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)
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()
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]))
