromComplaintNo(self, complaintno: int) -> str:\n\n        self.appCursor.execute(\n            \"SELECT branch_address FROM ecodb1.complaints WHERE complaint_number=%s\", (complaintno,))\n        for row in self.appCursor:\n            return row[0]\n\n    def updateJobCompletionDate(self, comaplaitno: int, date: str):\n        self.appCursor.execute(\n            \"update ecodb1.bills set job_completion_date=STR_TO_DATE(%s,'%d/%b/%Y') where quotation_id=%s\", (date, comaplaitno,))\n        self.appCursor.execute(\n            \"update ecodb1.quotations set is_done=1 where quotation_id=%s\", (comaplaitno,))\n        self.appDatabase.commit()\n\n    def fetchDoneQuotations(self, month: str, year: str) -> list:\n        self.appCursor.execute(\n            \"SELECT b.invoice_id ,q.complaint_number, b.job_completion_date , c.bank_zone, c.branch_address ,q.total_amount from ecodb1.complaints c join ecodb1.quotations q on c.complaint_number = q.complaint_number join ecodb1.bills b on b.quotation_id = q.quotation_id where q.is_done=1 and b.invoice_id like concat('______',%s,'-',%s)\", (month, year,))\n        quotationsList = []\n        for row in self.appCursor:\n            quotationsList.append(row)\n\n        return quotationsList\n\n    def fetchDoneQuotationsByZone(self, zone: str, month: str, year: str) -> list:\n        self.appCursor.execute(\n            \"SELECT b.invoice_id ,q.complaint_number, b.job_completion_date , c.bank_zone, c.branch_address ,q.total_amount from ecodb1.complaints c join ecodb1.quotations q on c.complaint_number = q.complaint_number join ecodb1.bills b on b.quotation_id = q.quotation_id where q.is_done=1 and c.bank_zone =upper(%s) and b.invoice_id like concat('______',%s,'-',%s)\", (zone, month, year,))\n        quotationsList = []\n        for row in self.appCursor:\n            quotationsList.append(row)\n\n        return quotationsList\n\n    def fetchDoneQuotationsByBank(self, bank: str, month: str, year: str) -> list:\n        self.appCursor.execute(\n            \"SELECT b.invoice_id ,q.complaint_number, b.job_completion_date , c.bank_zone, c.branch_address ,q.total_amount from ecodb1.complaints c join ecodb1.quotations q on c.complaint_number = q.complaint_number join ecodb1.bills b on b.quotation_id = q.quotation_id where q.is_done=1 and c.bank_name =upper(%s) and b.invoice_id like concat('______',%s,'-',%s)\", (bank, month, year,))\n        quotationsList = []\n        for row in self.appCursor:\n            quotationsList.append(row)\n\n        return quotationsList\n\n    def fetchDoneQuotationByComplaintNo(self, complaintno: int, month: str, year: str) -> list:\n        self.appCursor.execute(\n            \"SELECT b.invoice_id ,q.complaint_number, b.job_completion_date , c.bank_zone, c.branch_address ,q.total_amount from ecodb1.complaints c join ecodb1.quotations q on c.complaint_number = q.complaint_number join ecodb1.bills b on b.quotation_id = q.quotation_id where q.is_done=1 and q.complaint_number =%s and b.invoice_id like concat('______',%s,'-',%s)\", (complaintno, month, year,))\n        quotationsList = []\n        for row in self.appCursor:\n            quotationsList.append(row)\n\n        return quotationsList\n\n    def getZoneMaximumInvoiceId(self, zone: str):\n        print(zone[0])\n        query = \"SELECT invoice_id FROM `ecodb1`.`bills` WHERE invoice_id like %s ORDER by invoice_id DESC LIMIT 1\"\n        args = (zone[0],)\n        self.appCursor.execute(\n            query, (zone[0] + \"%\",))\n\n        for row in self.appCursor:\n            print(row)\n            return row[0]\n\n    def fetchDataForSummary(self, zone: str, month: str, year: str) -> list:\n        self.appCursor.execute(\n            \"SELECT c.branch_address, q.complaint_number,b.invoice_id , q.services_amount,q.tax,q.reimbursement_charges,q.total_amount from ecodb1.complaints c join ecodb1.quotations q on c.complaint_number = q.complaint_number join ecodb1.bills b on b.quotation_id = q.quotation_id where q.is_done=1 and c.bank_zone =upper(%s) and b.invoice_id like concat('______',%s,'-',%s)\", (zone, month, year,))\n        data = []\n        for row in self.appCursor:\n            data.append(row)\n        return data\n\n\nif __name__ == \"__main__\":\n    db = Database()\n    # print(len(db.fetchQuotationsByBank('Bank al-habib')))\n    # print((db.fetchDoneQuotations()))\n    # print(db.getZoneMaximumInvoiceId(zone=\"LHR\"))\n    # print(db.fetchTotalRows())\n    # print(db.deleteQuotation(1))\n    # print(db.getInvoiceIdFromComplaintNo(1))\n    # db.getdata()\n    print(db.fetchDataForSummary(\"LHR\", \"04\", \"2021\"))\n","sub_path":"Database.py","file_name":"Database.py","file_ext":"py","file_size_in_byte":16038,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"445142489","text":"import os\n\nfrom evaluator.properties import (\n    Density,\n    EnthalpyOfMixing,\n    EnthalpyOfVaporization,\n    ExcessMolarVolume,\n)\n\nfrom nistdataselection.analysis.plotting import (\n    plot_estimated_vs_reference,\n    plot_statistic,\n    plot_statistic_per_environment,\n)\nfrom nistdataselection.analysis.statistics import Statistics\nfrom nistdataselection.utils import SubstanceType\n\n\ndef main():\n\n    output_directory = \"plots\"\n    os.makedirs(output_directory, exist_ok=True)\n\n    # Define the types of property to plot.\n    property_types = [\n        (Density, SubstanceType.Pure),\n        (EnthalpyOfVaporization, SubstanceType.Pure),\n        (Density, SubstanceType.Binary),\n        (ExcessMolarVolume, SubstanceType.Binary),\n        (EnthalpyOfMixing, SubstanceType.Binary),\n    ]\n\n    study_names = [\n        \"openff-1.0.0\",\n        \"h_mix_rho_x\",\n        \"h_mix_rho_x_rho_pure\",\n        \"h_mix_rho_x_rho_pure_h_vap\",\n        \"rho_pure_h_vap\",\n    ]\n\n    plot_estimated_vs_reference(property_types, study_names, output_directory)\n\n    # Define the statistics to plot.\n    statistics = [Statistics.RMSE, Statistics.R2]\n\n    # Plot a summary of each statistic.\n    plot_statistic(statistics, output_directory)\n\n    statistics = [Statistics.RMSE]\n\n    # # Plot the statistics per environment\n    pure_properties = [x for x in property_types if x[1] == SubstanceType.Pure]\n    plot_statistic_per_environment(pure_properties, statistics, output_directory)\n\n    binary_properties = [x for x in property_types if x[1] == SubstanceType.Binary]\n    plot_statistic_per_environment(\n        binary_properties, statistics, output_directory, per_composition=True\n    )\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"studies/mixture_feasibility/benchmarking/analysis/expanded_set/plot_statistics.py","file_name":"plot_statistics.py","file_ext":"py","file_size_in_byte":1707,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"253075551","text":"from conf import settings, exchanges, blockexplorer\nimport pymysql\nimport threading\nimport logging\n\n\nclass CoinData(dict):\n\n    def __init__(self):\n        self = dict()\n\n    def add(self, coin_name, coin_id, trading_pair_list):\n        self[coin_name] = coin_id, trading_pair_list\n\n\ndef block_explorer_crawl(coin_id):\n    logger = logging.getLogger(__name__)\n\n    for blockchain_name, api in blockexplorer.block_list.items():\n        block_class = api.api_class\n        thread = block_class(api.url, logger, settings.headers, 3, coin_id, blockchain_name, settings.db_host,\n                             settings.db_user, settings.db_passwd, settings.db_name)\n        thread.daemon = True\n        thread.setName(blockchain_name)\n        thread.start()\n\n    main_thread = threading.currentThread()\n\n    for thread in threading.enumerate():\n        if thread is main_thread:\n            continue\n        thread.join()\n\n    logger.debug('Finished')\n\n\ndef exchange_crawl(exchange_to_id, trading_pair_id):\n    logger = logging.getLogger(__name__)\n\n    for ex_name, ex_api in exchanges.ex_list.items():\n        exchange_class = ex_api.api_class\n        thread = exchange_class(ex_api.delay, ex_api.url, settings.headers, exchange_to_id, trading_pair_id, logger,\n                                settings.db_host, settings.db_user, settings.db_passwd, settings.db_name)\n        thread.daemon = True\n        thread.setName(ex_name)\n        thread.start()\n\n    main_thread = threading.currentThread()\n\n    for thread in threading.enumerate():\n        if thread is main_thread:\n            continue\n        thread.join()\n\n    logger.debug('Finished')\n\n\ndef coin_crawl(trading_pair_id):\n    data = CoinData()\n    logger = logging.getLogger(\"Coin\")\n    logger.debug(\"Gathering information about trading pairs and coins.\")\n    _query_select = \"SELECT name, symbol, id FROM crypto_coin;\"\n    _query_select2 = \"SELECT price, price_change_percent24hr, volume FROM crypto_trading_pair_to_exchange \" \\\n                     \"WHERE trading_pair_id='%s';\"\n    _query_update = \"UPDATE crypto_fiat_to_coin \" \\\n                    \"SET price=%s, price_change_percent24hr=%s, volume_24hr=%s \" \\\n                    \"WHERE coin_id=%s and fiat_id='1';\"\n\n    try:\n        connection = pymysql.connect(host=settings.db_host, user=settings.db_user, passwd=settings.db_passwd,\n                                     db=settings.db_name, autocommit=False)\n        cursor = connection.cursor()\n        cursor.execute(_query_select)\n        \"\"\"\n        pairs_looking = []\n        test = {}\n\n        for row in cursor.fetchall():\n            print(\"Coin:\", row[0], \"Symbol:\", row[1], \"ID:\", row[2])\n            for pair in trading_pair_id.items():\n                if str(pair[0]).endswith(tuple(settings.end_trading_pair)):\n                    print(\"EZ\", pair[0])\n                    pairs_looking.append(pair[0])\n\n            data.add(row[0], row[2], [23, 24, 24])\n        print(data)\n        print(pairs_looking)\n       #2 print(trading_pair_id)\n\n        for item in trading_pair_id.items():\n            print(\"Name:\", item[0], \"ID:\", item[1])\n            if item[0] in pairs_looking:\n                test.update({item[0]: item[1]})\n\n        print(test)\n        \"\"\"\n        data = CoinData()\n        test = []\n        for row in cursor.fetchall():\n            print(\"Coin:\", row[0], \"Symbol:\", row[1])\n            new_end_trading_pairs = [str(row[1] + end_pair) for end_pair in settings.end_trading_pair]\n            for pairs in trading_pair_id.items():\n                if pairs[0] in new_end_trading_pairs:\n                    print(\"updating trading pair:\", pairs[0])\n                    cursor.execute(_query_select2 % (pairs[1]))\n                    print(\"P1\", pairs[1])\n                    print(\"P0\", pairs[0])\n                    avg_price = 0\n                    avg_change_percent = 0\n                    avg_volume = 0\n                    _count = 0\n\n                    for l_data in cursor.fetchall():\n                        print(\"Price:\", l_data[0], \"Change Percent:\", l_data[1], \"Volume:\", l_data[2])\n                        _count += 1\n                        avg_price += l_data[0]\n                        avg_change_percent += l_data[1]\n                        avg_volume += l_data[2]\n\n                    print(\"Avg price:\", (avg_price / _count), \"Count:\", _count)\n                    print(\"row2:\", row[2]) # row[0] is coin name, row[2] id of coin\n                    test.append(tuple((str(avg_price/_count), str(round((avg_change_percent/_count), 3)), str(round((avg_volume/_count), 0)), str(round(row[2])))))\n                    #data.add(row[0], str(avg_price/_count), str(round((avg_change_percent/_count), 3)), str(round((avg_volume/_count), 0)), row[2])\n                    print('fuck me', test)\n                    #cursor.execute(_query_update, ((avg_price/_count), round((avg_change_percent/_count), 3), round((avg_volume/_count), 0), row[2]))\n                    cursor.executemany(_query_update, test)\n                    del _count, avg_price, avg_change_percent, avg_volume\n\n        logger.debug(\"selecting coin symbols in database.\")\n        connection.commit()\n        connection.close()\n\n    except pymysql.ProgrammingError as error:\n        logger.error(error)\n    except pymysql.err.OperationalError as error:\n        logger.error(error)\n    except pymysql.err.InterfaceError as error:\n        logger.error(error)\n    except ZeroDivisionError as error:\n        logger.error(error)\n    finally:\n        logger.info(\"calculations are done.\")\n","sub_path":"multi.py","file_name":"multi.py","file_ext":"py","file_size_in_byte":5547,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"231122655","text":"import django_filters\nimport datetime\nfrom django.db.models import Q\n\nfrom apps.hotel.models import Hotel\n\n\nclass HotelFilterSet(django_filters.FilterSet):\n    name = django_filters.CharFilter(lookup_expr=\"icontains\", field_name=\"name\")\n    address = django_filters.CharFilter(lookup_expr=\"icontains\", field_name=\"address\")\n    adult = django_filters.CharFilter(lookup_expr=\"gte\"\n                                      , field_name=\"rooms__adult\")\n    children = django_filters.CharFilter(lookup_expr=\"gte\"\n                                      , field_name=\"rooms__children\")\n    day = django_filters.DateFromToRangeFilter(method=\"start_end_date\")\n    price = django_filters.RangeFilter(method=\"price_range\")\n    star = django_filters.CharFilter(method=\"star_choice\")\n\n    def filter_queryset(self, queryset):\n        qs = super().filter_queryset(queryset)\n        return qs.distinct()\n\n    def start_end_date(self, queryset, name, value):\n        d2 = value.start\n        d1 = value.stop\n        if d1 >= d2:\n            raise Exception(\"Date Error\")\n\n        query = Q(rooms__details__rent__start_date__gte=d1) | Q(rooms__details__rent__end_date__lte=d2) | \\\n                Q(rooms__details__rent__end_date__isnull=True)\n        return queryset.filter(query)\n\n    def price_range(self, queryset, name, value):\n        d2 = value.start\n        d1 = value.stop\n        query = Q()\n        if d1 and d2 and d1 < d2:\n            raise Exception(\"Price Error\")\n\n        if d1:\n            query &= Q(rooms__price__lte=d1)\n\n        if d2:\n            query &= Q(rooms__price__gte=d2)\n\n        return queryset.filter(query)\n\n    def star_choice(self, queryset, name, value):\n        query = Q()\n        try:\n            stars = [int(s) for s in value.split(',')]\n\n            for star in stars:\n                if star > 5 or star < 1:\n                    raise Exception(\"Rating Error\")\n                query |= Q(stars=star)\n        except Exception:\n            raise Exception(\"Rating Error\")\n        print(query)\n        return queryset.filter(query)\n\n    class Meta:\n        models = Hotel\n        exclude = [\"start\", \"end\"]\n\n\nclass RatingFilterSet(django_filters.FilterSet):\n    hotel = django_filters.CharFilter(field_name=\"hotel__id\")\n","sub_path":"apps/hotel/filters.py","file_name":"filters.py","file_ext":"py","file_size_in_byte":2240,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"466829484","text":"\"\"\"\nSortednp (sorted numpy) is a python package which provides methods to perform\nefficient set operations on sorted numpy arrays. This includes intersecting\nand merging sorted numpy arrays. The returned intersections and unions are\nalso sorted.\n\"\"\"\n\nfrom sortednp._internal import merge, intersect\n\n__version__ = \"0.2.1\"\n\nSIMPLE_SEARCH = 1\nBINARY_SEARCH = 2\nGALLOPING_SEARCH = 3\n\ndef resolve(obj):\n    \"\"\"\n    Helper function.\n\n    Check whether the given object is callable. If yes, return its return\n    value, otherwise return the object itself.\n    \"\"\"\n    return obj() if callable(obj) else obj\n\ndef kway_merge(*arrays, assume_sorted=True, **kwds):\n    \"\"\"\n    Merge all given arrays and return the result. Depending on the optional\n    flag assume_sorted, the function sorts the arrays before merging.\n\n    The method raises a TypeError, if the array count is zero.\n\n    The arguments can load the arrays on the fly. If an argument is callable,\n    its return value is used as an array instead of the argument itself. This\n    make it possible to load one array after another to avoid having all\n    arrays in memory at the same time..\n\n    Note on the performance: The function merges the arrays one-by-one. This\n    not the most performant implementation. Use the module heapq for more\n    efficient ways to merge sorted arrays.\n    \"\"\"\n    if not arrays:\n        raise TypeError(\"Merge expects at least one array.\")\n\n    arrays = list(arrays)\n    merge_result = arrays.pop()\n    merge_result = resolve(merge_result)\n    if not assume_sorted:\n        merge_result.sort()\n    for array in arrays:\n        array = resolve(array)\n        if not assume_sorted:\n            array.sort()\n        merge_result = merge(merge_result, array, **kwds)\n    return merge_result\n\ndef kway_intersect(*arrays, assume_sorted=True, **kwds):\n    \"\"\"\n    Intersect all given arrays and return the result. Depending on the\n    optional flag assume_sorted, the function sort sorts the arrays prior to\n    intersecting.\n\n    The method raises a TypeError, if the array count is zero.\n\n    The arguments can load the arrays on the fly. If an argument is callable,\n    its return value is used as an array instead of the argument itself. This\n    make it possible to load one array after another to avoid having all\n    arrays in memory at the same time..\n\n    Note on the performance: The function intersects the arrays one-by-one.\n    This is not the most performant implementation.\n    \"\"\"\n\n    if not arrays:\n        raise TypeError(\"Merge expects at least one array.\")\n\n    # start with smallest non-callable\n    inf = float('inf')\n    len_array = [(inf if callable(a) else len(a), a) for a in arrays]\n    len_array = sorted(len_array, key=lambda x: x[0])\n    arrays = [a for l, a in len_array]\n\n    intersect_result = arrays.pop()\n    intersect_result = resolve(intersect_result)\n    if not assume_sorted:\n        intersect_result.sort()\n    for array in arrays:\n        array = resolve(array)\n        if not assume_sorted:\n            array.sort()\n        intersect_result = intersect(intersect_result, array, **kwds)\n    return intersect_result\n","sub_path":"src/sortednp/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3136,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"282978539","text":"import sqlite3\r\nimport os\r\n\r\ndef convertToBinaryData(filename):\r\n    # path = os.getcwd() + filename\r\n    with open(filename, 'rb') as file:\r\n        binData = file.read()\r\n    return binData\r\n\r\ndef insertData(fellowID, name, img, email, desc, link):\r\n    try:\r\n        sqliteConn = sqlite3.connect('batch3.db')\r\n        cursor = sqliteConn.cursor()\r\n        print('Connected to DataBase')\r\n\r\n        # check if table exists create it if doesn't\r\n        createTableQuery = \"\"\" CREATE TABLE IF NOT EXISTS fellows\r\n                               (id INTEGER PRIMARY KEY, name TEXT NOT NULL, img BLOB NOT NULL,\r\n                               email TEXT NOT NULL, desc TEXT NOT NULL, link TEXT NOT NULL)\"\"\"\r\n        cursor.execute(createTableQuery)\r\n\r\n        sqliteInsertQuery = \"\"\" INSERT INTO fellows (id, name, img, email, desc, link) VALUES (?, ?, ?, ?, ?, ?) \"\"\"\r\n        photo = convertToBinaryData(img)\r\n\r\n        dataTuple = (fellowID, name, photo, email, desc, link)\r\n        cursor.execute(sqliteInsertQuery, dataTuple)\r\n        sqliteConn.commit()\r\n        print(f'Data belonging to {name} has been uploaded')\r\n        cursor.close()\r\n    except sqlite3.Error as err:\r\n        print(\" Failed to insert data to table\", err)\r\n    finally:\r\n        if (sqliteConn):\r\n            sqliteConn.close()\r\n\r\nfellowID = 364582020\r\nname = 'Kevin Karobia'\r\nimg = \"E:\\\\projects\\\\Batch 3 website\\\\static\\\\Kevin_Karobia.jpg\"\r\nemail = 'gkkarobia@gmail.com'\r\ndesc = \"If Pirus and Crips all got along They d probably gun me down by the end of this song Seem like the whole\"\r\nlink = 'https://sites.google.com/10academy.org/10-academy-batch-3-kevin'\r\n\r\ninsertData(fellowID, name, img, email, desc, link)\r\n","sub_path":"writeToDB.py","file_name":"writeToDB.py","file_ext":"py","file_size_in_byte":1694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"415835196","text":"\"\"\"\nRetrain the YOLO model for your own dataset.\n\"\"\"\n\nimport numpy as np\nimport json\nimport datetime\nimport os\nimport multiprocessing\nimport random\nos.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\" # so the IDs match nvidia-smi\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0,1\" # \"0, 1\" for multiple\n\nimport keras.backend as K\nimport tensorflow as tf\nimport keras\nfrom keras.layers import Input, Lambda\nfrom keras.models import Model\nfrom keras.optimizers import Adam\nfrom keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping\n\nconfig = tf.ConfigProto()\nconfig.gpu_options.allow_growth = True\nsession = tf.Session(config=config)\nK.set_session(session)\n\nfrom yolo3.model import preprocess_true_boxes, yolo_body, tiny_yolo_body, yolo_loss\nfrom yolo3.utils import mc3_get_random_data,get_random_data_own\n\nclass LRTensorBoard(TensorBoard):\n\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n\n    def on_epoch_end(self, epoch, logs=None):\n        logs.update({'lr': K.eval(self.model.optimizer.lr)})\n        super().on_epoch_end(epoch, logs)\n\n\ndef _main():\n    # adam, momentum or nesterov\n    opt_string_1 = \"adam\"\n    opt_string_2 = \"adam\"\n\n    LEARNING_RATE = 0.001\n    #using in lr decay\n    LR_DECAY = 0.95\n    #using in momentum and nesterov\n    MOMENTUM = 0.9\n    #using in adam\n    ADAM_BETA_1 = 0.9\n    ADAM_BETA_2 = 0.99\n    \n    BATCH_SIZE = 30\n    \n    IS_SAVE = True\n    \n    input_shape = (480,640) # multiple of 32, hw\n    \n    im_dir = '/data/mc_data/images_12k'\n    train_json_path = '../annotations/MC3_train.json'\n    val_json_path = '../annotations/MC3_val.json'\n    classes_path = 'model_data/mc3_classes.txt'\n    anchors_path = 'model_data/yolo_anchors.txt'\n#     weights_path = 'model_data/darknet53.h5'\n    weights_path = 'logs/20180726T1927_adam-adam/ep045-loss13.949-val_loss14.902.h5'\n    base_dir = './logs'\n    \n    f = open(train_json_path,'r')\n    train_info = json.load(f)\n    f.close()\n    f = open(val_json_path,'r')\n    val_info = json.load(f)\n    f.close()\n    \n    train_num = len(train_info)\n    val_num = len(val_info)\n    \n    now = datetime.datetime.now()\n    log_dir = os.path.join(base_dir,\"{:%Y%m%dT%H%M}_\".format(now)+opt_string_1+\"-\"+opt_string_2)\n#     log_dir = os.path.join(base_dir,\"{:%Y%m%dT%H%M}_\".format(now)+opt_string)\n    \n    class_names = get_classes(classes_path)\n    num_classes = len(class_names)\n    anchors = get_anchors(anchors_path)\n    \n    model = create_model(input_shape, anchors, num_classes,\n            freeze_body=2, weights_path=weights_path)\n    \n#     is_tiny_version = len(anchors)==6 # default setting\n#     if is_tiny_version:\n#         model = create_tiny_model(input_shape, anchors, num_classes,\n#             freeze_body=2, weights_path=weights_path)\n#     else:\n#         model = create_model(input_shape, anchors, num_classes,\n#             freeze_body=2, weights_path=weights_path) # make sure you know what you freeze\n\n    logging = LRTensorBoard(log_dir=log_dir)\n    checkpoint = ModelCheckpoint(os.path.join(log_dir,'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5'),\n                                monitor='val_loss',\n                                 save_weights_only=True, save_best_only=True, period=3)\n    reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1)\n    if IS_SAVE:\n        callbacks = [logging, checkpoint,reduce_lr]\n    else:\n        callbacks = [reduce_lr]\n    #early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1)\n    \n    if opt_string_1 == \"momentum\":\n        opt = keras.optimizers.SGD(lr=LEARNING_RATE,momentum=MOMENTUM)\n    if opt_string_1 == \"nesterov\":\n        opt = keras.optimizers.SGD(lr=LEARNING_RATE,momentum=MOMENTUM,nesterov=True)\n    if opt_string_1 == \"adam\":\n        opt = keras.optimizers.Adam(lr=LEARNING_RATE,beta_1=ADAM_BETA_1,\n                                    beta_2=ADAM_BETA_2)\n    \n    train_generator = mysequence(im_dir,train_info, BATCH_SIZE, input_shape, anchors, num_classes)\n    val_generator = mysequence(im_dir,val_info, BATCH_SIZE, input_shape, anchors, num_classes)\n    \n    # Train with frozen layers first, to get a stable loss.\n    # Adjust num epochs to your dataset. This step is enough to obtain a not bad model.\n    if True:\n        model.compile(optimizer=opt, loss={\n            # use custom yolo_loss Lambda layer.\n            'yolo_loss': lambda y_true, y_pred: y_pred})\n\n        print('Train on {} samples, val on {} samples, with batch size {}.'.format(train_num, val_num, BATCH_SIZE))\n        model.fit_generator(train_generator,\n#                 steps_per_epoch=1,\n                  steps_per_epoch=max(1, train_num//BATCH_SIZE),\n                validation_data=val_generator,\n                validation_steps=max(1, val_num//BATCH_SIZE),\n                epochs=50,\n                initial_epoch=0,\n                callbacks=callbacks,\n                workers = 7,\n#                 workers = multiprocessing.cpu_count(),\n                max_queue_size = 10,\n                use_multiprocessing = True\n                           )\n        model.save_weights(os.path.join(log_dir, 'trained_weights_stage_1.h5'))\n    \n    BATCH_SIZE = 5\n    train_generator = mysequence(im_dir,train_info, BATCH_SIZE, input_shape, anchors, num_classes)\n    val_generator = mysequence(im_dir,val_info, BATCH_SIZE, input_shape, anchors, num_classes)\n    \n    if opt_string_2 == \"momentum\":\n        opt = keras.optimizers.SGD(lr=LEARNING_RATE,momentum=MOMENTUM)\n    if opt_string_2 == \"nesterov\":\n        opt = keras.optimizers.SGD(lr=LEARNING_RATE,momentum=MOMENTUM,nesterov=True)\n    if opt_string_2 == \"adam\":\n        opt = keras.optimizers.Adam(lr=LEARNING_RATE,beta_1=ADAM_BETA_1,\n                                    beta_2=ADAM_BETA_2)\n    # Unfreeze and continue training, to fine-tune.\n    # Train longer if the result is not good.\n    if True:\n        for i in range(len(model.layers)):\n            model.layers[i].trainable = True\n        model.compile(optimizer=opt, loss={'yolo_loss': lambda y_true, y_pred: y_pred}) # recompile to apply the change\n        print('Unfreeze all of the layers.')\n\n        print('Train on {} samples, val on {} samples, with batch size {}.'.format(train_num, val_num, BATCH_SIZE))\n        model.fit_generator(train_generator,\n            steps_per_epoch=max(1, train_num//BATCH_SIZE),\n            validation_data=val_generator,\n            validation_steps=max(1, val_num//BATCH_SIZE),\n            epochs=100,\n            initial_epoch=50,\n            callbacks=callbacks,\n            workers = 7,\n#             workers = multiprocessing.cpu_count(),\n            max_queue_size = 10,\n            use_multiprocessing = True\n                           )\n        model.save_weights(os.path.join(log_dir, 'trained_weights_final.h5'))\n\n    # Further training if needed.\n\n\ndef get_classes(classes_path):\n    '''loads the classes'''\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\ndef get_anchors(anchors_path):\n    '''loads the anchors from a file'''\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\ndef create_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2,\n            weights_path='model_data/darknet53.h5'):\n    '''create the training model'''\n    K.clear_session() # get a new session\n    image_input = Input(shape=(None, None, 3))\n    h, w = input_shape\n    num_anchors = len(anchors)\n\n    y_true = [Input(shape=(h//{0:32, 1:16, 2:8}[l], w//{0:32, 1:16, 2:8}[l], \\\n        num_anchors//3, num_classes+5)) for l in range(3)]\n\n    model_body = yolo_body(image_input, num_anchors//3, num_classes)\n    print('Create YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes))\n\n    if load_pretrained:\n        model_body.load_weights(weights_path, by_name=True)\n        print('Load weights {}.'.format(weights_path))\n        if freeze_body in [1, 2]:\n            # Freeze darknet53 body or freeze all but 3 output layers.\n            num = (185, len(model_body.layers)-3)[freeze_body-1]\n            for i in range(num): model_body.layers[i].trainable = False\n            print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers)))\n\n    model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',\n        arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5})(\n        [*model_body.output, *y_true])\n    model = Model(inputs=[model_body.input, *y_true], outputs=model_loss)\n\n    return model\n\n# def create_tiny_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2,\n#             weights_path='model_data/darknet53.h5'):\n#     '''create the training model, for Tiny YOLOv3'''\n#     K.clear_session() # get a new session\n#     image_input = Input(shape=(None, None, 3))\n#     h, w = input_shape\n#     num_anchors = len(anchors)\n\n#     y_true = [Input(shape=(h//{0:32, 1:16}[l], w//{0:32, 1:16}[l], \\\n#         num_anchors//2, num_classes+5)) for l in range(2)]\n\n#     model_body = tiny_yolo_body(image_input, num_anchors//2, num_classes)\n#     print('Create Tiny YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes))\n\n#     if load_pretrained:\n#         model_body.load_weights(weights_path, by_name=True)\n#         print('Load weights {}.'.format(weights_path))\n#         if freeze_body in [1, 2]:\n#             # Freeze the darknet body or freeze all but 2 output layers.\n#             num = (20, len(model_body.layers)-2)[freeze_body-1]\n#             for i in range(num): model_body.layers[i].trainable = False\n#             print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers)))\n\n#     model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',\n#         arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.7})(\n#         [*model_body.output, *y_true])\n#     model = Model(inputs=[model_body.input, *y_true], outputs=model_loss)\n\n#     return model\n\n# def data_generator(im_dir,annotation_info,batch_size,input_shape,anchors,num_classes):\n#     ann_info = annotation_info\n#     index = 0\n#     n = len(ann_info)\n#     while True:\n#         image_data = []\n#         box_data = []\n#         for i in range(batch_size):\n#             if index==0:\n#                 np.random.shuffle(ann_info)\n#             info = ann_info[index]\n#             image, box = mc3_get_random_data(im_dir,info,input_shape)\n#             image_data.append(image)\n#             box_data.append(box)\n#             index = (index+1) % n\n#         image_data = np.array(image_data)\n#         box_data = np.array(box_data)\n#         y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes)\n#         yield [image_data, *y_true], np.zeros(batch_size)\n\n# def data_generator_wrapper(im_dir,annotation_info,batch_size, input_shape, anchors, num_classes):\n#     n = len(annotation_info)\n#     if n==0 or batch_size<=0: return None\n# #     return data_generator(im_dir,annotation_info, batch_size, input_shape, anchors, num_classes)\n#     return mysequence(im_dir,annotation_info, batch_size, input_shape, anchors, num_classes)\n\nclass mysequence(keras.utils.Sequence):\n    def __init__(self, im_dir,annotation_info,batch_size,input_shape,anchors,num_classes):\n        self.batch_size = batch_size\n        self.im_dir = im_dir\n        self.info = annotation_info\n        self.input_shape = input_shape\n        self.anchors = anchors\n        self.num_classes = num_classes\n    \n    def __len__(self):\n        return int(len(self.info) / self.batch_size) # the length is the number of batches\n    \n    def on_epoch_end(self):\n        random.shuffle(self.info)\n    \n    def __getitem__(self, batch_id):\n        image_data = []\n        box_data = []\n        for i in range(batch_id * self.batch_size, (batch_id+1) * self.batch_size):\n            n = len(self.info)\n            index = int(i % n)\n            info = self.info[index]\n#             image, box = mc3_get_random_data(self.im_dir,info,self.input_shape)\n            image, box = get_random_data_own(self.im_dir,info,self.input_shape)\n            #print(box)\n            image_data.append(image)\n            box_data.append(box)\n        image_data = np.array(image_data)\n        box_data = np.array(box_data)\n        y_true = preprocess_true_boxes(box_data, self.input_shape, self.anchors, self.num_classes)\n        return [image_data, *y_true], np.zeros(self.batch_size)\n\nif __name__ == '__main__':\n    _main()\n","sub_path":"mc3_yolo/mc3train.py","file_name":"mc3train.py","file_ext":"py","file_size_in_byte":12688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"483593070","text":"from django.urls import path, include\n\nfrom . import views\n\nurlpatterns = [\n    # ex: /polls/\n    path('', views.index, name='index'),\n    # ex: /polls/5/\n    path('<int:item_id>/', views.detail, name='detail'),\n    # Add Django site authentication urls (for login, logout, password management)\n    path('', include('django.contrib.auth.urls')),\n\n]\n","sub_path":"polls/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":349,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"49127959","text":"# def print_arr(arr):\n#     s = \"\"\n#     for a in arr:\n#         s += str(a) + \" \"\n#     print(s[:-1])\n\n# def put_col(X, col, arr, N):\n#     for i in range(N):\n#         if X[i][col] != 0 and X[i][col] != arr[i]:\n#             return False\n#     for i in range(N):\n#         X[i][col] = arr[i]\n#     return True\n\n# def put_row(X, row, arr, N):\n#     for i in range(N):\n#         if X[row][i] != 0 and X[row][i] != arr[i]:\n#             return False\n#     for i in range(N):\n#         X[row][i] = arr[i]\n#     return True\n\n# def solve(M, N):\n#     M.sort()\n#     print (M)\n\n#     X = [[0 for col in range(N)] for row in range(N)]\n\n#     col = 0\n#     row = 0\n#     # cnt = 0\n\n#     if M[0][0] == M[1][0]:\n#         put_row(X, 0, M[0], N)\n#         put_col(X, 0, M[1], N)\n#         # col += 1\n#         # row += 1\n#     else:\n#         print(\"missing first\")\n#         put_row(X, 0, M[0], N)\n#         # row += 1\n#         # col += 1\n\n#     for arr in M:\n#         if row < N and put_row(X, row, arr, N):\n#             row += 1\n#         elif col < N and put_col(X, col, arr, N):\n#             col += 1\n#         print(X)\n\n#     print(X)\n\n#     # find missing parts\n#     # rows\n#     for row in X:\n#         if row not in M:\n#             print_arr(row)\n\n#     # cols\n#     for i in range(N):\n#         col = []\n#         for j in range(N):\n#             col.append(X[j][i])\n#         if col not in M:\n#             print_arr(col)\n\ndef solve2(M, N):\n    count = {}\n    for arr in M:\n        for a in arr:\n            if a not in count:\n                count[a] = 0\n            count[a] += 1\n    #print(count)\n    # collect odd\n    odds = []\n    for key in count:\n        if count[key] % 2 == 1:\n            odds.append(key)\n    \n    odds.sort()\n    return odds\n\nT = int(input())\nfor t in range(0, T):\n    N = int(input())\n    M = []\n    for n in range(0, 2 * N - 1):\n        a = input()\n        row = []\n        for c in a.split(' '):\n            row.append(int(c))\n        M.append(row)\n    sol = solve2(M, N)\n    out = \"Case #%s: \" % (t+1)\n    for a in sol:\n        out += str(a) + \" \"\n    print(out[:-1])\n\n","sub_path":"codes/BuildLinks1.10/test_input/CJ_16_1/16_1_2_dinardo_sol.py","file_name":"16_1_2_dinardo_sol.py","file_ext":"py","file_size_in_byte":2108,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"190011383","text":"import os\nimport json\n\nfrom streamparse import Topology, ShellBolt, Stream\n\ndef setup_debug_hooks(topo_class):\n    inputs = []\n    graph = topo_class.to_flux_dict('')\n  \n    for spec in topo_class.specs:\n        for source_name, output in spec.outputs.items():\n            if source_name == \"error_handler_bolt\":\n                continue\n\n            inputs.append(spec[source_name])\n  \n    topo_class.debug_bolt = ShellBolt.spec(command='coffee', script='node/bolts/debug.coffee',\n                                           outputs=[Stream(fields=['config'])],\n                                           inputs=inputs)\n  \n    topo_class.debug_bolt.name = \"debug_bolt\"\n  \n    Topology.add_bolt_spec(topo_class.debug_bolt, topo_class.thrift_bolts)\n    topo_class.specs.append(topo_class.debug_bolt)\n  \n    directory = 'src/topology_graphs'\n\n    if not os.path.exists(directory):\n        os.makedirs(directory)\n\n    topo_name = topo_class.__module__\n    split_topo_name = topo_name.split('.')\n\n    if len(split_topo_name) > 1:\n        topo_name = split_topo_name[1]\n    \n    path = \"{}/{}.json\".format(directory, topo_name)\n    fullpath = os.path.abspath(path)\n  \n    with open(fullpath, 'w+') as outfile:\n        json.dump(graph, outfile, indent=2)\n","sub_path":"streamparse/topology_hooks.py","file_name":"topology_hooks.py","file_ext":"py","file_size_in_byte":1248,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"476158488","text":"\nimport numpy as np\nimport csv\nimport math\n\nfrom operator import itemgetter\nx_train =[]\ny_train = []\ntest_data =[]\ndef data_clean(x_train,y_train,test_data):\n    with open(\"test_data_3_b.csv\",encoding='utf-8-sig') as csvfile:\n        rows = csv.reader(csvfile,delimiter=',')\n        data = [data for data in rows]\n\n\n    for x in range(len(data)):\n        ins = []\n        for y in range(3):\n            ins.append(float(data[x][y]))\n        if(data[x][y+1] == \"M\"):\n            y_train.append(float(1))\n        else:\n            np.array(y_train.append(float(0)))\n        x_train.append(ins)\n\n\n    #print(np.array(x_train))\n    #print(np.array(y_train))\n    x = np.array(x_train)\n    y = np.array(y_train)\n    print(\"The given input data is\", x)\n    #print(\"x in data clean,\",x)\n    #print(\"y in data clean\",y)\n    #x_train = x\n    #y_train = y\n\n\n    with open(\"test.csv\",encoding='utf-8-sig') as csvfile:\n        rows = csv.reader(csvfile,delimiter=',')\n        dota = [dota for dota in rows]\n\n    for k in range(len(dota)):\n        ins = []\n        for l in range(3):\n            ins.append(float(dota[k][l]))\n\n        test_data.append(ins)\n    test = np.array(test_data)\n    #print(test_data)\n    return x,y,test\n\ndef normalise_data(x):\n    \" we can normalise by subtracting the maximum value by the minimum value and  then divide by the standard deviation of x or divide by the range\"\n\n    max_x = np.max(x,axis = 0)\n    min_x = np.min(x,axis = 0)\n    normalised_x = 1 - (max_x - x)/(max_x - min_x)\n    return normalised_x\n'''\ndef calculate_weighted_sum(X,weights):\n    #return sum(x*y for x,y in zip(X,weights))\n     print(\"x is \",X)\n     print(\"Weights is ,\",weights)\n     return -np.dot(X, weights.T)\n'''\ndef calculate_sigmoid(x,weight):\n    h = 1.0 / (1 + np.exp(-np.dot(x, weight.T)))\n    return h\n\ndef dot_product(a,b):\n    return sum(x*y for x,y in zip(a,b))\n\ndef calculate_mean(m):\n    return (sum(x for x in m))/len(m)\n#def transpose(mat):\n #   [[mat[i] for row in mat] for i in range(len(mat[0]))]\n\n\ndef calculate_gradient(weight,x,y):\n\n    #X = X - y.reshape(X.shape[0] -1)\n    #print(\"y in gradient descent\",y)\n    #print(\"weight in gradient descent\",weight)\n    #print(\"x in gradient descent\",x)\n    h = calculate_sigmoid(x,weight)\n    #print(\"h in gradient decent\",h)\n    first_calc = h - y.reshape(x.shape[0], -1)\n    final_calc = np.dot(first_calc.T, x)\n    #print(\"grad in gradient\",final_calc)\n    return final_calc\n\ndef calculate_cost(x,weight,y):\n    #print(\"x in cost\",x)\n    #print(\"weight in cost\",weight)\n    h = calculate_sigmoid(x,weight)\n    #print(\"y before squeeze\",y)\n    y = np.squeeze(y)\n    #print(\"y after squeeze\",y)\n\n    h = np.mean((-(y * np.log(h)) - ((1 - y)) * np.log(1 -h)))\n    #print(\"hh is \", h)\n    return h\n\n\ndef grad_descent(x, y, weight, learning, gradient):\n\n    cost = calculate_cost(x,weight, y)\n    iteration, change = 1, 1\n    while (change > gradient):\n        #iteration += 1\n        prev_cost = cost\n        # finding the weights and updating it after every itertation. The loop goes on unti the condition is met\n        weight = weight -  (learning * calculate_gradient(weight,x,y))\n        cost  =  calculate_cost(x,weight,y)\n        #print(\"cost in grad\",cost)\n\n        change = prev_cost - cost\n        #print(\"change in grad\", change)\n        iteration = iteration + 1\n        #print(\"iteration no is\",iteration)\n\n        #h = calculate_sigmoid(x, weight)\n\n        '''\n        difference = h - y\n        print(\"y in grad\",y)\n        gradient = calculate_gradient(difference, h, y)\n        weight = minimise_loss(weight, learning, gradient)\n\n        current_cost = calculate_cost(h, y)\n        change = prev_cost - current_cost\n        '''\n    return weight, iteration\ndef minimise_loss(weight, learning,gradient):\n    return weight - learning * gradient\n\ndef predict_val(weight,x):\n    prob = calculate_sigmoid(x,weight)\n    prediction = np.where(prob >= .5, 1, 0)\n    return np.squeeze(prediction)\n'''\ndef fit(x,y,no_of_iteration):\n    #weights = [1, 1, 1]\n    weights = np.matrix(np.zeros(4))\n    print(\"weights\")\n    print(weights)\n    for i in range(no_of_iteration):\n        prob = calculate_weighted_sum(x,weights)\n        h = calculate_sigmoid(x, prob)\n\n\n        print(\"y in fit\",y)\n        weight,iteration = grad_descent(x,y,weights,0.01,0.001)\n\n        result  = predict_val(x, weight)\n        print(\"Regression coefficients are\", weight)\n        print(\"no of iterations are\",iteration)\n        print(\"Predicted values are\",result)\n'''\n\n\n\n\nx,y,test = data_clean(x_train,y_train,test_data)\n#print(\"x value after data clean\",x)\n#print(\"y value after data clean\",y)\nX =x\n#print(\"Cap X is \",X)\n\n#X = np.hstack((np.matrix(np.ones(X.shape[0])).T, X))\n\nX = normalise_data(X)\nprint(\"the normalised input data is\",X)\none_column = np.ones((len(X),1))\nX = np.concatenate((one_column, X), axis = 1)\nweight = np.matrix(np.zeros(4))\n#print(\"xin main\", X)\n#print(\"y in main\", y)\n#print(\"beta in main\", weight)\nweight , n = grad_descent(X,y,weight,0.01,0.0005)\npredict = predict_val(weight,X)\nprint(\"the computed theta value is\")\nprint(\"difference between the actual output  and predicted output is as follows\")\nprint(\"Here '1' represents male and '0' represents female and The actual output is\",y)\nprint(\"The predicted output values are\",predict)\ny_count = np.sum(y == predict)\nprint(\"The calculated regression coefficients are\", weight)\nprint(\"total no of iterations taken\", n)\nprint(\"The count of the labels predicted\",y_count)\n#print(\"y out\",y)\n#fit(x,y,1)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"2_b_logistic_regression.py","file_name":"2_b_logistic_regression.py","file_ext":"py","file_size_in_byte":5539,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"174720195","text":"import tkinter\n\nfrom Elephant import Elephant\nfrom Monkey import Monkey\nfrom Giraffe import Giraffe\nfrom random import randint\n\n\nclass UserInterface:\n    \"\"\" Handles the user interface and logic/events\n\n        Attributes:\n            _window(tkinter.TK): the window that the UI elements will sit within.\n\n            _cur_row(int): counter for the row in which to add animals to the grid. Increments each time animal is added\n            Initialised to 3 to account for title, button and header rows.\n\n            _cur_hour(int): counter to keep track of the current time.\n\n            _animals(list): list of all animals in the zoo.\n\n            _labels(list): list of dictionaries. Each dictionary contains tkinter.Label objects for one row, one row\n            holds one animal, therefore _animals and _labels should be of the same length.\n\n            _time_label(tkinter.Label): Label that holds current time.\n\n    \"\"\"\n    _window = tkinter.Tk()\n    _cur_row = 3\n    _cur_hour = 1\n    _animals = []\n    _labels = []\n    _time_label = None\n\n    def __init__(self, animals):\n        \"\"\" Constructor. Sets out user interface elements in a grid.\n\n            The zoo simulation is presented in a tabular form, where each row represents one individual animal and\n            will have a column indicating:\n                * The species of the animal\n                * If the animal is alive\n                * If the animal can walk (hardcoded to a string(true) for anything other than elephants)\n                * current health of the animal to 2 decimal places.\n\n            Each column has a header\n\n            Has two buttons, one for incrementing time by 1 hour and one for feeding the animals.\n\n            Shows a clock formatted to 24hr time.\n\n        :param animals: List of animals to be included in the zoo.\n        \"\"\"\n        self._animals = animals\n        self._window.title('Zoo Simulation')\n\n        # Add frame to window to hold widgets\n        tkinter.Frame(self._window)\n        # Add column spanning width of window\n        tkinter.Label(self._window, text=\"Zoo Simulator\").grid(row=0, columnspan=4)\n\n        # Add row with the 2 buttons either side, with the current time in the middle.\n        tkinter.Button(self._window, text=\"+1 Hour\", command=self.increment_time).grid(row=1, column=0)\n        self._time_label = tkinter.Label(self._window, text=\"0{}:00\".format(self._cur_hour), fg=\"green\", bg=\"black\")\n        self._time_label.grid(row=1, column=1, columnspan=2)\n        tkinter.Button(self._window, text=\"Feed Animals\", command=self.feed_animals).grid(row=1, column=3)\n\n        # Add Column Headers for table\n        tkinter.Label(self._window, text=\"Animal\").grid(row=2, column=0)\n        tkinter.Label(self._window, text=\"Alive\").grid(row=2, column=1)\n        tkinter.Label(self._window, text=\"Can Walk?\").grid(row=2, column=2)\n        tkinter.Label(self._window, text=\"Health\").grid(row=2, column=3)\n\n        # Add Animals to zoo\n        self.add_animals()\n\n    def start(self):\n        \"\"\" Starts the main loop.\n\n        \"\"\"\n        self._window.mainloop()\n\n    def add_animals(self):\n        \"\"\" Adds all animals to the gui.\n\n            Creates a dictionary for each animal with the k,v pairs:\n                * name: Label showing species of the animal\n                * alive: Label showing weather the animal is alive\n                * can_walk: Label showing if animal can walk\n                * health: Label showing current health\n\n            Each of these labels are initialised for each animal and added to the grid layout of the window.\n        \"\"\"\n        for animal in self._animals:\n            # Create a label to display species and add to grid\n            name = tkinter.Label(self._window, text=animal.__class__.__name__)\n            name.grid(row=self._cur_row, column=0)\n\n            # Create a label to display if animal alive and add to grid.\n            alive = tkinter.Label(self._window, text=str(animal.get_alive()))\n            alive.grid(row=self._cur_row, column=1)\n\n            '''\n                Add label to display weather animal can walk. If Elephant. This could have been hard coded to true as \n                health is initialised to 100% and this method is only called once but wrote this way to account for\n                maintainability if more animals were added which start out unable to walk. \n            '''\n            if isinstance(animal, Elephant):\n                # Create can_walk label and add to grid\n                can_walk = tkinter.Label(self._window, text=str(animal.get_can_walk()))\n                can_walk.grid(row=self._cur_row, column=2)\n\n            else:\n                # Create can_walk label, hard coded to True and add to grid\n                can_walk = tkinter.Label(self._window, text=\"True\")\n                can_walk.grid(row=self._cur_row, column=2)\n\n            # Create a label to display health and add to grid.\n            health = tkinter.Label(self._window, text=str(animal.get_health()))\n            health.grid(row=self._cur_row, column=3)\n\n            # Add all labels to anonymous dictionary and add to labels instance variable\n            self._labels.append({\"name\": name, \"alive\": alive, \"can_walk\": can_walk, \"health\": health})\n\n            # Increment the current row.\n            self._cur_row += 1\n\n    def increment_time(self):\n        \"\"\" Increments the time forward by one hour. When corresponding button is pressed.\n\n            Time is displayed in 24 hour format. Checks to see if current hour is at the limit of 23 and resets to 0 if\n            true. Else increments hour by 1.\n\n            If current hour is only one digit then it will be displayed with a leading zero.\n\n            Health is decreased by a random value between 0 and 20%\n\n            Display is then updated to reflect this.\n\n        \"\"\"\n        if self._cur_hour == 23:\n            self._cur_hour = 0\n        else:\n            self._cur_hour += 1\n\n        if self._cur_hour > 9:\n            self._time_label.config(text=\"{}:00\".format(self._cur_hour))\n        else:\n            self._time_label.config(text=\"0{}:00\".format(self._cur_hour))\n        self.decrease_health()\n        self.update_health()\n\n    def decrease_health(self):\n        \"\"\" Decreases the health of each animal by a random percentage between 0 and 20 percent.\n        \"\"\"\n        for animal in self._animals:\n            animal.set_health(round(animal.get_health() - animal.get_health() * (randint(0, 20) / 100), 2))\n\n            # Call the check_alive method of the animal to update its _alive instance variable.\n            animal.check_alive()\n\n    def update_health(self):\n        \"\"\" Update the display with the new health, can_walk and alive values\n\n            For all rows in labels update the values to those of the animal in _animals with the corresponding index\n\n        \"\"\"\n        for l in range(len(self._labels)):\n            self._labels[l][\"health\"].config(text=str(self._animals[l].get_health()))\n            self._labels[l][\"alive\"].config(text=str(self._animals[l].get_alive()))\n\n            # Only update can_walk if animal is an elephant\n            if isinstance(self._animals[l], Elephant):\n                self._labels[l][\"can_walk\"].config(text=str(self._animals[l].get_can_walk()))\n\n    def feed_animals(self):\n        \"\"\" Update animals health by feeding them when the corresponding button is pressed.\n\n            Generate 3 random values between 10 and 25 percent (One for each type of animal)\n            Update the health of every animal of each species by it's random percentage ensuring that health cannot\n            exceed 100%.\n\n            Only update health on animals that are alive to prevent animals coming back to life.\n\n            Check the animal is alive after new health (If an elephant was <70% and after feeding does not revert to\n            >= 70%, it will need this updating).\n\n            Update display to reflect new health figures.\n\n        \"\"\"\n        increases = [randint(10, 25) for i in range(3)]\n        for animal in self._animals:\n            if isinstance(animal, Monkey) and animal.get_health() != 0:\n                animal.set_health(round(min(100, animal.get_health() + animal.get_health() * (increases[0] / 100)), 2))\n            else:\n                if isinstance(animal, Giraffe):\n                    animal.set_health(\n                        round(min(100, animal.get_health() + animal.get_health() * (increases[1] / 100)), 2))\n                else:\n                    animal.set_health(round(min(100, animal.get_health() + animal.get_health() * (increases[2])), 2))\n            animal.check_alive()\n        self.update_health()\n","sub_path":"User_InterFace.py","file_name":"User_InterFace.py","file_ext":"py","file_size_in_byte":8680,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"17435883","text":"import numpy as np\nimport random\nfrom multiprocessing import Pool\n\nclass GA(object):\n    #feval->crossValidFeval\n    # the last column of train and valid will be taken as the perdict value\n    def __init__(self, Xdata, estimator, feval, \n                 iter=200, r_sample=0.6, r_crossover=0.5, r_vary=0.01,\n                 r_keep_best=0.1, popsize = 1000, pTrain = 0.8,\n                 nfold = 5, verbose=False):\n        self.Xdata = Xdata\n        self.origin_estimator = estimator\n        self.estimator = estimator\n        self.verbose = verbose\n        self.iter = iter\n        self.r_sample = r_sample\n        self.r_crossover = r_crossover\n        self.r_vary = r_vary\n        self.r_keep_best = r_keep_best\n        self.popsize = popsize\n        self.feval = feval\n        self.pTrain = pTrain\n        self.nfold = nfold\n        #self._validate()\n\n    def _verbose(self, *args):\n        if self.verbose:\n            print(*args)\n\n    def _validate(self):\n        assert self.train.shape[1] == self.valid.shape[1]\n        assert self.iter > 0, 'iteration cnt should > 0'\n        assert self.r_sample > 0, 'r_sample is invalid'\n        assert self.estimator, 'estimator is invalid'\n\n    # axis = [0,1]: 0 means row, 1 means columns\n    def select(self):\n        return self._selectFeature()\n\n    # the smaller the better\n    def _selectFeatureScore(self, gene):\n        return self.feval(self.estimator, self.Xdata, gene, self.nfold)\n\n    def _selectFeature(self):\n        n_features = self.Xdata.shape[1] - 1  # the last feature is the value\n        n_sample = int(self.r_sample * n_features)\n\n        (best_gene, best_scores) = self._run( n_features, n_sample, self._selectFeatureScore )\n\n        best_sample = self.Xdata.T[:-1][best_gene].T\n        return (best_sample, best_gene, best_scores)\n\n    # generate a random array whose len=n_len, and have n_pos's value==True\n    def _randomSeries(self, n_len, n_pos):\n        gene = np.zeros(n_len, dtype=np.bool)\n\n        indexes = np.arange(n_len, dtype=np.int)\n        random.shuffle(indexes)\n\n        gene[indexes[:n_pos]] = True\n        return gene\n\n    def _gamblingBoard(self, scores):\n        # scores: the smaller the better\n        # possbilities: the bigger the better\n        # p = (alpha)^score\n\n        alpha = 0.8\n        tmp = list(map(lambda x: pow(alpha, x), scores))\n        tmp = tmp / np.sum(tmp)               # calculate p\n        possbilities = tmp / np.min(tmp)      # scale\n        return possbilities\n\n    def _generateDataIdx(self):\n        tr_len = np.int(self.pTrain*len(self.Xdata))\n        \n        rand_idx = np.arange(len(self.Xdata))\n        np.random.shuffle(rand_idx)\n        tr_idx = rand_idx[:tr_len]\n        vald_idx = rand_idx[tr_len:]\n        return tr_idx, vald_idx\n\n    def _generateData(self):\n        tr_idx, val_idx = self._generateDataIdx()\n        self.train = self.Xdata[tr_idx, :]\n        self.valid = self.Xdata[val_idx, :]\n        return\n\n    def _run(self, n_gene_units, n_sample, adapt_func):\n        if n_gene_units == n_sample:\n            return self.train\n        population = [self._randomSeries(n_gene_units, n_sample)\n                 for i in range(self.popsize)]\n        scoresHist = []\n        genesHist = []\n        for i in range(self.iter):\n            scores, population, gene = self._oneGeneration(population, adapt_func)\n            scoresHist.append(np.min(scores))\n            genesHist.append(gene)\n\n            m_genes = np.mean(population, axis = 0)\n            print(m_genes)\n\n            self._verbose('Generation {0:3}: Best score this gen:{1} Best socre:{1}'.format(i, np.min(scores) ,scoresHist[-1]))\n\n            np.save('trained_genes', gene)\n            np.save('hist_scores', scoresHist)\n            \n        self._verbose('Final best score:{0}'.format(scoresHist[-1]))\n        best_gene = genesHist[-1]\n        return (best_gene, scoresHist)\n            \n    def _getChildId(self, cumboard, sort_id):\n        val = random.uniform(0, cumboard[len(cumboard)-1])\n        ans = 0\n        for id, i in enumerate(cumboard):\n            if val <= i:\n                ans = sort_id[id]\n                break\n        return ans\n\n    def _swapGene(self, ch1, ch2):\n        return ch2, ch1\n\n    def _getChild(self, scores, genes, board):\n        cumboard = np.cumsum(np.sort(board))\n        sort_id = np.argsort(board)\n        idx = self._getChildId(cumboard, sort_id)\n        board[idx] = board[idx]-1\n        return genes[idx], scores[idx], board\n\n    def _oneGeneration(self, genes, adapt_func):\n        scores = [adapt_func(gene) for gene in genes]\n        board = self._gamblingBoard(scores)\n\n        best_gene = genes[np.argmin(scores)]\n        n_keep_best = int(len(genes) * self.r_keep_best)\n        bests_idx = np.array(np.argsort(scores)[:n_keep_best])\n        new_genes =(np.array(genes)[bests_idx, :]).tolist()\n\n\n        while(len(new_genes) < len(genes)):\n            ch1, sc1, board = self._getChild(scores, genes, board)\n            ch2, sc2, board = self._getChild(scores, genes, board)\n            if(sc1 > sc2):\n                ch1, ch2 = self._swapGene(ch1, ch2)\n            for j in range(len(ch1)):\n                do_cross = random.uniform(0, 1)\n                if(do_cross < self.r_crossover):\n                    ch2[j] = ch1[j]\n                do_mut = random.uniform(0, 1)\n                if(do_mut < self.r_vary):\n                    ch1[j] = not ch1[j]\n                do_mut = random.uniform(0, 1)\n                if(do_mut < self.r_vary):\n                    ch2[j] = not ch2[j]\n            new_genes.append(ch1)\n            new_genes.append(ch2)\n\n        return (scores, new_genes, best_gene)\n","sub_path":"genetics.py","file_name":"genetics.py","file_ext":"py","file_size_in_byte":5655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"17917104","text":"import torchvision.transforms as transforms\n\n# task selection\ntask = \"classify\"\n\n# select architecture\ncfg = \"arch/vgg16.cfg\"\ndata_config_path = \"cfg/wissenperson.data\"\n\n# data_transform\ntransform_train = transforms.Compose([\n    transforms.RandomCrop(32, padding=4),\n    transforms.RandomHorizontalFlip(),\n    transforms.ToTensor(),\n    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n])\n\ntransform_test = transforms.Compose([\n    transforms.ToTensor(),\n    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n])\n\n# pretrained model\n#pretrainedModel = \"./weights/darknet53.pth\"#\"./snapshot/yolov3SPP_best_model_0.6709.pkl\"#\n\n# resume from checkpoint\nresume = None#\"/home/wfw/HASCO/LaneDet_2018.12.12-/smartPytotch/classify/test/checkpoint.pth.tar\"#\"./weights/best.pt\"#\"./snapshot/yolov3-SPP_best_model.pkl\"#None#\n\nmax_epoch =300\ndisplay = 20\n\n# L2 regularizer\noptimizer = {0: {'optimizer': 'SGD',\n             'lr': 1e-3,\n             'momentum': 0.9,\n             'weight_decay': 5e-4} }\n             #'diffRate': [0.1, 1]} }\n\n# learning rate policy\nlearning_policy = {0: {'policy': 'multistep',\n                       'steps': [120, 160] }}\n                   #0: {'policy': 'burn-in',\n                   #    'burn-in': 100},\n                   #1: {'policy': 'poly',\n                   #    'power': 0.9} }\n\n# dataSet\ntrainList = \"/home/wfw/data/VOCdevkit/cifar10/train_data.txt\"\nvalList = \"/home/wfw/data/VOCdevkit/cifar10/test_data.txt\"\nvalLabelPath = \"/home/wfw/data/VOCdevkit/BerkeleyDet/Annotations/\"\ntrain_batch_size = 256\ntest_batch_size = 100\n\n# yolo param\nanchors = [[[44,76], [65,116], [117,164]],\n           [[15,18], [22,38], [29,56]],\n           [[7,10], [9,18], [14,30]]]\n#className = ['truck', 'person', 'bicycle', 'car', 'motorbike', 'bus']\nclassName = ['person']\nnumClasses = 1\niouThresh = 0.45\nconfThresh=0.24\n\njitter = 0.3\n#hue = 0.1\nhue = 0\nsaturation = 1.5\nexposure = 1.5\n\n# GPU SETTINGS\nCUDA_DEVICE = [0] # Enter device ID of your gpu if you want to run on gpu. Otherwise neglect.\nGPU_MODE = 1 # set to 1 if want to run on gpu.\n\n# SETTINGS FOR DISPLAYING ON TENSORBOARD\nvisdomTrain = False\nvisdomVal = False\n","sub_path":"arch/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":2183,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"32315559","text":"# Given the root to a binary tree, implement serialize(root),\n# which serializes the tree into a string, and deserialize(s),\n# which deserializes the string back into the tree.\n\nup = 0\n\n\nclass Node:\n    def __init__(self, val, left=None, right=None):\n        self.val = val\n        self.left = left\n        self.right = right\n\n\ndef deserialize(s):\n    global up\n    current_element = s.partition('|')[0]\n    next_element = s.partition('|')[2]\n    if current_element is '':\n        return None\n    up = up + 1\n    n = Node(current_element)\n    n.left = deserialize(next_element)\n    for i in range(0, pow(2, up-1)):\n        print(next_element.partition('|')[0])\n        next_element = next_element.partition('|')[2]\n    n.right = deserialize(next_element.partition('|')[2])\n    return n\n\n\ndef serialize(root):\n    serialization_string = \"\"\n    if root is None:\n        return \"|\"\n    else:\n        serialization_string = serialization_string + root.val + \"|\"\n        serialization_string = serialization_string + serialize(root.left) + \"\"\n        serialization_string = serialization_string + serialize(root.right) + \"\"\n\n    return serialization_string\n\n\ndef main():\n    node = Node('root', Node('left', Node('left.left')), Node('right'))\n    nn = deserialize(serialize(node))\n    assert deserialize(serialize(node)).left.left.val == 'left.left'\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"3_serialize_deserialize_bst.py","file_name":"3_serialize_deserialize_bst.py","file_ext":"py","file_size_in_byte":1385,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"74733308","text":"import os\nimport wave\nimport pyaudio\nimport requests\nimport speech_recognition as sr  # makes sure that Jarvis can understand what you're saying\nimport pyttsx3  # Enables text to speech so that Jarvis can respond on your commands\nimport datetime  # Enables Jarvis to get the real time and date\nimport wikipedia  # allows the program to search wikipedia for information's\nimport wolframalpha\nfrom phue import Bridge  # This library allow us to connect to a philips hue bridge\nimport pytz  # Enables us to define time zones\nimport json  # This enables us to work with json files\nimport webbrowser  # This enables us to open a web-browser in google\nfrom googlesearch import search  # Enables us to search google\nimport time  # Enables to set at timer for a quick break\n\n\nclass AudioFile:\n    chunk = 1024\n\n    def __init__(self, audio_file_name):\n        \"\"\" Init audio stream \"\"\"\n        self.wf = wave.open(audio_file_name, 'rb')\n        self.file_name = audio_file_name\n\n        self.p = pyaudio.PyAudio()\n        self.stream = self.p.open(\n            format=self.p.get_format_from_width(self.wf.getsampwidth()),\n            channels=self.wf.getnchannels(),\n            rate=self.wf.getframerate(),\n            output=True\n        )\n\n    def play(self):\n        \"\"\" Play entire file \"\"\"\n        self.wf.rewind()\n        data = self.wf.readframes(self.chunk)\n\n        while data:\n            self.stream.write(data)\n            data = self.wf.readframes(self.chunk)\n\n    def close(self):\n        \"\"\" Graceful shutdown \"\"\"\n        self.stream.close()\n        self.p.terminate()\n\n\nurl = \"https://app.resemble.ai/api/v1/projects/b9371d23/clips\"\nheaders = {\n    'Authorization': 'Token token=\"1x2oZ7SuwbuqXk1trIcJogtt\"',\n    'Content-Type': 'application/json'\n}\nresponse = requests.get(url, headers=headers).json()\n\nclips = {}\n\n# Load the possible clips files into a list\nfor clip in response['pods']:\n    title = clip['title'].replace(\" \", \"\")\n\n    if title not in clips:\n        clips[title] = {\n            'title': title,\n            'uuid': clip['uuid'],\n            'link': clip['link'],\n            'finished': clip['finished']\n        }\n    else:\n        print(f\"Warning: A clip with the name {title} has already been loaded\")\n\n\n# Load the audio file of a clip from a folder\ndef get_clip(clip_name, clip_folder_name):\n    if clip_name in clips:\n        audio_clip = clips[clip_name]\n        audio_file = None\n\n        if clip_folder_name and not os.path.isdir(\"AudioFiles/\" + clip_folder_name):\n            os.mkdir(\"AudioFiles/\" + clip_folder_name)\n\n        clip_folder_name = \"AudioFiles/\" + clip_folder_name\n\n        if os.path.isfile(clip_folder_name + \"/\" + clip_name + \".wav\"):\n            audio_file = AudioFile(clip_folder_name + \"/\" + clip_name + \".wav\")\n        else:\n            if clip['finished']:\n                with requests.Session() as req:\n                    download = req.get(audio_clip['link'], headers=headers)\n\n                    if download.status_code == 200:\n                        with open(clip_folder_name + \"/\" + clip_name + \".wav\", 'wb') as f:\n                            f.write(download.content)\n                    else:\n                        print(f\"Could not download audio file {clip_name}\")\n                audio_file = AudioFile(clip_folder_name + \"/\" + clip_name + \".wav\")\n                print(f\"Audio File {clip_name} successfully downloaded\")\n            else:\n                print(f\"Error: The audio file {clip_name} has not been created. Go to Resemble.ai and create the file.\")\n        return audio_file\n    else:\n        print(f\"Error: The clip {clip_name} does not exist.\")\n\n\naudio_files = {}\n\nif not os.path.isdir(\"AudioFiles\"):\n    os.mkdir(\"AudioFiles\")\n\n# Number\nfor i in range(1, 61):\n    audio_files[\"Number\" + str(i)] = get_clip(\"Number\" + str(i), \"Number\")\n\n# Greeting\nfor i in range(1, 4):\n    audio_files[\"Greeting\" + str(i)] = get_clip(\"Greeting\" + str(i), \"Greeting\")\n\n# Offer Help\naudio_files[\"OfferHelp\"] = get_clip(\"OfferHelp\", \"OfferHelp\")\n\n# Timer\naudio_files[\"TimerInvalidFormat\"] = get_clip(\"TimerInvalidFormat\", \"Timer\")\naudio_files[\"TimerSet\"] = get_clip(\"TimerSet\", \"Timer\")\naudio_files[\"And\"] = get_clip(\"And\", \"Timer\")\naudio_files[\"Hour\"] = get_clip(\"Hour\", \"Timer\")\naudio_files[\"Hours\"] = get_clip(\"Hours\", \"Timer\")\naudio_files[\"Minute\"] = get_clip(\"Minute\", \"Timer\")\naudio_files[\"Minutes\"] = get_clip(\"Minutes\", \"Timer\")\naudio_files[\"Second\"] = get_clip(\"Second\", \"Timer\")\naudio_files[\"Seconds\"] = get_clip(\"Seconds\", \"Timer\")\n\n# World Time\naudio_files[\"In\"] = get_clip(\"In\", \"WorldTime\")\naudio_files[\"ItIs\"] = get_clip(\"ItIs\", \"WorldTime\")\naudio_files[\"O\"] = get_clip(\"O\", \"WorldTime\")\n\naudio_files[\"Denmark\"] = get_clip(\"Denmark\", \"WorldTime\")\naudio_files[\"Copenhagen\"] = get_clip(\"Copenhagen\", \"WorldTime\")\naudio_files[\"England\"] = get_clip(\"England\", \"WorldTime\")\naudio_files[\"London\"] = get_clip(\"London\", \"WorldTime\")\n\n# Wikipedia\naudio_files[\"SearchingWikipedia\"] = get_clip(\"SearchingWikipedia\", \"Wikipedia\")\naudio_files[\"AccordingtoWikipedia\"] = get_clip(\"AccordingtoWikipedia\", \"Wikipedia\")\n\n# Open Webpage\naudio_files[\"OpeningWebpage\"] = get_clip(\"OpeningWebpage\", \"Webpage\")\naudio_files[\"WebpageNotFound\"] = get_clip(\"WebpageNotFound\", \"Webpage\")\n\nwith open(\"Data/KnownWebpages.json\") as known_webpages:\n    webpage_names = json.load(known_webpages)\n\n    for webpage in webpage_names:\n        audio_files[webpage['name'].replace(\" \", \"\")] = get_clip(webpage['name'].replace(\" \", \"\"), \"Webpage\")\n    known_webpages.close()\n\n# News\naudio_files[\"News\"] = get_clip(\"News\", \"News\")\n\n# Weather\naudio_files[\"CurrentWeather\"] = get_clip(\"CurrentWeather\", \"Weather\")\naudio_files[\"Is\"] = get_clip(\"Is\", \"Weather\")\naudio_files[\"WeatherTemperature\"] = get_clip(\"WeatherTemperature\", \"Weather\")\naudio_files[\"WeatherDegrees\"] = get_clip(\"WeatherDegrees\", \"Weather\")\n\nwith open('Data/WeatherDescriptions.txt', 'r') as weather_descriptions:\n    for description in weather_descriptions.readlines():\n        audio_files[description.strip().replace(\" \", \"\")] = get_clip(description.strip().replace(\" \", \"\"), \"Weather\")\n    weather_descriptions.close()\n\n# Initialize the pyttsx3 library\nengine = pyttsx3.init()\nvoices = engine.getProperty('voices')  # gets the voices for Jarvis\n\n# Jarvis's voice\nfor voice in voices:\n    if voice.languages[0] == 'en_GB':\n        engine.setProperty('voice', voice.id)\n        break\n\nrate = engine.getProperty('rate')\nengine.setProperty('rate', 180)\n\nvolume = engine.getProperty('volume')\nengine.setProperty('volume', 0.5)\n\n\n# This function enables Jarvis to speak\ndef speak(text):\n    engine.say(text)\n    engine.runAndWait()\n\n\n# play audio file or use pyttsx3 instead if the audio file is not available\ndef play_audio(audio_name, text):\n    if audio_name in audio_files and audio_files[audio_name]:\n        audio_files[audio_name].play()\n    else:\n        speak(text)\n\n\n# This function enables Jarvis to take commands\ndef take_command(respond=True):\n    recognition = sr.Recognizer()\n\n    with sr.Microphone() as source:\n        recognition.adjust_for_ambient_noise(source)  # reduce noise\n        print(\"Listening...\")\n\n        # this try function will help Jarvis understand what the user is saying\n        try:\n            audio = recognition.listen(source,\n                                       timeout=6)  # uses the function to listen for audio from speech_recognition library\n            input_statement = recognition.recognize_google(audio, language='en_GB')\n            print(f\"{input_statement}\")\n\n        # This exception will prevent run time errors\n        except sr.RequestError:\n            # API was unreachable or unresponsive\n            print(\"API was unreachable or unresponsive\")\n            return \"\"\n        except sr.UnknownValueError:\n            # speech was unintelligible\n            if respond:\n                speak(\"Pardon me, could you repeat that?\")\n            return \"\"\n        except sr.WaitTimeoutError:\n            # Time out\n            return \"\"\n        return input_statement\n\n\n# This function allows Jarvis to greet the user\ndef greet_user():\n    hour = datetime.datetime.now().hour  # gets the current hour\n\n    # This if statements checks the time and gives a shutting response\n    if 0 <= hour < 10:\n        play_audio(\"Greeting1\", \"Good morning sir, I hope you slept well\")\n    elif 10 <= hour < 12:\n        play_audio(\"Greeting1\", \"Good morning sir\")\n    elif 12 <= hour < 18:\n        play_audio(\"Greeting2\", \"Good afternoon sir\")\n    else:\n        play_audio(\"Greeting3\", \"Good evening sir\")\n\n    time.sleep(0.4)\n    print(\"How may I help you?\")\n    play_audio(\"OfferHelp\", \"How may I help you?\")\n\n\n\"\"\"\n# This section controls the light\nb = Bridge('10.24.9.200')\nb.connect()\nb.get_api()\n\n\ndef turn_lights_on_and_off_controls(user_light_command):\n    with open('Data/LightControls.json', 'r') as lc:\n        light_command = json.load(lc)\n\n        # Turn lights on, commands\n        for lightsOn in light_command['TurnOnLights']:\n            if lightsOn['command'].lower() in user_light_command:\n                b.set_group(1, 'on', True)\n        # Turn lights off, commands\n        for lightsOff in light_command['TurnOffLights']:\n            if lightsOff['command'].lower() in user_light_command:\n                b.set_group(1, 'on', False)\n        # Turn on the firs light, command\n        for firstLightOn in light_command['LightBulbOn1']:\n            if firstLightOn['command'].lower() in user_light_command:\n                b.set_light(1, 'on', True)\n        # Turn off the first light, command\n        for firstLightOff in light_command['LightBulbOff1']:\n            if firstLightOff['command'].lower() in user_light_command:\n                b.set_light(1, 'on', False)\n        # Turn on the second light, command\n        for secondLightOn in light_command['LightBulbOn2']:\n            if secondLightOn['command'].lower() in user_light_command:\n                b.set_light(2, 'on', True)\n        # Turn off the second light, command:\n        for secondLightOff in light_command['LightBulbOff2']:\n            if secondLightOff['command'].lower() in user_light_command:\n                b.set_light(2, 'on', False)\n        # Turn on the third light, command\n        for thirdLightOn in light_command['LightBulbOn3']:\n            if thirdLightOn['command'].lower() in user_light_command:\n                b.set_light(3, 'on', True)\n        # Turn off teh third light, command\n        for thirdLightOff in light_command['LightBulbOff3']:\n            if thirdLightOff['command'].lower() in user_light_command:\n                b.set_light(3, 'on', False)\n        # Turn on the fourth light, command\n        for fourthLighton in light_command['LightBulbOn4']:\n            if fourthLighton['command'].lower() in user_light_command:\n                b.set_light(4, 'on', True)\n        # Turn of the fourth light, command\n        for fourthLightOff in light_command['LightBulbOff4']:\n            if fourthLightOff['command'].lower() in user_light_command:\n                b.set_light(4, 'on', False)\n\"\"\"\n\n\n# This function allows Jarvis to set a timer from user specifications\ndef timer(input_statement):\n    words = input_statement.replace(\",\", \"\").replace(\"-\", \" \").split()\n    invalid_time_format = False\n    values_list = {}\n    duration = 0\n\n    # Loop through every word in the statement and look for digits and\n    # check if the word after the digit matches one of the keywords\n    for k in range(0, len(words)):\n        if words[k].isdigit() and k + 1 < len(words):\n            word = words[k + 1] if words[k + 1][-1] != \"s\" else words[k + 1][:len(words[k + 1]) - 1]\n\n            if word == \"second\" or word == \"minute\" or word == \"hour\":\n                if word not in values_list:\n                    values_list[word] = max(0, int(words[k]))\n                    values_list[word + \"_count\"] = 1\n                else:\n                    values_list[word + \"_count\"] += 1\n\n    resorted_values = {}\n\n    # Reorganize values so it's in the order of hour, minute, second\n    if 'hour' in values_list:\n        resorted_values['hour'] = values_list['hour']\n        resorted_values['hour_count'] = values_list['hour_count']\n    if 'minute' in values_list:\n        resorted_values['minute'] = values_list['minute']\n        resorted_values['minute_count'] = values_list['minute_count']\n    if 'second' in values_list:\n        resorted_values['second'] = values_list['second']\n        resorted_values['second_count'] = values_list['second_count']\n\n    timer_text = \"A timer has now been set for\"\n\n    # Construct the message that'll notify the user about the timer\n    for word in resorted_values:\n        if word in input_statement and values_list[word] > 0:\n            if values_list[word + \"_count\"] <= 1:\n                duration += values_list[word] * (1 if word == \"second\" else (60 if word == \"minute\" else 3600))\n                timer_text += \" \" + str(values_list[word]) + \" \" + word + (\"s\" if values_list[word] > 1 else \"\") + \",\"\n            else:\n                invalid_time_format = True\n\n    timer_text = ' and'.join(timer_text[:-1].rsplit(',', 1))\n\n    # If the timer contains multiple instances of the same keyword the format given is invalid\n    if invalid_time_format:\n        print(\"Invalid time format.\")\n        play_audio(\"TimerInvalidFormat\",\n                   \"I'm sorry but I can't start a timer with those specifications\")\n        return\n\n    hours, reminder = divmod(duration, 3600)\n    minutes, secs = divmod(reminder, 60)\n\n    print(\"Timer: {:02d}:{:02d}:{:02d}\".format(hours, minutes, secs))\n    play_audio(\"TimerSet\", timer_text)\n\n    timer_text_words = timer_text.split(\" \")\n\n    for word in timer_text_words:\n        if \"and\" in word or \"hour\" in word or \"minute\" in word or \"second\" in word:\n            play_audio(word.capitalize(), \"\")\n        elif word.isdigit():\n            play_audio(\"Number\" + word, word)\n\n    # Run the timer\n    while duration:\n        hours, reminder = divmod(duration, 3600)\n        minutes, secs = divmod(reminder, 60)\n        print(\"{:02d}:{:02d}:{:02d}\".format(hours, minutes, secs))\n        time.sleep(1)\n        duration -= 1\n\n    print(\"It is time\")\n    speak(\"It is time\")\n\n\n# This function allows Jarvis to tell the time of a capital around the world\ndef world_and_local_time(input_statement):\n    with open(\"Data/TimeZones.json\", 'r') as j:\n        time_zone = json.load(j)\n\n        # Loop through every continent and every country and tell the time of that country's capital\n        for continent in time_zone:\n            for country in time_zone[continent]:\n                if country['name'].lower() in input_statement or 'capital' in country and country[\n                    'capital'].lower() in input_statement:\n                    country_capital = country['capital'] if 'capital' in country else country['name']\n                    country_name = country['name'] if country['name'] != country_capital else continent\n                    time_string = continent + \"/\" + country_capital.replace(' ', '_')\n                    country_time = pytz.timezone(time_string)\n                    current_time = datetime.datetime.now(country_time).strftime(\"%H:%M\")\n                    print(\"In \" + f\"{country_capital}, \" + f\"{country_name}, it's \" + f\"{current_time}\")\n\n                    if country['name'] in audio_files or country_capital in audio_files:\n                        play_audio(\"In\", \"\")\n                        play_audio(country_capital, country_capital)\n                        time.sleep(0.4)\n                        play_audio(country['name'], country['name'])\n\n                        current_time_list = current_time.split(\":\")\n                        play_audio(\"ItIs\", \"\")\n\n                        for d in range(0, 2):\n                            hand_time = current_time_list[d][0].replace(\"0\", \"\") + current_time_list[d][1]\n\n                            if d == 1 and len(hand_time) == 1:\n                                play_audio(\"O\", \"\")\n\n                            play_audio(\"Number\" + hand_time, \"\")\n                    else:\n                        speak(\"In \" + f\"{country_capital}, \" + f\"{country_name}, it's \" + f\"{current_time}\")\n                    return\n\n        # If no country or capital was found in the input statement, simply tell the time of the default capital\n        country_time = pytz.timezone(\"Europe/Copenhagen\")\n        current_time = datetime.datetime.now(country_time).strftime(\"%H:%M\")\n        print(\"It's \" + f\"{current_time}\")\n        current_time_list = current_time.split(\":\")\n        play_audio(\"ItIs\", \"It's\")\n\n        for d in range(0, 2):\n            hand_time = current_time_list[d][0].replace(\"0\", \"\") + current_time_list[d][1]\n\n            if d == 1 and len(hand_time) == 1:\n                play_audio(\"O\", \"O\")\n\n            play_audio(\"Number\" + hand_time, hand_time)\n\n\n# This function will enable the user to search the wikipedia for answers\ndef search_wikipedia(input_statement):\n    search_text = input_statement[input_statement.index('wikipedia') + len('wikipedia') + 1:]\n\n    while not search_text:\n        speak(\"Pardon me, could you repeat that?