Esempio n. 1
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def corresponding_chromaticities_prediction_CMCCAT2000(experiment=1, **kwargs):
    """
    Returns the corresponding chromaticities prediction for CMCCAT2000
    chromatic adaptation model.

    Parameters
    ----------
    experiment : integer, optional
        {1, 2, 3, 4, 6, 8, 9, 11, 12}
        Breneman (1987) experiment number.
    \**kwargs : dict, optional
        Keywords arguments.

    Returns
    -------
    tuple
        Corresponding chromaticities prediction.

    Examples
    --------
    >>> from pprint import pprint
    >>> pr = corresponding_chromaticities_prediction_CMCCAT2000(2)
    >>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
    >>> pprint(pr)  # doctest: +SKIP
    [((0.207, 0.486), (0.20832101929657834, 0.47271680534693694)),
     ((0.449, 0.511), (0.44592707020371486, 0.50777351504395707)),
     ((0.263, 0.505), (0.26402624712986333, 0.4955361681706304)),
     ((0.322, 0.545), (0.33168840090358015, 0.54315801981008516)),
     ((0.316, 0.537), (0.32226245779851387, 0.53576245377085929)),
     ((0.265, 0.553), (0.27107058097430181, 0.5501997842556422)),
     ((0.221, 0.538), (0.22618269421847523, 0.52947407170848704)),
     ((0.135, 0.532), (0.14396930475660724, 0.51909841743126817)),
     ((0.145, 0.472), (0.14948357434418671, 0.45567605010224305)),
     ((0.163, 0.331), (0.15631720730028753, 0.31641514460738623)),
     ((0.176, 0.431), (0.17631993066748047, 0.41275893424542082)),
     ((0.244, 0.349), (0.22876382018951744, 0.3499324084859976))]
    """

    experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))

    illuminants = experiment_results.pop(0)
    XYZ_w = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_t)) * 100
    XYZ_wr = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_m)) * 100
    xy_wr = XYZ_to_xy(XYZ_wr)
    L_A1 = L_A2 = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES.get(
        experiment).Y

    prediction = []
    for result in experiment_results:
        XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t)) * 100
        XYZ_2 = chromatic_adaptation_CMCCAT2000(
            XYZ_1, XYZ_w, XYZ_wr, L_A1, L_A2)
        uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_wr), xy_wr)
        prediction.append(CorrespondingChromaticitiesPrediction(
            result.name,
            result.uvp_t,
            result.uvp_m,
            uvp))

    return tuple(prediction)
Esempio n. 2
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def corresponding_chromaticities_prediction_Fairchild1990(experiment=1):
    """
    Returns the corresponding chromaticities prediction for *Fairchild (1990)*
    chromatic adaptation model.

    Parameters
    ----------
    experiment : integer, optional
        {1, 2, 3, 4, 6, 8, 9, 11, 12}
        *Breneman (1987)* experiment number.

    Returns
    -------
    tuple
        Corresponding chromaticities prediction.

    References
    ----------
    :cite:`Breneman1987b`, :cite:`Fairchild1991a`, :cite:`Fairchild2013s`

    Examples
    --------
    >>> from pprint import pprint
    >>> pr = corresponding_chromaticities_prediction_Fairchild1990(2)
    >>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
    >>> pprint(pr)  # doctest: +SKIP
    [((0.207, 0.486), (0.2089528..., 0.4724034...)),
     ((0.449, 0.511), (0.4375652..., 0.5121030...)),
     ((0.263, 0.505), (0.2621362..., 0.4972538...)),
     ((0.322, 0.545), (0.3235312..., 0.5475665...)),
     ((0.316, 0.537), (0.3151390..., 0.5398333...)),
     ((0.265, 0.553), (0.2634745..., 0.5544335...)),
     ((0.221, 0.538), (0.2211595..., 0.5324470...)),
     ((0.135, 0.532), (0.1396949..., 0.5207234...)),
     ((0.145, 0.472), (0.1512288..., 0.4533041...)),
     ((0.163, 0.331), (0.1715691..., 0.3026264...)),
     ((0.176, 0.431), (0.1825792..., 0.4077892...)),
     ((0.244, 0.349), (0.2418904..., 0.3413401...))]
    """

    with domain_range_scale(1):
        experiment_results = list(BRENEMAN_EXPERIMENTS[experiment])

        illuminants = experiment_results.pop(0)
        XYZ_n = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_t))
        XYZ_r = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_m))
        xy_r = XYZ_to_xy(XYZ_r)
        Y_n = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES[experiment].Y

        prediction = []
        for result in experiment_results:
            XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t))
            XYZ_2 = chromatic_adaptation_Fairchild1990(XYZ_1, XYZ_n, XYZ_r,
                                                       Y_n)
            uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_r), xy_r)
            prediction.append(
                CorrespondingChromaticitiesPrediction(
                    result.name, result.uvp_t, result.uvp_m, uvp))

        return tuple(prediction)
Esempio n. 3
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    def test_n_dimensional_Luv_uv_to_xy(self):
        """
        Tests :func:`colour.models.cie_luv.Luv_uv_to_xy` definition
        n-dimensional arrays support.
        """

        uv = np.array([0.15085310, 0.48532971])
        xy = np.array([0.26414773, 0.37770001])
        np.testing.assert_almost_equal(
            Luv_uv_to_xy(uv),
            xy,
            decimal=7)

        uv = np.tile(uv, (6, 1))
        xy = np.tile(xy, (6, 1))
        np.testing.assert_almost_equal(
            Luv_uv_to_xy(uv),
            xy,
            decimal=7)

        uv = np.reshape(uv, (2, 3, 2))
        xy = np.reshape(xy, (2, 3, 2))
        np.testing.assert_almost_equal(
            Luv_uv_to_xy(uv),
            xy,
            decimal=7)
Esempio n. 4
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def corresponding_chromaticities_prediction_Fairchild1990(experiment=1,
                                                          **kwargs):
    """
    Returns the corresponding chromaticities prediction for Fairchild (1990)
    chromatic adaptation model.

    Parameters
    ----------
    experiment : integer, optional
        {1, 2, 3, 4, 6, 8, 9, 11, 12}
        Breneman (1987) experiment number.
    \**kwargs : dict, optional
        Keywords arguments.

    Returns
    -------
    tuple
        Corresponding chromaticities prediction.

    Examples
    --------
    >>> from pprint import pprint
    >>> pr = corresponding_chromaticities_prediction_Fairchild1990(2)
    >>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
    >>> pprint(pr)  # doctest: +SKIP
    [((0.207, 0.486), (0.2089528677990308, 0.47240345174230519)),
     ((0.449, 0.511), (0.43756528098582792, 0.51210303139041924)),
     ((0.263, 0.505), (0.26213623665658092, 0.49725385033264224)),
     ((0.322, 0.545), (0.3235312762825191, 0.54756652922585702)),
     ((0.316, 0.537), (0.3151390992740366, 0.53983332031574016)),
     ((0.265, 0.553), (0.26347459238415272, 0.55443357809543037)),
     ((0.221, 0.538), (0.22115956537655593, 0.53244703908294599)),
     ((0.135, 0.532), (0.13969494108553854, 0.52072342107668024)),
     ((0.145, 0.472), (0.1512288710743511, 0.45330415352961834)),
     ((0.163, 0.331), (0.17156913711903982, 0.30262647410866889)),
     ((0.176, 0.431), (0.18257922398137369, 0.40778921192793854)),
     ((0.244, 0.349), (0.24189049501108895, 0.34134012046930529))]
    """

    experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))

    illuminants = experiment_results.pop(0)
    XYZ_n = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_t)) * 100
    XYZ_r = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_m)) * 100
    xy_r = XYZ_to_xy(XYZ_r)
    Y_n = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES.get(experiment).Y

    prediction = []
    for result in experiment_results:
        XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t)) * 100
        XYZ_2 = chromatic_adaptation_Fairchild1990(
            XYZ_1, XYZ_n, XYZ_r, Y_n)
        uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_r), xy_r)
        prediction.append(CorrespondingChromaticitiesPrediction(
            result.name,
            result.uvp_t,
            result.uvp_m,
            uvp))

    return tuple(prediction)
Esempio n. 5
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def corresponding_chromaticities_prediction_VonKries(experiment=1,
                                                     transform='CAT02'):
    """
    Returns the corresponding chromaticities prediction for *Von Kries*
    chromatic adaptation model using given transform.

