python3-matplotlib 1.5.1-1ubuntu1 / usr / lib / python3 / dist-packages / matplotlib / tests / test_colors.py

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

from matplotlib.externals import six
import itertools
from distutils.version import LooseVersion as V

from nose.tools import assert_raises, assert_equal, assert_true

import numpy as np
from numpy.testing.utils import assert_array_equal, assert_array_almost_equal

import matplotlib.colors as mcolors
import matplotlib.cm as cm
import matplotlib.cbook as cbook
import matplotlib.pyplot as plt
from matplotlib.testing.decorators import (image_comparison,
                                           cleanup, knownfailureif)


def test_colormap_endian():
    """
    Github issue #1005: a bug in putmask caused erroneous
    mapping of 1.0 when input from a non-native-byteorder
    array.
    """
    cmap = cm.get_cmap("jet")
    # Test under, over, and invalid along with values 0 and 1.
    a = [-0.5, 0, 0.5, 1, 1.5, np.nan]
    for dt in ["f2", "f4", "f8"]:
        anative = np.ma.masked_invalid(np.array(a, dtype=dt))
        aforeign = anative.byteswap().newbyteorder()
        #print(anative.dtype.isnative, aforeign.dtype.isnative)
        assert_array_equal(cmap(anative), cmap(aforeign))


def test_BoundaryNorm():
    """
    Github issue #1258: interpolation was failing with numpy
    1.7 pre-release.
    """

    boundaries = [0, 1.1, 2.2]
    vals = [-1, 0, 1, 2, 2.2, 4]

    # Without interpolation
    expected = [-1, 0, 0, 1, 2, 2]
    ncolors = len(boundaries) - 1
    bn = mcolors.BoundaryNorm(boundaries, ncolors)
    assert_array_equal(bn(vals), expected)

    # ncolors != len(boundaries) - 1 triggers interpolation
    expected = [-1, 0, 0, 2, 3, 3]
    ncolors = len(boundaries)
    bn = mcolors.BoundaryNorm(boundaries, ncolors)
    assert_array_equal(bn(vals), expected)

    # more boundaries for a third color
    boundaries = [0, 1, 2, 3]
    vals = [-1, 0.1, 1.1, 2.2, 4]
    ncolors = 5
    expected = [-1, 0, 2, 4, 5]
    bn = mcolors.BoundaryNorm(boundaries, ncolors)
    assert_array_equal(bn(vals), expected)

    # a scalar as input should not trigger an error and should return a scalar
    boundaries = [0, 1, 2]
    vals = [-1, 0.1, 1.1, 2.2]
    bn = mcolors.BoundaryNorm(boundaries, 2)
    expected = [-1, 0, 1, 2]
    for v, ex in zip(vals, expected):
        ret = bn(v)
        assert_true(isinstance(ret, six.integer_types))
        assert_array_equal(ret, ex)
        assert_array_equal(bn([v]), ex)

    # same with interp
    bn = mcolors.BoundaryNorm(boundaries, 3)
    expected = [-1, 0, 2, 3]
    for v, ex in zip(vals, expected):
        ret = bn(v)
        assert_true(isinstance(ret, six.integer_types))
        assert_array_equal(ret, ex)
        assert_array_equal(bn([v]), ex)

    # Clipping
    bn = mcolors.BoundaryNorm(boundaries, 3, clip=True)
    expected = [0, 0, 2, 2]
    for v, ex in zip(vals, expected):
        ret = bn(v)
        assert_true(isinstance(ret, six.integer_types))
        assert_array_equal(ret, ex)
        assert_array_equal(bn([v]), ex)

    # Masked arrays
    boundaries = [0, 1.1, 2.2]
    vals = np.ma.masked_invalid([-1., np.NaN, 0, 1.4, 9])

    # Without interpolation
    ncolors = len(boundaries) - 1
    bn = mcolors.BoundaryNorm(boundaries, ncolors)
    expected = np.ma.masked_array([-1, -99, 0, 1, 2], mask=[0, 1, 0, 0, 0])
    assert_array_equal(bn(vals), expected)