\")\n        input_statement = take_command().lower()\n\n        if \"wikipedia\" in input_statement:\n            search_text = input_statement[input_statement.index('wikipedia') + len('wikipedia') + 1:]\n        else:\n            search_text = input_statement\n\n    play_audio(\"SearchingWikipedia\", \"Searching Wikipedia\")\n    result = wikipedia.summary(search_text, sentences=4)\n    play_audio(\"AccordingtoWikipedia\", \"According to wikipedia\")\n    print(result)\n    speak(result)\n\n\n# Open a webpage in google\ndef open_webpages(input_statement):\n    # Checks if a known webpage is in the JSON-file\n    for known_webpage in webpage_names:\n        if known_webpage['name'].lower() in input_statement:\n            play_audio(\"OpeningWebpage\", \"Opening\")\n            play_audio(known_webpage['name'], known_webpage['name'])\n            webbrowser.open_new_tab(known_webpage['link'])\n            return\n\n    play_audio(\"WebpageNotFound\", \"The webpage you are looking for don't exist in my list\")\n\n    search_text = input_statement[input_statement.index('open') + len('open') + 1:]\n    result_list = search(query=search_text, tld=\"co.in\", lang=\"en\", num=3, start=0, stop=3, pause=2.0)\n\n    for result in result_list:\n        print(result)\n\n\n# Open a news site\ndef news(input_statement):\n    webbrowser.open_new_tab(\"https://www.dr.dk/\")\n    play_audio(\"News\", \"Here are some news from Dr.dk\")\n\n\n# Tell the weather in a given capital\ndef weather(input_statement):\n    city_name = \"Copenhagen\"\n\n    with open(\"Data/TimeZones.json\", 'r') as j:\n        time_zone = json.load(j)\n\n        for continent in time_zone:\n            for country in time_zone[continent]:\n                if country['name'].lower() in input_statement or 'capital' in country and country[\n                    'capital'].lower() in input_statement:\n                    city_name = country[\n                        'capital'] if 'capital' in country else country['name']\n\n    api_key = \"826b9c92900e4a8a99571c3665261da6\"\n    base_url = \"http://api.openweathermap.org/data/2.5/weather\"\n    complete_url = base_url + \"?q=\" + city_name + \"&appid=\" + api_key\n    api_response = requests.get(complete_url)\n    api_response = api_response.json()\n\n    # --Possible Descriptions--\n\n    # clear sky\n    # few clouds\n    # scattered clouds\n    # broken clouds\n    # shower rain\n    # rain\n    # thunderstorm\n    # snow\n    # mist\n\n    if api_response[\"cod\"] != 404:\n        location = api_response['name']\n        temperature = round(api_response['main']['temp'] - 273.15)\n        weather_description = \" \".join([\n            word.capitalize()\n            for word in api_response['weather'][0]['description'].split(\" \")\n        ])\n\n        print((\"The current weather in {0} is {1} with a temperature of {2} \" +\n               \"degrees Celsius\").format(location, weather_description, temperature))\n\n        play_audio(\"CurrentWeather\", \"The current weather in\")\n        play_audio(city_name, city_name)\n        play_audio(\"Is\", \"Is\")\n        play_audio(weather_description.replace(\" \", \"\"), weather_description)\n        play_audio(\"WeatherTemperature\", \"With a temperature of\")\n        play_audio(\"Number\" + str(temperature), str(temperature))\n        play_audio(\"WeatherDegrees\", \"Degrees Celsius\")\n\n\ndef isfloat(input_str):\n    try:\n        float(input_str)\n        return True\n    except ValueError:\n        return False\n\n\n# Answer a mathematical or geographical question\ndef math(input_statement, second_try=False):\n    if not second_try:\n        print(\"Searching...\")\n        speak(\"Searching...\")\n\n    try:\n        client = wolframalpha.Client(\"X8K53V-A95P9W6UVW\")\n        if not second_try:\n            res = client.query(input_statement[input_statement.index('what is'):])\n        else:\n            res = client.query(input_statement[input_statement.index('what is') + len('what is') + 1:])\n\n        answer = next(res.results).text.split(\"\\n\")[0].split(\",\")[0].replace('...', '')\n        answer = answer[answer.rfind(\"|\") + 2:].capitalize() if answer.rfind(\"|\") != -1 else answer\n        answer = answer[:answer.find('.') + 5] if isfloat(answer) and '.' in answer else answer\n        print(answer)\n\n        if answer.isdigit():\n            play_audio(\"Number\" + answer, answer)\n        else:\n            play_audio(answer, answer)\n    except StopIteration:\n        if not second_try:\n            math(input_statement, second_try=True)\n        else:\n            speak(\"I'm sorry but I don't know the answer to that question\")\n\n\n# The things Jarvis shall do on start up\nprint(\"Loading your personal assistant Jarvis\")\n\n# Checks if the name of the file equals the main file.\nif __name__ == '__main__':\n    while True:\n        statement = take_command(respond=False).lower()  # Allows Jarvis to hear what you're saying\n\n        if statement and (\"hey jarvis\" in statement or \"hello there\" in statement):\n            greet_user()\n            statement = take_command().lower()\n\n            while not statement:\n                statement = take_command().lower()\n        else:\n            continue\n\n        if \"nevermind\" in statement or \"nothing\" in statement:\n            speak(\"Okay\")\n            continue\n\n        goodbye = False\n\n        with open(\"Data/GoodByeSayings.txt\", 'r') as file:\n            for goodbye_saying in file.readlines():\n                if goodbye_saying.strip() in statement:\n                    goodbye = True\n                    break\n\n        if goodbye:\n            print(\"Okay, see you later\")\n            speak(\"Okay, see you later\")\n            break\n\n        # Set timer\n        if \"timer\" in statement:\n            timer(statement)\n\n        # Give the time\n        elif \"time\" in statement:\n            world_and_local_time(statement)\n\n        # Light controls\n        # elif \"light\" in statement:\n        # turn_lights_on_and_off_controls(statement)\n\n        # Searching wikipedia for questions\n        elif 'wikipedia' in statement:\n            search_wikipedia(statement)\n\n        # news\n        elif 'news' in statement:\n            news(statement)\n\n        # search google\n\n        # Forecasting the weather\n        elif 'weather' in statement:\n            weather(statement)\n\n        # Math\n        elif 'what is' in statement or \"what's\" in statement:\n            math(statement.replace(\"what's\", \"what is\"))\n\n        # Open a webpage\n        elif \"open\" in statement:\n            open_webpages(statement)\n\n        else:\n            speak(\"I'm sorry but I can't help you with that\")\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":23620,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"609903908","text":"#\n# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements.  See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership.  The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); you may not use this file except in compliance\n# with the License.  You may obtain a copy of the License at\n#\n#   http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied.  See the License for the\n# specific language governing permissions and limitations\n# under the License.\n#\n\n\nfrom singa import singa_wrap as singa\nfrom singa import opt\nimport argparse\nimport train_largedata\n\nif __name__ == '__main__':\n    # use argparse to get command config: max_epoch, model, data, etc. for single gpu training\n    parser = argparse.ArgumentParser(\n        description='Training using the autograd and graph.')\n    parser.add_argument('model',\n                        choices=['resnet', 'xceptionnet', 'cnn', 'mlp'],\n                        default='cnn')\n    parser.add_argument('--epoch',\n                        '--max-epoch',\n                        default=10,\n                        type=int,\n                        help='maximum epochs',\n                        dest='max_epoch')\n    parser.add_argument('--bs',\n                        '--batch-size',\n                        default=64,\n                        type=int,\n                        help='batch size',\n                        dest='batch_size')\n    parser.add_argument('--lr',\n                        '--learning-rate',\n                        default=0.005,\n                        type=float,\n                        help='initial learning rate',\n                        dest='lr')\n    parser.add_argument('--op',\n                        '--option',\n                        default='fp32',\n                        choices=['fp32','fp16','partialUpdate','sparseTopK','sparseThreshold'],\n                        help='distibuted training options',\n                        dest='dist_option')  # currently partialUpdate support graph=False only\n    parser.add_argument('--spars',\n                        '--sparsification',\n                        default='0.05',\n                        type=float,\n                        help='the sparsity parameter used for sparsification, between 0 to 1',\n                        dest='spars')\n    parser.add_argument('--no-graph',\n                        '--disable-graph',\n                        default='True',\n                        action='store_false',\n                        help='disable graph',\n                        dest='graph')\n    parser.add_argument('--verbosity',\n                        '--log-verbosity',\n                        default=0,\n                        type=int,\n                        help='logging verbosity',\n                        dest='verbosity')\n\n    args = parser.parse_args()\n\n    sgd = opt.SGD(lr=args.lr, momentum=0.9, weight_decay=1e-5)\n    sgd = opt.DistOpt(sgd)\n\n    train_largedata.run(sgd.global_rank, sgd.world_size, sgd.local_rank, args.max_epoch,\n              args.batch_size, args.model, \"no\", sgd, args.graph,\n              args.verbosity, args.dist_option, args.spars)\n","sub_path":"examples/largedataset_cnn/train_mpi.py","file_name":"train_mpi.py","file_ext":"py","file_size_in_byte":3439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"622113654","text":"\"\"\"\nmrstacky\n\"\"\"\nimport argparse\nimport ConfigParser\nimport glob\nimport logging\nimport os.path\nimport re\nimport sys\n\nimport matplotlib\nmatplotlib.use('WXAgg')\nimport matplotlib.backends.backend_pdf as mplpdf\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ncore_file_name_pattern = re.compile(r'^(?P<core>\\w+\\d+)_(?P<core_number>\\d+).+(?P<spot>[Ss]pot_?\\d+(_\\d+)?b?)')\n\nlog = logging.getLogger(__name__)\nlog.setLevel(logging.DEBUG)\n\n\ndef mrstacky():\n    argparser = argparse.ArgumentParser()\n    argparser.add_argument('config_file_path')\n    cmd_line_args = argparser.parse_args()\n\n    config_file_parser = ConfigParser.SafeConfigParser(allow_no_value=True)\n    config_file_parser.optionxform = str\n    config_file_parser.read(cmd_line_args.config_file_path)\n\n    if config_file_parser.has_option('log', 'stdout_level'):\n        handler = logging.StreamHandler(stream=sys.stdout)\n        level = config_file_parser.get('log', 'stdout_level')\n        formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n        handler.setFormatter(formatter)\n        handler.setLevel(eval(level))\n        log.addHandler(handler)\n    else:\n        pass\n\n    if config_file_parser.has_option('log', 'file'):\n        log_file_path = config_file_parser.get('log', 'file')\n        handler = logging.FileHandler(log_file_path, mode='w')\n        level = config_file_parser.get('log', 'file_level')\n        formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n        handler.setFormatter(formatter)\n        handler.setLevel(eval(level))\n        log.addHandler(handler)\n    else:\n        pass\n\n    data_file_path_list = []\n    for data_file_glob in config_file_parser.options('data file globs'):\n        data_file_glob_path_list = glob.glob(data_file_glob)\n        log.info('{} files from glob {}'.format(len(data_file_glob_path_list), data_file_glob))\n        for data_file_path in data_file_path_list:\n            log.debug('data file: {}'.format(data_file_path))\n        data_file_path_list.extend(data_file_glob_path_list)\n\n    output_file_path = config_file_parser.options('output')[0]\n    log.info('writing figures to file {}'.format(output_file_path))\n    pp = mplpdf.PdfPages(output_file_path)\n\n\n    # core_groups looks like:\n    #     {\n    #         OTT3 : [file1, file2, ...]\n    #         TG3  : [file1, file2, ...]\n    #     }\n    core_groups = {}\n    # core_number_groups looks like:\n    #    {\n    #        OTT3_52 : [file1, file2, ...]\n    #        OTT3_57 : [file1, file2, ...]\n    #    }\n    ref_groups = {}\n    core_number_groups = {}\n    unrecognized_file_paths = []\n    for i, data_file_path in enumerate(data_file_path_list):\n        data_file_name = os.path.split(data_file_path)[1].lower()\n        print('data file name: {}'.format(data_file_name))\n        core_file_name_match = core_file_name_pattern.match(data_file_name)\n        if data_file_name.endswith('_standard.e'):\n            log.info('recognized reference file {}'.format(data_file_name))\n            core = 'reference'\n            core_number = 'reference'\n            core_number_string = 'reference'\n        elif core_file_name_match is not None:\n            log.info('recognized file name {}'.format(data_file_name))\n            core = core_file_name_match.group('core')\n            core_number = core_file_name_match.group('core_number')\n            spot = core_file_name_match.group('spot')\n            log.info('  spot: {}'.format(spot))\n\n            #core, core_number = data_file_name.split('_')[:2]\n            core = core.upper()\n            core_number_string = '{}_{}'.format(core, core_number)\n        else:\n            # failed to match core_file_name_pattern\n            unrecognized_file_paths.append(data_file_path)\n            raise Exception('unrecognized file: {}'.format(data_file_path))\n        log.info('{}: data file name: {}'.format(i, data_file_name))\n        log.info('  core: {} core number: {}'.format(core, core_number))\n        #if 'fe' in data_file_name:\n        #   print('rejected for Fe: {}'.format(data_file_name))\n        #else:\n        core_list = core_groups.setdefault(core, [])\n        core_list.append(data_file_path)\n        core_number_list = core_number_groups.setdefault(core_number_string, [])\n        core_number_list.append(data_file_path)\n\n    print('unrecognized files:')\n    for unrecognized_file_path in unrecognized_file_paths:\n        print('  {}'.format(unrecognized_file_path))\n\n    # make the big plot\n    core_groups_no_TG4 = core_groups.copy()\n    if 'TG4' in core_groups_no_TG4:\n        del core_groups_no_TG4['TG4']\n    make_stacked_plot(pp, core_groups_no_TG4, config_file_parser)\n\n    # make smaller plots\n    for core in core_groups.keys():\n        plot_groups = {\n            core_number: core_number_groups[core_number]\n            for core_number in core_number_groups.keys()\n            if core_number.startswith(core) or core_number == 'reference'\n        }\n        make_stacked_plot(pp, plot_groups, config_file_parser)\n    pp.close()\n\n    \"\"\"\n    # calling make_stacked_plot just to get core_data_list\n    core_data_list = make_stacked_plot(pp, core_groups)\n    pp.close()\n    # write a file with the data\n    with open('core_data_table_{}.txt'.format('_'.join(core_groups.keys())), 'w') as core_data_file:\n        # first column is row number\n        core_data_list.reverse()\n        core_data_file.write(',')\n        for (core_file_name, core_data) in core_data_list:\n            core_data_file.write('{},,'.format(core_file_name))\n        core_data_file.write('\\n')\n        core_data_file.write(',')\n        for (core_file_name, core_data) in core_data_list:\n            core_data_file.write('energy,absorbance,')\n\n        for row_index in xrange(310):\n            core_data_file.write('\\n')\n            core_data_file.write('{}'.format(row_index+1))\n            for (core_file_name, core_data) in core_data_list:\n                if core_data.shape[0] > row_index:\n                    core_data_file.write(',{},{}'.format(core_data[row_index, 0], core_data[row_index, 1]))\n                else:\n                    core_data_file.write(',,')\n    \"\"\"\n\n# iron:\nstart_e = 7100.0\nstop_e = 7180.0\nvline_es = [7120.0, 7129.0, 7132.0, 7137.0]\nfirst_e_tick = 7100.0\nlast_e_tick = 7175.0\nbase_line_step_size = 1.5\ncore_name_start =100\nlegend_start_x = 95\nlegend_start_y = 1.4\n\n# arsenic:\nstart_e = 11860.0\nstop_e = 11900.0\nvline_es = [11868.0, 11870.0, 11872.0, 11875.5]\nfirst_e_tick = 11860.0\nlast_e_tick = 11885.0\nbase_line_step_size = 3.5\ncore_name_start = 25.0\nlegend_start_x = 25.0\nlegend_start_y = 3.5\n\n\ndef make_stacked_plot(pp, core_groups, config_file_parser):\n    start_e = config_file_parser.getfloat('plot', 'start_energy')\n    stop_e = config_file_parser.getfloat('plot', 'stop_energy')\n    vline_es = [float(x) for x in config_file_parser.get('plot', 'vlines').split(',')]\n    first_e_tick = config_file_parser.getfloat('plot', 'first_energy_tick')\n    last_e_tick = config_file_parser.getfloat('plot', 'last_energy_tick')\n    step_e_tick = config_file_parser.getfloat('plot', 'energy_tick_step')\n    base_line_step_size = config_file_parser.getfloat('plot', 'base_line_step_size')\n    core_name_start = config_file_parser.getfloat('plot', 'core_name_start')\n    legend_start_x = config_file_parser.getfloat('plot', 'legend_start_x')\n    legend_start_y = config_file_parser.getfloat('plot', 'legend_start_y')\n\n    figure_width = config_file_parser.getfloat('plot', 'figure_width')\n    figure_height = config_file_parser.getfloat('plot', 'figure_height')\n    print('mrstacky figure shape: {},{}'.format(figure_width, figure_height))\n    fig = plt.figure(figsize=(figure_width, figure_height))\n\n    fig.suptitle('mr stacky')\n    ax = fig.add_subplot(1, 3, (1, 2))\n    base_line = 0.0\n\n    max_core_data_plus_base_line = 0.0\n\n    spot_name_pattern_re_str = re.compile(r'(?P<spot_name>spot_?\\d+)')\n    if config_file_parser.has_option('spot', 'spot_name'):\n        spot_name_pattern_re_str = config_file_parser.get('spot', 'spot_name')\n    log.info('found spot name regular expression {}'.format(spot_name_pattern_re_str))\n    spot_name_pattern = re.compile(spot_name_pattern_re_str)\n\n    annotation_energy_i = None\n    sorted_core_names = sorted(core_groups.keys(), reverse=True)\n    if 'reference' in sorted_core_names:\n        sorted_core_names.remove('reference')\n        sorted_core_names.append('reference')\n    core_data_list = []\n    legend_list = []\n    for core in sorted_core_names:\n        print(core)\n        core_list = core_groups[core]\n        core_line_plot_list = []\n        core_line_name_list = []\n        core_name_count = 0\n        for core_file_path in core_list:\n            core_file_name = os.path.split(core_file_path)[1]\n\n            core_data = np.loadtxt(core_file_path)\n            core_data_list.append((core_file_name, core_data))\n            print('in file {} data has shape {}'.format(core_file_name, core_data.shape))\n            start_i = np.argwhere(core_data[:, 0] > start_e)[0][0] #11860.0\n            #print('  start {} {}'.format(start_i, core_data[start_i,0]))\n            end_i = np.argwhere(core_data[:, 0] > stop_e)[0][0] #11900.0\n            #print('  end   {} {}'.format(end_i, core_data[end_i,0]))\n            core_data_plus_base_line = base_line + core_data[start_i:end_i, 1]\n            if max(core_data_plus_base_line) > max_core_data_plus_base_line:\n                max_core_data_plus_base_line = max(core_data_plus_base_line)\n            core_line, = ax.plot(core_data[start_i:end_i, 0], core_data_plus_base_line)\n            spot_name_pattern_match = spot_name_pattern.search(core_file_name.lower())\n            core_line_name = 'nothing!'\n            if spot_name_pattern_match is not None:\n                core_line_name = spot_name_pattern_match.group('spot_name')\n            #elif core_file_name.lower().find('spot') > 0:\n            #    core_line_name = core_file_name[core_file_name.lower().find('spot'):-2]\n            elif core_file_name.lower().find('_ref_als_cal.e') > 0:\n                core_line_name = core_file_name[:core_file_name.lower().find('_ref_als_cal.e')]\n            elif core_file_name.lower().find('_standard.e') > 0:\n                core_line_name = core_file_name[:core_file_name.lower().find('_standard.e')]\n\n            # keep a list of core_line plots and core_line_names to use in making legends\n            core_line_plot_list.append(core_line)\n            core_line_name_list.append(core_line_name)\n\n            \"\"\"\n            ax.annotate(\n                core_line_name,\n                xy=(11845.0, base_line + 1.5 - 0.3*core_name_count),\n                xycoords='data',\n                horizontalalignment='left',\n                verticalalignment='top',\n                color=core_line.get_color(),\n                fontsize=12\n            )\n            \"\"\"\n            core_name_count += 1\n\n        legend_column_count = 1\n        #if len(core_line_plot_list) > 5:\n        #    legend_column_count = 2\n        legend_list.append(\n            ax.legend(\n                core_line_plot_list,\n                core_line_name_list,\n                title=core,\n                loc=2,\n                fontsize=7,\n                ncol=legend_column_count,\n                bbox_to_anchor=(start_e + legend_start_x + 1.0, base_line + legend_start_y + 0.1), # 11886.0\n                bbox_transform=ax.transData\n            )\n        )\n        plt.setp(legend_list[-1].get_title(), fontsize=8)\n\n        # print core names such as 'OTT'\n        # try to put the end of the core name near the right side of the plot\n        print('length of {} is {}'.format(core, len(core)))\n        print('placing core name {} at x={}'.format(core, start_e + core_name_start - 0.9*len(core))) # 11885.0\n        ##ax.annotate(core, xy=(11885.0 - 0.8*len(core), base_line + 2.5), xycoords='data', fontsize=18)\n\n        print_core_name = False\n        if print_core_name:\n            plt.text(\n                start_e + core_name_start + 0.5, # - 0.9*len(core), 11884.5\n                base_line + base_line_step_size - 1.0,\n                core,\n                horizontalalignment='right',\n                fontsize=18\n            )\n        base_line += base_line_step_size\n        #ax.annotate('base line {}'.format(base_line), xy=(11840.0, base_line), xycoords='data')\n\n    for artist in legend_list:\n        ax.add_artist(artist)\n\n    ax.vlines(vline_es, -2.0, max_core_data_plus_base_line + 0.1, linestyles=\"dashed\", color=\"gray\")\n    ax.set_xlim((first_e_tick, last_e_tick))\n    ax.set_ylim((-0.1, max_core_data_plus_base_line + 0.1))  # was just base_line\n    xticks = np.arange(first_e_tick, last_e_tick, step_e_tick) #10.0\n    print('xticks: {}'.format(xticks))\n    ax.set_xticks(xticks)\n    ax.set_xticklabels(ax.get_xticks())\n    for tick in ax.xaxis.get_major_ticks():\n        tick.label.set_fontsize(7)\n    for tick in ax.yaxis.get_major_ticks():\n        tick.label.set_visible(False)\n    fig.savefig(pp, format='pdf', dpi=fig.dpi)\n\n    return core_data_list\n\n\nif __name__ == '__main__':\n    mrstacky()\n","sub_path":"mrstacky.py","file_name":"mrstacky.py","file_ext":"py","file_size_in_byte":13091,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"644007104","text":"class FpTreeNode:\n    def __init__(self, layer, node_tag='', val=0, parent=None, child_list=None):\n        if child_list is None:\n            child_list = []\n\n        self.layer = layer\n\n        self.node_tag = node_tag\n\n        self.val = val\n\n        self.parent = parent\n\n        self.child_list = child_list\n\n    def increase_val(self, inc_val=1):\n        self.val += inc_val\n\n    def add_child_node(self, child_node):\n        self.child_list.append(child_node)\n\n    def __str__(self):\n        return 'Node Name: %s, Value: %d' % (self.node_tag, self.val)\n\n\nclass FpTree:\n    def __init__(self, fre_list):\n        self.root = FpTreeNode(layer=0, node_tag='null')\n\n        self.fre_list = fre_list\n\n        self.lnk_tbl = None\n\n    def rearrange_ptn_as_fre_list(self, ptn):\n\n        rearranged_record = []\n\n        for (key, val) in self.fre_list:\n\n            if key in ptn:\n\n                rearranged_record.append(key)\n\n        return rearranged_record\n\n    def absorb_pattern(self, ptn, cnt):\n\n        tree_iter = self.root\n\n        layer_count = 0\n\n        ptn = self.rearrange_ptn_as_fre_list(ptn)\n\n        for ele in ptn:\n\n            next_node = None\n\n            for child_node in tree_iter.child_list:\n\n                if child_node.node_tag == ele:\n\n                    child_node.increase_val(cnt)\n\n                    next_node = child_node\n\n                    break\n\n            if next_node is None:\n\n                next_node = FpTreeNode(layer=layer_count + 1, node_tag=ele, val=cnt, parent=tree_iter)\n\n                tree_iter.add_child_node(next_node)\n\n            tree_iter = next_node\n\n            layer_count += 1\n\n        # tree_iter = self.root\n        #\n        # while tree_iter is not None:\n        #\n        #     print(tree_iter)\n        #\n        #     if len(tree_iter.child_list) > 0:\n        #\n        #         tree_iter = tree_iter.child_list[0]\n        #\n        #     else:\n        #\n        #         tree_iter = None\n\n    def gen_link_tbl(self):\n\n        link_tbl = {}\n\n        for (key, val) in self.fre_list:\n\n            link_tbl[key] = [val]\n\n        queue = []\n\n        for node in self.root.child_list:\n\n            queue.append(node)\n\n        while len(queue) > 0:\n\n            node = queue[0]\n\n            queue.pop(0)\n\n            for child_node in node.child_list:\n\n                queue.append(child_node)\n\n            link_tbl[node.node_tag].append(node)\n\n        return link_tbl\n\n    def gen_prefix_paths(self, key, update_lnk_tbl=False):\n\n        if self.lnk_tbl is None or update_lnk_tbl:\n\n            self.lnk_tbl = self.gen_link_tbl()\n\n        prefix_paths = {}\n\n        for node in self.lnk_tbl[key][1::]:\n\n            node_iter = node.parent\n\n            prefix_path = set()\n\n            while node_iter.node_tag != 'null':\n\n                prefix_path.add(node_iter.node_tag)\n\n                node_iter = node_iter.parent\n\n            if len(prefix_path) > 0: prefix_paths[frozenset(prefix_path)] = node.val\n\n        return prefix_paths\n\n    def is_empty(self):\n\n        return True if len(self.root.child_list) < 1 else False\n\n    def __str__(self):\n\n        layer_count = 0\n\n        quque = [self.root]\n\n        vis_str = 'Layer %d: ' % (layer_count)\n\n        while len(quque) > 0:\n\n            cur_node = quque[0]\n\n            if layer_count != cur_node.layer:\n\n                layer_count += 1\n\n                vis_str += '\\nLayer %d: ' % (layer_count)\n\n            vis_str += '(tag: %s, cnt: %d)' % (cur_node.node_tag, cur_node.val)\n\n            for ele in cur_node.child_list:\n\n                quque.append(ele)\n\n            quque.pop(0)\n\n        return vis_str\n\n    # def print_tree(self):\n    #\n    #     layer_count = 0\n    #\n    #     quque = [self.root]\n    #\n    #     print('Layer %d: ' % layer_count, end='')\n    #\n    #     while len(quque) > 0:\n    #\n    #         cur_node = quque[0]\n    #\n    #         if layer_count != cur_node.layer:\n    #\n    #             layer_count += 1\n    #\n    #             print( '\\nLayer %d: ' % (layer_count), end='')\n    #\n    #         print('(tag: %s, cnt: %d)' % (cur_node.node_tag, cur_node.val), end='')\n    #\n    #         for ele in cur_node.child_list:\n    #\n    #             quque.append(ele)\n    #\n    #         quque.pop(0)","sub_path":"FP-growth/py/fptools.py","file_name":"fptools.py","file_ext":"py","file_size_in_byte":4250,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"542632222","text":"from sklearn.manifold import TSNE\nimport matplotlib.pyplot as plt\n\nfp = open(\"lstm_100_embeddings.txt\", \"r\")\ndata = fp.read()\nfp.close()\nsplitdata = data.split(\"\\n\")\nlength = len(splitdata) - 1\nwords = []\nembedding = []\nfor i in range(length):\n\tif i % 2 == 0:\n\t\twords.append(splitdata[i])\n\telse:\n\t\tline = splitdata[i].split(' ')\n\t\trow = []\n\t\tfor j in line:\n\t\t\trow.append(float(j))\n\t\tembedding.append(row)\n\nmodel = TSNE(n_components=2, random_state=0, n_iter = 2000, perplexity = 50)\nembeds = model.fit_transform(embedding)\n# xmin = embedding[0][0]\n# xmax = embedding[0][0]\n# ymin = embedding[0][1]\n# ymax = embedding[0][1]\n# for i in range(len(embedding)):\n# \txmin = min(xmin, embedding[i][0])\n# \txmax = min(xmax, embedding[i][0])\n# \tymin = min(ymin, embedding[i][1])\n# \tymax = min(ymax, embedding[i][1])\n\n# print xmin, xmax, ymin, ymax\n\nx = [ele[0] for ele in embedding]\ny = [ele[1] for ele in embedding]\nplt.figure()\n# plt.autoscale(enable=True, axis='both', tight=None)\n# plt.xlim(xmin, xmax)\n# plt.ylim(ymin, ymax)\nplt.scatter(x, y, s = 0)\nfor i in range(len(words)):\n\tplt.annotate(words[i], xy = (x[i], y[i]))\nplt.show()","sub_path":"tsne.py","file_name":"tsne.py","file_ext":"py","file_size_in_byte":1125,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"614556003","text":"import sys\nimport requests\nimport json\nimport asyncio\nimport discord\n\nBASE_URL = \"http://pokeapi.co\"\n\ndef query_pokemon(pokenumber):\n\tresponse = requests.get(BASE_URL + \"/api/v1/pokemon/\" + str(pokenumber) + \"/\")\n\tif response.status_code == 200:\n\t\tdata = json.loads(response.text)\n\t\treturn data\n\telse:\n\t\tprint ('Error or some shit')\n\t\t\n\t\t\ndef query_pokeapi(resource_url):\n    url = '{0}{1}'.format(BASE_URL, resource_url)\n    response = requests.get(url)\n \n    if response.status_code == 200:\n        return json.loads(response.text)\n    return None\n\t\t\nclient = discord.Client()\n\nasync def handleMessage(message):\n    data = query_pokemon(str(message.content[8:]).lower())\n    name = data['name']\n    descDat = query_pokeapi(data['descriptions'][0]['resource_uri'])\n    desc = descDat['description']\n    reply = name + \": \" + desc\n    sprite = query_pokeapi(data['sprites'][0]['resource_uri'])\n    spriteURL = BASE_URL + sprite['image']\n    tmp = await client.send_message(message.channel, spriteURL)\n    tmp = await client.send_message(message.channel, reply)\n\n@client.event\nasync def on_ready():\n    print('Logged in as')\n    print(client.user.name)\n    print(client.user.id)\n    print('------')\n\n@client.event\nasync def on_message(message):\n\tif message.content.startswith(\"pokedex\"):\n\t\tawait handleMessage(message)\n\nclient.run('MjIwMDE3OTQ5NTc3MTgzMjMy.CqaLPQ.XcR7CrwiIzXCEUgiGeJWFxnljZ8')","sub_path":"pokebot.py","file_name":"pokebot.py","file_ext":"py","file_size_in_byte":1392,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"69691150","text":"# -*- coding: utf-8 -*-\nimport logging\nimport werkzeug\nfrom openerp import http\nfrom openerp.http import request\nfrom openerp.addons.tko_website_sale_pdf.controllers.search import advanced_search\nfrom openerp.addons.website_sale.controllers.main import table_compute, QueryURL, PPG, PPR\nfrom openerp.tools.translate import _\n\n__author__ = 'Rui Coelho'\n_logger = logging.getLogger(__name__)\n\n# key: lowercase options in listing type on website advanced_search\n_LISTING_TYPE_MAP = {\n    'venda': 'sale',\n    'arrendamento': 'rent',\n}\n\n# key: lowercase options in nature on website advanced_search\n_NATURE_MAP = {\n    'apartamentos': 'apartments',\n    'moradias': 'villas',\n}\n\n# key: lowercase options in listing type on website advanced_search\n_LISTING_TYPE_TAG_REF = {\n    'venda': 'tko_crm_pdf.product_listing_type_sale',\n    'arrendamento': 'tko_crm_pdf.product_listing_type_rental',\n}\n\n\nclass Search(advanced_search):\n\n    def _get_tag(self, pool, cr, uid, selectnegocio, context=None):\n        \"\"\"\n        Get the tag ID of associated with a listing type from the options available in the website advanced search\n        :param selectnegocio: \"Venda|Arrendamento\"\n        :returns: Tag ID, False if not found\n        \"\"\"\n        if selectnegocio and selectnegocio.lower() in _LISTING_TYPE_TAG_REF:\n            ref_parts = _LISTING_TYPE_TAG_REF[selectnegocio.lower()].split('.', 1)\n            ref_module = ref_parts[0]\n            ref_name = ref_parts[1]\n            tag = pool.get('ir.model.data').get_object_reference(cr, uid, ref_module, ref_name)\n\n            if tag:\n                return tag[1]\n\n        return False\n\n    def build_page_title(self, reference=None, listing_type=None, nature=None, counties=None):\n        # Needed for the translation to work\n        destroy_env = False\n        if not hasattr(self, 'env'):\n            setattr(self, 'env', http.request.env)\n            destroy_env = True\n\n        if reference is not None and len(reference.strip()) > 0:\n            return reference.upper()\n\n        title = ''\n        if listing_type is not None and len(listing_type) > 0:\n            title += _(_LISTING_TYPE_MAP.get(listing_type.lower(), '')) + ' '\n\n        if nature is not None and len(nature) > 0:\n            title += _(_NATURE_MAP.get(nature.lower(), 'real estates')) + ' '  # naive plural form :\\\n        else:\n            title += _('real estates') + ' '\n\n        if counties is not None and len(counties) > 0:\n            if len(title) > 0:\n                title += ' - '\n            title += ', '.join([county for county in counties])\n\n        if destroy_env:\n            delattr(self, 'env')\n\n        return title.title()\n\n    @http.route(['/shop/advanced_search', '/shop/advanced_search/page/<int:page>'],\n                type='http', auth=\"public\", website=True)\n    def shop(self, page=0, category=None, search='', **post):\n        cr, uid, context, pool = request.cr, request.uid, request.context, request.registry\n        \n        # we need to get the counties and parishes directly from the Werkezeug object,\n        # otherwise we lose multiple selections\n        for field in ('selectconcelho', 'selectfreguesia', 'selectzona'):\n            post[field] = request.httprequest.args.getlist(field)\n\n        self._log_search(cr, uid, post, context)\n\n        # Exclude the real estates that are already sold\n        model_data = pool.get('ir.model.data')\n        available = model_data.get_object_reference(cr, uid, '__export__', 'product_availability_12')[1]\n        reserved = model_data.get_object_reference(cr, uid, '__export__', 'product_availability_4')[1]\n\n        domain = request.website.sale_product_domain()\n        domain += [('website_published', '=', True), ('availability_id', 'in', (available, reserved))]\n\n        model_data = pool.get('ir.model.data')\n        enterprise_id = model_data.get_object_reference(cr, uid, 'tko_crm_pdf', 'product_nature_enterprise')[1]\n        domain += [('categ_id', '!=', enterprise_id)]\n        \n        tag_id = self._get_tag(pool, cr, uid, post.get('selectnegocio', ''), context)\n        if tag_id:\n            domain += [('categ_tag_ids', '=', tag_id)]\n        if post.get('referencia', False):\n            domain += [('default_code', 'ilike', post['referencia'])]\n        if post['selectconcelho']:\n            domain += ['|'] * (len(post['selectconcelho']) - 1)\n            domain += [('county_id.name', '=', county.upper()) for county in post['selectconcelho']]\n        if post['selectfreguesia']:\n            domain += ['|'] * (len(post['selectfreguesia']) - 1)\n            domain += [('parish_id.name', '=', parish.upper()) for parish in post['selectfreguesia']]\n        if post['selectzona']:\n            domain += ['|'] * (len(post['selectzona']) - 1)\n            domain += [('zone_id.name', '=', zone) for zone in post['selectzona']]\n        if post.get('selectnatureza', False):\n            domain += [('categ_id.advanced_search_link', '=', post['selectnatureza'])]\n        if post.get('tipologiade', False) not in (False, 'De', ''):\n            min_rooms = int(''.join(c for c in post['tipologiade'] if c.isdigit()))\n            domain += [('rooms', '>=', min_rooms)]\n        if post.get('tipologiaate', False) not in (False, u'Até', ''):\n            max_rooms = int(''.join(c for c in post['tipologiaate'] if c.isdigit()))\n            domain += ['|', ('rooms', '<=', max_rooms), ('rooms', '=', False)]\n        if post.get('precode', False) and post['precode'].isdigit():\n            if post.get('selectnegocio', '') == 'Venda':\n                domain += [('lst_price9', '>=', int(post['precode']))]\n            elif post.get('selectnegocio', '') == 'Arrendamento':\n                domain += ['|', '|', ('lst_price0', '>=', int(post['precode'])), ('lst_price2', '>=', int(post['precode'])), ('lst_price3', '>=', int(post['precode']))]\n            else:\n                sale_tag = self._get_tag(pool, cr, uid, 'Venda', context)\n                rent_tag = self._get_tag(pool, cr, uid, 'Arrendamento', context)\n                domain += ['|',\n                                '&',\n                                    ('categ_tag_ids', '=', sale_tag),\n                                    ('lst_price9', '>=', int(post['precode'])),\n                                '&',\n                                    ('categ_tag_ids', '=', rent_tag),\n                                    '|', '|', ('lst_price0', '>=', int(post['precode'])), ('lst_price2', '>=', int(post['precode'])), ('lst_price3', '>=', int(post['precode'])),\n                          ]\n        if post.get('precoate', False) and post['precoate'].isdigit():\n            precoate = int(post['precoate'])\n            if post.get('selectnegocio', '') == 'Venda':\n                domain += [('lst_price9', '<=', precoate)]\n            elif post.get('selectnegocio', '') == 'Arrendamento':\n                domain += ['|', ('lst_price0', '=', False), ('lst_price0', '<=', precoate), '|', ('lst_price2', '=', False), ('lst_price2', '<=', precoate), '|', ('lst_price3', '=', False), ('lst_price3', '<=', precoate)]\n            else:\n                sale_tag = self._get_tag(pool, cr, uid, 'Venda', context)\n                rent_tag = self._get_tag(pool, cr, uid, 'Arrendamento', context)\n                domain += ['|',\n                                '&',\n                                    ('categ_tag_ids', '=', sale_tag),\n                                    ('lst_price9', '<=', precoate),\n                                '&',\n                                    ('categ_tag_ids', '=', rent_tag),\n                                    '&', '|', ('lst_price0', '=', False), ('lst_price0', '<=', precoate), '&', '|', ('lst_price2', '=', False), ('lst_price2', '<=', precoate), '|', ('lst_price3', '=', False), ('lst_price3', '<=', precoate),\n                           ]\n        if post.get('selectestado', False) and post['selectestado'].isdigit():\n            domain += [('in_state', '=', int(post['selectestado']))]\n        \n        _logger.debug(\"Advanced search domain: %s\", domain)\n                \n        keep = QueryURL('/shop', category=category and int(category), search=search)\n\n        if not context.get('pricelist'):\n            pricelist = self.get_pricelist()\n            context['pricelist'] = int(pricelist)\n        else:\n            pricelist = pool.get('product.pricelist').browse(cr, uid, context['pricelist'], context)\n\n        product_obj = pool.get('product.product')\n\n        sort_order = self.get_order(post.get('sort'))\n\n        product_count = product_obj.search_count(cr, uid, domain, context=context)\n        pager = request.website.pager(url=\"/shop/advanced_search\", total=product_count, page=page, step=PPG, scope=7, url_args=post)\n        product_ids = product_obj.search(cr, uid, domain, limit=PPG, offset=pager['offset'], order=sort_order, context=context)\n        products = product_obj.browse(cr, uid, product_ids, context=context)\n\n        style_obj = pool['product.style']\n        style_ids = style_obj.search(cr, uid, [], context=context)\n        styles = style_obj.browse(cr, uid, style_ids, context=context)\n\n        category_obj = pool['product.public.category']\n        category_ids = category_obj.search(cr, uid, [], context=context)\n        categories = category_obj.browse(cr, uid, category_ids, context=context)\n        categs = filter(lambda x: not x.parent_id, categories)\n\n        attributes_obj = request.registry['product.attribute']\n        attributes_ids = attributes_obj.search(cr, uid, [], context=context)\n        attributes = attributes_obj.browse(cr, uid, attributes_ids, context=context)\n\n        from_currency = pool.get('product.price.type')._get_field_currency(cr, uid, 'list_price', context)\n        to_currency = pricelist.currency_id\n        compute_currency = lambda price: pool['res.currency']._compute(cr, uid, from_currency, to_currency, price, context=context)\n\n        search_fields = {k: v for (k, v) in post.items() if isinstance(v, basestring)}\n\n        page_title = self.build_page_title(reference=post.get('referencia', None), listing_type=post.get('selectnegocio', None), nature=post.get('selectnatureza', None), counties=post.get('selectconcelho', None))\n\n        values = {\n            'search': search,\n            'category': category,\n            'pager': pager,\n            'pricelist': pricelist,\n            'products': products,\n            'bins': table_compute().process(products),\n            'rows': PPR,\n            'styles': styles,\n            'categories': categs,\n            'attributes': attributes,\n            'compute_currency': compute_currency,\n            'keep': keep,\n            'style_in_product': lambda style, product: style.id in [s.id for s in product.website_style_ids],\n            'attrib_encode': lambda attribs: werkzeug.url_encode([('attrib', i) for i in attribs]),\n            'search_fields': search_fields,\n            'num_results': product_count,\n            'page_title': page_title if page_title is not None else False\n        }\n        \n        # If in case of a unit test we only want the list of product IDs\n        if 'output' in post and post['output'] == 'test':\n            import pickle\n            return request.make_response(pickle.dumps(product_ids))\n        \n        return request.website.render(\"website_sale.products\", values)\n\n    @http.route(['/shop/advanced_search/options'], type='json', auth=\"public\", methods=['POST'], website=True, multilang=False)\n    def advanced_search_options(self, field_name, lang=None, **kw):\n        \"\"\"\n        Return the list of possible options for the advanced search dropdown boxes\n        :param field_name: The name of the field\n        :param lang: The user language\n        :param kw:\n        :return: dictionary with the ID of the option and the name\n        \"\"\"\n        lang = lang or request.context.get('lang')\n        result = {}\n    \n        if field_name == 'product_state':\n            product_states = request.env['product.state'].with_context(lang=lang).search([('website_published', '=', True)])\n            for product_state in product_states:\n                result[product_state.id] = product_state.name\n    \n        return result\n","sub_path":"pdf_website/controllers/search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":12181,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"458547156","text":"import glob\nimport os\nimport sys\n \ndef main():\n    # 拡張子.jpgのファイルを取得する\n    args = sys.argv\n    img_path = './' + args[1] + '/*.jpg'\n    i = 1\n     \n    # image pathを取得する\n    file_lists = glob.glob(img_path)\n    print('変更前')\n    print(file_lists)\n     \n    # image nameを一括で変更する\n    for file_name in file_lists:\n      os.rename(file_name, './' + args[1] + '/traffic_' + \"%06.f.jpg\" % i)\n      i += 1\n     \n    li = glob.glob(img_path)\n    print('変更後')\n    print(li)\n\nif __name__ == \"__main__\":\n    main()","sub_path":"rename.py","file_name":"rename.py","file_ext":"py","file_size_in_byte":566,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"62800791","text":"\"\"\"\nProduct related URLs\n\"\"\"\n\nfrom django.conf.urls import url\nfrom rest_framework.routers import DefaultRouter\nfrom .views import (ProductViewSet, BrandViewSet, OrderViewSet,\n                    CartViewSet, BundleProductViewSet, StockViewSet,\n                    ImageViewSet, FavouriteViewSet, CompetitorViewSet,\n                    StockViewSet, StockMovementViewSet, export_products,\n                    WarrantyViewSet, SupplierCartViewSet, CarrierViewSet)\n\nROUTER = DefaultRouter()\n\nROUTER.register(r'product', ProductViewSet, base_name='product')\nROUTER.register(r'brand', BrandViewSet, base_name='brand')\nROUTER.register(r'order', OrderViewSet, base_name='order')\nROUTER.register(r'cart', CartViewSet, base_name='cart')\nROUTER.register(r'bundle', BundleProductViewSet, base_name='bundle')\nROUTER.register(r'image', ImageViewSet, base_name='image')\nROUTER.register(r'stock', StockViewSet, base_name='stock')\nROUTER.register(r'stockmovement', StockMovementViewSet,\n                base_name='stockmovement')\nROUTER.register(r'favourite', FavouriteViewSet, base_name='favourite')\nROUTER.register(r'competitor', CompetitorViewSet, base_name='competitor')\nROUTER.register(r'warranty', WarrantyViewSet, base_name='warranty')\nROUTER.register(r'adminorder', SupplierCartViewSet, base_name='admin-order')\nROUTER.register(r'carrier', CarrierViewSet, base_name='carrier')\n\nurlpatterns = ROUTER.urls + \\\n    [url(r'^export_products/$', export_products, name='export'), ]\n","sub_path":"src/product/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"72445592","text":"# Entorno similar a GNU/Octave\nfrom os                 import system\nfrom threading          import Thread\nfrom scipy.io           import loadmat, savemat\nfrom scipy.io.wavfile   import write    as wavwrite\nfrom matplotlib.pyplot  import figure, plot, title, grid, legend, show, xticks, yticks, xlabel, ylabel, axis, subplot, savefig, semilogx, semilogy, loglog, pcolormesh, colorbar, cm, subplots_adjust, rc, rcParams\n\n# Usar fuentes de TeX\nrc('text', usetex = True)\nrcParams['text.latex.unicode'] =  True\n\n# Tamaño base para las fuentes\nfs_base = 1\n\n# Índice de figuras\ni_fig = 0\n\nsubplots_adjust(hspace = 0.4)\n\ndef grabarImg(ruta):\n    def _aux():\n        print('Grabando \\'%s\\'...' % ruta, flush = True)\n        savefig(ruta)\n        print('Se terminó de grabar \\'%s\\'.' % ruta)\n            \n    Thread(target=_aux).start()\n\ndef traer_puntomat_al_namespace(ruta):\n    _mat_dict = loadmat(ruta)\n\n    from inspect import currentframe\n    frame = currentframe()\n    try:\n        for k, v in _mat_dict.items():\n            if k[:2] != '__':\n                frame.f_back.f_locals.update({k:v})\n                print('Se añadió %s al namespace. Tipo: %s.'  % (k, type(v)))\n    finally:\n        del frame\n","sub_path":"comun/matplotio.py","file_name":"matplotio.py","file_ext":"py","file_size_in_byte":1207,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"464449520","text":"import logging\nimport os\n\n\nDEBUG = False\nLOG_LEVEL = logging.INFO\nSECRET_KEY = os.environ.get('FLASK_SECRET_KEY')\n\nSQLALCHEMY_DATABASE_URI = os.environ.get('DATABASE_URI')\nSQLALCHEMY_TRACK_MODIFICATIONS = False\nSQLALCHEMY_ECHO = False\n","sub_path":"config/production.py","file_name":"production.py","file_ext":"py","file_size_in_byte":235,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"439403247","text":"\"\"\"комментарий к оценке\n\nRevision ID: aa15bf2bc6a8\nRevises: 1ed715a23a9f\nCreate Date: 2020-06-04 10:43:28.356248\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'aa15bf2bc6a8'\ndown_revision = '1ed715a23a9f'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('grades', sa.Column('comment', sa.Integer(), nullable=True))\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column('grades', 'comment')\n    # ### end Alembic commands ###\n","sub_path":"migrations/versions/aa15bf2bc6a8_комментарий_к_оценке.py","file_name":"aa15bf2bc6a8_комментарий_к_оценке.py","file_ext":"py","file_size_in_byte":679,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"70004764","text":"try:\n    from fbchat.models import *\n    from fbchat import Client\n    import thread_getter\n    from sys import platform, argv\n    from os import system\nexcept ImportError:\n    print('')\n    print('Cannot import the modules required for messenger-cli')\n    print('Did you run the setup script?')\n    print('If so... did you activate the venv before running')\n    print('You can run from start.sh or start.ps1 or you can activate the venv at __projectdir__/messenger-cli/messenger_environment')\n    print('')\n    quit()\n\n\ndef login():\n    username = input('enter username: ')\n    password = input('enter password: ')\n    return Client(username, password)\n\n\ndef print_thread(name, thread):\n    messages = client.fetchThreadMessages(thread_id=thread.uid, limit=20)\n    messages.reverse()\n    clear()\n    print(name + ': ')\n    for message in messages:\n        author = client.fetchUserInfo(message.author)[message.author]\n        if message.text != '':\n            print(author.name + ': ' + message.text)\n        else:\n            print(author.name + ' sent an image/attachment')\n\n\ndef send_message(thread, user_message, thread_type):\n    client.send(Message(text=user_message), thread_id=thread.uid, thread_type=thread_type)\n\n\n# going to be clearing the cmd on updates, so need a quick way on both os\nif platform == \"win32\":\n    clear = lambda: system('cls')\nelse:\n    clear = lambda: system('clear')\n\n# login details as args, can do better later\n# prompt if argv[1] is empty\n# use flags for more modes etc\nif len(argv) > 1:\n    client = Client(argv[1], argv[2])\nelse:\n    client = login()\n\n# this will return a thread by doing the search type specified in x\nclear()\nthread = thread_getter.recent_conversations(client=client)\nname = thread[1]\nthread = thread[0]\n\nwhile True:\n    print_thread(name, thread)\n    print('Enter Message:')\n    print('(or type !Q to quit)')\n    message = input()\n    if message == '!Q':\n        client.logout()\n        quit()\n    else:\n        send_message(thread, message, thread.type)\n\n\n\n# thread_type=thread_type\n\n# Gets the last 10 messages sent to the thread\n# messages = client.fetchThreadMessages(thread_id=\"<thread id>\", limit=10)\n# Since the message come in reversed order, reverse them\n# messages.reverse()\n","sub_path":"messenger-cli/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":2243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"38072303","text":"import numpy as np\nimport time\nimport wget\nimport os\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom skimage import io\nfrom skimage import color\nfrom skimage import transform\nfrom skimage.viewer import ImageViewer as image_viewer # oh image viewer you little bitchfucker listen here you think you're so damn cool and clever and edgy going against conventions and naming your fucking letiables with camelcase when you know FULL WELL that the convention is to use underscore well let me tell you what, you just got fucking outsmarted in the most humiliating and simply elegant manner possible. so maybe reconsider your fucking decision of being a special little snowflake hm? make the code a little more readable and gain a smidge of fucking respect from actual coders, or yknow, don't, not that I care since I can just rename your bullshit however I like since that's how python works, not that you'd know anything about that\nfrom net3 import M, n\nfrom urllib.error import HTTPError, URLError\nimport pickle\nimport sys\n\nif (not sys.argv[1]) or (sys.argv[1] == ''):\n    SystemExit('Input query string for google search.')\n\nsearch_query = sys.argv[1]\nget_request = 'https://www.google.com/search?tbm=isch&q=' + search_query.replace(' ', '+')\n\n# words = sys.argv[1].split('&q')[1].split('&oq')[0].split('=')[1].split('+')\n# print_string = 'Scraping google images for search query \"' + words[0]\n# for word in words[1::]:\n#     print_string = print_string + ' ' + word\n# print(print_string + '\"')\n\ntarget_image_size = M**2 * n\ntime_delay = 1\nshort_time_delay = 0.1\n\n# net_images_folder_id = '1K3gh-dgHkkIgV1XrNTIegzwmPc96VU0A'\n# creds = pickle.load(open('token.pickle', 'rb'))\n# service = build('drive', 'v3', credentials=creds)\n\nwd = webdriver.Chrome(executable_path = r'C:\\\\bin\\\\chromedriver.exe') # put driver in c:\\bin\nwd.get(get_request)\n\n# this is the right way of doing things\ndef find_by_xpath(xpath):\n    try:\n        out = wd.find_element_by_xpath(xpath)\n        return out\n    except:\n        return False\n\n# def upload_image(img_name, img_url):\n#     try:\n#         file_metadata = {'name': img_name, 'parents': [net_images_folder_id]}\n#         media = MediaFileUpload(img_url, mimetype='image/png')\n#         service.files().create(body = file_metadata, media_body = media, fields = 'id').execute()\n#     except:\n#         pass\n\ndef process_image(image, image_counter):\n    try:\n        # download image\n        src = image.get_attribute('src')\n        wget.download(src, 'images/temp.png')\n        # resize image to have an area of target_image_size, but maintain aspect ratio\n        edit_image = io.imread(os.path.abspath('images/temp.png'))\n        # convert to grayscale\n        edit_image = color.rgb2gray(edit_image)\n        h = len(edit_image)\n        w = len(edit_image[0])\n        aspect_ratio = w / h\n        hp = int(np.sqrt(target_image_size / aspect_ratio))\n        wp = int(np.sqrt(target_image_size * aspect_ratio))\n        edit_image = transform.resize(edit_image, (hp, wp))\n        # split image into MxM grids and save grids\n        \n        # split image into MxM grids and save grids\n        current_position = {'row': 0, 'col': 0}\n        # duplicates of pixels in more than one image are only allowed if that image has at max half duplicate pixels\n        while (hp - current_position['row']) > 0:\n            # if space left\n            while (wp - current_position['col']) > 0:\n                grid = None\n                # if current position is between M / 2 and M, so duplicate\n                if (wp - current_position['col']) < M:\n                    if (wp - current_position['col']) > (M / 2):\n                        current_position['col'] = wp - M\n                        grid = edit_image[current_position['row'] : current_position['row'] + M, current_position['col'] : current_position['col'] + M]\n                        current_position['col'] = wp + 1 # will stop the loops\n                    else:\n                        current_position['col'] = current_position['col'] + M\n                else:\n                    grid = edit_image[current_position['row'] : current_position['row'] + M, current_position['col'] : current_position['col'] + M]\n                    current_position['col'] = current_position['col'] + M\n\n                if grid is not None:\n                    img_name = '{0}.png'.format(image_counter)\n                    img_url = 'images/{0}'.format(img_name)\n                    io.imsave(img_url, grid)\n                    # upload_image(img_name, img_url)\n                    os.remove(img_url)\n                    image_counter = image_counter + 1\n            \n            current_position['col'] = 0\n\n            if (hp - (current_position['row'] + M)) < M:\n                if (hp - (current_position['row'] + M)) > (M / 2):\n                    current_position['row'] = hp - M\n                else:\n                    current_position['row'] = hp + 1 # will stop the loop\n            else:\n                current_position['row'] = current_position['row'] + M\n    except (ValueError, HTTPError, URLError):\n        pass\n\n    try: os.remove('images/temp.png')\n    except: pass\n    # return image_counter to keep track of counter SINCE YOU CAN'T PASS BY REFERENCE\n    return image_counter\n\ndef process_image_whole(image, image_counter):\n    counter_local = image_counter\n    try:\n        # download image\n        src = image.get_attribute('src')\n        wget.download(src, 'images/temp.png')\n        \n        edit_image = io.imread(os.path.abspath('images/temp.png'))\n\n        target_image_dimension = 100\n        edit_image = transform.resize(edit_image, (target_image_dimension, target_image_dimension), anti_aliasing=False)\n        \n        # convert to grayscale\n        edit_image = color.rgb2gray(edit_image)\n\n        # save image\n        io.imsave(f'images/net_images_whole/{image_counter}.png', edit_image)\n\n        counter_local += 1\n    except (HTTPError, URLError, ValueError):\n        pass\n    try: os.remove('images/temp.png')\n    except: pass\n    return counter_local\n\n# scroll to bottom\nwhile not find_by_xpath(\"//div[@class=\\\"OuJzKb Bqq24e\\\" and text()=\\\"Looks like you've reached the end\\\"]\"):\n    # load more button\n    wd.execute_script('window.scrollTo(0, document.body.scrollHeight);')\n    if find_by_xpath('//div[@class=\\'YstHxe\\']').is_displayed():\n        button = wd.find_element_by_xpath('//input[@class=\\'mye4qd\\']')\n        button.click()\nwd.execute_script('window.scrollTo(0, document.body.scrollHeight);')\n\nimage_counter = 505\nfor img in wd.find_elements_by_css_selector('img.rg_i'):\n    try:\n        img.click()\n        time.sleep(time_delay)\n    except Exception:\n        continue\n    for actual_image in wd.find_elements_by_xpath('//img[@class=\\'n3VNCb\\']'):\n        if actual_image.is_displayed():\n            image_counter = process_image_whole(actual_image, image_counter)\n    time.sleep(short_time_delay)","sub_path":"google_image_scraper.py","file_name":"google_image_scraper.py","file_ext":"py","file_size_in_byte":6951,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"264492854","text":"import sys\nimport numpy as np\nimport random\nimport math\nimport csv\nfrom visualization import plot_decision_boundary\nfrom random import shuffle\n\ninputfile = \"MNIST_train.csv\"\ncsvfile = open(inputfile, 'r')\ndata = list(csv.reader(csvfile))\n\nshuffle(data)\nshuffle(data)\n\nm = len(data)\nn = len(data[0])-1\nprint(\"mtrain={}\".format(m))\n\ntrainx = []\ntrainy = []\nfor i in range(m):\n\tfor j in range(n+1):\n\t\tdata[i][j] = float(data[i][j])\n\nfor i in range(m):\n\timage = data[i]\n\ttrainy.append(image[n])\n\ttrainx.append(image[:n])\n\n\tif(trainy[i] == 6):\n\t\ttrainy[i] = [1, 0]\n\telse:\n\t\ttrainy[i] = [0, 1]\n\n\n# number of inputs\n# n = 2\nbatchsize = 100\n# batchsize = 10\nlayers = [100]\n\nlayers.append(2)\nnumlayers = len(layers)\n\nth = []\nthd = []\nO = []\n\n# m=100\n\nfor i in range(numlayers):\n\tl = -1\n\tif(i == 0):\n\t\tl = n\n\telse:\n\t\tl = layers[i-1]\n\n\tth.append([[random.uniform(-0.05, 0.05) for k in range(l)]\n            for j in range(layers[i])])\n\tthd.append([[0 for k in range(l)] for j in range(layers[i])])\n\tO.append([[0 for u in range(m)] for j in range(layers[i])])\n\t# grad.append([np.array([0 for u in range(m)]) for j in range(layers[i])])\n\n\ndef sigmoid(x):\n\t# print(x)\n\tret = 1 / (1 + np.exp(-x))\n\t# print(ret)\n\treturn ret\n\n\nalpha = 0\neta = 0.003\nprint(eta)\nt = 0\nJold = 0\nepsilon = 0.01\nb=0\nwhile True:\n\n\tdelta = [[[0 for k in range(m)] for j in range(layers[i])]\n          for i in range(numlayers)]\n\n\tfor a in range(0, m, batchsize):\n\t\tt += 1\n\t\teta=0.1/math.sqrt(t)\n\n\t\tb = min(m, a+batchsize)\n\t\tprint((a, b))\n\t\tfor k in range(a, b):\n\t\t\tfor i in range(numlayers):\n\t\t\t\tinpt = []\n\t\t\t\tif(i == 0):\n\t\t\t\t\tinpt = trainx[k]\n\t\t\t\t\tfor j in range(layers[i]):\n\t\t\t\t\t\ttemp = 0\n\t\t\t\t\t\tthij=th[i][j]\n\t\t\t\t\t\tfor u in range(n):\n\t\t\t\t\t\t\ttemp += (inpt[u] * thij[u])\n\t\t\t\t\t\tO[i][j][k] = sigmoid(temp)\n\t\t\t\telse:\n\t\t\t\t\tinpt = O[i-1]\n\t\t\t\t\tfor j in range(layers[i]):\n\t\t\t\t\t\ttemp = 0\n\t\t\t\t\t\tthij = th[i][j]\n\t\t\t\t\t\tfor u in range(layers[i-1]):\n\t\t\t\t\t\t\ttemp += (inpt[u][k]*thij[u])\n\t\t\t\t\t\tO[i][j][k] = sigmoid(temp)\n\n\t\t# print(O)\n\n\t\tfor i in range(numlayers-1, -1, -1):\n\t\t\tfor j in range(layers[i]):\n\t\t\t\tOij=O[i][j]\t\n\t\t\t\tif(i == 0):\n\t\t\t\t\tfor u1 in range(n):\n\t\t\t\t\t\tgr = 0\n\t\t\t\t\t\tfor k in range(a, b):\n\t\t\t\t\t\t\tdelt = 0\n\t\t\t\t\t\t\tt1 = Oij[k]*(1-Oij[k])\n\t\t\t\t\t\t\tt2 = t1*trainx[k][u1]\n\t\t\t\t\t\t\tfor u in range(layers[i+1]):\n\t\t\t\t\t\t\t\tdelt += delta[i+1][u][k]*th[i+1][u][j]\n\t\t\t\t\t\t\t\tgr -= delta[i+1][u][k]*th[i+1][u][j] * t2\n\t\t\t\t\t\t\tdelt*=t1\n\n\t\t\t\t\t\t\tdelta[i][j][k] = delt\n\n\t\t\t\t\t\t# print(gr)\n\n\t\t\t\t\t\tth[i][j][u1] -= eta*gr\n\n\t\t\t\telse:\n\t\t\t\t\tfor u1 in range(layers[i-1]):\n\t\t\t\t\t\tdelt = 0\n\t\t\t\t\t\tgr = 0\n\t\t\t\t\t\tif(i == numlayers-1):\n\t\t\t\t\t\t\t# Oij=O[i][j]\n\t\t\t\t\t\t\tfor k in range(a, b):\n\t\t\t\t\t\t\t\t# delt = 0\n\t\t\t\t\t\t\t\tt1 = Oij[k]*(1-Oij[k])\n\t\t\t\t\t\t\t\tdelt = (trainy[k][j]-Oij[k])*t1\n\t\t\t\t\t\t\t\tgr -= (trainy[k][j]-Oij[k])*t1*O[i-1][u1][k]\n\t\t\t\t\t\t\t\tdelta[i][j][k] = delt\n\t\t\t\t\t\t\t# print(gr)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tfor k in range(a, b):\n\t\t\t\t\t\t\t\tdelt = 0\n\t\t\t\t\t\t\t\tt1 = Oij[k]*(1-Oij[k])\n\t\t\t\t\t\t\t\tt2 = t1*O[i-1][u1][k]\n\t\t\t\t\t\t\t\tfor u in range(layers[i+1]):\n\t\t\t\t\t\t\t\t\tdelt += delta[i+1][u][k]*th[i+1][u][j]\n\t\t\t\t\t\t\t\t\tgr -= delta[i+1][u][k]*th[i+1][u][j] * t2\n\t\t\t\t\t\t\t\tdelt*=t1\n\t\t\t\t\t\t\t\tdelta[i][j][k] = delt\n\n\t\t\t\t\t\tth[i][j][u1] -= eta*gr\n\n\n\t# batch only till here\n\n\tfor k in range(m):\n\t\tfor i in range(numlayers):\n\t\t\tinpt = []\n\t\t\tif(i == 0):\n\t\t\t\tinpt = trainx[k]\n\t\t\t\tfor j in range(layers[i]):\n\t\t\t\t\ttemp = 0\n\t\t\t\t\tthij=th[i][j]\n\t\t\t\t\tfor u in range(n):\n\t\t\t\t\t\ttemp += (inpt[u] * thij[u])\n\t\t\t\t\tO[i][j][k] = sigmoid(temp)\n\t\t\telse:\n\t\t\t\tinpt = O[i-1]\n\t\t\t\tfor j in range(layers[i]):\n\t\t\t\t\ttemp = 0\n\t\t\t\t\tthij=th[i][j]\n\t\t\t\t\tfor u in range(layers[i-1]):\n\t\t\t\t\t\ttemp += (inpt[u][k]*thij[u])\n\t\t\t\t\tO[i][j][k] = sigmoid(temp)\n\n\tJ = 0\n\tfor k in range(m):\n\t\tfor i in range(layers[numlayers-1]):\n\t\t\ttemp = (O[numlayers-1][i][k]-trainy[k][i])\n\t\t\tJ += temp*temp\n\tJ /= (2*m)\n\n\tprint(t)\n\tprint(J)\n\n\tif(abs(Jold-J) < epsilon):\n\t\tbreak\n\n\tJold = J\n\n\nfor k in range(m):\n\tfor i in range(numlayers):\n\t\tinpt = []\n\t\tif(i == 0):\n\t\t\tinpt = trainx[k]\n\t\t\tfor j in range(layers[i]):\n\t\t\t\ttemp = 0\n\t\t\t\tfor u in range(n):\n\t\t\t\t\ttemp += (inpt[u] * th[i][j][u])\n\t\t\t\tO[i][j][k] = sigmoid(temp)\n\t\telse:\n\t\t\tinpt = O[i-1]\n\t\t\tfor j in range(layers[i]):\n\t\t\t\ttemp = 0\n\t\t\t\tfor u in range(layers[i-1]):\n\t\t\t\t\ttemp += (inpt[u][k]*th[i][j][u])\n\t\t\t\tO[i][j][k] = sigmoid(temp)\n\ncorrect = 0\nfor k in range(m):\n\tans = -1\n\tma = -1\n\tfor i in range(layers[numlayers-1]):\n\t\ttemp = O[numlayers-1][i][k]\n\t\tif(temp > ma):\n\t\t\tma = temp\n\t\t\tans = i\n\t# print(ans)\n\tif(trainy[k][ans] == 1):\n\t\tcorrect += 1\n\nprint(\"Accuracy on train data={}%\".format(correct/m*100))\nprint((correct, m))\n\n\ninputfile = \"MNIST_test.csv\"\ncsvfile = open(inputfile, 'r')\ndata = list(csv.reader(csvfile))\nshuffle(data)\nshuffle(data)\n\nmtest = len(data)\n# mtest=100\n\nn = len(data[0])-1\ntestx = []\ntesty = []\nfor i in range(mtest):\n\tfor j in range(n+1):\n\t\tdata[i][j] = int(data[i][j])\n\nfor i in range(mtest):\n\timage = data[i]\n\ttesty.append(image[n])\n\ttestx.append(image[:n])\n\n\tif(testy[i] == 6):\n\t\ttesty[i] = [1, 0]\n\telse:\n\t\ttesty[i] = [0, 1]\n\n\n\nO = [[[0 for u in range(mtest)] for j in range(layers[i])]\n     for i in range(numlayers)]\n\nfor k in range(mtest):\n\tfor i in range(numlayers):\n\t\tinpt = []\n\t\tif(i == 0):\n\t\t\tinpt = testx[k]\n\t\t\tfor j in range(layers[i]):\n\t\t\t\ttemp = 0\n\t\t\t\tfor u in range(n):\n\t\t\t\t\ttemp += (inpt[u] * th[i][j][u])\n\t\t\t\tO[i][j][k] = sigmoid(temp)\n\t\telse:\n\t\t\tinpt = O[i-1]\n\t\t\tfor j in range(layers[i]):\n\t\t\t\ttemp = 0\n\t\t\t\tfor u in range(layers[i-1]):\n\t\t\t\t\ttemp += (inpt[u][k]*th[i][j][u])\n\t\t\t\tO[i][j][k] = sigmoid(temp)\n\ncorrect = 0\nfor k in range(mtest):\n\tans = -1\n\tma = -1\n\tfor i in range(layers[numlayers-1]):\n\t\ttemp = O[numlayers-1][i][k]\n\t\tif(temp > ma):\n\t\t\tma = temp\n\t\t\tans = i\n\n\tif(testy[k][ans] == 1):\n\t\tcorrect += 1\n\nprint(\"Accuracy on test data={}%\".format(correct/mtest*100))\nprint((correct, mtest))\n\n","sub_path":"2015CS10667_prakhar_kumar/Q2/q2c2.py","file_name":"q2c2.py","file_ext":"py","file_size_in_byte":5720,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"592723980","text":"# -*- coding: utf-8 -*-\n\nimport os\nimport pandas as pd\nfrom collections import deque\nimport matplotlib.pyplot as plt\nimport rrcf\n\n# Read data\ndef data_explore(is_show=True):\n    abs_path = os.path.abspath(__file__)\n    data_paths = abs_path.strip().split(\"/\")[:-1]\n    data_paths.extend([\"..\", \"resources\", \"website_flow.csv\"])\n    data_path = os.path.join(*data_paths)\n    if not data_path.startswith(\"/\"):\n        data_path = \"/\" + data_path\n    data_frame = pd.read_csv(data_path)\n    data_frame.sort_values([\"time\"], inplace=True)\n    data_frame[\"time\"] = pd.to_datetime(data_frame[\"time\"], unit=\"s\")\n    data_frame.set_index([\"time\"], inplace=True)\n    print(data_frame)\n\n    if is_show:\n        fig, ax = plt.subplots(3, figsize=(12, 7))\n        data_frame[\"bytes\"].plot(ax=ax[0])\n        data_frame[\"request\"].plot(ax=ax[1])\n        data_frame[\"num\"].plot(ax=ax[2])\n        ax[0].set_ylabel(\"bytes\")\n        ax[1].set_ylabel(\"request\")\n        ax[2].set_ylabel(\"num\")\n        plt.tight_layout()\n        plt.show()\n\n    return data_frame\n\n\ndef flow_detector():\n    # Set tree parameters\n    num_trees = 10\n    tree_size = 6000\n    history_queue = deque([], maxlen=tree_size)\n\n    n = 5000\n    data_frame = data_explore(is_show=False)\n    history_samples = data_frame[:n]\n    testing_samples = data_frame[n:]\n\n    anomaly_score = pd.Series(0.0, index=data_frame.index)\n\n    forest = []\n    for _ in range(num_trees):\n        tree = rrcf.RCTree(history_samples.values, index_labels=list(history_samples.index))\n        forest.append(tree)\n\n    for idx in history_samples.index:\n        cur_point = history_samples.ix[idx].values\n        avg_codisp = 0.0\n        for tree in forest:\n            avg_codisp += tree.codisp(idx) / num_trees\n        print('CoDisp for point ({index}) is {avg_codisp}'.format(index=idx, avg_codisp=avg_codisp))\n        anomaly_score[idx] = avg_codisp\n\n    for idx in list(history_samples.index):\n        history_queue.append(idx)\n\n    for idx in testing_samples.index:\n        cur_point = testing_samples.ix[idx].values\n        if len(history_queue) == tree_size:\n            old_index = history_queue.popleft()\n        else:\n            old_index = None\n        history_queue.append(idx)\n\n        avg_codisp = 0.0\n        for tree in forest:\n            if old_index is not None:\n                tree.forget_point(old_index)\n            tree.insert_point(point=cur_point, index=idx)\n            avg_codisp += tree.codisp(idx) / num_trees\n        print('CoDisp for point ({index}) is {avg_codisp}'.format(index=idx, avg_codisp=avg_codisp))\n        anomaly_score[idx] = avg_codisp\n\n    if True:\n        fig, ax = plt.subplots(4, figsize=(20, 8))\n        data_frame[\"bytes\"].plot(ax=ax[0])\n        data_frame[\"request\"].plot(ax=ax[1])\n        data_frame[\"num\"].plot(ax=ax[2])\n        anomaly_score.plot(ax=ax[3])\n\n        ax[0].set_ylabel(\"bytes\")\n        ax[1].set_ylabel(\"request\")\n        ax[2].set_ylabel(\"num\")\n        ax[3].set_ylabel(\"codisp_score\")\n        plt.tight_layout()\n        plt.show()\n\n\nflow_detector()\n","sub_path":"test/test_flow_detector.py","file_name":"test_flow_detector.py","file_ext":"py","file_size_in_byte":3050,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"615560314","text":"#[5, 4, 0, 3, 4, 1]\nimport sys\n\ndef graph(arr):\n\tmax_value  = max(arr) \n\tlenght_arr = len(arr)\n\tmatrix = []\n\tfor index,i in enumerate(range(lenght_arr,0,-1)):\n\t\tfor j in xrange(0,lenght_arr):\n\t\t\tif arr[j] >= i-1:\n\t\t\t\tsys.stdout.write(\"*\")\n\t\t\telse:\n\t\t\t\tsys.stdout.write(\" \")\n\t\tprint\n\t\narr = [5, 4, 0, 3, 4, 1]\ngraph(arr)","sub_path":"morning-training/histogram.py","file_name":"histogram.py","file_ext":"py","file_size_in_byte":319,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"46080163","text":"def test(N):\n    if N == 0:\n        return -1\n    result = [_ for _ in xrange(10)]\n    for i in xrange(1, 10001):\n        k = N * i\n        m = str(k)\n        for s in m:\n            temp = int(s)\n            if temp in result:\n                result.remove(int(s))\n        if len(result) == 0:\n            return k\n    return -1\n\n# print test(1)\n# print test(5)\n# print test(15)\n# print test(19)\n                    \nif __name__ == \"__main__\":\n    testcases = input()\n       \n    for caseNr in xrange(1, testcases + 1):\n        cipher = raw_input()\n        k = test(int(cipher))\n        r = \"\"\n        if k < 0:\n            r = \"INSOMNIA\"\n        else:\n            r = str(k)\n             \n        print(\"Case #%i: %s\" % (caseNr, r))\n","sub_path":"codes/CodeJamCrawler/16_0_1_neat/16_0_1_peterpans01_test.py","file_name":"16_0_1_peterpans01_test.py","file_ext":"py","file_size_in_byte":735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"583967312","text":"#!/opt/local/bin/python3\n# Jarik Oosting, 27-02-2015\n\nimport json\nfrom collections import namedtuple\n\ndef main():\n\n    with open(\"blood-die.json\", \"r\") as in_f:\n        file = json.load(in_f)\n        JsonTuple = namedtuple(\"JsonTuple\",(\"language\",\"classification\",\"blood\",\"die\"))\n\n        with open(\"result.json\",\"w\") as output:\n\n            for element in file:\n\n                element = [s.encode(\"utf-8\") for s in element]\n\n                elements = JsonTuple(element[0], element[1], element[2].split(), element[3].split())\n\n                [json.dump(element,output) for el in elements[2] if el in elements[3]]\n\nif __name__ == \"__main__\":\n    main()","sub_path":"json_filter.py","file_name":"json_filter.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"530718714","text":"\n\n#calss header\nclass _RIGHTLY():\n\tdef __init__(self,): \n\t\tself.name = \"RIGHTLY\"\n\t\tself.definitions = [u'behaving in a way that is suitable and acceptable: ', u'used to mean that something may or may not be morally correct, but it is a fact: ', u'in a correct or exact way: ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'adverbs'\n\n\n\tdef run(self, obj1, obj2):\n\t\tself.jsondata[obj2] = {}\n\t\tself.jsondata[obj2]['properties'] = self.name.lower()\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/adverbs/_rightly.py","file_name":"_rightly.py","file_ext":"py","file_size_in_byte":526,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"460079371","text":"\n\n\nfrom keras.models import Sequential\nfrom keras.layers import Conv2D, ZeroPadding2D, Activation, Input, concatenate\nfrom keras.models import Model\nfrom keras.layers.normalization import BatchNormalization\nfrom keras.layers.pooling import MaxPooling2D, AveragePooling2D\nfrom keras.layers.merge import Concatenate\nfrom keras.layers.core import Lambda, Flatten, Dense\nfrom keras.initializers import glorot_uniform\nfrom keras.engine.topology import Layer\nfrom keras import backend as K\nK.set_image_data_format('channels_first')\nimport cv2\nimport os\nimport numpy as np\nfrom numpy import genfromtxt\nimport pandas as pd\nimport tensorflow as tf\nfrom fr_utils import *\nfrom inception_blocks_v2 import *\n\n\nFRmodel = faceRecoModel(input_shape=(3, 96, 96))\n\n\ndef triplet_loss(y_true, y_pred, alpha = 0.2):\n    \"\"\"\n    Implementation of the triplet loss as defined by formula (3)\n    \n    Arguments:\n    y_true -- true labels, required when you define a loss in Keras, you don't need it in this function.\n    y_pred -- python list containing three objects:\n            anchor -- the encodings for the anchor images, of shape (None, 128)\n            positive -- the encodings for the positive images, of shape (None, 128)\n            negative -- the encodings for the negative images, of shape (None, 128)\n    \n    Returns:\n    loss -- real number, value of the loss\n    \"\"\"\n    \n    anchor, positive, negative = y_pred[0], y_pred[1], y_pred[2]\n    \n    ### START CODE HERE ### (≈ 4 lines)\n    # Step 1: Compute the (encoding) distance between the anchor and the positive, you will need to sum over axis=-1\n    pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, positive)), axis = -1)\n    # Step 2: Compute the (encoding) distance between the anchor and the negative, you will need to sum over axis=-1\n    neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, negative)), axis = -1)\n    # Step 3: subtract the two previous distances and add alpha.\n    basic_loss = tf.add(tf.subtract(pos_dist, neg_dist), alpha)\n    # Step 4: Take the maximum of basic_loss and 0.0. Sum over the training examples.\n    loss = tf.reduce_sum(tf.maximum(basic_loss, 0))\n    ### END CODE HERE ###\n    \n    return loss\n\n\n\n\nFRmodel.compile(optimizer = 'adam', loss = triplet_loss, metrics = ['accuracy'])\nload_weights_from_FaceNet(FRmodel)\n\n\n\ndatabase = {}\n\ndatabase[\"saksham\"] = img_to_encoding_another(\"images/saksham\", FRmodel)\ndatabase[\"shashi\"] = img_to_encoding_another(\"images/shashi\", FRmodel)\ndatabase[\"sajal\"] = img_to_encoding_another(\"images/sajal\", FRmodel)\n\ndef verify(image_path, identity, database, model):\n    \"\"\"\n    Function that verifies if the person on the \"image_path\" image is \"identity\".\n    \n    Arguments:\n    image_path -- path to an image\n    identity -- string, name of the person you'd like to verify the identity. Has to be a resident of the Happy house.\n    database -- python dictionary mapping names of allowed people's names (strings) to their encodings (vectors).\n    model -- your Inception model instance in Keras\n    \n    Returns:\n    dist -- distance between the image_path and the image of \"identity\" in the database.\n    door_open -- True, if the door should open. False otherwise.\n    \"\"\"\n    \n    ### START CODE HERE ###\n    \n    # Step 1: Compute the encoding for the image. Use img_to_encoding() see example above. (≈ 1 line)\n    encoding = img_to_encoding(image_path, model)\n    \n    # Step 2: Compute distance with identity's image (≈ 1 line)\n    dist = np.linalg.norm(encoding - database[identity])\n    \n    # Step 3: Open the door if dist < 0.7, else don't open (≈ 3 lines)\n    if dist < 0.7:\n        print(\"It's \" + str(identity) + \", welcome home!\")\n        door_open = True\n    else:\n        print(\"It's not \" + str(identity) + \", please go away\")\n        door_open = False\n        \n    ### END CODE HERE ###\n        \n    return dist, door_open\n\n\n\n\ndef who_is_it(image, database, model):\n    \n    encoding = img_to_encoding_without_path(image, model)\n    min_dist = 100\n    \n    for (name, db_enc) in database.items():\n        dist = 0\n        for i in range(0, db_enc.shape[0]):\n            dist += np.linalg.norm(encoding - db_enc[i])\n\n        dist = dist/db_enc.shape[0]\n        print(\"distance for \"+str(name)+\" is \"+str(dist) )\n        if dist < min_dist :\n            min_dist = dist\n            identity = name\n            \n    if min_dist > 0.7:\n        a = 5;\n        print(\"Not in the database.\")\n    else:\n        print (\"it's \" + str(identity) + \", the distance is \" + str(min_dist))\n        \n    return min_dist, identity\n\n\n# Younes is at the front-door and the camera takes a picture of him (\"images/camera_0.jpg\"). Let's see if your who_it_is() algorithm identifies Younes. \n\n\n\nface_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n\n\n\ndef detect(gray, frame):\n    faces = face_cascade.detectMultiScale(gray, 1.3, 5)\n    for (x, y, w, h) in faces:\n        #print(frame.shape)\n        y1 = max(y-int(h/2), 0)\n        y2 = min(y+h+int(h/2), frame.shape[0]-1)\n        x1 = max(x-int(w/2), 0)\n        x2 = min(x+w+int(w/2), frame.shape[1]-1)\n        crop_img = frame[y1:y2, x1:x2]\n        img = misc.imresize(crop_img, (96,96))\n        min_dist, identity = who_is_it(img, database, FRmodel)\n        #cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)\n        cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)\n        cv2.putText(frame, identity, (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1, cv2.LINE_AA)\n    return frame\n\n\n\nfrom scipy import misc\n\nvideo_capture = cv2.VideoCapture(0)\nwhile True:\n    _, frame = video_capture.read()\n    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n    canvas = detect(gray, frame)\n    cv2.imshow('Video', canvas)\n    if cv2.waitKey(1) & 0xFF == ord('q'):\n        break\nvideo_capture.release()\ncv2.destroyAllWindows()\n\n\n\n","sub_path":"Face Recognition for the Happy House - v3.py","file_name":"Face Recognition for the Happy House - v3.py","file_ext":"py","file_size_in_byte":5844,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"589825506","text":"'''\nCreated on Sep 24, 2015\n\n@author: mianmianba\n'''\n\nclass Calculate(object):\n    def compEqu(self, equStack, equSymbol):\n        if not equStack:\n            equStack.append(equSymbol)\n            return\n        if equStack[-1] == '*':\n            equStack.pop()\n            val1 = int(equStack.pop())\n            equStack.append(str(val1 * int(equSymbol)))\n        elif equStack[-1] == '/':\n            equStack.pop()\n            val1 = int(equStack.pop())\n            equStack.append(str(val1 / int(equSymbol)))\n        elif equStack[-1] == '+' or equStack[-1] == '-':\n            if len(equStack) == 4:\n                op = equStack.pop()\n                val2 = int(equStack.pop())\n                oldOp = equStack.pop()\n                if oldOp == '+':\n                    equStack.append(str(int(equStack.pop()) + val2))\n                elif oldOp == '-':\n                    equStack.append(str(int(equStack.pop()) - val2))\n                equStack.append(op)\n                equStack.append(equSymbol)\n            else:\n                equStack.append(equSymbol)\n                \n        \n    def solution(self, s):\n        \"\"\"\n        :type s: str\n        :rtype: int\n        \"\"\"\n        equStack = []\n        strLen = len(s)\n        cnt = 0\n        while cnt < strLen:\n            cc = s[cnt]\n            if cc >= '0' and cc <= '9':\n                curNumStr = cc\n                cnt = cnt + 1\n                while cnt < strLen: # either (cc is not a number) or (cnt == strLen and cc is a number) \n                    cc = s[cnt]\n                    if cc >= '0' and cc <= '9':\n                        curNumStr += cc\n                        cnt += 1\n                    else:\n                        break\n                self.compEqu(equStack, curNumStr)\n            if cc in '+-*/':\n                equStack.append(cc)\n            cnt += 1\n        if len(equStack) == 3:\n            val2 = int(equStack.pop())\n            op = equStack.pop()\n            if op == '+':\n                equStack.append(str(int(equStack.pop()) + val2))\n            elif op == '-':\n                equStack.append(str(int(equStack.pop()) - val2))\n            elif op == '*':\n                equStack.append(str(int(equStack.pop()) * val2))\n            elif op == '/':\n                equStack.append(str(int(equStack.pop()) / val2))\n        return int(equStack.pop())\n    \n            \n        \n                ","sub_path":"leetcodepy/src/problemset/BasicCalculatorII.py","file_name":"BasicCalculatorII.py","file_ext":"py","file_size_in_byte":2405,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"232232143","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom setuptools import setup\nimport os\nimport sys\n\nimport simplicity\n\n\nif sys.argv[-1] == 'publish':\n    os.system(\"python setup.py sdist upload\")\n    print(\"You probably want to also tag the version now:\")\n    print(\"  git tag -a %s -m 'version %s'\" % (\n            simplicity.