Python numpy.int16方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int16 [as 別名]
def wavefile_to_waveform(wav_file, features_type):
    data, sr = sf.read(wav_file)
    if features_type == 'vggish':
        tmp_name = str(int(np.random.rand(1)*1000000)) + '.wav'
        sf.write(tmp_name, data, sr, subtype='PCM_16')
        sr, wav_data = wavfile.read(tmp_name)
        os.remove(tmp_name)
        # sr, wav_data = wavfile.read(wav_file) # as done in VGGish Audioset
        assert wav_data.dtype == np.int16, 'Bad sample type: %r' % wav_data.dtype
        data = wav_data / 32768.0  # Convert to [-1.0, +1.0]
  
    # at least one second of samples, if not repead-pad
    src_repeat = data
    while (src_repeat.shape[0] < sr): 
        src_repeat = np.concatenate((src_repeat, data), axis=0)
        data = src_repeat[:sr]

    return data, sr 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int16 [as 別名]
def frompointer(pointer, count, dtype=float):
    '''Interpret a buffer that the pointer refers to as a 1-dimensional array.

    Args:
        pointer : int or ctypes pointer
            address of a buffer
        count : int
            Number of items to read.
        dtype : data-type, optional
            Data-type of the returned array; default: float.

    Examples:

    >>> s = numpy.ones(3, dtype=numpy.int32)
    >>> ptr = s.ctypes.data
    >>> frompointer(ptr, count=6, dtype=numpy.int16)
    [1, 0, 1, 0, 1, 0]
    '''
    dtype = numpy.dtype(dtype)
    count *= dtype.itemsize
    buf = (ctypes.c_char * count).from_address(pointer)
    a = numpy.ndarray(count, dtype=numpy.int8, buffer=buf)
    return a.view(dtype) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int16 [as 別名]
def test_no_offset_scale():
    # Specific tests of no-offset scaling
    SAW = SlopeArrayWriter
    # Floating point
    for data in ((-128, 127),
                  (-128, 126),
                  (-128, -127),
                  (-128, 0),
                  (-128, -1),
                  (126, 127),
                  (-127, 127)):
        aw = SAW(np.array(data, dtype=np.float32), np.int8)
        assert_equal(aw.slope, 1.0)
    aw = SAW(np.array([-126, 127 * 2.0], dtype=np.float32), np.int8)
    assert_equal(aw.slope, 2)
    aw = SAW(np.array([-128 * 2.0, 127], dtype=np.float32), np.int8)
    assert_equal(aw.slope, 2)
    # Test that nasty abs behavior does not upset us
    n = -2**15
    aw = SAW(np.array([n, n], dtype=np.int16), np.uint8)
    assert_array_almost_equal(aw.slope, n / 255.0, 5) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int16 [as 別名]
def test_writer_maker():
    arr = np.arange(10, dtype=np.float64)
    aw = make_array_writer(arr, np.float64)
    assert_true(isinstance(aw, SlopeInterArrayWriter))
    aw = make_array_writer(arr, np.float64, True, True)
    assert_true(isinstance(aw, SlopeInterArrayWriter))
    aw = make_array_writer(arr, np.float64, True, False)
    assert_true(isinstance(aw, SlopeArrayWriter))
    aw = make_array_writer(arr, np.float64, False, False)
    assert_true(isinstance(aw, ArrayWriter))
    assert_raises(ValueError, make_array_writer, arr, np.float64, False)
    assert_raises(ValueError, make_array_writer, arr, np.float64, False, True)
    # Does calc_scale get run by default?
    aw = make_array_writer(arr, np.int16, calc_scale=False)
    assert_equal((aw.slope, aw.inter), (1, 0))
    aw.calc_scale()
    slope, inter = aw.slope, aw.inter
    assert_false((slope, inter) == (1, 0))
    # Should run by default
    aw = make_array_writer(arr, np.int16)
    assert_equal((aw.slope, aw.inter), (slope, inter))
    aw = make_array_writer(arr, np.int16, calc_scale=True)
    assert_equal((aw.slope, aw.inter), (slope, inter)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int16 [as 別名]
def test_able_int_type():
    # The integer type cabable of containing values
    for vals, exp_out in (
        ([0, 1], np.uint8),
        ([0, 255], np.uint8),
        ([-1, 1], np.int8),
        ([0, 256], np.uint16),
        ([-1, 128], np.int16),
        ([0.1, 1], None),
        ([0, 2**16], np.uint32),
        ([-1, 2**15], np.int32),