    # With interpolation
    bn = mcolors.BoundaryNorm(boundaries, len(boundaries))
    expected = np.ma.masked_array([-1, -99, 0, 2, 3], mask=[0, 1, 0, 0, 0])
    assert_array_equal(bn(vals), expected)

    # Non-trivial masked arrays
    vals = np.ma.masked_invalid([np.Inf, np.NaN])
    assert_true(np.all(bn(vals).mask))
    vals = np.ma.masked_invalid([np.Inf])
    assert_true(np.all(bn(vals).mask))


def test_LogNorm():
    """
    LogNorm ignored clip, now it has the same
    behavior as Normalize, e.g., values > vmax are bigger than 1
    without clip, with clip they are 1.
    """
    ln = mcolors.LogNorm(clip=True, vmax=5)
    assert_array_equal(ln([1, 6]), [0, 1.0])


def test_PowerNorm():
    a = np.array([0, 0.5, 1, 1.5], dtype=np.float)
    pnorm = mcolors.PowerNorm(1)
    norm = mcolors.Normalize()
    assert_array_almost_equal(norm(a), pnorm(a))

    a = np.array([-0.5, 0, 2, 4, 8], dtype=np.float)
    expected = [0, 0, 1/16, 1/4, 1]
    pnorm = mcolors.PowerNorm(2, vmin=0, vmax=8)
    assert_array_almost_equal(pnorm(a), expected)
    assert_equal(pnorm(a[0]), expected[0])
    assert_equal(pnorm(a[2]), expected[2])
    assert_array_almost_equal(a[1:], pnorm.inverse(pnorm(a))[1:])

    # Clip = True
    a = np.array([-0.5, 0, 1, 8, 16], dtype=np.float)
    expected = [0, 0, 0, 1, 1]
    pnorm = mcolors.PowerNorm(2, vmin=2, vmax=8, clip=True)
    assert_array_almost_equal(pnorm(a), expected)
    assert_equal(pnorm(a[0]), expected[0])
    assert_equal(pnorm(a[-1]), expected[-1])

    # Clip = True at call time
    a = np.array([-0.5, 0, 1, 8, 16], dtype=np.float)
    expected = [0, 0, 0, 1, 1]
    pnorm = mcolors.PowerNorm(2, vmin=2, vmax=8, clip=False)
    assert_array_almost_equal(pnorm(a, clip=True), expected)
    assert_equal(pnorm(a[0], clip=True), expected[0])
    assert_equal(pnorm(a[-1], clip=True), expected[-1])


def test_Normalize():
    norm = mcolors.Normalize()
    vals = np.arange(-10, 10, 1, dtype=np.float)
    _inverse_tester(norm, vals)
    _scalar_tester(norm, vals)
    _mask_tester(norm, vals)


def test_SymLogNorm():
    """
    Test SymLogNorm behavior
    """
    norm = mcolors.SymLogNorm(3, vmax=5, linscale=1.2)
    vals = np.array([-30, -1, 2, 6], dtype=np.float)
    normed_vals = norm(vals)
    expected = [0., 0.53980074, 0.826991, 1.02758204]
    assert_array_almost_equal(normed_vals, expected)
    _inverse_tester(norm, vals)
    _scalar_tester(norm, vals)
    _mask_tester(norm, vals)

    # Ensure that specifying vmin returns the same result as above
    norm = mcolors.SymLogNorm(3, vmin=-30, vmax=5, linscale=1.2)
    normed_vals = norm(vals)
    assert_array_almost_equal(normed_vals, expected)


def _inverse_tester(norm_instance, vals):
    """
    Checks if the inverse of the given normalization is working.
    """
    assert_array_almost_equal(norm_instance.inverse(norm_instance(vals)), vals)


def _scalar_tester(norm_instance, vals):
    """
    Checks if scalars and arrays are handled the same way.
    Tests only for float.
    """
    scalar_result = [norm_instance(float(v)) for v in vals]
    assert_array_almost_equal(scalar_result, norm_instance(vals))


def _mask_tester(norm_instance, vals):
    """
    Checks mask handling
    """
    masked_array = np.ma.array(vals)
    masked_array[0] = np.ma.masked
    assert_array_equal(masked_array.mask, norm_instance(masked_array).mask)


