Python request.urlretrieve方法代碼示例

# 需要導入模塊: from urllib import request [as 別名]
# 或者: from urllib.request import urlretrieve [as 別名]
def maybe_download(filename, url, destination_dir, expected_bytes = None, force = False):

    filepath = os.path.join(destination_dir, filename)

    if force or not os.path.exists(filepath):
        if not os.path.exists(destination_dir):
            os.makedirs(destination_dir)

        print('Attempting to download: ' + filename)
        filepath, _ = urlretrieve(url, filepath, reporthook = progress_bar)
        print('Download complete!')

    statinfo = os.stat(filepath)

    if expected_bytes != None:
        if statinfo.st_size == expected_bytes:
            print('Found and verified: ' + filename)
        else:
            raise Exception('Failed to verify: ' + filename + '. Can you get to it with a browser?')
    else:
        print('Found: ' + filename)
        print('The size of the file: ' + str(statinfo.st_size))

    return filepath 

# 需要導入模塊: from urllib import request [as 別名]
# 或者: from urllib.request import urlretrieve [as 別名]
def _checkout(self, local_dir):
        user = self.config.get("user")
        repo = self.config.get("repo")

        version = self.config.get("version", "master")

        # TODO : Sanitize URL
        url = URL.format(user=user, repo=repo, version=version)
        logger.info("Downloading {}/{} from github".format(user, repo))
        try:
            (filename, headers) = urllib.urlretrieve(url)
        except URLError as e:
            raise RuntimeError("Failed to download '{}'. '{}'".format(url, e.reason))
        t = tarfile.open(filename)
        (cache_root, core) = os.path.split(local_dir)

        # Ugly hack to get the first part of the directory name of the extracted files
        tmp = t.getnames()[0]
        t.extractall(cache_root)
        os.rename(os.path.join(cache_root, tmp), os.path.join(cache_root, core)) 

# 需要導入模塊: from urllib import request [as 別名]
# 或者: from urllib.request import urlretrieve [as 別名]
def fetch_url(url, file_path, progress_bar=_report_hook, remote_checksum=None):
    """Download a remote file

    Fetch a dataset pointed to by ``url``, check its SHA-256 checksum for
    integrity, and save it to ``file_path``.

    Parameters
    ----------
    url : string
        URL of file to download
    file_path: string
        Path to the local file that will be created
    progress_bar : func callback, optional, default: built-in
        A callback to a function ``func(count, block_size, total_size)`` that
        will display a progress bar.
    remote_checksum : str, optional, default: None
        The expected SHA-256 checksum of the file.
    """
    urlretrieve(url, file_path, progress_bar)
    checksum = _sha256(file_path)
    if remote_checksum != None and remote_checksum != checksum:
        raise IOError("{} has an SHA256 checksum ({}) "
                      "differing from expected ({}), "
                      "file may be corrupted.".format(file_path, checksum,
                                                      remote_checksum)) 

# 需要導入模塊: from urllib import request [as 別名]
# 或者: from urllib.request import urlretrieve [as 別名]
def fetch_data():
    """Download the data."""
    train_file = tempfile.NamedTemporaryFile()
    test_file = tempfile.NamedTemporaryFile()
    req.urlretrieve("http://mlr.cs.umass.edu/ml/machine-learning-databases"
                    "/adult/adult.data", train_file.name)
    req.urlretrieve("http://mlr.cs.umass.edu/ml/machine-learning-databases/"
                    "adult/adult.test", test_file.name)

    df_train = pd.read_csv(train_file, names=COLUMNS, skipinitialspace=True)
    df_test = pd.read_csv(test_file, names=COLUMNS, skipinitialspace=True,
                          skiprows=1)

    df_train[LABEL_COLUMN] = (df_train["income_bracket"]
                              .apply(lambda x: ">50K" in x)).astype(int)
    df_test[LABEL_COLUMN] = (df_test["income_bracket"]
                             .apply(lambda x: ">50K" in x)).astype(int)

    return df_train, df_test 

# 需要導入模塊: from urllib import request [as 別名]
# 或者: from urllib.request import urlretrieve [as 別名]
def loadData(src):
    print('Downloading ' + src)
    fname, h = urlretrieve(src, './delete.me')
    print('Done.')
    try:
        print('Extracting files...')
        with tarfile.open(fname) as tar:
            tar.extractall()
        print('Done.')
        print('Preparing train set...')
        trn = np.empty((0, numFeature + 1), dtype=np.int)
        for i in range(5):
            batchName = './cifar-10-batches-py/data_batch_{0}'.format(i + 1)
            trn = np.vstack((trn, readBatch(batchName)))
        print('Done.')
        print('Preparing test set...')
        tst = readBatch('./cifar-10-batches-py/test_batch')
        print('Done.')
    finally:
        os.remove(fname)
    return (trn, tst) 