    Parameters
    ----------
    experiment : integer, optional
        {1, 2, 3, 4, 6, 8, 9, 11, 12}
        *Breneman (1987)* experiment number.
    transform : unicode, optional
        **{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
        'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
        'Bianco PC'}**,
        Chromatic adaptation transform.

    Returns
    -------
    tuple
        Corresponding chromaticities prediction.

    Examples
    --------
    >>> from pprint import pprint
    >>> pr = corresponding_chromaticities_prediction_VonKries(2, 'Bradford')
    >>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
    >>> pprint(pr)  # doctest: +SKIP
    [((0.207, 0.486), (0.2082014..., 0.4722922...)),
     ((0.449, 0.511), (0.4489102..., 0.5071602...)),
     ((0.263, 0.505), (0.2643545..., 0.4959631...)),
     ((0.322, 0.545), (0.3348730..., 0.5471220...)),
     ((0.316, 0.537), (0.3248758..., 0.5390589...)),
     ((0.265, 0.553), (0.2733105..., 0.5555028...)),
     ((0.221, 0.538), (0.2271480..., 0.5331317...)),
     ((0.135, 0.532), (0.1442730..., 0.5226804...)),
     ((0.145, 0.472), (0.1498745..., 0.4550785...)),
     ((0.163, 0.331), (0.1564975..., 0.3148795...)),
     ((0.176, 0.431), (0.1760593..., 0.4103772...)),
     ((0.244, 0.349), (0.2259805..., 0.3465291...))]
    """

    experiment_results = list(BRENEMAN_EXPERIMENTS[experiment])

    illuminants = experiment_results.pop(0)
    XYZ_w = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_t))
    XYZ_wr = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_m))
    xy_wr = XYZ_to_xy(XYZ_wr)

    prediction = []
    for result in experiment_results:
        XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t))
        XYZ_2 = chromatic_adaptation_VonKries(XYZ_1, XYZ_w, XYZ_wr, transform)
        uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_wr), xy_wr)
        prediction.append(
            CorrespondingChromaticitiesPrediction(result.name, result.uvp_t,
                                                  result.uvp_m, uvp))

    return tuple(prediction)
Esempio n. 6
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def corresponding_chromaticities_prediction_CIE1994(experiment=1):
    """
    Returns the corresponding chromaticities prediction for CIE 1994
    chromatic adaptation model.

    Parameters
    ----------
    experiment : integer, optional
        {1, 2, 3, 4, 6, 8, 9, 11, 12}
        Breneman (1987) experiment number.

    Returns
    -------
    tuple
        Corresponding chromaticities prediction.

    Examples
    --------
    >>> from pprint import pprint
    >>> pr = corresponding_chromaticities_prediction_CIE1994(2)
    >>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
    >>> pprint(pr)  # doctest: +SKIP
    [((0.207, 0.486), (0.2133909..., 0.4939794...)),
     ((0.449, 0.511), (0.4450345..., 0.5120939...)),
     ((0.263, 0.505), (0.2693262..., 0.5083212...)),
     ((0.322, 0.545), (0.3308593..., 0.5443940...)),
     ((0.316, 0.537), (0.3225195..., 0.5377826...)),
     ((0.265, 0.553), (0.2709737..., 0.5513666...)),
     ((0.221, 0.538), (0.2280786..., 0.5351592...)),
     ((0.135, 0.532), (0.1439436..., 0.5303576...)),
     ((0.145, 0.472), (0.1500743..., 0.4842895...)),
     ((0.163, 0.331), (0.1559955..., 0.3772379...)),
     ((0.176, 0.431), (0.1806318..., 0.4518475...)),
     ((0.244, 0.349), (0.2454445..., 0.4018004...))]
    """

    experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))

    illuminants = experiment_results.pop(0)
    xy_o1 = Luv_uv_to_xy(illuminants.uvp_t)
    xy_o2 = Luv_uv_to_xy(illuminants.uvp_m)
    # :math:`Y_o` is set to an arbitrary value in domain [18, 100].
    Y_o = 18
    E_o1 = E_o2 = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES.get(
        experiment).Y

    prediction = []
    for result in experiment_results:
        XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t)) * 100
        XYZ_2 = chromatic_adaptation_CIE1994(XYZ_1, xy_o1, xy_o2, Y_o, E_o1,
                                             E_o2)
        uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_o2), xy_o2)
        prediction.append(
            CorrespondingChromaticitiesPrediction(result.name, result.uvp_t,
                                                  result.uvp_m, uvp))

    return tuple(prediction)
Esempio n. 7
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def corresponding_chromaticities_prediction_CMCCAT2000(experiment=1):
    """
    Returns the corresponding chromaticities prediction for CMCCAT2000
    chromatic adaptation model.

    Parameters
    ----------
    experiment : integer, optional
        {1, 2, 3, 4, 6, 8, 9, 11, 12}
        Breneman (1987) experiment number.

    Returns
    -------
    tuple
        Corresponding chromaticities prediction.

    Examples
    --------
    >>> from pprint import pprint
    >>> pr = corresponding_chromaticities_prediction_CMCCAT2000(2)
    >>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
    >>> pprint(pr)  # doctest: +SKIP
    [((0.207, 0.486), (0.2083210..., 0.4727168...)),
     ((0.449, 0.511), (0.4459270..., 0.5077735...)),
     ((0.263, 0.505), (0.2640262..., 0.4955361...)),
     ((0.322, 0.545), (0.3316884..., 0.5431580...)),
     ((0.316, 0.537), (0.3222624..., 0.5357624...)),
     ((0.265, 0.553), (0.2710705..., 0.5501997...)),
     ((0.221, 0.538), (0.2261826..., 0.5294740...)),
     ((0.135, 0.532), (0.1439693..., 0.5190984...)),
     ((0.145, 0.472), (0.1494835..., 0.4556760...)),
     ((0.163, 0.331), (0.1563172..., 0.3164151...)),
     ((0.176, 0.431), (0.1763199..., 0.4127589...)),
     ((0.244, 0.349), (0.2287638..., 0.3499324...))]
    """

    experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))

    illuminants = experiment_results.pop(0)
    XYZ_w = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_t)) * 100
    XYZ_wr = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_m)) * 100
    xy_wr = XYZ_to_xy(XYZ_wr)
    L_A1 = L_A2 = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES.get(
        experiment).Y

    prediction = []
    for result in experiment_results:
        XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t)) * 100
        XYZ_2 = chromatic_adaptation_CMCCAT2000(XYZ_1, XYZ_w, XYZ_wr, L_A1,
                                                L_A2)
        uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_wr), xy_wr)
        prediction.append(
            CorrespondingChromaticitiesPrediction(result.name, result.uvp_t,
                                                  result.uvp_m, uvp))

    return tuple(prediction)
Esempio n. 8
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def chromaticity_diagram_colours_CIE1976UCS(
        samples=4096,
        cmfs='CIE 1931 2 Degree Standard Observer',
        antialiasing=True):
    """
    Plots the *CIE 1976 UCS Chromaticity Diagram* colours.