        ([0, 2**32], np.uint64),
        ([-1, 2**31], np.int64),
        ([-1, 2**64-1], None),
        ([0, 2**64-1], np.uint64),
        ([0, 2**64], None)):
        assert_equal(able_int_type(vals), exp_out) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int16 [as 別名]
def test_isolation(self):
        # Test image isolated from external changes to header and affine
        img_klass = self.image_class
        arr = np.arange(3, dtype=np.int16)
        aff = np.eye(4)
        img = img_klass(arr, aff)
        assert_array_equal(img.get_affine(), aff)
        aff[0,0] = 99
        assert_false(np.all(img.get_affine() == aff))
        # header, created by image creation
        ihdr = img.get_header()
        # Pass it back in
        img = img_klass(arr, aff, ihdr)
        # Check modifying header outside does not modify image
        ihdr.set_zooms((4,))
        assert_not_equal(img.get_header(), ihdr) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int16 [as 別名]
def test_calculate_scale():
    # Test for special cases in scale calculation
    npa = np.array
    # Here the offset handles it
    res = calculate_scale(npa([-2, -1], dtype=np.int8), np.uint8, True)
    assert_equal(res, (1.0, -2.0, None, None))
    # Not having offset not a problem obviously
    res = calculate_scale(npa([-2, -1], dtype=np.int8), np.uint8, 0)
    assert_equal(res, (-1.0, 0.0, None, None))
    # Case where offset handles scaling
    res = calculate_scale(npa([-1, 1], dtype=np.int8), np.uint8, 1)
    assert_equal(res, (1.0, -1.0, None, None))
    # Can't work for no offset case
    assert_raises(ValueError,
                  calculate_scale, npa([-1, 1], dtype=np.int8), np.uint8, 0)
    # Offset trick can't work when max is out of range
    res = calculate_scale(npa([-1, 255], dtype=np.int16), np.uint8, 1)
    assert_not_equal(res, (1.0, -1.0, None, None)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int16 [as 別名]
def test_can_cast():
    tests = ((np.float32, np.float32, True, True, True),
             (np.float64, np.float32, True, True, True),
             (np.complex128, np.float32, False, False, False),
             (np.float32, np.complex128, True, True, True),
             (np.float32, np.uint8, False, True, True),
             (np.uint32, np.complex128, True, True, True),
             (np.int64, np.float32, True, True, True),
             (np.complex128, np.int16, False, False, False),
             (np.float32, np.int16, False, True, True),
             (np.uint8, np.int16, True, True, True),
             (np.uint16, np.int16, False, True, True),
             (np.int16, np.uint16, False, False, True),
             (np.int8, np.uint16, False, False, True),
             (np.uint16, np.uint8, False, True, True),
             )
    for intype, outtype, def_res, scale_res, all_res in tests:
        assert_equal(def_res, can_cast(intype, outtype))
        assert_equal(scale_res, can_cast(intype, outtype, False, True))
        assert_equal(all_res, can_cast(intype, outtype, True, True)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int16 [as 別名]
def test_nifti1_init():
    bio = BytesIO()
    shape = (2,3,4)
    hdr = Nifti1Header()
    arr = np.arange(24, dtype=np.int16).reshape(shape)
    write_raw_data(arr, hdr, bio)
    hdr.set_slope_inter(2, 10)
    ap = ArrayProxy(bio, hdr)
    assert_true(ap.file_like == bio)
    assert_equal(ap.shape, shape)
    # Check there has been a copy of the header
    assert_false(ap.header is hdr)
    # Get the data
    assert_array_equal(np.asarray(ap), arr * 2.0 + 10)
    with InTemporaryDirectory():
        f = open('test.nii', 'wb')
        write_raw_data(arr, hdr, f)
        f.close()
        ap = ArrayProxy('test.nii', hdr)
        assert_true(ap.file_like == 'test.nii')
        assert_equal(ap.shape, shape)
        assert_array_equal(np.asarray(ap), arr * 2.0 + 10) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int16 [as 別名]
def testCreateBaseType(self):
        dt = hdf5dtype.createDataType('H5T_STD_U32BE')
        self.assertEqual(dt.name, 'uint32')
        self.assertEqual(dt.byteorder, '>')
        self.assertEqual(dt.kind, 'u')

