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()