Python numpy.right_shift方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import right_shift [as 別名]
def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod,
            np.greater, np.greater_equal, np.less, np.less_equal,
            np.equal, np.not_equal]

        a = np.array('1')
        b = 1
        c = np.array([1., 2.])
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b)
            assert_raises(TypeError, f, c, a) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import right_shift [as 別名]
def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import right_shift [as 別名]
def test_simple(self):
        input_arr = bitarray('10' * 64)

        self.chip['PIXEL_REG'][:] = input_arr
        self.chip['PIXEL_REG'][0] = 0
        self.chip.program_pixel_reg()

        ret = self.chip['DATA'].get_data()

        data0 = ret.astype(np.uint8)
        data1 = np.right_shift(ret, 8).astype(np.uint8)
        data = np.reshape(np.vstack((data1, data0)), -1, order='F')
        bdata = np.unpackbits(data)

        input_arr[0] = 0

        self.assertEqual(input_arr.tolist(), bdata.tolist()) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import right_shift [as 別名]
def bits_strip(bit_start, bit_count, value):
    """Extract specified bit from bit representation of integer value.

    Parameters
    ----------
    bit_start : int
        Starting index of the bits to extract (first bit has index 0)
    bit_count : int
        Number of bits starting from bit_start to extract
    value : int
        Number from which to extract the bits

    Returns
    -------
        int
        Value of the extracted bits

    """
    bit_mask = pow(2, bit_start + bit_count) - 1
    return np.right_shift(np.bitwise_and(value, bit_mask), bit_start) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import right_shift [as 別名]
def LCD_ShowImage(self,Image,Xstart,Ystart):
		if (Image == None):
			return
		imwidth, imheight = Image.size
		if imwidth != self.width or imheight != self.height:
			raise ValueError('Image must be same dimensions as display \
				({0}x{1}).' .format(self.width, self.height))
		img = np.asarray(Image)
		pix = np.zeros((self.width,self.height,2), dtype = np.uint8)
		pix[...,[0]] = np.add(np.bitwise_and(img[...,[0]],0xF8),np.right_shift(img[...,[1]],5))
		pix[...,[1]] = np.add(np.bitwise_and(np.left_shift(img[...,[1]],3),0xE0),np.right_shift(img[...,[2]],3))
		pix = pix.flatten().tolist()
		self.LCD_SetWindows(0, 0, self.width , self.height)
		GPIO.output(LCD_Config.LCD_DC_PIN, GPIO.HIGH)
		for i in range(0,len(pix),4096):
			LCD_Config.SPI_Write_Byte(pix[i:i+4096]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import right_shift [as 別名]
def test_binary_int_broadcast_1():
    for op, ref in [(relay.right_shift, np.right_shift),
                    (relay.left_shift, np.left_shift)]:
        x = relay.var("x", relay.TensorType((10, 4), "int32"))
        y = relay.var("y", relay.TensorType((5, 10, 1), "int32"))
        z = op(x, y)
        zz = run_infer_type(z)
        assert zz.checked_type == relay.TensorType((5, 10, 4), "int32")

        if ref is not None:
            x_shape = (10, 4)
            y_shape = (5, 10, 1)
            t1 = relay.TensorType(x_shape, 'int32')
            t2 = relay.TensorType(y_shape, 'int32')
            x_data = np.random.randint(1, 10000, size=(x_shape)).astype(t1.dtype)
            y_data = np.random.randint(1, 31, size=(y_shape)).astype(t2.dtype)
            func = relay.Function([x, y], z)
            ref_res = ref(x_data, y_data)

            for target, ctx in ctx_list():
                intrp = relay.create_executor("graph", ctx=ctx, target=target)
                op_res = intrp.evaluate(func)(x_data, y_data)
                tvm.testing.assert_allclose(op_res.asnumpy(), ref_res) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import right_shift [as 別名]
def np_float2np_bf16(arr):
    ''' Convert a numpy array of float to a numpy array
    of bf16 in uint16'''
    orig = arr.view('<u4')
    bias = np.bitwise_and(np.right_shift(orig, 16), 1) + 0x7FFF
    return np.right_shift(orig + bias, 16).astype('uint16') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import right_shift [as 別名]
def exprsco_to_rawsco(exprsco, clock=1789773.):
  rate, nsamps, exprsco = exprsco

  m = exprsco[:, :3, 0]
  m_zero = np.where(m == 0)

  m = m.astype(np.float32)
  f = 440 * np.power(2, ((m - 69) / 12))

  f_p, f_tr = f[:, :2], f[:, 2:]

  t_p = np.round((clock / (16 * f_p)) - 1)
  t_tr = np.round((clock / (32 * f_tr)) - 1)
  t = np.concatenate([t_p, t_tr], axis=1)

  t = t.astype(np.uint16)
  t[m_zero] = 0
  th = np.right_shift(np.bitwise_and(t, 0b11100000000), 8)
  tl = np.bitwise_and(t, 0b00011111111)

  rawsco = np.zeros((exprsco.shape[0], 4, 4), dtype=np.uint8)
  rawsco[:, :, 2:] = exprsco[:, :, 1:]
  rawsco[:, :3, 0] = th
  rawsco[:, :3, 1] = tl
  rawsco[:, 3, 1:] = exprsco[:, 3, :]