@image_comparison(baseline_images=['levels_and_colors'],
                  extensions=['png'])
def test_cmap_and_norm_from_levels_and_colors():
    data = np.linspace(-2, 4, 49).reshape(7, 7)
    levels = [-1, 2, 2.5, 3]
    colors = ['red', 'green', 'blue', 'yellow', 'black']
    extend = 'both'
    cmap, norm = mcolors.from_levels_and_colors(levels, colors, extend=extend)

    ax = plt.axes()
    m = plt.pcolormesh(data, cmap=cmap, norm=norm)
    plt.colorbar(m)

    # Hide the axes labels (but not the colorbar ones, as they are useful)
    for lab in ax.get_xticklabels() + ax.get_yticklabels():
        lab.set_visible(False)


def test_cmap_and_norm_from_levels_and_colors2():
    levels = [-1, 2, 2.5, 3]
    colors = ['red', (0, 1, 0), 'blue', (0.5, 0.5, 0.5), (0.0, 0.0, 0.0, 1.0)]
    clr = mcolors.colorConverter.to_rgba_array(colors)
    bad = (0.1, 0.1, 0.1, 0.1)
    no_color = (0.0, 0.0, 0.0, 0.0)
    masked_value = 'masked_value'

    # Define the test values which are of interest.
    # Note: levels are lev[i] <= v < lev[i+1]
    tests = [('both', None, {-2: clr[0],
                             -1: clr[1],
                             2: clr[2],
                             2.25: clr[2],
                             3: clr[4],
                             3.5: clr[4],
                             masked_value: bad}),

             ('min', -1, {-2: clr[0],
                          -1: clr[1],
                          2: clr[2],
                          2.25: clr[2],
                          3: no_color,
                          3.5: no_color,
                          masked_value: bad}),

             ('max', -1, {-2: no_color,
                          -1: clr[0],
                          2: clr[1],
                          2.25: clr[1],
                          3: clr[3],
                          3.5: clr[3],
                          masked_value: bad}),

             ('neither', -2, {-2: no_color,
                              -1: clr[0],
                              2: clr[1],
                              2.25: clr[1],
                              3: no_color,
                              3.5: no_color,
                              masked_value: bad}),
             ]

    for extend, i1, cases in tests:
        cmap, norm = mcolors.from_levels_and_colors(levels, colors[0:i1],
                                                    extend=extend)
        cmap.set_bad(bad)
        for d_val, expected_color in cases.items():
            if d_val == masked_value:
                d_val = np.ma.array([1], mask=True)
            else:
                d_val = [d_val]
            assert_array_equal(expected_color, cmap(norm(d_val))[0],
                               'Wih extend={0!r} and data '
                               'value={1!r}'.format(extend, d_val))

    assert_raises(ValueError, mcolors.from_levels_and_colors, levels, colors)


def test_rgb_hsv_round_trip():
    for a_shape in [(500, 500, 3), (500, 3), (1, 3), (3,)]:
        np.random.seed(0)
        tt = np.random.random(a_shape)
        assert_array_almost_equal(tt,
            mcolors.hsv_to_rgb(mcolors.rgb_to_hsv(tt)))
        assert_array_almost_equal(tt,
            mcolors.rgb_to_hsv(mcolors.hsv_to_rgb(tt)))


@cleanup
def test_autoscale_masked():
    # Test for #2336. Previously fully masked data would trigger a ValueError.
    data = np.ma.masked_all((12, 20))
    plt.pcolor(data)
    plt.draw()


def test_colors_no_float():
    # Gray must be a string to distinguish 3-4 grays from RGB or RGBA.