# 需要導入模塊: from urllib import request [as 別名]
# 或者: from urllib.request import urlretrieve [as 別名]
def loadData(src, cimg):
    gzfname, h = urlretrieve(src, './delete.me')
    try:
        with gzip.open(gzfname) as gz:
            n = struct.unpack('I', gz.read(4))
            if n[0] != 0x3080000:
                raise Exception('Invalid file: unexpected magic number.')
            n = struct.unpack('>I', gz.read(4))[0]
            if n != cimg:
                raise Exception('Invalid file: expected {0} entries.'.format(cimg))
            crow = struct.unpack('>I', gz.read(4))[0]
            ccol = struct.unpack('>I', gz.read(4))[0]
            if crow != 28 or ccol != 28:
                raise Exception('Invalid file: expected 28 rows/cols per image.')
            res = np.fromstring(gz.read(cimg * crow * ccol), dtype=np.uint8)
    finally:
        os.remove(gzfname)
    return res.reshape((cimg, crow * ccol)) 

# 需要導入模塊: from urllib import request [as 別名]
# 或者: from urllib.request import urlretrieve [as 別名]
def load_or_download_mnist_files(filename, num_samples, local_data_dir):

    if (local_data_dir):
        local_path = os.path.join(local_data_dir, filename)
    else:
        local_path = os.path.join(os.getcwd(), filename)

    if os.path.exists(local_path):
        gzfname = local_path
    else:
        local_data_dir = os.path.dirname(local_path)
        if not os.path.exists(local_data_dir):
            os.makedirs(local_data_dir)
        filename = "http://yann.lecun.com/exdb/mnist/" + filename
        print ("Downloading from" + filename, end=" ")
        gzfname, h = urlretrieve(filename, local_path)
        print ("[Done]")

    return gzfname 

# 需要導入模塊: from urllib import request [as 別名]
# 或者: from urllib.request import urlretrieve [as 別名]
def _install_module(self, fullname):
        top, username, repo, modname = self._parse_fullname(fullname)
        url = 'https://raw.githubusercontent.com/%s/%s/master/%s' % (username, repo, modname+'.py')
        print('Downloading: ', url)
        try:
            tmp_file, resp = urlretrieve(url)
            with open(tmp_file, 'r') as f:
                new_content = f.read()
            if new_content=='Not Found':
                raise InstallError('remote file does not exist')
        except IOError:
            raise InstallError('error downloading file')
        
        new = tmp_file
        old = self._install_path(fullname)
        updated = self._update_if_changed(old, new)
        if updated=='updated':
            print('Updating module: ', fullname)
        elif updated=='installed':
            print('Installing module: ', fullname)
        elif updated=='noaction':
            print('Using existing version: ', fullname) 

# 需要導入模塊: from urllib import request [as 別名]
# 或者: from urllib.request import urlretrieve [as 別名]
def thread_updater():
	while True:
		time.sleep(21600)
		if __version__ < latest_ver:
			logger.info(
				"Your version (%s%s) is out of date, the latest is %s, which has now be downloaded for you into the 'updates' subdirectory." % (
					type, __version__, latest_ver))
			newfilename = ntpath.basename(latestfile)
			if not os.path.isdir('updates'):
				os.mkdir('updates')
			requests.urlretrieve(latestfile, os.path.join('updates', newfilename))


############################################################
# playlist tools
############################################################ 

# 需要導入模塊: from urllib import request [as 別名]
# 或者: from urllib.request import urlretrieve [as 別名]
def load_logos(filename):
    '''
    Load logos from a geologos archive from <filename>

    <filename> can be either a local path or a remote URL.
    '''
    if filename.startswith('http'):
        log.info('Downloading GeoLogos bundle: %s', filename)
        filename, _ = urlretrieve(filename, tmp.path('geologos.tar.xz'))

    log.info('Extracting GeoLogos bundle')
    with contextlib.closing(lzma.LZMAFile(filename)) as xz:
        with tarfile.open(fileobj=xz, encoding='utf8') as tar:
            tar.extractall(tmp.root, members=tar.getmembers())

    log.info('Moving to the final location and cleaning up')
    if os.path.exists(logos.root):
        shutil.rmtree(logos.root)
    shutil.move(tmp.path('logos'), logos.root)
    log.info('Done') 

