def test_datalevels_visu_v():
a = np.array([-1., 0., 1.1, 1.9, 9.])
cm = mpl.cm.get_cmap('RdYlBu_r')
dl = DataLevels(a.reshape((5, 1)), cmap=cm, levels=[0, 1, 2, 3])
fig, ax = plt.subplots(1)
dl.visualize(ax=ax, orientation='vertical', add_values=True)
plt.tight_layout()
return fig
def test_datalevels_visu_v():
a = np.array([-1., 0., 1.1, 1.9, 9.])
cm = mpl.cm.get_cmap('RdYlBu_r')
dl = DataLevels(a.reshape((5, 1)), cmap=cm, levels=[0, 1, 2, 3])
fig, ax = plt.subplots(1)
dl.visualize(ax=ax, orientation='vertical', add_values=True)
plt.tight_layout()
return fig
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
try:
cm = mpl.cm.get_cmap('jet').copy()
except AttributeError:
import copy
cm = copy.deepcopy(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()
return fig
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()
return fig