    def gray_from_float_rgb():
        return mcolors.colorConverter.to_rgb(0.4)

    def gray_from_float_rgba():
        return mcolors.colorConverter.to_rgba(0.4)

    assert_raises(ValueError, gray_from_float_rgb)
    assert_raises(ValueError, gray_from_float_rgba)


@image_comparison(baseline_images=['light_source_shading_topo'],
                  extensions=['png'])
def test_light_source_topo_surface():
    """Shades a DEM using different v.e.'s and blend modes."""
    fname = cbook.get_sample_data('jacksboro_fault_dem.npz', asfileobj=False)
    dem = np.load(fname)
    elev = dem['elevation']
    # Get the true cellsize in meters for accurate vertical exaggeration
    #   Convert from decimal degrees to meters
    dx, dy = dem['dx'], dem['dy']
    dx = 111320.0 * dx * np.cos(dem['ymin'])
    dy = 111320.0 * dy
    dem.close()

    ls = mcolors.LightSource(315, 45)
    cmap = cm.gist_earth

    fig, axes = plt.subplots(nrows=3, ncols=3)
    for row, mode in zip(axes, ['hsv', 'overlay', 'soft']):
        for ax, ve in zip(row, [0.1, 1, 10]):
            rgb = ls.shade(elev, cmap, vert_exag=ve, dx=dx, dy=dy,
                           blend_mode=mode)
            ax.imshow(rgb)
            ax.set(xticks=[], yticks=[])


def test_light_source_shading_default():
    """Array comparison test for the default "hsv" blend mode. Ensure the
    default result doesn't change without warning."""
    y, x = np.mgrid[-1.2:1.2:8j, -1.2:1.2:8j]
    z = 10 * np.cos(x**2 + y**2)

    cmap = plt.cm.copper
    ls = mcolors.LightSource(315, 45)
    rgb = ls.shade(z, cmap)

    # Result stored transposed and rounded for for more compact display...
    expect = np.array([[[0.87, 0.85, 0.90, 0.90, 0.82, 0.62, 0.34, 0.00],
                        [0.85, 0.94, 0.99, 1.00, 1.00, 0.96, 0.62, 0.17],
                        [0.90, 0.99, 1.00, 1.00, 1.00, 1.00, 0.71, 0.33],
                        [0.90, 1.00, 1.00, 1.00, 1.00, 0.98, 0.51, 0.29],
                        [0.82, 1.00, 1.00, 1.00, 1.00, 0.64, 0.25, 0.13],
                        [0.62, 0.96, 1.00, 0.98, 0.64, 0.22, 0.06, 0.03],
                        [0.34, 0.62, 0.71, 0.51, 0.25, 0.06, 0.00, 0.01],
                        [0.00, 0.17, 0.33, 0.29, 0.13, 0.03, 0.01, 0.00]],

                       [[0.87, 0.79, 0.83, 0.80, 0.66, 0.44, 0.23, 0.00],
                        [0.79, 0.88, 0.93, 0.92, 0.83, 0.66, 0.38, 0.10],
                        [0.83, 0.93, 0.99, 1.00, 0.92, 0.75, 0.40, 0.18],
                        [0.80, 0.92, 1.00, 0.99, 0.93, 0.75, 0.28, 0.14],
                        [0.66, 0.83, 0.92, 0.93, 0.87, 0.44, 0.12, 0.06],
                        [0.44, 0.66, 0.75, 0.75, 0.44, 0.12, 0.03, 0.01],
                        [0.23, 0.38, 0.40, 0.28, 0.12, 0.03, 0.00, 0.00],
                        [0.00, 0.10, 0.18, 0.14, 0.06, 0.01, 0.00, 0.00]],

