Python numpy.bitwise_or方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_or [as 別名]
def labelcolormap(N=256):
    cmap = np.zeros((N, 3))
    for i in range(0, N):
        id = i
        r, g, b = 0, 0, 0
        for j in range(0, 8):
            r = np.bitwise_or(r, (bitget(id, 0) << 7-j))
            g = np.bitwise_or(g, (bitget(id, 1) << 7-j))
            b = np.bitwise_or(b, (bitget(id, 2) << 7-j))
            id = (id >> 3)
        cmap[i, 0] = r
        cmap[i, 1] = g
        cmap[i, 2] = b
    cmap = cmap.astype(np.float32) / 255
    return cmap


# -----------------------------------------------------------------------------
# Evaluation
# ----------------------------------------------------------------------------- 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_or [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 bitwise_or [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 bitwise_or [as 別名]
def test_values(self):
        for dt in self.bitwise_types:
            zeros = np.array([0], dtype=dt)
            ones = np.array([-1], dtype=dt)
            msg = "dt = '%s'" % dt.char

            assert_equal(np.bitwise_not(zeros), ones, err_msg=msg)
            assert_equal(np.bitwise_not(ones), zeros, err_msg=msg)

            assert_equal(np.bitwise_or(zeros, zeros), zeros, err_msg=msg)
            assert_equal(np.bitwise_or(zeros, ones), ones, err_msg=msg)
            assert_equal(np.bitwise_or(ones, zeros), ones, err_msg=msg)
            assert_equal(np.bitwise_or(ones, ones), ones, err_msg=msg)

            assert_equal(np.bitwise_xor(zeros, zeros), zeros, err_msg=msg)
            assert_equal(np.bitwise_xor(zeros, ones), ones, err_msg=msg)
            assert_equal(np.bitwise_xor(ones, zeros), ones, err_msg=msg)
            assert_equal(np.bitwise_xor(ones, ones), zeros, err_msg=msg)

            assert_equal(np.bitwise_and(zeros, zeros), zeros, err_msg=msg)
            assert_equal(np.bitwise_and(zeros, ones), zeros, err_msg=msg)
            assert_equal(np.bitwise_and(ones, zeros), zeros, err_msg=msg)
            assert_equal(np.bitwise_and(ones, ones), ones, err_msg=msg) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_or [as 別名]
def label_colormap(N=256):

    def bitget(byteval, idx):
        return ((byteval & (1 << idx)) != 0)

    cmap = np.zeros((N, 3))
    for i in range(0, N):
        id = i
        r, g, b = 0, 0, 0
        for j in range(0, 8):
            r = np.bitwise_or(r, (bitget(id, 0) << 7-j))
            g = np.bitwise_or(g, (bitget(id, 1) << 7-j))
            b = np.bitwise_or(b, (bitget(id, 2) << 7-j))
            id = (id >> 3)
        cmap[i, 0] = r
        cmap[i, 1] = g
        cmap[i, 2] = b
    cmap = cmap.astype(np.float32) / 255
    return cmap 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_or [as 別名]
def set_ufunc(self, scalar_op):
        # This is probably a speed up of the implementation
        if isinstance(scalar_op, theano.scalar.basic.Add):
            self.ufunc = numpy.add
        elif isinstance(scalar_op, theano.scalar.basic.Mul):
            self.ufunc = numpy.multiply
        elif isinstance(scalar_op, theano.scalar.basic.Maximum):
            self.ufunc = numpy.maximum
        elif isinstance(scalar_op, theano.scalar.basic.Minimum):
            self.ufunc = numpy.minimum
        elif isinstance(scalar_op, theano.scalar.basic.AND):
            self.ufunc = numpy.bitwise_and
        elif isinstance(scalar_op, theano.scalar.basic.OR):
            self.ufunc = numpy.bitwise_or
        elif isinstance(scalar_op, theano.scalar.basic.XOR):
            self.ufunc = numpy.bitwise_xor
        else:
            self.ufunc = numpy.frompyfunc(scalar_op.impl, 2, 1) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_or [as 別名]
def Hilditch_skeleton(binary_image):
    size = binary_image.size
    skel = np.zeros(binary_image.shape, np.uint8)

    elem = np.array([[0, 1, 0],
                     [1, 1, 1],
                     [0, 1, 0]])

    image = binary_image.copy()
    for _ in range(10000):
        eroded = ndi.binary_erosion(image, elem)
        temp = ndi.binary_dilation(eroded, elem)
        temp = image - temp
        skel = np.bitwise_or(skel, temp)
        image = eroded.copy()

        zeros = size - np.sum(image > 0)
        if zeros == size:
            break

    return skel 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_or [as 別名]
def loadDepthMap(self,filename):
        """
        Read a depth-map
        :param filename: file name to load
        :return: image data of depth image
        """

        img = Image.open(filename)
        # top 8 bits of depth are packed into green channel and lower 8 bits into blue
        assert len(img.getbands()) == 3
        r, g, b = img.split()
        r = np.asarray(r, np.int32)
        g = np.asarray(g, np.int32)
        b = np.asarray(b, np.int32)
        dpt = np.bitwise_or(np.left_shift(g, 8), b)
        imgdata = np.asarray(dpt, np.float32)

        return imgdata 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_or [as 別名]
def _get_slice_pixeldata(dicom_slice):
    """
    the slice and intercept calculation can cause the slices to have different dtypes
    we should get the correct dtype that can cover all of them

