Source code for fury.colormap

from __future__ import division, print_function

from warnings import warn
import json
from os.path import join as pjoin

import numpy as np
import vtk

from fury.data import DATA_DIR
# Allow import, but disable doctests if we don't have matplotlib
from fury.optpkg import optional_package
cm, have_matplotlib, _ = optional_package('matplotlib.cm')


def colormap_lookup_table(scale_range=(0, 1), hue_range=(0.8, 0),
                          saturation_range=(1, 1), value_range=(0.8, 0.8)):
    """Lookup table for the colormap.

    Parameters
    ----------
    scale_range : tuple
        It can be anything e.g. (0, 1) or (0, 255). Usually it is the mininum
        and maximum value of your data. Default is (0, 1).
    hue_range : tuple of floats
        HSV values (min 0 and max 1). Default is (0.8, 0).
    saturation_range : tuple of floats
        HSV values (min 0 and max 1). Default is (1, 1).
    value_range : tuple of floats
        HSV value (min 0 and max 1). Default is (0.8, 0.8).

    Returns
    -------
    lookup_table : vtkLookupTable

    """
    lookup_table = vtk.vtkLookupTable()
    lookup_table.SetRange(scale_range)
    lookup_table.SetTableRange(scale_range)

    lookup_table.SetHueRange(hue_range)
    lookup_table.SetSaturationRange(saturation_range)
    lookup_table.SetValueRange(value_range)

    lookup_table.Build()
    return lookup_table


def cc(na, nd):
    return na * np.cos(nd * np.pi / 180.0)


def ss(na, nd):
    return na * np.sin(nd * np.pi / 180.0)


def boys2rgb(v):
    """ boys 2 rgb cool colormap

    Maps a given field of undirected lines (line field) to rgb
    colors using Boy's Surface immersion of the real projective
    plane.
    Boy's Surface is one of the three possible surfaces
    obtained by gluing a Mobius strip to the edge of a disk.
    The other two are the crosscap and Roman surface,
    Steiner surfaces that are homeomorphic to the real
    projective plane (Pinkall 1986). The Boy's surface
    is the only 3D immersion of the projective plane without
    singularities.
    Visit http://www.cs.brown.edu/~cad/rp2coloring for further details.
    Cagatay Demiralp, 9/7/2008.

    Code was initially in matlab and was rewritten in Python for fury by
    the FURY Team. Thank you Cagatay for putting this online.

    Parameters
    ------------
    v : array, shape (N, 3) of unit vectors (e.g., principal eigenvectors of
       tensor data) representing one of the two directions of the
       undirected lines in a line field.

    Returns
    ---------
    c : array, shape (N, 3) matrix of rgb colors corresponding to the vectors
           given in V.

    Examples
    ----------

    >>> from fury import colormap
    >>> v = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
    >>> c = colormap.boys2rgb(v)
    """

    if v.ndim == 1:
        x = v[0]
        y = v[1]
        z = v[2]

    if v.ndim == 2:
        x = v[:, 0]
        y = v[:, 1]
        z = v[:, 2]

    x2 = x ** 2
    y2 = y ** 2
    z2 = z ** 2

    x3 = x * x2
    y3 = y * y2
    z3 = z * z2

    z4 = z * z2

    xy = x * y
    xz = x * z
    yz = y * z

    hh1 = .5 * (3 * z2 - 1) / 1.58
    hh2 = 3 * xz / 2.745
    hh3 = 3 * yz / 2.745
    hh4 = 1.5 * (x2 - y2) / 2.745
    hh5 = 6 * xy / 5.5
    hh6 = (1 / 1.176) * .125 * (35 * z4 - 30 * z2 + 3)
    hh7 = 2.5 * x * (7 * z3 - 3 * z) / 3.737
    hh8 = 2.5 * y * (7 * z3 - 3 * z) / 3.737
    hh9 = ((x2 - y2) * 7.5 * (7 * z2 - 1)) / 15.85
    hh10 = ((2 * xy) * (7.5 * (7 * z2 - 1))) / 15.85
    hh11 = 105 * (4 * x3 * z - 3 * xz * (1 - z2)) / 59.32
    hh12 = 105 * (-4 * y3 * z + 3 * yz * (1 - z2)) / 59.32