                       [[0.87, 0.75, 0.78, 0.73, 0.55, 0.33, 0.16, 0.00],
                        [0.75, 0.85, 0.90, 0.86, 0.71, 0.48, 0.23, 0.05],
                        [0.78, 0.90, 0.98, 1.00, 0.82, 0.51, 0.21, 0.08],
                        [0.73, 0.86, 1.00, 0.97, 0.84, 0.47, 0.11, 0.05],
                        [0.55, 0.71, 0.82, 0.84, 0.71, 0.20, 0.03, 0.01],
                        [0.33, 0.48, 0.51, 0.47, 0.20, 0.02, 0.00, 0.00],
                        [0.16, 0.23, 0.21, 0.11, 0.03, 0.00, 0.00, 0.00],
                        [0.00, 0.05, 0.08, 0.05, 0.01, 0.00, 0.00, 0.00]],

                       [[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
                        [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
                        [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
                        [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
                        [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
                        [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
                        [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
                        [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00]]]).T
    if (V(np.__version__) == V('1.9.0')):
        # Numpy 1.9.0 uses a 2. order algorithm on the edges by default
        # This was changed back again in 1.9.1
        expect = expect[1:-1, 1:-1, :]
        rgb = rgb[1:-1, 1:-1, :]

    assert_array_almost_equal(rgb, expect, decimal=2)


@knownfailureif((V(np.__version__) <= V('1.9.0')
                and V(np.__version__) >= V('1.7.0')))
# Numpy 1.9.1 fixed a bug in masked arrays which resulted in
# additional elements being masked when calculating the gradient thus
# the output is different with earlier numpy versions.
def test_light_source_masked_shading():
    """Array comparison test for a surface with a masked portion. Ensures that
    we don't wind up with "fringes" of odd colors around masked regions."""
    y, x = np.mgrid[-1.2:1.2:8j, -1.2:1.2:8j]
    z = 10 * np.cos(x**2 + y**2)

    z = np.ma.masked_greater(z, 9.9)

    cmap = plt.cm.copper
    ls = mcolors.LightSource(315, 45)
    rgb = ls.shade(z, cmap)

    # Result stored transposed and rounded for for more compact display...
    expect = np.array([[[0.90, 0.88, 0.91, 0.91, 0.84, 0.64, 0.36, 0.00],
                        [0.88, 0.96, 1.00, 1.00, 1.00, 0.97, 0.64, 0.18],
                        [0.91, 1.00, 1.00, 1.00, 1.00, 1.00, 0.74, 0.34],
                        [0.91, 1.00, 1.00, 0.00, 0.00, 1.00, 0.52, 0.30],
                        [0.84, 1.00, 1.00, 0.00, 0.00, 1.00, 0.25, 0.13],
                        [0.64, 0.97, 1.00, 1.00, 1.00, 0.23, 0.07, 0.03],
                        [0.36, 0.64, 0.74, 0.52, 0.25, 0.07, 0.00, 0.01],
                        [0.00, 0.18, 0.34, 0.30, 0.13, 0.03, 0.01, 0.00]],

                       [[0.90, 0.82, 0.85, 0.82, 0.68, 0.46, 0.24, 0.00],
                        [0.82, 0.91, 0.95, 0.93, 0.85, 0.68, 0.39, 0.10],
                        [0.85, 0.95, 1.00, 0.78, 0.78, 0.77, 0.42, 0.18],
                        [0.82, 0.93, 0.78, 0.00, 0.00, 0.78, 0.30, 0.15],
                        [0.68, 0.85, 0.78, 0.00, 0.00, 0.78, 0.13, 0.06],
                        [0.46, 0.68, 0.77, 0.78, 0.78, 0.13, 0.03, 0.01],
                        [0.24, 0.39, 0.42, 0.30, 0.13, 0.03, 0.00, 0.00],
                        [0.00, 0.10, 0.18, 0.15, 0.06, 0.01, 0.00, 0.00]],