__version__, simplicity.__version__))\n    print(\"  git push --tags\")\n    sys.exit()\n\nLONG_DESCRIPTION = open('README.rst').read()\nHISTORY = open('HISTORY.rst').read()\n\nsetup(\n    name='simplicity',\n    version=simplicity.__version__,\n    description=\"Converts ReStructuredText into JSON\",\n    long_description=LONG_DESCRIPTION + '\\n\\n' + HISTORY,\n    classifiers=[\n        \"Development Status :: 4 - Beta\",\n        \"License :: OSI Approved :: BSD License\",\n        \"Operating System :: OS Independent\",\n        \"Programming Language :: Python :: 2.7\",\n        \"Programming Language :: Python :: 3.3\",\n        \"Topic :: Software Development :: Libraries :: Python Modules\",\n        \"Topic :: Text Processing :: Markup\"\n    ],\n    keywords='python,json',\n    author=simplicity.__author__,\n    author_email='pydanny@gmail.com',\n    url='https://github.com/pydanny/simplicity',\n    license='MIT',\n    py_modules=['simplicity', ],\n    include_package_data=True,\n    zip_safe=False,\n    entry_points={\n        'console_scripts': [\n            'simplicity=simplicity:command_line_runner',\n        ]\n    },\n)\n\n# (*) Please direct queries to Github issue list, rather than to me directly\n#     Doing so helps ensure your question is helpful to other users.\n#     Queries directly to my email are likely to receive a canned response.\n#\n#     Many thanks for your understanding.\n","sub_path":"pypi_install_script/simplicity-0.6.4.tar/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1693,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"174394246","text":"\"\"\"Events for version control.\n\n\"\"\"\n__docformat__ = \"reStructuredText\"\n\nimport zope.interface\nimport zope.component.interfaces\nimport zope.app.versioncontrol.interfaces\n\n\nclass VersionEvent(zope.component.interfaces.ObjectEvent):\n\n    def __init__(self, object, info):\n        super(VersionEvent, self).__init__(object)\n        self.info = info\n\n\nclass VersionControlApplied(VersionEvent):\n\n    zope.interface.implements(\n        zope.app.versioncontrol.interfaces.IVersionControlApplied)\n\n    def __init__(self, object, info, message):\n        super(VersionControlApplied, self).__init__(object, info)\n        self.message = message\n\n    def __str__(self):\n        s = \"applied version control to %s\" % self.object\n        if self.message:\n            s = \"%s:\\n%s\" % (s, self.message)\n        return s\n\n\nclass VersionCheckedOut(VersionEvent):\n\n    zope.interface.implements(\n        zope.app.versioncontrol.interfaces.IVersionCheckedOut)\n\n    def __str__(self):\n        return \"checked out %s, version %s\" % (\n            self.object, self.info.version_id)\n\n\nclass VersionCheckedIn(VersionEvent):\n\n    zope.interface.implements(\n        zope.app.versioncontrol.interfaces.IVersionCheckedIn)\n\n    def __init__(self, object, info, message):\n        super(VersionCheckedIn, self).__init__(object, info)\n        self.message = message\n\n    def __str__(self):\n        return \"checked in %s, version %s\" % (\n            self.object, self.info.version_id)\n\n\nclass VersionReverted(VersionEvent):\n\n    zope.interface.implements(\n        zope.app.versioncontrol.interfaces.IVersionReverted)\n\n    def __str__(self):\n        return \"reverted %s to version %s\" % (\n            self.object, self.info.version_id)\n\n\nclass VersionUpdated(VersionEvent):\n\n    zope.interface.implements(\n        zope.app.versioncontrol.interfaces.IVersionReverted)\n\n    def __init__(self, object, info, old_version_id):\n        super(VersionUpdated, self).__init__(object, info)\n        self.old_version_id = old_version_id\n\n    def __str__(self):\n        return \"updated %s from version %s to %s\" % (\n            self.object, self.old_version_id, self.info.version_id)\n\n\nclass VersionRetrieved(VersionEvent):\n\n    zope.interface.implements(\n        zope.app.versioncontrol.interfaces.IVersionRetrieved)\n\n    def __str__(self):\n        return \"retrieved %s, version %s\" % (\n            self.object, self.info.version_id)\n\n\nclass VersionLabelled(VersionEvent):\n\n    zope.interface.implements(\n        zope.app.versioncontrol.interfaces.IVersionLabelled)\n\n    def __init__(self, object, info, label):\n        super(VersionLabelled, self).__init__(object, info)\n        self.label = label\n\n    def __str__(self):\n        return \"created label %s from version %s of %s\" % (\n            self.label, self.info.version_id, self.object)\n\n\nclass BranchCreated(VersionEvent):\n\n    zope.interface.implements(\n        zope.app.versioncontrol.interfaces.IVersionReverted)\n\n    def __init__(self, object, info, branch_id):\n        super(BranchCreated, self).__init__(object, info)\n        self.branch_id = branch_id\n\n    def __str__(self):\n        return \"created branch %s from version %s of %s\" % (\n            self.branch_id, self.info.version_id, self.object)\n","sub_path":"Zope3/tags/3.3.0/src/zope/app/versioncontrol/event.py","file_name":"event.py","file_ext":"py","file_size_in_byte":3217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"44549783","text":"# -*- coding: utf-8 -*-\n\nfrom astropy.table import Table, join\nimport os\nimport numpy as np\nimport numpy.ma as ma\nimport pdb\nimport shutil\nfrom gcwork import objects\nfrom gcwork import starset\nfrom gcwork import starTables\nfrom gcreduce import applyLocalDist\nfrom gcreduce import gcutil\nfrom flystar import transforms\nfrom flystar import examples\nfrom flystar import align\nfrom flystar import match\nfrom scipy import spatial\nfrom scipy import stats\nfrom scipy.stats import f\nfrom scipy import spatial\nfrom scipy.optimize import curve_fit\nfrom scipy import interpolate\nfrom astropy.io import fits\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nfrom astropy.stats import sigma_clipped_stats\nfrom astropy.stats import sigma_clip\n\nclass local_dist(object):\n    \"\"\"\n    NOTE: COMBINE IS NOT FULLY SUPPROTED YET!!!!\n    NOTE: READ_STARLIST IN ARCSEC CAN BE SIMPLIFIED BY USING FLYSTAR TO TRANSFORM RMS.LIS TO 06REF\n\n    this script is used to calculate local distortion map:\n    - files you need:\n        - root_06: the path to an 06-14 alignment which you want to use their poly fit\n                   as the reference.\n        - root_all: the path to an all epoch alignment which gives\n                   the starlists that you want to calculate distortion for\n\n          NOTE: TO GET AN ALLEPOCH ALIGNMENT, \n                you need to run an allepoch alignment after 06-14 alignment \n                manually without adding local distortion\n                (comment doit.apply_local_distortion; \n                 copy rms.lis file to rms_ld.lis to be able to run the following alignment\n                 no need to 1000 bootstrap alignment)\n\n    - usage example:\n        - root_all= '/Users/siyao/research/work/2_align/18_update/18_03_29/allNoDist/'\n          root_06= '/Users/siyao/research/work/2_align/18_01_12_OldHolo/18Feb_abs/06only/'\n          epochs = np.array(['05junlgs_kp', '05jullgs_kp', '15auglgs_kp', '16maylgs1_kp', '16jullgs1_nite2_kp', \n                    '17maylgs1_kp', '17auglgs1_kp', '17auglgs3_kp', '17maylgs3_h', '17auglgs2_h', '17auglgs3_h',\n                    '20180317nirc2_h', '20180317nirc2_kp', '20180322nirc2_h', '20180322nirc2_kp', '20180330nirc2_h','20180330nirc2_kp'])\n          local_dist = make_distortion.local_dist(root_06, root_all, epochs,combine=False)\n          #local_dist = make_distortion.local_dist_arcsec(root_06, root_all, epochs[2:],combine=True)\n          local_dist.run()\n\n    - method:\n        - use 06 alignment to infer each star's position at a certain epoch called \"label\"\n        - match label with starlist at this epoch\n        - use nth order lengendre transformation to calculate the distortion map\n        - use 100 bootstrap to calculate the distortion map uncertainty \n\n    - details:\n        - for each order, calculate distortion maps: \n            calc_distortion()\n    \"\"\"\n    def __init__(self, root_06, root_all, epochs, combine=False):\n        # root of the 06only alignment\n        self.root_06 = root_06\n\n        # root of the all epoch alignment\n        self.root_all = root_all\n\n        # star files that you want to calculate distortion for\n        self.epochs = epochs\n\n        # legendra order for local dist map\n        self.orders = range(2,10)\n    \n        # whether to use zero (TRUE) or nearest nonzero value (FALSE) for edges\n        self.zero = False\n\n        # whether you want to combine several distortion together or not, combine is not supported yet\n        self.combine = combine\n\n    def run(self):\n        epochs = self.epochs\n        orders = self.orders\n\n        for order in orders:\n            # creat a directory \"orderX\" to save distortion maps at different order\n            write2file = 'order' + str(order) + '/'\n            if not os.path.exists(write2file):\n                os.mkdir(write2file)\n            os.chdir(write2file)\n    \n            # copy the original starlists to current directory\n            for epoch in epochs:\n                starlist_file = self.root_all + 'lis/mag'+epoch+'_rms.lis'\n                shutil.copy(starlist_file, './')\n\n            # for each epoch, calculate distortion map\n            if self.combine == False:\n                self.calc_distortion(epochs, dist_order=order, zero=self.zero)\n            #else:\n            #    self.calc_distortion_combined(epochs, dist_order = order, zero=self.zero)\n    \n            # apply the new local distortion\n            for epoch in epochs:\n                applyLocalDist.go(epoch, 'kp', root=self.root_all, distDir='./')\n                starlist_file = self.root_all + 'lis/mag'+epoch+'_rms_ld.lis'\n                shutil.copy(starlist_file, './')\n\n                # make diagnostic plots\n                trans = self.find_trans(epoch)\n                self.plot_distortion(epoch, trans, dist_order=order)\n            \n            os.chdir('../')\n\n \n        # use f tests to find the best legendre transformation order\n        # will make f test plots, the best order needs to be determined by eye\n        # the best order happens when 1-p first reaches around zero\n    \n        if self.combine == False:\n            for epoch in epochs:\n                self.plot_ftest(epoch, orders)\n        #else:\n        #    self.plot_ftest_combined(epochs, orders)\n    \n    def bootstrap_indexes(self, data, n_samples=10000, size=None):\n        if size == None:\n            size = 1 \n        size *= data.shape[0]\n        size = int(size)\n        return np.random.randint(data.shape[0], size=(n_samples,size))\n    \n    def read_align(self):\n        \"\"\"\n        read 06alignment that increases to West\n        \"\"\"\n        root = self.root_06\n        align='align/align_d_rms_1000_abs_t'\n        poly='polyfit_4_trim/fit'\n        accel = 1        \n    \n        s = starset.StarSet(root + align)\n        s.loadPolyfit(root + poly, arcsec=1, accel=accel)\n        #x, y, xerr, yerr (arcsec) increase to east\n        x0 = s.getArray('fitXa.p')\n        y0 = s.getArray('fitYa.p')\n        x0e = s.getArray('fitXa.perr')\n        y0e = s.getArray('fitYa.perr')\n        #vx, vy, vxerr, vyerr (arcsec/yr)\n        vx = s.getArray('fitXa.v')\n        vy = s.getArray('fitYa.v')\n        vxe = s.getArray('fitXa.verr')\n        vye = s.getArray('fitYa.verr')\n        #ax, ay, axerr, ayerr (arcsec/yr2)\n        ax = s.getArray('fitXa.a')\n        ay = s.getArray('fitYa.a')\n        axe = s.getArray('fitXa.aerr')\n        aye = s.getArray('fitYa.aerr')\n        #t0 \n        t0 = s.getArray('fitXa.t0')\n        #mag, name\n        mag = s.getArray('mag')\n        name = s.getArray('name')\n        \n        # increase to east\n        label = Table()\n        label['name'] = name\n        label['m'] = mag\n        label['x0'] = x0*(-1.0)\n        label['y0'] = y0\n        label['x0e'] = x0e\n        label['y0e'] = y0e\n        label['vx'] = vx*(-1.0)\n        label['vy'] = vy\n        label['vxe'] = vxe\n        label['vye'] = vye\n        label['ax'] = ax*(-1.0)\n        label['ay'] = ay\n        label['axe'] = axe\n        label['aye'] = aye\n        label['t0'] = t0\n        return label\n    \n    def read_starlist(self, epoch):\n        \"\"\" read list of stars in certain epoch that increases to the West\"\"\"\n        root = self.root_all\n        align='align/align_d_rms_abs_t'\n        poly = 'polyfit_d/fit'\n        points = 'points_d/'\n        \n        # get the index of the epoch in alignment\n        t_epoch_info = Table.read(root + '/scripts/epochsInfo.txt', format='ascii')\n        idx_doalign = np.where(t_epoch_info['doAlign']==1)[0]\n        ee = np.where(t_epoch_info['epoch'][idx_doalign]==epoch)[0][0]\n\n        s = starset.StarSet(root + align)\n        s.loadPolyfit(root + poly)\n        s.loadPoints(root + points)\n\n        #x, y, xerr, yerr (arcsec), increase to east\n        x = s.getArrayFromEpoch(ee, 'x')\n        y = s.getArrayFromEpoch(ee, 'y')\n        xe_p = s.getArrayFromEpoch(ee, 'xerr_p')\n        xe_a = s.getArrayFromEpoch(ee, 'xerr_a')\n        xe = np.hypot(xe_p, xe_a)\n        ye_p = s.getArrayFromEpoch(ee, 'yerr_p')\n        ye_a = s.getArrayFromEpoch(ee, 'yerr_a')\n        ye = np.hypot(ye_p, ye_a)\n        name = s.getArray('name')\n        mag = s.getArray('mag')\n    \n        # find stars that are detected\n        # change to increase to west\n        idx = np.where(abs(x)<100)[0]\n        starlist = Table()\n        starlist['name'] = np.array(name)[idx]\n        starlist['year'] = s.getArray('years')[0][ee]\n        starlist['m'] = mag[idx]\n        starlist['x'] = x[idx]*(-1.0)\n        starlist['y'] = y[idx]\n        starlist['xe'] = xe[idx]\n        starlist['ye'] = ye[idx]\n\n        return starlist\n    \n    def sample_distortion(self, epoch, plot=False):\n        \"\"\"\n        select samples that are used in calculate distortion:\n        bright than 17, trim outlier, non-nan\n        - Input:\n            - epoch: read epoch to create starlist\n            - plot: make plot of sample cut\n\n        Output:\n        x_ref, y_ref, x_stalist, y_starlist, x_diff, y_diff, xe, ye\n        ref is the AO propagation result\"\"\"\n    \n        debug = True\n        if not os.path.exists('sample_%s.txt' %epoch):\n            # read starlist.\n            starlist = self.read_starlist(epoch=epoch)\n            t_starlist = starlist['year'][0]\n\n            # read AO only alignment which is called label here. \n            label = self.read_align()\n            # propogate label to t_starlist\n            ref = Table()\n            delta_t = t_starlist - label['t0']\n            ref['name'] = label['name']\n            ref['m'] = label['m']\n            r = np.hypot(label['x0'], label['y0'])\n            ref['x'] = np.zeros(len(label))\n            ref['xe'] = np.zeros(len(label))\n            ref['y'] = np.zeros(len(label))\n            ref['ye'] = np.zeros(len(label))\n\n            idx = np.where(r<=0.8)[0]\n            ref['x'][idx] = label['x0'][idx] + label['vx'][idx]*delta_t[idx] + 0.5*label['ax'][idx]*delta_t[idx]**2\n            ref['y'][idx] = label['y0'][idx] + label['vy'][idx]*delta_t[idx] + 0.5*label['ay'][idx]*delta_t[idx]**2\n            ref['xe'][idx] = np.sqrt(label['x0e'][idx]**2 + (label['vxe'][idx]*delta_t[idx])**2 + (0.5*label['axe'][idx]*delta_t[idx]**2)**2)\n            ref['ye'][idx] = np.sqrt(label['y0e'][idx]**2 + (label['vye'][idx]*delta_t[idx])**2 + (0.5*label['aye'][idx]*delta_t[idx]**2)**2)\n\n            idx = np.where(r>0.8)[0]\n            ref['x'][idx] = label['x0'][idx] + label['vx'][idx]*delta_t[idx] \n            ref['y'][idx] = label['y0'][idx] + label['vy'][idx]*delta_t[idx]\n            ref['xe'][idx] = np.sqrt(label['x0e'][idx]**2 + (label['vxe'][idx]*delta_t[idx])**2)\n            ref['ye'][idx] = np.sqrt(label['y0e'][idx]**2 + (label['vye'][idx]*delta_t[idx])**2)\n    \n            # match ref and starlist \n            idx_ref_match, idx_starlist_match, dr, dm = match.match(ref['x'], ref['y'], ref['m'], starlist['x'], starlist['y'], starlist['m'], dr_tol=0.1)\n            ref_match = ref[idx_ref_match]\n            starlist_match = starlist[idx_starlist_match]\n\n            # find stars k<17\n            if epoch[-1] == 'h':\n                mag_cut = 18\n            else:\n                mag_cut = 17\n            bright = np.where(ref_match['m']<mag_cut)[0]\n            if debug:\n                print('among %d detected star, %d are brighter than %d mag' %(len(ref_match), len(bright), mag_cut))\n    \n            # reject outliers in 2.2''*2.2'' boxes\n            x_ref = ref_match['x'][bright]\n            y_ref = ref_match['y'][bright]\n            x_diff = ref_match['x'][bright] - starlist_match['x'][bright]\n            y_diff = ref_match['y'][bright] - starlist_match['y'][bright] \n    \n            trim_outlier = []\n            x_edge = np.arange(-5.5, 7.5, 2.2)\n            y_edge = np.arange(-5.5, 7.5, 2.2)\n            for i in x_edge:\n                for j in y_edge:\n                    idx_box = np.where((x_ref>i) & (x_ref<i+2.2) & (y_ref>j) & (y_ref<j+2.2))[0] \n                    if len(idx_box) != 0:\n                        filtered_x = sigma_clip(x_diff[idx_box], sigma=2.5, iters=5)  \n                        filtered_y = sigma_clip(y_diff[idx_box], sigma=2.5, iters=5)  \n                        idx = np.where((filtered_x != False) & (filtered_y != False))[0]\n                        trim_outlier.extend(idx_box[idx])\n            if debug:\n                print('among %d stars, %d stars are trimmed as outliers\\n' %(len(bright),len(bright)-len(trim_outlier)))\n    \n            # plot all matched stars residual\n            #----------\n            if plot == True:\n                plt.figure(figsize=(10,10))\n                plt.clf()\n                qscale = 0.005\n                Q = plt.quiver(ref_match['x'], ref_match['y'], \n                        ref_match['x'] - starlist_match['x'], \n                        ref_match['y'] - starlist_match['y'], \n                        units='xy', scale=qscale, color='k')\n                plt.quiver(ref_match[bright]['x'], ref_match[bright]['y'], \n                        ref_match[bright]['x'] - starlist_match[bright]['x'], \n                        ref_match[bright]['y'] - starlist_match[bright]['y'], \n                        units='xy', scale=qscale, color='orange')\n                plt.quiver(ref_match[bright][trim_outlier]['x'], \n                        ref_match[bright][trim_outlier]['y'], \n                        ref_match[bright][trim_outlier]['x'] - starlist_match[bright][trim_outlier]['x'], \n                        ref_match[bright][trim_outlier]['y'] - starlist_match[bright][trim_outlier]['y'], \n                        units='xy', scale=qscale, color='r')\n                plt.quiverkey(Q, 0.9, 0.9, 0.001, '1mas')\n                plt.annotate('mag>%d' %mag_cut, color='k', xy=(0.1, 0.1), xycoords='axes fraction')\n                plt.annotate('outlier', color='orange', xy=(0.1, 0.07), xycoords='axes fraction')\n                plt.annotate('final', color='r', xy=(0.1, 0.04), xycoords='axes fraction')\n                plt.xlabel('x(arcsec)')\n                plt.ylabel('y(arcsec)')\n                plt.title('(ref-starlist) residule for all')\n                plt.savefig('%s_sample.png' %epoch, format='png')\n                plt.close()\n    \n            # get the final starlist and ref sample\n            ref = ref_match[bright][trim_outlier]\n            starlist = starlist_match[bright][trim_outlier]\n            name = ref['name']\n            x_ref = ref['x']\n            y_ref = ref['y']\n            x_starlist = starlist['x']\n            y_starlist = starlist['y']\n            x_diff = ref['x'] - starlist['x']\n            y_diff = ref['y'] - starlist['y']\n            #xe = starlist['xe']\n            #ye = starlist['xe']\n            xe = np.hypot(starlist['xe'], ref['xe'])\n            ye = np.hypot(starlist['ye'], ref['ye'])\n\n            # write the sample to a file\n            t = Table()\n            t['name'] = name\n            t['x_ref'] = x_ref\n            t['y_ref'] = y_ref\n            t['x_starlist'] = x_starlist\n            t['y_starlist'] = y_starlist\n            t['x_diff'] = x_diff\n            t['y_diff'] = y_diff\n            t['xe'] = xe\n            t['ye'] = ye\n            idx = np.argsort(name)\n            t[idx].write('sample_%s.txt' %(epoch), format='ascii.fixed_width')\n\n        else:\n            t = Table.read('sample_%s.txt' %(epoch), format='ascii.fixed_width')\n            name = t['name']\n            x_ref = t['x_ref']\n            y_ref = t['y_ref']\n            x_starlist = t['x_starlist']\n            y_starlist = t['y_starlist']\n            x_diff = t['x_diff']\n            y_diff = t['y_diff']\n            xe = t['xe']\n            ye = t['ye']\n        return name, x_ref, y_ref, x_starlist, y_starlist, x_diff, y_diff, xe, ye\n\n    def position_transform(self, x, y, transform):\n        \"\"\"\n        given the orginal position and distortion, calculate the transformed\n        position and distortion based on transformation object from\n        astropy.modeling.models.polynomial2D.\n        Input:\n            - x, y: original position (in arcsec)\n            - transform: transformation object from astropy.modeling.models.polynomial2D\n            \n        Outpus:\n            - x_new, y_new: transformed position (in pixel)\n        \"\"\"\n    \n        # Read transformation: Extract X, Y coefficients from transform\n        if transform.__class__.__name__ == 'four_paramNW':\n            Xcoeff = transform.px\n            Ycoeff = transform.py\n            order = 1\n        elif (transform.__class__.__name__ == 'PolyTransform'):\n            Xcoeff = transform.px.parameters\n            Ycoeff = transform.py.parameters\n            order = transform.order\n        else:\n            txt = 'Transform not yet supported by position_transform_from_object'\n            raise StandardError(txt)\n            \n        # How the transformation is applied depends on the type of transform.\n        # This can be determined by the length of Xcoeff, Ycoeff\n        N = order - 1\n    \n        x_new = 0\n        for i in range(0, N+2):\n            x_new += Xcoeff[i] * (x**i)\n        for j in range(1, N+2):\n            x_new += Xcoeff[N+1+j] * (y**j)\n        for i in range(1, N+1):\n            for j in range(1, N+2-i):\n                sub = int(2*N + 2 + j + (2*N+2-i) * (i-1)/2.)\n                x_new += Xcoeff[sub] * (x**i) * (y**j)\n    \n        y_new = 0\n        for i in range(0, N+2):\n            y_new += Ycoeff[i] * (x**i)\n        for j in range(1, N+2):\n            y_new += Ycoeff[N+1+j] * (y**j)\n        for i in range(1, N+1):\n            for j in range(1, N+2-i):\n                sub = int(2*N + 2 + j + (2*N+2-i) * (i-1)/2.)\n                y_new += Ycoeff[sub] * (x**i) * (y**j)\n    \n        return x_new, y_new\n\n    def distortion_transform(self, x, y, distx, disty, transform):\n        \"\"\"\n        given the orginal position and distortion, calculate the transformed\n        position and distortion based on transformation object from\n        astropy.modeling.models.polynomial2D.\n        Input:\n            - x, y: original position\n            - distx, disty: originial distortion\n            - transform: transformation object from astropy.modeling.models.polynomial2D\n            \n        Outpus:\n            - x_new, y_new: transformed position\n            - distx_new, disty_new: transformed position error\n        \"\"\"\n    \n        # Read transformation: Extract X, Y coefficients from transform\n        if transform.__class__.__name__ == 'four_paramNW':\n            Xcoeff = transform.px\n            Ycoeff = transform.py\n            order = 1\n        elif (transform.__class__.__name__ == 'PolyTransform'):\n            Xcoeff = transform.px.parameters\n            Ycoeff = transform.py.parameters\n            order = transform.order\n        else:\n            txt = 'Transform not yet supported by position_transform_from_object'\n            raise StandardError(txt)\n            \n        # How the transformation is applied depends on the type of transform.\n        # This can be determined by the length of Xcoeff, Ycoeff\n        N = order - 1\n    \n        x_new = 0\n        for i in range(0, N+2):\n            x_new += Xcoeff[i] * (x**i)\n        for j in range(1, N+2):\n            x_new += Xcoeff[N+1+j] * (y**j)\n        for i in range(1, N+1):\n            for j in range(1, N+2-i):\n                sub = int(2*N + 2 + j + (2*N+2-i) * (i-1)/2.)\n                x_new += Xcoeff[sub] * (x**i) * (y**j)\n    \n        y_new = 0\n        for i in range(0, N+2):\n            y_new += Ycoeff[i] * (x**i)\n        for j in range(1, N+2):\n            y_new += Ycoeff[N+1+j] * (y**j)\n        for i in range(1, N+1):\n            for j in range(1, N+2-i):\n                sub = int(2*N + 2 + j + (2*N+2-i) * (i-1)/2.)\n                y_new += Ycoeff[sub] * (x**i) * (y**j)\n    \n        # distx_new: partial(x_new, x) * distx + partial(x_new, y) * disty\n\t\t# partial(x_new,x) = scalex\n        scalex = 0\n        for i in range(1, N+2):\n            scalex += i * Xcoeff[i] * (x**(i-1))\n        for i in range(1, N+1):\n            for j in range(1, N+2-i):\n                sub = int(2*N + 2 + j + (2*N+2-i) * (i-1)/2.)\n                scalex += i * Xcoeff[sub] * (x**(i-1)) * (y**j)\n\n\t\t# partial(x_new,y) = scaley\n        scaley = 0\n        for j in range(1, N+2):\n            scaley += j * Xcoeff[N+1+j] * (y**(j-1))\n        for i in range(1, N+1):\n            for j in range(N+2-i):\n                sub = int(2*N + 2 + j + (2*N+2-i) * (i-1)/2.)\n                scaley += j * Xcoeff[sub] * (x**i) * (y**(j-1))\n        distx_new = scalex*distx + scaley*disty\n    \n        # disty_new: partial(y_new, x) * distx + partial(y_new, y) * disty\n\t\t# partial(y_new,x) = scalex\n        scalex = 0\n        for i in range(1, N+2):\n            scalex += i * Ycoeff[i] * (x**(i-1))\n        for i in range(1, N+1):\n            for j in range(1, N+2-i):\n                sub = int(2*N + 2 + j + (2*N+2-i) * (i-1)/2.)\n                scalex += i * Ycoeff[sub] * (x**(i-1)) * (y**j)\n\n\t\t# partial(y_new,y) = scaley\n        scaley = 0\n        for j in range(1, N+2):\n            scaley += j * Ycoeff[N+1+j] * (y**(j-1))\n        for i in range(1, N+1):\n            for j in range(N+2-i):\n                sub = int(2*N + 2 + j + (2*N+2-i) * (i-1)/2.)\n                scaley += j * Ycoeff[sub] * (x**i) * (y**(j-1))\n        disty_new = scalex*distx + scaley*disty\n    \n        return x_new, y_new, distx_new, disty_new\n\n    def find_trans(self, epoch):\n        # transform starlist from arcsec to pixel\n        # read  starlist in arcsec and pixel, all +West\n        starlist_arcsec = self.read_starlist(epoch)\n        starlist_file = self.root_all + 'lis/mag'+epoch+'_rms.lis'\n        starlist_pixel = Table.read(starlist_file, format='ascii',\n                                names=('name','m','t','x','y','xe','ye','snr','corr','n_frames','flux'))\n\n        # find the transformation between arcsec and pixel\n        #print('\\n')\n        print('using flystar to clacualte transformation between starlist in arcsec and starlist in pixel')\n        trans_file = 'trans_' + epoch + '.txt'\n        idx_arcsec, idx_pixel, translist = examples.align_starlists(starlist_arcsec, starlist_pixel, \n                order=2, N_loop=3, dr_tol=10, outFile=trans_file,briteN=100)\n        transModel=transforms.PolyTransform\n        trans = transModel(starlist_arcsec[idx_arcsec]['x'], starlist_arcsec[idx_arcsec]['y'], \n                starlist_pixel[idx_pixel]['x'], starlist_pixel[idx_pixel]['y'],order=2, weights=None)\n        self.trans = trans\n        return trans\n\n\n\n    def calc_distortion(self, epochs, dist_order=6, boot=True, zero=False):\n        \"\"\"\n        eg. star_file = '05junlgs_kp'\n        boot: whether use bootstrap to calculate error map or not\n        zero: whether to use zero to replace edge values or not\n              if not, we use the nearest unmasked star\n        \"\"\"\n    \n        # set the distortion map range in arcsec\n        x_min = -6\n        x_max = 6\n        y_min = -6\n        y_max = 6\n\n        points_x = np.arange(-6, 6, 0.01)\n        points_y = np.arange(-6, 6, 0.01)\n            \n        # legendre transformation is linear in [-1,1], find scale\n        x_scale = 2.0/(x_max - x_min) \n        x_shift = 1 - x_scale * x_max\n        y_scale = 2.0/(y_max - y_min) \n        y_shift = 1 - y_scale * y_max\n \n        # set the distortion map in pixel\n        points_xx, points_yy = np.meshgrid(points_x, points_y)\n        points_xx_scale = x_shift + x_scale*points_xx\n        points_yy_scale = y_shift + y_scale*points_yy\n\n        # interpolate to int pixel image\n        points_x_pixel = np.arange(1300)\n        points_y_pixel = np.arange(1300)\n        points_xx_pixel, points_yy_pixel = np.meshgrid(points_x_pixel, points_y_pixel)\n\n        \n        for epoch in epochs:\n            print(\"###########\")\n            print(\"running calc_distortion for %s at legendre order of %d\" %(epoch, dist_order))\n            print(\"###########\")\n    \n            # get the matched star\n            name,x_ref,y_ref,x_starlist,y_starlist,x_diff,y_diff,xe,ye = self.sample_distortion(epoch, plot=True)\n    \n            #####\n            ## calculate distortion map in arcsec using transformed ref and starlist (+W)\n            #####\n            print(\"distortion map in arcsec: calculation begins\")\n            print('---------------')\n\n            # find transformation between scaled ref and starlist\n            x_ref_scale = x_shift + x_scale*x_ref\n            y_ref_scale = y_shift + y_scale*y_ref\n            x_starlist_scale = x_shift + x_scale*x_starlist\n            y_starlist_scale = y_shift + y_scale*y_starlist\n            leg = transforms.LegTransform(x_ref_scale, y_ref_scale, x_starlist_scale, y_starlist_scale, dist_order, \n                weights=(1.0/np.sqrt(xe**2 + ye**2)))\n\n            # apply this transformation to the whole map\n            dist_x_scale, dist_y_scale = leg.evaluate(points_xx_scale, points_yy_scale)  \n    \n            # convert distortion back to pixel arcsec and get distortion solution\n            dist_x = (dist_x_scale - x_shift)/x_scale\n            dist_y = (dist_y_scale - y_shift)/y_scale\n            dist_x -= points_xx\n            dist_y -= points_yy\n            dist_x *= -1.0\n            dist_y *= -1.0\n\n            # write it to fits file +W\n            # here dist_x is in the same direction as x_ref - x_stalrist\n            hdu_x = fits.PrimaryHDU(dist_x)\n            hdulist_x = fits.HDUList([hdu_x])\n            hdulist_x.writeto('nirc2LocalDist_x_'+epoch+'_arcsec.fits', clobber=True)\n            hdu_y = fits.PrimaryHDU(dist_y)\n            hdulist_y = fits.HDUList([hdu_y])\n            hdulist_y.writeto('nirc2LocalDist_y_'+epoch+'_arcsec.fits', clobber=True)\n\n            print(\"distortion map in arcsec: done! \")\n\n            #####\n            ## write distortion map in pixel (+W)\n            #####\n            # find the transformation between arcsec and pixel\n            trans = self.find_trans(epoch)\n\n            # apply this transformation to distortion map and transform to pixel scale\n            xx_pixel, yy_pixel, dist_x_pixel, dist_y_pixel = self.distortion_transform(points_xx, points_yy, dist_x, dist_y, trans)\n\n            # interpolate to int pixel image\n            dist_x_pixel_itp = interpolate.griddata((xx_pixel.flatten(), yy_pixel.flatten()), dist_x_pixel.flatten(), (points_xx_pixel, points_yy_pixel), method='cubic', fill_value=0)\n            dist_y_pixel_itp = interpolate.griddata((xx_pixel.flatten(), yy_pixel.flatten()), dist_y_pixel.flatten(), (points_xx_pixel, points_yy_pixel), method='cubic', fill_value=0)\n\n            #write it to fits file\n            hdu_x = fits.PrimaryHDU(dist_x_pixel_itp)\n            hdulist_x = fits.HDUList([hdu_x])\n            hdulist_x.writeto('nirc2LocalDist_x_nn5_'+epoch+'_orig_nocut.fits', clobber=True)\n            hdu_y = fits.PrimaryHDU(dist_y_pixel_itp)\n            hdulist_y = fits.HDUList([hdu_y])\n            hdulist_y.writeto('nirc2LocalDist_y_nn5_'+epoch+'_orig_nocut.fits', clobber=True)\n \n            #####\n            # use bootstrap to calculate distortion uncertainties in pixel (+W)\n            #####\n            x_ref_all = x_ref\n            y_ref_all = y_ref\n            x_starlist_all = x_starlist\n            y_starlist_all = y_starlist\n            xe_all = xe\n            ye_all = ye\n            if boot == True:\n                n_samples = 100\n                idx_sub = self.bootstrap_indexes(x_ref_all, n_samples, 1)\n    \n                x_ref = x_ref_all[idx_sub]\n                y_ref = y_ref_all[idx_sub]\n                x_starlist = x_starlist_all[idx_sub]\n                y_starlist = y_starlist_all[idx_sub]\n                xe = xe_all[idx_sub]\n                ye = ye_all[idx_sub]\n    \n                X_dist = []\n                Y_dist = []\n    \n                print('distortion error map in arcsec using %d bootstrap: calculation begins (~10min)' %n_samples)\n                print('---------------')\n    \n                for i in range(n_samples):\n                    # find transformation between scaled ref and starlist\n                    x_ref_scale = x_shift + x_scale*x_ref[i]\n                    y_ref_scale = y_shift + y_scale*y_ref[i]\n                    x_starlist_scale = x_shift + x_scale*x_starlist[i]\n                    y_starlist_scale = y_shift + y_scale*y_starlist[i]\n    \n                    ## this is for debug:\n                    #plt.figure(figsize=(10,10))\n                    #plt.clf()\n                    #plt.quiver(x_ref_scale, y_ref_scale, x_ref_scale-x_starlist_scale, y_ref_scale-y_starlist_scale, scale=0.003)\n                    #plt.xlim(-1,1)\n                    #plt.ylim(-1,1)\n                    #plt.savefig('boot_{0}_{1}.png'.format(i, epoch))\n                    #plt.close()\n\n                    leg = transforms.LegTransform(x_ref_scale, y_ref_scale, x_starlist_scale, \n                            y_starlist_scale, dist_order,  weights=(1.0/np.sqrt(xe[i]**2 + ye[i]**2)))\n\n                    # apply this transformation to the whole map\n                    dist_x_scale, dist_y_scale = leg.evaluate(points_xx_scale, points_yy_scale)  \n    \n                    # convert distortion back to pixel arcsec and get distortion solution\n                    dist_x = (dist_x_scale - x_shift)/x_scale\n                    dist_y = (dist_y_scale - y_shift)/y_scale\n                    dist_x -= points_xx\n                    dist_y -= points_yy\n                    dist_x *= -1.0\n                    dist_y *= -1.0\n\n                    # apply this transformation to distortion map and transform to pixel scale\n                    xx_pixel, yy_pixel, dist_x_pixel, dist_y_pixel = self.distortion_transform(points_xx, points_yy, dist_x, dist_y, trans)\n\n                    # interpolate to int pixel image\n                    dist_x_pixel_itp = interpolate.griddata((xx_pixel.flatten(), yy_pixel.flatten()), dist_x_pixel.flatten(), \n                            (points_xx_pixel, points_yy_pixel), method='cubic', fill_value=0)\n                    dist_y_pixel_itp = interpolate.griddata((xx_pixel.flatten(), yy_pixel.flatten()), dist_y_pixel.flatten(), \n                            (points_xx_pixel, points_yy_pixel), method='cubic', fill_value=0)\n \n                    X_dist.append(dist_x_pixel_itp)\n                    Y_dist.append(dist_y_pixel_itp)\n    \n                dist_xerr = np.std(X_dist, axis=0)\n                dist_yerr = np.std(Y_dist, axis=0)\n\n                #write it to fits file\n                hdu_xe = fits.PrimaryHDU(dist_xerr)\n                hdulist_xe = fits.HDUList([hdu_xe])\n                hdulist_xe.writeto('nirc2LocalDist_xerr_nn5_'+epoch+'_orig_nocut.fits', clobber=True)\n                hdu_ye = fits.PrimaryHDU(dist_yerr)\n                hdulist_ye = fits.HDUList([hdu_ye])\n                hdulist_ye.writeto('nirc2LocalDist_yerr_nn5_'+epoch+'_orig_nocut.fits', clobber=True)\n\n                print(\"distortion error map in pixel: done! \\n\")\n\n            ## make the mask array\n            self.mask_dist(epoch, trans, limit_dist=0.3, limit_disterr=0.3, zero=zero)\n    \n        return\n\n    def calc_distortion_nn(self, epochs, knn=5, boot=True, zero=False):\n        \"\"\"\n        eg. star_file = '05junlgs_kp'\n        boot: whether use bootstrap to calculate error map or not\n        zero: whether to use zero to replace edge values or not\n              if not, we use the nearest unmasked star\n        \"\"\"\n    \n        # set the distortion map range in arcsec\n        x_min = -6\n        x_max = 6\n        y_min = -6\n        y_max = 6\n\n        points_x = np.arange(-6, 6, 0.01)\n        points_y = np.arange(-6, 6, 0.01)\n            \n        # legendre transformation is linear in [-1,1], find scale\n        x_scale = 2.0/(x_max - x_min) \n        x_shift = 1 - x_scale * x_max\n        y_scale = 2.0/(y_max - y_min) \n        y_shift = 1 - y_scale * y_max\n \n        # set the distortion map in pixel\n        points_xx, points_yy = np.meshgrid(points_x, points_y)\n        points_xx_scale = x_shift + x_scale*points_xx\n        points_yy_scale = y_shift + y_scale*points_yy\n\n        # interpolate to int pixel image\n        points_x_pixel = np.arange(1300)\n        points_y_pixel = np.arange(1300)\n        points_xx_pixel, points_yy_pixel = np.meshgrid(points_x_pixel, points_y_pixel)\n\n        \n        for epoch in epochs:\n            print(\"###########\")\n            print(\"running calc_distortion for %s using %d nearest neighbors\" %(epoch, knn))\n            print(\"###########\")\n    \n            # get the matched star\n            name,x_ref,y_ref,x_starlist,y_starlist,x_diff,y_diff,xe,ye = self.sample_distortion(epoch, plot=True)\n    \n            #####\n            ## calculate distortion map in arcsec using transformed ref and starlist (+W)\n            #####\n            print(\"distortion map in arcsec: calculation begins\")\n            print('---------------')\n\n            # loop through each point and average nn nearest number stars\n            n = len(points_x)\n            points = np.vstack((points_xx.ravel(), points_yy.ravel())).T\n            tree = spatial.KDTree(list(zip(x_starlist.ravel(), y_starlist.ravel())))\n            idx_near = tree.query(points, k=knn)[1]\n            dist_x = np.mean(x_diff[idx_near], axis=1)\n            dist_y = np.mean(y_diff[idx_near], axis=1)\n            dist_x = dist_x.reshape((n, n))\n            dist_y = dist_y.reshape((n, n))\n\n            # write it to fits file +W\n            # here dist_x is in the same direction as x_ref - x_stalrist\n            hdu_x = fits.PrimaryHDU(dist_x)\n            hdulist_x = fits.HDUList([hdu_x])\n            hdulist_x.writeto('nirc2LocalDist_x_'+epoch+'_arcsec.fits', clobber=True)\n            hdu_y = fits.PrimaryHDU(dist_y)\n            hdulist_y = fits.HDUList([hdu_y])\n            hdulist_y.writeto('nirc2LocalDist_y_'+epoch+'_arcsec.fits', clobber=True)\n\n            print(\"distortion map in arcsec: done! \")\n\n            #####\n            ## write distortion map in pixel (+W)\n            #####\n            # find the transformation between arcsec and pixel\n            trans = self.find_trans(epoch)\n\n            # apply this transformation to distortion map and transform to pixel scale\n            xx_pixel, yy_pixel, dist_x_pixel, dist_y_pixel = self.distortion_transform(points_xx, points_yy, dist_x, dist_y, trans)\n\n            # interpolate to int pixel image\n            dist_x_pixel_itp = interpolate.griddata((xx_pixel.flatten(), yy_pixel.flatten()), dist_x_pixel.flatten(), (points_xx_pixel, points_yy_pixel), method='cubic', fill_value=0)\n            dist_y_pixel_itp = interpolate.griddata((xx_pixel.flatten(), yy_pixel.flatten()), dist_y_pixel.flatten(), (points_xx_pixel, points_yy_pixel), method='cubic', fill_value=0)\n\n            #write it to fits file\n            hdu_x = fits.PrimaryHDU(dist_x_pixel_itp)\n            hdulist_x = fits.HDUList([hdu_x])\n            hdulist_x.writeto('nirc2LocalDist_x_nn5_'+epoch+'_orig_nocut.fits', clobber=True)\n            hdu_y = fits.PrimaryHDU(dist_y_pixel_itp)\n            hdulist_y = fits.HDUList([hdu_y])\n            hdulist_y.writeto('nirc2LocalDist_y_nn5_'+epoch+'_orig_nocut.fits', clobber=True)\n \n        return dist_x, dist_y, x_scale, x_shift, y_scale, y_shift\n\n   \n    def mask_dist(self, epoch, trans, limitd=50, limit_dist=0.3, limit_disterr=0.3, zero=False):\n        \"\"\"\n        based on the distortion error map, cut the edge:\n        1. the nearst star is far than limitd\n        or\n        2. the distortion value is larger than limit_dist\n        or\n        3. the distortion uncertainty is larger than limit_disterr\n        zero:\n            - True: interploate the edge with zero\n            - False: interploate the edge with the nearst unedge value\"\"\"\n\n        # read in the reference stars that are used to calcualte distortion map\n        name, x_ref, y_ref, x_diff, y_diff, xe, ye = self.find_residual(epoch, trans, ld=False)\n        x_star = x_ref\n        y_star = y_ref\n    \n        print('distortion map and distortion error map with edge cut: calculation begins (~20min)')\n        print('---------------')\n    \n        # read the uncut the distortion map\n        hdulist_x = fits.open('nirc2LocalDist_x_nn5_'+ epoch +'_orig_nocut.fits')\n        dist_x = hdulist_x[0].data\n        hdulist_y = fits.open('nirc2LocalDist_y_nn5_'+ epoch +'_orig_nocut.fits')\n        dist_y = hdulist_y[0].data\n        hdulist_xe = fits.open('nirc2LocalDist_xerr_nn5_'+ epoch +'_orig_nocut.fits')\n        dist_xe = hdulist_xe[0].data\n        hdulist_ye = fits.open('nirc2LocalDist_yerr_nn5_'+ epoch +'_orig_nocut.fits')\n        dist_ye = hdulist_ye[0].data\n        \n        ## find the large value cut index\n        #dist_e = np.hypot(dist_xe, dist_ye)\n        #edge_cut = (np.percentile(dist_e.ravel(), percent))\n        #idx_edge = np.where(dist_e>edge_cut)\n        #idx_edge = np.array(idx_edge).T\n        \n        # find the pixel with the nearest star more than limitd\n        x_range, y_range = dist_xe.shape\n        x_pixel, y_pixel = np.meshgrid(range(x_range), range(y_range))\n        pixel = np.vstack((x_pixel.ravel(), y_pixel.ravel())).T\n        p_ref = np.array([y_star, x_star]).T\n        distance, index = spatial.KDTree(p_ref).query(pixel)\n        idx_dis = pixel[np.where(distance>limitd)[0]]\n    \n        # find the distortion value larger than limit_dist\n        if limit_dist != None:\n            idx_dist = np.where((np.abs(dist_x)>limit_dist) | (np.abs(dist_y)>limit_dist))\n            idx_dist = np.array(idx_dist).T\n        # find the distortion uncertainty value larger than limit_dist\n            idx_disterr = np.where((np.abs(dist_xe)>limit_disterr) | (np.abs(dist_ye)>limit_disterr))\n            idx_disterr = np.array(idx_disterr).T\n    \n        # find the mask array\n        pixel_set = set(tuple(x) for x in pixel)\n        dis_set = set(tuple(x) for x in idx_dis)\n        dist_set = set(tuple(x) for x in idx_dist)\n        disterr_set = set(tuple(x) for x in idx_disterr)\n\n        idx_mask = np.array([x for x in (dis_set | dist_set | disterr_set)])\n        idx_unmask = np.array([x for x in (pixel_set - (dis_set | dist_set | disterr_set))])\n        \n        if zero == True:\n            for i in range(len(idx_mask)):\n                i_mask = idx_mask[i][0], idx_mask[i][1] \n                dist_x[i_mask] = 0\n                dist_y[i_mask] = 0\n                dist_xe[i_mask] = 0\n                dist_ye[i_mask] = 0\n        else:\n            # for those mask pixel, find the nearest pixel value\n            index = spatial.KDTree(idx_unmask).query(idx_mask)[1]\n            dist_x_unmask = [dist_x[i[0],i[1]] for i in idx_unmask]\n            dist_y_unmask = [dist_y[i[0],i[1]] for i in idx_unmask]\n            dist_xe_unmask = [dist_xe[i[0],i[1]] for i in idx_unmask]\n            dist_ye_unmask = [dist_ye[i[0],i[1]] for i in idx_unmask]\n    \n            for i in range(len(idx_mask)):\n                i_mask = idx_mask[i][0], idx_mask[i][1] \n                i_index = index[i] \n                i_unmask = idx_unmask[i_index][0], idx_unmask[i_index][1] \n                dist_x[i_mask] = dist_x[i_unmask]\n                dist_y[i_mask] = dist_y[i_unmask]\n                dist_xe[i_mask] = dist_xe[i_unmask]\n                dist_ye[i_mask] = dist_ye[i_unmask]\n        \n        hdu_x = fits.PrimaryHDU(dist_x)\n        hdulist_x = fits.HDUList(hdu_x)\n        hdulist_x.writeto('nirc2LocalDist_x_nn5_'+ epoch +'_orig.fits', clobber=True)\n        hdu_y = fits.PrimaryHDU(dist_y)\n        hdulist_y = fits.HDUList(hdu_y)\n        hdulist_y.writeto('nirc2LocalDist_y_nn5_'+ epoch +'_orig.fits', clobber=True)\n        hdu_xe = fits.PrimaryHDU(dist_xe)\n        hdulist_xe = fits.HDUList(hdu_xe)\n        hdulist_xe.writeto('nirc2LocalDist_xerr_nn5_'+ epoch +'_orig.fits', clobber=True)\n        hdu_ye = fits.PrimaryHDU(dist_ye)\n        hdulist_ye = fits.HDUList(hdu_ye)\n        hdulist_ye.writeto('nirc2LocalDist_yerr_nn5_'+ epoch +'_orig.fits', clobber=True) \n        print('distortion map and distortion error map with edge cut: done!\\n')\n        return\n    \n    def find_residual(self, epoch, trans, ld=False):\n        \"\"\"\n        find the residual between sample in 06alignment and rms.lis file\n        - input:\n            epoch: '05junlgs_kp'\n            ld: whether to compare with _rms_ld.lis or _rms.lis\n        - output:\n            x_ref: matched x (pixel)\n            y_ref: matched y (pixel)\n            x_diff: position offset in x (pixel)\n            y_diff: position offset in y (pixel)\n            xe: position uncertainty in x in starlist\n            ye: position uncertainty in y in starlist \"\"\"\n\n        # read in the sample in reference\n        # transform reference to pixel \n        name,x_ref_arcsec,y_ref_arcsec,x_starlist_arcsec,y_starlist_arcsec,x_diff,y_diff,xe,ye = self.sample_distortion(epoch)\n        x_ref, y_ref = self.position_transform(x_ref_arcsec, y_ref_arcsec, trans)\n\n        # match with rms.lis file\n        if ld:\n            starlist_file = 'mag'+epoch+'_rms_ld.lis'\n        else:\n            starlist_file = 'mag'+epoch+'_rms.lis'\n\n        starlist = Table.read(starlist_file, format='ascii',\n                                names=('name','m','t','x','y','xe','ye','snr','corr','n_frames','flux'))\n\n        idx1, idx2, dr, dm = match.match(x_ref, y_ref, np.zeros(len(x_ref)), starlist['x'], starlist['y'], np.zeros(len(starlist)), dr_tol=2)\n        name1 = name[idx1]\n        x_ref1 = x_ref[idx1]\n        y_ref1 = y_ref[idx1]\n        x_diff1 = x_ref[idx1] - starlist['x'][idx2]\n        y_diff1 = y_ref[idx1] - starlist['y'][idx2]\n\n        return name1, x_ref1, y_ref1, x_diff1, y_diff1, starlist['xe'][idx2], starlist['ye'][idx2]\n \n    def plot_distortion(self, epoch, trans, dist_order=6, qscale=0.005):\n    \n        print('plotting distortion map for %s at order of %d \\n' %(epoch, dist_order))\n\n        name1, x_ref1, y_ref1, x_diff1, y_diff1, xe1, ye1 = self.find_residual(epoch, trans, ld=False)\n   \n        # read in distortion\n        hdulist_x = fits.getdata('nirc2LocalDist_x_nn5_'+ epoch + '_orig.fits')\n        hdulist_y = fits.getdata('nirc2LocalDist_y_nn5_'+ epoch + '_orig.fits')\n    \n        # plot the distortion solution in x direction\n        vlimit = 0.2\n        plt.figure(figsize=(40,20))\n        plt.clf()\n        points_x, points_y = np.meshgrid(np.arange(1300), np.arange(1300))\n        plt.subplot(2,4,1)\n        fig = plt.imshow(hdulist_x, cmap=plt.cm.jet, vmin=-vlimit, vmax=vlimit, origin='lower')\n        cb = plt.colorbar(fig)\n        cb.set_label('distortion in x direction(pixel)')\n        Q = plt.quiver(x_ref1, y_ref1, x_diff1, y_diff1, units='xy', scale=qscale, color='k')\n        plt.quiverkey(Q, 0.9, 0.9, 1, '1 pixel')\n        plt.title('distortion for %s in x direction - order %d' %(epoch, dist_order))\n        plt.xlabel('x (pixel)')\n        plt.ylabel('y (pixel)')\n    \n        # plot the distortion solution in y direction\n        plt.subplot(2,4,5)\n        fig = plt.imshow(hdulist_y, cmap=plt.cm.jet, vmin=-vlimit, vmax=vlimit, origin='lower')\n        cb = plt.colorbar(fig)\n        cb.set_label('distortion in y direction(pixel)')\n        Q = plt.quiver(x_ref1, y_ref1, x_diff1, y_diff1, units='xy', scale=qscale, color='k')\n        plt.quiverkey(Q, 0.9, 0.9, 1, '1 pixel')\n        plt.title('distortion for %s in y direction - order %d' %(epoch, dist_order))\n        plt.xlabel('x (pixel)')\n        plt.ylabel('y (pixel)')\n\n        # read in distortion uncertainty\n        hdulist_xe = fits.getdata('nirc2LocalDist_xerr_nn5_'+ epoch + '_orig.fits')\n        hdulist_ye = fits.getdata('nirc2LocalDist_yerr_nn5_'+ epoch + '_orig.fits')\n    \n        # plot the distortion uncertianty solution in x direction\n        plt.subplot(2,4,2)\n        points_x, points_y = np.meshgrid(np.arange(1300), np.arange(1300))\n        fig = plt.imshow(hdulist_xe, cmap=plt.cm.jet, vmin=0, vmax=vlimit, origin='lower')\n        cb = plt.colorbar(fig)\n        cb.set_label('distortion uncertainty in x direction(pixel)')\n        plt.title('distortion uncertainty for %s in x direction - order %d' %(epoch, dist_order))\n        plt.xlabel('x (pixel)')\n        plt.ylabel('y (pixel)')\n    \n        # plot the distortion unceratinty solution in y direction\n        plt.subplot(2,4,6)\n        fig = plt.imshow(hdulist_ye, cmap=plt.cm.jet, vmin=0, vmax=vlimit, origin='lower')\n        cb = plt.colorbar(fig)\n        cb.set_label('distortion uncertainty in y direction(pixel)')\n        plt.title('distortion uncertainty for %s in y direction - order %d' %(epoch, dist_order))\n        plt.xlabel('x (pixel)')\n        plt.ylabel('y (pixel)')\n\n        # plot the distortion uncertianty sigma solution in x direction\n        plt.subplot(2,4,3)\n        points_x, points_y = np.meshgrid(np.arange(1300), np.arange(1300))\n        fig = plt.imshow(hdulist_x/hdulist_xe, cmap=plt.cm.jet, vmin=-3, vmax=3, origin='lower')\n        cb = plt.colorbar(fig)\n        cb.set_label('(distortion/distortion error) in x direction (sigma)')\n        plt.title('distortion simga for %s in x direction - order %d' %(epoch, dist_order))\n        plt.xlabel('x (pixel)')\n        plt.ylabel('y (pixel)')\n    \n        # plot the distortion unceratinty sigma solution in y direction\n        plt.subplot(2,4,7)\n        fig = plt.imshow(hdulist_y/hdulist_ye, cmap=plt.cm.jet, vmin=-3, vmax=3, origin='lower')\n        cb = plt.colorbar(fig)\n        cb.set_label('(distortion/distortion error) in y direction (sigma)')\n        plt.title('distortion simga for %s in y direction - order %d' %(epoch, dist_order))\n        plt.xlabel('x (pixel)')\n        plt.ylabel('y (pixel)')\n         \n        ## apply the local distortion and plot the residule again. \n        name2, x_ref2, y_ref2, x_diff2, y_diff2, xe2, ye2 = self.find_residual(epoch, trans, ld=True)\n\n        plt.subplot(2,4,4)\n        Q = plt.quiver(x_ref1, y_ref1, x_diff1, y_diff1, units='xy', scale=qscale, color='k')\n        plt.quiver(x_ref2, y_ref2, x_diff2, y_diff2, units='xy', scale=qscale, color='r', width=4)\n        plt.quiverkey(Q, 0.9, 0.9, 1, '1 pixel')\n        plt.text(300,20,'before distortion', color='k')\n        plt.text(600,20,'after distortion', color='r')\n        plt.xlabel('x(pixel)')\n        plt.ylabel('y(pixel)')\n        #plt.annotate('<$res_x$>=%.2f <$res_y$>=%.2f' %(np.mean(x_diff2),np.mean(y_diff2)), \n        #        xy=(0.05, 0.05), xycoords='axes fraction')\n        #plt.annotate(r'<$res_x^2$>=%.2f <$res_y^2$>=%.2f' %(np.mean(x_diff2**2),np.mean(y_diff2**2)),\n        #        xy=(0.05,0.1), xycoords='axes fraction')\n        plt.title('(ref-starlist) residule after applying new distortion')\n        plt.xlim(0,1300)\n        plt.xlim(0,1300)\n        plt.tight_layout()\n        plt.savefig('Distortion_%s.png' %(epoch))\n        plt.close()\n        return\n\n    def ftest_calc_chi(self, epoch, trans, order1, order2):\n        \"\"\"\n        to make f test between two distortion solution, first calcualte the chi2 for those 2 distortion\n        \"\"\"\n        root = os.getcwd()\n        os.chdir('order%d/'%(order1))\n\n        # read in the star_files at order1\n        name1, x_ref1, y_ref1, x_diff1, y_diff1, xe1, ye1 = self.find_residual(epoch, trans, ld=False)\n        name1_ld, x_ref1_ld, y_ref1_ld, x_diff1_ld, y_diff1_ld, xe1_ld, ye1_ld = self.find_residual(epoch, trans, ld=True)\n\n        name = np.intersect1d(name1_ld, name1)\n        idx = [list(name1).index(i) for i in name]\n        idx_ld = [list(name1_ld).index(i) for i in name]\n        name1 = name\n        x_diff1 = x_diff1_ld[idx_ld]\n        y_diff1 = y_diff1_ld[idx_ld]\n        xe1 = xe1[idx]\n        ye1 = ye1[idx]\n        \n        os.chdir(root)\n        os.chdir('order%d/'%(order2))\n    \n        # read in the star_files at order1\n        name2, x_ref2, y_ref2, x_diff2, y_diff2, xe2, ye2 = self.find_residual(epoch, trans, ld=False)\n        name2_ld, x_ref2_ld, y_ref2_ld, x_diff2_ld, y_diff2_ld, xe2_ld, ye2_ld = self.find_residual(epoch, trans, ld=True)\n\n        name = np.intersect1d(name2_ld, name2)\n        idx_ld = [list(name2_ld).index(i) for i in name]\n        idx = [list(name2).index(i) for i in name]\n        name2 = name\n        x_diff2 = x_diff2_ld[idx_ld]\n        y_diff2 = y_diff2_ld[idx_ld]\n        xe2 = xe2[idx]\n        ye2 = ye2[idx]\n        \n        os.chdir(root)\n        # find the common stars\n        name = np.intersect1d(name1, name2)\n        idx1 = [list(name1).index(i) for i in name]\n        idx2 = [list(name2).index(i) for i in name]\n    \n        # calculate chi2, Ndata, Npar\n        Ndata = len(name)*2\n        chix1 = (x_diff1[idx1]/xe1[idx1])**2 \n        chiy1 = (y_diff1[idx1]/ye1[idx1])**2 \n        chi1 = np.sum(chix1) + np.sum(chiy1)\n        Npar1 =  (order1+1)*(order1+1)*2\n    \n        chix2 = (x_diff2[idx2]/xe2[idx2])**2 \n        chiy2 = (y_diff2[idx2]/ye2[idx2])**2 \n        chi2 = np.sum(chix2) + np.sum(chiy2)\n        Npar2 = (order2+1)*(order2+1)*2\n    \n        return chi1, chi2, Ndata, Npar1, Npar2\n    \n    \n    def Ftest(self, epoch, trans, order1, order2):\n        \"\"\"\n        f test between two distoriton with different order\n        order 1 < order 2\n        f_ratio ~ 0 -> go to order2\n        f_ratio ~ 1 -> stay in order1\n        \"\"\"\n        chi1, chi2, Ndata, Npar1, Npar2 = self.ftest_calc_chi(epoch, trans, order1, order2)\n        df1 = Ndata - Npar1\n        df2 = Ndata - Npar2\n    \n        f_ratio = ((chi1 - chi2)/chi2)*((Ndata-Npar2)/(Npar2-Npar1))\n        p = stats.f.sf(f_ratio, Npar2-Npar1, Ndata-Npar2) \n        print('starlist    dist_order     chi2    Ndata   Npar    DOF')\n        print('%s    %d   %14.2f   %3d    %3d    %3d' %(epoch, order1, chi1, Ndata, Npar1, df1))\n        print('%s    %d   %14.2f   %3d    %3d    %3d' %(epoch, order2, chi2, Ndata, Npar2, df2))\n        print('from %d order to %d order, the f ratio = %.2f, correspond to p value = %.2f'\\\n                %(order1, order2, f_ratio, p) )\n    \n        f = open('Ftest_%s_%d_%d.txt' %(epoch, order1, order2), 'w')\n        f.write('starlist    dist_order     chi2    Ndata   Npar   DOF\\n')\n        f.write('%s    %d   %14.2f   %3d    %3d    %3d\\n' %(epoch, order1, chi1, Ndata, Npar1, df1))\n        f.write('%s    %d   %14.2f   %3d    %3d    %3d\\n' %(epoch, order2, chi2, Ndata, Npar2, df2))\n        f.write('from %d order to %d order, the f ratio = %.2f, correspond to p value = %.2f'\\\n                %(order1, order2, f_ratio, p))\n        f.close()\n        return f_ratio, p\n\n    def plot_ftest(self, epoch, order):\n        \"\"\"\n        plot f and p as a funciton of order to find the optimized order\n        \"\"\"\n        n = len(order) - 1\n        f_ratio = np.zeros(n)\n        p = np.zeros(n)\n        trans = self.find_trans(epoch)\n        for i in range(n):\n            order1 = order[i]\n            order2 = order[i+1]\n            f_ratio[i], p[i] = self.Ftest(epoch, trans, order1, order2)\n    \n        plt.figure(figsize=(10,10))\n        plt.plot(order[:-1], f_ratio)\n        plt.xlabel('order')\n        plt.ylabel('f_ratio')\n        plt.title('Ftest for %s' %(epoch))\n        plt.savefig('%s_ftest_f.png' %(epoch), format='png')\n        plt.close()\n    \n        plt.figure(figsize=(10,10))\n        plt.plot(order[:-1], 1-p)\n        plt.xlabel('order')\n        plt.ylabel('1-p')\n        plt.title('Ftest for %s' %(epoch))\n        plt.savefig('%s_ftest_p.png' %(epoch), format='png')\n        plt.close()\n        return\n    \n    #def calc_distortion_combined(self, epochs, dist_order=6, boot=True, zero=False, combine=True):\n    #    \"\"\"\n    #    !!! not fully support combine yet!!!\n    #    eg. star_file = '05junlgs_kp'\n    #    boot: whether use bootstrap to calculate error map or not\n    #    zero: whether to use zero to replace edge values or not\n    #          if not, we use the nearest unmasked star\n    #    \"\"\"\n    #\n    #    # set the distortion map range\n    #    x_min = -6\n    #    x_max = 6\n    #    y_min = -6\n    #    y_max = 6\n\n    #    points_x = np.arange(-6, 6, 0.01)\n    #    points_y = np.arange(-6, 6, 0.01)\n    #        \n    #    # legendre transformation is linear in [-1,1], find scale\n    #    x_scale = 2.0/(x_max - x_min) \n    #    x_shift = 1 - x_scale * x_max\n    #    y_scale = 2.0/(y_max - y_min) \n    #    y_shift = 1 - y_scale * y_max\n \n    #    # apply the scale in points\n    #    points_xx, points_yy = np.meshgrid(points_x, points_y)\n    #    points_xx_scale = x_shift + x_scale*points_xx\n    #    points_yy_scale = y_shift + y_scale*points_yy\n\n    #    # interpolate to int pixel image\n    #    points_x_pixel = np.arange(1300)\n    #    points_y_pixel = np.arange(1300)\n    #    points_xx_pixel, points_yy_pixel = np.meshgrid(points_x_pixel, points_y_pixel)\n\n    #    # for each epoch, calculate distortion map in arcsec\n    #    for epoch in epochs:\n    #        print(\"###########\")\n    #        print(\"running calc_distortion for %s at legendre order of %d\" %(epoch, dist_order))\n    #        print(\"###########\")\n    #\n    #        # get the matched star\n    #        #---------------\n    #        name,x_ref,y_ref,x_starlist,y_starlist,x_diff,y_diff,xe,ye = self.sample_distortion(epoch, plot=True)\n    #\n    #        #####\n    #        ## calculate distortion using transformed ref and starlist (arcsec)\n    #        #####\n    #        print('---------------')\n    #        print(\"distortion map in arcsec: calculation begins\")\n\n   \n    #        # find transformation between scaled ref and starlist\n    #        x_ref_scale = x_shift + x_scale*x_ref\n    #        y_ref_scale = y_shift + y_scale*y_ref\n    #        x_starlist_scale = x_shift + x_scale*x_starlist\n    #        y_starlist_scale = y_shift + y_scale*y_starlist\n    #        leg = transforms.LegTransform(x_ref_scale, y_ref_scale, x_starlist_scale, y_starlist_scale, dist_order, \n    #            weights=(1.0/np.sqrt(xe**2 + ye**2)))\n\n    #        # apply this transformation to the whole map\n    #        dist_x_scale, dist_y_scale = leg.evaluate(points_xx_scale, points_yy_scale)  \n    #\n    #        # convert distortion back to pixel arcsec and get distortion solution\n    #        dist_x = (dist_x_scale - x_shift)/x_scale\n    #        dist_y = (dist_y_scale - y_shift)/y_scale\n    #        dist_x -= points_xx\n    #        dist_y -= points_yy\n    #        dist_x *= -1.0\n    #        dist_y *= -1.0\n\n    #        #write it to fits file +W\n    #        ## here dist_x is in the same direction as x_ref - x_stalrist\n    #        hdu_x = fits.PrimaryHDU(dist_x)\n    #        hdulist_x = fits.HDUList([hdu_x])\n    #        hdulist_x.writeto('nirc2LocalDist_x_'+epoch+'_arcsec.fits', clobber=True)\n    #        hdu_y = fits.PrimaryHDU(dist_y)\n    #        hdulist_y = fits.HDUList([hdu_y])\n    #        hdulist_y.writeto('nirc2LocalDist_y_'+epoch+'_arcsec.fits', clobber=True)\n\n    #        print(\"distortion map in arcsec: done! \")\n\n    #        # use bootstrap to calculate distortion uncertainties too\n    #        #--------------\n    #        x_ref_all = x_ref\n    #        y_ref_all = y_ref\n    #        x_starlist_all = x_starlist\n    #        y_starlist_all = y_starlist\n    #        xe_all = xe\n    #        ye_all = ye\n    #        if boot == True:\n    #            n_samples = 100\n    #            idx_sub = self.bootstrap_indexes(x_ref_all, n_samples, 0.5)\n    #\n    #            x_ref = x_ref_all[idx_sub]\n    #            y_ref = y_ref_all[idx_sub]\n    #            x_starlist = x_starlist_all[idx_sub]\n    #            y_starlist = y_starlist_all[idx_sub]\n    #            xe = xe_all[idx_sub]\n    #            ye = ye_all[idx_sub]\n    #\n    #            X_dist = []\n    #            Y_dist = []\n    #\n    #            print('---------------')\n    #            print('distortion error map in arcsec using %d bootstrap: calculation begins (~5min)' %n_samples)\n    #\n    #            for i in range(n_samples):\n    #                # find transformation between scaled ref and starlist\n    #                x_ref_scale = x_shift + x_scale*x_ref[i]\n    #                y_ref_scale = y_shift + y_scale*y_ref[i]\n    #                x_starlist_scale = x_shift + x_scale*x_starlist[i]\n    #                y_starlist_scale = y_shift + y_scale*y_starlist[i]\n    #\n    #                leg = transforms.LegTransform(x_ref_scale, y_ref_scale, x_starlist_scale, \n    #                        y_starlist_scale, dist_order,  weights=(1.0/np.sqrt(xe[i]**2 + ye[i]**2)))\n\n    #                # apply this transformation to the whole map\n    #                dist_x_scale, dist_y_scale = leg.evaluate(points_xx_scale, points_yy_scale)  \n    #\n    #                # convert distortion back to pixel arcsec and get distortion solution\n    #                dist_x = (dist_x_scale - x_shift)/x_scale\n    #                dist_y = (dist_y_scale - y_shift)/y_scale\n    #                dist_x -= points_xx\n    #                dist_y -= points_yy\n    #                dist_x *= -1.0\n    #                dist_y *= -1.0\n\n    #                X_dist.append(dist_x)\n    #                Y_dist.append(dist_y)\n    #\n    #            dist_xerr = np.std(X_dist, axis=0)\n    #            dist_yerr = np.std(Y_dist, axis=0)\n\n    #            #write it to fits file\n    #            hdu_xe = fits.PrimaryHDU(dist_xerr)\n    #            hdulist_xe = fits.HDUList([hdu_xe])\n    #            hdulist_xe.writeto('nirc2LocalDist_xerr_'+epoch+'_arcsec.fits', clobber=True)\n    #            hdu_ye = fits.PrimaryHDU(dist_yerr)\n    #            hdulist_ye = fits.HDUList([hdu_ye])\n    #            hdulist_ye.writeto('nirc2LocalDist_yerr_'+epoch+'_arcsec.fits', clobber=True)\n\n    #            print(\"distortion error map in arcsec: done! \\n\")\n\n   \n    #    # combine distortion map in arcsec in needed\n    #    if combine:\n    #        x = []\n    #        y = []\n    #        xe = []\n    #        ye = []\n\n    #        # plot the 3 distortion map and combined distortion map\n    #        n = len(epochs)\n    #        #fig, axes = plt.subplots(2, n+1, figsize=(20, 10))\n    #\n    #        for i,epoch in enumerate(epochs):\n    #            hdulist_x = fits.getdata('nirc2LocalDist_x_'+ epoch +'_arcsec.fits')\n    #            hdulist_y = fits.getdata('nirc2LocalDist_y_'+ epoch +'_arcsec.fits')\n    #            hdulist_xe = fits.getdata('nirc2LocalDist_xerr_'+ epoch +'_arcsec.fits')\n    #            hdulist_ye = fits.getdata('nirc2LocalDist_yerr_'+ epoch +'_arcsec.fits')\n\n    #            x += hdulit_x\n    #            y += hdulit_y\n    #            xe += hdulit_xe\n    #            ye += hdulit_ye\n\n    #            #fig_sub = axes[0,0].imshow(hdulist_x, cmap=plt.cm.jet, vmin=-0.3, vmax=0.3)\n    #            #axes[0,i].set_title('%s in x ' %(epoch))\n    #\n    #            #fig_sub = axes[1,0].imshow(hdulist_y, cmap=plt.cm.jet, vmin=-0.3, vmax=0.3)\n    #            #axes[1,i].set_title('%s in y ' %(epoch))\n    #\n    #\n    #        # mean of all distortion maps\n    #        x = x/len(epochs)\n    #        y = y/len(epochs)\n    #        xe = xe/len(epochs)\n    #        ye = ye/len(epochs)\n\n    #        # write to new fits file\n    #        hdu_x = fits.PrimaryHDU(x)\n    #        hdulist_x = fits.HDUList(hdu_x)\n    #        hdulist_x.writeto('nirc2LocalDist_x_combine_arcsec.fits', clobber=True)\n    #        hdu_xe = fits.PrimaryHDU(xe)\n    #        hdulist_xe = fits.HDUList(hdu_xe)\n    #        hdulist_xe.writeto('nirc2LocalDist_xerr_combine_arcsec.fits', clobber=True)\n    #        hdu_y = fits.PrimaryHDU(y)\n    #        hdulist_y = fits.HDUList(hdu_y)\n    #        hdulist_y.writeto('nirc2LocalDist_y_combine_arcsec.fits', clobber=True)\n    #        hdu_ye = fits.PrimaryHDU(ye)\n    #        hdulist_ye = fits.HDUList(hdu_ye)\n    #        hdulist_ye.writeto('nirc2LocalDist_yerr_combine_arcsec.fits', clobber=True)\n    # \n    #        # plot the 3 distortion map and combined distortion map\n    #        #fig_sub = axes[0,n].imshow(x, cmap=plt.cm.jet, vmin=-0.3, vmax=0.3)\n    #        #axes[0,n].set_title('combined distortion in x ')\n    #\n    #        #fig_sub = axes[1,3].imshow(y, cmap=plt.cm.jet, vmin=-0.3, vmax=0.3)\n    #        #axes[1,n].set_title('combined distortion in y ')\n    #\n    #        #plt.savefig('combined_dist_arcsec.png', format=\"png\")\n    #\n \n    #        # write the distortion map  and distortion error map in pixel for combined epochs\n    #        # -----------------------\n\n    #        for epoch in epochs:\n    #            # find the transformation between arcsec and pixel\n    #            trans = self.find_trans(epoch)\n\n    #            # apply this transformation to distortion map and transform to pixel scale\n    #            x_pixel, y_pixel, dist_x_pixel, dist_y_pixel = self.distortion_transform(points_xx, points_yy, x, y, trans)\n\n    #            # interpolate to int pixel image\n    #            dist_x_pixel_itp = interpolate.griddata((x_pixel.flatten(), y_pixel.flatten()), dist_x_pixel.flatten(), (points_xx_pixel, points_yy_pixel), method='cubic', fill_value=0)\n    #            dist_y_pixel_itp = interpolate.griddata((x_pixel.flatten(), y_pixel.flatten()), dist_y_pixel.flatten(), (points_xx_pixel, points_yy_pixel), method='cubic', fill_value=0)\n\n    #            #write it to fits file\n    #            hdu_x = fits.PrimaryHDU(dist_x_pixel_itp)\n    #            hdulist_x = fits.HDUList([hdu_x])\n    #            hdulist_x.writeto('nirc2LocalDist_x_nn5_'+epoch+'_orig_nocut.fits', clobber=True)\n    #            hdu_y = fits.PrimaryHDU(dist_y_pixel_itp)\n    #            hdulist_y = fits.HDUList([hdu_y])\n    #            hdulist_y.writeto('nirc2LocalDist_y_nn5_'+epoch+'_orig_nocut.fits', clobber=True)\n    #\n    #\n    #            # write the distortion error map in pixel\n    #            # -----------------------\n    #            x_pixel, y_pixel, dist_xerr_pixel, dist_yerr_pixel = self.distortion_transform(points_xx, points_yy, xe, ye, trans)\n\n    #            # interpolate to int pixel image\n    #            dist_xerr_pixel_itp = interpolate.griddata((x_pixel.flatten(), y_pixel.flatten()), dist_xerr_pixel.flatten(), \n    #                    (points_xx_pixel, points_yy_pixel), method='cubic', fill_value=0)\n    #            dist_yerr_pixel_itp = interpolate.griddata((x_pixel.flatten(), y_pixel.flatten()), dist_yerr_pixel.flatten(), \n    #                    (points_xx_pixel, points_yy_pixel), method='cubic', fill_value=0)\n    #\n    #            hdu_xerr = fits.PrimaryHDU(dist_xerr_pixel_itp)\n    #            hdulist_xerr = fits.HDUList([hdu_xerr])\n    #            hdulist_xerr.writeto('nirc2LocalDist_xerr_nn5_'+ epoch +'_orig_nocut.fits', clobber=True)\n    #            hdu_yerr = fits.PrimaryHDU(dist_yerr_pixel_itp)\n    #            hdulist_yerr = fits.HDUList([hdu_yerr])\n    #            hdulist_yerr.writeto('nirc2LocalDist_yerr_nn5_'+ epoch +'_orig_nocut.fits', clobber=True)\n\n    #\n    #    ## make the mask array\n    #    for epoch in epochs:\n    #        self.mask_dist(epoch, limit_dist=0.3, limit_disterr=0.3, zero=zero)\n    #\n    #    # apply the new local distortion\n    #    for epoch in epochs:\n    #        applyLocalDist.go(epoch, 'kp', root=self.root_all, distDir='./')\n    #        #starlist_file = 'lis/mag'+epoch+'_rms.lis'\n    #        #shutil.copy(self.root_all + starlist_file, './')\n    #        #starlist_file = 'lis/mag'+epoch+'_rms_ld.lis'\n    #        #shutil.copy(self.root_all + starlist_file, './')\n\n    #        # make diagnostic plots\n    #        self.plot_distortion(epoch, dist_order=dist_order)\n    #\n    #    return\n \n    #def Ftest_combined(self, epochs, order1, order2):\n    #    \"\"\"\n    #    15, 16 are almost the same setup, so do ftests for combined 15 an 16\n    #    \"\"\"\n    #    chi1 = 0\n    #    chi2 = 0\n    #    Ndata = 0\n    #    Npar1 = 0\n    #    Npar2 = 0\n    #    for epoch in epochs:\n    #        chi1_temp, chi2_temp, Ndata_temp, Npar1_temp, Npar2_temp = self.ftest_calc_chi(star_file, order1, order2)\n    #        chi1 += chi1_temp\n    #        chi2 += chi2_temp\n    #        Ndata += Ndata_temp\n    #        Npar1 += Npar1_temp\n    #        Npar2 += Npar2_temp\n    #    df1 = Ndata - Npar1\n    #    df2 = Ndata - Npar2\n    #\n    #    epochs_name = ''\n    #    for epoch in epochs:\n    #        epochs_name += epoch\n    #\n    #    f_ratio = ((chi1 - chi2)/chi2)*((Ndata-Npar2)/(Npar2-Npar1))\n    #    p = stats.f.sf(f_ratio, Npar2-Npar1, Ndata-Npar2) \n    #    print('starlist    dist_order     chi2    Ndata   Npar    DOF')\n    #    print('%s    %d   %14.2f   %3d    %3d    %3d' %(epochs_name, order1, chi1, Ndata, Npar1, df1))\n    #    print('%s    %d   %14.2f   %3d    %3d    %3d' %(epochs_name, order2, chi2, Ndata, Npar2, df2))\n    #    print('from %d order to %d order, the f ratio = %.2f, correspond to p value = %.2f'\\\n    #            %(order1, order2, f_ratio, p) )\n    #\n    #    f = open('Ftest_%s_%d_%d.txt' %(epochs_name, order1, order2), 'w')\n    #    f.write('starlist    dist_order     chi2    Ndata   Npar   DOF\\n')\n    #    f.write('%s    %d   %14.2f   %3d    %3d    %3d\\n' %(epochs_name, order1, chi1, Ndata, Npar1, df1))\n    #    f.write('%s    %d   %14.2f   %3d    %3d    %3d\\n' %(epochs_name, order2, chi2, Ndata, Npar2, df2))\n    #    f.write('from %d order to %d order, the f ratio = %.2f, correspond to p value = %.2f \\n'\\\n    #            %(order1, order2, f_ratio, p))\n    #    f.close()\n    #    return f_ratio, p\n    #\n   \n    #def plot_ftest_combined(self, epochs, order):\n    #    \"\"\"\n    #    plot f and p as a funciton of order to find the optimized order for combined 15 and 16\n    #    \"\"\"\n    #    n = len(order) - 1\n    #    f_ratio = np.zeros(n)\n    #    p = np.zeros(n)\n    #    for i in range(n):\n    #        order1 = order[i]\n    #        order2 = order[i+1]\n    #        f_ratio[i], p[i] = F_test_combined(epochs, order1, order2)\n    #    \n    #    epochs_name = ''\n    #    for epoch in epochs:\n    #        epochs_name += epoch\n    #\n    #    plt.figure(figsize=(10,10))\n    #    plt.plot(order[1:], f_ratio)\n    #    plt.xlabel('order')\n    #    plt.ylabel('f_ratio')\n    #    plt.title('Ftest for %s' %(epochs_name))\n    #    plt.savefig('%s_ftest_f.png' %(epochs_name), format='png')\n    #    plt.close()\n    #\n    #    plt.figure(figsize=(10,10))\n    #    plt.plot(order[1:], 1-p)\n    #    plt.xlabel('order')\n    #    plt.ylabel('1-p')\n    #    plt.title('Ftest for %s' %(epochs_name))\n    #    plt.savefig('%s_ftest_p.png' %(epochs_name), format='png')\n    #    plt.close()\n    #    return\n    \ndef compare_distortion(root1, epoch1, name1, root2, epoch2, name2):\n    \"\"\"\n    compare two distortion map \n    \"\"\"\n    hdulist_x1 = fits.getdata(root1 + 'nirc2LocalDist_x_nn5_'+epoch1+'_orig.fits')\n    hdulist_y1 = fits.getdata(root1 + 'nirc2LocalDist_y_nn5_'+epoch1+'_orig.fits')\n    hdulist_xe1 = fits.getdata(root1 + 'nirc2LocalDist_xerr_nn5_'+epoch1+'_orig.fits')\n    hdulist_ye1 = fits.getdata(root1 + 'nirc2LocalDist_yerr_nn5_'+epoch1+'_orig.fits')\n\n    hdulist_x2 = fits.getdata(root2 + 'nirc2LocalDist_x_nn5_'+epoch2+'_orig.fits')\n    hdulist_y2 = fits.getdata(root2 + 'nirc2LocalDist_y_nn5_'+epoch2+'_orig.fits')\n    hdulist_xe2 = fits.getdata(root2 + 'nirc2LocalDist_xerr_nn5_'+epoch2+'_orig.fits')\n    hdulist_ye2 = fits.getdata(root2 + 'nirc2LocalDist_yerr_nn5_'+epoch2+'_orig.fits')\n\n    x_diff = hdulist_x1 - hdulist_x2\n    y_diff = hdulist_y1 - hdulist_y2\n    x_err = np.hypot(hdulist_xe1, hdulist_xe2)\n    y_err = np.hypot(hdulist_ye1, hdulist_ye2)\n\n    x_sigma = x_diff/x_err\n    y_sigma = y_diff/y_err\n\n    plt.figure(figsize=(10,10))\n    fig = plt.imshow(x_sigma, cmap=plt.cm.Spectral, vmin=-2.5, vmax=2.5, origin='lower')\n    cb = plt.colorbar(fig)\n    cb.set_label(r'$\\Delta$ distortion (sigma)')\n    plt.title(r'$\\Delta$(%s-%s) x' %(name1, name2))\n    plt.savefig('com_x_%s_%s_distortion.png' %(name1, name2), format='png')\n\n    plt.figure(figsize=(10,10))\n    fig = plt.imshow(y_sigma, cmap=plt.cm.Spectral, vmin=-2.5, vmax=2.5, origin='lower')\n    cb = plt.colorbar(fig)\n    cb.set_label(r'$\\Delta$ distortion (sigma)')\n    plt.title(r'$\\Delta$(%s-%s) y' %(name1, name2))\n    plt.savefig('com_y_%s_%s_distortion.png' %(name1, name2), format='png')\n\n    plt.figure(figsize=(10,10))\n    plt.clf()\n    plt.hist(x_sigma.ravel(), histtype='step', color='r', label='x')\n    plt.hist(y_sigma.ravel(), histtype='step', color='b', label='y')\n    plt.xlabel('sigma')\n    plt.legend()\n    plt.title(r'$\\Delta$(%s-%s) distortion' %(name1, name2))\n    plt.savefig('com_hist_%s_%s_distortion.png' %(name1, name2), format='png')\n    plt.close('all')\n    return\n        \n     \n### TEMP: how to skip copy_local_dist step in align_epochs_template.py\n#import numpy as np\n#from shutil import copyfile\n#epoch_tab = Table.read('epochsInfo.txt', format='ascii')\n#epochs_dist_idx = np.where((epoch_tab['isAO']==1) & (epoch_tab['doAlign']==1) & (epoch_tab['06setup']==0))[0]\n#epochs_dist = epoch_tab['epoch'][epochs_dist_idx]\n#for epoch in epochs_dist:\n#    in_file = '../lis/mag' + epoch + '_rms.lis'\n#    out_file = '../lis/mag' + epoch + '_rms_ld.lis'\n#    copyfile(in_file, out_file)\n#\n\nfrom shutil import copyfile\nepochs = np.array(['05junlgs_kp', '05jullgs_kp', '15auglgs_kp', '16maylgs1_kp', '16jullgs1_nite2_kp', \n                    '17maylgs1_kp', '17auglgs1_kp', '17auglgs3_kp', '17maylgs3_h', '17auglgs2_h', '17auglgs3_h',\n                    '20180317nirc2_h', '20180317nirc2_kp', '20180322nirc2_h', '20180322nirc2_kp', '20180330nirc2_h','20180330nirc2_kp'])\n#orders = np.array([5, 5, 6, 6, 6,  6, 6, 6, 6, 6, 6,  6, 6, 6, 6, 6, 6])\norders = np.array([8, 8, 8, 8, 7,  6, 6, 6, 7, 6, 6,  6, 6, 6, 6, 6, 6])\ndef copy_final_distortion(epochs, orders):\n    if not os.path.exists('final_distortion'):\n        os.mkdir('final_distortion')\n    if len(epochs)!=len(orders):\n        print('ERROR: epochs does not match orders')\n        sys.exit()\n\n    for i in range(len(epochs)):\n        epoch = epochs[i]\n        order = orders[i]\n        copyfile('order'+str(order)+'/Distortion_'+epoch+'.png', 'final_distortion/Distortion_'+epoch+'.png')\n        copyfile('order'+str(order)+'/nirc2LocalDist_x_nn5_'+epoch+'_orig.fits', 'final_distortion/'+'/nirc2LocalDist_x_nn5_'+epoch+'_orig.fits')\n        copyfile('order'+str(order)+'/nirc2LocalDist_xerr_nn5_'+epoch+'_orig.fits', 'final_distortion/'+'/nirc2LocalDist_xerr_nn5_'+epoch+'_orig.fits')\n        copyfile('order'+str(order)+'/nirc2LocalDist_y_nn5_'+epoch+'_orig.fits', 'final_distortion/'+'/nirc2LocalDist_y_nn5_'+epoch+'_orig.fits')\n        copyfile('order'+str(order)+'/nirc2LocalDist_yerr_nn5_'+epoch+'_orig.fits', 'final_distortion/'+'/nirc2LocalDist_yerr_nn5_'+epoch+'_orig.fits')\n        # copy the arcsec file\n        copyfile('order'+str(order)+'/nirc2LocalDist_x_'+epoch+'_arcsec.fits', 'final_distortion/'+'/nirc2LocalDist_x_'+epoch+'_arcsec.fits')\n        copyfile('order'+str(order)+'/nirc2LocalDist_y_'+epoch+'_arcsec.fits', 'final_distortion/'+'/nirc2LocalDist_y_'+epoch+'_arcsec.fits')\n\ndef debug_plot():\n    \n    epochs = np.array(['16maylgs1_kp'])\n    epochs_name = np.array(['16may_k'])\n    root = '/g/lu/scratch/siyao/work/2_align/18_update/18_04_27_linear_propagation/order6/'\n\n    epoch = epochs[0]\n    epoch_name = epochs_name[0]\n\n    x = fits.getdata(root + 'nirc2LocalDist_x_nn5_'+ epoch + '_orig.fits')\n    y = fits.getdata(root + 'nirc2LocalDist_y_nn5_'+ epoch + '_orig.fits')\n    xe = fits.getdata(root + 'nirc2LocalDist_xerr_nn5_'+ epoch + '_orig.fits')\n    ye = fits.getdata(root + 'nirc2LocalDist_yerr_nn5_'+ epoch + '_orig.fits')\n    idx = np.where(xe<0)[0]\n    if len(idx)>0:\n        print('%s has negative local distoriton' %epoch)\n\n    # find the non-edge vaules\n    x = x.ravel()\n    y = y.ravel()\n    xe = xe.ravel()\n    ye = ye.ravel()\n    idx = np.where((abs(x)<0.28) & (abs(y)<0.28) & (xe<0.28) & (ye<0.28))[0]\n    print('median: %20s: x=%5.3f  y=%5.3f  xe=%5.3f  ye=%5.3f' %(epoch, np.median(abs(x)[idx]), \n        np.median(abs(y)[idx]), np.median(xe[idx]), np.median(ye[idx])))\n    print('mean: %20s: x=%5.3f  y=%5.3f  xe=%5.3f  ye=%5.3f' %(epoch, np.mean(abs(x)[idx]), \n        np.mean(abs(y)[idx]), np.mean(xe[idx]), np.mean(ye[idx])))\n    print('median: {0:20s}: x/xe={1:1f}  y/ye={2:1f}'.format(epoch, np.median((abs(x)/xe)[idx]), np.median((abs(y)/ye)[idx])))\n \n    bins = np.linspace(0, 0.3, 20)\n    plt.figure(figsize=(20,10))\n    plt.clf()\n    plt.subplot(121)\n    plt.hist(abs(x), histtype='step', ls='--', color='r', bins=bins, label='abs(dist_x) with edge')\n    plt.hist(xe, histtype='step', color='b', ls='--', bins=bins, label='dist_xe with edge')\n    plt.hist(abs(x)[idx], histtype='step', color='r', bins=bins, label='abs(dist_x)')\n    plt.hist(xe[idx], histtype='step', color='b', bins=bins, label='dist_xe')\n    plt.legend()\n    plt.subplot(122)\n    plt.hist(abs(y), histtype='step', color='r', ls='--', bins=bins, label='abs(dist_y) with edge')\n    plt.hist(ye, histtype='step', color='b', ls='--', bins=bins, label='dist_ye with edge')\n    plt.hist(abs(y)[idx], histtype='step', color='r', bins=bins, label='abs(dist_y)')\n    plt.hist(ye[idx], histtype='step', color='b', bins=bins, label='dist_ye')\n    plt.legend()\n    plt.savefig(root+ 'debug_{0}.png'.format(epoch_name))\n    plt.close()\n\n","sub_path":"make_distortion.py","file_name":"make_distortion.py","file_ext":"py","file_size_in_byte":72966,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"71697426","text":"#!