    s0 = -23.0
    s1 = 227.9
    s2 = 251.0
    s3 = 125.0

    ss23 = ss(2.71, s0)
    cc23 = cc(2.71, s0)
    ss45 = ss(2.12, s1)
    cc45 = cc(2.12, s1)
    ss67 = ss(.972, s2)
    cc67 = cc(.972, s2)
    ss89 = ss(.868, s3)
    cc89 = cc(.868, s3)

    X = 0.0

    X = X + hh2 * cc23
    X = X + hh3 * ss23

    X = X + hh5 * cc45
    X = X + hh4 * ss45

    X = X + hh7 * cc67
    X = X + hh8 * ss67

    X = X + hh10 * cc89
    X = X + hh9 * ss89

    Y = 0.0

    Y = Y + hh2 * -ss23
    Y = Y + hh3 * cc23

    Y = Y + hh5 * -ss45
    Y = Y + hh4 * cc45

    Y = Y + hh7 * -ss67
    Y = Y + hh8 * cc67

    Y = Y + hh10 * -ss89
    Y = Y + hh9 * cc89

    Z = 0.0

    Z = Z + hh1 * -2.8
    Z = Z + hh6 * -0.5
    Z = Z + hh11 * 0.3
    Z = Z + hh12 * -2.5

    # scale and normalize to fit
    # in the rgb space

    w_x = 4.1925
    trl_x = -2.0425
    w_y = 4.0217
    trl_y = -1.8541
    w_z = 4.0694
    trl_z = -2.1899

    if v.ndim == 2:

        N = len(x)
        C = np.zeros((N, 3))

        C[:, 0] = 0.9 * np.abs(((X - trl_x) / w_x)) + 0.05
        C[:, 1] = 0.9 * np.abs(((Y - trl_y) / w_y)) + 0.05
        C[:, 2] = 0.9 * np.abs(((Z - trl_z) / w_z)) + 0.05

    if v.ndim == 1:

        C = np.zeros((3,))
        C[0] = 0.9 * np.abs(((X - trl_x) / w_x)) + 0.05
        C[1] = 0.9 * np.abs(((Y - trl_y) / w_y)) + 0.05
        C[2] = 0.9 * np.abs(((Z - trl_z) / w_z)) + 0.05

    return C


def orient2rgb(v):
    """Get Standard orientation 2 rgb colormap.

    v : array, shape (N, 3) of vectors not necessarily normalized

    Returns
    -------
    c : array, shape (N, 3) matrix of rgb colors corresponding to the vectors
           given in V.

    Examples
    --------
    >>> from fury import colormap
    >>> v = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
    >>> c = colormap.orient2rgb(v)

    """
    if v.ndim == 1:
        orient = v
        orient = np.abs(orient / np.linalg.norm(orient))

    elif v.ndim == 2:
        orientn = np.sqrt(v[:, 0] ** 2 + v[:, 1] ** 2 + v[:, 2] ** 2)
        orientn.shape = orientn.shape + (1,)
        orient = np.abs(v / orientn)
    else:
        raise IOError("Wrong vector dimension, It should be an array"
                      " with a shape (N, 3)")

    return orient


def line_colors(streamlines, cmap='rgb_standard'):
    """Create colors for streamlines to be used in actor.line.

