def test_polar_box():
# Remove this line when this test image is regenerated.
plt.rcParams['text.kerning_factor'] = 6
fig = plt.figure(figsize=(5, 5))
# PolarAxes.PolarTransform takes radian. However, we want our coordinate
# system in degree
tr = Affine2D().scale(np.pi / 180., 1.) + PolarAxes.PolarTransform()
# polar projection, which involves cycle, and also has limits in
# its coordinates, needs a special method to find the extremes
# (min, max of the coordinate within the view).
extreme_finder = angle_helper.ExtremeFinderCycle(20, 20,
lon_cycle=360,
lat_cycle=None,
lon_minmax=None,
lat_minmax=(0, np.inf))
grid_locator1 = angle_helper.LocatorDMS(12)
tick_formatter1 = angle_helper.FormatterDMS()
grid_helper = GridHelperCurveLinear(tr,
extreme_finder=extreme_finder,
grid_locator1=grid_locator1,
tick_formatter1=tick_formatter1)
ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper)
ax1.axis["right"].major_ticklabels.set_visible(True)
ax1.axis["top"].major_ticklabels.set_visible(True)
# let right axis shows ticklabels for 1st coordinate (angle)
ax1.axis["right"].get_helper().nth_coord_ticks = 0
# let bottom axis shows ticklabels for 2nd coordinate (radius)
ax1.axis["bottom"].get_helper().nth_coord_ticks = 1
fig.add_subplot(ax1)
ax1.axis["lat"] = axis = grid_helper.new_floating_axis(0, 45, axes=ax1)
axis.label.set_text("Test")
axis.label.set_visible(True)
axis.get_helper()._extremes = 2, 12
ax1.axis["lon"] = axis = grid_helper.new_floating_axis(1, 6, axes=ax1)
axis.label.set_text("Test 2")
axis.get_helper()._extremes = -180, 90
# A parasite axes with given transform
ax2 = ParasiteAxesAuxTrans(ax1, tr, "equal")
assert ax2.transData == tr + ax1.transData
# Anything you draw in ax2 will match the ticks and grids of ax1.
ax1.parasites.append(ax2)
ax2.plot(np.linspace(0, 30, 50), np.linspace(10, 10, 50))
ax1.set_aspect(1.)
ax1.set_xlim(-5, 12)
ax1.set_ylim(-5, 10)
ax1.grid(True)
def test_custom_transform():
class MyTransform(Transform):
input_dims = output_dims = 2
def __init__(self, resolution):
"""
Resolution is the number of steps to interpolate between each input
line segment to approximate its path in transformed space.
"""
Transform.__init__(self)
self._resolution = resolution
def transform(self, ll):
x, y = ll.T
return np.column_stack([x, y - x])
transform_non_affine = transform
def transform_path(self, path):
ipath = path.interpolated(self._resolution)
return Path(self.transform(ipath.vertices), ipath.codes)
transform_path_non_affine = transform_path
def inverted(self):
return MyTransformInv(self._resolution)
class MyTransformInv(Transform):
input_dims = output_dims = 2
def __init__(self, resolution):
Transform.__init__(self)
self._resolution = resolution
def transform(self, ll):
x, y = ll.T
return np.column_stack([x, y + x])
def inverted(self):
return MyTransform(self._resolution)
fig = plt.figure()
SubplotHost = host_subplot_class_factory(Axes)
tr = MyTransform(1)
grid_helper = GridHelperCurveLinear(tr)
ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper)
fig.add_subplot(ax1)
ax2 = ParasiteAxesAuxTrans(ax1, tr, "equal")
ax1.parasites.append(ax2)
ax2.plot([3, 6], [5.0, 10.])
ax1.set_aspect(1.)
ax1.set_xlim(0, 10)
ax1.set_ylim(0, 10)
ax1.grid(True)