/bin/env python\n# -*- coding: utf-8 -*-\n# encoding=utf-8 vi:ts=4:sw=4:expandtab:ft=python\n\"\"\"\ntest_unsqueeze_\n\"\"\"\nfrom apibase import APIBase\nimport paddle\nimport pytest\nimport numpy as np\n\n\nclass TestUnsqueeze(APIBase):\n    \"\"\"\n    test unsqueeze_\n    \"\"\"\n\n    def hook(self):\n        \"\"\"\n        implement\n        \"\"\"\n        self.types = [np.float32, np.float64, np.int8, np.int32, np.int64]\n        self.no_grad_var = [\"axis\"]\n        self.enable_backward = False\n        # self.static = True\n        # self.dygraph = True\n        # self.debug = True\n\n\nobj = TestUnsqueeze(paddle.unsqueeze_)\n\n\n@pytest.mark.api_base_unsqueeze__vartype\ndef test_unsqueeze__base():\n    \"\"\"\n    base\n    axis=int32\n    Cannot support x as bool tensor, it's a bug\n    \"\"\"\n    x = np.arange(6).reshape(2, 3)\n    axis = 1\n    res = np.expand_dims(x, axis)\n    obj.base(res=res, x=x, axis=axis)\n\n\n@pytest.mark.api_base_unsqueeze__exception\ndef test_unsqueeze_1():\n    \"\"\"\n    axis>max_dim\n    \"\"\"\n    x = np.arange(6).reshape(2, 3)\n    axis = 3\n    obj.exception(mode=\"c\", etype=\"InvalidArgument\", x=x, axis=axis)\n\n\n@pytest.mark.api_base_unsqueeze__parameters\ndef test_unsqueeze_2():\n    \"\"\"\n    axis=negtive_num\n    \"\"\"\n    x = np.arange(6).reshape(2, 3)\n    axis = -1\n    res = np.expand_dims(x, axis)\n    obj.run(res=res, x=x, axis=axis)\n\n\n@pytest.mark.api_base_unsqueeze__parameters\ndef test_unsqueeze_3():\n    \"\"\"\n    axis=list\n    \"\"\"\n    x = np.arange(6).reshape(2, 3)\n    axis = [0, 1]\n    res = np.expand_dims(x, axis)\n    obj.run(res=res, x=x, axis=axis)\n\n\n@pytest.mark.api_base_unsqueeze__parameters\ndef test_unsqueeze_4():\n    \"\"\"\n    axis=tuple\n    \"\"\"\n    x = np.arange(6).reshape(2, 3)\n    axis = (0, 1)\n    res = np.expand_dims(x, axis)\n    obj.run(res=res, x=x, axis=axis)\n\n\n@pytest.mark.api_base_unsqueeze__parameters\ndef test_unsqueeze_5():\n    \"\"\"\n    axis=tensor\n    \"\"\"\n    x = np.arange(6).reshape(2, 3)\n    axis = np.array([0, 1, 2])\n    res = np.expand_dims(x, axis=[0, 1, 2])\n    obj.run(res=res, x=x, axis=axis)\n","sub_path":"framework/api/paddlebase/test_unsqueeze_.py","file_name":"test_unsqueeze_.py","file_ext":"py","file_size_in_byte":2021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"262113956","text":"import setuptools\n\nwith open(\"README.md\", \"r\") as fh:\n    long_description = fh.read()\n\nsetuptools.setup(\n    name=\"topsis-antuanant\", # Replace with your own username\n    version=\"0.0.3\",\n    author=\"Anant\",\n    author_email=\"anantchopra95@gmail.com\",\n    description=\"Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in the 1980s as a multi-criteria decision making method. TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution, and greatest distance from the negative-ideal solution\",\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    url=\"https://github.com/Anant99-sys/topsis\",\n    packages=setuptools.find_packages(),\n    classifiers=[\n        \"Programming Language :: Python :: 3\",\n        \"License :: OSI Approved :: MIT License\",\n        \"Operating System :: OS Independent\",\n    ],\n    python_requires='>=3.7',\n)\n","sub_path":"pypi_install_script/topsis-antuanant-0.0.3.tar/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"319280215","text":"#!/usr/bin/env python3\n\n\"\"\"Unit Tests for Netflix.py\"\"\"\n\n# -------\n# imports\n# -------\n\nfrom io       import StringIO\nfrom unittest import main, TestCase\n\nfrom Netflix import netflix_read, netflix_eval, netflix_print, netflix_solve\n\n# -----------\n# TestNetflix\n# -----------\n\nclass TestNetflix(TestCase):\n    \"\"\"Unit Tests for Netflix.py\"\"\"\n    # ----\n    # read\n    # ----\n\n    def test_read_1(self):\n        \"\"\"Test netflix_read for a customer ID\"\"\"\n        string = '123\\n'\n        data_id, data_type = netflix_read(string)\n        self.assertEqual(data_id, 123)\n        self.assertEqual(data_type, 'customer')\n\n    def test_read_2(self):\n        \"\"\"Test netflix_read for a movie ID\"\"\"\n        string = '123:\\n'\n        data_id, data_type = netflix_read(string)\n        self.assertEqual(data_id, 123)\n        self.assertEqual(data_type, 'movie')\n\n    def test_read_3(self):\n        \"\"\"Test netflix_read for a string\"\"\"\n        string = 'abc\\n'\n        error = False\n        try:\n            netflix_read(string)\n        except ValueError:\n            error = True\n        self.assertTrue(error)\n\n    # ----\n    # eval\n    # ----\n\n    def test_eval_1(self):\n        \"\"\"Test netflix_eval\"\"\"\n        rating, actual = netflix_eval(30878, 1)\n        self.assertEqual(rating, 3.7255000228804325)\n        self.assertEqual(actual, 4)\n\n    def test_eval_2(self):\n        \"\"\"Test netflix_eval\"\"\"\n        rating, actual = netflix_eval(1952305, 10)\n        self.assertEqual(rating, 2.9539170532434964)\n        self.assertEqual(actual, 3)\n\n    def test_eval_3(self):\n        \"\"\"Test netflix_eval\"\"\"\n        rating, actual = netflix_eval(1485175, 10012)\n        self.assertEqual(rating, 3.782384142757868)\n        self.assertEqual(actual, 3)\n\n    def test_eval_4(self):\n        \"\"\"Test netflix_eval\"\"\"\n        rating, actual = netflix_eval(430376, 10014)\n        self.assertEqual(rating, 3.2289684823453744)\n        self.assertEqual(actual, 3)\n\n    # -----\n    # print\n    # -----\n\n    def test_print_1(self):\n        \"\"\"Test netflix_print for an int\"\"\"\n        writer = StringIO()\n        netflix_print(writer, 10)\n        self.assertEqual(writer.getvalue(), \"10\\n\")\n\n    def test_print_2(self):\n        \"\"\"Test netflix_print for a string\"\"\"\n        writer = StringIO()\n        netflix_print(writer, 'abc')\n        self.assertEqual(writer.getvalue(), \"abc\\n\")\n\n    def test_print_3(self):\n        \"\"\"Test netflix_print for 2 lines\"\"\"\n        writer = StringIO()\n        netflix_print(writer, 'abc\\n123')\n        self.assertEqual(writer.getvalue(), \"abc\\n123\\n\")\n\n    # -----\n    # solve\n    # -----\n\n    def test_solve_1(self):\n        \"\"\"Test netflix_solve\"\"\"\n        reader = StringIO(\"1123:\\n448549\\n2444222\\n1522889\\n\")\n        writer = StringIO()\n        netflix_solve(reader, writer)\n        self.assertEqual(writer.getvalue(), \"1123:\\n3.3\\n2.9\\n4.1\\nRMSE: 0.4148335185443161\\n\")\n\n    def test_solve_2(self):\n        \"\"\"Test netflix_solve\"\"\"\n        reader = StringIO(\"1:\\n1989766\\n\\n2380848\\n\")\n        writer = StringIO()\n        error = False\n        try:\n            netflix_solve(reader, writer)\n        except ValueError:\n            error = True\n        self.assertTrue(error)\n\n# ----\n# main\n# ----\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"cat3238-TestNetflix.py","file_name":"cat3238-TestNetflix.py","file_ext":"py","file_size_in_byte":3241,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"625684547","text":"import sys\nif sys.version_info.major < 3:\n    raise \"python3 required\"\n\nfrom gi.repository import AppStream\n\ndef capitalize(string):\n    if string and len(string) > 1:\n        return (string[0].upper() + string[1:])\n    else:\n        return (string)\n\nclass PkgInfo:\n    __slots__ = \"pkg_hash\", \"name\", \"display_name\", \"summary\", \"description\",    \\\n                \"icon\", \"screenshots\", \"url\", \"version\", \"categories\", \"refid\",  \\\n                \"remote\", \"remote_url\", \"kind\", \"arch\", \"branch\", \"commit\"\n\n    def __init__(self, pkg_hash):\n        # Saved stuff\n        self.pkg_hash = pkg_hash\n        self.name = None\n        # some flatpak-specific things\n        self.refid=\"\"\n        self.remote = \"\"\n        self.kind = 0\n        self.arch = \"\"\n        self.branch = \"\"\n        self.commit = \"\"\n        self.remote_url = \"\"\n\n        # Display info fetched by methods always\n        self.display_name = None\n        self.summary = None\n        self.description = None\n        self.version = None\n        self.icon = None\n        self.screenshots = []\n        self.url = None\n\n        # Runtime categories\n        self.categories = []\n\n    def __getstate__(self):\n        return (                                 \\\n        self.pkg_hash,       self.name,          \\\n        self.remote,         self.remote_url,    \\\n        self.kind,           self.arch,          \\\n        self.branch,         self.commit,        \\\n        self.refid                               \\\n        )\n\n    def __setstate__(self, state):\n        self.pkg_hash,       self.name,          \\\n        self.remote,         self.remote_url,    \\\n        self.kind,           self.arch,          \\\n        self.branch,         self.commit,        \\\n        self.refid                               = state\n\n        self.categories = []\n        self.clear_cached_info()\n\n    def clear_cached_info(self):\n        self.display_name = None\n        self.summary = None\n        self.description = None\n        self.icon = None\n        self.screenshots = []\n        self.version = None\n        self.url = None\n\nclass AptPkgInfo(PkgInfo):\n    def __init__(self, pkg_hash, apt_pkg):\n        super(AptPkgInfo, self).__init__(pkg_hash)\n        self.name = apt_pkg.name\n\n    def get_display_name(self, apt_pkg=None):\n        # fastest\n        if self.display_name:\n            return self.display_name\n\n        if apt_pkg:\n            self.display_name = apt_pkg.name.capitalize()\n\n        if not self.display_name:\n            self.display_name = self.name.capitalize()\n\n        self.display_name = self.display_name.replace(\":i386\", \"\")\n\n        return self.display_name\n\n    def get_summary(self, apt_pkg=None):\n        # fastest\n        if self.summary:\n            return self.summary\n\n        if apt_pkg and apt_pkg.candidate:\n            candidate = apt_pkg.candidate\n\n            summary = \"\"\n            if candidate.summary is not None:\n                summary = candidate.summary\n                summary = summary.replace(\"<\", \"&lt;\")\n                summary = summary.replace(\"&\", \"&amp;\")\n\n                self.summary = capitalize(summary)\n\n        if self.summary == None:\n            self.summary = \"\"\n\n        return self.summary\n\n    def get_description(self, apt_pkg=None):\n        # fastest\n        if self.description:\n            return self.description\n\n        if apt_pkg and apt_pkg.candidate:\n            candidate = apt_pkg.candidate\n\n            description = \"\"\n            if candidate.description != None:\n                description = candidate.description\n                description = description.replace(\"<p>\", \"\").replace(\"</p>\", \"\\n\")\n                for tags in [\"<ul>\", \"</ul>\", \"<li>\", \"</li>\"]:\n                    description = description.replace(tags, \"\")\n\n                self.description = capitalize(description)\n\n        if self.description == None:\n            self.description = \"\"\n\n        return self.description\n\n    def get_icon(self, apt_pkg=None):\n        return None # this is handled in mintinstall directly for now\n\n    def get_screenshots(self, apt_pkg=None):\n        return [] # handled in mintinstall for now\n\n    def get_version(self, apt_pkg=None):\n        if self.version:\n            return self.version\n\n        if apt_pkg:\n            if apt_pkg.is_installed:\n                self.version = apt_pkg.installed.version\n            else:\n                self.version = apt_pkg.candidate.version\n\n            if self.version == None:\n                self.version = \"\"\n\n        return self.version\n\n    def get_url(self, apt_pkg=None):\n        if self.url:\n            return self.url\n\n        if apt_pkg:\n            if apt_pkg.is_installed:\n                self.url = apt_pkg.installed.homepage\n            else:\n                self.url = apt_pkg.candidate.homepage\n\n        if self.url == None:\n            self.url = \"\"\n\n        return self.url\n\n\nclass FlatpakPkgInfo(PkgInfo):\n    def __init__(self, pkg_hash, remote, ref, remote_url, installed):\n        super(FlatpakPkgInfo, self).__init__(pkg_hash)\n\n        self.name = ref.get_name() # org.foo.Bar\n        self.remote = remote # \"flathub\"\n        self.remote_url = remote_url\n\n        self.refid = ref.format_ref() # app/org.foo.Bar/x86_64/stable\n        self.kind = ref.get_kind() # Will be app for now\n        self.arch = ref.get_arch()\n        self.branch = ref.get_branch()\n        self.commit = ref.get_commit()\n\n    def get_display_name(self, as_component=None):\n        # fastest\n        if self.display_name:\n            return self.display_name\n\n        if as_component:\n            display_name = as_component.get_name()\n\n            if display_name != None:\n                self.display_name = capitalize(display_name)\n\n        if self.display_name == None:\n            self.display_name = self.name\n\n        return self.display_name\n\n    def get_summary(self, as_component=None):\n        # fastest\n        if self.summary:\n            return self.summary\n\n        if as_component:\n            summary = as_component.get_summary()\n\n            if summary != None:\n                self.summary = summary\n\n        if self.summary == None:\n            self.summary = \"\"\n\n        return self.summary\n\n    def get_description(self, as_component=None):\n        # fastest\n        if self.description:\n            return self.description\n\n        if as_component:\n            description = as_component.get_description()\n\n            if description != None:\n                description = description.replace(\"<p>\", \"\").replace(\"</p>\", \"\\n\")\n                for tags in [\"<ul>\", \"</ul>\", \"<li>\", \"</li>\"]:\n                    description = description.replace(tags, \"\")\n                self.description = capitalize(description)\n\n        if self.description == None:\n            self.description = \"\"\n\n        return self.description\n\n    def get_icon(self, as_component=None):\n        if self.icon:\n            return self.icon\n\n        if as_component:\n            icons = as_component.get_icons()\n\n            if icons:\n                if icons[0].get_kind() == AppStream.IconKind.LOCAL:\n                    self.icon = icons[0].get_filename()\n                elif icons[0].get_kind() == AppStream.IconKind.STOCK:\n                    self.icon = icons[0].get_name()\n\n        if self.icon == None:\n            self.icon = self.name\n\n        return self.icon\n\n    def get_screenshots(self, as_component=None):\n        if len(self.screenshots) > 0:\n            return self.screenshots\n\n        if as_component:\n            screenshots = as_component.get_screenshots()\n\n            for ss in screenshots:\n                images = ss.get_images()\n\n                if len(images) == 0:\n                    continue\n\n                # FIXME: there must be a better way.  Finding an optimal size to use without just\n                # resorting to an original source.\n\n                best = None\n                largest = None\n\n                for image in images:\n                    if image.get_kind() == AppStream.ImageKind.SOURCE:\n                        continue\n\n                    w = image.get_width()\n\n                    if w > 500 and w < 625:\n                        best = image\n                        break\n\n                    if w > 625:\n                        continue\n\n                    if largest == None or (largest != None and largest.get_width() < w):\n                        largest = image\n\n                if best == None and largest == None:\n                    continue\n\n                if best == None:\n                    best = largest\n\n                if ss.get_kind() == AppStream.ScreenshotKind.DEFAULT:\n                    self.screenshots.insert(0, best.get_url())\n                else:\n                    self.screenshots.append(best.get_url())\n\n        return self.screenshots\n\n    def get_version(self, as_component=None):\n        if self.version:\n            return self.version\n\n        if as_component:\n            releases = as_component.get_releases()\n\n            if len(releases) > 0:\n                version = releases[0].get_version()\n\n                if version:\n                    self.version = version\n\n        if self.version == None:\n            self.version = \"\"\n\n        return self.version\n\n    def get_url(self, as_component=None):\n        if self.url:\n            return self.url\n\n        if as_component:\n            url = as_component.get_url(AppStream.UrlKind.HOMEPAGE)\n\n            if url != None:\n                self.url = url\n\n        if self.url == None:\n            self.url = self.remote_url\n\n        return self.url","sub_path":"usr/lib/python3/dist-packages/mintcommon/installer/pkgInfo.py","file_name":"pkgInfo.py","file_ext":"py","file_size_in_byte":9577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"46291666","text":"\"\"\"\n\n\"\"\"\nimport xml.etree.ElementTree as ET\nimport os\n\n\n\"\"\"\n\"28510101.xml\" CI CEE\n\n\"\"\"\n\nfname = \"28510101_2.xml\"\npath = r\"C:\\Users\\m.houska\\Documents\\_CIS\\Toyota\\GlobalReport\"\nxmlpath = os.path.join(path, fname)\n\ntree = ET.parse(xmlpath)\nroot = tree.getroot()\nouter_Data = root.find(\".//Responses/Response/Data\")\n\nns = {\n    \"ur\": 'https://ws.urplus.sk\"',\n    \"grpt\": \"urn:crif-cribiscz-GetGlobalReport:2013-05-03\",\n}\n\n\ndef clean_street_zip(subject):\n    street_number = subject.get(\"_HomeAddressStreet_Number\")\n    street = subject.get(\"_HomeAddressStreet_Raw\")\n    zip_ = subject.get(\"HomeAddressZip\")\n\n    if (street is not None) and (street_number is not None):\n        if street_number in street:\n            subject[\"HomeAddressStreet\"] = street\n        else:\n            subject[\"HomeAddressStreet\"] = street + \" \" + street_number\n\n    elif (street is None) and (street_number is not None):\n        subject[\"HomeAddressStreet\"] = street_number\n\n    elif street is not None:\n        subject[\"HomeAddressStreet\"] = street\n\n    if zip_ is not None:\n        subject[\"HomeAddressZip\"] = zip_.replace(\" \", \"\")\n\n\n# CompanyGlobalReport = outer_Data.find(\".//{urn:crif-cribiscz-GetGlobalReport:2013-05-03}CompanyGlobalReport\")\ncgr = outer_Data.find(\".//grpt:CompanyGlobalReport\", ns)  # CompanyGlobalReport\n\n\napplicant_parse = {\n    \"TradeName\": \"grpt:Name\",\n    \"RegistrationOfficeType\": \"grpt:DistrictCourt\",\n    \"RegistrationSectionFileNo\": \"grpt:InsertNumber\",\n    \"_RegistrationOfficeName_District\": \"grpt:District\",\n    \"_RegistrationOfficeName_Region\": \"grpt:Region\",\n    \"AresORDateEntry\": \"grpt:DateOfCreation\",  # BusinessCreation??\n    \"TaxRegistrationNumber\": \"grpt:VATID\",\n    \"PhoneNumber\": \"grpt:PhoneNumbers/grpt:PhoneNumber[1]/grpt:Number\",\n    \"Email\": \"grpt:Email\",\n    \"_Bank_Acc_Raw\": \"grpt:BankInfoList/grpt:BankInfo[1]/grpt:Value\",\n    \"_Bank_Acc_Type\": \"grpt:BankInfoList/grpt:BankInfo[1]/grpt:Type\",\n    \"_Bank_Acc_InacInd\": \"grpt:BankInfoList/grpt:BankInfo[1]/grpt:InacInd\",\n    \"_HomeAddressStreet_Raw\": \"grpt:Seat/grpt:Street\",\n    \"_HomeAddressStreet_Number\": \"grpt:Seat/grpt:StreetNumber\",\n    \"HomeAddressCity\": \"grpt:Seat/grpt:City\",\n    \"HomeAddressZip\": \"grpt:Seat/grpt:Zip\",\n    \"HomeAddressState\": \"grpt:Seat/grpt:Country\",\n}\n\n\napp = cgr.find(\"./grpt:CompanyIdentification\", ns)\napplicant = {}\n\nfor item in applicant_parse:\n    e = app.find(\"./\" + applicant_parse[item], ns)\n    if e is not None:\n        applicant[item] = e.text  # pro prázný element vrací None\n\n# RegistrationOfficeName\nro_district = applicant.get(\"_RegistrationOfficeName_District\")\nro_region = applicant.get(\"_RegistrationOfficeName_Region\")\n\nif (ro_district is not None) and (ro_region is not None):\n    if (ro_district.lower() in ro_region.lower()) or (\n        ro_region.lower() in ro_district.lower()\n    ):\n        if len(ro_district) > len(ro_region):\n            applicant[\"RegistrationOfficeName\"] = ro_district\n        else:\n            applicant[\"RegistrationOfficeName\"] = ro_region\n    else:\n        applicant[\"RegistrationOfficeName\"] = ro_region + \", \" + ro_district\n\nelif ro_region is not None:\n    applicant[\"RegistrationOfficeName\"] = ro_region\n\nelif ro_district is not None:\n    applicant[\"RegistrationOfficeName\"] = ro_district\n\n\n# předčíslí, čílo účtu, kod banky\nacc_type = applicant.get(\"_Bank_Acc_Type\")\nacc_inac_ind = applicant.get(\"_Bank_Acc_InacInd\")\nif acc_type == \"05\" and acc_inac_ind == \"00\":\n    _Bank_Acc_Raw = applicant.get(\"_Bank_Acc_Raw\")\n    _Bank_Acc_Raw_split = _Bank_Acc_Raw.split(\"/\")\n    applicant[\"BankCode\"] = _Bank_Acc_Raw_split[1]\n    account_split = _Bank_Acc_Raw_split[0].split(\"-\")\n    if len(account_split) == 2:\n        applicant[\"BankAccountNumber1\"] = account_split[0]  # předčíslí\n        applicant[\"BankAccountNumber\"] = account_split[1]  # číslo účtu\n    else:\n        applicant[\"BankAccountNumber\"] = account_split[0]\n\n\nclean_street_zip(applicant)\n# print (applicant)\n\n\nstatutory_parse = {\n    \"Name\": \"grpt:Name\",\n    \"Surname\": \"grpt:Surname\",\n    \"_HomeAddressStreet_Raw\": \"grpt:Address/grpt:Street\",\n    \"_HomeAddressStreet_Number\": \"grpt:Address/grpt:StreetNumber\",\n    \"HomeAddressState\": \"grpt:Address/grpt:Country\",\n    \"HomeAddressZip\": \"grpt:Address/grpt:Zip\",\n    \"DateOfBirth\": \"grpt:DateOfBirth\",\n}\n\n\n# stat = cgr.find(\"./grpt:OtherCompanyInformation/grpt:StatutoryList/grpt:Statutory[1]\", ns)\nstats = cgr.find(\"./grpt:OtherCompanyInformation/grpt:StatutoryList\", ns)\nstatutories = []\nfor stat in stats:\n    statutory = {}\n    for item in statutory_parse:\n        e = stat.find(\"./\" + statutory_parse[item], ns)\n        if e is not None:\n            statutory[item] = e.text  # pro prázný element vrací None\n    clean_street_zip(statutory)\n    statutories.append(statutory)\n\nresponse = {\"applicant\": applicant, \"statutories\": statutories}\n\n\n# CompanyGlobalReport = root.find(\"\")\n\n# /CN_Res_Direct/Responses/Response/Data\n\n# [namespace-uri()='https://ws.urplus.sk' and local-name()='CribisGetGlobalReportResponse'][1]/*[namespace-uri()='https://ws.urplus.sk' and local-name()='CribisGetGlobalReportResult'][1]/*[namespace-uri()='https://ws.urplus.sk' and local-name()='Data'][1]/*[namespace-uri()='urn:crif-cribiscz-GetGlobalReport:2013-05-03' and local-name()='CompanyGlobalReport'][1]\n","sub_path":"toyota/toyota_old/cze_global_report.py","file_name":"cze_global_report.py","file_ext":"py","file_size_in_byte":5281,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"619551064","text":"'''\nreturn the id of max occurance\n\nOutput Format\nPrint the type number of the most common bird;\nif two or more types of birds are equally common, \nchoose the type with the smallest ID number.\n\nlink: https://www.hackerrank.com/challenges/migratory-birds/problem\n'''\nimport sys\n\ndef migratoryBirds(n, ar):\n    count = dict()\n    for bird in ar:\n        if bird in count:\n            count[bird] += 1\n        else:\n            count[bird] = 1\n    return max(count, key=count.get)\n\nn = int(raw_input().strip())\nar = map(int, raw_input().strip().split(' '))\nresult = migratoryBirds(n, ar)\nprint(result)","sub_path":"hackerrank/Implementation/MigratoryBirds.py","file_name":"MigratoryBirds.py","file_ext":"py","file_size_in_byte":598,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"631830827","text":"import sys\nimport csv\nimport sqlite3 as lite\n#http://stackoverflow.com/questions/24245245/pyqt-qstandarditemmodel-how-to-get-a-row-as-a-list\nfrom PyQt4 import QtCore, QtGui, uic\n#def load_gui():\nwindow1 = uic.loadUiType(\"appendSQLTableView.ui\")[0]     # \nclass WindowClass(QtGui.QMainWindow, window1): \n        def __init__(self, parent=None):\n            QtGui.QMainWindow.__init__(self, parent)\n            self.setupUi(self)\n            self.data=[]\n            self.model = QtGui.QStandardItemModel(self)\n            self.tableView.setModel(self.model)\n            self.load_data()\n            self.tableView.doubleClicked.connect(self.show_selected)\n            self.btn1.clicked.connect(self.add_record)\n            self.btn2.clicked.connect(self.upd_record)\n\n        def load_data(self):\n            \n            con = lite.connect('tests.db')#opens a file\n            cur = con.cursor()#keeps track of the \"tape\" position\n            #parameter query - get criteria from Python\n            s='select * from TestResults' #not case sensitive inside the string\n            cur.execute(s)\n##            s='''SELECT Title,Artistid FROM '''+table_name+''' WHERE Artistid=? ;'''\n##            cur.execute(s,(my_id,)) #run our query\n            self.data = cur.fetchall() #query data comes back\n            #as a python 2D tuple  (read-only list)\n            #clear all previus data, so that our model doesn't get longer and longer duplicating the records\n            if len(self.data)>0:\n                for i in range(len(self.data)-1,-1):\n                    print(\"removing row\",i)\n                    self.data.pop(i)\n            self.model=QtGui.QStandardItemModel(self)\n            self.tableView.setModel(self.model)\n            for row in self.data:\n                        items = [\n                            QtGui.QStandardItem(str(field))\n                            for field in row\n                        ]\n                        self.model.appendRow(items)\n            #print(self.model.data(self.model.index(2,1)))\n            \n        def show_selected(self):\n            index = self.tableView.currentIndex() #returns currently selected cell\n            r=index.row() #returns the row of the currently selected cell\n            c=index.column()#returns the column of the currently selected cell\n            cell_contents=index.data()#returns the contents of the currently selected cell\n            print(\"index,r,c\",r,c,cell_contents)\n            self.lbl_data.setText(cell_contents)\n            self.lbl_row.setText(str(r) )\n            self.lbl_col.setText(str(c) )\n\n        def add_record(self):\n            name=self.txt_name.text()\n            test1=self.txt_test1.text()\n            test2=self.txt_test2.text()\n            test3=self.txt_test3.text()\n            con = lite.connect('tests.db')#opens a file\n            cur = con.cursor()#keeps track of the \"tape\" position\n            #parameter query - get criteria from Python\n            s='insert INTO TestResults (Name,Test1,Test2,Test3) VALUES(?,?,?,?)' #not case sensitive inside the string\n            #cur.execute(\"INSERT INTO Forms VALUES(?,?,?)\",(100,\"12BB\",\"Mr Bobson\"))\n            cur.execute(s,(name,test1,test2,test3))\n            con.commit()\n            self.load_data()\n        def upd_record(self):\n            index = self.tableView.currentIndex() #returns currently selected cell\n##            r=index.row() #returns the row of the currently selected cell\n##            c=index.column()#returns the column of the currently selected cell\n            cell_contents=index.data()#returns the contents of the currently selected cell\n            \n            id=self.model.data(self.model.index(index.row(), 0))\n            print(id,self.model.data(self.model.index(index.row(), 1)))\n            name=self.model.data(self.model.index(index.row(), 1))\n            test1=self.model.data(self.model.index(index.row(), 2))\n            test2=self.model.data(self.model.index(index.row(), 3))\n            test3=self.model.data(self.model.index(index.row(), 4))\n            print(id,name,test1,test2, test3)\n            self.model.data(self.model.index(4,1))\n            con = lite.connect('tests.db')#opens a file\n            cur = con.cursor()#keeps track of the \"tape\" position\n            #parameter query - get criteria from Python\n            s='UPDATE TestResults SET Name=?,Test1=?,Test2=?,Test3=? WHERE ID=?' #not case sensitive inside the string\n            #cur.execute(\"UPDATE Pupils SET Name=?, Form=? WHERE Id=?\",(newName,newForm,id))\n            cur.execute(s,(name,test1,test2,test3,id))\n            con.commit()\n            self.load_data()\n            \n\napp = QtGui.QApplication(sys.argv)\nw1 = WindowClass(None)\n\nw1.show()\napp.exec_()\n","sub_path":"Misc/pyqtListSQLInTableView.py","file_name":"pyqtListSQLInTableView.py","file_ext":"py","file_size_in_byte":4740,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"570946566","text":"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\nfrom __future__ import unicode_literals\n\nfrom fractions import Fraction\n\nfrom celery import shared_task\nfrom celery import Task\n\nfrom legacy.energy_use_reports.tasks import TaskProgressMixin\nfrom gridplatform.customers.models import Customer\nfrom gridplatform.trackuser.tasks import trackuser_task\nfrom gridplatform.trackuser import get_customer\nfrom gridplatform.trackuser import get_user\n\nfrom .models import BenchmarkProject\nfrom .models import payback_period_years\n\n\n@trackuser_task\n@shared_task(\n    timelimit=1860, soft_time_limit=1800,\n    name='legacy.projects.tasks.AnnualSavingsPotentialReportTask')\nclass AnnualSavingsPotentialReportTask(TaskProgressMixin, Task):\n    def run(self, data):\n        self.update_state(\n            state='PROGRESS',\n            meta={\n                'task_user_id': get_user().id,\n                'current': 0,\n                'total': 0\n            }\n        )\n\n        self.PROGRESS_TOTAL = len(data['projects'])\n        self.progress = 0\n\n        total_expected_annual_cost_savings = Fraction(0)\n        total_subsidy = Fraction(0)\n        total_investment = Fraction(0)\n        for project in BenchmarkProject.objects.filter(\n                id__in=data['projects'].keys()):\n            expected_annual_cost_savings = Fraction(\n                project.expected_savings_in_yearly_total_costs or 0)\n            subsidy = Fraction(project.subsidy or 0)\n            investment = Fraction(project.investment)\n\n            total_expected_annual_cost_savings += expected_annual_cost_savings\n            total_subsidy += subsidy\n            total_investment += investment\n\n            data['projects'][project.id]['baseline_annual_consumption'] = \\\n                project.baseline_annual_consumption\n            data['projects'][project.id]['expected_annual_cost_savings'] = \\\n                expected_annual_cost_savings\n            data['projects'][project.id]['baseline_annual_costs'] = \\\n                project.baseline_annual_costs\n            data['projects'][project.id]['subsidy'] = subsidy\n            data['projects'][project.id]['investment'] = investment\n            data['projects'][project.id]['expected_payback_period_years'] = \\\n                project.expected_payback_period_years\n            self.tick_progress()\n\n        total_expected_payback_period = payback_period_years(\n            total_investment,\n            total_subsidy,\n            total_expected_annual_cost_savings)\n\n        data['total_expected_annual_cost_savings'] = \\\n            total_expected_annual_cost_savings\n        data['total_subsidy'] = total_subsidy\n        data['total_investment'] = total_investment\n        data['total_expected_payback_period'] = total_expected_payback_period\n        data['currency_unit'] = \\\n            Customer.objects.get(id=get_customer().id).\\\n            get_currency_unit_display()\n        return data\n","sub_path":"legacy/projects/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":2933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"436465538","text":"#!/home/sgo/anaconda3/bin/python3\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.gridspec as gs\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nfrom matplotlib.colors import Normalize\nimport shutil,math\nimport pandas as pd\nimport numpy as np\nimport pkg_resources\nimport pykonal\nimport os,time,sys\n\nsv = pd.read_csv('svpC.csv')\nsva = pd.read_csv('svpA.csv')\n\n\nErad=6371.\nm_s_dp=[   10,   13,   16]\nm_s_km=[ 40.0,  0.0,  0.0] # cm/s/km\n#m_s_dp=[   10,   20,   30]\n#m_s_km=[ 1.08,  0.0,  0.0] # cm/s/km\n#m_s_dp=[    3,    6,   30]\n#m_s_km=[-0.60, 4.20,  0.0] # cm/s/km\nnrh,nth,nph=50,100,100\ndrh,dth,dph=0.1, (10./Erad)/float(nth-1), (10./Erad)/float(nth-1)\nsrh,sth,sph=30,50,50\n\nvv = pykonal.fields.ScalarField3D(coord_sys='spherical')\ndv = pykonal.fields.ScalarField3D(coord_sys='spherical')\nvv.min_coords = dv.min_coords = [Erad-5., np.pi/2, np.pi/2]\nvv.node_intervals = dv.node_intervals = [drh,dth,dph]\nvv.npts = dv.npts = [nrh,nth,nph]\nv2 = pykonal.fields.ScalarField3D(coord_sys='spherical')\nd2 = pykonal.fields.ScalarField3D(coord_sys='spherical')\nv2.min_coords = d2.min_coords = [Erad-5., np.pi/2, np.pi/2]\nv2.node_intervals = d2.node_intervals = [drh,dth,dph]\nv2.npts = d2.npts = [nrh,nth,nph]\n\na0 = [[] for j in range(nrh)]\n#svp = [[] for j in range(nrh)]\nfor j in range(nrh):\n  svd = [[] for i in range(nth)]\n  for i in range(nth):\n    svd[i] = np.array([sv.speed[j]*0.001] * nph)\n  a0[j] = [svd[0]]\n  for i in range(1,nth):\n    a0[j] = np.append(a0[j], [svd[i]], axis=0)\n###\na1 = [[] for j in range(nrh)]\n#svp = [[] for j in range(nrh)]\nfor j in range(0,m_s_dp[0]):\n  svd = [[] for i in range(nth)]\n  for i in range(nth):\n    for p in range(nph):\n      a      = [sv.speed[j]*0.001+m_s_km[0]*0.001*float(p-50)]\n      svd[i] = np.append(svd[i], a)\n  a1[j] = [svd[0]]\n  for i in range(1,nth):\n    a1[j] = np.append(a1[j], [svd[i]], axis=0)\nfor j in range(m_s_dp[0],m_s_dp[1]):\n  svd = [[] for i in range(nth)]\n  for i in range(nth):\n    for p in range(nph):\n      a      = [sv.speed[j]*0.001+m_s_km[1]*0.001*float(p-50)]\n      svd[i] = np.append(svd[i], a)\n  a1[j] = [svd[0]]\n  for i in range(1,nth):\n    a1[j] = np.append(a1[j], [svd[i]], axis=0)\nfor j in range(m_s_dp[1],nrh):\n  svd = [[] for i in range(nth)]\n  for i in range(nth):\n    for p in range(nph):\n      a      = [sv.speed[j]*0.001+m_s_km[2]*0.001*float(p-50)]\n      svd[i] = np.append(svd[i], a)\n  a1[j] = [svd[0]]\n  for i in range(1,nth):\n    a1[j] = np.append(a1[j], [svd[i]], axis=0)\nvv.values = a0\ndv.values = a0\nv2.values = a1\nd2.values = a1\n\n# Resample the velocity model (64, 1, 128) --> (1024, 1, 2048)\nrho_min, theta_min, phi_min = vv.min_coords\nrho_max, theta_max, phi_max = vv.max_coords\nnrho, ntheta, nphi = nrh, nth, nph\n\ndrho = (rho_max - rho_min) / (nrho - 1)\nrho = np.linspace(rho_min, rho_max, nrho)\n\ndtheta = (theta_max - theta_min) / (ntheta - 1)\ntheta = np.linspace(theta_min, theta_max, ntheta)\n\ndphi = (phi_max - phi_min) / (nphi - 1)\nphi = np.linspace(phi_min, phi_max, nphi)\n\nrtp = np.moveaxis(\n    np.stack(np.meshgrid(rho, theta, phi, indexing=\"ij\")),\n    0, \n    -1\n)\n###\nvv_new = vv.resample(rtp.reshape(-1, 3)).reshape(rtp.shape[:-1])\ndv_new = dv.resample(rtp.reshape(-1, 3)).reshape(rtp.shape[:-1])\nvv = pykonal.fields.ScalarField3D(coord_sys=\"spherical\")\ndv = pykonal.fields.ScalarField3D(coord_sys=\"spherical\")\nvv.min_coords = dv.min_coords = rho_min, theta_min, phi_min\nvv.node_intervals = dv.node_intervals = drho, dtheta, dphi\nvv.npts = dv.npts = nrho, ntheta, nphi\nvv.values = vv_new\ndv.values = dv_new\nvelocity = vv\n###\nv2_new = v2.resample(rtp.reshape(-1, 3)).reshape(rtp.shape[:-1])\nd2_new = d2.resample(rtp.reshape(-1, 3)).reshape(rtp.shape[:-1])\nv2 = pykonal.fields.ScalarField3D(coord_sys=\"spherical\")\nd2 = pykonal.fields.ScalarField3D(coord_sys=\"spherical\")\nv2.min_coords = d2.min_coords = rho_min, theta_min, phi_min\nv2.node_intervals = d2.node_intervals = drho, dtheta, dphi\nv2.npts = d2.npts = nrho, ntheta, nphi\nv2.values = v2_new\nd2.values = d2_new\nvelocity2 = v2\n###\n\nSRC_IDX = np.array([srh, sth, sph])\n\ntraveltime_fields = dict()\ntraveltime2_fields = dict()\nfor decimation_factor in range(7, -1, -1):\n    decimation_factor = 2**decimation_factor\n    \n    vv = velocity.values[::decimation_factor, :, ::decimation_factor]\n    solver = pykonal.EikonalSolver(coord_sys=\"spherical\")\n    solver.vv.min_coords = velocity.min_coords\n    solver.vv.node_intervals = velocity.node_intervals * decimation_factor\n    solver.vv.npts = vv.shape\n    solver.vv.values = vv\n    src_idx = tuple(SRC_IDX // decimation_factor - [1, 0, 1])\n    solver.traveltime.values[src_idx] = 0\n    solver.unknown[src_idx] = False\n    solver.trial.push(*src_idx)\n    solver.solve()\n    traveltime_fields[decimation_factor] = solver.traveltime\n    \n    v2 = velocity2.values[::decimation_factor, :, ::decimation_factor]\n    solver2 = pykonal.EikonalSolver(coord_sys=\"spherical\")\n    solver2.vv.min_coords = velocity2.min_coords\n    solver2.vv.node_intervals = velocity2.node_intervals * decimation_factor\n    solver2.vv.npts = v2.shape\n    solver2.vv.values = v2\n    src_idx = tuple(SRC_IDX // decimation_factor - [1, 0, 1])\n    solver2.traveltime.values[src_idx] = 0\n    solver2.unknown[src_idx] = False\n    solver2.trial.push(*src_idx)\n    solver2.solve()\n    traveltime2_fields[decimation_factor] = solver2.traveltime\n\nplt.close('all')\nfig = plt.figure(figsize=(5, 7.5))\n\nfig.text(0, 0.5, \"$y$ [km]\", ha=\"left\", va=\"center\", rotation=90)\nfig.text(0.5, 0.05, \"$x$ [km]\", ha=\"center\", va=\"bottom\")\n\ngridspec = gs.GridSpec(nrows=4, ncols=1, height_ratios=[0.04,0.32,0.06,1.05])\n#gridspec = gs.GridSpec(nrows=4, ncols=1, height_ratios=[0.03,0.82,0.03,1.64])\ncax00 = fig.add_subplot(gridspec[0, 0])\ncax01 = fig.add_subplot(gridspec[2, 0])\nax10 = fig.add_subplot(gridspec[1, 0])\nax11 = fig.add_subplot(gridspec[3, 0])\n\npanel = ord(\"a\")\nfor ax in (ax10, ax11):\n    ax.text(-0.05, 1.1, f\"({chr(panel)})\", ha=\"right\", va=\"top\", transform=ax.transAxes)\n    panel += 1\n\ntt0 = traveltime_fields[1]\ntt1 = traveltime2_fields[1]\nnodes = tt0.nodes\nxx = Erad-nodes[...,0] * np.sin(nodes[...,1]) * np.cos(nodes[...,2])\nyy = Erad-nodes[...,0] * np.sin(nodes[...,1]) * np.sin(nodes[...,2])-2.\n#print(nodes[...,0]*(1.-np.sin(nodes[...,1])))\n#print(xx-nodes[...,0]*(1.-np.sin(nodes[...,1])))\nzz = nodes[...,0]*(1.-np.sin(nodes[...,1]))\n#print(tt0.values[:,0])\nax10.set_ylim(0,3)\nqmesh = ax10.pcolormesh(\n    xx[:,0],\n    yy[:,0],\n    v2_new[:,0,:]-vv_new[:,0,:],\n    cmap=plt.get_cmap(\"seismic\"),\n    shading=\"gouraud\",\n    norm=Normalize(vmin=-0.1, vmax=0.1)\n)\ncbar = fig.colorbar(qmesh, cax=cax00, orientation=\"horizontal\", ticks=[-0.1,-0.05,0,0.05,0.1])\ncbar.set_label(\"d-Velocity [m/s]\")\ncbar.ax.xaxis.tick_top()\ncbar.ax.xaxis.set_label_position(\"top\")\ncbar.set_clim(-0.1,0.1)\n\nx1 = nodes[...,2]*Erad-nodes[0,0,0,2]*Erad\ny1 = nodes[...,1]*Erad-nodes[0,0,0,2]*Erad\nqmesh = ax11.pcolormesh(\n    x1[0,:],\n    y1[0,:],\n    tt1.values[0,:,:]-tt0.values[0,:,:],\n    cmap=plt.get_cmap(\"coolwarm\"),\n    shading=\"gouraud\",\n    norm=Normalize(vmin=-0.02, vmax=0.02)\n)\nax11.contour(\n    x1[0,:],\n    y1[0,:],\n    tt1.values[0,:,:]-tt0.values[0,:,:],\n    colors=\"k\",\n    levels=np.arange(-0.1, 0.1, 0.001),\n    linewidths=0.2,\n    linestyles=\"--\"\n)\ncbar = fig.colorbar(qmesh, cax=cax01, orientation=\"horizontal\", ticks=[-0.02,-0.01,0.01,0.02])\ncbar.set_label(\"Traveltime [s]\")\ncbar.ax.xaxis.tick_top()\ncbar.ax.xaxis.set_label_position(\"top\")\n\nax10.set_xticklabels([])\nax11.plot([5, 5], [2, 8], 'w-', lw=2)\nax11.plot([2, 8], [5, 5], 'w-', lw=2)\nax11.plot([5-3/math.sqrt(2), 5+3/math.sqrt(2)], [5-3/math.sqrt(2), 5+3/math.sqrt(2)], 'w-', lw=2)\nax11.plot([5-3/math.sqrt(2), 5+3/math.sqrt(2)], [5+3/math.sqrt(2), 5-3/math.sqrt(2)], 'w-', lw=2)\nax11.plot([5, 8, 5, 2, 5], [2, 5, 8, 5, 2], 'w-', lw=2)\nax11.plot([5, 6.5, 5, 3.5, 5], [3.5, 5, 6.5, 5, 3.5], 'w-', lw=2)\nax11.plot(5, 5, marker='*',markersize=4)\ncbar.set_clim(-0.02,0.02)\nplt.savefig('figure.png')\nshutil.copyfile(\"figure.png\", \"/mnt/owncloud_webdav/webdav/figureS.png\")\n","sub_path":"pykonal/Spherical_3Dfig5.py","file_name":"Spherical_3Dfig5.py","file_ext":"py","file_size_in_byte":8009,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"325461129","text":"import matplotlib.pyplot as plt#为方便简介为plt\nimport numpy as np#画图过程中会使用numpy\nimport pandas as pd#画图过程中会使用pandas\n\nx=np.linspace(-3,3,50)\ny1=2*x+1\ny2=x**2\nplt.figure(num=2,figsize=(8,5))\nplt.plot(x,y1,color='red',linewidth=2,linestyle='-')\nplt.plot(x,y2)#进行画图\nplt.xlim(-1,2)\nplt.ylim(-2,3)\nplt.xlabel(\"I'm x\")\nplt.ylabel(\"I'm y\")\nnew_ticks=np.linspace(-1,2,5)#小标从-1到2分为5个单位\nprint(new_ticks)\n#[-1.   -0.25  0.5   1.25  2.  ]\nplt.xticks(new_ticks)#进行替换新下标\nplt.yticks([-2,-1,1,2,],\n           [r'$really\\  \\beta_x\\ bad$','$bad$','$well$','$really\\ well$'])\nplt.show()\n\n\n'''\nticks\n'''\nx=np.linspace(-3,3,50)\ny1=2*x+1\ny2=x**2\nplt.figure(num=2,figsize=(8,5))\nplt.plot(x,y1,color='red',linewidth=2,linestyle='--')\nplt.plot(x,y2)#进行画图\nplt.xlim(-1,2)\nplt.ylim(-2,3)\nnew_ticks=np.linspace(-1,2,5)#小标从-1到2分为5个单位\nplt.xticks(new_ticks)#进行替换新下标\nplt.yticks([-2,-1,1,2,],\n           [r'$really\\ bad$','$bad$','$well$','$really\\ well$'])\nax=plt.gca()#gca=get current axis\nax.spines['right'].set_color('none')#边框属性设置为none 不显示\nax.spines['top'].set_color('none')\nax.xaxis.set_ticks_position('both')#使用xaxis.set_ticks_position设置x坐标刻度数字或名称的位置 所有属性为top、bottom、both、default、none\nax.spines['bottom'].set_position(('data', 0))#使用.spines设置边框x轴；使用.set_position设置边框位置，y=0位置 位置所有属性有outward、axes、data\nax.yaxis.set_ticks_position('left')\nax.spines['left'].set_position(('data',0))#坐标中心点在(0,0)位置\nplt.show()\n\n\n'''\nlegneds\n'''\nx=np.linspace(-3,3,50)\ny1=2*x+1\ny2=x**2\nplt.figure(num=2,figsize=(8,5))\nplt.xlim(-1,2)\nplt.ylim(-2,3)\nnew_ticks=np.linspace(-1,2,5)#小标从-1到2分为5个单位\nplt.xticks(new_ticks)#进行替换新下标\nplt.yticks([-2,-1,1,2,],\n           [r'$really\\ \\beta\\ \\alpha\\ bad$','bad','$well$','$really\\ well$'])\n\nl1,=plt.plot(x,y1,color='red',linewidth=2,linestyle='--',label='linear line')\nl2,=plt.plot(x,y2,label='square line')#进行画图\nplt.legend(loc='best')#显示在最好的位置\nplt.show()#显示图\n\nplt.legend(handles=[l1, l2], labels=['up', 'down'],  loc='best')\n#loc有很多参数 其中best自分配最佳位置\n'''\n 'best' : 0,          \n 'upper right'  : 1,\n 'upper left'   : 2,\n 'lower left'   : 3,\n 'lower right'  : 4,\n 'right'        : 5,\n 'center left'  : 6,\n 'center right' : 7,\n 'lower center' : 8,\n 'upper center' : 9,\n 'center'       : 10,\n '''\n\nx=np.linspace(-3, 3, 50)\ny=0.1*x\nplt.figure()\nplt.plot(x, y, linewidth=10, zorder=1)\nplt.ylim(-2, 2)\n\n#移动坐标轴\nax = plt.gca()\nax.spines['right'].set_color('none')\nax.spines['top'].set_color('none')\nax.xaxis.set_ticks_position('bottom')\nax.spines['bottom'].set_position(('data', 0))\nax.yaxis.set_ticks_position('left')\nax.spines['left'].set_position(('data', 0))\n\n#label.set_fontsize(12)重新调整字体大小 bbox设置目的内容的透明度相关参数 facecolor调节box前景色 edgecolor设置边框 alpha设置透明度 zorder设置图层顺序\nfor label in ax.get_xticklabels() + ax.get_yticklabels():\n    label.set_fontsize(12)\n    label.set_bbox(dict(facecolor='red', edgecolor='None', alpha=0.7, zorder=2))\nplt.show()\n\n\n'''\nnotation\n'''\nx=np.linspace(-3,3,50)\ny = 2*x + 1\nplt.figure(num=1, figsize=(8, 5))\nplt.plot(x, y,)\n\n#移动坐标轴\nax = plt.gca()\nax.spines['right'].set_color('none')\nax.spines['top'].set_color('none')\nax.xaxis.set_ticks_position('bottom')\nax.spines['bottom'].set_position(('data', 0))\nax.yaxis.set_ticks_position('left')\nax.spines['left'].set_position(('data', 0))\n\n#标注信息\nx0=1\ny0=2*x0+1\nplt.scatter(x0,y0,s=50,color='b')\nplt.plot([x0,x0],[y0,0],'k--',lw=2.5)#连接(x0,y0)(x0,0) k表示黑色 lw=2.5表示线粗细\n#xycoords='data'是基于数据的值来选位置，xytext=(+30,-30)和textcoords='offset points'对于标注位置描述和xy偏差值，arrowprops对图中箭头类型设置\nplt.annotate(r'$2x0+1=%s$' % y0, xy=(x0, y0), xycoords='data', xytext=(+30, -30),\n             textcoords='offset points', fontsize=16,\n             arrowprops=dict(arrowstyle='->', connectionstyle=\"arc3,rad=.2\"))\n#添加注视text（-3.7,3）表示选取text位置 空格需要用\\进行转译 fontdict设置文本字体             \nplt.text(-3.7, 3, r'$This\\ is\\ the\\ some\\ text. \\mu\\ \\sigma_i\\ \\alpha_t$',\n         fontdict={'size': 16, 'color': 'r'})\nplt.show()\n\n\n\n'''\nsubplot\n'''\nfig=plt.figure()\n#创建数据\nx=[1,2,3,4,5,6,7]\ny=[1,3,4,2,5,8,6]\n\n#绘制大图：假设大图的大小为10，那么大图被包含在由(1,1)开始，宽8高8的坐标系之中。\nleft, bottom, width, height = 0.1, 0.1, 0.8, 0.8\nax1 = fig.add_axes([left, bottom, width, height])  # main axes\nax1.plot(x, y, 'r')#绘制大图，颜色为red\nax1.set_xlabel('x')#横坐标名称为x\nax1.set_ylabel('y')\nax1.set_title('title')#图名称为title\n\n#绘制小图，注意坐标系位置和大小的改变\nax2 = fig.add_axes([0.2, 0.6, 0.25, 0.25])\nax2.plot(y, x, 'b')#颜色为buue\nax2.set_xlabel('x')\nax2.set_ylabel('y')\nax2.set_title('title inside 1')\n\n#绘制第二个小兔\nplt.axes([0.6, 0.2, 0.25, 0.25])\nplt.plot(y[::-1], x, 'g')#将y进行逆序\nplt.xlabel('x')\nplt.ylabel('y')\nplt.title('title inside 2')\nplt.show()\n\n\n'''\ndouble axis\n'''\nx=np.arange(0,10,0.1)\ny1=0.5*x**2\ny2=-1*y1\nfig, ax1 = plt.subplots()\n\nax2 = ax1.twinx()#镜像显示\nax1.plot(x, y1, 'g-')\nax2.plot(x, y2, 'b-')\n\nax1.set_xlabel('X data')\nax1.set_ylabel('Y1 data', color='g')#第一个y坐标轴\nax2.set_ylabel('Y2 data', color='b')#第二个y坐标轴\nplt.show()","sub_path":"python/pyplot2.py","file_name":"pyplot2.py","file_ext":"py","file_size_in_byte":5598,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"362480449","text":"from pandas import * \nimport pandasql\nimport numpy as np\nimport MySQLdb as mdb\nimport sys\nimport string\nimport pdb\nimport datetime\nfrom random import randint\nimport csv\n\n#Number of lineups per active day of EPL games (at least 5 games being played)\nnum_lineups = 10\n\nBoxscores_DATA = 'boxscores/nba_players.csv'\nPlayers_DATA = 'positions/nba_positions.csv'\n\ncsvfile = open('lineups/nba_lineups.csv', 'w')\nfieldnames = ['Date','G1','G2','G3','F1','F2','F3','C','U1','U2','Total']\nwriter = csv.DictWriter(csvfile, fieldnames=fieldnames)\nwriter.writeheader()\n\ndef main():\n    games = pandas.read_csv(Boxscores_DATA)\n    players = pandas.read_csv(Players_DATA)\n\n    games = games[['Year','Date','GameID','PlayerName','Fantasy_Points']]\n\n    games = merge(games, players, on=['Year','PlayerName'], how='left')\n\n    games_fields = ['Date',\n                    'GameID',\n                    'PlayerName',\n                    'Position',\n                    'Fantasy_Points']\n    games = games[games_fields]\n    games = games[games.Fantasy_Points > 0]\n\n    guards = games[games.Position.isin(['G','SG','PG'])].sort('Fantasy_Points')\n    guards = guards[int(0.25*len(guards)):]\n    forwards = games[games.Position.isin(['F','SF','PF'])].sort('Fantasy_Points')\n    forwards = forwards[int(0.25*len(forwards)):] \n    centers = games[games.Position =='C'].sort('Fantasy_Points')\n    centers = centers[int(0.25*len(centers)):]\n    utilities = games.sort('Fantasy_Points')\n    utilities = utilities[int(0.25*len(utilities)):]\n    \n\n    dates = games['Date'].unique()\n    dates.sort()\n    for date in dates:\n        gameids = games[games.Date == date]['GameID'].unique()\n        try:\n            for j in range(0, num_lineups):\n\n                date_guards = guards[guards.Date == date].reset_index()\n                date_forwards = forwards[forwards.Date == date].reset_index()\n                date_centers = centers[centers.Date == date].reset_index()\n\n                a = randint(0, len(date_guards)-1)\n                g1_points = date_guards['Fantasy_Points'][a]\n                date_guards = date_guards[date_guards.index != a].reset_index().drop('level_0', axis = 1)\n\n                b = randint(0, len(date_guards)-1)\n                g2_points = date_guards['Fantasy_Points'][b]\n                date_guards = date_guards[date_guards.index != b].reset_index().drop('level_0', axis = 1)\n\n                c = randint(0, len(date_guards)-1)\n                g3_points = date_guards['Fantasy_Points'][c]\n                date_guards = date_guards[date_guards.index != c].reset_index().drop('level_0', axis = 1)\n\n                d = randint(0, len(date_forwards)-1)\n                f1_points = date_forwards['Fantasy_Points'][d]\n                date_forwards = date_forwards[date_forwards.index != d].reset_index().drop('level_0', axis = 1)\n\n                e = randint(0, len(date_forwards)-1)\n                f2_points = date_forwards['Fantasy_Points'][e]\n                date_forwards = date_forwards[date_forwards.index != e].reset_index().drop('level_0', axis = 1)\n\n                f = randint(0, len(date_forwards)-1)\n                f3_points = date_forwards['Fantasy_Points'][f]\n                date_forwards = date_forwards[date_forwards.index != f].reset_index().drop('level_0', axis = 1)\n\n                g = randint(0, len(date_centers)-1)\n                c_points = date_centers['Fantasy_Points'][g]\n                date_centers = date_centers[date_centers.index != g].reset_index().drop('level_0', axis = 1)\n                date_utilities = date_guards.append(date_forwards).append(date_centers).reset_index().drop('level_0', axis = 1)\n\n                h = randint(0, len(date_utilities)-1)\n                u1_points = date_utilities['Fantasy_Points'][h]\n                date_utilities = date_utilities[date_utilities.index != h].reset_index().drop('level_0', axis = 1)\n\n                i = randint(0, len(date_utilities)-1)\n                u2_points = date_utilities['Fantasy_Points'][i]\n\n                lineup_points = g1_points + g2_points + g3_points + f1_points + f2_points + f3_points + c_points+ u1_points + u2_points\n\n                data = {'Date': date,\n                        'G1': g1_points,\n                        'G2': g2_points,\n                        'G3': g3_points,\n                        'F1': f1_points,\n                        'F2': f2_points,\n                        'F3': f3_points,\n                        'C': c_points,\n                        'U1': u1_points,\n                        'U2': u2_points,\n                        'Total': lineup_points}\n\n                if len(gameids) >5:\n\n                    writer.writerow(data)\n\n        except Exception:\n            pass\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"nba_lineup_simulation.py","file_name":"nba_lineup_simulation.py","file_ext":"py","file_size_in_byte":4742,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"39271959","text":"from django.shortcuts import render, redirect \nfrom django.http import HttpResponse\nfrom django.forms import inlineformset_factory\nfrom django.contrib.auth.forms import UserCreationForm\nfrom django.contrib import messages,auth\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nimport json\nimport urllib\nfrom searchx import settings\n\n# Create your views here.\n\ndef loginpage(request):\n    if request.method == 'POST':\n        #Login User\n        username = request.POST['username'] \n        password = request.POST['password'] \n        recaptcha_response = request.POST.get('g-recaptcha-response')\n        url = 'https://www.google.com/recaptcha/api/siteverify'\n        values = {\n            'secret' : settings.GOOGLE_RECAPTCHA_SECRET_KEY,\n            'response': recaptcha_response\n        }\n        data = urllib.parse.urlencode(values).encode()\n        req = urllib.request.Request(url, data=data)\n        response = urllib.request.urlopen(req)\n        result = json.loads(response.read().decode())\n        if result['success']:\n            user = auth.authenticate(username=username,password=password)\n            if user is not None:\n                auth.login(request,user)\n                messages.success(request,'You are now logged in')\n                return redirect('/user/user.html')\n            else:\n                messages.error(request, 'Invalid Credentials')\n                return redirect('/login')\n        else:\n            \n            messages.error(request, 'Invalid reCAPTCHA, Please Try again')\n            return redirect('/login')\n\ndef logoutUser(request):\n\tlogout(request)\n\treturn redirect('/')","sub_path":"searchx/login/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1729,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"476945376","text":"from django.conf import settings\nfrom django.conf.urls import url\nfrom django.conf.urls.i18n import i18n_patterns\nfrom django.contrib import admin\nfrom django.urls import include, path\nfrom drf_yasg import openapi\nfrom drf_yasg.views import get_schema_view\nfrom rest_framework import permissions, routers\n\nfrom map import views as map_views\nfrom traffic_control.views import (\n    additional_sign as additional_sign_views,\n    barrier as barrier_views,\n    mount as mount_views,\n    operational_area as operational_area_views,\n    owner as owner_views,\n    plan as plan_views,\n    road_marking as road_marking_views,\n    signpost as signpost_views,\n    traffic_light as traffic_light_views,\n    traffic_sign as traffic_sign_views,\n)\n\nrouter = routers.DefaultRouter()\nrouter.register(\n    \"barrier-plans\",\n    barrier_views.BarrierPlanViewSet,\n)\nrouter.register(\n    \"barrier-reals\",\n    barrier_views.BarrierRealViewSet,\n)\nrouter.register(\n    \"traffic-light-plans\",\n    traffic_light_views.TrafficLightPlanViewSet,\n)\nrouter.register(\n    \"traffic-light-reals\",\n    traffic_light_views.TrafficLightRealViewSet,\n)\nrouter.register(\n    \"traffic-sign-plans\",\n    traffic_sign_views.TrafficSignPlanViewSet,\n)\nrouter.register(\n    \"traffic-sign-reals\",\n    traffic_sign_views.TrafficSignRealViewSet,\n)\nrouter.register(\n    \"additional-sign-plans\",\n    additional_sign_views.AdditionalSignPlanViewSet,\n)\nrouter.register(\n    \"additional-sign-reals\",\n    additional_sign_views.AdditionalSignRealViewSet,\n)\nrouter.register(\n    \"additional-sign-content-plans\",\n    additional_sign_views.AdditionalSignContentPlanViewSet,\n)\nrouter.register(\n    \"additional-sign-content-reals\",\n    additional_sign_views.AdditionalSignContentRealViewSet,\n)\nrouter.register(\n    \"mount-plans\",\n    mount_views.MountPlanViewSet,\n)\nrouter.register(\n    \"mount-reals\",\n    mount_views.MountRealViewSet,\n)\nrouter.register(\n    \"signpost-plans\",\n    signpost_views.SignpostPlanViewSet,\n)\nrouter.register(\n    \"signpost-reals\",\n    signpost_views.SignpostRealViewSet,\n)\nrouter.register(\n    \"road-marking-plans\",\n    road_marking_views.RoadMarkingPlanViewSet,\n)\nrouter.register(\n    \"road-marking-reals\",\n    road_marking_views.RoadMarkingRealViewSet,\n)\nrouter.register(\n    \"traffic-control-device-types\",\n    traffic_sign_views.TrafficControlDeviceTypeViewSet,\n)\nrouter.register(\n    \"plans\",\n    plan_views.PlanViewSet,\n)\nrouter.register(\n    \"portal-types\",\n    mount_views.PortalTypeViewSet,\n)\nrouter.register(\n    \"mount-types\",\n    mount_views.MountTypeViewSet,\n)\nrouter.register(\n    \"operational-areas\",\n    operational_area_views.OperationalAreaViewSet,\n)\nrouter.register(\n    \"owners\",\n    owner_views.OwnerViewSet,\n)\nschema_view = get_schema_view(\n    openapi.Info(\n        title=\"City Infrastructure Platform REST API\",\n        default_version=\"v1\",\n        description=\"\"\"\n            <b>Traffic Control devices</b>\n\n            Provides REST API for Traffic Control devices, such as Traffic Signs, Traffic Lights, Barriers,\n            SignPosts, Mounts and Road Markings.\n\n            These devices have planned and realized entities in the platform and therefore also equivalent\n            REST-endpoints.\n\n            Entity location output format can be controlled via \"geo_format\" GET-parameter.\n            Supported formats are ewkt and geojson. EWKT is the default format.\n        \"\"\",\n        terms_of_service=\"https://opensource.org/licenses/MIT\",\n        license=openapi.License(name=\"MIT\"),\n    ),\n    public=True,\n    permission_classes=(permissions.AllowAny,),\n    validators=[\"ssv\"],\n)\n\nurlpatterns = [\n    path(\"ha/\", include(\"helusers.urls\", namespace=\"helusers\")),\n    path(\"v1/\", include((router.urls, \"traffic_control\"), namespace=\"v1\")),\n    path(\"auth/\", include(\"social_django.urls\", namespace=\"social\")),\n    url(\n        r\"^swagger(?P<format>\\.json|\\.yaml)$\",\n        schema_view.without_ui(cache_timeout=0),\n        name=\"schema-json\",\n    ),\n    url(\n        r\"^swagger/$\",\n        schema_view.with_ui(\"swagger\", cache_timeout=0),\n        name=\"schema-swagger-ui\",\n    ),\n    url(\n        r\"^redoc/$\", schema_view.with_ui(\"redoc\", cache_timeout=0), name=\"schema-redoc\"\n    ),\n    path(\"sentry-debug/\", lambda a: 1 / 0),\n]\n\nurlpatterns += i18n_patterns(\n    path(\"admin/\", admin.site.urls),\n    path(\"map/\", map_views.map_view, name=\"map-view\"),\n    path(\"map-config/\", map_views.map_config, name=\"map-config\"),\n)\n\nif settings.DEBUG:\n    from django.conf.urls.static import static\n\n    urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n","sub_path":"city-infrastructure-platform/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":4572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"579784834","text":"name = input(\"Enter file:\")\r\nif len(name) < 1 : name = \"mbox-short.txt\"\r\nhandle = open(name)\r\nlst = list()\r\nbox = dict()\r\nfor line in handle:\r\n    if not line.startswith('From '): continue\r\n    nl = line.split()\r\n    lst.append(nl[1])\r\n    box[nl[1]] = box.get(nl[1], 0) + 1\r\n\r\nbign = None\r\nfor s,n in box.items():\r\n    if bign is None or n > bign:\r\n        bign = n\r\n        bigs = s\r\n        \r\nprint(bigs,bign)\r\n","sub_path":"Ass_9.4_list+dict.py","file_name":"Ass_9.4_list+dict.py","file_ext":"py","file_size_in_byte":414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"121225035","text":"#!/usr/bin/env python3\n\nfrom tkinter import *\nfrom random import *\n\nGAME_SPEED = 50\n\n\nclass DefenseLaser:\n    def __init__(self, c):\n        self.c = c\n        self.lx = 640/2\n        self.ly = 480\n        self.id = c.create_line(self.lx, self.ly, self.lx, self.ly)\n\n    def shot_at(self, x, y):\n        self.c.coords(self.id, self.lx, self.ly, x, y)\n\n    def disengage(self):\n        self.shot_at(self.lx, self.ly)\n\n\nclass House:\n    def __init__(self, c, x):\n        self.c, y = c, 450\n        self.id = c.create_rectangle(x, y, x+30, y+30)\n        self.in_expl = False\n\n    def do_delete(self):\n        self.c.delete(self.id)\n\n\nclass Missile:\n    def __init__(self, c):\n        self.in_expl = False\n        self.explosion_r = 0\n        x = randint(0, 640)\n        a = (x, 0, x + 5, 10)\n        self.c = c\n        self.id = c.create_oval(*a)\n\n    def update(self):\n        # sprawdź czy rakieta jeszcze istnieje\n        if self.explosion_r > 10:\n            return False\n        if self.in_expl:\n            # rakieta nie skończyła jeszcze wybuchać, powiększ zasięg eksplozji\n            self.explosion_r += 1\n            x1, y1, x2, y2 = self.c.coords(self.id)\n            self.c.coords(self.id, x1-self.explosion_r, y1-self.explosion_r, x2+self.explosion_r, y2+self.explosion_r)\n        return True\n\n    def check_dammage_to_missile(self, m):\n        # sprzwdź czy ta rakieta zniszczyła rakietę m\n        if self.in_expl and not m.in_expl:\n            for o in self.c.find_overlapping(*self.c.coords(self.id)):\n                if m.id == o:\n                    return True\n        return False\n\n    def check_dammage_to_house(self, m):\n        # sprzwdź czy ta rakieta zniszczyła dom\n        for o in self.c.find_overlapping(*self.c.coords(self.id)):\n            if m.id == o:\n                return True\n        return False\n\n    def do_fly(self):\n        # utwórz nową pozycję\n        dx = randint(-3, 3)  # losowy wiatr\n        dy = 2\n        self.c.move(self.id, dx, dy)\n\n    def explode(self):\n        self.in_expl = True\n\n    def do_delete(self):\n        self.c.delete(self.id)\n\n\ntk = Tk()\ntk.bind('<Escape>', lambda e: tk.quit())\ntk.title('M i s s i l e  C o m m a n d')\ntk.geometry('640x480')\ntk.resizable(0, 0)\nc = Canvas(tk)\nstatus_l = c.create_text((100, 14), text='INCOMING MISSILES!')\nblink_stat = NORMAL\nblink_job = None\n\ndef do_blink_label():\n    global blink_stat, blink_job\n    c.itemconfigure(status_l, state=blink_stat)\n    if blink_stat == NORMAL:\n        blink_stat = HIDDEN\n    else:\n        blink_stat = NORMAL\n    blink_job = tk.after(250, do_blink_label)\n\ndo_blink_label()\nc.pack(fill=BOTH, expand=True)\ndef_l = DefenseLaser(c)\nhouses = [House(c, 30*i) for i in range(1, 21, 2)]\nmissiles = []\n\ndef on_btn_press(e):\n    # narysuj laser\n    def_l.shot_at(e.x, e.y)\n    # sprawdź każdą rakietę czy kliknięty punkt znajduje się w obrębie rakiety\n    for m in missiles:\n        x1, y1, x2, y2 = c.coords(m.id)\n        if x1 <= e.x <= x2 and y1 <= e.y <= y2:\n            # eksploduj rakietę\n            m.explode()\n\nc.bind('<ButtonPress-1>', on_btn_press)\nc.bind('<ButtonRelease-1>', lambda e: def_l.disengage())\n\n\ndef draw():\n    # czy dodać jakieś rakiety?\n    if random() > 0.95:\n        missiles.append(Missile(c))\n\n    # sprawdź status każdej rakiety...\n    i = 0\n    while i < len(missiles):\n        m = missiles[i]\n        # zdetonuj wszystkie rakiety w zasięgu wbuchu wszystkich\n        # wybuchających rakiet\n        for k in missiles:\n            if m.check_dammage_to_missile(k):\n                k.explode()\n        # zdetonuj każdą rakietę która uderzyła w dach domu\n        for h in houses:\n            if m.check_dammage_to_house(h):\n                m.explode()\n        # zniszcz wszystkei domy w zasięgu wybuchających rakiet\n        j = 0\n        while j < len(houses):\n            h = houses[j]\n            if m.check_dammage_to_house(h):\n                h.do_delete()\n                houses.remove(h)\n            j += 1\n\n        # zmień położenie rakiet\n        m.do_fly()\n\n        # zdetonuj każdą rakietę która uderzyła w ziemię\n        x1, y1, x2, y2 = c.coords(m.id)\n        if y2 >= 480:\n            # zrób wybuch\n            m.explode()\n\n        # jeśli rakieta skończyła wybuchać to usuń\n        # jeśli nie to zwiększ zasięg eksplozji\n        if not m.update():\n            m.do_delete()\n            missiles.remove(m)\n\n        i += 1\n\n    if not houses:\n        tk.after_cancel(blink_job)\n        c.itemconfigure(status_l, text='P R Z E G R A Ł E Ś', state=NORMAL)\n\n    tk.after(GAME_SPEED, draw)\n\nseed()\ndraw()\ntk.mainloop()\n","sub_path":"src/missile_command.py","file_name":"missile_command.py","file_ext":"py","file_size_in_byte":4630,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"106601258","text":"import logging\nimport datetime\n# ログレベルを DEBUG に変更\nnow = datetime.datetime.now()\n#filename = 'log_' + now.strftime('%Y%m%d_%H%M%S') + '.csv'\nformatter = '%(levelname)s : %(asctime)s : %(message)s'\n\nlogging.basicConfig(filename='logger.log',\n                    level=logging.DEBUG, format=formatter)\n# logging.StreamHandler()\n# 従来の出力\nlogging.info('error{}'.format('outputting error'))\nlogging.info('warning %s %s' % ('was', 'outputted'))\n# logging のみの書き方\na = 1\nlogging.info('info')\n","sub_path":"saclog/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":521,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"583929938","text":"from bisect import bisect_left, bisect_right\n\nN, Q = map(int, input().split())\nA = list(map(int, input().split()))\nX = [int(input()) for _ in range(Q)]\n\nA.reverse()\n\nevenA = [0] * (N + 1)\naccA = [0] * (N + 1)\n\nfor i in range(1, N + 1):\n    evenA[i] = evenA[i - 1] + (A[i - 1] if i % 2 == 1 else 0)\n    accA[i] = accA[i - 1] + A[i - 1]\n\nborders = []\nscores = []\n\nfor i in range(1, N // 2 + N % 2):\n    border = (A[i] + A[i * 2]) // 2 + 1\n    borders.append(border)\n\n    score = accA[i] + (evenA[N] - evenA[i * 2])\n    scores.append(score)\n\nscores.append(accA[N // 2 + N % 2])\n\nborders.reverse()\nscores.reverse()\n\nfor x in X:\n    i = bisect_right(borders, x)\n    print(scores[i])\n","sub_path":"AtCoder/other/エイジング_プログラミング_コンテスト_2019/d.py","file_name":"d.py","file_ext":"py","file_size_in_byte":678,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"596816207","text":"# The isBadVersion API is already defined for you.\n# @param version, an integer\n# @return a bool\n# def isBadVersion(version):\n\nclass Solution(object):\n    def firstBadVersion(self, n):\n        l = 1\n        h = n\n        while l<h:\n            m = (l+h)/2\n            if isBadVersion(m):\n                h = m\n            else:\n                l = m + 1\n        return l\n        ","sub_path":"First Bad Version.py","file_name":"First Bad Version.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"302178713","text":"\"\"\"This utility fills out nulls in the ident column by copying down\nthe last non-null value above.\n\nThis is useful for speeding up manual entry of idents.  As the typical case\nis there are many consecutive rows referring to the same person, one can\nfill in the ident for the first row referring to the person, and then use\nthis tool to copy that ident down to the remaining rows.\n\nObviously, this should be used with some care, as it is important that the\nfirst row for each person does have an ident entered.  Cases where there are\nmultiple people with the same surname do still need to be handled manually,\nas those records may be intermingled in the sort.\n\"\"\"\n\nimport pandas as pd\n\nif __name__ == '__main__':\n    import sys\n\n    fn = sys.argv[1]\n    df = pd.read_csv(fn, dtype=str, encoding='utf-8')\n    df['ident'] = df['ident'].fillna(method='pad')\n    df.to_csv(fn, index=False, encoding='utf-8')\n    \n","sub_path":"src/fillident.py","file_name":"fillident.py","file_ext":"py","file_size_in_byte":908,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"199268052","text":"lista = []\r\ncont = 0\r\nwhile True:\r\n    sair = ' '\r\n    lista.append(int(input('Digite um valor para pôr na lista: ')))\r\n    cont += 1\r\n    while sair not in 'SsNn':\r\n        sair = str(input('Você deseja continuar? [S/N] '))\r\n    if sair in 'Nn':\r\n        break\r\nprint(f'Foram digitados {cont} números')\r\nlista.sort(reverse=True)\r\nprint(f'A lista em forma decrescente é {lista}')\r\nif 5 in lista:\r\n    print('O valor 5 esta na lista')\r\nelse:\r\n    print('O valor 5 não esta na lista')","sub_path":"aula 17/desafio 081.py","file_name":"desafio 081.py","file_ext":"py","file_size_in_byte":487,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"338530377","text":"import sys\nimport os\nimport cv2\nimport time\nimport imutils\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow import keras\nimport math\n\ncandidateDirCount = 0\ncandidateArr = []\n\ndef detect (shapeC):\n\t\tshape = 'notFound'\n\n\t\t# The perimeter of the shape is used for the approxCountour. Approx Contour gives the vertices of the\n\t\t# shape, and the perimeter is used for convex hull. The larger the 0.04*perimiter is the less detailed\n\t\t# the approx countour becomes. IE if the value was 0.1 for a hand the countour would be a mitten as \n\t\t# spaces inbetween fingers would be skipped\n\t\tperimeter = cv2.arcLength(shapeC, True)\n\t\tapproxCountour = cv2.approxPolyDP(shapeC, 0.04 * perimeter, True)\n\t\tx = 0\n\t\ty = 0\n\t\twidth = 0\n\t\theigth = 0\n\n\t\t# approxCountour length will give the vertices of the shape\n\t\t# a four vertex shape is what we are looking for\n\t\tif len(approxCountour) == 4:\n\n\t\t\t# get bounding box info for the mask\n\t\t\t(x, y, width, heigth) = cv2.boundingRect(approxCountour)\n\t\t\tshape = \"rectangle\"\n\n\t\telse:\n\t\t\tshape = \"notFound\"\n\n\t\t# return the name of the shape x y width heigth\n\t\treturn [shape, x, y, width, heigth]\n\ndef getSquares(image, candidateDir):\n\t# resizing for better countour approximation given by the gray scale and thresholded image\n\t# save the ratio for later to convert y and x for mask\n\tresized = imutils.resize(image, width=300)\n\tratio = image.shape[0] / float(resized.shape[0])\n\n\t# starting value of threshold for mapping 0 -> 255 to, this gets incremented if the number of shapes found\n\t# is too small. If the number of shapes found is too small (<20) then it means a large portion of the image got\n\t# clumped together and some seperation is needed, which is what the while loop below does\n\tthreshVal = 100\n\tgray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)\n\tthresh = cv2.threshold(gray, threshVal, 255, cv2.THRESH_BINARY)[1]\n\tcountours = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\n\t# grab correct value of contours\n\tcountours = countours[0] if imutils.is_cv2() else countours[1]\n\n\t# see comment two above\n\twhile (len(countours)<20 and threshVal<255):\n\t\tthresh = cv2.threshold(gray, threshVal, 255, cv2.THRESH_BINARY)[1]\n\t\tcountours = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\t\tcountours = countours[0] if imutils.is_cv2() else countours[1]\n\t\tthreshVal += 5\n\n\n\tglobal candidateDirCount\n\tglobal candidateArr\n\n\t# loop over the contours\n\tfor c in countours:\n\t\t# compute the center of the contour pass to the detect method to check if rectangle\n\t\tM = cv2.moments(c)\n\t\tif M[\"m00\"] > 0:\n\t\t\tls = detect(c)\n\t\t\tshape = ls[0]\n\t\t \t\n\t\t \t# only do the rest if it is a rectangle\n\t\t\tif shape == 'notFound': continue\n\n\t\t\tx = int(ls[1] * ratio)\n\t\t\ty = int(ls[2] * ratio)\n\t\t\tw = int(ls[3] * ratio)\n\t\t\th = int(ls[4] * ratio)\n\n\t\t\t# multiply the contour by the resize ratio,\n\t\t\t# then draw the contours onto a mask and fill in\n\t\t\tc = c.astype(\"float\")\n\t\t\tc *= ratio\n\t\t\tc = c.astype(\"int\")\n\n\t\t\t# print(len(c))\n\t\t\tmask = np.zeros(image.shape[:2], dtype=\"uint8\") * 255\n\n\t\t\tcv2.drawContours(image, [c], -1, 1, 2)\n\n\t\t\t# cv2.imshow('Output', image)\n\t\t\t# cv2.waitKey(0)\n\n\n\t\t\tcv2.drawContours(mask, [c], -1, 1, 2)\n\t\t\tcv2.fillPoly(mask, pts = [c], color=(1,1,1))\n\t\t\t\n\t\t\t# apply the mask to the image and crop to just the masked area\n\t\t\tresult = cv2.bitwise_and(image.copy(), image.copy(), mask = mask)\n\t\t\tcroped = result[y:y+h, x:x+w].copy()\n\n\t\t\t# resize image to 128x128\n\t\t\tresized = cv2.resize(croped, (128, 128))\n\n\t\t\tcv2.imwrite(candidateDir+\"candidate_%d.png\" % candidateDirCount, resized)\n\t\t\tcandidateArr.append(resized)\n\t\t\tcandidateDirCount += 1\n\n\n#  The following is basic input checking until a valid video and \n# start frame are given\nvidName = \"\"\nvidBool = False\nvidFile = None\nsucces = None\nimage = None\n\nwhile(not vidBool):\n\tprint(\"Video filepath:\")\n\tvidName = input()\n\tif (not os.path.isfile(vidName)):\n\t\tprint(\"Please give a valid filepath\\n\")\n\telse:\n\t\tvidFile = cv2.VideoCapture(vidName)\n\t\tsuccess,image = vidFile.read()\n\t\tif(not success):\n\t\t\tprint(\"Filepath given does not provide a valid video file\\n\")\n\t\telse:\n\t\t\tvidBool = True\n\nmaxFrame = int(vidFile.get(cv2.CAP_PROP_FRAME_COUNT))-10\n\nprint()\n\nstartFrame = 0\nframeBool = False\nwhile(not frameBool):\n\tintBool = True\n\tprint(\"Frame to start count on:\")\n\ttry:\n\t\tstartFrame = int(input())\n\texcept:\n\t\tprint(\"Please give an integer\\n\")\n\t\tintBool = False\n\tif(intBool):\n\t\tif startFrame > maxFrame:\n\t\t\tprint(\"Start frame must be below \"+str(maxFrame)+\"\\n\")\n\t\telif startFrame < 0:\n\t\t\tprint(\"Start frame must be above 0\\n\")\n\t\telse:\n\t\t\tframeBool = True\n\ncurrentTime = time.time()\n\n# creating the folders to hold the files\nif(not os.path.exists(str(currentTime)+\"/\")):\n\tos.mkdir(str(currentTime)+\"/\")\n\nframeDir = str(currentTime)+\"/frames/\"\ncandidateDir = str(currentTime)+\"/candidates/\"\n\nos.mkdir(frameDir)\nos.mkdir(candidateDir)\ncount = 0\nframeCount = 0\nimageList = []\n\n# grabbing the frames using the startframe from user\nwhile success and count<10:\n\tif frameCount >= startFrame:\n\t\tcv2.imwrite(frameDir+\"frame_%d.jpg\" % frameCount, image)\n\t\timageList.append(image)\n\t\tcount += 1\n\tframeCount += 1\n\tsuccess,image = vidFile.read()\n\n# having each frame get its candidate stored in the candidates folder\nfor img in imageList:\n\tgetSquares(img, candidateDir)\n\nmodel = tf.keras.models.load_model(\n    'currentBest',\n    custom_objects=None,\n    compile=False\n)\n\nbestImage = None\nbestScore = 0\n\n# creating filewriter for the csv\nfileout = open(str(currentTime)+\"/scores.csv\", 'w')\nfileout.write(\"filename,is code block score,is not code block score]n\")\n\n# itterate through the candidates and score them\nfor image in os.listdir(os.getcwd()+\"/\"+candidateDir):\n\tif (image!='.DS_Store'):\n\t\tcurrImage = cv2.imread(os.getcwd()+'/'+candidateDir+image, cv2.IMREAD_GRAYSCALE)\n\t\tcurrImage = np.array(currImage)\n\t\tcurrImage = currImage.reshape(-1,128,128)\n\n\t\tcurrScore = model.predict(currImage)\n\n\t\tfileout.write(image+\",\"+str(currScore[0][1])+\",\"+str(currScore[0][0])+\"\\n\")\n\n\t\tif (currScore[0][1]>bestScore):\n\t\t\tbestScore = currScore[0][1]\n\t\t\tbestImage = cv2.imread(os.getcwd()+'/'+candidateDir+image)\n\nfileout.close()\n\n# select the best scored candidate for the cnnSelectedImage\nif bestScore > 0:\n\tcv2.imwrite(str(currentTime)+\"/cnnSelectedImage.png\", bestImage)\n\n# all of the below is to make the images a square that are outputted for the user to view\n# the images are formatted into a square concatanated image using np and have black images\n# appened at the end to round out the square\n\n# this is done for both candidates and frames\nrows = math.ceil(math.sqrt(len(candidateArr)))\n\nnpvertarr = []\n\ncount = 0\n\nfor i in range(0, rows):\n\tnewArr = []\n\tlengthForEach = rows\n\tfor j in range(0, rows):\n\t\tnewArr.append(candidateArr[count])\n\t\tcount += 1\n\t\tlengthForEach -= 1\n\t\tif(count>=len(candidateArr)):\n\t\t\tbreak\n\twhile(lengthForEach!=0):\n\t\tnewArr.append(np.zeros((128,128,3), dtype=np.uint8))\n\t\tlengthForEach -= 1\n\tnpvertarr.append(np.concatenate(newArr, axis=0))\n\tif(count>=len(candidateArr)):\n\t\tbreak\n\nnpVertConcat = np.concatenate(npvertarr, axis = 1)\n\nrows = math.ceil(math.sqrt(len(imageList)))\n\nimgListArr = []\n\ncount = 0\n\nfor i in range(0, rows):\n\tnewArr = []\n\tlengthForEach = rows\n\tfor j in range(0, rows):\n\t\tnewArr.append(imageList[count])\n\t\tcount += 1\n\t\tlengthForEach -= 1\n\t\tif(count>=len(imageList)):\n\t\t\tbreak\n\twhile(lengthForEach!=0):\n\t\tnewArr.append(np.zeros((len(imageList[0]),len(imageList[0][0]),3), dtype=np.uint8))\n\t\tlengthForEach -= 1\n\timgListArr.append(np.concatenate(newArr, axis=0))\n\tif(count>=len(imageList)):\n\t\tbreak\n\nframeConcat = np.concatenate(imgListArr, axis = 1)\n\n# showing to user\ncv2.imshow('Frames Togetger', frameConcat)\ncv2.waitKey()\ncv2.imshow('Candidates Together', npVertConcat)\ncv2.waitKey()\ncv2.imshow('Detected Code Block', bestImage)\ncv2.waitKey()\n\n\n\n","sub_path":"process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":7798,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"501077820","text":"class Solution:\n    def numRollsToTarget(self, d: int, f: int, target: int) -> int:\n        modulo = 10 ** 9 + 7\n\n        dp = [[0] * 1001 for _ in range(d)]\n        for i in range(f):\n            dp[0][i + 1] = 1\n\n        for dice in range(1, d):\n            for val in range(1001):\n                for face in range(1, f + 1):\n                    if val + face < 1001:\n                        dp[dice][val + face] += (dp[dice - 1][val] % modulo)\n        return dp[-1][target] % modulo","sub_path":"LeetCode/1100/1155.py","file_name":"1155.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"527838754","text":"import datetime\nimport gc\nimport random\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport pickle\nimport sys, os\nimport datetime\n\nfrom my_utils import mape, mpe, r2, is_local_symme_separate, train_forward_case, test_forward_case, \\\n    get_single_edge_index, get_single_edge_attr, count_parameters, graph2df\nfrom pnu_semiconductor.gnn_mdi_semi_inductive import calc_result\n\nsys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))\n\nfrom models.gnn_model_great import get_gnn\nfrom models.prediction_model import MLPNet\nfrom utils.plot_utils import plot_curve, plot_sample\nfrom utils.utils import build_optimizer, objectview, get_known_mask, mask_edge\n\n\ndef train_gnn_mdi(datas, args, log_path, logs, device=torch.device('cpu')):\n    model = get_gnn(datas[0], args).to(device)  # gnn모델을 가져옴. outputdim=64\n    impute_hiddens = list(map(int, args.impute_hiddens.split('_')))  # 이것도 문자열로 원래 나와있나봐. 여기선 args.impute_hiddens='64'\n    input_dim = args.node_dim * 2\n    output_dim = 1  # impute_model. 실제로 impute하는 모델. 앞의 과정은 어떻게 보면 전처리라고 볼 수도 있다. 실제 모델은 얘임.\n    # input_dim = 128, output_dim = 1. gnn의 outputdim = 64이다. 하지만 concat하기 때문에 128이 된 듯.\n    impute_model = MLPNet(input_dim, output_dim,\n                          # imputation에서 Oedge()로 예측한다 했는데 이는 MLP이다. 이것도 얘들이 만든 것이다. input_dim=128, output_dim=1\n                          hidden_layer_sizes=impute_hiddens,  # 기본값으로 64이고 impute_hiddens = [64]를 가지고 있다. 64layer MLP임.\n                          hidden_activation=args.impute_activation,  # relu\n                          dropout=args.dropout).to(device)  # 0\n    # if args.transfer_dir: # this ensures the valid mask is consistant. None이다.\n    #     load_path = './{}/test/{}/{}/'.format(args.domain,args.data,args.transfer_dir)\n    #     print(\"loading fron {} with {}\".format(load_path,args.transfer_extra))\n    #     model = torch.load(load_path+'model'+args.transfer_extra+'.pt',map_location=device)\n    #     impute_model = torch.load(load_path+'impute_model'+args.transfer_extra+'.pt',map_location=device)\n\n    trainable_parameters = list(model.parameters()) \\\n                           + list(\n        impute_model.parameters())  # type(model.parameters())=generator. generator[0] = tensor\n    param_cnt = count_parameters(model) + count_parameters(impute_model)\n    print(\"total trainable_parameters: \",param_cnt) # type(trainable_parameters[0])==torch.nn.parameter.Parameter\n\n    # build optimizer. Adam을 사용하고 별도의 scheduler를 사용하지 않는다.\n    scheduler, opt = build_optimizer(args, trainable_parameters)  # None, Adam\n\n    Train_loss = []\n    Test_rmse = []\n    Test_l1 = []  # MAE를 뜻함.\n    # Lr = []  # learning rate\n    # TestMAPE= []\n    # TestMPE= []\n    # TestR2 = []\n    obj = dict()\n    obj['args'] = args\n    obj['outputs'] = dict()\n    cnt = 0\n    # ------------------------------\n    for epoch in range(args.epochs):  # 학습시키는 부분. epoch기본값을 1로 수정함.\n        random.shuffle(datas)\n        for data_idx, data in enumerate(datas):  # 모든 그래프에 대해. data = graph. datas=[graph,,,, ]\n            torch.cuda.empty_cache()  # for out of memory\n            # clone할 필요 없다. cpu->gpu로 바꾼다고 해주는 것임...\n            x = data['grape'].x.clone().detach().to(device)  # tensor 복사. GPU로 바꾼다. 기존에는 CPU에 할당되어 있음.\n            all_train_edge_index = data['grape', 'grape', 'grape'].train_edge_index.clone().detach().to(\n                device)  # torch.size()=[2,11640]\n            all_train_edge_attr = data['grape', 'grape', 'grape'].train_edge_attr.clone().detach().to(\n                device)  # torch.size()=[11640,1]\n            all_train_labels = data['grape', 'grape', 'grape'].train_labels.clone().detach().to(\n                device)  # size()=5820. label = y가 아니라 결측치를 마스킹 한 후의 정답을 의미. 절반만 가지고 있다.\n\n            edge_index_locality = None\n            edge_index_symmetry = None\n            edge_sim_index = None\n            if is_local_symme_separate(args.model_types):\n                edge_index_locality = data['grape', 'locality', 'grape'].edge_index.clone().detach().to(device)\n                edge_index_symmetry = data['grape', 'symmetry', 'grape'].edge_index.clone().detach().to(device)\n\n            else:  # great1, EGSAGE\n                edge_sim_index = data['grape', 'similarity', 'grape'].edge_index.clone().detach().to(device)\n\n            edge_index_corr = data['grape', 'corr', 'grape'].edge_index.clone().detach().to(device)\n\n            # TODO : test는 샘플링 하면 안된다. 근데 train을 대입한다... test_input_edge_index vs test_edge_index : test_input_edge = gnn에 들어간다. test_edge_index = mlp에 들어간다.\n            test_input_edge_index = all_train_edge_index  # size=[2,11640]\n            test_input_edge_attr = all_train_edge_attr  # size=[11640, 1]\n\n            test_edge_index = data['grape', 'grape', 'grape'].test_edge_index.clone().detach().to(\n                device)  # size()=2,4840\n            test_edge_attr = data['grape', 'grape', 'grape'].test_edge_attr.clone().detach().to(\n                device)  # size()=4840, 1 # 안쓰인다.\n            test_labels = data['grape', 'grape', 'grape'].test_labels.clone().detach().to(\n                device)  # size()=2420 # test_labels는 attr에서 길이만 /2한 것이다.\n\n            # else: #바로 여기로\n            train_edge_index, train_edge_attr, train_labels = all_train_edge_index, all_train_edge_attr, all_train_labels\n\n            model.train()  # train mode로 진입. vs eval()\n            impute_model.train()\n            known_mask = get_known_mask(args.known, int(train_edge_attr.shape[0] / 2)).to(\n                device)  # edge dropout마스킹임. 