Пример #1
0
def test_datalevels_visu():

    a = np.array([-1., 0., 1.1, 1.9, 9.])
    cm = mpl.cm.get_cmap('RdYlBu_r')

    dl = DataLevels(a, cmap=cm, levels=[0, 1, 2, 3])
    dl.visualize(orientation='horizontal', add_values=True)

    dl = DataLevels(a.reshape((5,1)), cmap=cm, levels=[0, 1, 2, 3])
    dl.visualize(orientation='vertical', add_values=True)
Пример #2
0
def test_datalevels():

    plt.close()

    a = np.zeros((4, 5))
    a[0, 0] = -1
    a[1, 1] = 1.1
    a[2, 2] = 2.2
    a[2, 4] = 1.9
    a[3, 3] = 9

    cm = copy.copy(mpl.cm.get_cmap('jet'))
    cm.set_bad('pink')

    # fig, axes = plt.subplots(nrows=3, ncols=2)
    fig = plt.figure()
    ax = iter([fig.add_subplot(3, 2, i) for i in [1,2,3,4,5,6]])

    # The extended version should be automated
    c = DataLevels(levels=[0,1,2,3], data=a, cmap=cm)
    c.visualize(next(ax), title='levels=[0,1,2,3]')

    # Without min
    a[0, 0] = 0
    c = DataLevels(levels=[0,1,2,3], data=a, cmap=cm)
    c.visualize(next(ax), title='modified a for no min oob')

    # Without max
    a[3, 3] = 0
    c = DataLevels(levels=[0,1,2,3], data=a, cmap=cm)
    c.visualize(next(ax), title='modified a for no max oob')

    # Forced bounds
    c = DataLevels(levels=[0,1,2,3], data=a, cmap=cm, extend='both')
    c.visualize(next(ax), title="extend='both'")

    # Autom nlevels
    a[0, 0] = -1
    a[3, 3] = 9
    c = DataLevels(nlevels=127, vmin=0, vmax=3, data=a, cmap=cm)
    c.visualize(next(ax), title="Auto levels with oob data")

    # Missing data
    a[3, 0] = np.NaN
    c = DataLevels(nlevels=127, vmin=0, vmax=3, data=a, cmap=cm)
    c.visualize(next(ax), title="missing data")

    plt.tight_layout()
Пример #3
0
    def test_datalevels_output(self):

        # Test basic stuffs
        c = DataLevels(nlevels=2)
        assert_array_equal(c.levels, [0, 1])
        c.set_data([1, 2, 3, 4])
        assert_array_equal(c.levels, [1, 4])

        c = DataLevels(levels=[1, 2, 3])
        assert_array_equal(c.levels, [1, 2, 3])

        c = DataLevels(nlevels=10, data=[0, 9])
        assert_array_equal(c.levels, np.linspace(0, 9, num=10))
        self.assertTrue(c.extend == 'neither')

        c = DataLevels(nlevels=10, data=[0, 9], vmin=2, vmax=3)
        assert_array_equal(c.levels, np.linspace(2, 3, num=10))
        self.assertTrue(c.extend == 'both')
        c.set_extend('neither')
        self.assertTrue(c.extend == 'neither')
        with warnings.catch_warnings(record=True) as w:
            # Cause all warnings to always be triggered.
            warnings.simplefilter("always")
            # Trigger a warning.
            out = c.to_rgb()
            # Verify some things
            assert len(w) == 2
            assert issubclass(w[0].category, RuntimeWarning)
            assert issubclass(w[1].category, RuntimeWarning)

        c = DataLevels(nlevels=10, data=[2.5], vmin=2, vmax=3)
        assert_array_equal(c.levels, np.linspace(2, 3, num=10))
        self.assertTrue(c.extend == 'neither')
        c.update(dict(extend='both'))
        self.assertTrue(c.extend == 'both')
        self.assertRaises(AttributeError, c.update, dict(dummy='t'))

        c = DataLevels(nlevels=10, data=[0, 9], vmax=3)
        assert_array_equal(c.levels, np.linspace(0, 3, num=10))
        self.assertTrue(c.extend == 'max')

        c = DataLevels(nlevels=10, data=[0, 9], vmin=1)
        assert_array_equal(c.levels, np.linspace(1, 9, num=10))
        self.assertTrue(c.extend == 'min')

        c = DataLevels(nlevels=10, data=[0, 9], vmin=-1)
        assert_array_equal(c.levels, np.linspace(-1, 9, num=10))
        self.assertTrue(c.extend == 'neither')
        c.set_plot_params()
        self.assertTrue(c.extend == 'neither')
        assert_array_equal(c.vmin, 0)
        assert_array_equal(c.vmax, 9)
        c.set_plot_params(vmin=1)
        assert_array_equal(c.vmin, 1)
        c.set_data([-12, 8])
        assert_array_equal(c.vmin, 1)
        self.assertTrue(c.extend == 'min')
        c.set_data([2, 8])
        self.assertTrue(c.extend == 'neither')
        c.set_extend('both')
        self.assertTrue(c.extend == 'both')
        c.set_data([3, 3])
        self.assertTrue(c.extend == 'both')
        c.set_extend()
        self.assertTrue(c.extend == 'neither')

        # Test the conversion
        cm = mpl.colors.ListedColormap(['white', 'blue', 'red', 'black'])
        x = [-1, 0.9, 1.2, 2, 999, 0.8]
        c = DataLevels(levels=[0, 1, 2], data=x, cmap=cm)
        r = c.to_rgb()
        self.assertTrue(len(x) == len(r))
        self.assertTrue(c.extend == 'both')
        assert_array_equal(r, cm([0, 1, 2, 3, 3, 1]))

        x = [0.9, 1.2]
        c = DataLevels(levels=[0, 1, 2], data=x, cmap=cm, extend='both')
        r = c.to_rgb()
        self.assertTrue(len(x) == len(r))
        self.assertTrue(c.extend == 'both')
        assert_array_equal(r, cm([1, 2]))

        cm = mpl.colors.ListedColormap(['white', 'blue', 'red'])
        c = DataLevels(levels=[0, 1, 2], data=x, cmap=cm, extend='min')
        r = c.to_rgb()
        self.assertTrue(len(x) == len(r))
        assert_array_equal(r, cm([1, 2]))

        cm = mpl.colors.ListedColormap(['blue', 'red', 'black'])
        c = DataLevels(levels=[0, 1, 2], data=x, cmap=cm, extend='max')
        r = c.to_rgb()
        self.assertTrue(len(x) == len(r))
        assert_array_equal(r, cm([0, 1]))