    Parameters
    ----------
    samples : numeric, optional
        Samples count on one axis.
    cmfs : unicode, optional
        Standard observer colour matching functions used for diagram bounds.
    antialiasing : bool, optional
        Whether to apply anti-aliasing to the image.

    Other Parameters
    ----------------
    \**kwargs : dict, optional
        {:func:`colour.plotting.render`},
        Please refer to the documentation of the previously listed definition.

    Returns
    -------
    Figure
        Current figure or None.

    Examples
    --------
    >>> chromaticity_diagram_colours_CIE1976UCS()  # doctest: +SKIP
    """

    cmfs = get_cmfs(cmfs)

    illuminant = DEFAULT_PLOTTING_ILLUMINANT

    triangulation = Delaunay(Luv_to_uv(XYZ_to_Luv(cmfs.values, illuminant),
                                       illuminant),
                             qhull_options='Qu QJ')
    xx, yy = np.meshgrid(np.linspace(0, 1, samples),
                         np.linspace(1, 0, samples))
    xy = tstack((xx, yy))
    mask = (triangulation.find_simplex(xy) < 0).astype(DEFAULT_FLOAT_DTYPE)
    if antialiasing:
        kernel = np.array([
            [0, 1, 0],
            [1, 2, 1],
            [0, 1, 0],
        ]).astype(DEFAULT_FLOAT_DTYPE)
        kernel /= np.sum(kernel)
        mask = convolve(mask, kernel)

    mask = 1 - mask[:, :, np.newaxis]

    XYZ = xy_to_XYZ(Luv_uv_to_xy(xy))

    RGB = normalise_maximum(XYZ_to_sRGB(XYZ, illuminant), axis=-1)

    return np.dstack([RGB, mask])
Esempio n. 9
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    def test_n_dimensional_Luv_uv_to_xy(self):
        """
        Tests :func:`colour.models.cie_luv.Luv_uv_to_xy` definition
        n-dimensional arrays support.
        """

        uv = np.array([0.37720213, 0.50120264])
        xy = np.array([0.54369558, 0.32107944])
        np.testing.assert_almost_equal(Luv_uv_to_xy(uv), xy, decimal=7)

        uv = np.tile(uv, (6, 1))
        xy = np.tile(xy, (6, 1))
        np.testing.assert_almost_equal(Luv_uv_to_xy(uv), xy, decimal=7)

        uv = np.reshape(uv, (2, 3, 2))
        xy = np.reshape(xy, (2, 3, 2))
        np.testing.assert_almost_equal(Luv_uv_to_xy(uv), xy, decimal=7)
Esempio n. 10
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    def test_Luv_uv_to_xy(self):
        """
        Tests :func:`colour.models.cie_luv.Luv_uv_to_xy` definition.
        """

        np.testing.assert_almost_equal(Luv_uv_to_xy(
            np.array([0.37720213, 0.50120264])),
                                       np.array([0.54369558, 0.32107944]),
                                       decimal=7)

        np.testing.assert_almost_equal(Luv_uv_to_xy(
            np.array([0.14536327, 0.52992069])),
                                       np.array([0.29777734, 0.48246445]),
                                       decimal=7)

        np.testing.assert_almost_equal(Luv_uv_to_xy(
            np.array([0.16953603, 0.30039234])),
                                       np.array([0.18582824, 0.14633764]),
                                       decimal=7)
Esempio n. 11
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    def test_Luv_uv_to_xy(self):
        """
        Tests :func:`colour.models.cie_luv.Luv_uv_to_xy` definition.
        """

        np.testing.assert_almost_equal(Luv_uv_to_xy(
            (0.20048615433157738,
             0.4654903849082484)), (0.31352792378977895, 0.32353408235422665),
                                       decimal=7)

        np.testing.assert_almost_equal(Luv_uv_to_xy(
            (0.46412000081619281,
             0.54388500014670993)), (0.6867305880410077, 0.3576684816384643),
                                       decimal=7)

        np.testing.assert_almost_equal(Luv_uv_to_xy(
            (0.18133000048542355,
             0.40268999998517152)), (0.2455958975694641, 0.2424039944946324),
                                       decimal=7)
Esempio n. 12
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    def test_Luv_uv_to_xy(self):
        """
        Tests :func:`colour.models.cie_luv.Luv_uv_to_xy` definition.
        """

        np.testing.assert_almost_equal(
            Luv_uv_to_xy(np.array([0.15085310, 0.48532971])),
            np.array([0.26414773, 0.37770001]),
            decimal=7)

        np.testing.assert_almost_equal(
            Luv_uv_to_xy(np.array([0.31125983, 0.51970032])),
            np.array([0.50453169, 0.37440000]),
            decimal=7)

        np.testing.assert_almost_equal(
            Luv_uv_to_xy(np.array([0.30069387, 0.50794847])),
            np.array([0.47670437, 0.35790000]),
            decimal=7)
Esempio n. 13
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    def test_nan_Luv_uv_to_xy(self):
        """
        Tests :func:`colour.models.cie_luv.Luv_uv_to_xy` definition nan
        support.
        """

        cases = [-1.0, 0.0, 1.0, -np.inf, np.inf, np.nan]
        cases = set(permutations(cases * 3, r=2))
        for case in cases:
            uv = np.array(case)
            Luv_uv_to_xy(uv)
Esempio n. 14
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def plot_chromaticity_diagram_colours(
        samples=256,
        diagram_opacity=1.0,
        diagram_clipping_path=None,
        cmfs='CIE 1931 2 Degree Standard Observer',
        method='CIE 1931',
        **kwargs):
    """
    Plots the *Chromaticity Diagram* colours according to given method.

    Parameters
    ----------
    samples : numeric, optional
        Samples count on one axis.
    diagram_opacity : numeric, optional
        Opacity of the *Chromaticity Diagram* colours.
    diagram_clipping_path : array_like, optional
        Path of points used to clip the *Chromaticity Diagram* colours.
    cmfs : unicode, optional
        Standard observer colour matching functions used for
        *Chromaticity Diagram* bounds.
    method : unicode, optional
        **{'CIE 1931', 'CIE 1960 UCS', 'CIE 1976 UCS'}**,
        *Chromaticity Diagram* method.

    Other Parameters
    ----------------
    \\**kwargs : dict, optional
        {:func:`colour.plotting.artist`, :func:`colour.plotting.render`},
        Please refer to the documentation of the previously listed definitions.

    Returns
    -------
    tuple
        Current figure and axes.

    Examples
    --------
    >>> plot_chromaticity_diagram_colours()  # doctest: +SKIP

    .. image:: ../_static/Plotting_Plot_Chromaticity_Diagram_Colours.png
        :align: center
        :alt: plot_chromaticity_diagram_colours
    """

    settings = {'uniform': True}
    settings.update(kwargs)

    figure, axes = artist(**settings)

    method = method.upper()

    cmfs = first_item(filter_cmfs(cmfs).values())

    illuminant = COLOUR_STYLE_CONSTANTS.colour.colourspace.whitepoint

    ii, jj = np.meshgrid(
        np.linspace(0, 1, samples), np.linspace(1, 0, samples))
    ij = tstack([ii, jj])

    if method == 'CIE 1931':
        XYZ = xy_to_XYZ(ij)
        spectral_locus = XYZ_to_xy(cmfs.values, illuminant)
    elif method == 'CIE 1960 UCS':
        XYZ = xy_to_XYZ(UCS_uv_to_xy(ij))
        spectral_locus = UCS_to_uv(XYZ_to_UCS(cmfs.values))
    elif method == 'CIE 1976 UCS':
        XYZ = xy_to_XYZ(Luv_uv_to_xy(ij))
        spectral_locus = Luv_to_uv(
            XYZ_to_Luv(cmfs.values, illuminant), illuminant)
    else:
        raise ValueError(
            'Invalid method: "{0}", must be one of '
            '{\'CIE 1931\', \'CIE 1960 UCS\', \'CIE 1976 UCS\'}'.format(
                method))

    RGB = normalise_maximum(
        XYZ_to_plotting_colourspace(XYZ, illuminant), axis=-1)

    polygon = Polygon(
        spectral_locus
        if diagram_clipping_path is None else diagram_clipping_path,
        facecolor='none',
        edgecolor='none')
    axes.add_patch(polygon)
    # Preventing bounding box related issues as per
    # https://github.com/matplotlib/matplotlib/issues/10529
    image = axes.imshow(
        RGB,
        interpolation='bilinear',
        extent=(0, 1, 0, 1),
        clip_path=None,
        alpha=diagram_opacity)
    image.set_clip_path(polygon)

    settings = {'axes': axes}
    settings.update(kwargs)

    return render(**kwargs)
Esempio n. 15
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def convert_experiment_results_Breneman1987(experiment):
    """
    Converts *Breneman (1987)* experiment results to a
    :class:`colour.CorrespondingColourDataset` class instance.