0.7  masking tensor. 길이=5820. train_label이 5820개 이다.\n            # 1 : known. not edge dropout ; 0 : dropout. # TODO : 절반만 known_mask로 처리함. 왜? 그냥 둘로 나눈다음에 다시 2배로 늘리고.. 의미없어 보이는데 왜 이렇게 했을까?\n            # node는 가리는 것 없다. 다 안다. edge만 가린다.\n            double_known_mask = torch.cat((known_mask, known_mask), dim=0)  # len = 11640 train index인듯.\n\n            known_edge_index, known_edge_attr = mask_edge(train_edge_index, train_edge_attr, double_known_mask,\n                                                          True)  # edge에 대해서 maksing. 얘들이 최종이다.\n\n            del known_mask  # TODO : for memory\n            del double_known_mask\n            gc.collect()\n            opt.zero_grad()\n\n            input_obj = {'x': x, 'known_edge_attr': known_edge_attr, 'known_edge_index': known_edge_index,\n                   'edge_index_locality': edge_index_locality,\n                   'edge_index_symmetry': edge_index_symmetry, 'edge_sim_index': edge_sim_index,\n                   'edge_index_corr': edge_index_corr}\n            x_embd = train_forward_case(model=model, args=args, obj=input_obj)\n            # x_embd에 node vector, edge vector 모두 포함되어 있다. 그래서 tensor(1002, 64)이다..\n\n            pred = impute_model([x_embd[train_edge_index[0]], x_embd[\n                train_edge_index[1]]])  # MLP에 넣는다. # inputdim = 128, outputdim = 1. list로 넣는다. outputdim=1이라서 이렇게 나온다.\n            # train_edge_index[0] : edge start idx, train_edge_index[1] : edge end idx\n            # train_edge_index = tensor(2,1170420)\n\n            # if hasattr(args,'ce_loss') and args.ce_loss: #false\n            #     pred_train = pred[:int(train_edge_attr.shape[0] / 2)]\n            # else:\n            pred_train = pred[:int(train_edge_attr.shape[0] / 2),\n                         0]  # TODO : 왜 또 반으로 나누는 것일까? -> 2배가 들어있다. edge가 중복으로 들어있기 때문에 pred도 중복으로 들어가 있음.\n            # if args.loss_mode == 1: # false\n            #     pred_train[known_mask] = train_labels[known_mask]\n            label_train = train_labels\n\n            # if hasattr(args,'ce_loss') and args.ce_loss: # false\n            #     loss = F.cross_entropy(pred_train,train_labels)\n            # else:\n            loss = F.mse_loss(pred_train, label_train)  # missing data imputation에도 label이 필요하다. y는 필요 없다. label은 필요\n            # pred_train = tensor(836684,)\n            # label_train = tensor(836684, 1)\n\n            loss.backward()  # 여기 역전파\n            opt.step()\n            train_loss = loss.item()\n            Train_loss.append(train_loss)\n\n            # if scheduler is not None: # false\n            #     scheduler.step(epoch)\n            # for param_group in opt.param_groups:  # True\n            #     Lr.append(param_group['lr'])\n\n            # ------------------------------\n            torch.cuda.empty_cache()  # for out of memory\n            # TODO : epoch마다 평가하는게 맞나??? 맞는듯. 그래야 변화를 관찰할 수 있다. 그리고 에폭을 일단 많이 한 뒤에 최적 에폭을 찾으면 된다. 에폭이 작으면 힘들다.\n            # 마지막에 한번 하는 것 보다 더 많은 정보를 얻을 수 있음.\n            model.eval()  # 평가모드로 들어감. 에폭을 한번 할 때 마다 평가를 한다.?\n            impute_model.eval()\n            with torch.no_grad():\n\n                # 바로 여기로\n                test_input_obj = {'x': x, 'test_input_edge_attr':test_input_edge_attr, 'test_input_edge_index': test_input_edge_index,\n                       'edge_index_locality': edge_index_locality,\n                       'edge_index_symmetry': edge_index_symmetry, 'edge_sim_index': edge_sim_index,\n                       'edge_index_corr': edge_index_corr}\n                x_embd = test_forward_case(model=model, args=args, obj=test_input_obj)\n                pred = impute_model(\n                    [x_embd[test_edge_index[0], :], x_embd[test_edge_index[1], :]])  # Tensor(11640,1) 임베딩 값으로 edge를 예측\n                pred_test = pred[:int(test_edge_attr.shape[0] / 2), 0]\n                label_test = test_labels\n                mse = F.mse_loss(pred_test, label_test)\n                test_rmse = np.sqrt(mse.item())\n                test_l1 = F.l1_loss(pred_test, label_test).item()  # l1_loss() : MAE\n                # r2_score = r2(y_true=label_test, y_pred=pred_test).item()\n\n                Test_rmse.append(test_rmse)\n                Test_l1.append(test_l1)\n                # TestMAPE.append(mape_score)\n                # TestMPE.append(mpe_score)\n                # TestR2.append(r2_score)\n\n                # logs['time'].append((f'epoch{epoch}',datetime.datetime.now()))\n                if data_idx * (epoch + 1) % 50 == 0:\n                    print('data_idx : ', data_idx, 'epoch: ', epoch)\n                    print('loss: ', train_loss)\n                    # if args.valid > 0.:\n                    #     print('valid rmse: ', valid_rmse)\n                    #     print('valid l1: ', valid_l1)\n                    print('test rmse: ', test_rmse)\n                    print('test l1: ', test_l1)\n    test_res={'model' : []}\n    model.eval()\n    impute_model.eval()\n    for data_idx, data in enumerate(datas):  # 모든 그래프에 대해. data = graph. datas=[graph,,,, ]\n        with torch.no_grad():\n            # _s = single, col마다 값 구하기.\n            # 전체 index, attr\n            edge_index = data['grape', 'grape', 'grape'].edge_index.clone().detach().to(\n                device)  # size()=2,4840\n            edge_attr = data['grape', 'grape', 'grape'].edge_attr.clone().detach().to(\n                device)  # size()=4840, 1\n\n            edge_index_single = get_single_edge_index(edge_index)\n            edge_attr_single = get_single_edge_attr(edge_attr)\n\n            # test_input = known\n            known_edge_index_single = test_input_edge_index[:, :test_input_edge_index.shape[1] // 2].detach()\n            known_edge_attr_single = test_input_edge_attr[:test_input_edge_attr.shape[0] // 2, 0].detach()\n\n            test_edge_index_single = get_single_edge_index(test_edge_index)\n            # col_range = range(982, edge_index[0].max()+1)  # col의 node인덱스 범위\n            last_col_idx = edge_index[0].max() + 1\n            col_range = range(last_col_idx - args.col_len, last_col_idx)  # col의 node인덱스 범위\n            label_df = graph2df(test_edge_index, torch.cat([test_labels] * 2), col_range[0], col_range)\n            pred_df = graph2df(test_edge_index, torch.cat([pred_test] * 2), col_range[0], col_range)\n            # label_li = []\n            # pred_li = []\n            # for i in col_range:  # 여긴 얼마 안걸린다.\n            #     label, pred = calc_result(i=i, edge_attr_single=edge_attr_single, edge_index_single=edge_index_single,\n            #                               known_edge_attr_single=known_edge_attr_single,\n            #                               known_edge_index_single=known_edge_index_single, pred_test=pred_test,\n            #                               test_edge_index_single=test_edge_index_single, test_labels=test_labels,\n            #                               type='model')\n            #     # nrmse.append(rmse/std)\n            #     label_li.append(label.tolist())\n            #     # std_li.append(std)\n            #     pred_li.append(pred.tolist())\n            # tmp = {}\n            # tmp['label_li'] = label_li\n            # tmp['pred_li'] = pred_li\n            # test_res['model'].append(tmp)\n\n\n\n\n    # pred_train = pred_train.detach().cpu().numpy()\n    # label_train = label_train.detach().cpu().numpy()\n    # pred_test = pred_test.detach().cpu().numpy()\n    # label_test = label_test.detach().cpu().numpy()\n\n    # curves는 중요하지만 outputs는 딱히 중요하지 않다.\n    obj['curves'] = dict()  # obj = dict()임.\n    obj['curves']['train_loss'] = Train_loss\n    # if args.valid > 0.: # 이 조건이 매우 많다. 근데 한번에 처리해야지 이런건.. 클래스로 분리하거나..\n    #     obj['curves']['valid_rmse'] = Valid_rmse\n    #     obj['curves']['valid_l1'] = Valid_l1\n    obj['curves']['test_rmse'] = Test_rmse\n    obj['curves']['test_l1'] = Test_l1\n    obj['model']=test_res['model']\n    obj['cnt_param'] = param_cnt\n    # 젤 마지막 웨이퍼만 들어가 있어서 의미없다.\n    # obj['outputs']['final_pred_train'] = pred_train\n    # obj['outputs']['label_train'] = label_train\n    # obj['outputs']['final_pred_test'] = pred_test\n    # obj['outputs']['label_test'] = label_test\n\n    logs['time'].append(('end', datetime.datetime.now()))\n    obj['time'] = logs['time']\n\n\n    print(f'start : {logs[\"time\"][0]}')\n    print(f'end : {logs[\"time\"][-1]}')\n    print(f'time : {logs[\"time\"][-1][1] - logs[\"time\"][0][1]}')\n\n    now = datetime.datetime.now()\n    file_name = str(now)[:11] + f'{now.hour}시{now.minute}분'  # 파일명에 : 가들어갈 수 없다.\n    # for i, wf in enumerate(test_res['model']): # wf = dict\n    #     pickle.dump(wf, open(log_path + 'test_res'+str(i) + '.pkl', \"wb\"))  # pkl로 dict를 저장한다.\n\n    pickle.dump(obj, open(log_path + file_name + '.pkl', \"wb\"))  # pkl로 dict를 저장한다.\n\n    if args.save_model:\n        torch.save(model, log_path + 'model.pt')\n        torch.save(impute_model, log_path + 'impute_model.pt')\n\n","sub_path":"pnu_semiconductor/gnn_mdi_semi_transductive.py","file_name":"gnn_mdi_semi_transductive.py","file_ext":"py","file_size_in_byte":15631,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"341135208","text":"#-*- coding: utf-8 -*-\n#接收32报警信息\n\nfrom socket import *\n#import 32client\n\nHOST=''\nPORT=8091 #设置侦听端口\nBUFSIZ=1024\nADDR=(HOST,PORT)\nsock=socket(AF_INET, SOCK_STREAM)\nsock.bind(ADDR)\nsock.listen(5)\n\n\nwhile(1):\n\tprint('等待接入，侦听端口:%d' % (PORT))\n\ttcpClientSock,addr=sock.accept()\n\tprint('接受连接，客户端地址：',addr)\n\tdata=tcpClientSock.recv(BUFSIZ)\n\tprint(data)\n#\t32client(data)\n\t#tcpClientSock.send(data)\n\ttcpClientSock.close()\n","sub_path":"PI/32server.py","file_name":"32server.py","file_ext":"py","file_size_in_byte":475,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"403878691","text":"import time\nimport sys\nimport numpy\nfrom SALPY_ATDome import *\nmgr = SAL_ATDome(1)\nmgr.salEvent(\"ATDome_logevent_summaryState\")\nprint(\"ATDome_summaryState logger ready\")\nevent = ATDome_logevent_summaryStateC()\nwhile True:\n  retval = mgr.getEvent_summaryState(event)\n  print(\"priority: \" + str(event.priority) + \"\\nsummaryState: \" + str(event.summaryState))\n  if retval==0:\n    print(\"Event ATDome summaryState received\")\n  time.sleep(0.1)\nmgr.salShutdown()\nexit()\n\n","sub_path":"ts_ATDome/ATDome_EventLogger_summaryState.py","file_name":"ATDome_EventLogger_summaryState.py","file_ext":"py","file_size_in_byte":465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"10130956","text":"from keras.callbacks import Callback\n\n\nclass MAPCallback(Callback):\n\n    def __init__(self, train_generator, validation_generator, yolo):\n        super(MAPCallback, self).__init__()\n        self.train_generator = train_generator\n        self.validation_generator = validation_generator\n        self.yolo = yolo\n\n    def on_epoch_end(self, epoch, logs=None):\n        average_precisions = self.yolo.evaluate(self.train_generator)\n        for label, average_precision in average_precisions.items():\n            label = self.yolo.labels[label]\n            logs[label] = average_precision\n        mAP = sum(average_precisions.values()) / len(average_precisions)\n        logs['mAP'] = mAP\n\n        average_precisions = self.yolo.evaluate(self.validation_generator)\n        for label, average_precision in average_precisions.items():\n            label = self.yolo.labels[label]\n            logs[\"val_\"+label] = average_precision\n        mAP = sum(average_precisions.values()) / len(average_precisions)\n        logs['val_mAP'] = mAP\n        return logs\n\n\nclass FloydhubLoggerCallback(Callback):\n\n    def on_epoch_end(self, epoch, logs=None):\n        for metric, value in logs.items():\n            print('{{\"metric\": \"{}\", \"value\": {}}}'.format(metric, value))\n","sub_path":"kerolo2/callbacks/floydhub_logger.py","file_name":"floydhub_logger.py","file_ext":"py","file_size_in_byte":1252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"49574858","text":"\"\"\"\nLoads campus map from provided .pgm files, analyzes, prints building info\nGiven the -p flag, pickles the campus map to campus_map.pkl for easy reloading\n\"\"\"\n__author__=\"Kaitlin Huben <kjh2141@columbia.edu>\"\n__date__ =\"$April 14, 2015\"\n\nimport sys\nfrom campusMap import CampusMap\nimport pickle\n\ndef errwrite(msg):\n\tsys.stderr.write(msg)\n\ndef usage():\n\terrwrite('Usage:\\n\\tpython part1.py [-p]\\n')\n\terrwrite('\\t-p to pickle to campus_map.pkl\\n')\n\tsys.exit()\n\t\ndef main(argv):\n\tif not ((len(argv)==1) or (len(argv)==2 and argv[1]==\"-p\")):\n\t\tusage()\n\t\n\terrwrite(\"Loading map, analyzing buildings...\")\n\tcampus_map = CampusMap()\n\terrwrite(\"done.\\n\")\n\n\tcampus_map.printBuildingInfo()\n\n\tif len(argv) == 2:\n\t\tpkl_file = open(\"campus_map.pkl\", \"w\")\n\t\tpickle.dump(campus_map, pkl_file)\n\t\nif __name__ == '__main__':\n\tmain(sys.argv)","sub_path":"assignment3/part1.py","file_name":"part1.py","file_ext":"py","file_size_in_byte":823,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"217061962","text":"# Prediction of rate changes (binary) using Bag-of-words\r\n\r\nmyfileloc = 'C:\\\\Users\\\\9888120\\\\Desktop\\\\My codes\\\\'\r\n\r\nimport sys\r\nsys.path.append(myfileloc+'utility.py')\r\nfrom sklearn.model_selection import train_test_split, cross_val_score,GridSearchCV\r\nfrom utility import *\r\n\r\nimport pickle\r\nimport parfit.parfit as pf\r\n\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom sklearn.metrics import classification_report, roc_auc_score, auc,roc_curve\r\nfrom sklearn.model_selection import ParameterGrid\r\nfrom sklearn.svm import SVC,LinearSVC\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nimport random\r\nseed = 999\r\nrandom.seed(seed)\r\n\r\n\r\n# Load Bag-of-words vectors(inputs) and labels(outputs)\r\nbow_vector = pickle.load(open(myfileloc + \"bow_vector.p\", \"rb\"))\r\ndf_label = pickle.load( open( myfileloc + \"df_label.p\", \"rb\" ) )\r\n\r\n# Split data into trainning set (80%) and test set (20%)\r\nX_train, X_test, y_train, y_test = train_test_split(bow_vector, df_label, train_size=0.8, shuffle = False)\r\n\r\n\r\n# Apply Machine learning classifiers\r\n## Logistic regression with L1\r\n### Hyper-Parameters to be determined\r\ngrid = {'C': np.logspace(-5, 5, 10),'penalty': ['l1'], 'random_state': [999]}\r\nparamGrid = ParameterGrid(grid)\r\n### 10-fold cross-validation on training set to determine the optimal Hyper-Parameters\r\nlog_bestModel_l1, bestScore, allModels, allScores = pf.bestFit(LogisticRegression(), paramGrid, X_train, y_train, X_test, y_test, nfolds=10, metric = roc_auc_score, scoreLabel = \"AUC\")\r\n\r\n### Performance on testing set\r\nplot_performance(model= log_bestModel_l1, model_names='Logistic L1', X_test=X_test, y_test=y_test)\r\n\r\n## Logistic regression with L2\r\n### Hyper-Parameters to be determined\r\ngrid = {'C': np.logspace(-5, 5, 10), 'penalty': ['l2']}\r\nparamGrid = ParameterGrid(grid)\r\n### 10-fold cross-validation on training set to determine the optimal Hyper-Parameters\r\nlog_bestModel_l2, bestScore, allModels, allScores = pf.bestFit(LogisticRegression(), paramGrid, X_train, y_train, X_test, y_test, nfolds=10, metric = roc_auc_score, scoreLabel = \"AUC\")\r\n### Performance on testing set\r\nplot_performance(model= log_bestModel_l2, model_names='Logistic L2', X_test=X_test, y_test=y_test)\r\n\r\n## SVM linear kernel with L1\r\n### Hyper-Parameters to be determined\r\ngrid = {'C': np.logspace(-5, 5, 10),'penalty': ['l1'], 'loss': ['squared_hinge'], 'dual': [False]}\r\nparamGrid = ParameterGrid(grid)\r\n### 10-fold cross-validation on training set to determine the optimal Hyper-Parameters\r\nsvm_linear_l1_bestModel, bestScore, allModels, allScores = pf.bestFit(LinearSVC(), paramGrid, X_train, y_train, X_test, y_test, nfolds=10, metric = roc_auc_score, scoreLabel = \"AUC\")\r\n### Performance on testing set\r\nplot_performance(model= svm_linear_l1_bestModel, model_names='SVM linear kernel L1', X_test=X_test, y_test=y_test, linear_svm = True)\r\n\r\n## SVM linear kernel with L2\r\n### Hyper-Parameters to be determined\r\ngrid = {'C': np.logspace(-5, 5, 10),'penalty': ['l2'], 'loss': ['squared_hinge'], 'dual': [True]}\r\nparamGrid = ParameterGrid(grid)\r\n### 10-fold cross-validation on training set to determine the optimal Hyper-Parameters\r\nsvm_linear_l2_bestModel, bestScore, allModels, allScores = pf.bestFit(LinearSVC(), paramGrid, X_train, y_train, X_test, y_test, nfolds=10, metric = roc_auc_score, scoreLabel = \"AUC\")\r\n### Performance on testing set\r\nplot_performance(model= svm_linear_l2_bestModel, model_names='SVM linear kernel L2', X_test=X_test, y_test=y_test, linear_svm = True)\r\n\r\n\r\n\r\n## Random Forest\r\n### Hyper-Parameters to be determined\r\nparam_grid = {'bootstrap': [True], 'max_depth': [80, 90, 100, 110], 'max_features': [2, 3], 'min_samples_leaf': [3, 4, 5], 'min_samples_split': [8, 10, 12], 'n_estimators': [5, 10, 15, 20, 50, 100,200, 300, 1000]\r\n}\r\nparamGrid = ParameterGrid(param_grid)\r\n### 10-fold cross-validation on training set to determine the optimal Hyper-Parameters\r\nrf_bestModel, bestScore, allModels, allScores = pf.bestFit(RandomForestClassifier(), paramGrid, X_train, y_train, X_test, y_test, nfolds=10, metric = roc_auc_score, scoreLabel = \"AUC\")\r\n### Performance on testing set\r\nplot_performance(model= rf_bestModel, model_names='Random forest', X_test=X_test, y_test=y_test)\r\n","sub_path":"Project 02 prediction using BoW.py","file_name":"Project 02 prediction using BoW.py","file_ext":"py","file_size_in_byte":4218,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"465338228","text":"#爬虫调度器\nimport DataOutput\nimport UrlManager\nimport GetSrcList\n\n\nclass SpiderManager():\n    def __init__(self):# 自动执行\n        self.getlist = GetSrcList.GetSrcList()\n        self.urls=UrlManager.UrlManager()\n        self.output=DataOutput.DataOutput()\n\n    def splider(self):\n        jpglist =self.getlist.selenium_do()\n        print(len(jpglist))\n        print(jpglist)\n        for jpg in jpglist:\n            self.urls.add_url(jpg)\n        while self.urls.has_url():\n            url=self.urls.get_url()\n            #try:\n            self.output.down_mp4_jpg(url)\n            # except:\n            #      print('!!!!!!!!!!!!!!!warning')\n\n\nif __name__ == '__main__':\n    myspider=SpiderManager()\n    myspider.splider()\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"spider_ins/SpiderManager.py","file_name":"SpiderManager.py","file_ext":"py","file_size_in_byte":747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"211413497","text":"import graphene\nfrom graphene_django import DjangoObjectType\nfrom .models import Track\n\nclass TrackType(DjangoObjectType):\n    class Meta:\n        model=Track\n    \nclass Query(graphene.ObjectType):\n    tracks=graphene.List(TrackType)\n\n    def resolve_tracks(self,info):\n        return Track.objects.all()\n\nclass CreateTrack(graphene.Mutation):\n    track=graphene.Field(TrackType)\n    class Arguments:\n        title=graphene.String()\n        description=graphene.String()\n        url=graphene.String()\n        \n    def mutate(self,info,title,description,url):\n        user=info.context.user\n        if user.is_anonymous:\n            raise Exception(\"Log in to create track\")\n        track=Track(title=title, description=description, url=url, posted_by=user)\n        track.save()\n        return CreateTrack(track=track)\n\nclass UpdateTrack(graphene.Mutation):\n    track=graphene.Field(TrackType)\n    class Arguments:\n        track_id=graphene.Int(required=True)\n        title=graphene.String()\n        description=graphene.String()\n        url=graphene.String()\n    \n    def mutate(self,info,track_id,title,description,url):\n        user=info.context.user\n        track=Track.objects.get(id=track_id)\n        if track.posted_by != user:\n            raise Exception(\"Log in to update track\")\n        track.title=title\n        track.description=description\n        track.url=url\n        track.save()\n        return UpdateTrack(track=track)\n\n\nclass Mutation(graphene.ObjectType):\n    create_track=CreateTrack.Field()\n    update_track=UpdateTrack.Field()","sub_path":"06.Update/app/tracks/schema.py","file_name":"schema.py","file_ext":"py","file_size_in_byte":1547,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"437761339","text":"#!/usr/bin/env python3\n\nfrom flask import Blueprint, render_template, request, g, session \n\nfrom core import model \n\ncontroller = Blueprint('dashboard',__name__,url_prefix='/dashboard')\n\n@controller.route('/',methods=['GET'])\ndef show_dashboard():\n\n\tusername = g.user\n\tuser_login = model.get_username(username)\n\n\n\tleaderboard = model.get_leaderboard_by_portfolio_value()\n\n\tprofitloss = model.get_leaderboard_by_profit_loss()\n\n\ttop_stocks, shares, percentages = model.get_top_ten_stocks()\n\n\treturn render_template('homepage.html',  user_login=user_login, leaderboard=leaderboard, profitloss=profitloss,top_stocks=sorted(zip(percentages,top_stocks,shares),reverse=True))\n\n\n\n\n@controller.before_request\ndef before_request():\n\tg.user = None\n\tif 'user' in session:\n\t\tg.user = session['user']\n","sub_path":"run/core/controllers/dashboard.py","file_name":"dashboard.py","file_ext":"py","file_size_in_byte":787,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"170970104","text":"#TV-recording sorter, compresser. Linux only\n\nimport os\nfrom datetime import date, datetime\nx = datetime.now()\nyear = str(x.year)\nmonth = x.strftime(\"%Y_\"\"%b\") #month with name\nmonth1 = str(x.month) #current month\nif month1 > str(9):\n    current = year+\"-\"+str(month1)\nelse:\n    current = year+\"-0\"+str(month1) # current year and month\nfiles =  current +\"*\"\ndef delete_source():\n    confirmation = input(\"should i delete the source file? Y/N \")\n    if confirmation in ['y', 'y', \"i\", \"I\", \"yes\"]:\n        os.system(\"rm \" + files)\n\nif not os.path.exists(month): # if the folder exists skip it\n    os.mkdir(month)\n\nos.system('ls /home/logan/TV-tapes/') \nautomatic = input (\"\\n \\n Should i go semi-automatic? Y/N \")\n\nif automatic in ['Y',\"y\",\"i\",\"I\",\"yes\"]:\n    outputfile = input(\"\\n \\n what should the output file be named? \")\n    lrz = str((\"ffmpeg -i \"+ current  +\"* \" \"-c:v libx265 \" + month+ \"/\" +outputfile + \".mkv && beep -f 600\"))\n    \nelse:\n    files = \"~/TV-tapes/\" + input(\"\\n \\n Which file should i compress? \") + \"* \"\n    os.system('ls '+ files)\n    outputfile = input(\"\\n \\n what should the output file be named? \")\n    lrz = str((\"ffmpeg -i \" + files +  \"-c:v libx265 \" + month+ \"/\" +outputfile + \".mkv && beep -f 600\"))\n \n\nprint (current)\n#print (lrz)\n#os.system(lrz)\n#delete_source()","sub_path":"TV-recording-sorter_Dry.py","file_name":"TV-recording-sorter_Dry.py","file_ext":"py","file_size_in_byte":1298,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"297767367","text":"\"\"\"\nDescription: Numpy implementation of optimizers to minimize using loss using\n             loss functions and applying gradients\nauthor: syedmohsinbukhari@googlemail.com\n\"\"\"\n\nimport numpy as np\n\nfrom .models_sequential import SequentialModel\n\n\nclass Optimizers:\n    def __init__(self, learning_rate, model):\n        assert isinstance(model, SequentialModel)\n        assert type(learning_rate) == float\n\n        self.model = model\n        self.lr = np.array(learning_rate)\n\n    def apply_grads(self):\n        pass\n\n\nclass SGD(Optimizers):\n    def apply_grads(self):\n        new_weights = []\n\n        for i in range(len(self.model.layers)):\n            w, b = self.model.layers[i]\n            dw, db = self.model.layer_grads[i]\n\n            w = np.subtract(w, np.multiply(self.lr, dw))\n            b = np.subtract(b, np.multiply(self.lr, db))\n\n            new_weights.append((w, b))\n\n        self.model.layers = new_weights\n","sub_path":"experiments/backprop_numpy/optimizers.py","file_name":"optimizers.py","file_ext":"py","file_size_in_byte":925,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"528075424","text":"\"\"\"Unit tests for the `config` module.\"\"\"\n# pylint: disable=no-self-use,redefined-outer-name\n\nfrom unittest.mock import patch, Mock\nfrom copy import copy\n\nimport pytest\n\nfrom gdm.config import Source, Config, load\n\nfrom .conftest import FILES\n\n\n@pytest.fixture\ndef source():\n    return Source(repo='repo', dir='dir', rev='rev', link='link')\n\n\nclass TestSource:\n\n    def test_init_defaults(self):\n        \"\"\"Verify a source has a default revision.\"\"\"\n        source = Source('http://mock.git', 'mock_dir')\n\n        assert 'http://mock.git' == source.repo\n        assert 'mock_dir' == source.dir\n        assert 'master' == source.rev\n        assert None is source.link\n\n    def test_init_rev(self):\n        \"\"\"Verify the revision can be customized.\"\"\"\n        source = Source('http://mock.git', 'mock_dir', 'v1.0')\n\n        assert 'v1.0' == source.rev\n\n    def test_init_link(self):\n        \"\"\"Verify the link can be set.\"\"\"\n        source = Source('http://mock.git', 'mock_dir', link='mock/link')\n\n        assert 'mock/link' == source.link\n\n    def test_init_error(self):\n        \"\"\"Verify the repository and directory are required.\"\"\"\n        with pytest.raises(ValueError):\n            Source('', 'mock_dir')\n        with pytest.raises(ValueError):\n            Source('http://mock.git', '')\n\n    def test_repr(self, source):\n        \"\"\"Verify sources can be represented.\"\"\"\n        assert \"<source 'repo' @ 'rev' in 'dir' <- 'link'>\" == repr(source)\n\n    def test_repr_no_link(self, source):\n        \"\"\"Verify sources can be represented.\"\"\"\n        source.link = None\n\n        assert \"<source 'repo' @ 'rev' in 'dir'>\" == repr(source)\n\n    def test_eq(self, source):\n        source2 = copy(source)\n        assert source == source2\n\n        source2.dir = \"dir2\"\n        assert source != source2\n\n    def test_lt(self):\n        sources = [\n            Source('zzz', '123'),\n            Source('bbb', '456'),\n            Source('ccc', '456'),\n            Source('BBB', 'AAA'),\n            Source('AAA', 'AAA'),\n        ]\n\n        assert sources == sorted(sources)\n\n    def test_identify_missing(self, source, tmpdir):\n        \"\"\"Verify a missing source identifies as unknown.\"\"\"\n        tmpdir.chdir()\n        with patch('os.path.isdir', Mock(return_value=False)):\n            assert (str(tmpdir), '<missing>', '<unknown>') == source.identify()\n\n    def test_lock_uses_the_identity_rev(self, source):\n        source.identify = Mock(return_value=('path2', 'dir2', 'abc123'))\n\n        source2 = source.lock()\n\n        assert 'abc123' == source2.rev\n        assert 'dir' == source2.dir\n\n\nclass TestConfig:\n\n    def test_init_defaults(self):\n        \"\"\"Verify a configuration has a default filename and location.\"\"\"\n        config = Config('mock/root')\n\n        assert 'mock/root' == config.root\n        assert 'gdm.yml' == config.filename\n        assert 'gdm_sources' == config.location\n        assert [] == config.sources\n\n    def test_init_filename(self):\n        \"\"\"Verify the filename can be customized.\"\"\"\n        config = Config('mock/root', 'mock.custom')\n\n        assert 'mock.custom' == config.filename\n        assert 'gdm_sources' == config.location\n\n    def test_init_location(self):\n        \"\"\"Verify the location can be customized.\"\"\"\n        config = Config('mock/root', location='.gdm')\n\n        assert 'gdm.yml' == config.filename\n        assert '.gdm' == config.location\n\n    def test_path(self):\n        \"\"\"Verify a configuration's path is correct.\"\"\"\n        config = Config('mock/root')\n\n        assert \"mock/root/gdm.yml\" == config.path\n\n    @pytest.mark.integration\n    def test_install_and_list(self):\n        \"\"\"Verify the correct dependencies are installed.\"\"\"\n        config = Config(FILES)\n\n        count = config.install_deps()\n        assert 7 == count\n\n        deps = list(config.get_deps())\n        assert 7 == len(deps)\n        assert 'https://github.com/jacebrowning/gdm-demo' == deps[0][1]\n        assert 'eb37743011a398b208dd9f9ef79a408c0fc10d48' == deps[0][2]\n        assert 'https://github.com/jacebrowning/gdm-demo' == deps[1][1]\n        assert 'ddbe17ef173538d1fda29bd99a14bab3c5d86e78' == deps[1][2]\n        assert 'https://github.com/jacebrowning/gdm-demo' == deps[2][1]\n        assert 'fb693447579235391a45ca170959b5583c5042d8' == deps[2][2]\n        assert 'https://github.com/jacebrowning/gdm-demo' == deps[3][1]\n        # master branch always changes --------------------- deps[3][2]\n        assert 'https://github.com/jacebrowning/gdm-demo' == deps[4][1]\n        # master branch always changes --------------------- deps[4][2]\n        assert 'https://github.com/jacebrowning/gdm-demo' == deps[5][1]\n        assert '7bd138fe7359561a8c2ff9d195dff238794ccc04' == deps[5][2]\n        assert 'https://github.com/jacebrowning/gdm-demo' == deps[6][1]\n        assert '2da24fca34af3748e3cab61db81a2ae8b35aec94' == deps[6][2]\n\n        assert 5 == len(list(config.get_deps(depth=2)))\n\n        assert 3 == len(list(config.get_deps(depth=1)))\n\n        assert 0 == len(list(config.get_deps(depth=0)))\n\n    @pytest.mark.integration\n    def test_install_with_dirs(self):\n        \"\"\"Verify the dependency list can be filtered.\"\"\"\n        config = Config(FILES)\n\n        count = config.install_deps('gdm_2', 'gdm_3')\n        assert 2 == count\n\n    def test_install_with_dirs_unknown(self):\n        \"\"\"Verify zero dependencies are installed with an unknown dependency.\"\"\"\n        config = Config(FILES)\n\n        count = config.install_deps('foobar')\n        assert 0 == count\n\n    def test_install_with_depth_0(self):\n        \"\"\"Verify an install depth of 0 installs nothing.\"\"\"\n        config = Config(FILES)\n\n        count = config.install_deps(depth=0)\n        assert 0 == count\n\n    @pytest.mark.integration\n    def test_install_with_depth_1(self):\n        \"\"\"Verify an install depth of 1 installs the direct dependencies.\"\"\"\n        config = Config(FILES)\n\n        count = config.install_deps(depth=1)\n        assert 3 == count\n\n    @pytest.mark.integration\n    def test_install_with_depth_2(self):\n        \"\"\"Verify an install depth of 2 installs 1 level of nesting.\"\"\"\n        config = Config(FILES)\n\n        count = config.install_deps(depth=2)\n        assert 5 == count\n\n\nclass TestLoad:\n\n    def test_load_from_directory_with_config_file(self):\n        config = load(FILES)\n\n        assert None is not config\n\n    def test_load_from_directory_without_config_file(self, tmpdir):\n        tmpdir.chdir()\n\n        config = load()\n\n        assert None is config\n","sub_path":"gdm/test/test_config.py","file_name":"test_config.py","file_ext":"py","file_size_in_byte":6487,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"214846273","text":"import decimal\nfrom unittest import TestCase\n\nimport pydantic\nfrom pydantic import ValidationError\n\nfrom piccolo.columns import JSON, JSONB, Array, Numeric, Secret, Text, Varchar\nfrom piccolo.columns.column_types import ForeignKey\nfrom piccolo.table import Table\nfrom piccolo.utils.pydantic import create_pydantic_model\n\n\nclass TestVarcharColumn(TestCase):\n    def test_varchar_length(self):\n        class Director(Table):\n            name = Varchar(length=10)\n\n        pydantic_model = create_pydantic_model(table=Director)\n\n        with self.assertRaises(ValidationError):\n            pydantic_model(name=\"This is a really long name\")\n\n        pydantic_model(name=\"short name\")\n\n\nclass TestNumericColumn(TestCase):\n    \"\"\"\n    Numeric and Decimal are the same - so we'll just Numeric.\n    \"\"\"\n\n    def test_numeric_digits(self):\n        class Movie(Table):\n            box_office = Numeric(digits=(5, 1))\n\n        pydantic_model = create_pydantic_model(table=Movie)\n\n        with self.assertRaises(ValidationError):\n            # This should fail as there are too much numbers after the decimal\n            # point\n            pydantic_model(box_office=decimal.Decimal(\"1.11\"))\n\n        with self.assertRaises(ValidationError):\n            # This should fail as there are too much numbers in total\n            pydantic_model(box_office=decimal.Decimal(\"11111.1\"))\n\n        pydantic_model(box_office=decimal.Decimal(\"1.0\"))\n\n    def test_numeric_without_digits(self):\n        class Movie(Table):\n            box_office = Numeric()\n\n        try:\n            create_pydantic_model(table=Movie)\n        except TypeError:\n            self.fail(\n                \"Creating numeric field without\"\n                \" digits failed in pydantic model.\"\n            )\n        else:\n            self.assertTrue(True)\n\n\nclass TestSecretColumn(TestCase):\n    def test_secret_param(self):\n        class TopSecret(Table):\n            confidential = Secret()\n\n        pydantic_model = create_pydantic_model(table=TopSecret)\n        self.assertEqual(\n            pydantic_model.schema()[\"properties\"][\"confidential\"][\"extra\"][\n                \"secret\"\n            ],\n            True,\n        )\n\n\nclass TestArrayColumn(TestCase):\n    def test_array_param(self):\n        class Band(Table):\n            members = Array(base_column=Varchar(length=16))\n\n        pydantic_model = create_pydantic_model(table=Band)\n\n        self.assertEqual(\n            pydantic_model.schema()[\"properties\"][\"members\"][\"items\"][\"type\"],\n            \"string\",\n        )\n\n\nclass TestTextColumn(TestCase):\n    def test_text_format(self):\n        class Band(Table):\n            bio = Text()\n\n        pydantic_model = create_pydantic_model(table=Band)\n\n        self.assertEqual(\n            pydantic_model.schema()[\"properties\"][\"bio\"][\"format\"],\n            \"text-area\",\n        )\n\n\nclass TestColumnHelpText(TestCase):\n    \"\"\"\n    Make sure that columns with `help_text` attribute defined have the\n    relevant text appear in the schema.\n    \"\"\"\n\n    def test_help_text_present(self):\n        help_text = \"In millions of US dollars.\"\n\n        class Movie(Table):\n            box_office = Numeric(digits=(5, 1), help_text=help_text)\n\n        pydantic_model = create_pydantic_model(table=Movie)\n        self.assertEqual(\n            pydantic_model.schema()[\"properties\"][\"box_office\"][\"extra\"][\n                \"help_text\"\n            ],\n            help_text,\n        )\n\n\nclass TestTableHelpText(TestCase):\n    \"\"\"\n    Make sure that tables with `help_text` attribute defined have the\n    relevant text appear in the schema.\n    \"\"\"\n\n    def test_help_text_present(self):\n        help_text = \"Movies which were released in cinemas.\"\n\n        class Movie(Table, help_text=help_text):\n            name = Varchar()\n\n        pydantic_model = create_pydantic_model(table=Movie)\n        self.assertEqual(\n            pydantic_model.schema()[\"help_text\"],\n            help_text,\n        )\n\n\nclass TestJSONColumn(TestCase):\n    def test_default(self):\n        class Movie(Table):\n            meta = JSON()\n            meta_b = JSONB()\n\n        pydantic_model = create_pydantic_model(table=Movie)\n\n        json_string = '{\"code\": 12345}'\n\n        model_instance = pydantic_model(meta=json_string, meta_b=json_string)\n        self.assertEqual(model_instance.meta, json_string)\n        self.assertEqual(model_instance.meta_b, json_string)\n\n    def test_deserialize_json(self):\n        class Movie(Table):\n            meta = JSON()\n            meta_b = JSONB()\n\n        pydantic_model = create_pydantic_model(\n            table=Movie, deserialize_json=True\n        )\n\n        json_string = '{\"code\": 12345}'\n        output = {\"code\": 12345}\n\n        model_instance = pydantic_model(meta=json_string, meta_b=json_string)\n        self.assertEqual(model_instance.meta, output)\n        self.assertEqual(model_instance.meta_b, output)\n\n    def test_validation(self):\n        class Movie(Table):\n            meta = JSON()\n            meta_b = JSONB()\n\n        for deserialize_json in (True, False):\n            pydantic_model = create_pydantic_model(\n                table=Movie, deserialize_json=deserialize_json\n            )\n\n            json_string = \"error\"\n\n            with self.assertRaises(pydantic.ValidationError):\n                pydantic_model(meta=json_string, meta_b=json_string)\n\n    def test_json_format(self):\n        class Movie(Table):\n            features = JSON()\n\n        pydantic_model = create_pydantic_model(table=Movie)\n\n        self.assertEqual(\n            pydantic_model.schema()[\"properties\"][\"features\"][\"format\"],\n            \"json\",\n        )\n\n\nclass TestExcludeColumn(TestCase):\n    def test_all(self):\n        class Computer(Table):\n            CPU = Varchar()\n            GPU = Varchar()\n\n        pydantic_model = create_pydantic_model(Computer, exclude_columns=())\n\n        properties = pydantic_model.schema()[\"properties\"]\n        self.assertIsInstance(properties[\"GPU\"], dict)\n        self.assertIsInstance(properties[\"CPU\"], dict)\n\n    def test_exclude(self):\n        class Computer(Table):\n            CPU = Varchar()\n            GPU = Varchar()\n\n        pydantic_model = create_pydantic_model(\n            Computer,\n            exclude_columns=(Computer.CPU,),\n        )\n\n        properties = pydantic_model.schema()[\"properties\"]\n        self.assertIsInstance(properties.get(\"GPU\"), dict)\n        self.assertIsNone(properties.get(\"CPU\"))\n\n    def test_exclude_all_manually(self):\n        class Computer(Table):\n            GPU = Varchar()\n            CPU = Varchar()\n\n        pydantic_model = create_pydantic_model(\n            Computer,\n            exclude_columns=(Computer.GPU, Computer.CPU),\n        )\n\n        self.assertEqual(pydantic_model.schema()[\"properties\"], {})\n\n    def test_exclude_all_meta(self):\n        class Computer(Table):\n            GPU = Varchar()\n            CPU = Varchar()\n\n        pydantic_model = create_pydantic_model(\n            Computer,\n            exclude_columns=tuple(Computer._meta.columns),\n        )\n\n        self.assertEqual(pydantic_model.schema()[\"properties\"], {})\n\n    def test_invalid_column_str(self):\n        class Computer(Table):\n            CPU = Varchar()\n            GPU = Varchar()\n\n        with self.assertRaises(ValueError):\n            create_pydantic_model(\n                Computer,\n                exclude_columns=(\"CPU\",),\n            )\n\n    def test_invalid_column_different_table(self):\n        class Computer(Table):\n            CPU = Varchar()\n            GPU = Varchar()\n\n        class Computer2(Table):\n            SSD = Varchar()\n\n        with self.assertRaises(ValueError):\n            create_pydantic_model(Computer, exclude_columns=(Computer2.SSD,))\n\n    def test_invalid_column_different_table_same_type(self):\n        class Computer(Table):\n            CPU = Varchar()\n            GPU = Varchar()\n\n        class Computer2(Table):\n            CPU = Varchar()\n\n        with self.assertRaises(ValueError):\n            create_pydantic_model(Computer, exclude_columns=(Computer2.CPU,))\n\n\nclass TestIncludeColumn(TestCase):\n    def test_include(self):\n        class Computer(Table):\n            CPU = Varchar()\n            GPU = Varchar()\n\n        pydantic_model = create_pydantic_model(\n            Computer,\n            include_columns=(Computer.CPU,),\n        )\n\n        properties = pydantic_model.schema()[\"properties\"]\n        self.assertIsInstance(properties.get(\"CPU\"), dict)\n        self.assertIsNone(properties.get(\"GPU\"))\n\n    def test_include_exclude_error(self):\n        class Computer(Table):\n            CPU = Varchar()\n            GPU = Varchar()\n\n        with self.assertRaises(ValueError):\n            create_pydantic_model(\n                Computer,\n                exclude_columns=(Computer.CPU,),\n                include_columns=(Computer.CPU,),\n            )\n\n\nclass TestNestedModel(TestCase):\n    def test_nested_models(self):\n        class Country(Table):\n            name = Varchar(length=10)\n\n        class Director(Table):\n            name = Varchar(length=10)\n            country = ForeignKey(Country)\n\n        class Movie(Table):\n            name = Varchar(length=10)\n            director = ForeignKey(Director)\n\n        MovieModel = create_pydantic_model(table=Movie, nested=True)\n\n        #######################################################################\n\n        DirectorModel = MovieModel.__fields__[\"director\"].type_\n\n        self.assertTrue(issubclass(DirectorModel, pydantic.BaseModel))\n\n        director_model_keys = [i for i in DirectorModel.__fields__.keys()]\n        self.assertEqual(director_model_keys, [\"name\", \"country\"])\n\n        #######################################################################\n\n        CountryModel = DirectorModel.__fields__[\"country\"].type_\n\n        self.assertTrue(issubclass(CountryModel, pydantic.BaseModel))\n\n        country_model_keys = [i for i in CountryModel.__fields__.keys()]\n        self.assertEqual(country_model_keys, [\"name\"])\n\n    def test_cascaded_args(self):\n        \"\"\"\n        Make sure that arguments passed to ``create_pydantic_model`` are\n        cascaded to nested models.\n        \"\"\"\n\n        class Country(Table):\n            name = Varchar(length=10)\n\n        class Director(Table):\n            name = Varchar(length=10)\n            country = ForeignKey(Country)\n\n        class Movie(Table):\n            name = Varchar(length=10)\n            director = ForeignKey(Director)\n\n        MovieModel = create_pydantic_model(\n            table=Movie, nested=True, include_default_columns=True\n        )\n\n        #######################################################################\n\n        DirectorModel = MovieModel.__fields__[\"director\"].type_\n\n        self.assertTrue(issubclass(DirectorModel, pydantic.BaseModel))\n\n        director_model_keys = [i for i in DirectorModel.__fields__.keys()]\n        self.assertEqual(director_model_keys, [\"id\", \"name\", \"country\"])\n\n        #######################################################################\n\n        CountryModel = DirectorModel.__fields__[\"country\"].type_\n\n        self.assertTrue(issubclass(CountryModel, pydantic.BaseModel))\n\n        country_model_keys = [i for i in CountryModel.__fields__.keys()]\n        self.assertEqual(country_model_keys, [\"id\", \"name\"])\n\n\nclass TestDBColumnName(TestCase):\n    def test_db_column_name(self):\n        \"\"\"\n        Make sure that the Pydantic model has an alias if ``db_column_name``\n        is specified for a column.\n        \"\"\"\n\n        class Band(Table):\n            name = Varchar(db_column_name=\"regrettable_column_name\")\n\n        BandModel = create_pydantic_model(table=Band)\n\n        model = BandModel(regrettable_column_name=\"test\")\n\n        self.assertTrue(model.name == \"test\")\n\n\nclass TestSchemaExtraKwargs(TestCase):\n    def test_schema_extra_kwargs(self):\n        \"\"\"\n        Make sure that the ``schema_extra_kwargs`` arguments are reflected in\n        Pydantic model's schema.\n        \"\"\"\n\n        class Band(Table):\n            name = Varchar()\n\n        model = create_pydantic_model(Band, visible_columns=(\"name\",))\n        self.assertEqual(model.schema()[\"visible_columns\"], (\"name\",))\n","sub_path":"tests/utils/test_pydantic.py","file_name":"test_pydantic.py","file_ext":"py","file_size_in_byte":12150,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"493871757","text":"__author__ = 'laila'\n\n\nimport csv\nimport json\n\n\nwith open('raw_unemployment.txt') as f:\n    reader = csv.reader(f, dialect='excel-tab')\n    dic = {}\n    for row in reader:\n        dic[row[0]] = float(row[1])\n    with open('parsed_unemployment', 'w') as f1:\n        json.dump(dic, f1, sort_keys=True, indent=4,ensure_ascii=False)","sub_path":"data/parse_unemployment.py","file_name":"parse_unemployment.py","file_ext":"py","file_size_in_byte":328,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"544308820","text":"import json, sqlalchemy\nfrom flask import session\nfrom app import genericConect, bots_per_page\nfrom app.commons import datetimeconv\nfrom app.plays.models import PlayModel\nfrom app.trading.models import TradingModel\nfrom app.trading.models import ExchangeModel\nfrom app.users.models import UsersModel\nfrom decimal import *\nfrom dateutil.parser import parse\nfrom datetime import datetime, timedelta\nimport requests\nimport pandas as pd\n\nclass OrderBookModel():\n\n    def orderBookListByPair(self, data):\n        params = []\n        custom_res = dict()\n        custom_res['data'] = ''\n        custom_res['code'] = 200\n        data_all = []\n        dict_data = {}\n        list_buy = []\n        list_sell = []\n        try:\n            exchange = ExchangeModel()\n            formatos = (exchange.Select_FormatExchange({'idExchange':1}))['data']\n            url = [f[4] for f in formatos if f[3]=='BB']\n            url_orderbook = url[0]\n            response = requests.post(url=url_orderbook,data=json.dumps(data),headers={'Content-Type': 'application/json'})\n\n            if response.status_code!=200:\n                custom_res['data'] = '[WARNING]  No data, check endpoint.'\n                custom_res['code'] = response.status_code\n                return custom_res\n                \n            res = json.loads(response.content)\n            \"\"\"\n            Begin Processing\n            \"\"\"\n\n            # Seteo de diccionario BUY(response sell se convierte en buy)\n            if type(res['SELL']) is list:\n                for i in res['SELL']:\n                    buy_dict = {}\n                    buy_dict['username'] = i['username']\n                    buy_dict['hora'] = i['Hora']\n                    buy_dict['idOrder'] = i['idOrder']\n                    buy_dict['price'] = i['Price']\n                    buy_dict['flag'] = i['flag']\n                    buy_dict['amount'] = i['Amount']\n                    buy_dict['pair'] = i['Pair']\n                    list_buy.append(buy_dict)\n            dict_data['buy'] = list_buy\n\n            # Seteo de diccionario SELL(response buy se convierte en sell)\n            if type(res['BUY']) is list:\n                for i in res['BUY']:\n                    sell_dict = {}\n                    sell_dict['username'] = i['username']\n                    sell_dict['hora'] = i['Hora']\n                    sell_dict['idOrder'] = i['idOrder']\n                    sell_dict['price'] = i['Price']\n                    sell_dict['flag'] = i['flag']\n                    sell_dict['amount'] = i['Amount']\n                    sell_dict['pair'] = i['Pair']\n                    list_sell.append(sell_dict)\n            dict_data['sell'] = list_sell\n            data_all.append(dict_data)\n            \"\"\"\n            End Processing\n            \"\"\"\n\n            custom_res['data'] = data_all if data_all else '[WARNING] Data empty from store procedure'\n            custom_res['code'] = 200 if data_all else 400\n            return custom_res\n        except sqlalchemy.exc.InternalError as e:\n            raise e","sub_path":"app/orderBook/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"247175076","text":"# A set is a list of values that can be compared to other sets using opeartors such as\n# intersection, difference, symmetric_difference, and union.\n\nmydict = {\"Mack\": {\"Age\": 16, \"Color\": \"Purple\", \"Sophomore\":True}}\nmydict.update({\"Phin\": {\"Age\": 15, \"Color\": \"Red\", \"Sophomore\":False}})\nmydict.update({\"Bob\": {\"Age\": 19, \"Color\": \"Maroon\", \"Sophomore\":False}})\n\nfor key in mydict.keys():\n    name = key\n    age = mydict[key][\"Age\"]\n    color = mydict[key][\"Color\"]\n    if mydict[key][\"Sophomore\"]: sophomore = \"is\"\n    if not mydict[key][\"Sophomore\"]: sophomore = \"is not\"\n    print(f\"{name} is {age} years old, his favorite color is {color}, and he {sophomore} a sophomore\")\n\n# This program fulfills the requirements because the dictionary 'mydict' holds 3 keys,\n# houses four mixed types (int, string, bool, and another dict), and retrieves\n# values when keys are given\n","sub_path":"greeneDictQuiz.py","file_name":"greeneDictQuiz.py","file_ext":"py","file_size_in_byte":874,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"80586867","text":"import torch\nimport util\nimport copy\nimport numpy as np\nimport torch.nn as nn\nfrom net.alexnet import AlexNet\nfrom net.quantization import weight_quantization, Quantize, Quantize_2, FracBits, Shift_Right_Bits, ShiftRight, Quantize_torch, FracBits_torch\nimport torch.nn.functional as F\nimport torchvision\nfrom torchvision import datasets, transforms\nfrom torch.nn.modules.module import Module\nfrom net.prune import PruningModule, MaskedLinear\nfrom net.models import LeNet_5_onnx\n\nnp.set_printoptions(suppress=True)\nnp.set_printoptions(threshold=np.inf)\n\nno_cuda = False\nuse_cuda = not no_cuda and torch.cuda.is_available()\ndevice = torch.device(\"cuda:0\" if use_cuda else 'cpu')\n\noutput_shift_dict = {}\nstats = {}\nbatch_count = 0\nfc_size = 0\n\ntest_loader = torch.utils.data.DataLoader(\n    datasets.MNIST('data', train=False, transform=transforms.Compose([\n                       transforms.ToTensor(),\n                       transforms.Normalize((0.1307,), (0.3081,))\n                   ])), batch_size=200, shuffle=False)\n\ndef test(model, quant=False):\n    model.eval()\n    test_loss = 0\n    correct = 0\n    flag = False\n    count = 0\n    with torch.no_grad():\n        for data, target in test_loader:\n            data, target = data.to(device), target.to(device)\n            if quant == True:\n                data = Quantize_torch(data.cpu(), FracBits_torch(data.cpu()))\n                # data = Quantize_2(data.cpu(), 8)\n                data = data.to(device)\n\n            # output = model(data)\n            output = mnist_quant_activate_inference(model, data)\n            test_loss += F.cross_entropy(output, target, reduction='sum').item()\n            pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability\n            correct += pred.eq(target.data.view_as(pred)).sum().item()\n            count += 1\n\n        test_loss /= len(test_loader.dataset)\n        accuracy = 100. * correct / len(test_loader.dataset)\n        print(f'Test set: Average loss: {test_loss:.4f}, Accuracy: {correct}/{len(test_loader.dataset)} ({accuracy:.2f}%)')\n    return accuracy\n\ndef Quant_activate(data):\n    out = Quantize(data.cpu().detach().numpy(), FracBits(data.cpu().detach().numpy()))\n    out = torch.from_numpy(out.astype('float')).type(torch.FloatTensor)\n    out = out.to(device)\n    return out\n\ndef Shiftright_activate(data, shift_right_bit):\n    x = ShiftRight(data.cpu().detach().numpy().astype(int), shift_right_bit)\n    x = torch.from_numpy(x.astype('double')).type(torch.FloatTensor)\n    x = x.to(device)\n    return x\n\ndef update_stats(stats, key, shift_bit):\n    \n    if key not in stats:\n        stats[key] = {\"shift_bit\": 0, \"batch_count\": 1}\n    else:\n        stats[key][\"shift_bit\"] += shift_bit\n        stats[key][\"batch_count\"] += 1\n    \n    return stats\n\ndef mnist_quant_activate_inference(model, data):\n    x = data\n    global batch_count\n    global stats\n    global fc_size\n    batch_count += 1\n\n    # Conv1\n    x = model.conv1(x)\n    # print(\"conv1_output_size:{}\".format(x.size()))\n    shift_right_bit = Shift_Right_Bits(x.cpu().detach().numpy().astype(int))\n    x = Shiftright_activate(x, shift_right_bit)\n    name = \"CONV0\"\n    output_shift_dict.update({name: shift_right_bit})\n    stats = update_stats(stats, name, shift_right_bit)\n\n    x = F.relu(x)\n    x = F.max_pool2d(x, kernel_size=(2, 2), stride=2)\n    # print(\"maxpool1_output_size:{}\".format(x.size()))\n\n    # Conv2\n    x = model.conv2(x)\n    # print(\"conv2_output_size:{}\".format(x.size()))\n    shift_right_bit = Shift_Right_Bits(x.cpu().detach().numpy().astype(int))\n    x = Shiftright_activate(x, shift_right_bit)\n    name = \"CONV1\"\n    output_shift_dict.update({name: shift_right_bit})\n    stats = update_stats(stats, name, shift_right_bit)\n\n    x = F.relu(x)\n    x = F.max_pool2d(x, kernel_size=(3, 3), stride=3)\n    # print(\"maxpool2_output_size:{}\".format(x.size()))\n    fc_size = x.cpu().detach().numpy().shape[1:]\n    # print(fc_size)\n    # Fully-connected\n    x = torch.flatten(x, 1)\n    x = model.fc1(x)\n    shift_right_bit = Shift_Right_Bits(x.cpu().detach().numpy().astype(int))\n    x = Shiftright_activate(x, shift_right_bit)\n    name = \"FC0\"\n    output_shift_dict.update({name: shift_right_bit})\n    stats = update_stats(stats, name, shift_right_bit)\n\n    x = F.log_softmax(x, dim=1)\n    \n    return x\n\ndef GetLayerChannels(model):\n    channels_list = []\n    for layer in model.children():\n        if isinstance(layer, nn.Conv2d):\n            channels_list.append(layer.weight.data.size()[0])\n        elif isinstance(layer, nn.Linear):\n            channels_list.append(layer.weight.data.size()[1])\n    \n    return channels_list\n\nconv_list = [\"conv1\", \"conv2\"]\n\nmodel = torch.load(\"./saves/mnist_noprune_9911.ptmodel\")\nprint(model)\nutil.print_nonzeros(model)\nprint(\"--- Quantization ---\")\nmodel = weight_quantization(model)\nmodel = model.to(device)\ntest(model, True)\nprint(stats)\nfor key in stats:\n    output_shift_dict[key] = int(np.round(stats[key][\"shift_bit\"] / stats[key][\"batch_count\"]))\n\nprint(output_shift_dict)\nprint(\"--- Output weight ---\")\nconv_output_channel = [8, 16]\ncount = 0\n# channels_list = GetLayerChannels(model)\n\n\nmodel_weight = open(\"cortexm_weight.h\",\"w\")\n# cortexm_weight_0_8_256_conv1_20.h\nfor m in output_shift_dict:\n    model_weight.write(\"#define \")\n    model_weight.write(str(m) + \"_OUT_SHIFT \")\n    model_weight.write(str(output_shift_dict[m]))\n    model_weight.write(\"\\n\")\n\nmodel_weight.write(\"\\n\")\n\nfor i in range(len(conv_output_channel)):\n    model_weight.write(\"#define \")\n    model_weight.write(\"CONV\" + str(i) + \"_BIAS {\")\n    for k in range(int(conv_output_channel[i])):\n        if k == int(conv_output_channel[i]) - 1:\n            model_weight.write('0')\n        else:\n            model_weight.write(\"0, \")\n\n    model_weight.write(\"}\")\n    model_weight.write(\"\\n\")\n\nmodel_weight.write(\"#define \")\nmodel_weight.write(\"FC0_BIAS {\")\nfor i in range(10):\n    if i == 9:\n        model_weight.write('0')\n    else:\n        model_weight.write(\"0, \")\n\nmodel_weight.write(\"}\")\nmodel_weight.write(\"\\n\")\n\nmodel_weight.write(\"\\n\")\n\nfor k in range(2):\n    # weight_name = \"feature.\" + k + \".module.weight\"    \n    conv1_weight = model.state_dict()[\"conv\" + str(k+1) + \".weight\"].cpu().numpy().astype(int)\n    conv1_weight = conv1_weight.transpose(0, 2, 3, 1)\n    print(\"weight_shape:{}\".format(conv1_weight.shape))\n    conv1_weight = conv1_weight.flatten().astype(int)\n\n    model_weight.write(\"#define \")\n    model_weight.write(\"CONV\" + str(count) + \"_WEIGHT {\")\n    count += 1\n\n    for i in range(len(conv1_weight)):\n        if i == len(conv1_weight) - 1:\n            model_weight.write(str(conv1_weight[i]))\n        else:\n            model_weight.write(str(conv1_weight[i]) + ', ')\n\n    model_weight.write(\"}\")\n    model_weight.write(\"\\n\")\n    model_weight.write(\"\\n\")\n\nfc_reshape = [10]\nfor i in range(len(fc_size)):\n    fc_reshape.append(fc_size[i])\n\nprint(fc_reshape)\n\nweight_name = \"fc1.weight\"\nfc_weight = model.state_dict()[weight_name].cpu().numpy().astype(int)\nfc_weight = fc_weight.reshape(fc_reshape).transpose(0,2,3,1).reshape(10, fc_weight.shape[1])\nprint(\"fc_weight:{}\".format(fc_weight.shape))\nfc_weight = fc_weight.flatten().astype(int)\nmodel_weight.write(\"#define \")\ndefname = \"FC\" + \"0\" + \"_WEIGHT {\"\nmodel_weight.write(defname)\nfor j in range(len(fc_weight)):\n    if j == len(fc_weight) - 1:\n        model_weight.write(str(fc_weight[j]))\n    else:\n        model_weight.write(str(fc_weight[j]) + ', ')\n\nmodel_weight.write(\"}\")\nmodel_weight.write(\"\\n\")\nmodel_weight.close()\n","sub_path":"inference_mnist.py","file_name":"inference_mnist.py","file_ext":"py","file_size_in_byte":7528,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"51694851","text":"#!/usr/bin/python\n#\n# Copyright 2018-2021 Polyaxon, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#      http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport os\nimport sys\nimport time\nimport uuid\n\nfrom collections.abc import Mapping\nfrom datetime import datetime\nfrom typing import Dict, List, Optional, Sequence, Tuple, Union\n\nimport click\nimport polyaxon_sdk\nimport ujson\n\nfrom polyaxon_sdk import V1Run\nfrom polyaxon_sdk.rest import ApiException\nfrom urllib3.exceptions import HTTPError\n\nfrom polyaxon import settings\nfrom polyaxon.cli.errors import handle_cli_error\nfrom polyaxon.client.client import PolyaxonClient\nfrom polyaxon.client.decorators import client_handler\nfrom polyaxon.constants.metadata import META_COPY_ARTIFACTS\nfrom polyaxon.containers.contexts import CONTEXT_MOUNT_ARTIFACTS, CONTEXT_OFFLINE_ROOT\nfrom polyaxon.env_vars.getters import (\n    get_artifacts_store_name,\n    get_entity_full_name,\n    get_project_error_message,\n    get_project_or_local,\n    get_run_info,\n    get_run_or_local,\n)\nfrom polyaxon.exceptions import PolyaxonClientException\nfrom polyaxon.lifecycle import LifeCycle, V1StatusCondition, V1Statuses\nfrom polyaxon.logger import logger\nfrom polyaxon.managers.ignore import IgnoreConfigManager\nfrom polyaxon.polyaxonfile import check_polyaxonfile\nfrom polyaxon.polyboard.artifacts import V1ArtifactKind, V1RunArtifact\nfrom polyaxon.polyboard.events import V1Events\nfrom polyaxon.polyboard.logging.streamer import get_logs_streamer\nfrom polyaxon.polyflow import V1Matrix, V1Operation, V1RunKind\nfrom polyaxon.schemas.types import V1ArtifactsType\nfrom polyaxon.stores.polyaxon_store import PolyaxonStore\nfrom polyaxon.utils.code_reference import get_code_reference\nfrom polyaxon.utils.date_utils import file_modified_since\nfrom polyaxon.utils.formatting import Printer\nfrom polyaxon.utils.fqn_utils import to_fqn_name\nfrom polyaxon.utils.hashing import hash_value\nfrom polyaxon.utils.http_utils import absolute_uri\nfrom polyaxon.utils.list_utils import to_list\nfrom polyaxon.utils.path_utils import (\n    check_or_create_path,\n    delete_path,\n    get_base_filename,\n    get_dirs_under_path,\n    get_files_in_path_context,\n)\nfrom polyaxon.utils.query_params import get_logs_params, get_query_params\nfrom polyaxon.utils.tz_utils import now\nfrom polyaxon.utils.validation import validate_tags\n\n\nclass RunClient:\n    \"\"\"RunClient is a client to communicate with Polyaxon runs endpoints.\n\n    If no values are passed to this class,\n    Polyaxon will try to resolve the owner, project, and run uuid from the environment:\n     * If you have a configured CLI, Polyaxon will use the configuration of the cli.\n     * If you have a cached run using the CLI,\n       the client will default to that cached run unless you override the values.\n     * If you use this client in the context of a job or a service managed by Polyaxon,\n       a configuration will be available to resolve the values based on that run.\n\n    If you intend to create a new run instance or to list runs,\n    only the `owner` and `project` parameters are required.\n\n    You can always access the `self.client` to execute more APIs.\n\n    Properties:\n        project: str.\n        owner: str.\n        run_uuid: str.\n        run_data: V1Run.\n        status: str.\n        namespace: str.\n        client: [PolyaxonClient](/docs/core/python-library/polyaxon-client/)\n\n    Args:\n        owner: str, optional, the owner is the username or\n             the organization name owning this project.\n        project: str, optional, project name owning the run(s).\n        run_uuid: str, optional, run uuid.\n        client: [PolyaxonClient](/docs/core/python-library/polyaxon-client/), optional,\n             an instance of a configured client, if not passed,\n             a new instance will be created based on the available environment.\n\n    Raises:\n        PolyaxonClientException: If no owner and/or project are passed and Polyaxon cannot\n             resolve the values from the environment.\n    \"\"\"\n\n    @client_handler(check_no_op=True)\n    def __init__(\n        self,\n        owner: str = None,\n        project: str = None,\n        run_uuid: str = None,\n        client: PolyaxonClient = None,\n        is_offline: bool = None,\n    ):\n        self._is_offline = (\n            is_offline if is_offline is not None else settings.CLIENT_CONFIG.is_offline\n        )\n        try:\n            owner, project = get_project_or_local(\n                get_entity_full_name(owner=owner, entity=project)\n            )\n        except PolyaxonClientException:\n            pass\n\n        if project is None:\n            if settings.CLIENT_CONFIG.is_managed:\n                owner, project, _run_uuid = get_run_info()\n                run_uuid = run_uuid or _run_uuid\n            elif not self._is_offline:\n                raise PolyaxonClientException(\n                    \"Please provide a valid project, \"\n                    \"or make sure this operation is managed by Polyaxon.\"\n                )\n\n        error_message = get_project_error_message(owner, project)\n        if error_message and not self._is_offline:\n            raise PolyaxonClientException(error_message)\n\n        self._client = client\n        self._owner = owner\n        self._project = project\n        self._run_uuid = (\n            get_run_or_local(run_uuid)\n            if not self._is_offline\n            else run_uuid or uuid.uuid4().hex\n        )\n        default_runtime = (\n            V1RunKind.JOB\n            if self._is_offline or not settings.CLIENT_CONFIG.is_managed\n            else None\n        )\n        self._run_data = polyaxon_sdk.V1Run(\n            owner=self._owner,\n            project=self._project,\n            uuid=self._run_uuid,\n            kind=default_runtime,\n            runtime=default_runtime,\n            is_managed=False if self._is_offline else None,\n        )\n        self._namespace = None\n        self._results = {}\n        self._lineages = {}\n\n    @property\n    def client(self):\n        if self._client:\n            return self._client\n        self._client = PolyaxonClient()\n        return self._client\n\n    @property\n    def status(self) -> str:\n        return self._run_data.status\n\n    @property\n    def namespace(self) -> str:\n        if self._namespace:\n            return self._namespace\n        if (\n            self.run_data\n            and self.run_data.settings\n            and self.run_data.settings.namespace\n        ):\n            self._namespace = self.run_data.settings.namespace\n        else:\n            self._namespace = self.get_namespace()\n        return self._namespace\n\n    @property\n    def owner(self) -> str:\n        return self._owner\n\n    @property\n    def project(self) -> str:\n        return self._project\n\n    @property\n    def run_uuid(self) -> str:\n        return self._run_uuid\n\n    @property\n    def run_data(self):\n        return self._run_data\n\n    @client_handler(check_no_op=True)\n    def get_inputs(self) -> Dict:\n        \"\"\"Gets the run's inputs.\n        Returns:\n            dict, all the run inputs/params.\n        \"\"\"\n        return self._run_data.inputs\n\n    @client_handler(check_no_op=True)\n    def get_outputs(self) -> Dict:\n        \"\"\"Gets the run's outputs.\n        Returns:\n             dict, all the run outputs/metrics.\n        \"\"\"\n        return self._run_data.outputs\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def refresh_data(self):\n        \"\"\"Fetches the run data from the api.\"\"\"\n        self._run_data = self.client.runs_v1.get_run(\n            self.owner, self.project, self.run_uuid\n        )\n\n    def _update(\n        self, data: Union[Dict, polyaxon_sdk.V1Run], async_req: bool = True\n    ) -> V1Run:\n        if self._is_offline:\n            return self.run_data\n        response = self.client.runs_v1.patch_run(\n            owner=self.owner,\n            project=self.project,\n            run_uuid=self.run_uuid,\n            body=data,\n            async_req=async_req,\n        )\n        if not async_req:\n            self._run_data = response\n        return response\n\n    @client_handler(check_no_op=True)\n    def update(\n        self, data: Union[Dict, polyaxon_sdk.V1Run], async_req: bool = False\n    ) -> V1Run:\n        \"\"\"Updates a run based on the data passed.\n\n        [Run API](/docs/api/#operation/PatchRun)\n\n        Args:\n            data: Dict or V1Run, required.\n            async_req: bool, optional, default: False, execute request asynchronously.\n\n        Returns:\n            V1Run, run instance from the response.\n        \"\"\"\n        if self._is_offline:\n            for k in data:\n                setattr(self._run_data, k, getattr(data, k, None))\n        return self._update(data=data, async_req=async_req)\n\n    def _create(\n        self, data: Union[Dict, polyaxon_sdk.V1OperationBody], async_req: bool = False\n    ) -> V1Run:\n        response = self.client.runs_v1.create_run(\n            owner=self.owner,\n            project=self.project,\n            body=data,\n            async_req=async_req,\n        )\n        if not async_req:\n            self._run_data = response\n            self._run_uuid = self._run_data.uuid\n            self._run_data.status = V1Statuses.CREATED\n            self._namespace = None\n            self._results = {}\n            self._lineages = {}\n        return response\n\n    @client_handler(check_no_op=True)\n    def create(\n        self,\n        name: str = None,\n        description: str = None,\n        tags: Union[str, Sequence[str]] = None,\n        content: Union[str, Dict, V1Operation] = None,\n        is_managed: bool = True,\n        pending: Optional[str] = None,\n        meta_info: Optional[Dict] = None,\n    ) -> V1Run:\n        \"\"\"Creates a new run based on the data passed.\n\n        N.B. Create methods are only useful if you want to create a run programmatically,\n        if you run a component/operation from the CLI/UI an instance will be created automatically.\n\n        This is a generic create function, you can check other methods for creating runs:\n          * from yaml: `create_from_polyaxonfile`\n          * from url: `create_from_url`\n          * from hub: `create_from_hub`\n\n        > **Note**: If the `content` param is not passed, the run will be marked as non-managed.\n\n        [Run API](/docs/api/#operation/CreateRun)\n\n        Args:\n            name: str, optional, it will override the name in the operation if provided.\n            description: str, optional,\n                 it will override the description in the operation if provided.\n            tags: str or List[str], optional, list of tags,\n                 it will override the tags in the operation if provided.\n            content: str or Dict or V1Operation, optional.\n            is_managed: bool, flag to create a managed run.\n            pending: str, to specify if the run is pending approval (requires human validation) or pending upload.  # noqa\n            meta_info: dict, meta info to create the run with.\n\n        Returns:\n            V1Run, run instance from the response.\n        \"\"\"\n        tags = validate_tags(tags)\n        if self._is_offline:\n            self._run_data.name = name\n            self._run_data.description = description\n            self._run_data.tags = tags\n            self._run_data.owner = self._owner\n            self._run_data.project = self._project\n            if not self._run_uuid:\n                self._run_uuid = uuid.uuid4().hex\n            self.run_data.uuid = self._run_uuid\n            return self.run_data\n        if not content:\n            is_managed = False\n        elif not isinstance(content, (str, Mapping, V1Operation)):\n            raise PolyaxonClientException(\n                \"Received an invalid content: {}\".format(content)\n            )\n        if content:\n            if isinstance(content, Mapping):\n                content = V1Operation.from_dict(content)\n            content = (\n                content if isinstance(content, str) else content.to_dict(dump=True)\n            )\n        data = polyaxon_sdk.V1OperationBody(\n            name=name,\n            description=description,\n            tags=tags,\n            content=content,\n            is_managed=is_managed,\n            pending=pending,\n            meta_info=meta_info,\n        )\n        self._create(data=data, async_req=False)\n        return self.run_data\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def create_from_polyaxonfile(\n        self,\n        polyaxonfile: str,\n        name: str = None,\n        description: str = None,\n        tags: Union[str, Sequence[str]] = None,\n        params: Dict = None,\n        matrix: Union[Dict, V1Matrix] = None,\n        presets: List[str] = None,\n        queue: str = None,\n        nocache: bool = None,\n        cache: Union[int, str, bool] = None,\n        approved: Union[int, str, bool] = None,\n    ) -> V1Run:\n        \"\"\"Creates a new run based on a polyaxonfile.\n\n        N.B. Create methods are only useful if you want to create a run programmatically,\n        if you run a component/operation from the CLI/UI an instance will be created automatically.\n\n        [Run API](/docs/api/#operation/CreateRun)\n\n        Args:\n            polyaxonfile: str, path to the polyaxonfile containing a YAML/Json specification.\n                 The polyaxonfile should contain a\n                 [V1Component](/docs/core/specification/component/) or an\n                 [V1Operation](/docs/core/specification/operation/).\n            name: str, optional,\n                 it will override the name in the operation if provided.\n            description: str, optional,\n                 it will override the description in the operation if provided.\n            tags: str or List[str], optional, list of tags,\n                 it will override the tags in the operation if provided.\n            params: dict, optional, a dictionary of parameters that will be\n                 used to resolve the component's inputs/outputs.\n            matrix: dict or V1Matrix, a matrix definition.\n            presets: List[str], optional, the name of the\n                 [presets](/docs/core/scheduling-presets/).\n            queue: str, optional, the name of the\n                 [queue](/docs/core/scheduling-strategies/queues/) to assign the run to.\n            nocache: bool, optional, DEPRECATED Please use `cache='f'`\n                 simple flag to disable\n                 [cache check](/docs/automation/helpers/cache/).\n                 If passed and the Polyaxonfile has cache section,\n                 it will be patched with `disabled: true`.\n            cache: Union[int, str, bool], optional, simple flag to enable/disable\n                 [cache check](/docs/automation/helpers/cache/).\n                 If passed and the Polyaxonfile will be patched with `disabled: true/false`.\n                 e.g. `cache=1`, `cache='yes'`, `cache=False`, `cache='t'`, ...\n            approved: Union[int, str, bool], optional, simple flag to enable/disable\n                 human in the loop validation without changing the polyaxonfile,\n                 similar to `isApproved: true/false`,\n                 [manual approval](/docs/core/scheduling-strategies/manual-approval/).\n                 Can be used with yes/no, y/n, false/true, f/t, 1/0. \"\n                 \"e.g. `approved=1`, `approved='yes'`, `approved=False`, `approved='t'`, ...\"\n\n        Returns:\n            V1Run, run instance from the response.\n        \"\"\"\n        op_spec = check_polyaxonfile(\n            polyaxonfile=polyaxonfile,\n            params=params,\n            matrix=matrix,\n            presets=presets,\n            queue=queue,\n            nocache=nocache,\n            cache=cache,\n            approved=approved,\n            verbose=False,\n            is_cli=False,\n        )\n        return self.create(\n            name=name, description=description, tags=tags, content=op_spec\n        )\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def create_from_url(\n        self,\n        url: str,\n        name: str = None,\n        description: str = None,\n        tags: Union[str, Sequence[str]] = None,\n        params: Dict = None,\n        matrix: Union[Dict, V1Matrix] = None,\n        presets: List[str] = None,\n        queue: str = None,\n        nocache: bool = None,\n        cache: Union[int, str, bool] = None,\n        approved: Union[int, str, bool] = None,\n    ) -> V1Run:\n        \"\"\"Creates a new run from a url containing a Polyaxonfile specification.\n\n        N.B. Create methods are only useful if you want to create a run programmatically,\n        if you run a component/operation from the CLI/UI an instance will be created automatically.\n\n        [Run API](/docs/api/#operation/CreateRun)\n\n        Args:\n            url: str, url containing a YAML/Json specification.\n                 The url's polyaxonfile should contain a\n                 [V1Component](/docs/core/specification/component/) or an\n                 [V1Operation](/docs/core/specification/operation/).\n            name: str, optional, it will override the name in the operation if provided.\n            description: str, optional,\n                 it will override the description in the operation if provided.\n            tags: str or List[str], optional, list of tags,\n                 it will override the tags in the operation if provided.\n            params: dict, optional, a dictionary of parameters that will be\n                 used to resolve the component's inputs/outputs.\n            matrix: dict or V1Matrix, a matrix definition.\n            presets: List[str], optional, the name of the\n                 [presets](/docs/core/scheduling-presets/).\n            queue: str, optional, the name of the\n                 [queue](/docs/core/scheduling-strategies/queues/) to assign the run to.\n            nocache: bool, optional, DEPRECATED Please use `cache='f'`\n                 simple flag to disable\n                 [cache check](/docs/automation/helpers/cache/).\n                 If passed and the Polyaxonfile has cache section,\n                 it will be patched with `disabled: true`.\n            cache: Union[int, str, bool], optional, simple flag to enable/disable\n                 [cache check](/docs/automation/helpers/cache/).\n                 If passed and the Polyaxonfile will be patched with `disabled: true/false`.\n                 e.g. `cache=1`, `cache='yes'`, `cache=False`, `cache='t'`, ...\n            approved: Union[int, str, bool], optional, simple flag to enable/disable\n                 human in the loop validation without changing the polyaxonfile,\n                 similar to `isApproved: true/false`,\n                 [manual approval](/docs/core/scheduling-strategies/manual-approval/).\n                 Can be used with yes/no, y/n, false/true, f/t, 1/0. \"\n                 \"e.g. `approved=1`, `approved='yes'`, `approved=False`, `approved='t'`, ...\"\n\n        Returns:\n            V1Run, run instance from the response.\n        \"\"\"\n        op_spec = check_polyaxonfile(\n            url=url,\n            params=params,\n            matrix=matrix,\n            presets=presets,\n            queue=queue,\n            nocache=nocache,\n            cache=cache,\n            approved=approved,\n            verbose=False,\n            is_cli=False,\n        )\n        return self.create(\n            name=name, description=description, tags=tags, content=op_spec\n        )\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def create_from_hub(\n        self,\n        component: str,\n        name: str = None,\n        description: str = None,\n        tags: Union[str, Sequence[str]] = None,\n        params: Dict = None,\n        matrix: Union[Dict, V1Matrix] = None,\n        presets: str = None,\n        queue: str = None,\n        nocache: bool = None,\n        cache: Union[int, str, bool] = None,\n        approved: Union[int, str, bool] = None,\n    ) -> V1Run:\n        \"\"\"Creates a new run from the hub based on the component name.\n\n        N.B. Create methods are only useful if you want to create a run programmatically,\n        if you run a component/operation from the CLI/UI an instance will be created automatically.\n\n        If the component has required inputs, you should pass the params.\n\n        [Run API](/docs/api/#operation/CreateRun)\n\n        Args:\n            component: str, name of the hub component.\n            name: str, optional, it will override the name in the component if provided.\n            description: str, optional,\n                 it will override the description in the component if provided.\n            tags: str or List[str], optional, list of tags,\n                 it will override the tags in the component if provided.\n            params: dict, optional, a dictionary of parameters that will be\n                 used to resolve the component's inputs/outputs.\n            matrix: dict or V1Matrix, a matrix definition.\n            presets: List[str], optional, the name of the\n                 [presets](/docs/core/scheduling-presets/).\n            queue: str, optional, the name of the\n                 [queue](/docs/core/scheduling-strategies/queues/) to assign the run to.\n            nocache: bool, optional, DEPRECATED Please use `cache='f'`\n                 simple flag to disable\n                 [cache check](/docs/automation/helpers/cache/).\n                 If passed and the Polyaxonfile has cache section,\n                 it will be patched with `disabled: true`.\n            cache: Union[int, str, bool], optional, simple flag to enable/disable\n                 [cache check](/docs/automation/helpers/cache/).\n                 If passed and the Polyaxonfile will be patched with `disabled: true/false`.\n                 e.g. `cache=1`, `cache='yes'`, `cache=False`, `cache='t'`, ...\n            approved: Union[int, str, bool], optional, simple flag to enable/disable\n                 human in the loop validation without changing the polyaxonfile,\n                 similar to `isApproved: true/false`,\n                 [manual approval](/docs/core/scheduling-strategies/manual-approval/).\n                 Can be used with yes/no, y/n, false/true, f/t, 1/0. \"\n                 \"e.g. `approved=1`, `approved='yes'`, `approved=False`, `approved='t'`, ...\"\n\n        Returns:\n            V1Run, run instance from the response.\n        \"\"\"\n        op_spec = check_polyaxonfile(\n            hub=component,\n            params=params,\n            matrix=matrix,\n            presets=presets,\n            queue=queue,\n            nocache=nocache,\n            cache=cache,\n            approved=approved,\n            verbose=False,\n            is_cli=False,\n        )\n        return self.create(\n            name=name, description=description, tags=tags, content=op_spec\n        )\n\n    @client_handler(check_no_op=True)\n    def log_status(\n        self,\n        status: str,\n        reason: str = None,\n        message: str = None,\n        last_transition_time: datetime = None,\n        last_update_time: datetime = None,\n    ):\n        \"\"\"Logs a new run status.\n\n        <blockquote class=\"info\">\n        N.B. If you are executing a managed run, you don't need to call this method manually.\n        This method is only useful for manual runs outside of Polyaxon.