def apply_roi(self, roi, override_mode=None):
# Force redraw to get rid of ROI. We do this because applying the
# subset state below might end up not having an effect on the viewer,
# for example there may not be any layers, or the active subset may not
# be one of the layers. So we just explicitly redraw here to make sure
# a redraw will happen after this method is called.
self.redraw()
if len(self.layers) == 0:
return
x_date = 'datetime' in self.state.x_kinds
y_date = 'datetime' in self.state.y_kinds
if x_date or y_date:
roi = roi.transformed(xfunc=mpl_to_datetime64 if x_date else None,
yfunc=mpl_to_datetime64 if y_date else None)
use_transform = self.state.plot_mode != 'rectilinear'
subset_state = roi_to_subset_state(
roi,
x_att=self.state.x_att,
x_categories=self.state.x_categories,
y_att=self.state.y_att,
y_categories=self.state.y_categories,
use_pretransform=use_transform)
if use_transform:
subset_state.pretransform = ProjectionMplTransform(
self.state.plot_mode, self.axes.get_xlim(),
self.axes.get_ylim(), self.axes.get_xscale(),
self.axes.get_yscale())
self.apply_subset_state(subset_state, override_mode=override_mode)
def test_wedge_polar_mpl_transform():
angles = np.deg2rad(np.array([0, 45, 90, 135, 180]))
radii = np.array([0.1, 0.2, 0.3, 0.4, 0.5])
transform = ProjectionMplTransform('polar', [0, np.pi], [0, 0.5], 'linear',
'linear')
x, y = transform(angles, radii)
assert_allclose(x, np.array([0.6, 0.64142136, 0.5, 0.21715729, 0]))
# For just the upper half, y is between 0.25 and 0.75
assert_allclose(y, np.array([0.25, 0.39142136, 0.55, 0.53284271, 0.25]))
new_transform = roundtrip_transform(transform)
new_x, new_y = new_transform(angles, radii)
assert_allclose(new_x, x, rtol=1e-14)
assert_allclose(new_y, y, rtol=1e-14)
def test_log_polar_mpl_transform():
angles = np.deg2rad(np.array([0, 90, 180]))
radii = np.array([10, 100, 1000])
transform = ProjectionMplTransform('polar', [0, 2 * np.pi], [1, 10000],
'linear', 'log')
x, y = transform(angles, radii)
expected_x = np.array([0.5, 0.5, 0.25])
expected_y = np.array([0.5, 0.625, 0.5])
assert_allclose(x, expected_x)
assert_allclose(y, expected_y)
new_transform = roundtrip_transform(transform)
new_x, new_y = new_transform(angles, radii)
assert_allclose(new_x, x, rtol=1e-14)
assert_allclose(new_y, y, rtol=1e-14)
def test_lambert_mpl_projection():
transform = ProjectionMplTransform('lambert', [-np.pi, np.pi],
[-np.pi / 2, np.pi / 2], 'linear',
'linear')
long = np.deg2rad(np.array([0, -90, 0, 45]))
lat = np.deg2rad(np.array([0, 0, 45, -45]))
x, y = transform(long, lat)
expected_x = np.array([0.5, 0.14644661, 0.5, 0.64433757])
expected_y = np.array([0.5, 0.5, 0.69134172, 0.29587585])
assert_allclose(x, expected_x)
assert_allclose(y, expected_y)
new_transform = roundtrip_transform(transform)
new_x, new_y = new_transform(long, lat)
assert_allclose(new_x, x, rtol=1e-14)
assert_allclose(new_y, y, rtol=1e-14)
def test_hammer_mpl_transform():
transform = ProjectionMplTransform('hammer', [-np.pi, np.pi],
[-np.pi / 2, np.pi / 2], 'linear',
'linear')
long = np.deg2rad(np.array([0, -90, 0, 45]))
lat = np.deg2rad(np.array([0, 0, 45, -45]))
x, y = transform(long, lat)
expected_x = np.array([0.5, 0.22940195, 0.5, 0.60522557])
expected_y = np.array([0.5, 0.5, 0.77059805, 0.22503235])
assert_allclose(x, expected_x)
assert_allclose(y, expected_y)
new_transform = roundtrip_transform(transform)