    :type dicom_slice: pydicom object
    :param dicom_slice: slice to get the pixeldata for
    """
    data = dicom_slice.pixel_array
    # fix overflow issues for signed data where BitsStored is lower than BitsAllocated and PixelReprentation = 1 (signed)
    # for example a hitachi mri scan can have BitsAllocated 16 but BitsStored is 12 and HighBit 11
    if dicom_slice.BitsAllocated != dicom_slice.BitsStored and \
            dicom_slice.HighBit == dicom_slice.BitsStored - 1 and \
            dicom_slice.PixelRepresentation == 1:
        if dicom_slice.BitsAllocated == 16:
            data = data.astype(numpy.int16)  # assert that it is a signed type
        max_value = pow(2, dicom_slice.HighBit) - 1
        invert_value = -1 ^ max_value
        data[data > max_value] = numpy.bitwise_or(data[data > max_value], invert_value)
        pass
    return apply_scaling(data, dicom_slice) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_or [as 別名]
def loadDepthMap(self, filename):
        """
        Read a depth-map
        :param filename: file name to load
        :return: image data of depth image
        """

        img = Image.open(filename)
        # top 8 bits of depth are packed into green channel and lower 8 bits into blue
        assert len(img.getbands()) == 3
        r, g, b = img.split()
        r = np.asarray(r, np.int32)
        g = np.asarray(g, np.int32)
        b = np.asarray(b, np.int32)
        dpt = np.bitwise_or(np.left_shift(g, 8), b)
        imgdata = np.asarray(dpt, np.float32)

        return imgdata 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_or [as 別名]
def _pack_encoded_values(encoded_values, bits):
    # TODO optimize with np.packbits for bits == 1
    if bits == 0:
        return bytes()
    else:
        values_per_32bit = 32 // bits
        assert np.all(encoded_values == encoded_values & ((1 << bits) - 1))
        padded_values = np.empty(
            values_per_32bit * ceil_div(len(encoded_values), values_per_32bit),
            dtype="<I")
        padded_values[:len(encoded_values)] = encoded_values
        padded_values[len(encoded_values):] = 0
        packed_values = functools.reduce(
            np.bitwise_or,
            (padded_values[shift::values_per_32bit] << (shift * bits)
             for shift in range(values_per_32bit)))
        return packed_values.tobytes() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_or [as 別名]
def show(self, output_array):
        

        import _rpi_ws281x as ws # pylint: disable=import-error

        # Typecast the array to int
        output_array = output_array.clip(0, 255).astype(int)

        # sort the colors. grb
        g = np.left_shift(output_array[1][:].astype(int), 16) # pylint: disable=assignment-from-no-return
        r = np.left_shift(output_array[0][:].astype(int), 8) # pylint: disable=assignment-from-no-return    
        b = output_array[2][:].astype(int)
        rgb = np.bitwise_or(np.bitwise_or(r, g), b).astype(int)

        # You can only use ws2811_leds_set with the custom version.
        #ws.ws2811_leds_set(self.channel, rgb)
        for i in range(self._led_count):
            ws.ws2811_led_set(self.channel, i, rgb[i].item())


        resp = ws.ws2811_render(self._leds)

        if resp != ws.WS2811_SUCCESS:
            message = ws.ws2811_get_return_t_str(resp)
            raise RuntimeError('ws2811_render failed with code {0} ({1})'.format(resp, message)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_or [as 別名]
def show(self, pixels):
        """Writes new LED values to the Raspberry Pi's LED strip
        Raspberry Pi uses the rpi_ws281x to control the LED strip directly.
        This function updates the LED strip with new values.
        """
        # Truncate values and cast to integer
        n_pixels = pixels.shape[1]
        pixels = pixels.clip(0, 255).astype(int)
        # Optional gamma correction
        pixels = _GAMMA_TABLE[pixels]
        # Encode 24-bit LED values in 32 bit integers
        r = np.left_shift(pixels[0][:].astype(int), 8)
        g = np.left_shift(pixels[1][:].astype(int), 16)
        b = pixels[2][:].astype(int)
        rgb = np.bitwise_or(np.bitwise_or(r, g), b)
        # Update the pixels
        for i in range(n_pixels):
            self.strip.setPixelColor(i, neopixel.Color(rgb[i]))
        self.strip.show() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_or [as 別名]
def _update_pi():
    """Writes new LED values to the Raspberry Pi's LED strip

    Raspberry Pi uses the rpi_ws281x to control the LED strip directly.
    This function updates the LED strip with new values.
    """
    global pixels, _prev_pixels
    # Truncate values and cast to integer
    pixels = np.clip(pixels, 0, 255).astype(int)
    # Optional gamma correction
    p = _gamma[pixels] if config.settings["configuration"]["SOFTWARE_GAMMA_CORRECTION"] else np.copy(pixels)
    # Encode 24-bit LED values in 32 bit integers
    r = np.left_shift(p[0][:].astype(int), 8)
    g = np.left_shift(p[1][:].astype(int), 16)
    b = p[2][:].astype(int)
    rgb = np.bitwise_or(np.bitwise_or(r, g), b)
    # Update the pixels
    for i in range(config.settings["configuration"]["N_PIXELS"]):
        # Ignore pixels if they haven't changed (saves bandwidth)
        if np.array_equal(p[:, i], _prev_pixels[:, i]):
            continue
        strip._led_data[i] = rgb[i]
    _prev_pixels = np.copy(p)
    strip.show()