\n        </blockquote>\n\n        N.B you will probably use one of the simpler methods:\n            * log_succeeded\n            * log_stopped\n            * log_failed\n            * start\n            * end\n\n        [Run API](/docs/api/#operation/CreateRunStatus)\n\n        Args:\n            status: str, a valid [Statuses](/docs/core/specification/lifecycle/) value.\n            reason: str, optional, reason or service issuing the status change.\n            message: str, optional, message to log with this status.\n            last_transition_time: datetime, default `now`.\n            last_update_time: datetime, default `now`.\n        \"\"\"\n        reason = reason or \"PolyaxonClient\"\n        self._run_data.status = status\n        current_date = now()\n        status_condition = V1StatusCondition(\n            type=status,\n            status=True,\n            reason=reason,\n            message=message,\n            last_transition_time=last_transition_time or current_date,\n            last_update_time=last_update_time or current_date,\n        )\n        if self._is_offline:\n            self._run_data.status_conditions = self._run_data.status_conditions or []\n            self._run_data.status_conditions.append(status_condition)\n            if status == polyaxon_sdk.V1Statuses.CREATED:\n                self._run_data.created_at = current_date\n            LifeCycle.set_started_at(self._run_data)\n            LifeCycle.set_finished_at(self._run_data)\n            return\n        self.client.runs_v1.create_run_status(\n            owner=self.owner,\n            project=self.project,\n            uuid=self.run_uuid,\n            body={\"condition\": status_condition},\n            async_req=True,\n        )\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def get_statuses(\n        self, last_status: str = None\n    ) -> Tuple[str, List[V1StatusCondition]]:\n        \"\"\"Gets the run's statuses.\n\n        [Run API](/docs/api/#operation/GetRunStatus)\n\n        Args:\n            last_status: str, a valid [Statuses](/docs/core/specification/lifecycle/) value.\n\n        Returns:\n            Tuple[str, List[Conditions]], last status and ordered status conditions.\n        \"\"\"\n        try:\n            response = self.client.runs_v1.get_run_statuses(\n                self.owner, self.project, self.run_uuid\n            )\n            if not last_status:\n                return response.status, response.status_conditions\n            if last_status == response.status:\n                return last_status, []\n\n            _conditions = []\n            for c in reversed(response.status_conditions):\n                if c.type == last_status:\n                    break\n                _conditions.append(c)\n\n            return response.status, reversed(_conditions)\n\n        except (ApiException, HTTPError) as e:\n            raise PolyaxonClientException(\"Api error: %s\" % e) from e\n\n    def _wait_for_condition(self, statuses: List[str] = None):\n        statuses = to_list(statuses, check_none=True)\n\n        def condition():\n            if statuses:\n                return last_status in statuses\n            return LifeCycle.is_done(last_status)\n\n        last_status = None\n        while not condition():\n            if last_status:\n                time.sleep(settings.CLIENT_CONFIG.watch_interval)\n            last_status, _conditions = self.get_statuses(last_status)\n            yield last_status, _conditions\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def wait_for_condition(\n        self, statuses: List[str] = None, print_status: bool = False\n    ):\n        \"\"\"Waits for the run's last status to meet a condition.\n\n        If a list of statuses is passed, it will wait for the condition:\n         * last status is one of the statuses passed.\n\n        Otherwise, it will wait until the user interrupts the function or\n        when the run reaches a final status.\n\n        N.B. if you want to watch the statuses and receive the status/conditions,\n        please use `watch_statuses` instead which yields the results.\n        \"\"\"\n        for status, conditions in self._wait_for_condition(statuses):\n            self._run_data.status = status\n            if print_status:\n                print(\"Last received status: {}\\n\".format(status))\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def watch_statuses(self, statuses: List[str] = None):\n        \"\"\"Watches run statuses.\n\n        If statuses is passed the watch will wait for a condition:\n         * last status is one of the statuses passed.\n\n        Otherwise, it will watch until the user interrupts it or\n        when the run reaches a final status.\n\n        N.B. if you just want to wait for a status condition without expecting a yield,\n        please use `wait_for_condition` instead\n\n        Yields:\n            Tuple[status, List[conditions]]:\n                This function will yield the last status and condition for every check.\n        \"\"\"\n        for status, conditions in self._wait_for_condition(statuses):\n            self._run_data.status = status\n            yield status, conditions\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def get_logs(self, last_file=None, last_time=None) -> \"V1Logs\":\n        \"\"\"Gets the run's logs.\n\n        This method return up-to 2000 line logs per request.\n\n        Returns:\n            V1Logs\n        \"\"\"\n        params = get_logs_params(last_file=last_file, last_time=last_time)\n        return self.client.runs_v1.get_run_logs(\n            self.namespace, self.owner, self.project, self.run_uuid, **params\n        )\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def watch_logs(self, hide_time: bool = False, all_info: bool = False):\n        \"\"\"Watches run logs.\n\n        Args:\n            hide_time: bool, optional, default: False, remove time information from log lines.\n            all_info: bool, optional, default: False, show all information about log lines.\n        \"\"\"\n        return get_run_logs(\n            client=self, hide_time=hide_time, all_info=all_info, follow=True\n        )\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def get_events(\n        self,\n        kind: V1ArtifactKind,\n        names: List[str],\n        orient: str = None,\n        force: bool = False,\n    ):\n        \"\"\"Gets the run's events\n\n        Args:\n            kind: str, a valid `V1ArtifactKind`.\n            names: List[str], list of events to return.\n            orient: str, csv or dict.\n            force: bool, force reload the events.\n        \"\"\"\n        return self.client.runs_v1.get_run_events(\n            self.namespace,\n            self.owner,\n            self.project,\n            self.run_uuid,\n            kind=kind,\n            names=names,\n            orient=orient,\n            force=force,\n        )\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def get_multi_run_events(\n        self,\n        kind: V1ArtifactKind,\n        runs: List[str],\n        names: List[str],\n        orient: str = None,\n        force: bool = False,\n    ):\n        logger.warning(\"This method is deprecated in favor of `get_multi_run_events`\")\n        return self.get_multi_run_events(\n            kind=kind, runs=runs, names=names, orient=orient, force=force\n        )\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def get_multi_run_events(\n        self,\n        kind: V1ArtifactKind,\n        runs: List[str],\n        names: List[str],\n        orient: str = None,\n        force: bool = False,\n    ):\n        \"\"\"Gets multi-run events.\n\n        Args:\n            kind: str, a valid `V1ArtifactKind`.\n            runs: List[str], list of run uuids to return events for.\n            names: List[str], list of events to return.\n            orient: str, csv or dict.\n            force: bool, force reload the events.\n        \"\"\"\n        return self.client.runs_v1.get_multi_run_events(\n            self.namespace,\n            self.owner,\n            self.project,\n            kind=kind,\n            names=names,\n            runs=runs,\n            orient=orient,\n            force=force,\n        )\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def get_artifact(self, path: str, stream: bool = True, force: bool = False):\n        \"\"\"Gets the run's artifact.\n\n        Args:\n            path: str, the relative path of the artifact to return.\n            stream: bool, optional, default: True, whether to stream the artifact content.\n            force: bool, force reload the artifact.\n\n        Returns:\n            str.\n        \"\"\"\n        return self.client.runs_v1.get_run_artifact(\n            namespace=self.namespace,\n            owner=self.owner,\n            project=self.project,\n            uuid=self.run_uuid,\n            path=path,\n            stream=stream,\n            force=force,\n            _preload_content=True,\n        )\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def download_artifact(self, path: str, force: bool = False, path_to: str = None):\n        \"\"\"Downloads a single run artifact.\n\n        Args:\n            path: str, the relative path of the artifact to return.\n            path_to: str, optional, path to download to.\n            force: bool, force reload the artifact.\n\n        Returns:\n            str\n        \"\"\"\n        url = PolyaxonStore.URL.format(\n            namespace=self.namespace,\n            owner=self.owner,\n            project=self.project,\n            uuid=self.run_uuid,\n            subpath=\"artifact\",\n        )\n        url = absolute_uri(url=url, host=self.client.config.host)\n        if force:\n            url = \"{}?force=true\".format(url)\n\n        return PolyaxonStore(client=self).download_file(\n            url=url, path=path, path_to=path_to\n        )\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def download_artifacts(\n        self,\n        path: str = \"\",\n        path_to: str = None,\n        untar: bool = True,\n        delete_tar: bool = True,\n        extract_path: str = None,\n    ):\n        \"\"\"Downloads a subpath containing multiple run artifacts.\n\n        Args:\n            path: str, the relative path of the artifact to return.\n            path_to: str, optional, path to download to.\n            untar: bool, optional, default: true.\n            delete_tar: bool, optional, default: true.\n            extract_path: str, optional.\n\n        Returns:\n            str.\n        \"\"\"\n        url = PolyaxonStore.URL.format(\n            namespace=self.namespace,\n            owner=self.owner,\n            project=self.project,\n            uuid=self.run_uuid,\n            subpath=\"artifacts\",\n        )\n        url = absolute_uri(url=url, host=self.client.config.host)\n\n        return PolyaxonStore(client=self).download_file(\n            url=url,\n            path=path,\n            untar=untar,\n            path_to=path_to,\n            delete_tar=delete_tar and untar,\n            extract_path=extract_path,\n        )\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def upload_artifact(\n        self,\n        filepath: str,\n        path: str = None,\n        untar: bool = False,\n        overwrite: bool = True,\n        show_progress: bool = True,\n    ):\n        \"\"\"Uploads a single artifact to the run's artifacts store path.\n\n        Args:\n            filepath: str, the filepath to upload.\n            path: str, optional, path to upload to, otherwise it will be on the run's root path.\n            untar: bool, optional, if the file uploaded is tar.gz and\n                 it should be decompressed on the artifacts store.\n            overwrite: bool, optional, if the file uploaded should overwrite any previous content.\n            show_progress: bool, to show a progress bar.\n\n        Returns:\n            str\n        \"\"\"\n        url = PolyaxonStore.URL.format(\n            namespace=self.namespace,\n            owner=self.owner,\n            project=self.project,\n            uuid=self.run_uuid,\n            subpath=\"artifact\",\n        )\n        url = absolute_uri(url=url, host=self.client.config.host)\n\n        return PolyaxonStore(client=self).upload_file(\n            url=url,\n            filepath=filepath,\n            path=path or \"\",\n            untar=untar,\n            overwrite=overwrite,\n            show_progress=show_progress,\n        )\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def upload_artifacts_dir(\n        self,\n        dirpath: str,\n        path: str = \"\",\n        overwrite: bool = True,\n        relative_to: str = None,\n    ):\n        \"\"\"Uploads a full directory to the run's artifacts store path.\n\n        > This function crawls all files to upload and uses `upload_artifacts`.\n\n        Args:\n            dirpath: str, the dirpath to upload.\n            path: str, the relative path of the artifact to return.\n            overwrite: bool, optional, if the file uploaded should overwrite any previous content.\n            relative_to: str, optional, if the path uploaded is not the current dir,\n                 and you want to cancel the relative path.\n\n        Returns:\n            str.\n        \"\"\"\n        files = IgnoreConfigManager.get_unignored_filepaths(dirpath)\n        return self.upload_artifacts(\n            files=files,\n            path=path or \"\",\n            overwrite=overwrite,\n            relative_to=relative_to,\n        )\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def upload_artifacts(\n        self,\n        files: List[str],\n        path: str = \"\",\n        overwrite: bool = True,\n        relative_to: str = None,\n    ):\n        \"\"\"Uploads a multiple artifacts to the run's artifacts store path.\n\n        Args:\n            files: List[str], list of files to upload.\n            path: str, the relative path of the artifact to return.\n            overwrite: bool, optional, if the file uploaded should overwrite any previous content.\n            relative_to: str, optional, if the path uploaded is not the current dir,\n                 and you want to cancel the relative path.\n\n        Returns:\n            str.\n        \"\"\"\n        url = PolyaxonStore.URL.format(\n            namespace=self.namespace,\n            owner=self.owner,\n            project=self.project,\n            uuid=self.run_uuid,\n            subpath=\"artifacts\",\n        )\n        url = absolute_uri(url=url, host=self.client.config.host)\n\n        return PolyaxonStore(client=self).upload_dir(\n            url=url,\n            path=path,\n            files=files,\n            overwrite=overwrite,\n            relative_to=relative_to,\n        )\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def delete_artifact(self, path: str):\n        \"\"\"Deletes a single run artifact.\n\n        Args:\n            path: str, the relative path of the artifact to return.\n        \"\"\"\n        self.client.runs_v1.delete_run_artifact(\n            namespace=self.namespace,\n            owner=self.owner,\n            project=self.project,\n            uuid=self.run_uuid,\n            path=path,\n        )\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def delete_artifacts(self, path: str):\n        \"\"\"Deletes a subpath containing multiple run artifacts.\n\n        Args:\n            path: str, the relative path of the artifact to return.\n        \"\"\"\n        return self.client.runs_v1.delete_run_artifacts(\n            namespace=self.namespace,\n            owner=self.owner,\n            project=self.project,\n            uuid=self.run_uuid,\n            path=path,\n        )\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def get_artifacts_tree(self, path: str = \"\"):\n        \"\"\"Return the artifacts tree based on the path.\n\n        Args:\n            path: str, the relative path of the artifact tree to return.\n\n        Returns:\n            V1ArtifactTree.\n        \"\"\"\n        return self.client.runs_v1.get_run_artifacts_tree(\n            namespace=self.namespace,\n            owner=self.owner,\n            project=self.project,\n            uuid=self.run_uuid,\n            path=path,\n        )\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def stop(self):\n        \"\"\"Stops the current run.\"\"\"\n        self.client.runs_v1.stop_run(\n            self.owner,\n            self.project,\n            self.run_uuid,\n        )\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def approve(self):\n        \"\"\"Stops the current run.\"\"\"\n        self.client.runs_v1.approve_run(\n            self.owner,\n            self.project,\n            self.run_uuid,\n        )\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def invalidate(self):\n        \"\"\"Invalidates the current run.\"\"\"\n        self.client.runs_v1.invalidate_run(\n            self.owner,\n            self.project,\n            self.run_uuid,\n        )\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def restart(\n        self,\n        override_config=None,\n        copy: bool = False,\n        copy_dirs: List[str] = None,\n        copy_files: List[str] = None,\n        name: str = None,\n        description: str = None,\n        tags: Union[str, Sequence[str]] = None,\n        **kwargs,\n    ):\n        \"\"\"Restarts the current run\n\n        Args:\n            override_config: Dict or str, optional,\n                 config to use for overriding the original run's config.\n            copy: bool, optional, default: False, to restart with copy mechanism.\n            copy_dirs: List[str], optional, default: None or all in copy mode, list of dirs to copy.\n            copy_files: List[str], optional, default: None or all in copy mode, list of files to copy.  # noqa\n            name: str, optional, default: None, name to use for the restarted run.\n            description: str, optional, default: None, description to use for the restarted run.\n            tags: list[str], optional, default: None, tags to use for the restarted run.\n\n        Returns:\n            V1Run instance.\n        \"\"\"\n        body = polyaxon_sdk.V1Run(content=override_config)\n        if name:\n            body.name = name\n        if description:\n            body.description = description\n        if tags:\n            tags = validate_tags(tags)\n            body.tags = tags\n        if copy or copy_dirs or copy_files:\n            if copy_dirs or copy_files:\n                copy_dirs = to_list(copy_dirs, check_none=True)\n                copy_files = to_list(copy_files, check_none=True)\n                copy_artifacts = V1ArtifactsType()\n                if copy_dirs:\n                    copy_artifacts.dirs = [\n                        \"{}/{}\".format(self.run_uuid, cp) for cp in copy_dirs\n                    ]\n                if copy_files:\n                    copy_artifacts.files = [\n                        \"{}/{}\".format(self.run_uuid, cp) for cp in copy_files\n                    ]\n                body.meta_info = {META_COPY_ARTIFACTS: copy_artifacts.to_dict()}\n            return self.client.runs_v1.copy_run(\n                self.owner, self.project, self.run_uuid, body=body, **kwargs\n            )\n        else:\n            return self.client.runs_v1.restart_run(\n                self.owner, self.project, self.run_uuid, body=body, **kwargs\n            )\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def resume(self, override_config=None, **kwargs):\n        \"\"\"Resumes the current run\n\n        Args:\n            override_config: Dict or str, optional,\n                 config to use for overriding the original run's config.\n\n        Returns:\n            V1Run instance.\n        \"\"\"\n        body = polyaxon_sdk.V1Run(content=override_config)\n        return self.client.runs_v1.resume_run(\n            self.owner, self.project, self.run_uuid, body=body, **kwargs\n        )\n\n    @client_handler(check_no_op=True)\n    def set_description(self, description: str, async_req: bool = True):\n        \"\"\"Sets a new description for the current run.\n\n        Args:\n            description: str, the description to set.\n            async_req: bool, optional, default: False, execute request asynchronously.\n        \"\"\"\n        self._run_data.description = description\n        self._update({\"description\": description}, async_req=async_req)\n\n    @client_handler(check_no_op=True)\n    def set_name(self, name: str, async_req: bool = True):\n        \"\"\"Sets a new name for the current run.\n\n        Args:\n            name: str, the name to set.\n            async_req: bool, optional, default: False, execute request asynchronously.\n        \"\"\"\n        self._run_data.name = name\n        self._update({\"name\": name}, async_req=async_req)\n\n    @client_handler(check_no_op=True)\n    def log_inputs(self, reset: bool = False, async_req: bool = True, **inputs):\n        \"\"\"Logs or resets new inputs/params for the current run.\n\n\n        > **Note**: If you are starting a run from the CLI/UI\n        > polyaxon will track all inputs from the Polyaxonfile,\n        > so you generally don't need to set them manually.\n        > But you can always add or reset these params/inputs once your code starts running.\n\n        Args:\n            reset: bool, optional, if True, it will reset the whole inputs state.\n                 Note that Polyaxon will automatically populate the inputs based\n                 on the Polyaxonfile inputs definition and params passed.\n            async_req: bool, optional, default: False, execute request asynchronously.\n            inputs: **kwargs, e.g. param1=value1, param2=value2, ...\n        \"\"\"\n        inputs = {to_fqn_name(k): v for k, v in inputs.items()}\n        patch_dict = {\"inputs\": inputs}\n        if reset is False:\n            patch_dict[\"merge\"] = True\n            self._run_data.inputs = self._run_data.inputs or {}\n            self._run_data.inputs.update(inputs)\n        else:\n            self._run_data.inputs = inputs\n        self._update(patch_dict, async_req=async_req)\n\n    @client_handler(check_no_op=True)\n    def log_outputs(self, reset: bool = False, async_req: bool = True, **outputs):\n        \"\"\"Logs a new outputs/results for the current run.\n\n\n        Args:\n            reset: bool, optional, if True, it will reset the whole outputs state.\n                 Note that Polyaxon will automatically populate some outputs based\n                 on the Polyaxonfile outputs definition and params passed.\n            async_req: bool, optional, default: False, execute request asynchronously.\n            outputs: **kwargs, e.g. output1=value1, metric2=value2, ...\n        \"\"\"\n        outputs = {to_fqn_name(k): v for k, v in outputs.items()}\n        patch_dict = {\"outputs\": outputs}\n        if reset is False:\n            patch_dict[\"merge\"] = True\n            self._run_data.outputs = self._run_data.outputs or {}\n            self._run_data.outputs.update(outputs)\n        else:\n            self._run_data.outputs = outputs\n        self._update(patch_dict, async_req=async_req)\n\n    @client_handler(check_no_op=True)\n    def log_meta(self, reset: bool = False, async_req: bool = True, **meta):\n        \"\"\"Logs meta_info for the current run.\n\n        > **Note**: Use carefully! The meta information is used by\n        > Polyaxon internally to perform several information.\n\n        Polyaxon Client already uses this method to log information\n        about several events and artifacts, Polyaxon API/Scheduler uses\n        this information to set meta information about the run.\n\n        An example use case for this method is to update the concurrency\n        of a pipeline to increase/decrease the initial value:\n        ```python\n        >>> from polyaxon.client import RunClient\n        >>> client = RunClient()\n        >>> client.log_meta(concurrency=5)\n        ```\n\n        Args:\n            reset: bool, optional, if True, it will reset the whole meta info state.\n            async_req: bool, optional, default: False, execute request asynchronously.\n            meta: **kwargs, e.g. concurrency=10, has_flag=True, ...\n        \"\"\"\n        meta = {to_fqn_name(k): v for k, v in meta.items()}\n        patch_dict = {\"meta_info\": meta}\n        if reset is False:\n            patch_dict[\"merge\"] = True\n            self._run_data.meta_info = self._run_data.meta_info or {}\n            self._run_data.meta_info.update(meta)\n        else:\n            self._run_data.meta_info = meta\n        self._update(patch_dict, async_req=async_req)\n\n    @client_handler(check_no_op=True)\n    def log_tags(\n        self,\n        tags: Union[str, Sequence[str]],\n        reset: bool = False,\n        async_req: bool = True,\n    ):\n        \"\"\"Logs new tags for the current run.\n\n        Args:\n            tags: str or List[str], tag or tags to log.\n            reset: bool, optional, if True, it will reset the whole tags state.\n                 Note that Polyaxon will automatically populate the tags based\n                 on the Polyaxonfile.\n            async_req: bool, optional, default: False, execute request asynchronously.\n        \"\"\"\n        tags = validate_tags(tags)\n        patch_dict = {\"tags\": tags}\n        if reset is False:\n            patch_dict[\"merge\"] = True\n            self._run_data.tags = self._run_data.tags or []\n            self._run_data.tags += [t for t in tags if t not in self._run_data.tags]\n        else:\n            self._run_data.tags = tags\n        self._update(patch_dict, async_req=async_req)\n\n    @client_handler(check_no_op=True)\n    def start(self):\n        \"\"\"Sets the current run to `running` status.\n\n        <blockquote class=\"info\">\n        N.B. If you are executing a managed run, you don't need to call this method manually.\n        This method is only useful for manual runs outside of Polyaxon.\n        </blockquote>\n        \"\"\"\n        self.log_status(polyaxon_sdk.V1Statuses.RUNNING, message=\"Operation is running\")\n\n    def _log_end_status(\n        self,\n        status: str,\n        reason: str = None,\n        message: str = None,\n    ):\n        \"\"\"Sets the current run to `status` status.\n\n        <blockquote class=\"info\">\n        N.B. If you are executing a managed run, you don't need to call this method manually.\n        This method is only useful for manual runs outside of Polyaxon.\n        </blockquote>\n\n        Args:\n            status: str, a valid [Statuses](/docs/core/specification/lifecycle/) value.\n            reason: str, optional, reason or service issuing the status change.\n            message: str, optional, message to log with this status.\n        \"\"\"\n        if self.status in LifeCycle.DONE_VALUES:\n            return\n        self.log_status(status=status, reason=reason, message=message)\n        time.sleep(\n            0.1\n        )  # Just to give the opportunity to the worker to pick the message\n\n    @client_handler(check_no_op=True)\n    def log_succeeded(self, message=\"Operation has succeeded\"):\n        \"\"\"Sets the current run to `succeeded` status.\n\n        <blockquote class=\"info\">\n        N.B. If you are executing a managed run, you don't need to call this method manually.\n        This method is only useful for manual runs outside of Polyaxon.\n        </blockquote>\n        \"\"\"\n        self._log_end_status(status=polyaxon_sdk.V1Statuses.SUCCEEDED, message=message)\n\n    @client_handler(check_no_op=True)\n    def log_stopped(self, message=\"Operation is stopped\"):\n        \"\"\"Sets the current run to `stopped` status.\n\n        <blockquote class=\"info\">\n        N.B. If you are executing a managed run, you don't need to call this method manually.\n        This method is only useful for manual runs outside of Polyaxon.\n        </blockquote>\n        \"\"\"\n        self._log_end_status(status=polyaxon_sdk.V1Statuses.STOPPED, message=message)\n\n    @client_handler(check_no_op=True)\n    def log_failed(self, reason: str = None, message: str = None):\n        \"\"\"Sets the current run to `failed` status.\n\n        <blockquote class=\"info\">\n        N.B. If you are executing a managed run, you don't need to call this method manually.\n        This method is only useful for manual runs outside of Polyaxon.\n        </blockquote>\n\n        Args:\n            reason: str, optional, reason or service issuing the status change.\n            message: str, optional, message to log with this status.\n        \"\"\"\n        self._log_end_status(\n            status=polyaxon_sdk.V1Statuses.FAILED,\n            reason=reason,\n            message=message,\n        )\n\n    def _log_has_events(self):\n        if not self._has_meta_key(\"has_events\"):\n            self.log_meta(has_events=True)\n\n    def _log_has_metrics(self):\n        data = {}\n        if not self._has_meta_key(\"has_metrics\"):\n            data[\"has_metrics\"] = True\n        if not self._has_meta_key(\"has_events\"):\n            data[\"has_events\"] = True\n        if data:\n            self.log_meta(**data)\n\n    def _log_has_model(self):\n        if not self._has_meta_key(\"has_model\"):\n            self.log_meta(has_model=True)\n\n    @client_handler(check_no_op=True)\n    def log_code_ref(self, code_ref: Dict = None, is_input: bool = True):\n        \"\"\"Logs code reference as a\n        lineage information with the code_ref dictionary in the summary field.\n\n        In offline\n\n        Args:\n            code_ref: dict, optional, if not provided,\n                 Polyaxon will detect the code reference from the git repo in the current path.\n            is_input: bool, if the code reference is an input or outputs.\n        \"\"\"\n        code_ref = code_ref or get_code_reference()\n        if code_ref and \"commit\" in code_ref:\n            artifact_run = V1RunArtifact(\n                name=code_ref.get(\"commit\"),\n                kind=V1ArtifactKind.CODEREF,\n                summary=code_ref,\n                is_input=is_input,\n            )\n            self.log_artifact_lineage(body=artifact_run)\n\n    @client_handler(check_no_op=True)\n    def log_data_ref(\n        self,\n        name: str,\n        hash: str = None,\n        path: str = None,\n        content=None,\n        summary: Dict = None,\n        is_input: bool = True,\n    ):\n        \"\"\"Logs data reference.\n\n        Args:\n            name: str, name of the data.\n            hash: str, optional, default = None, the hash version of the data,\n                 if not provided it will be calculated based on the data in the content.\n            path: str, optional, path of where the data is coming from.\n            summary: Dict, optional, additional summary information to log about data\n                 in the lineage table.\n            is_input: bool, if the data reference is an input or outputs.\n            content: the data content.\n        \"\"\"\n        summary = summary or {}\n        if hash:\n            summary[\"hash\"] = hash\n        elif content is not None:\n            summary[\"hash\"] = hash_value(content)\n        if path is not None:\n            summary[\"path\"] = path\n        if name:\n            artifact_run = V1RunArtifact(\n                name=name,\n                kind=V1ArtifactKind.DATA,\n                path=path,\n                summary=summary,\n                is_input=is_input,\n            )\n            self.log_artifact_lineage(body=artifact_run)\n\n    @client_handler(check_no_op=True)\n    def log_artifact_ref(\n        self,\n        path: str,\n        kind: V1ArtifactKind,\n        name: str = None,\n        hash: str = None,\n        content=None,\n        summary: Dict = None,\n        is_input: bool = False,\n        rel_path: str = None,\n    ):\n        \"\"\"Logs an artifact reference with custom kind.\n\n        Logging a generic file reference to the lineage table:\n\n        ```python\n        >>> # Get outputs artifact path\n        >>> asset_path = tracking.get_outputs_path(\"test.txt\")\n        >>> with open(asset_path, \"w\") as f:\n        >>>     f.write(\"Artifact content.\")\n        >>> # Log reference to the lineage table\n        >>> # Name of the artifact will default to test\n        >>> tracking.log_artifact_ref(path=asset_path, kind=V1ArtifactKind.FILE)\n        ```\n\n        **Note**: This is a generic method that is used by `log_file_ref` and `log_model_ref`.\n\n        Args:\n            path: str, filepath, the name is extracted from the filepath.\n            kind: V1ArtifactKind, the artifact kind.\n            name: str, if the name is passed it will be used instead of the filename from the path.\n            hash: str, optional, default = None, the hash version of the file,\n                 if not provided it will be calculated based on the file content.\n            content: the file content.\n            summary: Dict, optional, additional summary information to log about data\n                 in the lineage table.\n            is_input: bool, if the file reference is an input or outputs.\n            rel_path: str, optional relative path to the run artifacts path.\n        \"\"\"\n        summary = summary or {}\n        summary[\"path\"] = path\n        if hash:\n            summary[\"hash\"] = hash\n        elif content is not None:\n            summary[\"hash\"] = hash_value(content)\n        name = name or get_base_filename(path)\n        rel_path = get_rel_asset_path(\n            path=path, rel_path=rel_path, is_offline=self._is_offline\n        )\n        if name:\n            artifact_run = V1RunArtifact(\n                name=to_fqn_name(name),\n                kind=kind,\n                path=rel_path,\n                summary=summary,\n                is_input=is_input,\n            )\n            self.log_artifact_lineage(body=artifact_run)\n\n    @client_handler(check_no_op=True)\n    def log_model_ref(\n        self,\n        path: str,\n        name: str = None,\n        framework: str = None,\n        summary: Dict = None,\n        is_input: bool = False,\n        rel_path: str = None,\n    ):\n        \"\"\"Logs model reference.\n\n         > **Note**: The difference between this method and the `log_model`\n         > is that this one does not copy or move the asset, it only registers a lineage reference.\n         > If you need the model asset to be on the `artifacts_path` or the `outputs_path`\n         > you have to copy it manually using a relative path to\n         > `self.get_artifacts_path` or `self.get_outputs_path`.\n\n         ```python\n        >>> # Get outputs artifact path\n        >>> asset_path = tracking.get_outputs_path(\"model/model_data.h5\")\n        >>> with open(asset_path, \"w\") as f:\n        >>>     f.write(\"Artifact content.\")\n        >>> # Log reference to the lineage table\n        >>> # Name of the artifact will default to model_data\n        >>> tracking.log_model_ref(path=asset_path)\n        ```\n\n        Args:\n            path: str, filepath, the name is extracted from the filepath.\n            name: str, if the name is passed it will be used instead of the filename from the path.\n            framework: str, optional ,name of the framework\n            summary: Dict, optional, additional summary information to log about data\n                 in the lineage table.\n            is_input: bool, if the file reference is an input or outputs.\n            rel_path: str, optional relative path to the run artifacts path.\n        \"\"\"\n        summary = summary or {}\n        summary[\"framework\"] = framework\n        self._log_has_model()\n        return self.log_artifact_ref(\n            path=path,\n            kind=V1ArtifactKind.MODEL,\n            name=name,\n            summary=summary,\n            is_input=is_input,\n            rel_path=rel_path,\n        )\n\n    @client_handler(check_no_op=True)\n    def log_file_ref(\n        self,\n        path: str,\n        name: str = None,\n        hash: str = None,\n        content=None,\n        summary: Dict = None,\n        is_input: bool = False,\n        rel_path: str = None,\n    ):\n        \"\"\"Logs file reference.\n\n        Args:\n            path: str, filepath, the name is extracted from the filepath.\n            name: str, if the name is passed it will be used instead of the filename from the path.\n            hash: str, optional, default = None, the hash version of the file,\n                 if not provided it will be calculated based on the file content.\n            content: the file content.\n            summary: Dict, optional, additional summary information to log about data\n                 in the lineage table.\n            is_input: bool, if the file reference is an input or outputs.\n            rel_path: str, optional relative path to the run artifacts path.\n        \"\"\"\n        return self.log_artifact_ref(\n            path=path,\n            kind=V1ArtifactKind.FILE,\n            name=name,\n            hash=hash,\n            content=content,\n            summary=summary,\n            is_input=is_input,\n            rel_path=rel_path,\n        )\n\n    @client_handler(check_no_op=True)\n    def log_dir_ref(\n        self,\n        path: str,\n        name: str = None,\n        summary: Dict = None,\n        is_input: bool = False,\n        rel_path: str = None,\n    ):\n        \"\"\"Logs dir reference.\n\n        Args:\n            path: str, dir path, the name is extracted from the path.\n            name: str, if the name is passed it will be used instead of the dirname from the path.\n            summary: Dict, optional, additional summary information to log about data\n                 in the lineage table.\n            is_input: bool, if the dir reference is an input or outputs.\n            rel_path: str, optional relative path to the run artifacts path.\n        \"\"\"\n        name = name or os.path.basename(path)\n        rel_path = get_rel_asset_path(\n            path=path, rel_path=rel_path, is_offline=self._is_offline\n        )\n        summary = summary or {}\n        summary[\"path\"] = path\n        if name:\n            artifact_run = V1RunArtifact(\n                name=to_fqn_name(name),\n                kind=V1ArtifactKind.DIR,\n                path=rel_path,\n                summary=summary,\n                is_input=is_input,\n            )\n            self.log_artifact_lineage(body=artifact_run)\n\n    def _has_meta_key(self, key: str):\n        return (\n            self.run_data\n            and self.run_data.meta_info\n            and self.run_data.meta_info.get(key, False)\n        )\n\n    @client_handler(check_no_op=True)\n    def log_tensorboard_ref(\n        self,\n        path: str,\n        name: str = \"tensorboard\",\n        is_input: bool = False,\n        rel_path: str = None,\n    ):\n        \"\"\"Logs dir reference.\n\n        Args:\n            path: str, path to the tensorboard logdir.\n            name: str, if the name is passed it will be used instead of the dirname from the path.\n            is_input: bool, if the tensorboard reference is an input or outputs\n            rel_path: str, optional relative path to run the artifacts path.\n        \"\"\"\n        if not self._has_meta_key(\"has_tensorboard\"):\n            rel_path = get_rel_asset_path(\n                path=path, rel_path=rel_path, is_offline=self._is_offline\n            )\n            artifact_run = V1RunArtifact(\n                name=to_fqn_name(name),\n                kind=V1ArtifactKind.TENSORBOARD,\n                path=rel_path,\n                summary={\"path\": path},\n                is_input=is_input,\n            )\n            self.log_artifact_lineage(body=artifact_run)\n            self.log_meta(has_tensorboard=True)\n\n    @client_handler(check_no_op=True)\n    def log_artifact_lineage(\n        self,\n        body: Union[Dict, List[Dict], V1RunArtifact, List[V1RunArtifact]],\n        async_req: bool = True,\n    ):\n        \"\"\"Logs an artifact lineage.\n\n        > **Note**: This method can be used to log manual lineage objects, it is used internally\n        > to log model/file/artifact/code refs\n\n        Args:\n            body: dict or List[dict] or V1RunArtifact or List[V1RunArtifact], body of the lineage.\n            async_req: bool, optional, default: False, execute request asynchronously.\n        \"\"\"\n        if self._is_offline:\n            for b in to_list(body, check_none=True):\n                if not isinstance(b, V1RunArtifact):\n                    b = V1RunArtifact.read(b)\n                self._lineages[b.name] = b\n            return\n        self.client.runs_v1.create_run_artifacts_lineage(\n            self.owner,\n            self.project,\n            self.run_uuid,\n            body=body,\n            async_req=async_req,\n        )\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def get_namespace(self):\n        \"\"\"Fetches the run namespace.\"\"\"\n        return self.client.runs_v1.get_run_namespace(\n            self.owner,\n            self.project,\n            self.run_uuid,\n        ).namespace\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def delete(self):\n        \"\"\"Deletes the current run.\"\"\"\n        return self.client.runs_v1.delete_run(self.owner, self.project, self.run_uuid)\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def list(\n        self, query: str = None, sort: str = None, limit: int = None, offset: int = None\n    ):\n        \"\"\"Lists runs under the current owner - project.\n\n        [Run API](/docs/api/#operation/ListRuns)\n\n        Args:\n            query: str, optional, query filters, please refer to\n                 [Run PQL](/docs/core/query-language/runs/#query)\n            sort: str, optional, fields to order by, please refer to\n                 [Run PQL](/docs/core/query-language/runs/#sort)\n            limit: int, optional, limit of runs to return.\n            offset: int, optional, offset pages to paginate runs.\n\n        Returns:\n            List[V1Run], list of run instances.\n        \"\"\"\n        params = get_query_params(\n            limit=limit or 20, offset=offset, query=query, sort=sort\n        )\n        return self.client.runs_v1.list_runs(self.owner, self.project, **params)\n\n    @client_handler(check_no_op=True, check_offline=True)\n    def list_children(\n        self, query: str = None, sort: str = None, limit: int = None, offset: int = None\n    ):\n        \"\"\"Lists run's children if the current run has a pipeline.\n\n        [Run API](/docs/api/#operation/ListRuns)\n\n        Args:\n            query: str, optional, query filters, please refer to\n                 [Project PQL](/docs/core/query-language/runs/#query)\n            sort: str, optional, fields to order by, please refer to\n                 [Project PQL](/docs/core/query-language/runs/#sort)\n            limit: int, optional, limit of runs to return.\n            offset: int, optional, offset pages to paginate runs.\n\n        Returns:\n            List[V1Run], list of run instances.\n        \"\"\"\n        params = get_query_params(limit=limit, offset=offset, query=query, sort=sort)\n        query = params.get(\"query\")\n        query = query + \"&\" if query else \"?\"\n        query += \"pipeline={}\".format(self.run_uuid)\n        params[\"query\"] = query\n\n        return self.client.runs_v1.list_runs(self.owner, self.project, **params)\n\n    def _collect_events_summaries(\n        self,\n        events_path: str,\n        events_kind: str,\n        last_check: Optional[datetime],\n        is_system_resource: bool = False,\n    ) -> Tuple[List, Dict]:\n        current_events_path = os.path.join(events_path, events_kind)\n\n        summaries = []\n        last_values = {}\n        connection_name = get_artifacts_store_name()\n        with get_files_in_path_context(current_events_path) as files:\n            for f in files:\n                if last_check and not file_modified_since(\n                    filepath=f, last_time=last_check\n                ):\n                    continue\n\n                event_name = os.path.basename(f).split(\".plx\")[0]\n                event = V1Events.read(kind=events_kind, name=event_name, data=f)\n                if event.df.empty:\n                    continue\n\n                # Get only the relpath from run uuid\n                event_rel_path = get_rel_asset_path(path=f, is_offline=self._is_offline)\n                summary = event.get_summary()\n                run_artifact = V1RunArtifact(\n                    name=event_name,\n                    kind=V1ArtifactKind.SYSTEM if is_system_resource else events_kind,\n                    connection=connection_name,\n                    summary=summary,\n                    path=event_rel_path,\n                    is_input=False,\n                )\n                summaries.append(run_artifact)\n                if events_kind == V1ArtifactKind.METRIC:\n                    last_values[event_name] = summary[V1ArtifactKind.METRIC][\"last\"]\n\n        return summaries, last_values\n\n    def _sync_events_summaries(\n        self,\n        last_check: Optional[datetime],\n        events_path: str,\n        is_system_resource: bool = False,\n    ):\n        # check if there's a path to sync\n        if not events_path or not os.path.exists(events_path):\n            return\n\n        # crawl dirs\n        summaries = []\n        last_values = {}\n        set_last_values = not is_system_resource\n\n        for events_kind in get_dirs_under_path(events_path):\n            _summaries, _last_values = self._collect_events_summaries(\n                events_path=events_path,\n                events_kind=events_kind,\n                last_check=last_check,\n                is_system_resource=is_system_resource,\n            )\n            summaries += _summaries\n            if set_last_values:\n                last_values.update(_last_values)\n\n        if summaries:\n            self.log_artifact_lineage(summaries)\n        if set_last_values and last_values:\n            self.log_outputs(**last_values)\n\n    @client_handler(check_no_op=True)\n    def sync_events_summaries(self, last_check: Optional[datetime], events_path: str):\n        \"\"\"Syncs all tracked events and auto-generates summaries and lineage data.\n\n        > **Note**: Both `in-cluster` and `offline` modes will manage syncing events summaries\n        > automatically, so you should not call this method manually.\n        \"\"\"\n        self._sync_events_summaries(\n            last_check=last_check,\n            events_path=events_path,\n            is_system_resource=False,\n        )\n\n    @client_handler(check_no_op=True)\n    def sync_system_events_summaries(\n        self, last_check: Optional[datetime], events_path: str\n    ):\n        \"\"\"Syncs all tracked system events and auto-generates summaries and lineage data.\n\n        > **Note**: Both `in-cluster` and `offline` modes will manage syncing events summaries\n        > automatically, so you should not call this method manually.\n        \"\"\"\n        self._sync_events_summaries(\n            last_check=last_check,\n            events_path=events_path,\n            is_system_resource=True,\n        )\n\n    @client_handler(check_no_op=True)\n    def persist_offline_run(self, artifacts_path: str):\n        \"\"\"Persists an offline run to a local path.\n\n        > **Note**: You generally do not need to call this method manually,\n        > When the `offline` mode is enabled, this method is triggered automatically at the end.\n        \"\"\"\n        if not self._is_offline or not self.run_data:\n            logger.debug(\n                \"Persist offline run call failed. \"\n                \"Make sure that the offline mode is enabled and that run_data is provided.\"\n            )\n            return\n        if not artifacts_path or not os.path.exists(artifacts_path):\n            check_or_create_path(artifacts_path, is_dir=True)\n        run_path = \"{}/run_data.json\".format(artifacts_path)\n        with open(run_path, \"w\") as config_file:\n            config_file.write(\n                ujson.dumps(self.client.sanitize_for_serialization(self.run_data))\n            )\n\n        if not self._lineages:\n            logger.debug(\"Persist offline run call did not find any lineage data. \")\n            return\n\n        lineages_path = \"{}/lineages.json\".format(artifacts_path)\n        with open(lineages_path, \"w\") as config_file:\n            config_file.write(\n                ujson.dumps(\n                    [\n                        self.client.sanitize_for_serialization(l)\n                        for l in self._lineages.values()\n                    ]\n                )\n            )\n\n    @classmethod\n    @client_handler(check_no_op=True)\n    def load_offline_run(\n        cls, artifacts_path: str, run_client: Union[\"RunClient\", \"Run\"] = None\n    ) -> Union[\"RunClient\", \"Run\"]:\n        \"\"\"Loads an offline run from a local path.\n\n        > **Note**: When the `offline` mode is enabled, and the run uuid is provided,\n        > this method is triggered automatically to load last checkpoint.\n        \"\"\"\n        run_path = \"{}/run_data.json\".format(artifacts_path)\n        if not os.path.isfile(run_path):\n            logger.info(f\"Offline data was not found: {run_path}\")\n            return\n        with open(run_path, \"r\") as config_file:\n            config_str = config_file.read()\n            run_config = polyaxon_sdk.V1Run(**ujson.loads(config_str))\n            if run_client:\n                run_client._owner = run_config.owner\n                run_client._project = run_config.project\n                run_client._run_uuid = run_config.uuid\n            else:\n                run_client = cls(\n                    owner=run_config.owner,\n                    project=run_config.project,\n                    run_uuid=run_config.uuid,\n                )\n            run_client._run_data = run_config\n            logger.info(f\"Offline data loaded from: {run_path}\")\n\n        lineages_path = \"{}/lineages.json\".format(artifacts_path)\n        if not os.path.isfile(lineages_path):\n            logger.info(f\"Offline lineage data was not found: {lineages_path}\")\n            return\n        with open(lineages_path, \"r\") as config_file:\n            config_str = config_file.read()\n            lineages = [V1RunArtifact(**l) for l in ujson.loads(config_str)]\n            run_client._lineages = {l.name: l for l in lineages}\n            logger.info(f\"Offline lineage data loaded from: {run_path}\")\n\n        return run_client\n\n    @client_handler(check_no_op=True)\n    def sync_offline_run(\n        self,\n        artifacts_path: str = None,\n        load_offline_run: bool = False,\n        upload_artifacts: bool = True,\n        clean: bool = False,\n    ):\n        \"\"\"Syncs an offline run to Polyaxon's API and artifacts store.\"\"\"\n        if artifacts_path and load_offline_run:\n            self._run_uuid = None\n            self._run_data = None\n            self.load_offline_run(artifacts_path=artifacts_path, run_client=self)\n\n        # We ensure that the is_offline is False\n        is_offline = self._is_offline\n        self._is_offline = False\n\n        if not self.run_data:\n            logger.warning(\n                \"Sync offline run failed. Make sure that run_data is provided.\"\n            )\n            return\n        self.client.runs_v1.sync_run(\n            owner=self.owner,\n            project=self.project,\n            body=self.run_data,\n            async_req=False,\n        )\n        logger.info(f\"Offline data for run {self.run_data.uuid} synced\")\n        if self._lineages:\n            self.log_artifact_lineage(\n                [l for l in self._lineages.values()], async_req=False\n            )\n            logger.info(f\"Offline lineage data for run {self.run_data.uuid} synced\")\n        else:\n            logger.info(\"Sync offline run failed. No lineage data found.\")\n            return\n\n        if artifacts_path and upload_artifacts:\n            self.upload_artifacts_dir(\n                dirpath=artifacts_path,\n                path=\"/\",\n                overwrite=True,\n                relative_to=artifacts_path,\n            )\n            logger.info(f\"Offline artifacts for run {self.run_data.uuid} uploaded\")\n\n        if clean:\n            delete_path(artifacts_path)\n\n        # Reset is_offline\n        self._is_offline = is_offline\n\n\ndef get_run_logs(\n    client: RunClient,\n    hide_time: bool = False,\n    all_containers: bool = False,\n    all_info: bool = False,\n    follow: bool = False,\n):\n    def get_logs(last_file=None, last_time=None):\n        try:\n            response = client.get_logs(last_file=last_file, last_time=last_time)\n            get_logs_streamer(\n                show_timestamp=not hide_time,\n                all_containers=all_containers,\n                all_info=all_info,\n            )(response)\n            return response\n        except (ApiException, HTTPError) as e:\n            if not follow:\n                handle_cli_error(\n                    e,\n                    message=\"Could not get logs for run `{}`.\".format(client.run_uuid),\n                )\n                sys.exit(1)\n\n    def handle_status(last_status: str = None):\n        if not last_status:\n            return {\"status\": None}\n\n        click.echo(\n            \"{}\".format(\n                Printer.add_status_color({\"status\": last_status}, status_key=\"status\")[\n                    \"status\"\n                ]\n            )\n        )\n        return last_status\n\n    def handle_logs():\n        is_done = False\n        last_file = None\n        _status = None\n        files = []\n        last_transition_time = now()\n        last_status, conditions = client.get_statuses()\n        if conditions:\n            last_transition_time = conditions[0].last_transition_time\n\n        while not LifeCycle.is_done(last_status) and not LifeCycle.is_running(\n            last_status\n        ):\n            time.sleep(settings.CLIENT_CONFIG.watch_interval)\n            last_status, conditions = client.get_statuses()\n            if conditions:\n                last_transition_time = conditions[0].last_transition_time\n            if _status != last_status:\n                _status = handle_status(last_status)\n\n        if LifeCycle.is_done(last_status):\n            last_time = None\n        else:\n            last_time = last_transition_time\n\n        checks = 0\n        while not is_done:\n            response = get_logs(last_time=last_time, last_file=last_file)\n\n            if response:\n                last_time = response.last_time\n                last_file = response.last_file\n                files = response.files\n            else:\n                last_time = None\n                last_file = None\n\n            # Follow logic\n            if not any([last_file, last_time]) or checks > 3:\n                if follow:\n                    last_status, _ = client.get_statuses()\n                    if _status != last_status:\n                        _status = handle_status(last_status)\n                    is_done = LifeCycle.is_done(last_status)\n                    if not is_done:\n                        checks = 0\n                else:\n                    is_done = True\n            if last_time and not follow:\n                is_done = True\n\n            if not is_done:\n                if last_file:\n                    if len(files) > 1 and last_file != files[-1]:\n                        time.sleep(1)\n                    else:\n                        is_done = True\n                else:\n                    time.sleep(settings.CLIENT_CONFIG.watch_interval)\n            checks += 1\n\n    handle_logs()\n\n\ndef get_rel_asset_path(\n    path: str = None, rel_path: str = None, is_offline: bool = False\n):\n    if not path or rel_path:\n        return rel_path\n    artifacts_root = CONTEXT_OFFLINE_ROOT if is_offline else CONTEXT_MOUNT_ARTIFACTS\n    if artifacts_root in path:\n        try:\n            return os.path.relpath(path, artifacts_root)\n        except Exception as e:\n            logger.debug(\"could not calculate relative path %s\", e)\n\n    return rel_path or path\n","sub_path":"src/polyaxon/client/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":78964,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"636522493","text":"# -*- coding: UTF-8 -*-\nimport csv\nimport sqlite3\nfrom io import StringIO\nfrom flask import g, Flask, render_template, request, session, redirect, url_for, make_response\nfrom flask_mail import Mail, Message\nfrom config import *\n\napp = Flask(__name__)\napp.config['MAIL_SERVER'] = MAIL_SERVER\napp.config['MAIL_PORT'] = MAIL_PORT\napp.config['MAIL_USERNAME'] = MAIL_USERNAME\napp.config['MAIL_PASSWORD'] = MAIL_PASSWORD\napp.config['MAIL_USE_TLS'] = MAIL_USE_TLS\napp.config['MAIL_USE_SSL'] = MAIL_USE_SSL\napp.secret_key = secret_key\n\nmail = Mail(app)\n\n\ndef get_db():\n    db = getattr(g, '_database', None)\n    if db is None:\n        db = g._database = sqlite3.connect(DATABASE)\n        db.execute('''CREATE TABLE IF NOT EXISTS attendants( \n        id INTEGER PRIMARY KEY AUTOINCREMENT,\n        email TEXT NOT NULL, \n        name TEXT NOT NULL,\n        university TEXT NOT NULL,\n        integration1 INT NOT NULL,\n        integration2 INT NOT NULL,\n        ifPaper INT NOT NULL,\n        paperTopic TEXT NULL,\n        abstract TEXT NULL,\n        ifInvoice INT NOT NULL,\n        invoiceData TEXT NULL)''')\n\n    return db\n\n\n@app.teardown_appcontext\ndef close_connection(exception):\n    db = getattr(g, '_database', None)\n    if db is not None:\n        db.close()\n\n\n@app.route('/')\ndef main_page():\n    return render_template('base.html')\n\n\n@app.route('/competition')\ndef competition():\n    return render_template('competition.html')\n\n\n@app.route('/schedule')\ndef schedule():\n    return render_template('schedule.html')\n\n\n@app.route('/register')\ndef register_page():\n    return render_template('register.html')\n\n\n@app.route('/registrationConfirm', methods=['POST'])\ndef register_attendant():\n    try:\n        cursor = get_db().execute('SELECT COUNT(*) FROM attendants WHERE email = ?', (request.form['email'],))\n        if cursor.fetchall()[0][0] == 0:\n            get_db().execute('INSERT INTO attendants VALUES(NULL, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)',\n                             (request.form['email'], request.form['name'], request.form['university'],\n                              1 if request.form.get('integration') == 'on' else 0,\n                              1 if request.form.get('banquet') == 'on' else 0,\n                              1 if request.form.get('ifPaper') == 'on' else 0,\n                              request.form['title'],\n                              request.form['abstract'].replace('\\r', ' ').replace('\\n', ' '),\n                              1 if request.form.get('ifInvoice') == 'on' else 0,\n                              request.form['invoiceData'].replace('\\r', ' ').replace('\\n', ' ')\n                              ))\n            get_db().commit()\n            cursor = get_db().execute('SELECT * FROM attendants WHERE email = ?', (request.form['email'],))\n            id = cursor.fetchall()[0][0]\n            message = Message('Rejestracja na konferencję PTM Nowe Pokolenie', sender=app.config['MAIL_USERNAME'],\n                              reply_to='kmsuj7+ptm@gmail.com', recipients=[request.form['email']])\n            message.html = render_template('email.html', data=request.form, id=id)\n            mail.send(message)\n        else:\n            return render_template('registerEmailPresent.html')\n    except BaseException as e:\n        print(e)\n        return render_template('registerError.html')\n    return render_template('registerConfirmed.html')\n\n\n@app.route('/admin')\ndef admin():\n    if 'username' in session:\n        cur = get_db().execute(\"SELECT * FROM attendants\")\n        data = cur.fetchall()\n        return render_template('admin/adminAttendants.html', attendants=data)\n    return render_template('admin/login.html')\n\n\n@app.route('/adminLogin', methods=['POST'])\ndef admin_login():\n    if request.form.get('password') == SECRET_PASS:\n        session['username'] = 'admin'\n    return redirect(url_for('admin'))\n\n\n@app.route('/adminLogout')\ndef admin_logout():\n    if 'username' in session:\n        session.pop('username')\n    return redirect(url_for('main_page'))\n\n\n@app.route('/adminRemoveAttendant/<int:a_id>')\ndef remove_attendant(a_id):\n    if 'username' in session:\n        get_db().execute('DELETE FROM attendants WHERE id = ?', (a_id,))\n        get_db().commit()\n        return redirect(url_for('admin'))\n    else:\n        return redirect(url_for('main_page'))\n\n\n@app.route('/adminGetCSV')\ndef get_csv():\n    if 'username' in session:\n        si = StringIO()\n        cw = csv.writer(si)\n        cur = get_db().execute('SELECT * FROM attendants')\n        rows = cur.fetchall()\n        cw.writerow([i[0] for i in cur.description])\n        for row in rows:\n            row_encoded = [x.encode('utf-8') if type(x) == 'str' else x for x in row]\n            cw.writerow(row_encoded)\n        response = make_response(si.getvalue())\n        response.headers['Content-Disposition'] = 'attachment; filename=attendants.csv'\n        response.headers['Content-type'] = 'text/csv'\n        return response\n    else:\n        return redirect(url_for('main_page'))\n\n\nif __name__ == '__main__':\n    app.run()\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":5040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"562996987","text":"bemenet = open(\"be1.txt\", \"r\")\n\nbemenet_meretek = bemenet.readline().split()\nN, M = int(bemenet_meretek[0]), int(bemenet_meretek[1])\n\nadatok = []\n\nfor i in range(N):\n    sor = bemenet.readline().rstrip()\n    sor = sor.split(\" \")\n    # sor=[int(a) for a in sor]\n    # sor= list(map(int, sor))\n    for j in range(M):\n        sor[j] = int(sor[j])\n    adatok.append(sor)\n\nbemenet.close()\n\nkimenet = open(\"be2_javitott.txt\", \"a\")\n\n\nfor i in range(len(adatok)):\n    for j in range(len(adatok[i])):\n        adatok[i][j] = str(adatok[i][j])\n\n# print(adatok)\n\nprint(\" \".join(adatok[0]))\n\nfor lista in adatok:\n    kimenet.write(\" \".join(lista))\n    kimenet.write(\"\\n\")\n\nkimenet.close()\n","sub_path":"python/10_15/elorejelzesek atlaga/file.py","file_name":"file.py","file_ext":"py","file_size_in_byte":676,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"637143066","text":"import numpy as np\nimport torch\nfrom absl.testing import absltest as test\nfrom absl.testing import parameterized\n\nfrom pyctr.examples.torchscript.torchscript import specialize\n\n\ndef random_inputs_numpy(batch_size, max_seq_len, input_size, hidden_size):\n    inputs = np.random.normal(size=(batch_size, 2 * max_seq_len, input_size))\n    seq_len = np.arange(max_seq_len)  # Not random for equal computation.\n    w = np.random.normal(size=(input_size + hidden_size, hidden_size))\n    b = np.random.normal(size=(hidden_size,))\n    init_state = np.zeros((batch_size, hidden_size))\n    return inputs, seq_len, w, b, init_state\n\n\ndef random_inputs_torch(batch_size, max_seq_len, input_size, hidden_size):\n    np_inputs = random_inputs_numpy(batch_size, max_seq_len, input_size, hidden_size)\n    return tuple(torch.tensor(a) for a in np_inputs)\n\n\nclass TorchscriptTest(parameterized.TestCase):\n    @parameterized.parameters(\n        (\n            torch.tensor([1, 2, 3]),\n            torch.tensor([4, 3, 1]),\n            torch.tensor([1]),\n            torch.tensor([10]),\n            torch.tensor([10, 11, 19]),\n            True,\n        ),\n        (\n            torch.tensor([1, 2, 3]),\n            torch.tensor([4, 3, 1]),\n            torch.tensor([[1]]),\n            torch.tensor([[10]]),\n            torch.tensor([10, 11, 19]),\n            False,\n        ),\n    )\n    def test_if_basic(self, w_1, w_2, b_1, b_2, x, cond: bool):\n        def pytorch_test_fn(w_1, w_2, b_1, b_2, x, cond: bool):\n            return w_1 @ x + b_1 if cond else w_2 @ x + b_2\n\n        def if_basic_fn(w_1, w_2, b_1, b_2, x, cond: bool):\n            if cond:\n                return (w_1 @ x) + b_1\n            else:\n                return (w_2 @ x) + b_2\n\n        fn, ast = specialize(if_basic_fn, w_1, w_2, b_1, b_2, x, cond)\n\n        fn_result = fn(w_1, w_2, b_1, b_2, x, cond)\n        pytorch_result = pytorch_test_fn(w_1, w_2, b_1, b_2, x, cond)\n\n        self.assertEqual(fn_result, pytorch_result)\n\n    @parameterized.parameters((torch.tensor(5), torch.tensor(5)))\n    def test_statements(self, w, x):\n        def statements_basic(w, x):\n            t = torch.mm(w, x)\n            ret = torch.tanh(t)\n            return ret\n\n        fn, ast = specialize(statements_basic, w, x)\n\n    @parameterized.parameters(random_inputs_torch(10, 10, 50, 256))\n    def test_rnn(self, ins, seq_len, w, b, init_state):\n        def rnn_basic(ins, seq_len, w, b, init_state):\n            inputs_time_major = torch.transpose(ins, 1, 0)\n            state = init_state\n            for i in range(int(torch.max(seq_len))):\n                x = inputs_time_major[i]\n                h = torch.cat((x, state), 1)\n                state = torch.tanh(torch.add(torch.mm(h, w), b))  # Basic RNN cell\n            return state\n\n        fn, ast = specialize(rnn_basic, ins, seq_len, w, b, init_state)\n\n        fn_result = fn(ins, seq_len, w, b, init_state)\n        pytorch_result = rnn_basic(ins, seq_len, w, b, init_state)\n        self.assertTrue(bool((fn_result == pytorch_result).all()))\n\n\nif __name__ == \"__main__\":\n    test.main()\n","sub_path":"pyctr/examples/torchscript/torchscript_test.py","file_name":"torchscript_test.py","file_ext":"py","file_size_in_byte":3078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"218609681","text":"class Solution(object):\n    def letterCombinations(self, digits: str) -> list:\n        if digits == '':\n            return []\n        return self.letterCombinationsIter(digits, len(digits)-1)\n\n    def letterCombinationsIter(self, digits, end_index: int) -> list:\n        if end_index < 0:\n            return ['']\n        else:\n            num = int(digits[end_index])\n            letters = Solution.getLetter(num)\n            leftCombinations = self.letterCombinationsIter(digits, end_index-1)\n            combinations = []\n            for combination in leftCombinations:\n                for letter in letters:\n                    combinations.append(Solution.insertLetter(\n                        letter, combination, len(combination)+1))\n            return combinations\n\n    @staticmethod\n    def getLetter(num: int) -> list:\n        letters = ['a', 'b', 'c',\n                   'd', 'e', 'f',\n                   'g', 'h', 'i',\n                   'j', 'k', 'l',\n                   'm', 'n', 'o',\n                   'p', 'q', 'r', 's',\n                   't', 'u', 'v',\n                   'w', 'x', 'y', 'z']\n        if num >= 2 and num <= 6:\n            index = 3*(num-2)\n            return letters[index:index+3]\n        elif num == 7:\n            return letters[15:19]\n        elif num == 8:\n            return letters[19:22]\n        elif num == 9:\n            return letters[22:26]\n        else:\n            return []\n\n    @staticmethod\n    def insertLetter(letter: str, string: str, index: int) -> str:\n        \"\"\"\n        docstring\n        \"\"\"\n        if index == 0:\n            return letter+string\n        elif index == len(string):\n            return string+letter\n        else:\n            return string[0:index]+letter+string[index:]\n\n\nif __name__ == \"__main__\":\n    test_digits = \"39\"\n    test_sol = Solution()\n    actual_combination = test_sol.letterCombinations(test_digits)\n    print(actual_combination)\n","sub_path":"Letter-Combination/letter_combination.py","file_name":"letter_combination.py","file_ext":"py","file_size_in_byte":1921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"543665136","text":"#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n\ncount = -1\nscore = total =0\nmaxnum = score\n\nwhile score != -1:\n    count += 1\n    total += score\n    if score > maxnum:\n        position = count\n        maxnum = score\n    score = int(input(\"\"))\n\nprint(total)\nprint(\"{0:.1f}\".format(total/count))\nprint(maxnum)\nprint(position)","sub_path":"大一/python/2/14.py","file_name":"14.py","file_ext":"py","file_size_in_byte":322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"633877817","text":"#!/usr/bin/env python\n# coding=utf-8\n\n\n\"\"\"\n    2016-7-14:\n    网口配置--> 策略路由精简配置（仅供测试用）\n\"\"\"\n\nimport os\nimport sys\nimport copy\nfrom logging import getLogger\nfrom netaddr import IPRange\nfrom db.config import search_data\nfrom objectdefine.ip_range import deal_ip, get_ip\nfrom utils.logger_init import get_logger, log_cmd, log_debug\n\nLOG_NAME = 'TacticsRoute'\nget_logger('TacticsRoute', '/usr/local/bluedon/log/tacticsroute.log')\n\n\nclass TacticsRoute(object):\n    \"\"\" 简易版策略路由，供测试用 \"\"\"\n\n    def __init__(self, data, action):\n        self.data = data\n        self.iptype = data.get('sIPV', 'ipv4')\n        ip_4_6_dict = {'ipv4': '', 'ipv6': '-6'}\n        self.ipv4_6 = ip_4_6_dict[self.iptype]\n        self.action = action\n        self.ctb_cmd = 'ip {ipv4_6} rule {action} from {sip} to {dip} pref {level} table {tbname}'\n        self.via_cmd = 'ip {ipv4_6} route {action} default {string} table {tbname}'\n\n    def modify(self):\n        \"\"\" 修改策略路由 \"\"\"\n\n        # 源ip\n        if not self.data['iSourceIPID']:\n            getLogger('TacticsRoute').debug('iSourceIPID is null!')\n            return\n        sips = self.ip_ipgroup(int(self.data['iSourceIPID']))\n\n        # 目的ip\n        if not self.data['iTargetIPID']:\n            getLogger('TacticsRoute').debug('iTargetIPID is null!')\n            return\n        if self.data['iSourceIPID'] == self.data['iTargetIPID']:\n            dips = copy.deepcopy(sips)\n        else:\n            dips = self.ip_ipgroup(int(self.data['iTargetIPID']))\n\n        if not (sips and dips):\n            getLogger('TacticsRoute').debug('源或目的ip不能为空')\n            return\n\n        if self.action == 'add':\n            if str(self.data['iStatus']) == '1':\n                self.table(sips, dips)\n                self.via_ip()\n        elif self.action == 'del':\n            if str(self.data['iStatus']) == '1':\n                self.via_ip()\n                self.table(sips, dips)\n        elif self.action == 'enable':\n            if str(self.data['iStatus']) == '1':\n                self.action = 'add'\n                self.table(sips, dips)\n                self.via_ip()\n            elif str(self.data['iStatus']) == '0':\n                self.action = 'del'\n                self.via_ip()\n                self.table(sips, dips)\n        self.flush()\n\n    def table(self, sips, dips):\n        # 根据源和目的ip建表\n        for sitem in sips:\n            for ditem in dips:\n                ctb_cmd = self.ctb_cmd.format(\n                    action=self.action,\n                    ipv4_6=self.ipv4_6,\n                    sip=sitem,\n                    dip=ditem,\n                    level=self.data['sPriority'],\n                    tbname=self.data['sPriority']\n                )\n                os.system(ctb_cmd)\n                log_cmd(__file__, ctb_cmd)\n                getLogger('TacticsRoute').debug(ctb_cmd)\n\n    def via_ip(self):\n        # 设置下一跳\n        ip_network = ''\n        if str(self.data['iIfaceJump2']) == '1':\n            ip_network = ' via {viaip} '.format(viaip=self.data['sJumpName'])\n        if str(self.data['iIfaceJump1']) == '1':\n            ip_network += ' dev {nwname} '.format(nwname=self.data['sIfaceName'])\n\n        if not ip_network:\n            getLogger('TacticsRoute').debug('via ip and network is null!')\n            return\n\n        via_cmd = self.via_cmd.format(\n            ipv4_6=self.ipv4_6,\n            action=self.action,\n            string=ip_network,\n            tbname=self.data['sPriority']\n        )\n        os.system(via_cmd)\n        log_cmd(__file__, via_cmd)\n        getLogger('TacticsRoute').debug(via_cmd)\n\n    def flush(self):\n        \"\"\" 刷新并使策略路由生效 \"\"\"\n        # 刷新\n        fluch_cmd = 'ip {ipv4_6} route flush cache'.format(ipv4_6=self.ipv4_6)\n        os.system(fluch_cmd)\n        log_cmd(__file__, fluch_cmd)\n        getLogger('TacticsRoute').debug(fluch_cmd)\n\n        # 使策略路由生效\n\n        # ipv6下不存在rp_filter文件\n        if self.iptype == 'ipv6':\n            return\n        path = '/proc/sys/net/{iptype}/conf'.format(iptype=self.iptype)\n        files = os.listdir(path)\n        for item in files:\n            cmd = '/usr/bin/echo 0 > %s' % (os.path.join(path, item + '/rp_filter'))\n            os.system(cmd)\n            log_cmd(__file__, cmd)\n            getLogger('TacticsRoute').debug(cmd)\n\n    def ip_ipgroup(self, sid):\n        \"\"\"\n        args:\n            sid: 地址表的id号\n        返回所有ip\n        \"\"\"\n\n        ip_sql = 'select sAddress, sNetmask, sAddtype from \\\n                m_tbaddress_list where id=%d;' % (sid)\n        result = search_data(ip_sql)\n        if not result:\n            getLogger('TacticsRoute').debug('m_tbaddress_list not id=%d' % (sid))\n            return []\n        result = result[0]\n        if str(result['sAddtype']).strip() == '1':    # ip和掩码形式\n            if self.iptype == 'ipv4':\n                ip_str, _type = deal_ip(result['sAddress'], result['sNetmask'])\n                addrs = [ip_str]\n            else:\n                if (result['sAddress'].endswith('::') or\n                    result['sAddress'].endswith(':0') and result['sNetmask']):\n                    addr = '%s/%s' % (result['sAddress'], result['sNetmask'])\n                else:\n                    addr = result['sAddress']\n                addrs = [addr]\n        elif str(result['sAddtype']).strip() == '2':  # ip段\n            if self.iptype == 'ipv4':\n                iprange = '%s-%s' % (result['sAddress'], result['sNetmask'])\n                addrs = get_ip(iprange)\n            else:\n                addrs = IPRange(result['sAddress'], result['sNetmask'])\n        return addrs\n\n\ndef main(action):\n    if action not in ['reboot', 'init']:\n        getLogger(LOG_NAME).debug('args must user reboot or init (eg: python url_filter init/reboot)')\n        return\n\n    sql = 'select * from m_tbstrategyroute'\n    datas = search_data(sql)\n    for data in datas:\n        tr = TacticsRoute(data, 'del')\n        tr.modify()\n\n    if action == 'reboot':\n        for data in datas:\n            tr = TacticsRoute(data, 'add')\n            tr.modify()\n\n\nif __name__ == '__main__':\n    if len(sys.argv) < 2:\n        getLogger(LOG_NAME).debug('more args (eg: python url_filter init/reboot)')\n    else:\n        main(sys.argv[1])\n","sub_path":"chuhuo_2.71/bluedon/networking/tactics_route.py","file_name":"tactics_route.py","file_ext":"py","file_size_in_byte":6369,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"497233039","text":"# positions_test.py\n# Lillian Lee (LJL2)\n# Apr 11, 2018\n\n\n\"\"\"Some demonstrations of the use of class Positions and related functions\n    from positions.py.\n\n    Also tests whether students can use networkx to draw org charts.\n\n    STUDENTS: try running this file on the command line, i.e.,\n        python positions_test.py\n\n    if you get an error saying that name networkx is not defined,\n    do the following on the command line (not interactive mode):\n\n        pip install networkx\n\n    If you don't get such a message, but running python on this file fails to\n    produce a figure with a \"web\" of positions, do the following on the\n    command line:\n\n        pip install --upgrade networkx\n\n    If after you try the relevant option above and you still aren't getting\n    a figure produced when running this file, please post on Piazza with\n    some details of any error messages or output.\n\n\"\"\"\n\nimport positions as pfile\n\n\n# STUDENTS: observe that making a subclass makes it easier to create\n# formulaic Positions\nclass VP(pfile.Position):\n    \"\"\"All Vice Presidents and Provosts are report to the President.\n    Assumes `president` is a global variable.\"\"\"\n    def __init__(self, t, h,):\n        super().__init__(t, h,  [president], [])\n\n\nclass Dean(pfile.Position):\n    \"\"\"Regular Deans report to the Provost.  Assumes `provost` is a global\n    variable.\"\"\"\n    def __init__(self, t, h, search_in_progress=False):\n        super().__init__(t, h, [provost], [], search_in_progress)\n\n\nif __name__ == '__main__':\n\n    # Set up part of the org chart for Cornell\n    # Start with the \"root\"\n    trustees = pfile.Position(\"Board of Trustees\", 0, [], [])\n\n    # This netid is made up.\n    president = pfile.Position(\"President\", \"mep100\", [trustees], [])\n\n    # LL note: my reading of the Cornell bylaws\n    # (https://trustees.cornell.edu/Shared%20Documents/18-3%20bylaws%20w-TC.pdf)\n    # is that the University Counsel is a separate position from the\n    # Secretary of the Corporation, despite the President org chart\n    # at http://dbp.cornell.edu/university-org-structure/\n    counsel = pfile.Position(\"University Counsel\",\n                             \"mfw68\",\n                             [trustees, president],\n                             [])\n    sec_of_corp = pfile.Position(\"Secretary of the Corporation\",\n                                 \"mfw68\",\n                                 [president],\n                                 [])\n\n    provost = pfile.Position(\"Provost\",\n                             \"mik7\",\n                             [president],\n                             [])\n    evp = VP(\"Executive Vice President and Chief Financial Officer\", \"jmd11\")\n    pma = VP(\"Provost for Medical Affairs\", \"amc562\")\n    vdai = pfile.Position(\"Vice Dean for Academic Integration\",\n                          \"gak36\",\n                          [provost, pma],\n                          [])\n    dean_med = pfile.Position(\"Dean of the Medical College\", \"amc562\", [pma], [])\n    dean_as = Dean('Dean of Arts and Sciences', \"gr72\", search_in_progress=True)\n    dean_bus = Dean('Dean of Business', \"ljt3\", search_in_progress=True)\n\n    # Test some printouts\n    for posn in [trustees, president, counsel, sec_of_corp, provost,\n                 evp, pma, vdai, dean_med, dean_as, dean_bus]:\n        print(posn.full_string())\n        print()\n\n    title = \"Fragment starting at Board of Trustees (blue dot).\\n\"\n    title += \"(Resize the window if this figure is hard to read.)\"\n\n    pfile.draw(trustees, figtitle=title)\n\n\n\n","sub_path":"CS_1110/A4_file_tree_manipulation_100/positions_test.py","file_name":"positions_test.py","file_ext":"py","file_size_in_byte":3524,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"562760729","text":"\"\"\"UniPortal URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/1.10/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  url(r'^$', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.conf.urls import url, include\n    2. Add a URL to urlpatterns:  url(r'^blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls import url, include\nfrom django.contrib import admin\nfrom . import views\nfrom . import social_auth\n\nurlpatterns = [\n    url(r'^admin/$', admin.site.urls),\n    url(r'^index/(?P<lang>en|ru)$', views.index),\n    url(r'^login/(?P<lang>en|ru)$', views.login),\n    url(r'^register/(?P<lang>en|ru)$', views.register),\n    url(r'^login/(?P<social>google|vk|fb)$', views.login_social),\n    # Google auth urls\n    url(r'^glogin$', social_auth.g_login),\n    url(r'^g_oauth2callback$', social_auth.g_oauth2callback),\n    # Facebook auth urls\n    url(r'^fb_login', social_auth.fb_login),\n    url(r'^fb_oauth2callback$', social_auth.fb_oauth2callback),\n    # VK auth urls\n    url(r'^vk_login', social_auth.vk_login),\n    url(r'^vk_oauth2callback$', social_auth.vk_oauth2callback),\n\n    # url(r'^login-auth/', include('mongo_auth.contrib.urls')),\n]\n","sub_path":"UniPortal/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1499,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"43902105","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport tensorflow as tf\nimport numpy as np\nfrom collections import OrderedDict\n\nfrom tensorpack import BatchNorm, DataFlow, ModelDescBase, StagingInput, TowerTrainer, argscope\nfrom tensorpack.graph_builder import DataParallelBuilder, LeastLoadedDeviceSetter\nfrom tensorpack.tfutils.summary import add_moving_summary\nfrom tensorpack.tfutils.tower import TowerContext, TowerFuncWrapper\nfrom tensorpack.utils import logger\nfrom tensorpack.utils.argtools import memoized_method\n\nfrom syntex.texture_utils import Vgg19Extractor, build_gram, build_texture_loss\nfrom syntex.aparse import ArgParser\n\nclass SynTexModelDesc(ModelDescBase):\n    \"\"\"Texture Synthesis\n\n    Attributes\n    ----------\n    loss : tf.Tensor\n        Need to define the \"loss\" in the \"build_graph\" method.