    Parameters
    ----------
    streamlines : sequence of ndarrays
    cmap : ('rgb_standard', 'boys_standard')

    Returns
    -------
    colors : ndarray

    """
    if cmap == 'rgb_standard':
        col_list = [orient2rgb(streamline[-1] - streamline[0])
                    for streamline in streamlines]

    if cmap == 'boys_standard':
        col_list = [boys2rgb(streamline[-1] - streamline[0])
                    for streamline in streamlines]

    return np.vstack(col_list)


lowercase_cm_name = {'blues': 'Blues', 'accent': 'Accent'}
dipy_cmaps = None


def get_cmap(name):
    """Make a callable, similar to maptlotlib.pyplot.get_cmap."""
    if name.lower() == "accent":
        warn("The `Accent` colormap is deprecated as of version" +
             " 0.2 of Fury and will be removed in a future " +
             "version. Please use another colormap",
             PendingDeprecationWarning)

    global dipy_cmaps
    if dipy_cmaps is None:
        filename = pjoin(DATA_DIR, "dipy_colormaps.json")
        with open(filename) as f:
            dipy_cmaps = json.load(f)

    desc = dipy_cmaps.get(name)
    if desc is None:
        return None

    def simple_cmap(v):
        """Emulate matplotlib colormap callable."""
        rgba = np.ones((len(v), 4))
        for i, color in enumerate(('red', 'green', 'blue')):
            x, y0, _ = zip(*desc[color])
            # Matplotlib allows more complex colormaps, but for users who do
            # not have Matplotlib fury makes a few simple colormaps available.
            # These colormaps are simple because y0 == y1, and therefor we
            # ignore y1 here.
            rgba[:, i] = np.interp(v, x, y0)
        return rgba

    return simple_cmap


def create_colormap(v, name='plasma', auto=True):
    """Create colors from a specific colormap and return it
    as an array of shape (N,3) where every row gives the corresponding
    r,g,b value. The colormaps we use are similar with those of matplotlib.

    Parameters
    ----------
    v : (N,) array
        vector of values to be mapped in RGB colors according to colormap
    name : str.
        Name of the colormap. Currently implemented: 'jet', 'blues',
        'accent', 'bone' and matplotlib colormaps if you have matplotlib
        installed. For example, we suggest using 'plasma', 'viridis' or
        'inferno'. 'jet' is popular but can be often misleading and we will
        deprecate it the future.
    auto : bool,
        if auto is True then v is interpolated to [0, 10] from v.min()
        to v.max()

    Notes
    -----
    FURY supports a few colormaps for those who do not use Matplotlib, for
    more colormaps consider downloading Matplotlib (see matplotlib.org).

    """
    if not have_matplotlib:
        msg = "You do not have Matplotlib installed. Some colormaps"
        msg += " might not work for you. Consider downloading Matplotlib."
        warn(msg)

    if name.lower() == 'jet':
        msg = 'Jet is a popular colormap but can often be misleading'
        msg += 'Use instead plasma, viridis, hot or inferno.'
        warn(msg, PendingDeprecationWarning)

    if v.ndim > 1:
        msg = 'This function works only with 1d arrays. Use ravel()'
        raise ValueError(msg)

    if auto:
        v = np.interp(v, [v.min(), v.max()], [0, 1])
    else:
        v = np.clip(v, 0, 1)

    # For backwards compatibility with lowercase names
    newname = lowercase_cm_name.get(name) or name

    get_colormap = cm.get_cmap if have_matplotlib else get_cmap
    colormap = get_colormap(newname)
    if colormap is None:
        e_s = "Colormap {} is not yet implemented ".format(name)
        raise ValueError(e_s)

    rgba = colormap(v)
    rgb = rgba[:, :3].copy()
    return rgb


def _lab_delta(x, y):
    dL = y[:, 0] - x[:, 0]  # L
    dA = y[:, 1] - x[:, 1]  # A
    dB = y[:, 2] - x[:, 2]  # B
    return np.sqrt(dL**2 + dA**2 + dB**2)


def _rgb_lab_delta(x, y):
    labX = _rgb2lab(x)
    labY = _rgb2lab(y)
    return _lab_delta(labX, labY)


def _rgb2xyz(rgb):
    var_R = rgb[:, 0] / 255  # R from 0 to 255
    var_G = rgb[:, 1] / 255  # G from 0 to 255
    var_B = rgb[:, 2] / 255  # B from 0 to 255