    Parameters
    ----------
    experiment : integer
        {1, 2, 3, 4, 6, 8, 9, 11, 12}
        *Breneman (1987)* experiment number.

    Returns
    -------
    CorrespondingColourDataset
        :class:`colour.CorrespondingColourDataset` class instance.

    Examples
    --------
    >>> from pprint import pprint
    >>> pprint(tuple(convert_experiment_results_Breneman1987(2)))
    ... # doctest: +ELLIPSIS
    (2,
     array([ 0.9582463...,  1.        ,  0.9436325...]),
     array([ 0.9587332...,  1.        ,  0.4385796...]),
     array([[ 388.125     ,  405.        ,  345.625     ],
           [ 266.8957925...,  135.        ,   28.5983365...],
           [ 474.5717821...,  405.        ,  222.75     ...],
           [ 538.3899082...,  405.        ,   24.8944954...],
           [ 178.7430167...,  135.        ,   19.6089385...],
           [ 436.6749547...,  405.        ,   26.5483725...],
           [ 124.7746282...,  135.        ,   36.1965613...],
           [  77.0794172...,  135.        ,   60.5850563...],
           [ 279.9390889...,  405.        ,  455.8395127...],
           [ 149.5808157...,  135.        ,  498.7046827...],
           [ 372.1113689...,  405.        ,  669.9883990...],
           [ 212.3638968...,  135.        ,  414.6704871...]]),
     array([[ 400.1039651...,  405.        ,  191.7287234...],
           [ 271.0384615...,  135.        ,   13.5      ...],
           [ 495.4705323...,  405.        ,  119.7290874...],
           [ 580.7967033...,  405.        ,    6.6758241...],
           [ 190.1933701...,  135.        ,    7.4585635...],
           [ 473.7184115...,  405.        ,   10.2346570...],
           [ 135.4936014...,  135.        ,   20.2376599...],
           [  86.4689781...,  135.        ,   35.2281021...],
           [ 283.5396281...,  405.        ,  258.1775929...],
           [ 119.7044335...,  135.        ,  282.6354679...],
           [ 359.9532224...,  405.        ,  381.0031185...],
           [ 181.8271461...,  135.        ,  204.0661252...]]),
     array(1500),
     array(1500),
     0.3,
     0.3,
     {})
    """

    valid_experiment_results = (1, 2, 3, 4, 6, 8, 9, 11, 12)
    assert experiment in valid_experiment_results, (
        '"Breneman (1987)" experiment result must be one of "{0}"!'.format(
            valid_experiment_results))

    samples_luminance = [
        0.270,
        0.090,
        0.270,
        0.270,
        0.090,
        0.270,
        0.090,
        0.090,
        0.270,
        0.090,
        0.270,
        0.090,
    ]

    experiment_results = list(BRENEMAN_EXPERIMENTS[experiment])
    illuminant_chromaticities = experiment_results.pop(0)
    Y_r = Y_t = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES[experiment].Y
    B_r = B_t = 0.3

    XYZ_t, XYZ_r = xy_to_XYZ(
        np.hstack([
            Luv_uv_to_xy(illuminant_chromaticities[1:3]),
            np.full([2, 1], Y_r)
        ])) / Y_r

    xyY_cr, xyY_ct = [], []
    for i, experiment_result in enumerate(experiment_results):
        xyY_cr.append(
            np.hstack([
                Luv_uv_to_xy(experiment_result[2]), samples_luminance[i] * Y_r
            ]))
        xyY_ct.append(
            np.hstack([
                Luv_uv_to_xy(experiment_result[1]), samples_luminance[i] * Y_t
            ]))

    XYZ_cr = xyY_to_XYZ(xyY_cr)
    XYZ_ct = xyY_to_XYZ(xyY_ct)

    return CorrespondingColourDataset(experiment, XYZ_r, XYZ_t, XYZ_cr, XYZ_ct,
                                      Y_r, Y_t, B_r, B_t, {})
Esempio n. 16
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def CIE_1976_UCS_chromaticity_diagram_colours_plot(
        surface=1.25,
        spacing=0.00075,
        cmfs='CIE 1931 2 Degree Standard Observer',
        **kwargs):
    """
    Plots the *CIE 1976 UCS Chromaticity Diagram* colours.

    Parameters
    ----------
    surface : numeric, optional
        Generated markers surface.
    spacing : numeric, optional
        Spacing between markers.
    cmfs : unicode, optional
        Standard observer colour matching functions used for diagram bounds.
    \*\*kwargs : \*\*
        Keywords arguments.

    Returns
    -------
    bool
        Definition success.

    Examples
    --------
    >>> CIE_1976_UCS_chromaticity_diagram_colours_plot()  # doctest: +SKIP
    True
    """

    cmfs, name = get_cmfs(cmfs), cmfs

    illuminant = ILLUMINANTS.get('CIE 1931 2 Degree Standard Observer').get(
        'D50')

    Luvs = [XYZ_to_Luv(value, illuminant) for key, value in cmfs]

    u, v = tuple(zip(*([Luv_to_uv(x) for x in Luvs])))

    path = matplotlib.path.Path(tuple(zip(u, v)))
    x_dot, y_dot, colours = [], [], []
    for i in np.arange(0, 1, spacing):
        for j in np.arange(0, 1, spacing):
            if path.contains_path(matplotlib.path.Path([[i, j], [i, j]])):
                x_dot.append(i)
                y_dot.append(j)

                XYZ = xy_to_XYZ(Luv_uv_to_xy((i, j)))
                RGB = normalise(XYZ_to_sRGB(XYZ, illuminant))

                colours.append(RGB)

    pylab.scatter(x_dot, y_dot, color=colours, s=surface)

    settings = {
        'no_ticks': True,
        'bounding_box': [0, 1, 0, 1],
        'bbox_inches': 'tight',
        'pad_inches': 0
    }
    settings.update(kwargs)

    bounding_box(**settings)
    aspect(**settings)

    return display(**settings)
Esempio n. 17
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def plot_chromaticity_diagram_colours(
        samples=256,
        diagram_opacity=1.0,
        diagram_clipping_path=None,
        cmfs='CIE 1931 2 Degree Standard Observer',
        method='CIE 1931',
        **kwargs):
    """
    Plots the *Chromaticity Diagram* colours according to given method.

    Parameters
    ----------
    samples : numeric, optional
        Samples count on one axis.
    diagram_opacity : numeric, optional
        Opacity of the *Chromaticity Diagram* colours.
    diagram_clipping_path : array_like, optional
        Path of points used to clip the *Chromaticity Diagram* colours.
    cmfs : unicode or XYZ_ColourMatchingFunctions, optional
        Standard observer colour matching functions used for computing the
        spectral locus boundaries. ``cmfs`` can be of any type or form
        supported by the :func:`colour.plotting.filter_cmfs` definition.
    method : unicode, optional
        **{'CIE 1931', 'CIE 1960 UCS', 'CIE 1976 UCS'}**,
        *Chromaticity Diagram* method.

