示例#1
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 def theta(self, eta):
     """The canonical parameter, theta, that
     corresponds to the natural parameter, eta."""
     assert self._check_shape(eta)
     W = 2. * eta[self.k:].reshape((self.k, self.k))
     mu = solve(W, eta[:self.k])
     return (mu, W)
示例#2
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def linear_solution_bias_estimate(readings):
    delta_matrix = delta(readings)
    num_sensors = len(delta_matrix)
    target_vector = [
        sum([delta_matrix[k, i] for i in range(num_sensors) if i != k])
        for k in range(num_sensors)
    ] + [0]

    def diagonal(i, j):
        if i == j:
            return num_sensors - 1
        else:
            return -1

    matrix = [[diagonal(i, j) for i in range(num_sensors)] + [1]
              for j in range(num_sensors)] + [[1] * num_sensors + [0]]
    return solve(matrix, target_vector)[:num_sensors]
示例#3
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文件: color.py 项目: DavidB-CMU/moped
def adapt_whitepoint(src, dst):
    """ Compute the adaptation matrix for converting XYZ tristimulus values from
    a one standard illuminant to another using the Bradford transform.

    This implementation follows the presentation on
        http://www.color.org/chadtag.html

    The results are cached.

    Parameters
    ----------
    src : str
    dst : str
        The names of the standard illuminants to convert from and to,
        respectively. Valid values are the keys of `whitepoints` like 'D65' or
        'CIE A'.

    Returns
    -------
    adapt : float array (3, 3)
        Matrix-multiply this matrix against XYZ values with the `src` whitepoint
        to get XYZ values with the `dst` whitepoint.
    """

    # Check the cache first.
    key = (src, dst)
    if key not in adapt_whitepoint.cache:
        bradford = np.array([[0.8951, 0.2664, -0.1614], [-0.7502, 1.7135, 0.0367], [0.0389, -0.0685, 1.0296]])

        src_whitepoint = whitepoints[src][-1]
        src_rgb = np.dot(bradford, src_whitepoint)
        dst_whitepoint = whitepoints[dst][-1]
        dst_rgb = np.dot(bradford, dst_whitepoint)

        scale = dst_rgb / src_rgb
        adapt_whitepoint.cache[key] = solve(bradford, scale[:, np.newaxis] * bradford)

    adapt = adapt_whitepoint.cache[key]
    return adapt
示例#4
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文件: color.py 项目: DavidB-CMU/moped
        [0.2434974736455316, 0.8523911562030849, -0.0515994646411065],
        [-0.3958579552426224, 1.1655483851630273, 0.0837969419671409],
        [0.0, 0.0, 0.6185822095756526],
    ]
)
xyz_from_lms = inv(lms_from_xyz)

# The transformation from XYZ to the ATD opponent colorspace. These are
# "official" values directly from Guth 1980.
atd_from_xyz = np.array([[0.0, 0.9341, 0.0], [0.7401, -0.6801, -0.1567], [-0.0061, -0.0212, 0.0314]])
xyz_from_atd = inv(atd_from_xyz)

# Now we need to compute the intermediate transformations between LMS and ATD.
# We derive these directly from the other two instead of specifying potentially
# truncated values.
atd_from_lms = solve(lms_from_xyz.T, atd_from_xyz.T).T
lms_from_atd = solve(atd_from_xyz.T, lms_from_xyz.T).T


# XYZ white-point coordinates
#  from http://www.aim-dtp.net/aim/technology/cie_xyz/cie_xyz.htm
whitepoints = {
    "CIE A": ["Normal incandescent", triwhite(0.4476, 0.4074)],
    "CIE B": ["Direct sunlight", triwhite(0.3457, 0.3585)],
    "CIE C": ["Average sunlight", triwhite(0.3101, 0.3162)],
    "CIE E": ["Normalized reference", triwhite(1.0 / 3, 1.0 / 3)],
    "D50": ["Bright tungsten", triwhite(0.3457, 0.3585)],
    "D55": ["Cloudy daylight", triwhite(0.3324, 0.3474)],
    "D65": ["Daylight", triwhite(0.312713, 0.329016)],
    "D75": ["?", triwhite(0.299, 0.3149)],
    "D93": ["low-quality old CRT", triwhite(0.2848, 0.2932)],