        dt = hdf5dtype.createDataType('H5T_STD_I16LE')
        self.assertEqual(dt.name, 'int16')
        self.assertEqual(dt.kind, 'i')

        dt = hdf5dtype.createDataType('H5T_IEEE_F64LE')
        self.assertEqual(dt.name, 'float64')
        self.assertEqual(dt.kind, 'f')

        dt = hdf5dtype.createDataType('H5T_IEEE_F32LE')
        self.assertEqual(dt.name, 'float32')
        self.assertEqual(dt.kind, 'f')

        typeItem = { 'class': 'H5T_INTEGER', 'base': 'H5T_STD_I32BE' }
        typeSize = hdf5dtype.getItemSize(typeItem)
        dt = hdf5dtype.createDataType(typeItem)
        self.assertEqual(dt.name, 'int32')
        self.assertEqual(dt.kind, 'i')
        self.assertEqual(typeSize, 4) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int16 [as 別名]
def synthesize(frames,filename,stride,sr=16000,deemph=0,ymax=0.98,normalize=False):
    # Generate stream
    y=torch.zeros((len(frames)-1)*stride+len(frames[0]))
    for i,x in enumerate(frames):
        y[i*stride:i*stride+len(x)]+=x
    # To numpy & deemph
    y=y.numpy().astype(np.float32)
    if deemph>0:
        y=deemphasis(y,alpha=deemph)
    # Normalize
    if normalize:
        y-=np.mean(y)
        mx=np.max(np.abs(y))
        if mx>0:
            y*=ymax/mx
    else:
        y=np.clip(y,-ymax,ymax)
    # To 16 bit & save
    wavfile.write(filename,sr,np.array(y*32767,dtype=np.int16))
    return y

######################################################################################################################## 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int16 [as 別名]
def save_wav(audio, output_wav_file):
    wav.write(output_wav_file, 16000, np.array(np.clip(np.round(audio), -2**15, 2**15-1), dtype=np.int16))
    print('output dB', db(audio)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int16 [as 別名]
def _convert(self, vals):
        res = {}
        for k, v in vals.items():
            if isinstance(v, (np.int, np.int8, np.int16, np.int32, np.int64)):
                v = int(v)
            elif isinstance(v, (np.float, np.float16, np.float32, np.float64)):
                v = float(v)
            elif isinstance(v, Labels):
                v = list(v)
            elif isinstance(v, np.ndarray):
                v = v.tolist()
            elif isinstance(v, dict):
                v = self._convert(v)
            res[k] = v
        return res 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int16 [as 別名]
def _toscalar(v):
    if isinstance(v, (np.float16, np.float32, np.float64,
                      np.uint8, np.uint16, np.uint32, np.uint64,
                      np.int8, np.int16, np.int32, np.int64)):
        return np.asscalar(v)
    else:
        return v 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int16 [as 別名]
def convert(self, complex_iq):
        intlv = self._interleave(complex_iq)
        clipped = self._clip(intlv, limit=1.0)
        converted = 2047. * clipped
        bladerf_out = converted.astype(np.int16)
        return bladerf_out