    Other Parameters
    ----------------
    \\**kwargs : dict, optional
        {:func:`colour.plotting.artist`, :func:`colour.plotting.render`},
        Please refer to the documentation of the previously listed definitions.

    Returns
    -------
    tuple
        Current figure and axes.

    Examples
    --------
    >>> plot_chromaticity_diagram_colours()  # doctest: +ELLIPSIS
    (<Figure size ... with 1 Axes>, <...AxesSubplot...>)

    .. image:: ../_static/Plotting_Plot_Chromaticity_Diagram_Colours.png
        :align: center
        :alt: plot_chromaticity_diagram_colours
    """

    settings = {'uniform': True}
    settings.update(kwargs)

    _figure, axes = artist(**settings)

    method = method.upper()

    cmfs = first_item(filter_cmfs(cmfs).values())

    illuminant = CONSTANTS_COLOUR_STYLE.colour.colourspace.whitepoint

    ii, jj = np.meshgrid(np.linspace(0, 1, samples),
                         np.linspace(1, 0, samples))
    ij = tstack([ii, jj])

    # NOTE: Various values in the grid have potential to generate
    # zero-divisions, they could be avoided by perturbing the grid, e.g. adding
    # a small epsilon. It was decided instead to disable warnings.
    with suppress_warnings(python_warnings=True):
        if method == 'CIE 1931':
            XYZ = xy_to_XYZ(ij)
            spectral_locus = XYZ_to_xy(cmfs.values, illuminant)
        elif method == 'CIE 1960 UCS':
            XYZ = xy_to_XYZ(UCS_uv_to_xy(ij))
            spectral_locus = UCS_to_uv(XYZ_to_UCS(cmfs.values))
        elif method == 'CIE 1976 UCS':
            XYZ = xy_to_XYZ(Luv_uv_to_xy(ij))
            spectral_locus = Luv_to_uv(XYZ_to_Luv(cmfs.values, illuminant),
                                       illuminant)
        else:
            raise ValueError(
                'Invalid method: "{0}", must be one of '
                '[\'CIE 1931\', \'CIE 1960 UCS\', \'CIE 1976 UCS\']'.format(
                    method))

    RGB = normalise_maximum(XYZ_to_plotting_colourspace(XYZ, illuminant),
                            axis=-1)

    polygon = Polygon(spectral_locus if diagram_clipping_path is None else
                      diagram_clipping_path,
                      facecolor='none',
                      edgecolor='none')
    axes.add_patch(polygon)
    # Preventing bounding box related issues as per
    # https://github.com/matplotlib/matplotlib/issues/10529
    image = axes.imshow(RGB,
                        interpolation='bilinear',
                        extent=(0, 1, 0, 1),
                        clip_path=None,
                        alpha=diagram_opacity)
    image.set_clip_path(polygon)

    settings = {'axes': axes}
    settings.update(kwargs)

    return render(**kwargs)
Esempio n. 18
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def corresponding_chromaticities_prediction_CIE1994(experiment=1, **kwargs):
    """
    Returns the corresponding chromaticities prediction for CIE 1994
    chromatic adaptation model.

    Parameters
    ----------
    experiment : integer, optional
        {1, 2, 3, 4, 6, 8, 9, 11, 12}
        Breneman (1987) experiment number.
    \**kwargs : dict, optional
        Keywords arguments.

    Returns
    -------
    tuple
        Corresponding chromaticities prediction.

    Examples
    --------
    >>> from pprint import pprint
    >>> pr = corresponding_chromaticities_prediction_CIE1994(2)
    >>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
    >>> pprint(pr)  # doctest: +SKIP
    [((0.207, 0.486), (0.21339093279517196, 0.49397945742298016)),
     ((0.449, 0.511), (0.4450345313098153, 0.5120939085633327)),
     ((0.263, 0.505), (0.26932620724691858, 0.50832124608390727)),
     ((0.322, 0.545), (0.33085939370840811, 0.54439408389253441)),
     ((0.316, 0.537), (0.3225195584183046, 0.53778269440789594)),
     ((0.265, 0.553), (0.2709737181087471, 0.5513666373694861)),
     ((0.221, 0.538), (0.22807869730753863, 0.53515923458385406)),
     ((0.135, 0.532), (0.14394366662060523, 0.53035769204585748)),
     ((0.145, 0.472), (0.15007438031976222, 0.48428958620888679)),
     ((0.163, 0.331), (0.15599555781959967, 0.37723798698131394)),
     ((0.176, 0.431), (0.18063180902005657, 0.45184759430042898)),
     ((0.244, 0.349), (0.24544456656434688, 0.40180048388092021))]
    """

    experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))

    illuminants = experiment_results.pop(0)
    xy_o1 = Luv_uv_to_xy(illuminants.uvp_t)
    xy_o2 = Luv_uv_to_xy(illuminants.uvp_m)
    # :math:`Y_o` is set to an arbitrary value in domain [18, 100].
    Y_o = 18
    E_o1 = E_o2 = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES.get(
        experiment).Y

    prediction = []
    for result in experiment_results:
        XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t)) * 100
        XYZ_2 = chromatic_adaptation_CIE1994(
            XYZ_1, xy_o1, xy_o2, Y_o, E_o1, E_o2)
        uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_o2), xy_o2)
        prediction.append(CorrespondingChromaticitiesPrediction(
            result.name,
            result.uvp_t,
            result.uvp_m,
            uvp))

    return tuple(prediction)
Esempio n. 19
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def CIE_1976_UCS_chromaticity_diagram_colours_plot(
        surface=1,
        samples=4096,
        cmfs='CIE 1931 2 Degree Standard Observer',
        **kwargs):
    """
    Plots the *CIE 1976 UCS Chromaticity Diagram* colours.

    Parameters
    ----------
    surface : numeric, optional
        Generated markers surface.
    samples : numeric, optional
        Samples count on one axis.
    cmfs : unicode, optional
        Standard observer colour matching functions used for diagram bounds.
    \**kwargs : dict, optional
        Keywords arguments.

    Returns
    -------
    bool
        Definition success.

    Examples
    --------
    >>> CIE_1976_UCS_chromaticity_diagram_colours_plot()  # doctest: +SKIP
    True
    """

    if is_scipy_installed(raise_exception=True):
        from scipy.spatial import Delaunay

        settings = {'figure_size': (64, 64)}
        settings.update(kwargs)

        canvas(**settings)

        cmfs = get_cmfs(cmfs)

        illuminant = DEFAULT_PLOTTING_ILLUMINANT

        triangulation = Delaunay(Luv_to_uv(XYZ_to_Luv(cmfs.values, illuminant),
                                           illuminant),
                                 qhull_options='QJ Qf')
        xx, yy = np.meshgrid(np.linspace(0, 1, samples),
                             np.linspace(0, 1, samples))
        xy = tstack((xx, yy))
        xy = xy[triangulation.find_simplex(xy) > 0]

        XYZ = xy_to_XYZ(Luv_uv_to_xy(xy))

        RGB = normalise(XYZ_to_sRGB(XYZ, illuminant), axis=-1)

        x_dot, y_dot = tsplit(xy)

        pylab.scatter(x_dot, y_dot, color=RGB, s=surface)

        settings.update({
            'x_ticker': False,
            'y_ticker': False,
            'bounding_box': (0, 1, 0, 1),
            'bbox_inches': 'tight',
            'pad_inches': 0
        })
        settings.update(kwargs)

        ax = matplotlib.pyplot.gca()
        matplotlib.pyplot.setp(ax, frame_on=False)

        boundaries(**settings)
        decorate(**settings)

        return display(**settings)
Esempio n. 20
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def plot_spectral_locus(cmfs='CIE 1931 2 Degree Standard Observer',
                        spectral_locus_colours=None,
                        spectral_locus_labels=None,
                        method='CIE 1931',
                        **kwargs):
    """
    Plots the *Spectral Locus* according to given method.