    idx = var_R > 0.04045
    var_R[idx] = ((var_R[idx] + 0.055) / 1.055) ** 2.4
    idx = np.logical_not(idx)
    var_R[idx] = var_R[idx] / 12.92

    idx = var_G > 0.04045
    var_G[idx] = ((var_G[idx] + 0.055) / 1.055) ** 2.4
    idx = np.logical_not(idx)
    var_G[idx] = var_G[idx] / 12.92

    idx = var_B > 0.04045
    var_B[idx] = ((var_B[idx] + 0.055) / 1.055) ** 2.4
    idx = np.logical_not(idx)
    var_B[idx] = var_B[idx] / 12.92

    var_R = var_R * 100
    var_G = var_G * 100
    var_B = var_B * 100

    # Observer. = Illuminant = D65
    X = var_R * 0.4124 + var_G * 0.3576 + var_B * 0.1805
    Y = var_R * 0.2126 + var_G * 0.7152 + var_B * 0.0722
    Z = var_R * 0.0193 + var_G * 0.1192 + var_B * 0.9505

    return np.c_[X, Y, Z]


def _xyz2lab(xyz):
    ref_X = 095.047
    ref_Y = 100.000
    ref_Z = 108.883
    var_X = xyz[:, 0] / ref_X
    var_Y = xyz[:, 1] / ref_Y
    var_Z = xyz[:, 2] / ref_Z

    idx = var_X > 0.008856
    var_X[idx] = var_X[idx] ** (1/3)
    idx = np.logical_not(idx)
    var_X[idx] = (7.787 * var_X[idx]) + (16. / 116.)

    idx = var_Y > 0.008856
    var_Y[idx] = var_Y[idx] ** (1/3)
    idx = np.logical_not(idx)
    var_Y[idx] = (7.787 * var_Y[idx]) + (16. / 116.)

    idx = var_Z > 0.008856
    var_Z[idx] = var_Z[idx] ** (1/3)
    idx = np.logical_not(idx)
    var_Z[idx] = (7.787 * var_Z[idx]) + (16. / 116.)

    L = (116 * var_Y) - 16
    A = 500 * (var_X - var_Y)
    B = 200 * (var_Y - var_Z)

    return np.c_[L, A, B]


def _lab2xyz(lab):
    var_Y = (lab[:, 0] + 16) / 116.0
    var_X = lab[:, 1] / 500.0 + var_Y
    var_Z = var_Y - lab[:, 2] / 200.0

    if var_Y**3 > 0.008856:
        var_Y = var_Y**3
    else:
        var_Y = (var_Y - 16. / 116.) / 7.787

    if var_X**3 > 0.008856:
        var_X = var_X**3
    else:
        var_X = (var_X - 16. / 116.) / 7.787

    if var_Z**3 > 0.008856:
        var_Z = var_Z**3
    else:
        var_Z = (var_Z - 16. / 116.) / 7.787

    ref_X = 095.047
    ref_Y = 100.000
    ref_Z = 108.883
    X = ref_X * var_X
    Y = ref_Y * var_Y
    Z = ref_Z * var_Z

    return np.c_[X, Y, Z]


def _xyz2rgb(xyz):
    var_X = xyz[:, 0] / 100  # X from 0 to  95.047
    var_Y = xyz[:, 1] / 100  # Y from 0 to 100.000
    var_Z = xyz[:, 2] / 100  # Z from 0 to 108.883

    var_R = var_X * 03.2406 + var_Y * -1.5372 + var_Z * -0.4986
    var_G = var_X * -0.9689 + var_Y * 01.8758 + var_Z * 00.0415
    var_B = var_X * 00.0557 + var_Y * -0.2040 + var_Z * 01.0570

    if var_R > 0.0031308:
        var_R = 1.055 * (var_R**(1/2.4)) - 0.055
    else:
        var_R = 12.92 * var_R

    if var_G > 0.0031308:
        var_G = 1.055 * (var_G**(1/2.4)) - 0.055
    else:
        var_G = 12.92 * var_G

    if var_B > 0.0031308:
        var_B = 1.055 * (var_B**(1/2.4)) - 0.055
    else:
        var_B = 12.92 * var_B

    R = var_R * 255
    G = var_G * 255
    B = var_B * 255

    return np.c_[R, G, B]


def _rgb2lab(rgb):
    tmp = _rgb2xyz(rgb)
    return _xyz2lab(tmp)


def _lab2rgb(lab):
    tmp = _lab2xyz(lab)
    return _xyz2rgb(tmp)


def distinguishable_colormap(bg=(0, 0, 0), exclude=[], nb_colors=None):
    """Generate colors that are maximally perceptually distinct.