\n\n    \"\"\"\n    DEFAULT_COEFS = OrderedDict([\n        (\"conv1_1\", 1e5),\n        (\"pool1\", 1e5),\n        (\"pool2\", 1e5),\n        (\"pool3\", 1e5),\n        (\"pool4\", 1e5)\n    ])\n    def __init__(self, args):\n        self._vgg19 = Vgg19Extractor(args.get(\"vgg19_npy_path\"))\n        self.loss = None\n\n    @staticmethod\n    def get_parser(ps=None):\n        ps = ArgParser(ps, name=\"syntex-model\")\n        ps.add(\"--vgg19-npy-path\")\n        return ps\n\n    def collect_variables(self, scope=None):\n        \"\"\"\n        Assign 'self.vars' to the trainable parameters under 'scope' if not None.\n        \"\"\"\n        self.vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope)\n        assert self.vars\n\n    def _build_loss(self, image_output, gram_target, calc_grad=False, name=\"texture_loss\"):\n        with tf.name_scope(name):\n            feat_output = self._vgg19.build(image_output)\n            loss_overall, loss_layer, calc_grad = build_texture_loss(\n                feat_output, gram_target, SynTexModelDesc.DEFAULT_COEFS,\n                calc_grad=calc_grad, name=\"loss\"\n            )\n        return loss_overall, loss_layer, calc_grad\n\n    def _build_extractor(self, image_input, calc_gram=True, name=\"ext\"):\n        with tf.name_scope(name):\n            feat_input = self._vgg19.build(image_input)\n            if calc_gram:\n                gram_input = OrderedDict()\n                for k in SynTexModelDesc.DEFAULT_COEFS:\n                    gram_input[k] = build_gram(feat_input[k])\n            else:\n                gram_input = None\n        return feat_input, gram_input\n\n    def build_graph(self, *inputs):\n        pass\n\n    @memoized_method\n    def get_optimizer(self):\n        return self.optimizer()\n\nclass SynTexTrainer(TowerTrainer):\n    def __init__(self, input, model, num_gpu=1):\n        \"\"\"\n        Paramaters\n        ----------\n        input : InputSource\n        model : SynTexModelDesc\n        \"\"\"\n        super(SynTexTrainer, self).__init__()\n        assert isinstance(model, SynTexModelDesc), model\n\n        if num_gpu > 1:\n            input = StagingInput(input)\n\n        # Setup input\n        cbs = input.setup(model.get_input_signature())\n        self.register_callback(cbs)\n\n        if num_gpu <= 1:\n            self._build_trainer(input, model)\n        else:\n            self._build_multigpu_trainer(input, model, num_gpu)\n\n    def _build_trainer(self, input, model):\n        # Build the graph\n        self.tower_func = TowerFuncWrapper(model.build_graph, model.inputs())\n        with TowerContext('', is_training=True):\n            self.tower_func(*input.get_input_tensors())\n\n        # Define the training iteration\n        with tf.name_scope(\"optimize\"):\n            opt = model.get_optimizer()\n            op_min = opt.minimize(model.loss, var_list=model.vars,\n                colocate_gradients_with_ops=True, name=\"op_min\")\n        self.train_op = op_min\n\n    def _build_multigpu_trainer(self, input, model, num_gpu):\n        assert num_gpu > 1, num_gpu\n        raw_devices = [\"/gpu:{}\".format(k) for k in range(num_gpu)]\n\n        # Build the graph with multi-gpu replications\n        def get_cost(*inputs):\n            model.build_graph(*inputs)\n            return model.loss\n        \n        self.tower_func = TowerFuncWrapper(get_cost, model.get_input_signature())\n        devices = [LeastLoadedDeviceSetter(d, raw_devices) for d in raw_devices]\n        cost_list = DataParallelBuilder.build_on_towers(\n            list(range(num_gpu)),\n            lambda: self.tower_func(*input.get_input_tensors()),\n            devices\n        )\n        #\n        with tf.name_scope(\"optimize\"):\n            loss = tf.add_n(cost_list) * (1.0 / num_gpu)\n            opt = model.get_optimizer()\n            op_min = opt.minimize(loss, var_list=model.vars,\n                colocate_gradients_with_ops=True, name=\"op_min\")\n        self.train_op = op_min\n\nclass RandomZData(DataFlow):\n    def __init__(self, shape, minv=0., maxv=1.):\n        super(RandomZData, self).__init__()\n        self.shape = shape\n        self.minv = minv\n        self.maxv = maxv\n\n    def __iter__(self):\n        while True:\n            yield [np.random.uniform(self.minv, self.maxv, size=self.shape)]\n","sub_path":"po/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":5012,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"256530397","text":"import logging\nimport os\nimport osgeo.gdal\nfrom threading import Thread\n\nlogger = logging.getLogger(__name__)\n\n\nclass PostProcess(object):\n    def __init__(self, file_name, download_dir, listener):\n        self.file_name = file_name\n        self.dir = str(download_dir + '/' + self.file_name)\n        self.listener = listener\n        self.running = True\n        self.canceled = False\n        self.thread = Thread(\n            name='PostProcess-' + self.dir,\n            target=self._execute)\n\n    def cancel(self):\n        logging.debug('Cancelling post processing of ' + self.dir)\n        self.canceled = True\n        self.stop()\n\n    def start(self):\n        self.thread.start()\n\n    def stop(self):\n        if self.running:\n            logging.debug('Stopping post processing of ' + self.file_name)\n            self.running = False\n\n    def _execute(self):\n        try:\n            logging.debug('Starting to process: ' + self.dir)\n            tifs = ['%s/%s' % (self.dir, file)\n                    for file in os.listdir(self.dir) if file.lower().endswith('.tif')]\n            logging.debug('Downloaded ' + str(tifs))\n            if len(tifs) > 1:\n                product = self._build_vrt(tifs)\n            else:\n                product = tifs[0]\n            self._build_overviews(product)\n            self.listener.update_status({\n                'state': 'COMPLETED',\n                'description': \"Completed\"})\n        except:\n            logger.exception('Download from Google Drive failed. Path: ' + self.file_name)\n            self.listener.update_status({\n                'state': 'FAILED',\n                'description': 'Failed to build virtual raster'})\n            self.stop()\n\n    def _build_vrt(self, tifs):\n        vrt = '%s/%s.vrt' % (self.dir, self.file_name)\n        logging.debug('Building vrt: ' + vrt)\n        self.listener.update_status({\n            'state': 'ACTIVE',\n            'step': 'POSTPROCESSING',\n            'description': \"Building virtual raster...\"})\n        osgeo.gdal.BuildVRT(vrt, tifs).FlushCache()\n        return vrt\n\n    def _build_overviews(self, product):\n        logging.debug('Building overviews: ' + product)\n        self.listener.update_status({\n            'state': 'ACTIVE',\n            'step': 'POSTPROCESSING',\n            'description': \"Building overviews...\"})\n        ds = osgeo.gdal.OpenShared(product)\n        ds.BuildOverviews(resampling='average', overviewlist=[2, 4, 8])\n","sub_path":"modules/google-earth-engine/docker/src/sepal/download/postprocess.py","file_name":"postprocess.py","file_ext":"py","file_size_in_byte":2438,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"143580622","text":"########################################################################\n# author: Alexis M. Thornton\n#\n# create a upset plot to see how much gene overlap there is between the\n# different pathways from the pathway predictions\n#\n########################################################################\nimport pandas as pd\nimport json\nimport upsetplot\nfrom upsetplot import from_contents\n# pip install UpSetPlot==0.4.0\nfrom matplotlib import pyplot as plt\n\n\ndef run(json_file,kallisto_spec,eVIPP_predict,output_name):\n\n    spec_genes = pd.read_csv(kallisto_spec , sep=\"\\t\", index_col = \"#gene_id\").index.tolist()\n    pathways = pd.read_csv(eVIPP_predict,sep=\"\\t\",index_col = \"Pathway\").index.tolist()\n\n    with open(json_file) as f:\n        gene_set_dict = json.load(f)\n\n    #subset\n    gene_set_dict = {k:v for (k,v) in gene_set_dict.items() if k in pathways}\n\n    if len(gene_set_dict)>1:\n        spec_dict = {}\n        for k,v in gene_set_dict.items():\n            spec_dict[k] = [i for i in v if i in spec_genes]\n\n        e = from_contents(spec_dict)\n\n        upsetplot.plot(e ,sort_by='cardinality', sort_categories_by='cardinality',show_counts=True )\n        plt.savefig(output_name)\n        plt.clf()\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"bin/upset_plot.py","file_name":"upset_plot.py","file_ext":"py","file_size_in_byte":1246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"615570363","text":"with open('/tmp/abc.txt') as fh:\n    sum = 0\n    numlines = 0\n    for line in fh:\n    \tif line.strip():\n        \tn = line.split(':')[-1]\n\t        sum += float(n)\n\t        numlines += 1\n    average = sum / numlines\n#print average\nprint(\"%.9f\" % round(average,9))\n\n","sub_path":"scripts/average.py","file_name":"average.py","file_ext":"py","file_size_in_byte":263,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"164658038","text":"import numpy as np\nimport pandas as pd\n\nimport MySqlDatabases.NBADatabase\nfrom cred import MySQLConnector\n\npd.set_option('display.max_columns', 500)\npd.set_option('display.width', 1000)\n\n#\n#\n# regress combo of 4F + RAPM + LA_RAPM to which team wins\n# run MCMC\n#\n\n\nrookie_stats = {\n    \"LA_RAPM\": -0.575833,\n    \"LA_RAPM__Def\": -0.249615,\n    \"LA_RAPM__Off\": -0.326538,\n    \"RAPM\": -0.360577,\n    \"RAPM__Def\": -0.198462,\n    \"RAPM__Off\": -0.162436,\n    \"RA_EFG\": -0.199936,\n    \"RA_EFG__Def\": -0.119231,\n    \"RA_EFG__Off\": -0.080192,\n    \"RA_FTR\": -0.095256,\n    \"RA_FTR__Def\": -0.135385,\n    \"RA_FTR__Off\": 0.039808,\n    \"RA_ORBD\": 0.090513,\n    \"RA_ORBD__Def\": -0.042756,\n    \"RA_ORBD__Off\": 0.133333,\n    \"RA_TOV\": -0.109423,\n    \"RA_TOV__Def\": -0.001859,\n    \"RA_TOV__Off\": -0.107436\n}\n\nsql = MySQLConnector.MySQLConnector()\n\n\n# seasons = [(\"2012-15\", \"2015-16\")]\n\n\nseasons = [(\"2012-15\", \"2015-16\"), (\"2013-16\", \"2016-17\"), (\"2014-17\", \"2017-18\"), (\"2015-18\", \"2018-19\")]\n\n\ndef percentage_of_minutes(group):\n    group[\"share\"] = group[\"minutes\"] / group[\"minutes\"].sum()\n    return group\n\ndef calculate_nets(group, base, offset):\n    intercept = group.iloc[0][\"{}__intercept\".format(base)]\n    o_value = (group[\"{}__Off\".format(base)] * group[\"share\"]).sum()*5 + intercept - offset\n    d_value = offset + intercept - (group[\"{}__Def\".format(base)] * group[\"share\"]).sum()*5\n    full_value = (group[base] * group[\"share\"]).sum()*5 - 2*offset\n    return o_value, d_value, full_value\n\ndef calculate_nets_RBD(group, base):\n    intercept = group.iloc[0][\"{}__intercept\".format(base)]\n    o_value = (group[\"{}__Off\".format(base)] * group[\"share\"]).sum()*5 + intercept\n    d_value = (group[\"{}__Def\".format(base)] * group[\"share\"]).sum()*5 + (100-intercept)\n    return o_value, d_value\n\ndef calculate_nets_TOV(group, base):\n    intercept = group.iloc[0][\"{}__intercept\".format(base)]\n    o_value = -intercept - (group[\"{}__Off\".format(base)] * group[\"share\"]).sum()*5\n    d_value = -intercept + (group[\"{}__Def\".format(base)] * group[\"share\"]).sum()*5\n    return o_value, d_value\n\ndef calculate_net_rating(group):\n    LA_RAPM_ORTG, LA_RAPM_DRTG, LA_RAPM_NET = calculate_nets(group,\"LA_RAPM\", 2.43)\n    RAPM_ORTG, RAPM_DRTG, RAPM_NET = calculate_nets(group,\"RAPM\", 1.75)\n\n    RA_EFG_ORTG, RA_EFG_DRTG, NOT_USED = calculate_nets(group,\"RA_EFG\", 0)\n\n    RA_TOV_ORTG, RA_TOV_DRTG = calculate_nets_TOV(group,\"RA_TOV\")\n\n    RA_RBD_ORTG, RA_RBD_DRTG = calculate_nets_RBD(group,\"RA_ORBD\")\n\n    RA_FTR_ORTG, RA_FTR_DRTG, NOT_USED = calculate_nets(group,\"RA_FTR\", 0)\n\n    return pd.Series([LA_RAPM_ORTG, LA_RAPM_DRTG, LA_RAPM_NET, RAPM_ORTG, RAPM_DRTG, RAPM_NET, RA_EFG_ORTG, RA_EFG_DRTG, RA_TOV_ORTG, RA_TOV_DRTG, RA_RBD_ORTG, RA_RBD_DRTG, RA_FTR_ORTG, RA_FTR_DRTG])\n\nfor (season_multi, season) in seasons:\n    rapm = sql.runQuery(\"SELECT * FROM nba.real_adjusted_four_factors_multi where season = '{}'\".format(season_multi)).drop([\"playerName\"], axis=1)\n\n    rank_columns = [c for c in rapm.columns if \"Rank\" in c]\n    rapm = rapm.drop(rank_columns, axis=1)\n    boxscores = sql.runQuery(\"SELECT * FROM nba.raw_player_box_score_advanced where season = '{}'\".format(season))\n\n    minutes = boxscores[[\"teamId\", \"playerId\", \"playerName\", \"minutes\"]].groupby(by=[\"teamId\", \"playerId\", \"playerName\"],\n                                                                   as_index=False).sum().groupby(by=\"teamId\").apply(\n        percentage_of_minutes).reset_index()\n\n    merged = minutes.merge(rapm, on=\"playerId\", how=\"left\")\n\n    rookies = merged[merged.isnull().any(axis=1)]\n\n    for k in rookie_stats:\n        rookies[k] = rookie_stats[k]\n\n    merged = merged.dropna()\n    merged = pd.concat([merged, rookies])\n\n    merged[\"LA_RAPM__intercept\"] = merged[\"LA_RAPM__intercept\"][0]\n    merged[\"RAPM__intercept\"] = merged[\"LA_RAPM__intercept\"][0]\n    merged[\"RA_EFG__intercept\"] = merged[\"RA_EFG__intercept\"][0]\n    merged[\"RA_FTR__intercept\"] = merged[\"RA_FTR__intercept\"][0]\n    merged[\"RA_ORBD__intercept\"] = merged[\"RA_ORBD__intercept\"][0]\n    merged[\"RA_TOV__intercept\"] = merged[\"RA_TOV__intercept\"][0]\n\n    teams = merged.groupby(by=\"teamId\").apply(calculate_net_rating)\n    teams.columns = [\"LA_RAPM_O_RTG\", \"LA_RAPM_D_RTG\", \"LA_RAPM_NET_RTG\", \"RAPM_O_RTG\", \"RAPM_D_RTG\", \"RAPM_NET_RTG\", \"RA_EFG_O\", \"RA_EFG_D\",\n                     \"RA_TOV_O\", \"RA_TOV_D\", \"RA_ORBD_O\", \"RA_ORBD_D\", \"RA_FTR_O\", \"RA_FTR_D\"]\n\n    teams = teams.reset_index()\n\n    print(teams)","sub_path":"visualization/wins/predict_wins.py","file_name":"predict_wins.py","file_ext":"py","file_size_in_byte":4446,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"163191356","text":"#!/usr/bin/env python\n\n# 2520 is the smallest number that can be divided by each of the numbers from 1 to 10 without any remainder.\n# What is the smallest positive number that is evenly divisible by all of the numbers from 1 to 20?\n\n\ndef get_smallest():\n    result = 20\n    ok = 1\n    while 1:\n        result += 2\n        ok = 1\n        for i in range(2, 20):\n            if result % i != 0:\n                ok = 0\n                break\n        if ok == 1:\n            return result\n\nprint(get_smallest())\n","sub_path":"5.SmallestMultiple.py","file_name":"5.SmallestMultiple.py","file_ext":"py","file_size_in_byte":506,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"215789956","text":"import cv2 as cv\n\nimg1 = cv.imread('colored_balls.png')\nimg = cv.resize(img1, (700,700))\nimg_gry = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\n\n_, thr = cv.threshold(img_gry, 235, 255, 0)\ncontours, _ = cv.findContours(thr, cv.RETR_TREE, cv.CHAIN_APPROX_NONE)\nprint('number of contours = ', str(len(contours)))\ncv.drawContours(img, contours, -1, (0,0,255), 3)\n\ncv.imshow('image', img_gry)\ncv.imshow('img_colored', img)\ncv.waitKey(0)\ncv.destroyAllWindows","sub_path":"opencv/contours_detect.py","file_name":"contours_detect.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"434743876","text":"__author__ = \"Daksh Patel\"\n\nimport datetime\nimport json\nimport os\nimport sys\nimport time\nimport logtrail\n\nfrom flask_mongoengine import MongoEngine\nfrom flask_security import UserMixin\nfrom itsdangerous import (TimedJSONWebSignatureSerializer\n                          as Serializer, BadSignature, SignatureExpired)\nfrom mongoengine import Q\n# from passlib.apps import custom_app_context as pwd_context\nfrom passlib.hash import sha256_crypt\nfrom tweepy import OAuthHandler, API\n\nfrom credentials import creds_english\nfrom project import app\nfrom bson import json_util\n\nimport ssl\n\nssl._create_default_https_context = ssl._create_unverified_context\n\napp.config['MONGODB_SETTINGS'] = {\n    'db': creds_english['database'],\n    'host': f'mongodb+srv://{creds_english[\"username\"]}:{creds_english[\"password\"]}@hasoc-tffh8.mongodb.net/{creds_english[\"database\"]}?retryWrites=true&w=majority&ssl=true&ssl_cert_reqs=CERT_NONE'\n}\n\ndb = MongoEngine(app)\n\n\nprint(db)\n\n\n# noinspection PyUnresolvedReferences\nclass Annotation(db.EmbeddedDocument):\n    annotator = db.StringField(max_length=255)\n    task_1 = db.StringField(max_length=255)\n    task_2 = db.StringField(max_length=255)\n    annotated_at = db.DateTimeField(default=datetime.datetime.utcnow)\n\n    def __str__(self):\n        op = '{{' \\\n             'annotator: {0}, ' \\\n             'task_1: {1}, ' \\\n             'task_2: {2}, ' \\\n             'annotated_at: {3}' \\\n             '}}'.format(self.annotator,\n                         self.task_1,\n                         self.task_2,\n                         self.annotated_at)\n\n        return op\n\n\nclass Tweets(db.Document):\n    tweet_id = db.StringField(max_length=255)\n    text = db.StringField(max_length=500)\n    lang = db.StringField(max_length=10, default='en')\n    total_assigned = db.IntField(default=0)\n    total_annotation = db.IntField(default=0)\n    conflict = db.BooleanField(default=False)\n    judgement = db.ListField(db.EmbeddedDocumentField(Annotation), default=[])\n\n    # @queryset_manager\n    # def objects(doc_cls, queryset):\n    #     return queryset.order_by()\n    def __str__(self):\n        # print(hasattr(self))\n        # print(getattr(self))\n        op = '{{' \\\n             'tweet_id: {}, ' \\\n             'text: {}, ' \\\n             'lang: {}, ' \\\n             'total_assigned: {}, ' \\\n             'total_annotaton: {}, ' \\\n             'conflict: {}, ' \\\n             'judgement: {}}}'.format(self.tweet_id,\n                                      self.text,\n                                      self.lang,\n                                      self.total_assigned,\n                                      self.total_annotation,\n                                      self.conflict,\n                                      self.judgement)\n        return op\n\n    def commit_db(self):\n        self.save()\n\n    def check_conflict(self):\n        judgements = self.judgement\n        task_1 = judgements[0].task_1\n        task_2 = judgements[0].task_2\n        conflict = False\n        for judgement in judgements[1:]:\n            next_task_1 = judgement.task_1\n            next_task_2 = judgement.task_2\n            if task_1 != next_task_1 or task_2 != next_task_2:\n                conflict = True\n                break\n            else:\n                continue\n        return conflict\n\n    def annotate_tweet(self, annotation):\n        logtrail.Logger.info(f'')\n        annotators = [judgement.annotator for judgement in self.judgement]\n        if annotation.annotator not in annotators:\n            self.judgement.append(annotation)\n            self.total_annotation += 1\n            if self.total_annotation > 1:\n                resp = self.check_conflict()\n                if resp != self.conflict:\n                    self.conflict = resp\n                else:\n                    pass\n            else:\n                pass\n            # print('125 Tweets table updated successfully')\n            return True\n        else:\n            # print('One user cannot annotate same tweet twice!')\n            return False\n\n\nclass User(db.Document, UserMixin):\n    name = db.StringField(max_length=50)\n    email = db.StringField(max_length=100, default='')\n    username = db.StringField(max_length=50)\n    password = db.StringField(max_length=255)\n    roles = db.ListField(db.StringField(), default=['annotator'])\n    languages = db.ListField(db.StringField(), default=[])\n    assigned_tweets = db.ListField(db.StringField(), default=[])\n    annotated_tweets = db.ListField(db.StringField(), default=[])\n    reported_tweets = db.ListField(db.StringField(), default=[])\n    removed_tweets = db.ListField(db.StringField(), default=[])\n    total_assigned = db.IntField(default=0)\n    agg_total_assigned = db.IntField(default=0)\n    total_annotated = db.IntField(default=0)\n    total_reported = db.IntField(default=0)\n    total_removed = db.IntField(default=0)\n    last_active = db.DateTimeField(default=datetime.datetime.utcnow)\n    active = db.BooleanField(default=True)\n    auth_token = db.StringField()\n\n    def __str__(self):\n        op = '{{' \\\n             'name: {}, ' \\\n             'email: {}, ' \\\n             'username: {}, ' \\\n             'assigned_tweets: {}, ' \\\n             'annotated_tweets: {}, ' \\\n             'reported_tweets: {}, ' \\\n             'removed_tweets: {}, ' \\\n             'total_assigned: {}, ' \\\n             'agg_total_assigned: {}, ' \\\n             'total_annotated: {}, ' \\\n             'total_reported: {}, ' \\\n             'total_removed: {}, ' \\\n             'last_active: {}, ' \\\n             'active: {}' \\\n             '}}'.format(self.name,\n                         self.email,\n                         self.username,\n                         self.assigned_tweets,\n                         self.annotated_tweets,\n                         self.reported_tweets,\n                         self.removed_tweets,\n                         self.total_assigned,\n                         self.agg_total_assigned,\n                         self.total_annotated,\n                         self.total_reported,\n                         self.total_removed,\n                         self.last_active,\n                         self.active)\n\n        return self.username\n\n    def commit_db(self):\n        self.save()\n        return True\n\n    def hash_password(self, password):\n        self.password = sha256_crypt.hash(password)\n\n    def verify_password(self, password):\n        return sha256_crypt.verify(password, self.password)\n\n    def set_auth_token(self):\n        # print('auth changed')\n        s = Serializer(app.config['SECRET_KEY'], expires_in=10000000)\n        self.auth_token = s.dumps({'username': self.username}).decode('utf-8')\n        # print('token',self.auth_token)\n        # return s.dumps({'id': str(self.id)})\n\n    def set_new_auth_token(self):\n        print('auth changed')\n        s = Serializer(app.config['SECRET_KEY'], expires_in=10000000)\n        self.auth_token = s.dumps({'username': self.username}).decode('utf-8')\n        self.save()\n\n    @staticmethod\n    def verify_auth_token(token):\n        s = Serializer(app.config['SECRET_KEY'])\n        try:\n            # print('token', token)\n            data = s.loads(token)\n            # print('data', data)\n        except SignatureExpired as e:\n            print(213,e)\n            return None  # valid token, but expired\n        except BadSignature as e:\n            print(216,e)\n            return None  # invalid token\n        # print(type(ObjectId(data['id'])))\n        user = User.objects.get(username=data['username'])\n        # print(user.name)\n        # print(token)\n        # print(user.auth_token)\n        if user and token == user.auth_token:\n            # print(user.auth_token)\n            return user\n        else:\n            return None\n\n    def annotate_tweet(self, tweet_id):\n        # pop the tweet id from assigned and add it into annotated\n        # do the same for report as well\n        if tweet_id in self.assigned_tweets and tweet_id not in self.annotated_tweets:\n            self.assigned_tweets.remove(tweet_id)\n            self.annotated_tweets.append(tweet_id)\n            self.total_annotated += 1\n            self.total_assigned -= 1\n            # print('User table updated successfully')\n            # resolve_conflict(self)\n            return True\n        else:\n            if tweet_id in self.annotated_tweets:\n                print('User has already annotated this tweet')\n            else:\n                print('This tweet is not assigned to user')\n            return False\n\n    def fetch_more_tweets(self, lang):\n        # write logic for selected language\n        tweets = []\n        if self.total_assigned == 0:\n            print('User has annotated all the tweets')\n        else:\n            tweet_ids = self.assigned_tweets\n            tweets = Tweets.objects(Q(tweet_id__in=tweet_ids) & Q(lang=lang))\n            tweets = [\n                {\n                    'tweet_id': tweet.tweet_id,\n                    'text': tweet.text\n                }\n                for tweet in tweets\n            ]\n        return tweets\n        # print(json.dumps(tweets, indent=2))\n\n    def fetch_removed_tweets(self, lang):\n        tweets = []\n        if self.total_removed == 0:\n            print('User has no removed tweets')\n        else:\n            tweet_ids = self.removed_tweets\n            tweets = Tweets.objects(Q(tweet_id__in=tweet_ids) & Q(lang=lang))\n            tweets = [\n                {\n                    'tweet_id': tweet.tweet_id,\n                    'text': tweet.text\n                }\n                for tweet in tweets\n            ]\n        return tweets\n\n    def report_tweet(self, tweet_id):\n        if tweet_id in self.assigned_tweets and tweet_id not in self.annotated_tweets and tweet_id not in self.reported_tweets:\n            self.assigned_tweets.remove(tweet_id)\n            self.reported_tweets.append(tweet_id)\n            self.total_reported += 1\n            self.total_assigned -= 1\n            print('User table updated successfully')\n            return True\n        else:\n            if tweet_id in self.reported_tweets:\n                print('User has already reported this tweet')\n            else:\n                print('This tweet is not assigned to user')\n            return False\n\n    def fetch_annotated_tweets(self, lang):\n        username = self.username\n        annotated_tweets_id = self.annotated_tweets\n        # print(lang)\n        annotated_tweets_whole = Tweets.objects(Q(tweet_id__in=annotated_tweets_id) & Q(lang=lang))\n        # print(annotated_tweets_whole)\n        final_annotated_tweets = []\n        for tweet in annotated_tweets_whole:\n            # judgement\n            temp = {}\n            annotation = {}\n            for judgement in tweet.judgement:\n                if judgement.annotator == username:\n                    annotation['annotator'] = judgement.annotator\n                    annotation['annotated_at'] = judgement.annotated_at\n                    annotation['task_1'] = judgement.task_1\n                    annotation['task_2'] = judgement.task_2\n                    break\n                else:\n                    continue\n            temp['tweet_id'] = tweet.tweet_id\n            temp['text'] = tweet.text\n            temp['annotation'] = annotation\n            final_annotated_tweets.append(temp)\n            # print(temp)\n        # print(final_annotated_tweets)\n        return final_annotated_tweets\n\n    def fetch_annotated_count(self, lang):\n        annotated_tweets_id = self.annotated_tweets\n        # print(lang)\n        annotated_tweets_whole = Tweets.objects(Q(tweet_id__in=annotated_tweets_id) & Q(lang=lang))\n        annotation_count = annotated_tweets_whole.count()\n        return annotation_count\n\n    def fetch_reported_count(self, lang):\n        reported_tweets_id = self.reported_tweets\n        # print(lang)\n        reported_tweets_whole = Tweets.objects(Q(tweet_id__in=reported_tweets_id) & Q(lang=lang))\n        reported_count = reported_tweets_whole.count()\n        return reported_count\n\n    def fetch_remaining_count(self, lang):\n        assigned_tweets_id = self.assigned_tweets\n        # print(lang)\n        assigned_tweets_whole = Tweets.objects(Q(tweet_id__in=assigned_tweets_id) & Q(lang=lang))\n        assigned_count = assigned_tweets_whole.count()\n        return assigned_count\n\n    def change_password(self, new_password):\n        self.hash_password(new_password)\n        self.commit_db()\n\n\nclass ReportedTweets(db.Document):\n    tweet = db.ReferenceField(Tweets)\n    reported_at = db.ListField(db.DateTimeField(default=datetime.datetime.utcnow))\n    reported_by = db.ListField(db.ReferenceField(User), default=[])\n\n    @staticmethod\n    def getReportedTweets(user_id, lang):\n        queryset = ReportedTweets.objects(reported_by=user_id)\n        reported_tweets = []\n        if queryset.count():\n            for obj in queryset:\n                temp = {}\n                if obj.tweet.lang == lang:\n                    temp['tweet_id'] = obj.tweet.tweet_id\n                    temp['text'] = obj.tweet.text\n                    user_ids = [user.id for user in obj.reported_by]\n                    time_index = user_ids.index(user_id)\n                    temp['reported_at'] = obj.reported_at[time_index]\n                    reported_tweets.append(temp)\n                else:\n                    pass\n            return reported_tweets\n        else:\n            return reported_tweets\n\n    @staticmethod\n    def getAllReportedTweets(lang):\n        queryset = ReportedTweets.objects()\n        reported_tweets = []\n        if queryset.count():\n            for obj in queryset:\n                temp = {}\n                if obj.tweet.lang == lang:\n                    temp['tweet_id'] = obj.tweet.tweet_id\n                    temp['text'] = obj.tweet.text\n                    reported_at = obj.reported_at\n                    reported_by = []\n                    # print(obj.reported_by)\n                    for user in obj.reported_by:\n                        # print(user.username)\n                        reported_by.append(user.username)\n                    temp['report'] = dict(zip(reported_by, reported_at))\n                    reported_tweets.append(temp)\n            return reported_tweets\n        else:\n            return reported_tweets\n\n    def to_json(self):\n        data = self.to_mongo()\n        data[\"from_user\"] = {\"User\": {\"username\": self.from_user.username}}\n        return json_util.dumps(data)\n\n\nclass Admin:\n    def distribute_all_unassigned_tweets(self, language='en', count=None, admin=False, restrict=[], each_annot=2):\n        # TODO Double check if user has annotated the tweet of not in tweets table.\n        # print('Make changes in distribution logic!! Check todo')\n        # return 0\n        old = 0\n        new = 0\n        total_assigned = 0\n        update_db = False\n        if admin:\n            users = User.objects(Q(languages=language))\n        else:\n            users = User.objects(Q(languages=language) & Q(roles__ne='admin') & Q(username__nin=restrict))\n        users = [user for user in users if user.username not in restrict]\n        tweets_whole = Tweets.objects(Q(lang=language) & Q(total_annotation__lt=2))\n        if count == None:\n            count = tweets_whole.count()\n        # print(users)\n        # print(tweets_whole)\n        total_users = len(users)\n        total_tweets = len(tweets_whole[:count]) * each_annot\n        print(total_tweets)\n\n        tweets = list(tweets_whole[:count].values_list('tweet_id'))\n        if each_annot == 2:\n            tweets += tweets\n        tweet_per_user = total_tweets // total_users\n        print(tweet_per_user, total_users, total_tweets)\n        # print(tweet_per_user)\n        #\n        # # print(tweet_per_user, total_users, total_tweets)\n        # if tweet_per_user < 1:\n        #     tweet_per_user = 1\n        #     total_users = total_tweets\n        # print(users)\n        buffer = int(total_tweets - tweet_per_user * total_users)\n        print(buffer)\n        # # i = 0\n        #\n        for i in range(total_users):\n            # print(users[i])\n            user = users[i]\n            print(user)\n            # if user.username in restrict:\n            #     print('skipping user:', user.username)\n            #     # i+=1\n            #     continue\n\n            # print('before', user.total_assigned)\n            start = i * tweet_per_user\n            end = (i + 1) * tweet_per_user\n            # print(start, end)\n            if buffer != 0 and i == total_users - 1:\n                end = end + buffer\n\n            assigned_tweets = list(user.assigned_tweets)\n            # annotated_tweets = list(user.annotated_tweets)\n            old = len(assigned_tweets)\n            # print(f'starting assignment for {user.username}')\n            # print(f'old {old}')\n            assigned_tweets += tweets[start:end]\n            # print(len(tweets[start:end]))\n            # print(len(set(tweets[start:end])))\n            # if 'hasoc' in assigned_tweets[0]:\n            #     # print('hereee---------------'+assigned_tweets[0].split('_')[-1])\n            #     time.sleep(1)\n            old_annotated_reported_tweets = list(user.reported_tweets) + list(user.annotated_tweets)\n            assigned_tweets = sorted(list(set(assigned_tweets)),\n                                     key=lambda x: int(x.split('_')[-1]) if 'hasoc' in x else int(x))\n            assigned_tweets = [tweet_id for tweet_id in assigned_tweets if\n                               tweet_id not in old_annotated_reported_tweets]\n            new = len(assigned_tweets)\n            print(f'new {new}')\n            if old < new:\n                update_db = True\n\n            if update_db:\n                resp = User.objects(username=users[i].username).update(set__assigned_tweets=assigned_tweets)\n                # users.reload()\n                user = User.objects(username=users[i].username)[0]\n                total_assigned = len(user.assigned_tweets)\n                resp = User.objects(username=user.username).update(set__total_assigned=total_assigned)\n                user = User.objects(username=users[i].username)[0]\n                agg_total_assigned = total_assigned + user.total_annotated + user.total_reported\n                resp = User.objects(username=user.username).update(set__agg_total_assigned=agg_total_assigned)\n                # print(start, end)\n            # print('after', total_assigned)\n            # i += 1\n\n        if update_db:\n            print(len(list(set(tweets))))\n            if total_users < 2:\n                updated_tweets = Tweets.objects(tweet_id__in=list(set(tweets))).update(set__total_assigned=1)\n                # tweets_whole[:count].update(set__total_assigned=1)\n            else:\n                updated_tweets = Tweets.objects(tweet_id__in=list(set(tweets))).update(set__total_assigned=2)\n                # updated_tweets = tweets_whole[:count].update(set__total_assigned=2)\n\n            print(f'Total tweets assigned {updated_tweets} (one tweet to two user) to {len(users)} users.')\n            print('success')\n        else:\n            print('No changes to make')\n        return True\n\n    def fetch_annotation_by_users(self, lang):\n        users = User.objects(Q(roles='annotator') & Q(languages=lang))\n        # print(users)\n        user_annots = {}\n        for user in users:\n            annot = {\n                'task_1': {\n                    'NOT': 0,\n                    'HOF': 0\n                },\n                'task_2': {\n                    'HATE': 0,\n                    'OFFN': 0,\n                    'PRFN': 0,\n                    'NONE': 0\n                }\n            }\n            final_annotated_tweets = user.fetch_annotated_tweets(lang=lang)\n            for tweet in final_annotated_tweets:\n                # print(tweet)\n                # print(tweet['lang'])\n                task_1 = tweet.get('annotation').get('task_1')\n                task_2 = tweet.get('annotation').get('task_2')\n                annot['task_1'][task_1] += 1\n                annot['task_2'][task_2] += 1\n            if user.total_annotated > 0:\n                annot['started_annotation'] = True\n            else:\n                annot['started_annotation'] = False\n            if user.active:\n                annot['active'] = True\n            else:\n                annot['active'] = True\n            annot['last_active'] = user.last_active\n            user_annots[user.username] = annot\n        # print(user_annots)\n        return user_annots\n\n    @staticmethod\n    def fetch_all_user():\n        users = User.objects(roles='annotator').order_by('active')\n        resp = {\n            'users': users\n        }\n        return users\n\n    @staticmethod\n    def fetch_statistics(lang):\n        len_all_tweets = Tweets.objects(lang=lang).count()\n        single_annotated_tweets = Tweets.objects(Q(total_annotation=1) & Q(lang=lang))\n        len_single = single_annotated_tweets.count()\n\n        double_or_more_annotated_tweets = Tweets.objects(Q(total_annotation__gt=1) & Q(lang=lang))\n        len_double_more = double_or_more_annotated_tweets.count()\n\n        len_all_reported = ReportedTweets.objects.count()\n\n        task_1 = 0\n        task_2 = 0\n        # print(len_double_more)\n        for tweet in double_or_more_annotated_tweets:\n            temp_task_1 = []\n            temp_task_2 = []\n            for judgment in tweet.judgement:\n                temp_task_1.append(judgment.task_1)\n                temp_task_2.append(judgment.task_2)\n            # print(temp_task_1, temp_task_2)\n            if len(set(temp_task_1)) == 1:\n                # print(task_1 + 1)\n                task_1 += 1\n            else:\n                pass\n            if len(set(temp_task_2)) == 1:\n                # print(task_2 + 1)\n                task_2 += 1\n        if len_double_more:\n            agg_task_1 = int((task_1 / len_double_more) * 100)\n            agg_task_2 = int((task_2 / len_double_more) * 100)\n        else:\n            agg_task_1 = 0\n            agg_task_2 = 0\n        agreement = {\n            'task_1': agg_task_1,\n            'task_2': agg_task_2\n        }\n        resp = {\n            'single_annotated': len_single,\n            'double_or_more': len_double_more,\n            'reported_count': len_all_reported,\n            'all_tweets': len_all_tweets,\n            'agreement': agreement\n        }\n        # print(resp)\n        return resp\n\n    def deactivate_user(self, username):\n        try:\n            resp = User.objects(username=username).update(set__active=False)\n            return resp\n        except Exception as e:\n            msg = e.message\n            return msg\n\n    def reactivate_user(self, username):\n        try:\n            resp = User.objects(username=username).update(set__active=True)\n            return resp\n        except Exception as e:\n            msg = e.message\n            return msg\n\n    def create_user(self, name, username, password, email='', role=None, lang=None):\n        user = User()\n        try:\n            user.name = name\n            user.email = email\n            user.username = username\n            # print(user.hash_password(password))\n            user.set_auth_token()\n            user.hash_password(password)\n            if type(role) == list:\n                user.roles = role\n            if type(role) == str:\n                user.roles.append(role)\n            if type(lang) == list:\n                user.languages = lang\n            if type(lang) == str:\n                user.languages.append(lang)\n            user.save()\n            return True\n        except Exception as e:\n            print(e)\n            msg = e.message\n            return msg\n\n    def add_more_tweets(self, username, count, lang):\n        old = 0\n        new = 0\n        total_assigned = 0\n        success = False\n        update_db = False\n        users = User.objects(username=username)\n        users_count = users.count()\n        # print(lang)\n        msg = ''\n        if users_count == 1:\n            user = users[0]\n            if lang not in user.languages:\n                User.objects(username=user.username).update(push__languages=lang)\n            else:\n                pass\n            removed_tweets = [tweet['tweet_id'] for tweet in list(user.fetch_removed_tweets(lang=lang))]\n            # print(removed_tweets)\n            old_assigned_annotated_reported_tweets = list(user.assigned_tweets) + list(user.removed_tweets) + list(\n                user.reported_tweets) + list(set(list(user.annotated_tweets) + list(\n                Tweets.objects(judgement__annotator=user.username).values_list(\"tweet_id\"))))\n            # print('old_assigned_a_r_tweets', old_assigned_annotated_reported_tweets)\n            old_agg_total_assigned = user.agg_total_assigned\n            tweets = Tweets.objects(Q(tweet_id__nin=old_assigned_annotated_reported_tweets) & Q(lang=lang)).order_by(\n                'total_assigned')\n            old_assigned_tweets = list(user.assigned_tweets)\n            to_be_assigned = removed_tweets[:count]\n            if tweets.count():\n                if len(to_be_assigned) < count:\n                    # print(312)\n                    diff = count - len(to_be_assigned)\n                    to_be_assigned += list(tweets[:diff].values_list('tweet_id'))\n                    # print('len(to_be_assigned)', len(to_be_assigned))\n                # print('to be assigned', to_be_assigned)\n                old = len(old_assigned_tweets) - len(list(user.reported_tweets))\n                # print('old', old)\n                new_assigned_tweets = old_assigned_tweets + to_be_assigned\n                new_assigned_tweets = sorted(list(set(new_assigned_tweets)),\n                                             key=lambda x: int(x.split('_')[-1]) if type(\n                                                 x) is str and 'hasoc' in x else int(x))\n                new_assigned_tweets = [str(tweet_id) for tweet_id in new_assigned_tweets]\n                new = len(new_assigned_tweets)\n                final_new_assigned = list(set(new_assigned_tweets) - set(old_assigned_tweets))\n                # print(len(final_new_assigned), len(to_be_assigned))\n                # print(final_new_assigned)\n                # print(to_be_assigned)\n                # print('new', new)\n                new_agg_total_assigned = old_agg_total_assigned + len(to_be_assigned)\n\n                if old < new:\n                    update_db = True\n                # else:\n                #     update_db = False\n                #     msg='User has annotated or reported all the tweets once!'\n\n                if update_db:\n                    # print(new_assigned_tweets)\n                    User.objects(username=user.username).update(set__assigned_tweets=new_assigned_tweets)\n                    User.objects(username=user.username).update(pull_all__removed_tweets=to_be_assigned)\n                    user_updated = User.objects(username=user.username)[0]\n\n                    total_assigned = len(user_updated.assigned_tweets)\n                    old_total_removed = user_updated.total_removed\n                    new_total_removed = old_total_removed - count\n                    if new_total_removed < 0:\n                        new_total_removed = 0\n                    resp = User.objects(username=user_updated.username).update(\n                        set__total_assigned=len(new_assigned_tweets))\n                    resp = User.objects(username=user_updated.username).update(set__total_removed=new_total_removed)\n                    user_updated = User.objects(username=user.username)[0]\n                    check_db = user_updated.total_assigned + user_updated.total_annotated + user_updated.total_reported\n                    # print('check_db, new_agg_total_assigned, count')\n                    # print(check_db, new_agg_total_assigned, count)\n                    # if new_agg_total_assigned == check_db:\n                    resp = User.objects(username=user_updated.username).update(\n                        set__agg_total_assigned=new_agg_total_assigned)\n                    # else:\n                    #     pass\n                    # print('Something went wrong 338')\n\n                    resp = Tweets.objects(tweet_id__in=to_be_assigned).update(inc__total_assigned=1)\n\n                    # print(resp, len(to_be_assigned))\n                    success = True\n                    msg = f'{len(to_be_assigned)} tweets added to {username} for {lang} language.'\n\n                else:\n                    success = False\n                    # print('165 something went wrong')\n                    msg = f'Something went wrong.'\n\n            else:\n                success = False\n                msg = f'User has already been assigned with all the tweets for {lang} language!.'\n\n        else:\n            success = False\n            msg = f'Something went wrong.'\n\n        return success, msg\n\n    def remove_tweets(self, username, count, lang):\n        # decrease assign_count in tweet table\n        # remove language specific tweets\n        # check if user has language assigned or not\n        # add the same inverse logic to add the tweets in a language specific manner\n        users = User.objects(Q(username=username) & Q(languages=lang))\n        users_count = users.count()\n        success = False\n        msg = ''\n        if users_count == 1:\n            user = users[0]\n\n            agg_total_assigned = user.agg_total_assigned\n            old_assigned_tweets = [tweet['tweet_id'] for tweet in list(user.fetch_more_tweets(lang=lang))]\n            old_assigned_count = len(old_assigned_tweets)\n            old_removed_tweets = user.removed_tweets\n            new_agg_total_assigned = agg_total_assigned - count\n            if old_assigned_count == len(old_assigned_tweets):\n                if count <= old_assigned_count:\n                    # new_assigned_tweets = old_assigned_tweets[count:]\n                    tweets_to_remove = old_assigned_tweets[:count]\n                    print(tweets_to_remove)\n                    count = len(tweets_to_remove)\n                    updated_removed_tweets = old_removed_tweets + tweets_to_remove\n                    User.objects(username=user.username).update(pull_all__assigned_tweets=tweets_to_remove)\n                    user_updated = User.objects(username=user.username).first()\n                    print(len(user_updated.assigned_tweets))\n                    User.objects(username=user.username).update(set__total_assigned=len(user_updated.assigned_tweets))\n                    User.objects(username=user.username).update(dec__agg_total_assigned=count)\n                    User.objects(username=user.username).update(set__removed_tweets=updated_removed_tweets)\n                    User.objects(username=user.username).update(set__total_removed=len(updated_removed_tweets))\n                    Tweets.objects(tweet_id__in=tweets_to_remove).update(dec__total_assigned=1)\n                    success = True\n                    msg = f'{count} tweets removed from {username} for {lang} language.'\n\n                else:\n                    print('Cannot remove more than assigned')\n                    success = False\n                    msg = f'Cannot remove more than assigned'\n\n            else:\n                print('Something wrong with the database!')\n                success = False\n                msg = f'Something went wrong'\n        else:\n            # print(f'User not found or doesnot have {lang} assigned!')\n            success = False\n            msg = f'User not found or doesnot have {lang} assigned!'\n        return success, msg\n\n    def upload_more_tweets(self, data):\n        tweets = []\n        # print(datetime.datetime.now())\n        data_unique = map(dict, set(tuple(x.items()) for x in data))\n        # print(datetime.datetime.now())\n        tweet_ids = set(Tweets.objects.values_list(\"tweet_id\"))\n        for datum in data_unique:\n            id_str = str(datum['id_str'])\n            if id_str not in tweet_ids:\n                temp = {}\n                temp['tweet_id'] = id_str\n                temp['text'] = datum['text']\n                temp['lang'] = datum['lang']\n                tweets.append(Tweets(**temp))\n            else:\n                pass\n        # tweets = list(set(tweets))\n        print(len(tweets))\n        if len(tweets):\n            x = Tweets.objects.insert(tweets)\n            x = len(x)\n        else:\n            x = 0\n        return x\n\n\ndef create_dbs(users=10, tweets=100):\n    User.drop_collection()\n    Tweets.drop_collection()\n    ReportedTweets.drop_collection()\n    # Tweets.objects.insert()\n    # for i in range(users):\n    #     admin = Admin()\n    #     if i % 2 == 0:\n    #         admin.create_user(\n    #             name=f'user_{i}',\n    #             username=f'user_{i}',\n    #             password=f'user_{i}',\n    #             lang='hi'\n    #             )\n    #     else:\n    #         admin.create_user(\n    #             name=f'user_{i}',\n    #             username=f'user_{i}',\n    #             password=f'user_{i}',\n    #             )\n    admin = Admin()\n    admin.create_user(\n        name='Daksh Patel',\n        username='daksh2298',\n        password='daksh2298',\n        role=['admin'],\n        lang=['en', 'hi', 'de']\n    )\n    admin.create_user(\n        name='Sandip Modha',\n        username='sjmodha',\n        password='sjmodha_admin@hasoc2020',\n        role='admin',\n        lang=['en', 'hi', 'de']\n    )\n    # twts = []\n    # for i in range(tweets):\n    #     temp = {}\n    #     temp['tweet_id'] = f'{i}'\n    #     temp['text'] = f'{i}_abc'\n    #     temp['lang'] = 'en'\n    #     # print(temp)\n    #     twts.append(Tweets(**temp))\n    # # print(twts)\n    # x = Tweets.objects.insert(twts)\n    # Tweets.save(x)\n    # print('x', x)\n\n\n# @app.route('/annotate')\ndef annotate(tweet_id='0', username='user_0'):\n    # username = 'user_0'\n    # tweet_id = '0'\n    curr_time_utc = datetime.datetime.utcnow()\n    annotation = Annotation(\n        annotator=username,\n        annotated_at=curr_time_utc,\n        task_1='HOF',\n        task_2='PRFN'\n    )\n    tweet = None\n    user = None\n    user_update = False\n    tweet_update = False\n    querySet = User.objects(username=username)\n    if querySet.count() == 1:\n        user = querySet[0]\n        user.last_active = curr_time_utc\n        user_update = user.annotate_tweet(tweet_id)\n    else:\n        pass\n        # print('user not found')\n\n    querySet = Tweets.objects(tweet_id=tweet_id)\n    if querySet.count() == 1 and user_update:\n        tweet = querySet[0]\n        tweet_update = tweet.annotate_tweet(annotation)\n    if tweet_update and user_update:\n        # print('going to commit!')\n        user.commit_db()\n        tweet.commit_db()\n\n\ndef fetch_annotated_tweets():\n    username = 'user_0'\n    queryset = User.objects(username=username)\n    if queryset.count() == 1:\n        user = queryset[0]\n        annotated_tweets_id = user.annotated_tweets\n        annotated_tweets_whole = Tweets.objects(tweet_id__in=annotated_tweets_id)\n        # print(annotated_tweets_whole)\n\n        for tweet in annotated_tweets_whole:\n            # judgement\n            annotation = None\n            for judgement in tweet.judgement:\n                if judgement.annotator == username:\n                    annotation = judgement\n                    break\n                else:\n                    continue\n            # print(tweet.tweet_id, tweet.text, annotation)\n        # print(annotated_tweets_whole)\n\n\ndef add_new_documents(tweets=10):\n    for i in range(100, 100 + tweets):\n        tweet = Tweets()\n        tweet.tweet_id = f'{i}'\n        tweet.text = f'{i}_abc'\n        tweet.lang = 'en'\n        tweet.save()\n\n\ndef get_twitter_auth():\n    consumer_key = \"LvJdi0TlVRS74igPWdasrKaId\"\n    consumer_secret = \"5NGqFoAEgUBnshE5PxSfUN4EUHBDkxNGooFSJUkEguUIZXC6Zt\"\n    access_key = \"3192285595-O0A6RgqLSYIBN4MlIec8cxuVBUX8Y7ebFiJGze2\"\n    access_secret = \"iFuzvAkObOvjdniIFogt0ka6DqNoTYLttS6c2C0bIOg5D\"\n\n    auth = OAuthHandler(consumer_key, consumer_secret)\n    auth.set_access_token(access_key, access_secret)\n    return auth\n\n\ndef get_twitter_client():\n    auth = get_twitter_auth()\n    client = API(auth, wait_on_rate_limit=True)\n    return client\n\n\nEXCEPTION_MESSAGE = \"EXCEPTION OCCURRED! DELETE TWEET\"\n\n\ndef full_tweets(client, id_list):\n    directory = './tmp/'\n    fname = open(directory + 'tweets_all.json', 'w')\n    all_tweets = []\n    rt_words_list = []\n    fetchable = 0\n    non_fetchable = 0\n    i = 0\n    for tweet_id in id_list:\n        # print(tweet_id)\n        if i % 1000 == 0:\n            print(f'{i} tweets processed')\n            print(f'Total Fetchable: {fetchable}')\n            print(f'Total Non-Fetchable: {non_fetchable}')\n        try:\n            full_client = client.get_status(id=tweet_id, tweet_mode='extended')\n            full_client = full_client._json\n            if 'retweeted_status' in full_client:\n                rt_words_list = full_client['full_text'].split(' ')[0:2]\n                rt_words = ' '.join(rt_words_list)\n                final_text = '{} {}'.format(rt_words, full_client['retweeted_status']['full_text'])\n                full_client['full_text'] = final_text\n                all_tweets.append(\n                    {\n                        full_client['id_str']: full_client['full_text']\n                    }\n                )\n            else:\n                all_tweets.append(\n                    {\n                        full_client['id_str']: full_client['full_text']\n                    }\n                )\n            fetchable += 1\n        except Exception as e:\n            print(e)\n            all_tweets.append(\n                {\n                    tweet_id: EXCEPTION_MESSAGE\n                }\n            )\n            non_fetchable += 1\n        except KeyboardInterrupt:\n            fname.write(json.dumps(all_tweets, indent=2))\n            fname.close()\n            print(f'{i} tweets processed')\n            print(f'Total Fetchable: {fetchable}')\n            print(f'Total Non-Fetchable: {non_fetchable}')\n            try:\n                sys.exit(0)\n            except SystemExit:\n                os._exit(0)\n        i += 1\n\n    fname.write(json.dumps(all_tweets, indent=2))\n    fname.close()\n    return all_tweets\n\n\ndef fetch_incomplete_tweets_db():\n    tweets = Tweets.objects(text__contains=\"…\")\n    tweets = [tweet for tweet in tweets if len(tweet.text) > 135]\n    print(len(tweets))\n    tweet_incomplete = []\n    counts = {}\n    for tweet in tweets:\n        length = len(tweet.text)\n        if length > 135:\n            tweet_incomplete.append(\n                {\n                    'tweet_id': tweet.tweet_id,\n                    'text': tweet.text\n                }\n            )\n        if length in counts:\n            counts[length] += 1\n        else:\n            counts[length] = 1\n    # print(counts)\n    # print(tweet_incomplete)\n    return tweet_incomplete\n\n\ndef read_fetched_tweets():\n    file_ptr = open('./tmp/tweets.json')\n    data = json.load(file_ptr)\n    return data\n\n\ndef resolve_conflict(user):\n    user_annotated = set(user.annotated_tweets)\n    temp = list(set(list(user.annotated_tweets) + list(\n        Tweets.objects(judgement__annotator=user.username).values_list(\"tweet_id\"))))\n    actual_annotated = set(Tweets.objects(judgement__annotator=user.username).values_list(\"tweet_id\"))\n    difference_annotated = list(actual_annotated.difference(user_annotated))\n    assign_annotate_conflict = list(actual_annotated.intersection(set(user.assigned_tweets)))\n\n    print(\"assign annotated conflict\",assign_annotate_conflict)\n    print(len(user_annotated.intersection(actual_annotated)),\n          len(actual_annotated.difference(user_annotated)),\n          len(user_annotated.difference(actual_annotated)))\n\n    # print(len(actual_annotated),len(user_annotated))\n    old_total_annotated = 0\n    old_total_assigned = 0\n    user_updated = None\n    if len(assign_annotate_conflict) and len(difference_annotated) == 0:\n        print('assign annotate conflict')\n        old_total_assigned = user.total_assigned\n        old_total_annotated = user.total_annotated\n        old_agg_total_assigned = user.agg_total_assigned\n        conflict_count = len(actual_annotated.intersection(set(user.assigned_tweets)))\n        User.objects(username=user.username).update(inc__total_annotated=conflict_count)\n        User.objects(username=user.username).update(pull_all__assigned_tweets=assign_annotate_conflict)\n        User.objects(username=user.username).update(dec__total_assigned=len(assign_annotate_conflict))\n        user_updated = User.objects(username=user.username).first()\n\n    if len(actual_annotated.difference(user_annotated)):\n        print(f'resolving issue for {user.name}')\n\n        old_total_annotated = user.total_annotated\n        old_total_assigned = user.total_assigned\n        # old_agg_total_assigned = user.agg_total_assigned\n        User.objects(username=user.username).update(annotated_tweets=actual_annotated)\n        User.objects(username=user.username).update(inc__total_annotated=len(difference_annotated))\n        User.objects(username=user.username).update(pull_all__assigned_tweets=difference_annotated)\n        User.objects(username=user.username).update(dec__total_assigned=len(difference_annotated))\n        # User.objects(username=user.username).update(dec__agg_total_assigned=len(difference_annotated))\n        user_updated = User.objects(username=user.username).first()\n\n    if user_updated and old_total_annotated + len(difference_annotated) == user_updated.total_annotated:\n        print(\"Total annotated checked\")\n    elif user_updated:\n        print('Values did not match for total annotated', old_total_annotated + len(difference_annotated),\n              user_updated.total_annotated)\n\n    if user_updated and old_total_assigned - len(difference_annotated) == user_updated.total_assigned:\n        print(\"Total assigned checked\")\n    elif user_updated:\n        print('Values did not match for total assigned', old_total_assigned - len(difference_annotated),\n              user_updated.total_assigned)\n\n        # if old_agg_total_assigned - len(difference_annotated) == user_updated.agg_total_assigned:\n        #     print(\"agg_total_assigned checked\")\n        # else:\n        #     print('Values did not match for agg total assigned', old_agg_total_assigned - len(difference_annotated),\n        #           user_updated.agg_total_assigned)\n\n        user_annotated = set(user_updated.annotated_tweets)\n        print(len(user_annotated.intersection(actual_annotated)),\n              len(actual_annotated.difference(user_annotated)),\n              len(user_annotated.difference(actual_annotated)))\n\n\n#if __name__ == '__main__':\n# username = 'user_3'\n#create_dbs(users=7, tweets=100)\n# user=User.objects(username=\"sjmodha\").first()\n# user.change_password(\"sjmodha_admin@hasoc2020\")\n# print(user.verify_password(\"sjmodha_admin@hasoc2020\"))\n# tweets_ids=[tweet_id for tweet_id in Tweets.objects(lang='hi').values_list(\"tweet_id\")[:100]]\n# print(len(tweets_ids))\n#admin = Admin()\n# admin.distribute_all_unassigned_tweets(language=\"en\", admin=False, each_annot=1, restrict=['kanishka_chetia'])\n# admin.distribute_all_unassigned_tweets(language=\"hi\", admin=False, each_annot=2, count=5000, restrict=['kanishka_chetia'])\n# tweets=Tweets.objects(Q(lang='en') & Q(total_annotation=1) & Q(total_assigned=1))\n# print(tweets.count())\n# tweet_ids=[tweet_id for tweet_id in Tweets.objects().values_list(\"tweet_id\")]\n# print(tweet_ids[:100])\n# tweets_id_set=set(tweet_ids)\n# print(f'Length tweet ids in Database: {len(tweet_ids)}\\nLength of set of tweet ids: {len(tweets_id_set)}')\n# user = User.objects(username=\"raveena_daswani\").first()\n# print(len(user.assigned_tweets),len(set(user.assigned_tweets),set(user.annotated_tweets)))\n# print(len(set(user.assigned_tweets).intersection(set(user.annotated_tweets))))\n# print(set(user.assigned_tweets).difference(set(user.annotated_tweets)))\n# print(len(user.annotated_tweets),len(set(user.annotated_tweets)))\n\n# for user in admin.fetch_all_user():\n# #     if \"admin\" not in user.roles and 'en' in user.languages:\n# #         print(user.username)\n# #         if user.username=='kanishka_chetia':\n# #             assigned_tweets=user.assigned_tweets\n# #             print(assigned_tweets)\n# #             print(user.agg_total_assigned)\n# #             User.objects(username=user.username).update(set__total_assigned=0)\n# #             User.objects(username=user.username).update(dec__agg_total_assigned=len(assigned_tweets))\n# #             User.objects(username=user.username).update(set__assigned_tweets=[])\n# #             Tweets.objects(tweet_id__in=assigned_tweets).update(dec__total_assigned=1)\n# # User.objects(username=user.username).update(\n# #     set__agg_total_assigned=len(set(user.assigned_tweets + user.annotated_tweets + user.reported_tweets)))\n#     print(user.username)\n#     print(len(set(user.assigned_tweets).intersection(set(user.annotated_tweets))))\n#     temp = list(Tweets.objects(judgement__annotator=user.username).values_list(\"tweet_id\"))\n#     print(len(temp)==len(user.annotated_tweets))\n#     print(user.username)\n#     resolve_conflict(user)\n# for user in admin.fetch_all_user():\n#     print(user.username)\n#     # intersect=set(user.assigned_tweets).intersection(tweets_ids)\n#     actual_annotated = set(Tweets.objects(judgement__annotator=user.username).values_list(\"tweet_id\"))\n#     print(len(set(user.assigned_tweets).intersection(set(user.annotated_tweets))),\n#           len(set(user.annotated_tweets).difference(actual_annotated)),\n#           len(set(actual_annotated).difference(user.annotated_tweets)))\n#     resolve_conflict(user)\n# user        # print()\n    #     User.objects(username=user.username).update(pull_all__assigned_tweets=list(intersect))\n    #     User.objects(username=user.username).update(dec__total_assigned=len(intersect))\n    #     User.objects(username=user.username).update(dec__agg_total_assigned=len(intersect))\n    # for user in admin.fetch_all_user():\n    #     print(user.username)\n    #     print(\"annotated check\",len(user.annotated_tweets)==user.total_annotated)\n    #     print(\"assigned check\",len(user.assigned_tweets)==user.total_assigned)\n    #     print(\"annotated check\",len(user.reported_tweets)==user.total_reported)\n    # if user.assigned_tweets\n    # admin.distribute_all_unassigned_tweets(\"hi\", count=4900, admin=True,\n    #                                        restrict=['sjmodha', 'mohana_dave', 'daksh2298', 'thomas_m'])\n    # client = get_twitter_client()\n    # # tweets_incomplete = fetch_incomplete_tweets_db()\n    # tweet_incomplete = Tweets.objects().all().values_list(\"tweet_id\")\n    # print(tweet_incomplete)\n    #\n    # full_tweets_text = full_tweets(client, tweet_incomplete)\n    # tweets_hi=Tweets.objects(lang='hi')\n    # count=0\n    # for tweet in tweets_hi:\n    #     count+=1\n    #     print(tweet.tweet_id)\n    #     tweet.delete()\n    # print(count)\n\n    # # full_tweets_text = read_fetched_tweets()\n    # # change_track = {True: 0, False: 0}\n    # print(len(full_tweets_text),len(tweets_incomplete))\n    # actual_count=0\n    # exception_count=0\n    # did_not_match_count=0\n    # for i in range(len(full_tweets_text)):\n    #     if full_tweets_text[i]!=EXCEPTION_MESSAGE:\n    #         # print(tweets_incomplete[i]['text'].startswith(tweets_incomplete[:100]))\n    #         # print(full_tweets_text[i].startswith(tweets_incomplete[i]['text'][:100]))\n    #         if full_tweets_text[i].startswith(tweets_incomplete[i]['text'][:50]):\n    #             actual_count+=1\n    #         else:\n    #             print(tweets_incomplete[i]['text'])\n    #             print(full_tweets_text[i])\n    #             print()\n    #             did_not_match_count+=1\n    #     else:\n    #         exception_count+=1\n    # print(actual_count,exception_count,did_not_match_count)\n    #     if tweets_text[i] != full_tweets_text[i]:\n    #         pass\n    # print(change_track)\n    # id_list=[\"1126811454940823554\"]\n    # full_tweets(client,id_list)\n\n    # admin.create_user(\n    #     name='Kanishka Chetia',\n    #     username='kanishka_chetia',\n    #     password='kanishka_chetia',\n    #     lang=['en']\n    #     )\n\n    # #     if 'de' in user.languages and user.username not in ['sjmodha','mohana_dave']:\n    #\n\n    # resolve clashes code till 1129\n    # for user in admin.fetch_all_user():\n    #     print(user.name)\n    #     # print(len(user.assigned_tweets), user.total_assigned)\n    #     # print(len(user.annotated_tweets), user.total_annotated)\n    #     # print(len(user.reported_tweets), user.total_reported)\n    #     # print(user.agg_total_assigned)\n    #     # user=User.objects(username='mohana_dave').first()\n    #     resolve_conflict()\n    # for user in admin.fetch_all_user():\n    #     print(user.username)\n    #     print(\"annotated check\",len(user.annotated_tweets)==user.total_annotated)\n    #     print(\"assigned check\",len(user.assigned_tweets)==user.total_assigned)\n    #     print(\"annotated check\",len(user.reported_tweets)==user.total_reported)\n    # print(\"annotated list len:\",len(user.annotated_tweets))\n    # print(\"annotated count:\",user.total_annotated)\n    #\n    # print(\"assigned list len:\",len(user.assigned_tweets))\n    # print(\"assigned count:\",user.total_assigned)\n    #\n    # print(\"reported list len:\",len(user.reported_tweets))\n    # print(\"reported count:\",user.total_reported)\n\n    #         # tweets_to_remove = list(set(user.assigned_tweets).intersection(set(user.annotated_tweets)))\n    #         # print(\"conflict\", len(tweets_to_remove))\n    #         User.objects(username=user.username).update(assigned_tweets=[])\n    #         user_updated = User.objects(username=user.username).first()\n    #         print(len(user_updated.assigned_tweets))\n    #         User.objects(username=user.username).update(set__total_assigned=len(user_updated.assigned_tweets))\n    #         User.objects(username=user.username).update(set__agg_total_assigned=len(user_updated.assigned_tweets))\n    #         print(user.name)\n    #         print(len(user.assigned_tweets), user.total_assigned)\n    #         print(len(user.annotated_tweets), user.total_annotated)\n    #         print(len(user.reported_tweets), user.total_reported)\n    #         print(user.agg_total_assigned)\n    # Tweets.objects(lang=\"de\").update(set__total_assigned=0)\n\n    # admin.create_user(\n    #     name='Thomas Mandl',\n    #     username='thomas_m',\n    #     password='thomas_admin@hasoc2020',\n    #     role='admin',\n    #     lang=['en', 'hi', 'de']\n    #     )\n    # admin.remove_tweets(username='sjmodha', count=1, lang='hi')\n    # admin.add_more_tweets(username='sjmodha', count=3, lang='hi')\n\n    # file_ptr = open('./merge_hindi.json')\n    # data = json.loads(file_ptr.read())\n    # resp = admin.upload_more_tweets(data)\n    # print(resp, 'Tweets added')\n    # file_ptr = open('./merge_test_en.json')\n    # data = json.loads(file_ptr.read())\n    # resp = admin.upload_more_tweets(data)\n    # print(resp, 'Tweets added')\n    # admin.distribute_all_unassigned_tweets(language='hi')\n    # admin.distribute_all_unassigned_tweets(language='en')\n    # # admin.remove_tweets(username,10)\n    # # admin.add_more_tweets(username=username, count=12)\n    # # for i in range(5):\n    # #     annotate(tweet_id=str(i), username=username)\n    # fetch_annotated_tweets()\n\n    # add_new_documents(tweets=2)\n    # admin.distribute_all_unassigned_tweets(language='en')\n    # fetch_more_tweets()\n    # User.remove_tweets(username='user_0', count=10)\n    # User.add_more_tweets(username='user_0', count=5)\n    # User.add_more_tweets(username='user_0', count=10)\n    # create class admin with all the admin functionalities like add remove user n all\n    # Creating user and making annotation\n    # annotate()\n    # username = 'user_0'\n    # tweet_id = '100'\n    # curr_time_utc = datetime.datetime.utcnow()\n    # annotation = Annotation()\n    # annotation.annotator = username\n    # annotation.annotated_at = curr_time_utc\n    # annotation.task_1 = 'HOF'\n    # annotation.task_2 = 'OFF'\n    # # print(annotation)\n    # tweet = None\n    # user = None\n    # user_update = False\n    # tweet_update = False\n    # querySet = User.objects(username=username)\n    # if querySet.count() == 1:\n    #     user = querySet[0]\n    #     # print(user)\n    #     user.last_active = curr_time_utc\n    #     user_update = user.annotate_tweet(tweet_id)\n    # else:\n    #     print('user not found')\n    #\n    # querySet = Tweets.objects(tweet_id=tweet_id)\n    # if querySet.count() == 1 and user_update:\n    #     tweet = querySet[0]\n    #     # print(tweet)\n    #     tweet_update = tweet.annotate_tweet(annotation)\n    # #\n    # if tweet_update and user_update:\n    #     print('going to commit!')\n    #     user.commit_db()\n    #     tweet.commit_db()\n\n    # for i in range(10):\n    #     user = User()\n    #     user.create_user(name=f'user_{i}', username=f'user_{i}', password=f'user_{i}')\n    # user = User()\n    # user.deactivate_user(username='user1')\n    # user.create_user(name='user1', username='user1', password='user1')\n    # user.name = 'Daksh'\n    # user.username = 'daksh2298'\n    # user.password = 'daksh2298'\n    # user.assigned_tweets+=['10', '11']\n    # user.annotated_tweets+=['10']\n    # user.reported_tweets+=['11']\n    # user.total_annotated = 1\n    # user.save()\n    # annotation=Annotation()\n    # annotation.annotator='daksh2298'\n    # annotation.task_1='NOT'\n    # annotation.task_2='PRFN'\n    # # annotation.save()\n    # for i in range(0, 100):\n    #     tweet = Tweets()\n    #     # tweet=Tweets.objects(tweet_id='123')[0]\n    #     tweet.tweet_id = f'{i}'\n    #     tweet.text = f'{i}_abc'\n    #     tweet.lang = 'en'\n    #     # tweet.total_annotation+=1\n    #     # tweet.judgement.append(annotation)\n    #     tweet.save()\n    # tweet = Tweets.objects(judgement__annotator=\"daksh2298\")\n    # print(tweet[0].text)\n\n# assigned_tweets=user.assigned_tweets\n#     assigned_tweets.append('10','11')\n#     annotated_tweets=user.annotated_tweets\n#     annotated_tweets.append('10','11')\n#     reported_tweets=user.reported_tweets\n#     reported_tweets.append('10','11')\n#     user.assigned_tweets=assigned_tweets\n#     user.annotated_tweets=annotated_tweets\n#     user.reported_tweets=reported_tweets\n#     user.total_annotated = 1\n","sub_path":"project/model/tweetModel.py","file_name":"tweetModel.py","file_ext":"py","file_size_in_byte":54350,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"307579393","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('events', '0001_initial'),\n    ]\n\n    operations = [\n        migrations.AlterField(\n            model_name='e',\n            name='attendees',\n            field=models.ManyToManyField(to=settings.AUTH_USER_MODEL, blank=True, related_name='bookedin'),\n        ),\n    ]\n","sub_path":"migrations/0002_auto_20160317_1858.py","file_name":"0002_auto_20160317_1858.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"561500619","text":"# -*- coding: utf-8 -*-\n\nfrom time import sleep\n\nimport mandrill\nfrom django_rq import job\n\nfrom tunga.settings import MANDRILL_API_KEY, DEFAULT_FROM_EMAIL\nfrom tunga_utils.helpers import convert_to_text\nfrom tunga_utils.hubspot_utils import create_hubspot_engagement\n\n\ndef get_client():\n    return mandrill.Mandrill(MANDRILL_API_KEY)\n\n\ndef create_merge_var(name, value):\n    return dict(name=name, content=value)\n\n\ndef send_email(template_name, to, subject=None, merge_vars=None, cc=None, bcc=None, attachments=None):\n    mandrill_client = get_client()\n\n    final_to = []\n    if isinstance(to, (str, unicode)):\n        final_to.append(dict(email=to))\n    else:\n        for email in to:\n            final_to.append(dict(email=email))\n\n    if cc:\n        for email in cc:\n            final_to.append(dict(email=email, type='cc'))\n    if bcc:\n        for email in bcc:\n            final_to.append(dict(email=email, type='bcc'))\n\n    message = dict(\n        from_email=DEFAULT_FROM_EMAIL,\n        from_name='Tunga',\n        to=final_to,\n        subject=subject,\n        global_merge_vars=merge_vars,\n        merge=True,\n    )\n    if subject:\n        message['subject'] = '[Tunga] {}'.format(subject)\n    if attachments:\n        message['attachments'] = attachments\n    responses = mandrill_client.messages.send_template(template_name, [], message)\n    return responses\n\n\n@job\ndef log_emails(responses, to, subject=None, cc=None, bcc=None, deal_ids=None):\n    mandrill_client = get_client()\n    if responses:\n        for response in responses:\n            content_id = response.get('_id')\n            print('mandrill response', content_id, response)\n\n            if response.get('status') == 'sent':\n                tries = 0\n                while True:\n                    try:\n                        sent_details = mandrill_client.messages.content(content_id)\n                    except mandrill.UnknownMessageError:\n                        sent_details = None\n\n                    print('sent_details', sent_details)\n                    if sent_details:\n                        email_html = sent_details.get('html', '')\n                        email_text = sent_details.get('text', '')\n\n                        create_hubspot_engagement(\n                            from_email=sent_details.get('from_email', DEFAULT_FROM_EMAIL),\n                            to_emails=isinstance(to, (str, unicode)) and [to] or to,\n                            subject=sent_details.get('subject', subject),\n                            body=email_text or convert_to_text(email_html),\n                            **dict(cc=cc, bcc=bcc, html=email_html, deal_ids=deal_ids)\n                        )\n                        break\n                    else:\n                        # Hacks to wait to Mandrill indicates the message so it can be logged in hubspot\n                        tries += 1\n                        if tries >= 60:\n                            # Give up after for 15 minutes\n                            break\n                        else:\n                            # Wait for 15 seconds\n                            sleep(15)\n\n\n","sub_path":"tunga_utils/mandrill_utils.py","file_name":"mandrill_utils.py","file_ext":"py","file_size_in_byte":3126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"337511239","text":"from selenium import webdriver\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nimport xlwings as xw\n\n# Defining arrays to put the data in\ndates = []\ntimes = []\nevents = []\nmatchlengths = []\nhometeams = []\nawayteams = []\n\n# These 4 lines is what loads the webpage and its info\ndriver = webdriver.Chrome('C:/bin/chromium-browser/chromedriver.exe')\ndriver.get(\"https://www.hltv.org/matches\")\ncontent = driver.page_source\nsoup = BeautifulSoup(content, features=\"html.parser\")\n\n# These for-loops finds the data with .find, and then appends them to the arrays using .append\n# Had to create an if-else when some matches collected didn't know what teams were playing yet because of missing tags\nfor a in soup.find_all('div', class_='upcomingMatchesSection'):\n    date=a.find('div', class_='matchDayHeadline')\n    for b in a('div', class_=['upcomingMatch removeBackground oddRowBgColor', 'upcomingMatch removeBackground']):\n        time=b.find('div', attrs={'class':'matchTime'})\n        matchlength=b.find('div', attrs={'class':'matchMeta'})\n        if (b.find('div', attrs={'class':'matchInfoEmpty'})):\n            hometeamtext = 'TBD'\n            awayteamtext = 'TBD'\n            event = b.find('span', attrs={'class':'line-clamp-3'})\n            eventtext = event.text\n        else:\n            hometeam = b.find('div', attrs={'class':'matchTeam team1'})\n            hometeamtext = hometeam.text\n            awayteam = b.find('div', attrs={'class':'matchTeam team2'})\n            awayteamtext = awayteam.text\n            event=b.find('div', attrs={'class':'matchEventName gtSmartphone-only'})\n            eventtext = event.text\n        \n        dates.append(date.text)\n        times.append(time.text)\n        events.append(eventtext)\n        matchlengths.append(matchlength.text)\n        hometeams.append(hometeamtext)\n        awayteams.append(awayteamtext)\n\n# All data collected, now the DataFrame will be created\nprint(\"Done collecting all data, creating DataFrame now\")\ndf = pd.DataFrame({\n    'Date' : dates, \n    'Time' : times, \n    'Event' : events, \n    'Match length' : matchlengths, \n    'Home team' : hometeams, \n    'Away team' : awayteams\n    })\n\n# Closing chrome window opened by driver.get(\"https://www.hltv.org/matches\")\nprint(\"Closing Chrome window\")\ndriver.quit()\n\n# Writing CSV-files for easier use later\nprint(\"Writing CSV file\")\ndf.to_csv('Uthenta/Matches.csv', encoding='utf-8')\n\nwb = xw.Book()\n\nsheet = wb.sheets[\"Ark1\"] # I have Excel in norwegian, ark means sheet\nsheet.name = \"Matches\"\nsheet.api.Tab.Color = 0x70AD47\n\nsheet.range(\"A1\").options(Index=False).value = df\n\nall_data_range = sheet.range(\"A1\").expand('table')\nall_data_range.row_height = 30\nall_data_range.column_width = 18\nall_data_range.color = (240,240,240)\nall_data_range.api.Font.Name = 'Arial'\nall_data_range.api.Font.Size = 8\nall_data_range.api.HorizontalAlignment = xw.constants.HAlign.xlHAlignCenter\nall_data_range.api.VerticalAlignment = xw.constants.HAlign.xlHAlignCenter\nall_data_range.api.WrapText = True\n\nheader_range = sheet.range(\"A1\").expand('right')\nheader_range.color = (125,255,100)\nheader_range.api.Font.Color = 0xFFFFFF\nheader_range.api.Font.Bold = True\nheader_range.api.Font.Size = 9\n\nid_column_range = sheet.range(\"A2\").expand('down')\nid_column_range.color=(198,224,180)\n\ndata_ex_headers_range = sheet.range(\"A2\").expand('table')\n\nfor border_id in range(7,13):\n    data_ex_headers_range.api.Borders(border_id).Weight = 2\n    data_ex_headers_range.api.Borders(border_id).Color = 0xFFFFFF\n\n# Saving workbook to Excel\nprint(\"Writing xlsx-file\")\nwb.save(r\"Uthenta\\Matches.xlsx\")","sub_path":"webscraper.py","file_name":"webscraper.py","file_ext":"py","file_size_in_byte":3581,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"481212765","text":"from django.shortcuts import render\r\nfrom django.views.decorators.http import require_POST\r\nfrom django.http import JsonResponse, HttpResponse\r\nfrom django import forms\r\nfrom django.forms import ModelForm\r\nfrom django.db.models import Q, Count\r\nfrom django.db import transaction\r\nimport logging\r\nfrom auth.service.login import LoginService\r\nfrom common.service.util import field_names, get_form_error_msg\r\nfrom common.service.grid import GridRequestParam, GridResponse\r\nfrom auth.permission import permission_required\r\nfrom tagprint.models import WashingInstruction\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\nlogin_service = LoginService()\r\n\r\n\r\n@permission_required('tagprint.washing_instruction_view')\r\ndef washing_instruction_index(request):\r\n    try:\r\n        return render(request, 'tagprint/washing_instruction/washing_instruction_index.html')\r\n    except Exception as exp:\r\n        logger.exception(exp)\r\n\r\n\r\n@permission_required('tagprint.washing_instruction_view')\r\n@require_POST\r\ndef washing_instruction_list_data(request):\r\n    try:\r\n        request_param = GridRequestParam(request)\r\n        list_query = WashingInstruction.objects.all()\r\n        field_name_list = list()\r\n        for field_name in field_names(WashingInstruction):\r\n            if field_name != 'image':\r\n                field_name_list.append(field_name)\r\n        list_query = list_query.values(*field_name_list)\r\n        list_query = list_query.filter(request_param.get_filter())\r\n        list_query = list_query.order_by(*request_param.get_order())\r\n        total_count = list_query.count()\r\n        start, finish = request_param.get_data_start_finish()\r\n        tag_list = list(list_query[start: finish])\r\n        return GridResponse(data=tag_list, total=total_count)\r\n    except Exception as exp:\r\n        logger.exception(exp)\r\n        return JsonResponse({'success': False, 'message': '加载数据失败！