    Parameters
    ----------
    cmfs : unicode, optional
        Standard observer colour matching functions defining the
        *Spectral Locus*.
    spectral_locus_colours : array_like or unicode, optional
        *Spectral Locus* colours, if ``spectral_locus_colours`` is set to
        *RGB*, the colours will be computed according to the corresponding
        chromaticity coordinates.
    spectral_locus_labels : array_like, optional
        Array of wavelength labels used to customise which labels will be drawn
        around the spectral locus. Passing an empty array will result in no
        wavelength labels being drawn.
    method : unicode, optional
        **{'CIE 1931', 'CIE 1960 UCS', 'CIE 1976 UCS'}**,
        *Chromaticity Diagram* method.

    Other Parameters
    ----------------
    \\**kwargs : dict, optional
        {:func:`colour.plotting.artist`, :func:`colour.plotting.render`},
        Please refer to the documentation of the previously listed definitions.

    Returns
    -------
    tuple
        Current figure and axes.

    Examples
    --------
    >>> plot_spectral_locus(spectral_locus_colours='RGB')  # doctest: +SKIP

    .. image:: ../_static/Plotting_Plot_Spectral_Locus.png
        :align: center
        :alt: plot_spectral_locus
    """

    if spectral_locus_colours is None:
        spectral_locus_colours = COLOUR_STYLE_CONSTANTS.colour.dark

    settings = {'uniform': True}
    settings.update(kwargs)

    figure, axes = artist(**settings)

    method = method.upper()

    cmfs = first_item(filter_cmfs(cmfs).values())

    illuminant = COLOUR_STYLE_CONSTANTS.colour.colourspace.whitepoint

    wavelengths = cmfs.wavelengths
    equal_energy = np.array([1 / 3] * 2)

    if method == 'CIE 1931':
        ij = XYZ_to_xy(cmfs.values, illuminant)
        labels = ((390, 460, 470, 480, 490, 500, 510, 520, 540, 560, 580, 600,
                   620, 700)
                  if spectral_locus_labels is None else spectral_locus_labels)
    elif method == 'CIE 1960 UCS':
        ij = UCS_to_uv(XYZ_to_UCS(cmfs.values))
        labels = ((420, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540,
                   550, 560, 570, 580, 590, 600, 610, 620, 630, 645, 680)
                  if spectral_locus_labels is None else spectral_locus_labels)
    elif method == 'CIE 1976 UCS':
        ij = Luv_to_uv(XYZ_to_Luv(cmfs.values, illuminant), illuminant)
        labels = ((420, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540,
                   550, 560, 570, 580, 590, 600, 610, 620, 630, 645, 680)
                  if spectral_locus_labels is None else spectral_locus_labels)
    else:
        raise ValueError(
            'Invalid method: "{0}", must be one of '
            '{\'CIE 1931\', \'CIE 1960 UCS\', \'CIE 1976 UCS\'}'.format(
                method))

    pl_ij = tstack([
        np.linspace(ij[0][0], ij[-1][0], 20),
        np.linspace(ij[0][1], ij[-1][1], 20)
    ]).reshape(-1, 1, 2)
    sl_ij = np.copy(ij).reshape(-1, 1, 2)

    if spectral_locus_colours.upper() == 'RGB':
        spectral_locus_colours = normalise_maximum(
            XYZ_to_plotting_colourspace(cmfs.values), axis=-1)

        if method == 'CIE 1931':
            XYZ = xy_to_XYZ(pl_ij)
        elif method == 'CIE 1960 UCS':
            XYZ = xy_to_XYZ(UCS_uv_to_xy(pl_ij))
        elif method == 'CIE 1976 UCS':
            XYZ = xy_to_XYZ(Luv_uv_to_xy(pl_ij))
        purple_line_colours = normalise_maximum(
            XYZ_to_plotting_colourspace(XYZ.reshape(-1, 3)), axis=-1)
    else:
        purple_line_colours = spectral_locus_colours

    for slp_ij, slp_colours in ((pl_ij, purple_line_colours),
                                (sl_ij, spectral_locus_colours)):
        line_collection = LineCollection(
            np.concatenate([slp_ij[:-1], slp_ij[1:]], axis=1),
            colors=slp_colours)
        axes.add_collection(line_collection)

    wl_ij = dict(tuple(zip(wavelengths, ij)))
    for label in labels:
        i, j = wl_ij[label]

        index = bisect.bisect(wavelengths, label)
        left = wavelengths[index - 1] if index >= 0 else wavelengths[index]
        right = (wavelengths[index]
                 if index < len(wavelengths) else wavelengths[-1])

        dx = wl_ij[right][0] - wl_ij[left][0]
        dy = wl_ij[right][1] - wl_ij[left][1]

        ij = np.array([i, j])
        direction = np.array([-dy, dx])

        normal = (np.array([-dy, dx]) if np.dot(
            normalise_vector(ij - equal_energy), normalise_vector(direction)) >
                  0 else np.array([dy, -dx]))
        normal = normalise_vector(normal) / 30

        label_colour = (spectral_locus_colours
                        if is_string(spectral_locus_colours) else
                        spectral_locus_colours[index])
        axes.plot(
            (i, i + normal[0] * 0.75), (j, j + normal[1] * 0.75),
            color=label_colour)

        axes.plot(i, j, 'o', color=label_colour)

        axes.text(
            i + normal[0],
            j + normal[1],
            label,
            clip_on=True,
            ha='left' if normal[0] >= 0 else 'right',
            va='center',
            fontdict={'size': 'small'})

    settings = {'axes': axes}
    settings.update(kwargs)

    return render(**kwargs)
Esempio n. 21
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def plot_spectral_locus(
    cmfs: Union[MultiSpectralDistributions, str, Sequence[Union[
        MultiSpectralDistributions,
        str]], ] = "CIE 1931 2 Degree Standard Observer",
    spectral_locus_colours: Optional[Union[ArrayLike, str]] = None,
    spectral_locus_opacity: Floating = 1,
    spectral_locus_labels: Optional[Sequence] = None,
    method: Union[Literal["CIE 1931", "CIE 1960 UCS", "CIE 1976 UCS"],
                  str] = "CIE 1931",
    **kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
    """
    Plot the *Spectral Locus* according to given method.

    Parameters
    ----------
    cmfs
        Standard observer colour matching functions used for computing the
        spectral locus boundaries. ``cmfs`` can be of any type or form
        supported by the :func:`colour.plotting.filter_cmfs` definition.
    spectral_locus_colours
        Colours of the *Spectral Locus*, if ``spectral_locus_colours`` is set
        to *RGB*, the colours will be computed according to the corresponding
        chromaticity coordinates.
    spectral_locus_opacity
        Opacity of the *Spectral Locus*.
    spectral_locus_labels
        Array of wavelength labels used to customise which labels will be drawn
        around the spectral locus. Passing an empty array will result in no
        wavelength labels being drawn.
    method
        *Chromaticity Diagram* method.

    Other Parameters
    ----------------
    kwargs
        {:func:`colour.plotting.artist`, :func:`colour.plotting.render`},
        See the documentation of the previously listed definitions.

    Returns
    -------
    :class:`tuple`
        Current figure and axes.