new_x, new_y = new_transform(long, lat)
assert_allclose(new_x, x, rtol=1e-14)
assert_allclose(new_y, y, rtol=1e-14)
def test_aitoff_mpl_transform():
transform = ProjectionMplTransform('aitoff', [-np.pi, np.pi],
[-np.pi / 2, np.pi / 2], 'linear',
'linear')
long = np.deg2rad(np.array([0, -90, 0, 45]))
lat = np.deg2rad(np.array([0, 0, 45, -45]))
x, y = transform(long, lat)
expected_x = np.array([0.5, 0.25, 0.5, 0.59771208])
expected_y = np.array([0.5, 0.5, 0.75, 0.24466602])
assert_allclose(x, expected_x)
assert_allclose(y, expected_y)
new_transform = roundtrip_transform(transform)
new_x, new_y = new_transform(long, lat)
assert_allclose(new_x, x, rtol=1e-14)
assert_allclose(new_y, y, rtol=1e-14)
def test_mollweide_mpl_projection():
transform = ProjectionMplTransform('mollweide', [-np.pi, np.pi],
[-np.pi / 2, np.pi / 2], 'linear',
'linear')
long = np.deg2rad(np.array([0, -90, 0, 45]))
lat = np.deg2rad(np.array([0, 0, 45, -45]))
x, y = transform(long, lat)
expected_x = np.array([0.5, 0.25, 0.5, 0.60076564])
expected_y = np.array([0.5, 0.5, 0.79587254, 0.20412746])
assert_allclose(x, expected_x)
assert_allclose(y, expected_y)
new_transform = roundtrip_transform(transform)
new_x, new_y = new_transform(long, lat)
assert_allclose(new_x, x, rtol=1e-14)
assert_allclose(new_y, y, rtol=1e-14)
def test_simple_polar_mpl_transform():
angles = np.deg2rad(np.array([0, 45, 90, 180, 300, 810, 0, 0]))
radii = np.array([0, 5, 4, 1, 0, 2, -10, 10])
transform = ProjectionMplTransform('polar', [0, 2 * np.pi], [-5, 5],
'linear', 'linear')
x, y = transform(angles, radii)
expected_x = np.array(
[0.75, 0.8535533905932736, 0.5, 0.2, .625, 0.5, np.nan, 1.25])
expected_y = np.array([
0.5, 0.8535533905932736, 0.95, 0.5, 0.28349364905389035, 0.85, np.nan,
0.5
])
assert_allclose(x, expected_x)
assert_allclose(y, expected_y)
new_transform = roundtrip_transform(transform)
new_x, new_y = new_transform(angles, radii)
assert_allclose(new_x, x, rtol=1e-14)
assert_allclose(new_y, y, rtol=1e-14)
def apply_roi(self, roi, override_mode=None):
# Force redraw to get rid of ROI. We do this because applying the
# subset state below might end up not having an effect on the viewer,
# for example there may not be any layers, or the active subset may not
# be one of the layers. So we just explicitly redraw here to make sure
# a redraw will happen after this method is called.
self.redraw()
if len(self.layers) == 0:
return
x_date = 'datetime' in self.state.x_kinds
y_date = 'datetime' in self.state.y_kinds
if x_date or y_date:
roi = roi.transformed(xfunc=mpl_to_datetime64 if x_date else None,
yfunc=mpl_to_datetime64 if y_date else None)
use_transform = self.state.plot_mode != 'rectilinear'
subset_state = roi_to_subset_state(
roi,
x_att=self.state.x_att,
x_categories=self.state.x_categories,
y_att=self.state.y_att,
y_categories=self.state.y_categories,
use_pretransform=use_transform)
if use_transform:
transform = ProjectionMplTransform(self.state.plot_mode,
self.axes.get_xlim(),
self.axes.get_ylim(),
self.axes.get_xscale(),
self.axes.get_yscale())
# If we're using degrees, we need to staple on the degrees -> radians conversion beforehand
if self.using_polar() and self.state.angle_unit == 'degrees':
transform = partial(
MatplotlibScatterMixin._radian_pretransform,
transform=transform)
subset_state.pretransform = transform
self.apply_subset_state(subset_state, override_mode=override_mode)