# 需要導入模塊: from urllib import request [as 別名]
# 或者: from urllib.request import urlretrieve [as 別名]
def import_shapers(logger):
    (_, zip_path) = tempfile.mkstemp()
    (_, http_message) = request.urlretrieve(url, zip_path)
    zip_file = ZipFile(zip_path)
    ex_dir = tempfile.mkdtemp()
    zip_file.extractall(ex_dir)
    shapefiles = glob.glob1(ex_dir, "*.shp")
    lm = LayerMapping(Parcel, "/data/shapefiles/M274TaxPar.shp", {
        "shape_leng": "SHAPE_Leng",
        "shape_area": "SHAPE_Area",
        "map_par_id": "MAP_PAR_ID",
        "loc_id": "LOC_ID",
        "poly_type": "POLY_TYPE",
        "map_no": "MAP_NO",
        "source": "SOURCE",
        "plan_id": "PLAN_ID",
        "last_edit": "LAST_EDIT",
        "town_id": "TOWN_ID",
        "shape": "POLYGON"
    }) 

# 需要導入模塊: from urllib import request [as 別名]
# 或者: from urllib.request import urlretrieve [as 別名]
def _load_cifar10():
    def unflatten(images):
        return np.transpose(images.reshape((images.shape[0], 3, 32, 32)),
                            [0, 2, 3, 1])

    with tempfile.NamedTemporaryFile() as f:
        request.urlretrieve(URLS['cifar10'], f.name)
        tar = tarfile.open(fileobj=f)
        train_data_batches, train_data_labels = [], []
        for batch in range(1, 6):
            data_dict = scipy.io.loadmat(tar.extractfile(
                'cifar-10-batches-mat/data_batch_{}.mat'.format(batch)))
            train_data_batches.append(data_dict['data'])
            train_data_labels.append(data_dict['labels'].flatten())
        train_set = {'images': np.concatenate(train_data_batches, axis=0),
                     'labels': np.concatenate(train_data_labels, axis=0)}
        data_dict = scipy.io.loadmat(tar.extractfile(
            'cifar-10-batches-mat/test_batch.mat'))
        test_set = {'images': data_dict['data'],
                    'labels': data_dict['labels'].flatten()}
    train_set['images'] = _encode_png(unflatten(train_set['images']))
    test_set['images'] = _encode_png(unflatten(test_set['images']))
    return dict(train=train_set, test=test_set) 

# 需要導入模塊: from urllib import request [as 別名]
# 或者: from urllib.request import urlretrieve [as 別名]
def _load_cifar100():
    def unflatten(images):
        return np.transpose(images.reshape((images.shape[0], 3, 32, 32)),
                            [0, 2, 3, 1])

    with tempfile.NamedTemporaryFile() as f:
        request.urlretrieve(URLS['cifar100'], f.name)
        tar = tarfile.open(fileobj=f)
        data_dict = scipy.io.loadmat(tar.extractfile('cifar-100-matlab/train.mat'))
        train_set = {'images': data_dict['data'],
                     'labels': data_dict['fine_labels'].flatten()}
        data_dict = scipy.io.loadmat(tar.extractfile('cifar-100-matlab/test.mat'))
        test_set = {'images': data_dict['data'],
                    'labels': data_dict['fine_labels'].flatten()}
    train_set['images'] = _encode_png(unflatten(train_set['images']))
    test_set['images'] = _encode_png(unflatten(test_set['images']))
    return dict(train=train_set, test=test_set)

# Load a custom dataset from a local directory. 

# 需要導入模塊: from urllib import request [as 別名]
# 或者: from urllib.request import urlretrieve [as 別名]
def check_or_download_inception(inception_path):
    """ Checks if the path to the inception file is valid, or downloads
        the file if it is not present. """
    INCEPTION_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
    if inception_path is None:
        inception_path = '/tmp'
    inception_path = pathlib.Path(inception_path)
    model_file = inception_path / 'classify_image_graph_def.pb'
    if not model_file.exists():
        print("Downloading Inception model")
        from urllib import request
        import tarfile
        fn, _ = request.urlretrieve(INCEPTION_URL)
        with tarfile.open(fn, mode='r') as f:
            f.extract('classify_image_graph_def.pb', str(model_file.parent))
    return str(model_file) 

# 需要導入模塊: from urllib import request [as 別名]
# 或者: from urllib.request import urlretrieve [as 別名]
def download_url(url, destination=None, progress_bar=True):
    def my_hook(t):
        last_b = [0]

        def inner(b=1, bsize=1, tsize=None):
            if tsize is not None:
                t.total = tsize
            if b > 0:
                t.update((b - last_b[0]) * bsize)
            last_b[0] = b

        return inner

    if progress_bar:
        with tqdm(unit='B', unit_scale=True, miniters=1, desc=url.split('/')[-1]) as t:
            filename, _ = urlretrieve(url, filename=destination, reporthook=my_hook(t))
    else:
        filename, _ = urlretrieve(url, filename=destination)