    Examples
    --------
    >>> plot_spectral_locus(spectral_locus_colours='RGB')  # doctest: +ELLIPSIS
    (<Figure size ... with 1 Axes>, <...AxesSubplot...>)

    .. image:: ../_static/Plotting_Plot_Spectral_Locus.png
        :align: center
        :alt: plot_spectral_locus
    """

    method = validate_method(method,
                             ["CIE 1931", "CIE 1960 UCS", "CIE 1976 UCS"])

    spectral_locus_colours = optional(spectral_locus_colours,
                                      CONSTANTS_COLOUR_STYLE.colour.dark)

    settings: Dict[str, Any] = {"uniform": True}
    settings.update(kwargs)

    _figure, axes = artist(**settings)

    cmfs = cast(MultiSpectralDistributions,
                first_item(filter_cmfs(cmfs).values()))

    illuminant = CONSTANTS_COLOUR_STYLE.colour.colourspace.whitepoint

    wavelengths = list(cmfs.wavelengths)
    equal_energy = np.array([1 / 3] * 2)

    if method == "cie 1931":
        ij = XYZ_to_xy(cmfs.values, illuminant)
        labels = cast(
            Tuple,
            optional(
                spectral_locus_labels,
                (
                    390,
                    460,
                    470,
                    480,
                    490,
                    500,
                    510,
                    520,
                    540,
                    560,
                    580,
                    600,
                    620,
                    700,
                ),
            ),
        )
    elif method == "cie 1960 ucs":
        ij = UCS_to_uv(XYZ_to_UCS(cmfs.values))
        labels = cast(
            Tuple,
            optional(
                spectral_locus_labels,
                (
                    420,
                    440,
                    450,
                    460,
                    470,
                    480,
                    490,
                    500,
                    510,
                    520,
                    530,
                    540,
                    550,
                    560,
                    570,
                    580,
                    590,
                    600,
                    610,
                    620,
                    630,
                    645,
                    680,
                ),
            ),
        )
    elif method == "cie 1976 ucs":
        ij = Luv_to_uv(XYZ_to_Luv(cmfs.values, illuminant), illuminant)
        labels = cast(
            Tuple,
            optional(
                spectral_locus_labels,
                (
                    420,
                    440,
                    450,
                    460,
                    470,
                    480,
                    490,
                    500,
                    510,
                    520,
                    530,
                    540,
                    550,
                    560,
                    570,
                    580,
                    590,
                    600,
                    610,
                    620,
                    630,
                    645,
                    680,
                ),
            ),
        )

    pl_ij = np.reshape(
        tstack([
            np.linspace(ij[0][0], ij[-1][0], 20),
            np.linspace(ij[0][1], ij[-1][1], 20),
        ]),
        (-1, 1, 2),
    )
    sl_ij = np.copy(ij).reshape(-1, 1, 2)

    purple_line_colours: Optional[Union[ArrayLike, str]]
    if str(spectral_locus_colours).upper() == "RGB":
        spectral_locus_colours = normalise_maximum(XYZ_to_plotting_colourspace(
            cmfs.values),
                                                   axis=-1)

        if method == "cie 1931":
            XYZ = xy_to_XYZ(pl_ij)
        elif method == "cie 1960 ucs":
            XYZ = xy_to_XYZ(UCS_uv_to_xy(pl_ij))
        elif method == "cie 1976 ucs":
            XYZ = xy_to_XYZ(Luv_uv_to_xy(pl_ij))

        purple_line_colours = normalise_maximum(XYZ_to_plotting_colourspace(
            np.reshape(XYZ, (-1, 3))),
                                                axis=-1)
    else:
        purple_line_colours = spectral_locus_colours

    for slp_ij, slp_colours in (
        (pl_ij, purple_line_colours),
        (sl_ij, spectral_locus_colours),
    ):
        line_collection = LineCollection(
            np.concatenate([slp_ij[:-1], slp_ij[1:]], axis=1),
            colors=slp_colours,
            alpha=spectral_locus_opacity,
            zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_scatter,
        )
        axes.add_collection(line_collection)

    wl_ij = dict(zip(wavelengths, ij))
    for label in labels:
        ij_l = wl_ij.get(label)

        if ij_l is None:
            continue

        ij_l = as_float_array([ij_l])
        i, j = tsplit(ij_l)

        index = bisect.bisect(wavelengths, label)
        left = wavelengths[index - 1] if index >= 0 else wavelengths[index]
        right = (wavelengths[index]
                 if index < len(wavelengths) else wavelengths[-1])

        dx = wl_ij[right][0] - wl_ij[left][0]
        dy = wl_ij[right][1] - wl_ij[left][1]

        direction = np.array([-dy, dx])

        normal = (np.array([-dy, dx]) if np.dot(
            normalise_vector(ij_l - equal_energy),
            normalise_vector(direction),
        ) > 0 else np.array([dy, -dx]))
        normal = normalise_vector(normal) / 30

        label_colour = (
            spectral_locus_colours if is_string(spectral_locus_colours) else
            spectral_locus_colours[index]  # type: ignore[index]
        )
        axes.plot(
            (i, i + normal[0] * 0.75),
            (j, j + normal[1] * 0.75),
            color=label_colour,
            alpha=spectral_locus_opacity,
            zorder=CONSTANTS_COLOUR_STYLE.zorder.background_line,
        )

        axes.plot(
            i,
            j,
            "o",
            color=label_colour,
            alpha=spectral_locus_opacity,
            zorder=CONSTANTS_COLOUR_STYLE.zorder.background_line,
        )

        axes.text(
            i + normal[0],
            j + normal[1],
            label,
            clip_on=True,
            ha="left" if normal[0] >= 0 else "right",
            va="center",
            fontdict={"size": "small"},
            zorder=CONSTANTS_COLOUR_STYLE.zorder.background_label,
        )

    settings = {"axes": axes}
    settings.update(kwargs)

    return render(**kwargs)
Esempio n. 22
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def plot_chromaticity_diagram_colours(
    samples: Integer = 256,
    diagram_colours: Optional[Union[ArrayLike, str]] = None,
    diagram_opacity: Floating = 1,
    diagram_clipping_path: Optional[ArrayLike] = None,
    cmfs: Union[MultiSpectralDistributions, str, Sequence[Union[
        MultiSpectralDistributions,
        str]], ] = "CIE 1931 2 Degree Standard Observer",
    method: Union[Literal["CIE 1931", "CIE 1960 UCS", "CIE 1976 UCS"],
                  str] = "CIE 1931",
    **kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
    """
    Plot the *Chromaticity Diagram* colours according to given method.

    Parameters
    ----------
    samples
        Samples count on one axis when computing the *Chromaticity Diagram*
        colours.
    diagram_colours
        Colours of the *Chromaticity Diagram*, if ``diagram_colours`` is set
        to *RGB*, the colours will be computed according to the corresponding
        coordinates.
    diagram_opacity
        Opacity of the *Chromaticity Diagram*.
    diagram_clipping_path
        Path of points used to clip the *Chromaticity Diagram* colours.
    cmfs
        Standard observer colour matching functions used for computing the
        spectral locus boundaries. ``cmfs`` can be of any type or form
        supported by the :func:`colour.plotting.filter_cmfs` definition.
    method
        *Chromaticity Diagram* method.

    Other Parameters
    ----------------
    kwargs
        {:func:`colour.plotting.artist`, :func:`colour.plotting.render`},
        See the documentation of the previously listed definitions.

    Returns
    -------
    :class:`tuple`
        Current figure and axes.

