10 examples of 'python read file into array' in Python

9
def read_file():
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    # Text file containing words for training
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    training_file = 'belling_the_cat.txt'
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    content=[]
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    with open(training_file,'r') as f:
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        for line in f.readlines():
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            # line 表示读到数据的每一行，linelist是按照空格切分成一个list
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            linelist=line.strip().split()
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            for i in linelist:
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                content.append(i.strip())
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    content=np.array(content)
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    content=np.reshape(content,[-1,])  #shape (204,1)
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    return content

53
def read_file(f):
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    """ 
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    Open the text file containing deep learning classification results for files. The decision variables is automatically opened from utils folder
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    :param f: text filename          
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    :returns:                                                                                                         
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      - sounds: sounds that were analyzed
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      - lc: labels for each sound
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    """
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    with open(f, 'r') as i_file:
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        files = i_file.read().split('\n')
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    sounds = np.array([i.split(" ")[0].split(".json")[0] for i in np.array(files)])
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    labels = np.array([i.split(" ") for i in np.array(files)])
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    lc = []
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    for lb in labels:
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        try: 
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            lc.append(lb[1])
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        except:
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            continue
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    decisions = np.loadtxt('utils/data.txt')
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    return sounds, np.array(lc), decisions

50
def file_reader(filename, shape, data_type = None, **kwds):
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    # TODO: write the reader...
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    dc = np.fromfile(filename, **kwds)
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    if len(shape) == 3:
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        dc = dc.reshape((shape[2], shape[1], shape[0])).swapaxes(1,2)
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        if data_type is None:
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            data_type = 'SI'
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    elif len(shape) == 2:
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        dc = dc.reshape(shape).T
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        if data_type is None:
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            data_type = 'Image'
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    return [{'mapped_parameters':{
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				'data_type' : data_type,
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				'name' : filename,
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				}, 
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			'data':dc,

52
def readArray(file, count, func):
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    result = []
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    for i in range(count):
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        result.append(func(file))
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    return result

4
def read_array_from_binary_file(filename, shape, dtype=numpy.float64, order='F'):
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    array = numpy.fromfile(filename, dtype=dtype)
6
    if array.shape[0] != shape[0]*shape[1]*shape[2]:
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        raise ValueError("Incorrect grid size %d x %d x %d" %shape)
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    array = array.reshape(shape, order=order)
9
    return array

164
def file_read(fname, arr, size):
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    return

13
def read_file(filename):
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	return np.matrix([map(float, line.strip('\n').split('\t')) for line in open(filename)])

1049
def _array_from_file(infile, dtype, count, sep):
1050
    """Create a numpy array from a file or a file-like object."""
1051
1052
    if isfile(infile):
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1054
        global CHUNKED_FROMFILE
1055
        if CHUNKED_FROMFILE is None:
1056
            if sys.platform == 'darwin' and LooseVersion(platform.mac_ver()[0]) < LooseVersion('10.9'):
1057
                CHUNKED_FROMFILE = True
1058
            else:
1059
                CHUNKED_FROMFILE = False
1060
1061
        if CHUNKED_FROMFILE:
1062
            chunk_size = int(1024 ** 3 / dtype.itemsize)  # 1Gb to be safe
1063
            if count < chunk_size:
1064
                return np.fromfile(infile, dtype=dtype, count=count, sep=sep)
1065
            else:
1066
                array = np.empty(count, dtype=dtype)
1067
                for beg in range(0, count, chunk_size):
1068
                    end = min(count, beg + chunk_size)
1069
                    array[beg:end] = np.fromfile(infile, dtype=dtype, count=end - beg, sep=sep)
1070
                return array
1071
        else:
1072
            return np.fromfile(infile, dtype=dtype, count=count, sep=sep)
1073
    else:
1074
        # treat as file-like object with "read" method; this includes gzip file
1075
        # objects, because numpy.fromfile just reads the compressed bytes from
1076
        # their underlying file object, instead of the decompressed bytes
1077
        read_size = np.dtype(dtype).itemsize * count
1078
        s = infile.read(read_size)
1079
        return np.fromstring(s, dtype=dtype, count=count, sep=sep)

83
def read_file_and_fill_arr(file_descr, arr):
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  for line in file_descr.readlines():
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    if line.startswith('#'): continue
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    phi_str, psi_str, value_str = line.split()
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    phi = int(float(phi_str))
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    psi = int(float(psi_str))
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    value = float(value_str)
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    arr[(phi+179)//2][(psi+179)//2] = value

5
def read_h5_array(f5, var_name):
6
7
    try:
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        nx = f5['Nx'].value
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        ny = f5['Ny'].value
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        nz = f5['Nz'].value
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    except:
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        nx,ny,nz = np.shape(f5[var_name])
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        return f5[var_name]
14
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    # column-ordered data with image convention (x horizontal, y vertical, ..)
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    # i.e., so-called fortran ordering
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    val = f5[var_name][:]
18
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    #print("reshaping 1D array of {} into multiD with {} {} {}".format(len(val), nx,ny,nz))
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    # reshape to python format; from Fortran image to C matrix
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    val = np.reshape(val, (nz, ny, nx))
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    val = val.ravel(order='F').reshape((nx,ny,nz))
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    return val