import platform import numpy as np import matplotlib.pyplot as plt from matplotlib.path import Path from matplotlib.projections import PolarAxes from matplotlib.ticker import FuncFormatter from matplotlib.transforms import Affine2D, Transform from matplotlib.testing.decorators import image_comparison from mpl_toolkits.axisartist import SubplotHost from mpl_toolkits.axes_grid1.parasite_axes import host_axes_class_factory from mpl_toolkits.axisartist import angle_helper from mpl_toolkits.axisartist.axislines import Axes from mpl_toolkits.axisartist.grid_helper_curvelinear import \ GridHelperCurveLinear @image_comparison(['custom_transform.png'], style='mpl20', tol=0 if platform.machine() == 'x86_64' else 0.04) def test_custom_transform(): plt.rcParams.update({"xtick.direction": "in", "ytick.direction": "inout"}) 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_axes_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 = ax1.get_aux_axes(tr, viewlim_mode="equal") ax2.plot([3, 6], [5.0, 10.]) ax1.set_aspect(1.) ax1.set_xlim(0, 10) ax1.set_ylim(0, 10) ax1.grid(True) @image_comparison(['polar_box.png'], style='mpl20', tol=0.04) def test_polar_box(): plt.rcParams.update({"xtick.direction": "inout", "ytick.direction": "out"}) 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_helper = GridHelperCurveLinear( tr, extreme_finder=extreme_finder, grid_locator1=angle_helper.LocatorDMS(12), tick_formatter1=angle_helper.FormatterDMS(), tick_formatter2=FuncFormatter(lambda x, p: "eight" if x == 8 else f"{int(x)}"), ) 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().set_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().set_extremes(-180, 90) # A parasite axes with given transform ax2 = ax1.get_aux_axes(tr, viewlim_mode="equal") assert ax2.transData == tr + ax1.transData # Anything you draw in ax2 will match the ticks and grids of ax1. 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) @image_comparison(['axis_direction.png'], style='mpl20', tol=0.04) def test_axis_direction(): 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). # 20, 20 : number of sampling points along x, y direction 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) for axis in ax1.axis.values(): axis.set_visible(False) fig.add_subplot(ax1) ax1.axis["lat1"] = axis = grid_helper.new_floating_axis( 0, 130, axes=ax1, axis_direction="left") axis.label.set_text("Test") axis.label.set_visible(True) axis.get_helper().set_extremes(0.001, 10) ax1.axis["lat2"] = axis = grid_helper.new_floating_axis( 0, 50, axes=ax1, axis_direction="right") axis.label.set_text("Test") axis.label.set_visible(True) axis.get_helper().set_extremes(0.001, 10) ax1.axis["lon"] = axis = grid_helper.new_floating_axis( 1, 10, axes=ax1, axis_direction="bottom") axis.label.set_text("Test 2") axis.get_helper().set_extremes(50, 130) axis.major_ticklabels.set_axis_direction("top") axis.label.set_axis_direction("top") grid_helper.grid_finder.grid_locator1.set_params(nbins=5) grid_helper.grid_finder.grid_locator2.set_params(nbins=5) ax1.set_aspect(1.) ax1.set_xlim(-8, 8) ax1.set_ylim(-4, 12) ax1.grid(True)