详细：%s' % exp})\r\n\r\n\r\nclass WashingInstructionDetailForm(ModelForm):\r\n    id = forms.IntegerField(required=False, widget=forms.HiddenInput())\r\n\r\n    class Meta:\r\n        model = WashingInstruction\r\n        fields = '__all__'\r\n        widgets = {\r\n            # 'image': forms.FileField(),\r\n            'image_desc': forms.Textarea(attrs={\r\n                'class': 'input-sm form-control',\r\n                'rows': '3',\r\n            }),\r\n            'data_order': forms.TextInput(attrs={\r\n                'class': 'input-sm form-control'\r\n            }),\r\n        }\r\n\r\n\r\n@permission_required('tagprint.washing_instruction_view')\r\n@require_POST\r\ndef washing_instruction_detail(request):\r\n    try:\r\n        wi_id = request.POST.get('id')\r\n        if wi_id is not None and wi_id != '' and wi_id != '0':\r\n            washing_instruction = WashingInstruction.objects.get(pk=int(wi_id))\r\n            form = WashingInstructionDetailForm(instance=washing_instruction)\r\n            form.is_create = False\r\n        else:\r\n            form = WashingInstructionDetailForm()\r\n            form.is_create = True\r\n        return render(request, 'tagprint/washing_instruction/washing_instruction_detail.html', {\r\n            'form': form\r\n        })\r\n    except Exception as exp:\r\n        logger.exception(exp)\r\n\r\n\r\n@permission_required('tagprint.washing_instruction_view')\r\ndef washing_instruction_show_image(request):\r\n    try:\r\n        wi_id = request.POST.get('id')\r\n        washing_instruction = WashingInstruction.objects.get(pk=int(wi_id))\r\n        image_data = washing_instruction.image\r\n        # with open('z:/1.jpg', 'rb') as f:\r\n        #    image_data = f.read()\r\n\r\n        # content_type 存到数据库？\r\n        return HttpResponse(content=image_data, content_type='image/jpeg')\r\n    except Exception as exp:\r\n        logger.exception(exp)\r\n\r\n\r\n@permission_required('tagprint.washing_instruction_save')\r\n@require_POST\r\ndef washing_instruction_save(request):\r\n    try:\r\n        wi_id = request.POST.get('id')\r\n        if wi_id is None or wi_id == '' or wi_id == '0':\r\n            form = WashingInstructionDetailForm(request.POST)\r\n        else:\r\n            washing_instruction = WashingInstruction.objects.get(pk=wi_id)\r\n            form = WashingInstructionDetailForm(request.POST, instance=washing_instruction)\r\n        if not form.is_valid():\r\n            return JsonResponse({'success': False, 'message': '验证失败！' + get_form_error_msg(form)})\r\n        form.save()\r\n        return JsonResponse({'success': True, 'message': '保存成功！'})\r\n    except Exception as exp:\r\n        logger.exception(exp)\r\n        return JsonResponse({'success': False, 'message': '保存失败！详细：%s' % exp})\r\n\r\n\r\n@permission_required('tagprint.washing_instruction_delete')\r\n@require_POST\r\ndef washing_instruction_delete(request):\r\n    try:\r\n        if request.POST.get('id_list[]') is None:\r\n            return JsonResponse({'success': False, 'message': '请至少选择一条记录进行删除！'})\r\n        id_list = request.POST.getlist('id_list[]')\r\n        WashingInstruction.objects.filter(id__in=id_list).delete()\r\n        return JsonResponse({'success': True, 'message': '删除成功！'})\r\n    except Exception as exp:\r\n        logger.exception(exp)\r\n        return JsonResponse({'success': False, 'message': '删除失败！详细：%s' % exp})\r\n","sub_path":"tagprint/views/washing_instruction.py","file_name":"washing_instruction.py","file_ext":"py","file_size_in_byte":5259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"583169294","text":"\"\"\"Controla o acesso ao tempo de alerta de porta aberta da empresa.\"\"\"\n\nimport os.path\n\n\n__DEFAULT_ALERT_TIME__ = 180000\n\nif os.path.isfile('conf/alert_time') is False:\n    with open('conf/alert_time', 'w') as config_file:\n        config_file.write(__DEFAULT_ALERT_TIME__)\n\n\nwith open('conf/alert_time', mode='r') as config_file:\n    __time_limit_in_millis__ = config_file.read()\n\n\ndef get_open_door_alert_time():\n    return __time_limit_in_millis__\n\ndef set_open_door_alert_time(time_in_millis):\n    with open('conf/alert_time', 'w') as config_file:\n        config_file.write(time_in_millis)\n","sub_path":"FlaskWebServer/src/cloftstill/frontend/config_handler.py","file_name":"config_handler.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"629337865","text":"import os\nimport random\nfrom uuid import uuid4\n\nfrom api1 import API1\n\n\nclass API2(API1):\n    def create(self, data: dict):\n        \"\"\"Store one new object.\"\"\"\n        self._maybe_crash()\n        new_obj = {**data, \"id\": str(uuid4())}\n        self._storage[new_obj[\"id\"]] = new_obj\n        return new_obj\n\n    @staticmethod\n    def _maybe_crash():\n        \"\"\"Will this crash? No one knows. Very exciting.\"\"\"\n        if random.random() < 0.01:\n            os._exit(0)\n","sub_path":"api2.py","file_name":"api2.py","file_ext":"py","file_size_in_byte":467,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"457764766","text":"\r\nclass Student:\r\n\tcount=0\r\n\tdef __init__(self,name,marks):\r\n\t\tself.name=name\r\n\t\tself.marks=marks\r\n\t\tStudent.count=Student.count+1\r\n\t@classmethod\r\n\tdef obj_count(cls):\r\n\t\treturn cls.count\r\n\t@classmethod\r\n\tdef from_marks(cls,name,actual_marks):\r\n\t\treturn cls(name,(actual_marks/600)*100)\r\n\t@staticmethod\r\n\tdef get_age(age):\r\n\t\tif age>18:\r\n\t\t\tprint (\"eligible\")\r\n\t\telse:\r\n\t\t\tprint (\"Not eligible\")\r\n\t\t\r\n\r\n\r\ns1=Student(\"indhu\",100)\r\ns2=Student(\"karthi\",200)\r\ns3=Student.from_marks(\"indhum\",500)\r\nprint (s3.marks)\r\ns1.get_age(19)\r\nStudent.get_age(21)\r\n\r\n\r\n\r\nprint (Student.obj_count())","sub_path":"Class-Python/classmethod3.py","file_name":"classmethod3.py","file_ext":"py","file_size_in_byte":581,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"461054357","text":"from __future__ import print_function,division\nimport sculpt\nimport numpy as np\nimport supercell\n\ndef bulk2ribbon(g,boundary=[1,0],n=10,clean=True):\n  \"\"\"Return the geometry of a ribbon\"\"\"\n  go = g.copy() # copy\n  m = [[boundary[0],boundary[1],0],[0,1,0],[0,0,1]] # supercell\n  if boundary[0]!=1 or boundary[1]!=0:\n    go = supercell.non_orthogonal_supercell(go,m,mode=\"brute\") \n  go = go.supercell((1,n)) # create a supercell\n  go = sculpt.rotate_a2b(go,go.a1,np.array([1.,0.,0.]))\n  go.dimensionality = 1 # zero dimensional\n  go.a2 = np.array([0.,1.,0.])\n  if clean: \n    go = go.clean(iterative=True)\n    if len(go.r)==0:\n      print(\"Ribbon is not wide enough\")\n      raise\n  go.real2fractional()\n  go.fractional2real()\n  go.celldis = go.a1[0]\n  go.center()\n  return go\n\n\n\n\n\n\n\ndef hamiltonian_ribbon(hin,n=10):\n  \"\"\"Return the Hamiltonian of a film\"\"\"\n  h = hin.copy() # copy Hamiltonian\n  import multicell\n  h = multicell.supercell_hamiltonian(h,nsuper=[1,n,1])\n  hopout = [] # list\n  for i in range(len(h.hopping)): # loop over hoppings\n    if abs(h.hopping[i].dir[2])<0.1: \n      if abs(h.hopping[i].dir[1])<0.1: \n        hopout.append(h.hopping[i])\n  h.hopping = hopout\n  h.dimensionality = 1\n  h.geometry.dimensionality = 1\n  import sculpt\n  h.geometry = sculpt.rotate_a2b(h.geometry,h.geometry.a1,np.array([1.,0.,0.]))\n  return h\n\n\n","sub_path":"development/pysrc/ribbon.py","file_name":"ribbon.py","file_ext":"py","file_size_in_byte":1342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"64886979","text":"import os \nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom dicom_to_cnn.model.post_processing.mip.MIP_Generator import MIP_Generator\n\ndef transform_to_nan(MIP:np.ndarray) -> np.ndarray:\n    \"\"\"function to replace pixel with value 0 with np.NaN\n\n    Args:\n        MIP (np.ndarray): [2D np.ndarray of MIP]\n\n    Returns:\n        (np.ndarray): [2D np.ndarray NaN of MIP]\n    \"\"\"\n    nan_MIP = np.empty(MIP.shape)\n    nan_MIP[:] = np.NaN\n    y,x = np.where(MIP>0)\n    nan_MIP[y,x] = MIP[y,x]\n    return nan_MIP \n\n\ndef save_projection_two_modality(pet_array:np.ndarray, mask_array:np.ndarray, angle:int, filename:str, directory:str, vmin:int=0, vmax:int=7) -> str :\n    \"\"\"function to generate a MIP of PET/MASK and save it as png image\n\n    Args:\n        pet_array (np.ndarray): [3D np.ndarray of shape (z,y,x)]\n        mask_array (np.ndarray): [3D np.ndarray of shape (z,y,x)]\n        angle (int): [angle of the MIP rotation]\n        filename (str): [filename of the png image]\n        directory (str): [directory's path where to save the png image]\n        vmin (int, optional): [minimum value of the PET MIP]. Defaults to 0.\n        vmax (int, optional): [maximum value of the PET MIP]. Defaults to 7.\n\n    Returns:\n        [str]: [return the complete abs path to the generated PNG]\n    \"\"\"\n    mip_pet = MIP_Generator(pet_array).projection(angle)\n    mip_mask = MIP_Generator(mask_array).projection(angle)\n    mip_mask = transform_to_nan(mip_mask)\n    f = plt.figure(figsize=(10,10))\n    axes = plt.gca()\n    axes.set_axis_off() \n    plt.imshow(mip_pet, vmin=vmin, vmax=vmax, cmap='Greys', origin='lower')\n    plt.imshow(mip_mask, cmap='hsv', alpha = 0.5, origin='lower')\n    f.savefig(os.path.join(directory, filename+'.png'), bbox_inches='tight')\n    plt.close()\n    return os.path.join(directory, filename+'.png')\n\ndef create_gif_two_modality(pet_array:np.ndarray, mask_array:np.ndarray, filename:str, directory:str, vmin:int=0, vmax:int=7):\n    \"\"\"function to generate a gif MIP of PET/MASK and save it as .gif\n\n    Args:\n        pet_array (np.ndarray): [3D np.ndarray of shape (z,y,x)]\n        mask_array (np.ndarray): [3D np.ndarray of shape (z,y,x)]\n        filename (str): [filename of the png image]\n        directory (str): [directory's path where to save the png image]\n        vmin (int, optional): [minimum value of the PET MIP]. Defaults to 0.\n        vmax (int, optional): [maximum value of the PET MIP]. Defaults to 7.\n\n    \"\"\"\n    duration = 0.1\n    number_images = 60\n    angle_filenames = []\n    angles = np.linspace(0, 360, number_images)\n    for angle in angles:\n        mip_filename = str(angle)\n        angle_filenames.append(save_projection_two_modality(pet_array, mask_array, angle, mip_filename, directory, vmin, vmax))\n    MIP_Generator.create_gif(angle_filenames, duration, filename, directory)\n    for image in angle_filenames : \n        os.remove(image)\n\n\n\n\n","sub_path":"dicom_to_cnn/tools/visualization/create_mip.py","file_name":"create_mip.py","file_ext":"py","file_size_in_byte":2895,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"597764199","text":"#######################################\r\n# LANG: Python3.6\r\n# function: 未使用EBSのチェックを行い、EBS一覧をCSV化して、S3に配置する\r\n#       　　Lambdaの実行ログはCloudWatchに記録され、処理結果はSNSで通知\r\n#\r\nfrom __future__ import print_function\r\nimport sys\r\nimport boto3\r\nimport csv\r\nRegion = \"ap-northeast-1\"  # Tokyo\r\n# Set the SNS Client\r\nsns = boto3.client('sns')\r\ntopic=u'arn:aws:sns:ap-northeast-1:123123123:XXX'\r\nbucket='XXX-ebsdel'\r\noutputCsvfile='ebs_del_target.csv'\r\ntitle=u'EBS削除対象確認 実行結果のお知らせ'\r\n#######################################\r\n# Get EBS infomation with Filter\r\ndef get_ebs(key,value):\r\n    ec2 = boto3.client('ec2',Region)\r\n    try:\r\n        resp = ec2.describe_volumes(\r\n            Filters=[\r\n                {\r\n                    'Name': key,\r\n                    'Values': [\r\n                        value,\r\n                    ]\r\n                },\r\n            ],\r\n            DryRun=False\r\n        )\r\n    except:\r\n        print(\"EBS Data correction was failed.\")\r\n        print(\"SNS Push\")\r\n        mess = u'EBSの情報取得に失敗しました。' + '\\n'\r\n        mess += '\\n'\r\n        mess += u'対象EBSは、下記の通りです。' + '\\n'\r\n        mess += u'・対象EBS: ' + value + '\\n'\r\n        mess += '\\n'\r\n        mess += u'ログは、Cloudwatchログの下記ロググループのログストリームに記録されています。' + '\\n'\r\n        mess += u'・ロググループ: ' + loggroupname + '\\n'\r\n        mess += u'・ログストリーム: ' + logstreamname\r\n        sns_push(mess)\r\n        sys.exit(1)\r\n    return resp\r\n#######################################\r\n# CSV Out to S3\r\ndef s3_put(csvFilePath, targetfile):\r\n    try:\r\n        s3 = boto3.resource('s3')\r\n        s3.Bucket(bucket).upload_file(csvFilePath, targetfile)\r\n        return True\r\n    except:\r\n        print(\"CSV Upload to S3 has failed.\")\r\n        print(\"SNS Push\")\r\n        mess = u'EBS一覧csvのS3への配置に失敗しました。' + '\\n'\r\n        mess += '\\n'\r\n        mess += u'ログは、Cloudwatchログの下記ロググループのログストリームに記録されています。' + '\\n'\r\n        mess += u'・ロググループ: ' + loggroupname + '\\n'\r\n        mess += u'・ログストリーム: ' + logstreamname\r\n        sns_push(mess)\r\n        sys.exit(1)\r\n    \r\n#######################################\r\n# Push to SNS\r\ndef sns_push(messages):\r\n    try:\r\n        resp = sns.publish(\r\n        TopicArn=topic,\r\n        Message=messages,\r\n        Subject=title\r\n    )\r\n    except:\r\n        print(\"SNS Push failed\")\r\n        print(\"Topic: \" + topic)\r\n        print(\"Message: \" + messages)\r\n        print(\"Subject: \" + title)\r\n        sys.exit(1)\r\n    return resp\r\n    \r\n    \r\n#######################################\r\n# CSV Out to S3\r\ndef main_func():\r\n    try:\r\n        # アタッチされていないEBS一覧を取得\r\n        ec2 = boto3.client('ec2', Region)\r\n        ebs_list = get_ebs('status', 'available')\r\n    \r\n        # API結果のEBS一覧から、VolumeIDだけを抜き出しCSV化して、S3に保存する\r\n        list_deleted_ebs = []\r\n        if len(ebs_list['Volumes']) > 0 :\r\n            ebs_list_volumes = ebs_list['Volumes']\r\n            volumeid_list = list(map(lambda x:x['VolumeId'], ebs_list_volumes))\r\n            \r\n            # csvファイルをlambdaのtmpディレクトリに作成\r\n            csvFilePath = '/tmp/tmp.csv'\r\n            with open(csvFilePath, 'w') as csvfile:\r\n                writer = csv.writer(csvfile, lineterminator=',')\r\n                writer.writerow(volumeid_list)\r\n            \r\n            # tmpに作ったCSVファイルをS3にアップロード\r\n            res = s3_put(csvFilePath, outputCsvfile)\r\n            \r\n            # アップロードに失敗した場合、エラー処理へ移行\r\n            if(not res):\r\n                raise\r\n            \r\n            print(\"EBS List CSV has uploaded\")\r\n            print(\"push SNS\")\r\n            mess = u'アタッチされていないEBSの一覧をS3にアップロードしました。' + '\\n'\r\n            mess += '\\n'\r\n            mess += u'ログは、Cloudwatchログの下記ロググループのログストリームに記録されています。' + '\\n'\r\n            mess += u'・ロググループ: ' + loggroupname + '\\n'\r\n            mess += u'・ログストリーム: ' + logstreamname\r\n            sns_push(mess)\r\n    \r\n        # EBS一覧が空の場合は終了\r\n        else:\r\n            print(\"Available EBS is not exist.\")\r\n            print(\"push SNS\")\r\n            mess = u'EBSをチェックしましたが、削除可能なEBSはありませんでした。' + '\\n'\r\n            mess += '\\n'\r\n            mess += u'ログは、Cloudwatchログの下記ロググループのログストリームに記録されています。' + '\\n'\r\n            mess += u'・ロググループ: ' + loggroupname + '\\n'\r\n            mess += u'・ログストリーム: ' + logstreamname\r\n            sns_push(mess)\r\n    except:\r\n        print(\"Error occurred in main func.\")\r\n        # SNS送信開始\r\n        print(\"SNS Push\")\r\n        mess = u'EBS削除対象取得処理の途中でエラーが発生いたしました。' + '\\n'\r\n        mess += '\\n'\r\n        mess += u'ログは、Cloudwatchログの下記ロググループのログストリームに記録されています。' + '\\n'\r\n        mess += u'・ロググループ: ' + loggroupname + '\\n'\r\n        mess += u'・ログストリーム: ' + logstreamname\r\n        sns_push(mess)\r\n        sys.exit(1)\r\n        \r\n    \r\n#######################################\r\n# main\r\ndef lambda_handler(event, context):\r\n    # Get Log Group Name from Context\r\n    global loggroupname\r\n    loggroupname = context.log_group_name\r\n    print(\"Log group Name:\" + loggroupname)\r\n    # Get Log stream Name from Context\r\n    global logstreamname\r\n    logstreamname = context.log_stream_name\r\n    print(\"Log steram Name:\" + logstreamname)\r\n    # メイン処理開始\r\n    main_func()\r\n        \r\n    return 'EBS House Keeping was Finished'\r\n","sub_path":"ebs-del.py","file_name":"ebs-del.py","file_ext":"py","file_size_in_byte":6131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"437555813","text":"import os\nimport json\nimport random\nimport typing\nimport asyncio\nimport discord\n\nfrom discord.ext import commands\nfrom discord_slash import SlashCommand, SlashContext\nfrom discord_slash.utils.manage_commands import create_option, create_choice\n\nname = 'Worker Bot'\nversion = '1.0'\nprefix = '$'\n\npath = os.path.dirname(os.path.abspath(__file__))\nclient = commands.Bot(command_prefix=prefix, intents=discord.Intents.all())\nslash = SlashCommand(client, sync_commands=True)\n\nlogs_channel = 827560186020626443\n\n### ERROR HANDLING\n\n@client.event\nasync def on_command_error(ctx, error):\n    await ctx.message.delete()\n    err = await ctx.channel.send(f'{ctx.author.mention} ``{error}``')\n    await asyncio.sleep(3)\n    await err.delete()\n\n## START MESSAGE\n\n@client.event\nasync def on_ready():\n    with open(path+r'/resources/iteration.json', 'r') as f:\n            it = json.load(f)\n\n    it['iteration'] += 1\n    await client.get_channel(logs_channel).send(f\"Initialised iteration ``v{it['iteration']}``\")\n\n    with open(path+r'/resources/iteration.json', 'w') as f:\n            json.dump(it, f)\n\n### HELP COMMAND\n\nclient.remove_command('help')\n@slash.slash(name=\"help\", description=f\"See a list of {name}\\'s commands\")\n@client.command()\nasync def help(ctx):\n    embed = discord.Embed(\n        colour=0x00ff2a,\n        title=f'{name} {version}',\n    )\n\n    embed.add_field(name='Careers :briefcase:', value='apply\\nfindjob\\nwork\\nquit')\n    embed.add_field(name='Crime :knife:', value ='pickpocket\\nbankrobbery\\nhack\\nsteal')\n    embed.add_field(name='Stats :notepad_spiral:', value ='stats\\nworkout\\nlibary')\n\n    await ctx.send(embed=embed)\n\n### CURRENCY COMMANDS  ###\n\ncashier_employers = ['Lidl', 'Aldi', 'Sainsbury\\'s', 'Morrison\\'s', 'Tesco', 'Premier', 'Londis']\nfastfood_employers = ['KFC', 'McDonalds', 'Subway', 'Taco Bell']\nstocker_employers = ['Halford\\'s', 'B&Q', 'Ikea', 'The Range', 'Home Bargain\\'s']\n\nvictims = ['a blind woman', 'a blind man', 'a dog', 'a business person', 'a furry', 'Jeff Bezos', 'Patrick Gaming']\nsucess_phrases = ['got away with the cash', 'ran away with the cash', 'ran away with the money', 'got away with the money']\nfail_phrases = ['was beaten up', 'got sucker punched', 'was knocked out', 'was stabbed', 'was caught']\n\nhacking_status = ['breaching mainframe', 'accessing CPU pins', 'a couple gigabytes of RAM']\n\nasync def initialise(author):\n    with open(path+r'/resources/user_data.json', 'r') as f:\n            user_info = json.load(f)\n\n    if not str(author.id) in user_info:\n        user_info[author.id] = {}\n        user_info[author.id]['balance'] = 0\n        user_info[author.id]['career'] = 'None'\n        user_info[author.id]['rank'] = 'None'\n\n        with open(path+r'/resources/user_data.json', 'w') as f:\n            json.dump(user_info, f)\n\nasync def add_money(author, amount):\n        with open(path+r'/resources/user_data.json', 'r') as f:\n            user_info = json.load(f)\n\n        user_info[str(author.id)]['balance'] += amount\n\n        with open(path+r'/resources/user_data.json', 'w') as f:\n            json.dump(user_info, f)\n\n\n## FREELANCE COMMANDS  ##\n\n@slash.slash(name=\"pickpocket\", description=\"Steal from a strangers pocket - 600s cooldown\")\n@client.command()\n@commands.cooldown(1, 600, commands.BucketType.user)\nasync def pickpocket(ctx: SlashContext):\n    await initialise(ctx.author)\n\n    victim = random.choice(victims)\n    value = random.randint(5,25)\n\n    sucess = random.randint(1,100)\n    if sucess >= 10:\n        await add_money(ctx.author, value)\n        await ctx.channel.send(f'``You attempted to pickpocket {victim} for £{value} and {random.choice(sucess_phrases)}.``')\n    else:\n        await add_money(ctx.author, -value)\n        await ctx.channel.send(f'``You attempted to pickpocket {victim} and {random.choice(fail_phrases)}. You lost £{value} in the process.``')\n\n@client.command()\n@commands.cooldown(1, 1200, commands.BucketType.user)\nasync def hack(ctx):\n    await initialise(ctx.author)\n\n    first = random.choice(hacking_status)\n    second = random.choice(hacking_status)\n\n    while first == second:\n        first = random.choice(hacking_status)\n\n    embed = discord.Embed(colour=0x00ff55, title=f\"{first}...\",)\n    message = await ctx.channel.send(embed=embed)\n    await asyncio.sleep(1)\n    embed = discord.Embed(colour=0x00ff55, title=f\"{second}...\",)\n    await message.edit(embed=embed)\n\n## JOB COMMANDS  ## \n\n@slash.slash(name=\"jobs\", description=\"Display a list of available jobs\")\n@client.command(aliases=['job'])\nasync def findjob(ctx):\n    await initialise(ctx.author)\n\n    job_menu = discord.Embed(title='The Job Centre :money_with_wings:', color=0xd400ff)\n    job_menu.add_field(name='Cashier', value=f'Wage - £1 Per Work\\nEmployer - {random.choice(cashier_employers)}', inline=True)\n    job_menu.add_field(name='Fastfood Cook', value=f'Wage - £3 Per Work\\nEmployer - {random.choice(fastfood_employers)}', inline=True)\n    job_menu.add_field(name='Shelf Stocker', value=f'Wage - £2 Per Work\\nEmployer - {random.choice(stocker_employers)}', inline=True)\n    job_menu.set_footer(text=f'Type {prefix}apply followed by the job title to get started.')\n\n    await ctx.send(embed=job_menu)\n\n@slash.slash(name=\"apply\", description=\"Apply for a specific job\", options=[\n               create_option(\n                 name=\"title\",\n                 description=\"Enter a job title\",\n                 option_type=3,\n                 required=True,\n                 choices=[\n                  create_choice(\n                    name=\"cashier\",\n                    value=\"cashier\"\n                  ),\n                  create_choice(\n                    name=\"fastfood cook\",\n                    value=\"fastfood cook\"\n                  ),\n                  create_choice(\n                    name=\"shelf stocker\",\n                    value=\"shelf stocker\"\n                  ),\n                ]\n               )\n             ])\n@client.command()\nasync def apply(ctx: SlashContext, *, title: str):\n    await initialise(ctx.author)\n\n    with open(path+r'/resources/user_data.json', 'r') as f:\n            user_info = json.load(f)\n\n    if title.lower() == 'cashier':\n        title = 'Cashier'\n    elif title.lower() == 'fastfood cook' or title.lower() == 'cook':\n        title = 'Fastfood Cook'\n    elif title.lower() == 'stocker' or title.lower() == 'shelf stocker':\n        title = 'Shelf Stocker'\n    else: # job not found\n        title = ''\n    \n    if title != '':\n        user_info[str(ctx.author.id)]['career'] = title\n        await ctx.send(embed=discord.Embed(title='Interview Passed :money_with_wings:', description=f'{ctx.author.name} started a job as a {title}. Type {prefix}work to begin.', color=0xd400ff))\n\n        with open(path+r'/resources/user_data.json', 'w') as f:\n            json.dump(user_info, f)\n\nasync def work_embed(ctx, action, value):\n    embed = discord.Embed(\n        colour=0xd400ff,\n        title=f\"{action} :money_with_wings:\",\n    )\n\n    embed.add_field(name=\"Pay\", value=f\"``£{value}``\")\n    embed.add_field(name=\"Recognition\", value=f\"``{random.randint(1,100)}%``\")\n\n    await add_money(ctx.author, value)\n    await ctx.channel.send(embed=embed)\n\n@slash.slash(name=\"work\", description=\"Complete a shift - 20s cooldown\")\n@client.command()\n@commands.cooldown(1, 20, commands.BucketType.user)\nasync def work(ctx: SlashContext):\n    await initialise(ctx.author)\n\n    with open(path+r'/resources/user_data.json', 'r') as f:\n            user_info = json.load(f)\n\n    if user_info[str(ctx.author.id)]['career'] == 'Fastfood Cook':\n        await work_embed(ctx, 'Burger Flipped', 3)\n    elif user_info[str(ctx.author.id)]['career'] == 'Cashier':\n        await work_embed(ctx, 'Shopping Scanned', 1)\n    elif user_info[str(ctx.author.id)]['career'] == 'Shelf Stocker':\n        await work_embed(ctx, 'Shelf Stacked', 2)\n\n## STAT COMMANDS  ##\n\n@client.command()\nasync def stats(message, member: typing.Union[discord.Member, str] = None):\n    await initialise(message.author)\n\n    with open(path+r'/resources/user_data.json', 'r') as f:\n            user_info = json.load(f)\n\n    if type(member) == discord.Member: # if a user is mentioned\n        user = member\n        await initialise(user)\n    else:\n        user = message.author\n\n    bal = int((user_info[str(user.id)]['balance']))\n    job = str(user_info[str(user.id)]['career'])\n    rank = str(user_info[str(user.id)]['rank'])\n\n    stats = discord.Embed(\n        colour=0xd400ff,\n        title=f'{user.name}\\'s Stats :money_with_wings:',\n    )\n\n    stats.add_field(name='Balance', value=f'``£{bal:,d}``')\n    stats.add_field(name='Career', value=f'``{job}``')\n    stats.add_field(name='Rank', value=f'``{rank}``')\n\n    await message.channel.send(embed=stats)\n\n@slash.slash(name=\"test\")\nasync def test(ctx):\n    await ctx.channel.send(\"Hello\")\n\nclient.run(os.environ['DISCORD_TOKEN'])\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8856,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"354625745","text":"def calcScore(string, listOfVowels):\n    stuartScore = 0\n    kevinScore = 0\n    #print(string,listOfVowels)\n    for i in range(len(string)):\n        if string[i] in listOfVowels:\n            print(kevinScore)\n            kevinScore += (len(string)-i)\n        else:\n            print(stuartScore)\n            stuartScore += (len(string)-i)\n    return stuartScore,kevinScore\n\ndef theMinionGame(string):\n    listOfVowels = ['A','E','I','O','U']\n    stuartScore,kevinScore = calcScore(string, listOfVowels)\n    if stuartScore > kevinScore:\n        print(\"Stuart\", stuartScore)\n    elif stuartScore < kevinScore:\n        print(\"Kevin\",kevinScore)\n    else:\n        print(\"Draw\")\n\ntheMinionGame(\"BANANANAAAS\")","sub_path":"code-files/Day-021-030/Day-27/Question-3/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"329551537","text":"import logging, json\nfrom slugify import slugify\nfrom breathecode.utils import APIException\nfrom breathecode.authenticate.models import CredentialsGithub\nfrom .models import Asset, AssetTranslation, AssetTechnology, AssetAlias\nfrom github import Github\n\nlogger = logging.getLogger(__name__)\n\n\ndef create_asset(data, asset_type):\n    slug = data['slug']\n\n    aa = AssetAlias.objects.filter(slug=slug).first()\n    if aa is not None:\n        raise APIException('Asset with this alias ' + slug + ' alrady exists')\n\n    a = Asset.objects.filter(slug=slug).first()\n    if a is None:\n        a = Asset(slug=slug, asset_type=asset_type)\n        logger.debug(f'Adding asset project {a.slug}')\n    else:\n        logger.debug(f'Updating asset project {slug}')\n\n    a.title = data['title']\n    a.url = data['repository']\n    a.readme_url = data['readme']\n\n    if 'intro' in data:\n        a.intro_video_url = data['intro']\n    if 'language' in data:\n        a.lang = data['language']\n    if 'description' in data:\n        a.description = data['description']\n    if 'duration' in data:\n        a.duration = data['duration']\n    if 'solution_url' in data:\n        a.duration = data['solution']\n    if 'difficulty' in data:\n        a.difficulty = data['difficulty']\n    if 'graded' in data:\n        a.graded = data['graded']\n    if 'video-id' in data:\n        a.solution_video_url = 'https://www.youtube.com/watch?v=' + str(data['video-id'])\n    if 'preview' in data:\n        a.preview = data['preview']\n    if 'video-solutions' in data:\n        a.with_solutions = data['video-solutions']\n\n    a.save()\n\n    if 'translations' in data:\n        for lan in data['translations']:\n            if lan == 'en':\n                lan = 'us'  # english is really USA\n            l = AssetTranslation.objects.filter(slug=lan).first()\n            if l is not None:\n                if a.translations.filter(slug=lan).first() is None:\n                    a.translations.add(l)\n            else:\n                logger.debug(f'Ignoring language {lan} because its not added as a possible AssetTranslation')\n\n    aa = AssetAlias(slug=slug, asset=a)\n    aa.save()\n\n\ndef sync_with_github(asset_slug, author_id=None):\n\n    try:\n\n        asset = Asset.objects.filter(slug=asset_slug).first()\n        if asset is None:\n            raise Exception(f'Asset with slug {asset_slug} not found when attempting to sync with github')\n\n        if asset.author is not None:\n            author_id = asset.author.id\n\n        if author_id is None:\n            raise Exception(\n                f'System does not know what github credentials to use to retrive asset info for: {asset_slug}'\n            )\n\n        org_name, repo_name = get_url_info(asset.url)\n\n        credentials = CredentialsGithub.objects.filter(user__id=author_id).first()\n        if credentials is None:\n            raise Exception(\n                f'Github credentials for this user {author_id} not found when sync asset {asset_slug}')\n\n        g = Github(credentials.token)\n        repo = g.get_repo(f'{org_name}/{repo_name}')\n        readme_file = repo.get_contents('README.md')\n\n        learn_file = None\n        try:\n            learn_file = repo.get_contents('learn.json')\n        except:\n            try:\n                learn_file = repo.get_contents('.learn/learn.json')\n            except:\n                try:\n                    learn_file = repo.get_contents('bc.json')\n                except:\n                    try:\n                        learn_file = repo.get_contents('.learn/bc.json')\n                    except:\n                        raise Exception('No configuration learn.json or bc.json file was found')\n\n        asset.readme = str(readme_file.content)\n\n        if learn_file is not None:\n            config = json.loads(learn_file.decoded_content.decode('utf-8'))\n            asset.config = config\n            if 'title' in config:\n                asset.title = config['title']\n            if 'description' in config:\n                asset.description = config['description']\n\n            if 'preview' in config:\n                asset.preview = config['preview']\n            else:\n                raise Exception(f'Missing preview URL')\n\n            if 'video-id' in config:\n                asset.solution_video_url = 'https://www.youtube.com/watch?v=' + str(config['video-id'])\n                asset.with_video = True\n\n            if 'duration' in config:\n                asset.duration = config['duration']\n            if 'difficulty' in config:\n                asset.difficulty = config['difficulty']\n            if 'solution' in config:\n                asset.solution = config['solution']\n                asset.with_solutions = True\n\n            if 'language' in config:\n                asset.lang = config['language']\n            elif 'syntax' in config:\n                asset.lang = config['syntax']\n\n            if 'translations' in config:\n                for lang in config['translations']:\n                    language = AssetTranslation.objects.filter(slug__iexact=lang).first()\n                    if language is None:\n                        raise Exception(f\"Language '{lang}' not found\")\n                    asset.translations.add(language)\n\n            if 'technologies' in config:\n                for tech_slug in config['technologies']:\n                    _slug = slugify(tech_slug)\n                    technology = AssetTechnology.objects.filter(slug__iexact=_slug).first()\n                    if technology is None:\n                        technology = AssetTechnology(slug=_slug, title=tech_slug)\n                        technology.save()\n                    asset.technologies.add(technology)\n\n        asset.status_text = 'Successfully Synched'\n        asset.status = 'OK'\n        asset.save()\n    except Exception as e:\n        asset.status_text = str(e)\n        asset.status = 'ERROR'\n        asset.save()\n        logger.error(f'Error updating {asset.slug} from github: ' + str(e))\n\n    logger.debug(f'Successfully re-synched asset {asset_slug} with github')\n    return asset.status\n\n\ndef get_url_info(url: str):\n    last_slash_index = url.rfind('/')\n    last_suffix_index = url.rfind('.git')\n    if last_suffix_index < 0:\n        last_suffix_index = len(url)\n\n    if last_slash_index < 0 or last_suffix_index <= last_slash_index:\n        raise Exception('Badly formatted url {}'.format(url))\n\n    repo_name_position = last_slash_index + 1\n    repo_name = url[repo_name_position:last_suffix_index]\n\n    last_slash_index = url[0:repo_name_position - 2].rfind('/')\n    org_name = url[last_slash_index + 1:repo_name_position - 1]\n\n    return org_name, repo_name\n","sub_path":"breathecode/registry/actions.py","file_name":"actions.py","file_ext":"py","file_size_in_byte":6628,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"520683626","text":"\n\nfrom xai.brain.wordbase.nouns._subcompact import _SUBCOMPACT\n\n#calss header\nclass _SUBCOMPACTS(_SUBCOMPACT, ):\n\tdef __init__(self,): \n\t\t_SUBCOMPACT.__init__(self)\n\t\tself.name = \"SUBCOMPACTS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"subcompact\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_subcompacts.py","file_name":"_subcompacts.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"425578151","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nimport os\nimport pickle\nimport argparse\nimport _init_paths\nimport numpy as np\nfrom utils.config import cfg\nfrom models.train_Solver_HICO import train_net\nfrom networks.SIGAN_HICO import SIGAN\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n\n\ndef parse_args():\n    parser = argparse.ArgumentParser(description='Train an iCAN on VCOCO')\n    parser.add_argument('--num_iteration', dest='max_iters', help='Number of iterations to perform', default=300000, type=int)\n    parser.add_argument('--model', dest='model', help='Select model', default='SIGCN_HICO', type=str)\n    parser.add_argument('--Pos_augment', dest='Pos_augment',\n                        help='Number of augmented detection for each one. (By jittering the object detections)',\n                        default=30, type=int)\n    parser.add_argument('--Neg_select', dest='Neg_select', help='Number of Negative example selected for each image',\n                        default=60, type=int)\n    parser.add_argument('--Restore_flag', dest='Restore_flag', help='How many ResNet blocks are there?', default=5, type=int)\n    parser.add_argument('--skebox', dest='use_skebox', help='use_skebox or not', action='store_true', default=False)\n    parser.add_argument('--bp', dest='use_bodypart', help='use_bodypart or not', action='store_true', default=False)\n    parser.add_argument('--pm', dest='use_pm', help='use posemap or not', action='store_true', default=False)\n    parser.add_argument('--u', dest='use_u', help='use union box or not', action='store_true', default=False)\n    parser.add_argument('--sg', dest='use_sg', help='use posegraph or not', action='store_true', default=False)\n    parser.add_argument('--sg_att', dest='use_sg_att', help='use Spatial posegraph or not', action='store_true', default=False)\n    parser.add_argument('--ag', dest='use_ag', help='use Appearance posegraph or not', action='store_true', default=False)\n    parser.add_argument('--ag_att', dest='use_ag_att', help='use posegraph or not', action='store_true', default=False)\n    parser.add_argument('--bi', dest='use_binary', help='use binary or not', action='store_true', default=False)\n    parser.add_argument('--Hsolo', dest='use_Hsolo', help='use Hsolo or not', action='store_true', default=False)\n    parser.add_argument('--tmp', dest='tmp', help='use tmp or not', action='store_true', default=False)\n    parser.add_argument('--cuda', dest='cuda', help='cuda device id', default='0', type=str)\n    args = parser.parse_args()\n    return args\n\n\nif __name__ == '__main__':\n    args = parse_args()\n    if args.tmp:\n        Trainval_GT = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_HICO_with_pose_tmp.pkl', \"rb\"))\n        Trainval_N = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_Neg_HICO_with_pose_tmp.pkl', \"rb\"))\n    else:\n        Trainval_GT = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_Part_GT_HICO_with_pose.pkl', \"rb\"))\n        Trainval_N = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_Neg_HICO_with_pose.pkl', \"rb\"))\n\n    np.random.seed(cfg.RNG_SEED)\n    if cfg.TRAIN_MODULE_CONTINUE == 1:\n        weight = cfg.ROOT_DIR + '/Weights/{:s}/HOI_iter_420000.ckpt'.format(args.model)  # from ckpt which you wish to continue\n    else:\n        weight = cfg.ROOT_DIR + '/Weights/res50_faster_rcnn_iter_1190000.ckpt'  # only restore ResNet weights\n\n    # output directory where the models are saved\n    output_dir = cfg.ROOT_DIR + '/Weights/' + args.model + '/'\n\n    print('Use skebox: ' + str(args.use_skebox))\n    print('Use bodypart: ' + str(args.use_bodypart))\n    print('Use posemap: ' + str(args.use_pm))\n    print('Use union box: ' + str(args.use_u))\n    print('Use S graph: ' + str(args.use_sg))\n    print('Use S graph att: ' + str(args.use_sg_att))\n    print('Use A graph: ' + str(args.use_ag))\n    print('Use A graph att: ' + str(args.use_ag_att))\n    print('Use binary: ' + str(args.use_binary))\n    print('Use Hsolo: ' + str(args.use_Hsolo))\n    print('Pose norm:' + str(cfg.POSENORM))\n\n    net = SIGAN(use_skebox=args.use_skebox, use_bodypart=args.use_bodypart,\n                use_pm=args.use_pm, use_u=args.use_u,\n                use_sg=args.use_sg, use_sg_att=args.use_sg_att,\n                use_ag=args.use_ag, use_ag_att=args.use_ag_att,\n                use_binary=args.use_binary, use_Hsolo=args.use_Hsolo)\n\n    train_net(net, Trainval_GT, Trainval_N, output_dir, weight,\n              args.Pos_augment, args.Neg_select, args.Restore_flag,\n              max_iters=args.max_iters)\n","sub_path":"tools/Train_HICO.py","file_name":"Train_HICO.py","file_ext":"py","file_size_in_byte":4554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"246608918","text":"from aip import *\nimport os\n\n\nAPP_ID = '10379743'\nAPI_KEY = 'QGGvDG2yYiVFvujo6rlX4SvD'\nSECRET_KEY = 'PcEAUvFO0z0TyiCdhwrbG97iVBdyb3Pk'\n\n\n\nclass OcrManager(object):\n\n    def __init__(self):\n        self.aipOcr = AipOcr( APP_ID, API_KEY, SECRET_KEY )\n        self.options = \\\n            {   'detect_direction' : 'true'\n                # , 'language_type' : 'CHN_ENG'\n            }\n\n\n    def get_file_content( self, filePath ) :\n        with open( filePath, 'rb' ) as fp :\n            return fp.read( )\n\n\n    def BD_DecodeImage(self, local_file:str) -> str:\n        result = \\\n        self.aipOcr.webImage( self.get_file_content( local_file ), self.options )\n        return result\n\n\nif __name__ ==\"__main__\":\n    ocr_mgr = OcrManager()\n    img_dir = os.path.join(os.getcwd(), \"content\")\n    for img_file in os.listdir( img_dir):\n        img_path = os.path.join(img_dir, img_file)\n        txt = ocr_mgr.BD_DecodeImage( img_path )\n        print( txt )\n\n\n","sub_path":"Ocr.py","file_name":"Ocr.py","file_ext":"py","file_size_in_byte":950,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"273611964","text":"import pymongo \nimport os\nimport datetime\nfrom recent_date import get_default_day\nfrom filter import slang_filter\nfrom post_wash import post_wash\n# 디버깅을 위한 코드, 추후에 전체 수정 필요\nt = datetime.datetime.now()\nfilter_list = [\"페미\",\"냄져\",\"한남\",\"자댕이\",'조팔',\"씨발\",'섹스','개년','개새끼',\n'씹','셋스','느개비','좆','노무현',\"느개비\",'느금마','니애미','빠구리','시발년'\n,'시발새끼','느그앰','느그미','노무혐','빠9리','시발롬','시발련','창년','보빨러','사까시'\n,'걸레년','걸레련','보빨','4카시','사카시','봊','보전깨','니미','오피누','오피녀','고무통'\n,'이기야','놈딱','북딱','지잡','십색기','십색갸','십색꺄']\n\n#확장 필요\ndb_name = 'pookle'\nip = 'localhost'\nport = 27017\n\ndef db_manage(mode, coll_name = None, doc = None, is_first = None):\n\tdb = db_access()\n\tif coll_name != None and mode != \"renewal_date\":\n\t\tcoll = db[coll_name]\n\telse:\n\t\tcoll = db['recent_date']\n\t\n\n\tif mode == \"add\":\n\t\taddok = 0\n\t\tfor i in doc:\n\t\t\tcnt = 0\n\t\t\tfor j in coll.find({'title':i['title']},\\\n\t\t\t\t\t\t\t\t\t{'_id':0,'title':1}).\\\n\t\t\t\t\t\t\t\t\t\tsort([(\"date\", -1)]):\n\t\t\t\tif i['title'] == j['title']:\n\t\t\t\t\tcnt = 1\n\t\t\t\t\tprint(\"중복처리: \",i['title'])\n\t\t\t\t\tbreak\n\n\t\t\tfor j in filter_list:\n\t\t\t\tif i[\"title\"].find(j) != -1:\n\t\t\t\t\tprint(\"부적합 게시물: \",i['title'],\":\", j)\n\t\t\t\t\tcnt = 1\n\t\t\t\t\tbreak\n\t\t\t\telif i[\"post\"] != 0 and  i[\"post\"] != 1 :\n\t\t\t\t\tif i['post'].find(j) != -1:\n\t\t\t\t\t\tprint(\"부적합 게시물: \",i['title'],\":\", j)\n\t\t\t\t\t\tcnt = 1\n\t\t\t\t\t\tbreak\n\n\t\t\tif '디시인사이드' in i['tag'] and slang_filter(i['title'] + i['post']):\n\t\t\t\tprint(\"부적합 게시물2: \",i['title'],\":\")\n\t\t\t\tcnt = 1\n\n\t\t\tif cnt == 0:\n\t\t\t\t# i 변수가 하나의 게시물\n\t\t\t\t# 여기다가 추가하고픈 게시물의 원하는 칼럼 붙여넣으면 됨 \n\t\t\t\t# ex) i.update({\"추가할 항목\":\"추가할 값\"})\n\t\t\t\ti.update({\"fav_cnt\":0})\n\t\t\t\ti.update({\"view\":0})\n\t\t\t\taddok += 1\n\t\t\t\tif  i[\"post\"] != 0 and  i[\"post\"] != 1:\n\t\t\t\t\ti['post'] = post_wash(i['post'])\n\t\t\t\tcoll.insert(i)\n\t\t\telse:\n\t\t\t\tcontinue\n\n\t\treturn addok\n\n\n\telif mode == \"renewal_date\":\n\t\tif is_first == True:\n\t\t\tdb['recent_date'].insert(doc)\n\t\telse:\n\t\t\tdb['recent_date'].update({\"name\":coll_name}, doc)\n\n\n\telif mode == \"get_recent\":\n\t\treturn db['recent_date'].find_one({\"name\":coll_name})\n\n\n\telif mode ==\"old_remove\":\n\t\tday = get_default_day(365)\n\n\t\tfor col in db.collection_names():\n\t\t\tif col.startswith(\"PK_\") \\\n\t\t\tand col != \"PK_etc\" and col != \"PK_pknu_lecture\":\n\t\t\t\tdb[col].remove({\"date\":{\"$lt\":day}})\n\n\ndef db_access():\n\tclient = pymongo.MongoClient(ip,port)\n\tdb = client[db_name]\n\treturn db\n\n\t\n\t\n\t \t\n\n\t \n\n\n\n","sub_path":"src/db_manager.py","file_name":"db_manager.py","file_ext":"py","file_size_in_byte":2692,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"390209276","text":"N = int(input())\nfor c in range(N):\n    X = int(input())\n    primo = 0\n    for d in range(1, X+1):\n        if X % d == 0:\n            primo += 1\n    if primo == 2:\n        print(X, \"eh primo\")\n    else:\n        print(X, \"nao eh primo\")\n","sub_path":"1165 - Número Primo.py","file_name":"1165 - Número Primo.py","file_ext":"py","file_size_in_byte":236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"106918396","text":"class laurent:\n\n    def __init__(self, powers):\n        self.powers = powers\n\n    def __add__(self, other):\n        summed = {exp : self.powers.get(exp, 0) + other.powers.get(exp, 0) for exp in set(self.powers) | set(other.powers)}\n        return laurent(summed)\n\n    def __mul__(self, other):\n        mult = {}\n        for j in list(self.powers):\n            for k in list(other.powers):\n                mult[j+k] = mult.get(j+k, 0) + self.powers[j]*other.powers[k]\n        return laurent(mult)\n    \ndef toText(poly, var = 't'):\n    text = ''\n    for exp in sorted(poly.powers):\n        if poly.powers[exp] != 0:\n            if poly.powers[exp] < 0:\n                text = text + ' - '\n            elif poly.powers[exp] > 0 and text != '':\n                text = text + ' + '\n            if exp == 0:\n                text = text + '1'\n            elif abs(poly.powers[exp]) > 1:\n                text = text + str(abs(poly.powers[exp])) + '*' + var + '^' + str(exp)\n            else:\n                text = text + var + '^' + str(exp)\n    if text == '':\n        text = '0'\n    if text[0] == ' ':\n        text = text[1:]\n    return text\n\ndef substitution(poly):\n    substPowers = {exp/4 : poly.powers[exp] for exp in poly.powers.keys()}\n    return laurent(substPowers)\n\ndef power(poly, exp):\n    res = one\n    for i in range(exp):\n        res = res*poly\n    return res\n\n# constants\nA = laurent({1 : 1})\nB = laurent({-1 : 1})\nC = laurent({2 : -1, -2 : -1})\none = laurent({0 : 1})\n        \n    \n","sub_path":"laurent.py","file_name":"laurent.py","file_ext":"py","file_size_in_byte":1492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"530139796","text":"from __future__ import print_function\nfrom processors import Processors\nfrom settings import endpoints, path, recipients2\nimport sys, os, requests\n\n\nclass StockPrices(Processors):\n    def __init__(self, _from, _to):\n        self._from = _from\n        self._to = _to\n        self.count = 0\n        self.ticker_processed = 0\n        Processors.__init__(self)\n\n    def main_function(self):\n        log_path = path + '/logs/prices_{}.log'.format(self.log_date)\n\n        with open(log_path, 'w') as self.fh, requests.Session() as self.session:        \n            self.conn, self.cur = self.mysql_connect()\n            self.get_dates()\n            self.get_tickers()\n            self.get_prices()\n            self.conn.commit()\n            self.conn.close()\n\n    def ticker_exceptions(self, ticker):\n        if ticker == 'GB':\n            return 'ITGR'\n        elif ticker == 'GMT':\n            return 'GMTA'\n        elif ticker == 'IACI':\n            return 'IAC'\n        elif ticker == 'JDSU':\n            return 'VIAV'\n        elif ticker == 'LG':\n            return 'SR'\n        elif ticker == 'MHFI':\n            return 'SPGI'\n        elif ticker == 'OSGB':\n            return 'OSG'\n        elif ticker == 'SLTC':\n            return 'DTRM'\n        elif ticker == 'SPW':\n            return 'SPXC'\n        else:\n            return ticker\n\n    def get_tickers(self):\n        tickers = []\n\n        #with requests.Session() as self.session:\n        data = self.send_request(self.company_url, 'company')\n        self.company_total = data['result_count']\n       \n        urls = [self.company_url + '?page_number={}'.format(x) for x in range(2, data['total_pages']+1)]\n        urls += [self.company_url]\n\n        for url in urls:\n            data = self.send_request(url, 'company')\n\n            for d in data['data']:\n                if d['ticker'] == None:\n                    continue\n                else:\n                    tickers.append(d['ticker'])\n\n        self.urls = [(self.price_url.format(ticker, self._from, self._to), ticker) for ticker in tickers]\n        self.total = len(self.urls)\n\n    def get_prices(self):\n        count = 0\n\n        #with requests.Session() as self.session:\n        for row in self.urls:\n            self.get_prices2(row)\n            count += 1\n            print(self.total, count, row[1])\n\n    def get_prices2(self, t):\n        url = t[0]\n        ticker = self.ticker_exceptions(t[1])\n\n        self.ticker_processed += 1\n        data = self.send_request(url, 'price')\n\n        try:\n            message = data['errors']\n            print(message)\n            self.send_email(str(message), recipients2)\n            os._exit(1)\n        except:\n            for d in data['data']:\n                self._mysql(d, ticker)\n\n                if data['total_pages'] > 1:\n                    for x in range(2, data['total_pages'] + 1):\n                        _url = url + '&page_number={}'.format(x)\n                        data = self.send_request(_url, 'price')\n                        self.count += 1\n                        for d in data['data']:\n                            self._mysql(d, ticker)\n\n    def report(self):\n        self.rep = \"Stock prices: {}.\\n\\n\".format(self.count)\n        print(self.rep)\n\n    def clear_data(self, v):\n        if v == 'None' or v == 'True' or v == 'False' or v == 'true' or v == 'false' or v == 'none':\n            return 0\n        else:\n            return v\n\n    def _mysql(self, d, ticker):\n        self.count += 1\n\n        try:\n            ex_dividend = self.clear_data(d['ex_dividend'])\n        except:\n            ex_dividend = 0\n        try:\n            volume = self.clear_data(d['volume'])\n        except:\n            volume = 0\n        try:\n            adj_low = self.clear_data(d['adj_low'])\n        except:\n            adj_low = 0\n        try:\n            adj_open = self.clear_data(d['adj_open'])\n        except:\n            adj_open = 0\n        try:\n            adj_close = self.clear_data(d['adj_close'])\n        except:\n            adj_close = 0\n        try:\n            high = self.clear_data(d['high'])\n        except:\n            high = 0\n        try:\n            adj_volume = self.clear_data(d['adj_volume'])\n        except:\n            adj_volume = 0\n        try:\n            low = self.clear_data(d['low'])\n        except:\n            low = 0\n        try:\n            date = d['date']\n        except:\n            date = None\n        try:\n            close = self.clear_data(d['close'])\n        except:\n            close = 0\n        try:\n            _open = self.clear_data(d['open'])\n        except:\n            _open = 0\n        try:\n            adj_high = self.clear_data(d['adj_high'])\n        except:\n            adj_high = 0\n        try:\n            split_ratio = self.clear_data(d['split_ratio'])\n        except:\n            split_ratio = 0\n\n        sql = \"\"\"REPLACE INTO stock_prices(\n            ex_dividend,\n            volume,\n            adj_low,\n            adj_open,\n            adj_close,\n            high,\n            adj_volume,\n            low,\n            date,\n            close,\n            open,\n            adj_high,\n            split_ratio,\n            ticker\n        ) VALUES (\"{}\", \"{}\", \"{}\", \"{}\", \"{}\", \"{}\", \"{}\", \"{}\", \"{}\", \"{}\", \"{}\", \"{}\", \"{}\", \"{}\")\"\"\".format(\n            ex_dividend,\n            volume,\n            adj_low,\n            adj_open,\n            adj_close,\n            high,\n            adj_volume,\n            low,\n            date,\n            close,\n            _open,\n            adj_high,\n            split_ratio,\n            ticker\n        )\n\n        try:\n            self.cur.execute(sql)\n        except Exception as e:\n            self.fh.write('{}\\n{}\\n\\n'.format(sql, e))\n            print(e, '\\n', sql)\n\n\nif __name__ == \"__main__\":\n    _from = sys.argv[1]\n    _to = sys.argv[2]\n\n    StockPrices(_from, _to)\n\n","sub_path":"python/archive/python/load_stock_prices.py","file_name":"load_stock_prices.py","file_ext":"py","file_size_in_byte":5850,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"523885360","text":"import logging\r\n\r\nfrom celery.bin.worker import worker as celery_worker\r\nimport click\r\nfrom sqlalchemy.sql import func\r\n\r\nfrom scoreengine import (\r\n    celery_app,\r\n    config,\r\n    models,\r\n    runner,\r\n    utils,\r\n)\r\n\r\n\r\ncli = click.Group()\r\n\r\n\r\n@cli.group()\r\ndef db():\r\n    \"\"\"Database queries (including initialization).\"\"\"\r\n\r\n\r\n@db.command()\r\ndef init():\r\n    \"\"\"Initialize the database.\"\"\"\r\n    logging.getLogger('scoreengine').setLevel(config['logging']['level'])\r\n    utils.init_db_from_config()\r\n\r\n\r\n@db.command()\r\ndef list_services():\r\n    \"\"\"List configured services.\"\"\"\r\n    with utils.session_scope() as session:\r\n        services = session.query(models.Service).all()\r\n        for service in services:\r\n            click.echo(service)\r\n\r\n\r\n@db.command()\r\ndef list_teams():\r\n    \"\"\"List configured teams.\"\"\"\r\n    with utils.session_scope() as session:\r\n        teams = session.query(models.Team).all()\r\n        for team in teams:\r\n            click.echo(team)\r\n\r\n\r\n@cli.command()\r\n@click.option('--use-task-queue/--no-task-queue',\r\n              help='Whether or not a task queue (Celery) should be used.')\r\n@click.option('--reset', is_flag=True,\r\n              help='Discard all previous rounds and checks.')\r\n@click.option('--resume', is_flag=True,\r\n              help='Continue where the last round stopped.')\r\n@click.option('--start-round', default=1, type=click.IntRange(min=1),\r\n              help='Round number to start checks at.')\r\ndef run(use_task_queue, reset, resume, start_round):\r\n    \"\"\"Perform scheduling of round checks for scoring.\"\"\"\r\n    logging.getLogger('scoreengine').setLevel(config['logging']['level'])\r\n\r\n    if reset:\r\n        with utils.session_scope() as session:\r\n            session.query(models.Round).delete()\r\n\r\n    if resume:\r\n        with utils.session_scope() as session:\r\n            max_round = session.query(func.max(models.Round.number)).first()[0]\r\n        if max_round is not None:\r\n            start_round = max_round + 1\r\n\r\n    runner.Runner(start_round).run(use_task_queue=use_task_queue)\r\n\r\n\r\n@cli.command()\r\ndef worker():\r\n    \"\"\"Process checks on the Celery task queue.\"\"\"\r\n    celery_app.autodiscover_tasks(['scoreengine.tasks'])\r\n    celery_worker(app=celery_app).run(**config['celery']['worker'])\r\n\r\n\r\n@cli.command()\r\n@click.option('--service', '-s', 'services', type=int, multiple=True,\r\n              help='ID of service to be checked.')\r\n@click.option('--team', '-t', 'teams', type=int, multiple=True,\r\n              help='ID of team to be checked.')\r\ndef check(services, teams):\r\n    \"\"\"Perform one-time checks as a dry-run (without the task queue).\"\"\"\r\n    results = runner.perform_round_checks(\r\n        current_round=None,\r\n        use_task_queue=False,\r\n        only_enabled=False,\r\n        service_ids=services,\r\n        team_ids=teams,\r\n    )\r\n    for result in results:\r\n        passed = result['passed']\r\n        click.secho(\r\n            '{result}:\\t{team:15} | {service:20} | {check}'.format(\r\n                check='{}.{}'.format(\r\n                    result['check']['file_name'],\r\n                    result['check']['function_name']\r\n                ),\r\n                result='Pass' if passed else 'Fail',\r\n                service='[#{}] {}'.format(\r\n                    result['service_id'],\r\n                    result['service_name'],\r\n                ),\r\n                team='[#{}] {}'.format(\r\n                    result['team_id'],\r\n                    result['team_name'],\r\n                ),\r\n            ),\r\n            fg='green' if passed else 'red'\r\n        )\r\n        if not passed:\r\n            click.echo('\\n'.join(result['output']))\r\n","sub_path":"scoreengine/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":3636,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"526887866","text":"from tree_data import FileSystemTree\nfrom population import PopulationTree\nfrom treemap_visualiser import run_visualisation, run_treemap_population\n\nPATH = r'C:\\Tcwin45'\n\ndef _sort_subtrees(tree):\n    \"\"\"Sort the subtrees of <tree> in alphabetical order.\n\n    This is recursive, and affects all levels of the tree.\n\n    @type tree: AbstractTree\n    @rtype: None\n    \"\"\"\n    if not tree.is_empty():\n        for subtree in tree._subtrees:\n            _sort_subtrees(subtree)\n\n        tree._subtrees.sort(key=lambda t: t._root)\n\nif __name__ == '__main__':\n    file_tree = FileSystemTree(PATH)\n    _sort_subtrees(file_tree)\n    #pop_tree = PopulationTree(True)\n    #_sort_subtrees(pop_tree)\n    for i in file_tree.generate_treemap((0, 0, 1024, 768-30)):\n        print(i[0])\n    run_visualisation(file_tree)","sub_path":"tester.py","file_name":"tester.py","file_ext":"py","file_size_in_byte":802,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"242976929","text":"__author__ = 'Filippo'\n\n''' Only top\nimport csv\n\nartistsPerYear = {}\n\nfor i in range(1940,2011):\n    with open(\"../data/artistsByYear/\"+str(i)+'artist.csv', 'rb') as csvfile:\n        year=[]\n        spamreader = csv.reader(csvfile, delimiter=',', quotechar='\"')\n        spamreader.next()\n        for row in spamreader:\n            if row[0] != \"Artist\":\n                year.append({\"name\":row[0].decode('latin-1').encode(\"utf-8\"),\"value\":row[1]})\n        artistsPerYear[i]=year\n\n\ndecadeForTop = {}\ntopTen = []\nfor i in [1940,1950,1960,1970,1980,1990,2000]:\n    with open(\"../data/artistsByDecade/\"+str(i)+'sArtist.csv', 'rb') as csvfile:\n        spamreader = csv.reader(csvfile, delimiter=',', quotechar='\"')\n        spamreader.next()\n        j = 0\n        for row in spamreader:\n            j+=1\n            if j<11:\n                topTen.append(row[0])\n                decadeForTop[row[0]]=i\n\nprint topTen\nprint len(topTen)\n\n\nres=[]\nperArtist = {}\n\nfor i in artistsPerYear.keys():\n    yearArray = artistsPerYear[i]\n    for art in yearArray:\n        if art[\"name\"] in topTen:\n            if not(art[\"name\"] in perArtist.keys()):\n                perArtist[art[\"name\"]]=[]\n            res.append({\"name\":art[\"name\"],\"value\":art[\"value\"],\"date\":i,\"decade\":decadeForTop[art[\"name\"]]})\n\nfor i in range(1940,2011):\n    for j in topTen:\n        alreadyThere = False\n        for k in res:\n            if (k[\"date\"]==i and k[\"name\"]==j):\n                alreadyThere = True\n                break\n        if (not alreadyThere):\n            res.append({\"name\":j,\"value\":\"0\",\"date\":i,\"decade\":decadeForTop[j]})\n\ndef comparer(x,y):\n    return (topTen.index(x[\"name\"]) - topTen.index(y[\"name\"]))*100 - (y[\"date\"]-x[\"date\"])\n\norderedRes = sorted(res,cmp = comparer)\n'''\n'''\nfor i in perArtist.keys():\n    res.append(perArtist[i])\n'''\nimport csv\n\nartistsPerYear = {}\n\nfor i in range(1940,2011):\n    with open(\"../data/artistsByYear/\"+str(i)+'artist.csv', 'rb') as csvfile:\n        year=[]\n        spamreader = csv.reader(csvfile, delimiter=',', quotechar='\"')\n        spamreader.next()\n        for row in spamreader:\n            if row[0] != \"Artist\":\n                year.append({\"name\":row[0].decode('latin-1').encode(\"utf-8\"),\"value\":row[1]})\n        artistsPerYear[i]=year\n\nallData = {}\n\nfor year in artistsPerYear.keys():\n    for t in artistsPerYear[year]:\n        if t[\"name\"] in allData.keys():\n            allData[t[\"name\"]].append({\"year\":year,\"value\":t[\"value\"]})\n        else:\n            allData[t[\"name\"]] = [{\"year\":year,\"value\":t[\"value\"]}]\n\n\nimport json\nwith open(\"../data/allArtistsStaticData\", 'w') as csvfile:\n    json.dump(allData,csvfile)\n","sub_path":"script/artistArrayCreator.py","file_name":"artistArrayCreator.py","file_ext":"py","file_size_in_byte":2647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"236708990","text":"from mcpi.minecraft import Minecraft\r\nmc = Minecraft.create()\r\nx,y,z=mc.player.getTilePos()\r\ndef Tree (x,y,z):\r\n    mc.setBlocks(x-1,y,z-1,x,y+5,z,17)\r\n    mc.setBlocks(x-3,y+4,z-3,x+2,y+7,z+2,161)\r\n    \r\n    \r\nfor i in range(10):\r\n    for f in range(10):\r\n        for l in range(10):\r\n        \r\n            Tree(x+i*5,y+i*f,z+i*l)\r\n        \r\n\r\n        \r\n        \r\n        \r\n        \r\n              ","sub_path":"26.py","file_name":"26.py","file_ext":"py","file_size_in_byte":399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"226181159","text":"from collections import defaultdict\nimport requests\n\ntry:\n    import simplejson as json \nexcept ImportError:\n    import json\n\ndef tree():\n    return defaultdict(tree)\n\nsentilo = \"http://sistemic.udea.edu.co:9091\"\nkey = \"2362b24b814b12688f7d9d705bce6e283e9ddba47c519c61d80035d22b91afeb\"\nproveedor = \"provider2\"\ncomponente = \"component2\"\n\ndef enviar_dato(sensor, dato, tiempo=\"\", posicion=\"\", detallado = False):\n    headers = {'IDENTITY_KEY': key, 'Content-Type': 'application/json'}\n    \n    if detallado:\n        url = sentilo + '/data/' + proveedor + '/' + sensor\n        # payload\n        root = tree()\n        obs = tree()\n        #observation\n        obs[\"value\"] = dato\n        obs[\"timestamp\"] = tiempo \n        obs[\"location\"]  = posicion \n        root[\"observations\"] = [obs]\n        payload = json.dumps(root, ensure_ascii=False).encode('utf8')\n        # end payload\n        try:\n            response = requests.put(url= url, verify=False, data = payload, headers=headers)\n            if response.status_code in (201, 200):\n                print(\"dato enviado\")\n            else:\n                return payload\n        except requests.exceptions.RequestException as e:\n            return \"error de conexión: {}\".format(e)\n    else:\n        try:\n            url = sentilo + '/data/' + proveedor + '/' + sensor + '/' + str(dato) \n            response = requests.put(url= url, verify=False, headers=headers)\n            if response.status_code in (201, 200):\n                print(\"dato enviado\")\n            else:\n                return \"verifique los argumentos\"\n        except requests.exceptions.RequestException as e:\n            return \"error de conexión: {}\".format(e)\n\ndef suscribir_muestra(sensor, reenvio=\"localhost\"):\n    headers = {'IDENTITY_KEY': key, 'Content-Type': 'application/json'}\n    url = sentilo + '/subscribe/data/' + proveedor + '/' + sensor\n    # payload\n    root = tree()\n    #observation\n    root[\"endpoint\"] = reenvio\n    payload = json.dumps(root, ensure_ascii=False).encode('utf8')\n    # end payload\n    try:\n        response = requests.put(url= url, verify=False, data = payload, headers=headers)\n        if response.status_code in (201, 200):\n            print(\"suscripción realizada\")\n        else:\n           print(response)\n    except requests.exceptions.RequestException as e:\n        return \"error de conexión: {}\".format(e)\n\ndef orden_sentilo(sensor, order=\"run\"):\n    headers = {'IDENTITY_KEY': key, 'Content-Type': 'application/json'}\n    url = sentilo + '/order/' + proveedor + '/' + sensor\n    # payload\n    root = tree()\n    #observation\n    root[\"order\"] = order\n    payload = json.dumps(root, ensure_ascii=False).encode('utf8')\n    # end payload\n    try:\n        response = requests.put(url= url, verify=False, data = payload, headers=headers)\n        if response.status_code in (201, 200):\n            print(\"orden enviada\")\n        else:\n           print(response)\n    except requests.exceptions.RequestException as e:\n        return \"error de conexión: {}\".format(e)","sub_path":"Semillero/sentilo/sentilo.py","file_name":"sentilo.py","file_ext":"py","file_size_in_byte":3027,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"614246731","text":"import torch\nimport torch.nn.functional as F\nimport torch.nn as nn\nimport numpy as np\nimport cv2\n\ndef save_attention_map(model, testing_rgb, testing_lidar, testing_mask):\n    x_global, x_local, global_attn, local_attn = model(testing_rgb, testing_lidar, testing_mask)\n    predict_depth = get_predicted_depth(x_global, x_local, global_attn, local_attn)\n    \n    b, _, h, w = x_global.size()\n\n    pred_attn = torch.zeros((b, 2, h, w)).to(x_global.device)#torch.zeros_like(color_path_dense) # b x 2 x 128 x 256\n    pred_attn[:, 0, :, :] = global_attn\n    pred_attn[:, 1, :, :] = local_attn\n    pred_attn = F.softmax(pred_attn, dim=1) # b x 2 x 128 x 256\n\n    global_attn, local_attn = pred_attn[:, 0, :, :], pred_attn[:, 1, :, :]\n    print(global_attn.size(), torch.max(global_attn), torch.min(global_attn))\n    print(x_global.size(), torch.max(x_global), torch.min(x_global))\n\n    cv2.imwrite(\"global.png\", x_global.squeeze(0).detach().numpy().transpose(1,2,0))\n    cv2.imwrite(\"local.png\", x_local.squeeze(0).detach().numpy().transpose(1,2,0))\n    cv2.imwrite(\"predict_depth.png\", predict_depth.squeeze(0).detach().numpy().transpose(1,2,0))\n    cv2.imwrite(\"global_attn.png\", global_attn.detach().numpy().transpose(1,2,0)*255)\n    cv2.imwrite(\"local_attn.png\", local_attn.detach().numpy().transpose(1,2,0)*255)\n    exit()\ndef get_predicted_depth(color_path_dense, normal_path_dense, color_attn, normal_attn):\n    \"\"\"Use raw model output to generate dense of color pathway, normal path way, and integrated result\n    Returns: predicted_dense, pred_color_path_dense, pred_normal_path_dense\n    \"\"\"\n    # get predicted dense depth from 2 pathways\n    b, _, h, w = color_path_dense.size()\n    pred_color_path_dense = color_path_dense[:, 0, :, :] # b x 128 x 256\n    pred_normal_path_dense = normal_path_dense[:, 0, :, :]\n\n    # get attention map of 2 pathways\n    color_attn = torch.squeeze(color_attn) # b x 128 x 256\n    normal_attn = torch.squeeze(normal_attn) # b x 128 x 256\n\n    # softmax 2 attention map\n    pred_attn = torch.zeros((b, 2, h, w)).to(color_path_dense.device)#torch.zeros_like(color_path_dense) # b x 2 x 128 x 256\n    pred_attn[:, 0, :, :] = color_attn\n    pred_attn[:, 1, :, :] = normal_attn\n    pred_attn = F.softmax(pred_attn, dim=1) # b x 2 x 128 x 256\n\n    color_attn, normal_attn = pred_attn[:, 0, :, :], pred_attn[:, 1, :, :]\n\n    # get predicted dense from weighted sum of 2 path way\n    predicted_dense = pred_color_path_dense * color_attn + pred_normal_path_dense * normal_attn # b x 128 x 256\n\n    predicted_dense = predicted_dense.unsqueeze(1)\n    pred_color_path_dense = pred_color_path_dense.unsqueeze(1) \n    pred_normal_path_dense = pred_normal_path_dense.unsqueeze(1)\n\n    return predicted_dense\n\ndef get_depth_and_normal(model, rgb, lidar, mask):\n    \"\"\"Given model and input of model, get dense depth and surface normal\n\n    Returns:\n    predicted_dense: b x c x h x w\n    pred_surface_normal: b x c x h x w\n    \"\"\"\n    model.eval()\n    with torch.no_grad():\n        x_global, x_local, global_attn, local_attn = model(rgb, lidar, mask)\n    predicted_dense = get_predicted_depth(x_global, x_local, global_attn, local_attn)\n\n    return predicted_dense\n\n\n\n\ndef normal_to_0_1(img):\n    \"\"\"Normalize image to [0, 1], used for tensorboard visualization.\"\"\"\n    return (img - torch.min(img)) / (torch.max(img) - torch.min(img))\n\n\ndef normal_loss(pred_normal, gt_normal, gt_normal_mask):\n    \"\"\"Calculate loss of surface normal (in the stage N)\n\n    Params:\n    pred: b x 3 x 128 x 256\n    normal_gt: b x 3 x 128 x 256\n    normal_mask: b x 3 x 128 x 256\n    \"\"\"\n\n    valid_mask = (gt_normal_mask > 0.0).detach()\n\n    #pred_n = pred.permute(0,2,3,1)\n    pred_normal = pred_normal[valid_mask]\n    gt_normal = gt_normal[valid_mask]\n\n    pred_normal = pred_normal.contiguous().view(-1,3)\n    pred_normal = F.normalize(pred_normal)\n    gt_normal = gt_normal.contiguous().view(-1, 3)\n\n    loss_function = nn.CosineEmbeddingLoss()\n    loss = loss_function(pred_normal, gt_normal, torch.Tensor(pred_normal.size(0)).to(pred_normal.device).fill_(1.0))\n    return loss\n\n\n\ndef get_depth_loss(dense, gt):\n    \"\"\"\n    dense: b x 1 x 128 x 256\n    c_dense: b x 1 x 128 x 256\n    n_dense: b x 1 x 128 x 256\n    gt: b x 1 x 128 x 256\n    params: b x 3 x 128 x 256\n    normals: b x 128 x 256 x 3\n    \"\"\"\n    valid_mask = (gt > 0.0).detach() # b x 1 x 128 x 256\n    gt = gt[valid_mask]\n    dense = dense[valid_mask]\n\n    criterion = nn.MSELoss()\n    loss = torch.sqrt(criterion(dense, gt))\n\n    \n    return loss\n\n\n\n\ndef get_loss(predicted_dense, gt_depth):\n    return get_depth_loss(predicted_dense, gt_depth)\n\n\n","sub_path":"training/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"183164450","text":"from tkinter import *\r\nimport time\r\nimport random\r\n\r\n\r\nwindow = Tk()\r\n\r\n\r\nrows = 30\r\ncols = 30\r\nplayer = (rows//2,cols//2)\r\nend = (cols-2,rows-2)\r\n\r\n\r\nWidth = 500/rows\r\n\r\nplatform = Canvas(window, width = cols*Width, height = rows*Width)\r\nplatform.pack()\r\n\r\ndef render_grid():\r\n    global rows, cols, Width, player, end\r\n    for i in range(cols):\r\n        for j in range(rows):\r\n            platform.create_rectangle(i*Width, j*Width,(i+1)*Width, (j+1)*Width, fill=\"yellow\", width = 1)\r\n\r\n    platform.create_rectangle(player[0]*Width, player[1]*Width,(player[0]+1)*Width, (player[1]+1)*Width, fill=\"white\", width = 1)\r\n    platform.create_rectangle(end[0]*Width, end[1]*Width,(end[0]+1)*Width, (end[1]+1)*Width, fill=\"green\", width = 1)\r\n\r\n\r\n\r\nrender_grid()\r\n\r\ndef render_trivial_path(path):\r\n    global Width\r\n    for i in path:\r\n        for (j,k) in i:\r\n            platform.create_rectangle(j * Width, k * Width, (j + 1) * Width, (k + 1) * Width, fill=\"red\", width=1)\r\n            time.sleep(0.0000000000001)\r\n\r\n\r\ndef render_path(path):\r\n    global Width\r\n    for (i,j) in path:\r\n        platform.create_rectangle(i*Width, j*Width,(i+1)*Width, (j+1)*Width, fill=\"blue\", width = 1)\r\n        time.sleep(0.1)\r\n\r\ndef start_game():\r\n    window.mainloop()","sub_path":"Board6.py","file_name":"Board6.py","file_ext":"py","file_size_in_byte":1253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"506293912","text":"'''\n123. Best Time to Buy and Sell Stock III \nDifficulty: Hard\nSay you have an array for which the ith element is the price of a given stock on day i.\nDesign an algorithm to find the maximum profit. You may complete at most two transactions.\n\nNote:\nYou may not engage in multiple transactions at the same time (ie, you must sell the stock before you buy again).\n'''\nclass Solution(object):\n    def maxProfit(self, prices):\n        \"\"\"\n        :type prices: List[int]\n        :rtype: int\n        \"\"\"\n        if prices is None or len(prices) == 0:\n            return 0\n        \n        left = [0 for i in range(len(prices))]\n        minLeft = prices[0]\n        for i in range(1, len(prices)):\n            minLeft = min(minLeft, prices[i])\n            left[i] = max(left[i-1], prices[i]-minLeft)\n        \n        right = [0 for i in range(len(prices))]\n        maxRight = prices[len(prices)-1]\n        for i in range(len(prices)-2, -1, -1):\n            maxRight = max(maxRight, prices[i])\n            right[i] = max(right[i+1], maxRight - prices[i])\n        \n        result = 0\n        for i in range(len(prices)):\n            result = max(left[i]+right[i], result)\n        \n        return result","sub_path":"common/best_time_to_buy_and_sell_stock_iii.py","file_name":"best_time_to_buy_and_sell_stock_iii.py","file_ext":"py","file_size_in_byte":1193,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"307012548","text":"import hashlib\nimport logging\nfrom functools import wraps\n\nfrom .index import RemoteIndex, RemoteIndexNoop\n\nlogger = logging.getLogger(__name__)\n\n\ndef index_locked(f):\n    @wraps(f)\n    def wrapper(obj, named_cache, remote, *args, **kwargs):\n        if hasattr(remote, \"index\"):\n            with remote.index:\n                return f(obj, named_cache, remote, *args, **kwargs)\n        return f(obj, named_cache, remote, *args, **kwargs)\n\n    return wrapper\n\n\nclass Remote:\n    \"\"\"Cloud remote class.\n\n    Provides methods for indexing and garbage collecting trees which contain\n    DVC remotes.\n    \"\"\"\n\n    INDEX_CLS = RemoteIndex\n\n    def __init__(self, tree):\n        self.tree = tree\n        self.repo = tree.repo\n\n        config = tree.config\n        url = config.get(\"url\")\n        if url:\n            index_name = hashlib.sha256(url.encode(\"utf-8\")).hexdigest()\n            self.index = self.INDEX_CLS(\n                self.repo, index_name, dir_suffix=self.tree.CHECKSUM_DIR_SUFFIX\n            )\n        else:\n            self.index = RemoteIndexNoop()\n\n    def __repr__(self):\n        return \"{class_name}: '{path_info}'\".format(\n            class_name=type(self).__name__,\n            path_info=self.tree.path_info or \"No path\",\n        )\n\n    @property\n    def cache(self):\n        return getattr(self.repo.cache, self.tree.scheme)\n\n    def hashes_exist(self, hashes, jobs=None, name=None):\n        \"\"\"Check if the given hashes are stored in the remote.\n\n        There are two ways of performing this check:\n\n        - Traverse method: Get a list of all the files in the remote\n            (traversing the cache directory) and compare it with\n            the given hashes. Cache entries will be retrieved in parallel\n            threads according to prefix (i.e. entries starting with, \"00...\",\n            \"01...\", and so on) and a progress bar will be displayed.\n\n        - Exists method: For each given hash, run the `exists`\n            method and filter the hashes that aren't on the remote.\n            This is done in parallel threads.\n            It also shows a progress bar when performing the check.\n\n        The reason for such an odd logic is that most of the remotes\n        take much shorter time to just retrieve everything they have under\n        a certain prefix (e.g. s3, gs, ssh, hdfs). Other remotes that can\n        check if particular file exists much quicker, use their own\n        implementation of hashes_exist (see ssh, local).\n\n        Which method to use will be automatically determined after estimating\n        the size of the remote cache, and comparing the estimated size with\n        len(hashes). To estimate the size of the remote cache, we fetch\n        a small subset of cache entries (i.e. entries starting with \"00...\").\n        Based on the number of entries in that subset, the size of the full\n        cache can be estimated, since the cache is evenly distributed according\n        to hash.\n\n        Returns:\n            A list with hashes that were found in the remote\n        \"\"\"\n        # Remotes which do not use traverse prefix should override\n        # hashes_exist() (see ssh, local)\n        assert self.tree.TRAVERSE_PREFIX_LEN >= 2\n\n        hashes = set(hashes)\n        indexed_hashes = set(self.index.intersection(hashes))\n        hashes -= indexed_hashes\n        logger.debug(\"Matched '{}' indexed hashes\".format(len(indexed_hashes)))\n        if not hashes:\n            return indexed_hashes\n\n        if len(hashes) == 1 or not self.tree.CAN_TRAVERSE:\n            remote_hashes = self.tree.list_hashes_exists(hashes, jobs, name)\n            return list(indexed_hashes) + remote_hashes\n\n        # Max remote size allowed for us to use traverse method\n        remote_size, remote_hashes = self.tree.estimate_remote_size(\n            hashes, name\n        )\n\n        traverse_pages = remote_size / self.tree.LIST_OBJECT_PAGE_SIZE\n        # For sufficiently large remotes, traverse must be weighted to account\n        # for performance overhead from large lists/sets.\n        # From testing with S3, for remotes with 1M+ files, object_exists is\n        # faster until len(hashes) is at least 10k~100k\n        if remote_size > self.tree.TRAVERSE_THRESHOLD_SIZE:\n            traverse_weight = (\n                traverse_pages * self.tree.TRAVERSE_WEIGHT_MULTIPLIER\n            )\n        else:\n            traverse_weight = traverse_pages\n        if len(hashes) < traverse_weight:\n            logger.debug(\n                \"Large remote ('{}' hashes < '{}' traverse weight), \"\n                \"using object_exists for remaining hashes\".format(\n                    len(hashes), traverse_weight\n                )\n            )\n            return (\n                list(indexed_hashes)\n                + list(hashes & remote_hashes)\n                + self.tree.list_hashes_exists(\n                    hashes - remote_hashes, jobs, name\n                )\n            )\n\n        logger.debug(\"Querying '{}' hashes via traverse\".format(len(hashes)))\n        remote_hashes = set(\n            self.tree.list_hashes_traverse(\n                remote_size, remote_hashes, jobs, name\n            )\n        )\n        return list(indexed_hashes) + list(hashes & set(remote_hashes))\n\n    @classmethod\n    @index_locked\n    def gc(cls, named_cache, remote, jobs=None):\n        tree = remote.tree\n        used = set(named_cache.scheme_keys(\"local\"))\n\n        if tree.scheme != \"\":\n            used.update(named_cache.scheme_keys(tree.scheme))\n\n        removed = False\n        # hashes must be sorted to ensure we always remove .dir files first\n        for hash_ in sorted(\n            tree.all(jobs, str(tree.path_info)),\n            key=tree.is_dir_hash,\n            reverse=True,\n        ):\n            if hash_ in used:\n                continue\n            path_info = tree.hash_to_path_info(hash_)\n            if tree.is_dir_hash(hash_):\n                # backward compatibility\n                # pylint: disable=protected-access\n                tree._remove_unpacked_dir(hash_)\n            tree.remove(path_info)\n            removed = True\n\n        if removed and hasattr(remote, \"index\"):\n            remote.index.clear()\n        return removed\n","sub_path":"dvc/remote/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":6191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}
+{"seq_id":"616741976","text":"import os, yaml\n\nf = open('../submission_metadata.yml')\n\ndataMap = yaml.load(f)\n\nf.close()\n\nif not os.path.isdir('../StudentSubcs60'):\n    os.system('mkdir ../StudentSubcs60')\n\nmkdir = \"mkdir ../StudentSubcs60/{}\"\ncpdir = \"cp -a ../{}/. ../StudentSubcs60/{}/\"\n# cpfiles = \"cp ../{}/Picture.java ../{}/Picture.java\"\nnames = ['AnnikaMammen', 'ArthurChang', 'BandarAlKabbani', 'DylanMcgarvey', 'IbrahimIbeElhari', 'MohnishMoShah', 'PabloVenegas', 'SerenaColyer']\n\nfor key in dataMap:\n    name = dataMap[key][':submitters'][0][':name'].replace(' ', '')\n    name = name.replace('(','')\n    name = name.replace(')','')\n    name2 = None\n    try:\n        name2 = dataMap[key][':submitters'][1][':name'].replace(' ', '')\n        name2 = name2.replace('(','')\n        name2 = name2.replace(')','')\n    \n    except:\n        pass\n\n    if name in names:\n        # print(name)\n        if not os.path.isdir('../StudentSubcs60/'+name):\n            mkdircommand = mkdir.format(name)\n            os.system(mkdircommand)\n            \n        if os.path.isdir('../'+key):\n            cpcommand = cpdir.format(key, name)\n            os.system(cpcommand)\n\n        else:\n            os.system(\"cp ../\" +key+ \" ../StudentSubcs60/\" +name+ \"/\"+ key)\n\n    if name2 and name2 in names:\n        # print(name2)\n        if not os.path.isdir('../StudentSubcs60/'+name2):\n            mkdircommand = mkdir.format(name2)\n            os.system(mkdircommand)\n            \n        if os.path.isdir('../'+key):\n            cpcommand = cpdir.format(key, name2)\n            os.system(cpcommand)\n\n        else:\n            os.system(\"cp ../\" +key+ \" ../StudentSubcs60/\" +name2+ \"/\"+ key)\n        # if os.path.isdir('/'+name):\n\n\n    # mkdircommand = mkdir.format(name)\n    # cpcommand = cpfiles.format(key, name)\n    \n    # os.system(mkdircommand)\n    # os.system(cpcommand)\n    # run_moss += ' ../' + name + '/' + filetocheck\n\n# print(run_moss)\n\n# dirls = os.listdir('../')\n# filetocheck = 'Picture.java'\n# run_moss = \"perl moss.pl -l java -b Skeleton.java -d\"\n\n# for folder in os.listdir('../'):\n#     if 'scripts' not in folder:\n#         run_moss += ' ../' + folder + '/' + filetocheck\n\n# os.system(run_moss)","sub_path":"kevinFiles/submission_collection/scripts/get_subs.py","file_name":"get_subs.py","file_ext":"py","file_size_in_byte":2169,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"11"}