    This function generates a set of colors which are distinguishable
    by reference to the "Lab" color space, which more closely matches
    human color perception than RGB. Given an initial large list of possible
    colors, it iteratively chooses the entry in the list that is farthest (in
    Lab space) from all previously-chosen entries. While this "greedy"
    algorithm does not yield a global maximum, it is simple and efficient.
    Moreover, the sequence of colors is consistent no matter how many you
    request, which facilitates the users' ability to learn the color order
    and avoids major changes in the appearance of plots when adding or
    removing lines.

    Parameters
    ----------
    bg : tuple (optional)
        Background RGB color, to make sure that your colors are also
        distinguishable from the background. Default: (0, 0, 0).
    exclude : list of tuples (optional)
        Additional RGB colors to be distinguishable from.
    nb_colors : int (optional)
        Number of colors desired. Default: generate as many colors as needed.

    Returns
    -------
    iterable of ndarray
        If `nb_colors` is provided, returns a list of RBG colors.
        Otherwise, yields the next RBG color maximally perceptually
        distinct from previous ones.

    Examples
    --------
    >>> from dipy.viz.colormap import distinguishable_colormap
    >>> # Generate 5 colors
    >>> [c for i, c in zip(range(5), distinguishable_colormap())]
    [array([ 0.,  1.,  0.]),
     array([ 1.,  0.,  1.]),
     array([ 1.        ,  0.75862069,  0.03448276]),
     array([ 0.        ,  1.        ,  0.89655172]),
     array([ 0.        ,  0.17241379,  1.        ])]


    Notes
    -----
    Code was initially in matlab and was rewritten in Python for dipy by
    the Dipy Team. Thank you Tim Holy for putting this online. Visit
    http://www.mathworks.com/matlabcentral/fileexchange/29702 for the
    original implementation (v1.2), 14 Dec 2010 (Updated 07 Feb 2011).

    """
    NB_DIVISIONS = 30  # This constant come from the original code.

    # Generate a sizable number of RGB triples. This represents our space of
    # possible choices. By starting in RGB space, we ensure that all of the
    # colors can be generated by the monitor.

    colors_to_exclude = np.array([bg] + exclude)
    # Divisions along each axis in RGB space.
    x = np.linspace(0, 1, NB_DIVISIONS)
    R, G, B = np.meshgrid(x, x, x)
    rgb = np.c_[R.flatten(), G.flatten(), B.flatten()]

    lab = _rgb2lab(rgb)
    bglab = _rgb2lab(colors_to_exclude)

    def _generate_next_color():
        lastlab = bglab[0]
        mindist2 = np.ones(len(rgb)) * np.inf
        for bglab_i in bglab[1:]:
            dist2 = np.sum((lab-bglab_i)**2, axis=1)
            # Dist2 to closest previously-chosen color.
            mindist2 = np.minimum(dist2, mindist2)

        while True:
            dX = lab - lastlab  # Displacement of last from all colors on list.
            dist2 = np.sum(dX**2, axis=1)  # Square distance.
            # Dist2 to closest previously-chosen color.
            mindist2 = np.minimum(dist2, mindist2)
            # Find the entry farthest from all previously-chosen colors.
            idx = np.argmax(mindist2)
            yield rgb[idx]

            lastlab = lab[idx]

    if nb_colors is not None:
        return [c for i, c in zip(range(nb_colors), _generate_next_color())]

    return _generate_next_color()