    Examples
    --------
    >>> plot_chromaticity_diagram_colours(diagram_colours='RGB')
    ... # doctest: +ELLIPSIS
    (<Figure size ... with 1 Axes>, <...AxesSubplot...>)

    .. image:: ../_static/Plotting_Plot_Chromaticity_Diagram_Colours.png
        :align: center
        :alt: plot_chromaticity_diagram_colours
    """

    method = validate_method(method,
                             ["CIE 1931", "CIE 1960 UCS", "CIE 1976 UCS"])

    settings: Dict[str, Any] = {"uniform": True}
    settings.update(kwargs)

    _figure, axes = artist(**settings)

    diagram_colours = cast(
        ArrayLike,
        optional(diagram_colours,
                 HEX_to_RGB(CONSTANTS_COLOUR_STYLE.colour.average)),
    )

    cmfs = cast(MultiSpectralDistributions,
                first_item(filter_cmfs(cmfs).values()))

    illuminant = CONSTANTS_COLOUR_STYLE.colour.colourspace.whitepoint

    if method == "cie 1931":
        spectral_locus = XYZ_to_xy(cmfs.values, illuminant)
    elif method == "cie 1960 ucs":
        spectral_locus = UCS_to_uv(XYZ_to_UCS(cmfs.values))
    elif method == "cie 1976 ucs":
        spectral_locus = Luv_to_uv(XYZ_to_Luv(cmfs.values, illuminant),
                                   illuminant)

    use_RGB_diagram_colours = str(diagram_colours).upper() == "RGB"
    if use_RGB_diagram_colours:
        ii, jj = np.meshgrid(np.linspace(0, 1, samples),
                             np.linspace(1, 0, samples))
        ij = tstack([ii, jj])

        # NOTE: Various values in the grid have potential to generate
        # zero-divisions, they could be avoided by perturbing the grid, e.g.
        # adding a small epsilon. It was decided instead to disable warnings.
        with suppress_warnings(python_warnings=True):
            if method == "cie 1931":
                XYZ = xy_to_XYZ(ij)
            elif method == "cie 1960 ucs":
                XYZ = xy_to_XYZ(UCS_uv_to_xy(ij))
            elif method == "cie 1976 ucs":
                XYZ = xy_to_XYZ(Luv_uv_to_xy(ij))

        diagram_colours = normalise_maximum(XYZ_to_plotting_colourspace(
            XYZ, illuminant),
                                            axis=-1)

    polygon = Polygon(
        spectral_locus
        if diagram_clipping_path is None else diagram_clipping_path,
        facecolor="none" if use_RGB_diagram_colours else np.hstack(
            [diagram_colours, diagram_opacity]),
        edgecolor="none" if use_RGB_diagram_colours else np.hstack(
            [diagram_colours, diagram_opacity]),
        zorder=CONSTANTS_COLOUR_STYLE.zorder.background_polygon,
    )
    axes.add_patch(polygon)

    if use_RGB_diagram_colours:
        # Preventing bounding box related issues as per
        # https://github.com/matplotlib/matplotlib/issues/10529
        image = axes.imshow(
            diagram_colours,
            interpolation="bilinear",
            extent=(0, 1, 0, 1),
            clip_path=None,
            alpha=diagram_opacity,
            zorder=CONSTANTS_COLOUR_STYLE.zorder.background_polygon,
        )
        image.set_clip_path(polygon)

    settings = {"axes": axes}
    settings.update(kwargs)

    return render(**kwargs)
Esempio n. 23
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def CIE_1976_UCS_chromaticity_diagram_colours_plot(
        surface=1,
        samples=4096,
        cmfs='CIE 1931 2 Degree Standard Observer',
        **kwargs):
    """
    Plots the *CIE 1976 UCS Chromaticity Diagram* colours.

    Parameters
    ----------
    surface : numeric, optional
        Generated markers surface.
    samples : numeric, optional
        Samples count on one axis.
    cmfs : unicode, optional
        Standard observer colour matching functions used for diagram bounds.

    Other Parameters
    ----------------
    \**kwargs : dict, optional
        {:func:`boundaries`, :func:`canvas`, :func:`decorate`,
        :func:`display`},
        Please refer to the documentation of the previously listed definitions.

    Returns
    -------
    Figure
        Current figure or None.

    Examples
    --------
    >>> CIE_1976_UCS_chromaticity_diagram_colours_plot()  # doctest: +SKIP
    """

    settings = {'figure_size': (64, 64)}
    settings.update(kwargs)

    canvas(**settings)

    cmfs = get_cmfs(cmfs)

    illuminant = DEFAULT_PLOTTING_ILLUMINANT

    triangulation = Delaunay(Luv_to_uv(XYZ_to_Luv(cmfs.values, illuminant),
                                       illuminant),
                             qhull_options='QJ Qf')
    xx, yy = np.meshgrid(np.linspace(0, 1, samples),
                         np.linspace(0, 1, samples))
    xy = tstack((xx, yy))
    xy = xy[triangulation.find_simplex(xy) > 0]

    XYZ = xy_to_XYZ(Luv_uv_to_xy(xy))

    RGB = normalise_maximum(XYZ_to_sRGB(XYZ, illuminant), axis=-1)

    x_dot, y_dot = tsplit(xy)

    pylab.scatter(x_dot, y_dot, color=RGB, s=surface)

    settings.update({
        'x_ticker': False,
        'y_ticker': False,
        'bounding_box': (0, 1, 0, 1)
    })
    settings.update(kwargs)

    ax = matplotlib.pyplot.gca()
    matplotlib.pyplot.setp(ax, frame_on=False)

    boundaries(**settings)
    decorate(**settings)

    return display(**settings)
Esempio n. 24
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def corresponding_chromaticities_prediction_VonKries(experiment=1,
                                                     transform='CAT02'):
    """
    Returns the corresponding chromaticities prediction for Von Kries
    chromatic adaptation model using given transform.

    Parameters
    ----------
    experiment : integer, optional
        {1, 2, 3, 4, 6, 8, 9, 11, 12}
        Breneman (1987) experiment number.
    transform : unicode, optional
        **{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
        'Fairchild, 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
        'Bianco PC'}**,
        Chromatic adaptation transform.

    Returns
    -------
    tuple
        Corresponding chromaticities prediction.

    Examples
    --------
    >>> from pprint import pprint
    >>> pr = corresponding_chromaticities_prediction_VonKries(2, 'Bradford')
    >>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
    >>> pprint(pr)  # doctest: +SKIP
    [((0.207, 0.486), (0.20820148430638033, 0.47229226819364528)),
     ((0.449, 0.511), (0.44891022948064191, 0.50716028901449561)),
     ((0.263, 0.505), (0.26435459360846608, 0.49596314494922683)),
     ((0.322, 0.545), (0.33487309037107632, 0.54712207251983425)),
     ((0.316, 0.537), (0.32487581236911361, 0.53905899356457776)),
     ((0.265, 0.553), (0.27331050571632376, 0.55550280647813977)),
     ((0.221, 0.538), (0.22714800102072819, 0.53313179748041983)),
     ((0.135, 0.532), (0.14427303768336433, 0.52268044497913713)),
     ((0.145, 0.472), (0.14987451889726533, 0.45507852741116867)),
     ((0.163, 0.331), (0.15649757464732098, 0.31487959772753954)),
     ((0.176, 0.431), (0.17605936460371163, 0.41037722722471409)),
     ((0.244, 0.349), (0.22598059059292835, 0.34652914678030416))]
    """

    experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))

    illuminants = experiment_results.pop(0)
    XYZ_w = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_t))
    XYZ_wr = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_m))
    xy_wr = XYZ_to_xy(XYZ_wr)

    prediction = []
    for result in experiment_results:
        XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t))
        XYZ_2 = chromatic_adaptation_VonKries(XYZ_1, XYZ_w, XYZ_wr, transform)
        uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_wr), xy_wr)
        prediction.append(CorrespondingChromaticitiesPrediction(
            result.name,
            result.uvp_t,
            result.uvp_m,
            uvp))

    return tuple(prediction)