                       [[0.90, 0.79, 0.81, 0.76, 0.58, 0.35, 0.17, 0.00],
                        [0.79, 0.88, 0.92, 0.88, 0.73, 0.50, 0.24, 0.05],
                        [0.81, 0.92, 1.00, 0.50, 0.50, 0.53, 0.22, 0.09],
                        [0.76, 0.88, 0.50, 0.00, 0.00, 0.50, 0.12, 0.05],
                        [0.58, 0.73, 0.50, 0.00, 0.00, 0.50, 0.03, 0.01],
                        [0.35, 0.50, 0.53, 0.50, 0.50, 0.02, 0.00, 0.00],
                        [0.17, 0.24, 0.22, 0.12, 0.03, 0.00, 0.00, 0.00],
                        [0.00, 0.05, 0.09, 0.05, 0.01, 0.00, 0.00, 0.00]],

                       [[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
                        [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
                        [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
                        [1.00, 1.00, 1.00, 0.00, 0.00, 1.00, 1.00, 1.00],
                        [1.00, 1.00, 1.00, 0.00, 0.00, 1.00, 1.00, 1.00],
                        [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
                        [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
                        [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00]]]).T

    assert_array_almost_equal(rgb, expect, decimal=2)


def test_light_source_hillshading():
    """Compare the current hillshading method against one that should be
    mathematically equivalent. Illuminates a cone from a range of angles."""

    def alternative_hillshade(azimuth, elev, z):
        illum = _sph2cart(*_azimuth2math(azimuth, elev))
        illum = np.array(illum)

        dy, dx = np.gradient(-z)
        dy = -dy
        dz = np.ones_like(dy)
        normals = np.dstack([dx, dy, dz])
        dividers = np.zeros_like(z)[..., None]
        for i, mat in enumerate(normals):
            for j, vec in enumerate(mat):
                dividers[i, j, 0] = np.linalg.norm(vec)
        normals /= dividers
        # once we drop support for numpy 1.7.x the above can be written as
        # normals /= np.linalg.norm(normals, axis=2)[..., None]
        # aviding the double loop.

        intensity = np.tensordot(normals, illum, axes=(2, 0))
        intensity -= intensity.min()
        intensity /= intensity.ptp()
        return intensity

    y, x = np.mgrid[5:0:-1, :5]
    z = -np.hypot(x - x.mean(), y - y.mean())

    for az, elev in itertools.product(range(0, 390, 30), range(0, 105, 15)):
        ls = mcolors.LightSource(az, elev)
        h1 = ls.hillshade(z)
        h2 = alternative_hillshade(az, elev, z)
        assert_array_almost_equal(h1, h2)


def test_light_source_planar_hillshading():
    """Ensure that the illumination intensity is correct for planar
    surfaces."""

    def plane(azimuth, elevation, x, y):
        """Create a plane whose normal vector is at the given azimuth and
        elevation."""
        theta, phi = _azimuth2math(azimuth, elevation)
        a, b, c = _sph2cart(theta, phi)
        z = -(a*x + b*y) / c
        return z

    def angled_plane(azimuth, elevation, angle, x, y):
        """Create a plane whose normal vector is at an angle from the given
        azimuth and elevation."""
        elevation = elevation + angle
        if elevation > 90:
            azimuth = (azimuth + 180) % 360
            elevation = (90 - elevation) % 90
        return plane(azimuth, elevation, x, y)

    y, x = np.mgrid[5:0:-1, :5]
    for az, elev in itertools.product(range(0, 390, 30), range(0, 105, 15)):
        ls = mcolors.LightSource(az, elev)

        # Make a plane at a range of angles to the illumination
        for angle in range(0, 105, 15):
            z = angled_plane(az, elev, angle, x, y)
            h = ls.hillshade(z)
            assert_array_almost_equal(h, np.cos(np.radians(angle)))


def _sph2cart(theta, phi):
    x = np.cos(theta) * np.sin(phi)
    y = np.sin(theta) * np.sin(phi)
    z = np.cos(phi)
    return x, y, z


def _azimuth2math(azimuth, elevation):
    """Converts from clockwise-from-north and up-from-horizontal to
    mathematical conventions."""
    theta = np.radians((90 - azimuth) % 360)
    phi = np.radians(90 - elevation)
    return theta, phi


if __name__ == '__main__':
    import nose
    nose.runmodule(argv=['-s', '--with-doctest'], exit=False)