  return (clock, rate, nsamps, rawsco) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import right_shift [as 別名]
def compactbit(b):
        nSamples, nbits = b.shape
        nwords = (nbits + 7) / 8
        B = np.hstack([np.packbits(b[:, i*8:(i+1)*8][:, ::-1], 1)
                       for i in xrange(nwords)])
        residue = nbits % 8
        if residue != 0:
            B[:, -1] = np.right_shift(B[:, -1], 8 - residue)

        return B 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import right_shift [as 別名]
def image_to_data(self, image, rotation=0):
        """Generator function to convert a PIL image to 16-bit 565 RGB bytes."""
        # NumPy is much faster at doing this. NumPy code provided by:
        # Keith (https://www.blogger.com/profile/02555547344016007163)
        pb = np.rot90(np.array(image.convert('RGB')), rotation // 90).astype('uint8')

        result = np.zeros((self._width, self._height, 2), dtype=np.uint8)
        result[..., [0]] = np.add(np.bitwise_and(pb[..., [0]], 0xF8), np.right_shift(pb[..., [1]], 5))
        result[..., [1]] = np.add(np.bitwise_and(np.left_shift(pb[..., [1]], 3), 0xE0), np.right_shift(pb[..., [2]], 3))
        return result.flatten().tolist() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import right_shift [as 別名]
def test_shift():
    # explicit specify the output type
    verify_broadcast_binary_ele(
        (2, 1, 2), None, topi.right_shift, np.right_shift,
        dtype="int32", rhs_min=0, rhs_max=32)

    verify_broadcast_binary_ele(
        (1, 2, 2), (2,), topi.left_shift, np.left_shift,
        dtype="int32", rhs_min=0, rhs_max=32)

    verify_broadcast_binary_ele(
        (1, 2, 2), (2,), topi.left_shift, np.left_shift,
        dtype="int8", rhs_min=0, rhs_max=32) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import right_shift [as 別名]
def run(self, tensor: np.ndarray, data_format: str = 'NHWC') -> np.ndarray:
        """Pack a tensor.

        Args:
            tensor (np.ndarray): Input tensor.
            data_format (str): Order of dimension. This defaults to 'NHWC', where 'N' is
                the number of kernels, 'H' and 'W' are the height and
                width, and 'C' is the depth / the number of channels.

        Returns:
            np.ndarray: Quantized tensor.

        """

        wordsize = self.wordsize

        if (tensor >= (2 ** self.bitwidth)).any():
            raise ValueError("all value of input tensor must be less than bit width ({})".format(self.bitwidth))

        output_size = tensor.size // wordsize
        output_size += 1 if tensor.size % wordsize != 0 else 0
        output_size *= self.bitwidth

        tensor_flat = tensor.flatten(order='C').astype(np.uint32)
        output = np.zeros(output_size, dtype=np.uint32)
        oi = 0
        for i in range(0, tensor.size, wordsize):
            if i + wordsize < tensor.size:
                sliced_tensor = tensor_flat[i:i + wordsize]
            else:
                sliced_tensor = tensor_flat[i:]

            for _ in range(0, self.bitwidth):
                output[oi] = self._pack_to_word(np.bitwise_and(sliced_tensor, 1))
                oi += 1
                sliced_tensor = np.right_shift(sliced_tensor, 1)

        return output.reshape([1, output_size]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import right_shift [as 別名]
def _rgb_integers_to_components(self, rgb_integers):
        red_mask = 0x00FF0000
        green_mask = 0x0000FF00
        blue_mask =  0x000000FF
        masks = np.asarray([[red_mask, green_mask, blue_mask]])
        masked_rgb_components = np.bitwise_and(rgb_integers, masks)

        red_shifted = np.right_shift(masked_rgb_components[:,0], 16)
        green_shifted = np.right_shift(masked_rgb_components[:,1], 8)
        blue_shifted =  np.right_shift(masked_rgb_components[:,2], 0)
        return np.array([red_shifted, green_shifted, blue_shifted]).transpose() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import right_shift [as 別名]
def _transform_color_int_to_rgba(np_values):
    np_values[np_values == gxapi.iDUMMY] = 0
    a = (np.right_shift(np_values, 24) & 0xFF).astype(np.uint8)
    b = (np.right_shift(np_values, 16) & 0xFF).astype(np.uint8)
    g = (np.right_shift(np_values, 8) & 0xFF).astype(np.uint8)
    r = (np_values & 0xFF).astype(np.uint8)
    # the values for color grids actually do not contain alphas but just
    # 0 or 1 to indicate if the color is valid or not
    a[a > 0] = 255
    return np.array([r, g, b, a]).transpose() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import right_shift [as 別名]
def load_gated(root_dir, sample, slice):
    path = os.path.join(root_dir, 'gated{}_10bit'.format(slice), sample + '.png')
    img = cv2.imread(path, -1)
    img = np.right_shift(img, 2).astype(np.uint8)  # convert from 10bit to 8bit

    return img