gallery_python.zip

""" ================= Custom projection ================= Showcase Hammer projection by alleviating many features of Matplotlib. """ import numpy as np import matplotlib from matplotlib.axes import Axes import matplotlib.axis as maxis from matplotlib.patches import Circle from matplotlib.path import Path from matplotlib.projections import register_projection import matplotlib.spines as mspines from matplotlib.ticker import FixedLocator, Formatter, NullLocator from matplotlib.transforms import Affine2D, BboxTransformTo, Transform rcParams = matplotlib.rcParams # This example projection class is rather long, but it is designed to # illustrate many features, not all of which will be used every time. # It is also common to factor out a lot of these methods into common # code used by a number of projections with similar characteristics # (see geo.py). class GeoAxes(Axes): """ An abstract base class for geographic projections """ class ThetaFormatter(Formatter): """ Used to format the theta tick labels. Converts the native unit of radians into degrees and adds a degree symbol. """ def __init__(self, round_to=1.0): self._round_to = round_to def __call__(self, x, pos=None): degrees = round(np.rad2deg(x) / self._round_to) * self._round_to return f"{degrees:0.0f}\N{DEGREE SIGN}" RESOLUTION = 75 def _init_axis(self): self.xaxis = maxis.XAxis(self) self.yaxis = maxis.YAxis(self) # Do not register xaxis or yaxis with spines -- as done in # Axes._init_axis() -- until GeoAxes.xaxis.clear() works. # self.spines['geo'].register_axis(self.yaxis) def clear(self): # docstring inherited super().clear() self.set_longitude_grid(30) self.set_latitude_grid(15) self.set_longitude_grid_ends(75) self.xaxis.set_minor_locator(NullLocator()) self.yaxis.set_minor_locator(NullLocator()) self.xaxis.set_ticks_position('none') self.yaxis.set_ticks_position('none') self.yaxis.set_tick_params(label1On=True) # Why do we need to turn on yaxis tick labels, but # xaxis tick labels are already on? self.grid(rcParams['axes.grid']) Axes.set_xlim(self, -np.pi, np.pi) Axes.set_ylim(self, -np.pi / 2.0, np.pi / 2.0) def _set_lim_and_transforms(self): # A (possibly non-linear) projection on the (already scaled) data # There are three important coordinate spaces going on here: # # 1. Data space: The space of the data itself # # 2. Axes space: The unit rectangle (0, 0) to (1, 1) # covering the entire plot area. # # 3. Display space: The coordinates of the resulting image, # often in pixels or dpi/inch. # This function makes heavy use of the Transform classes in # ``lib/matplotlib/transforms.py.`` For more information, see # the inline documentation there. # The goal of the first two transformations is to get from the # data space (in this case longitude and latitude) to axes # space. It is separated into a non-affine and affine part so # that the non-affine part does not have to be recomputed when # a simple affine change to the figure has been made (such as # resizing the window or changing the dpi). # 1) The core transformation from data space into # rectilinear space defined in the HammerTransform class. self.transProjection = self._get_core_transform(self.RESOLUTION) # 2) The above has an output range that is not in the unit # rectangle, so scale and translate it so it fits correctly # within the axes. The peculiar calculations of xscale and # yscale are specific to an Aitoff-Hammer projection, so don't # worry about them too much. self.transAffine = self._get_affine_transform() # 3) This is the transformation from axes space to display # space. self.transAxes = BboxTransformTo(self.bbox) # Now put these 3 transforms together -- from data all the way # to display coordinates. Using the '+' operator, these # transforms will be applied "in order". The transforms are # automatically simplified, if possible, by the underlying # transformation framework. self.transData = \ self.transProjection + \ self.transAffine + \ self.transAxes # The main data transformation is set up. Now deal with # gridlines and tick labels. # Longitude gridlines and ticklabels. The input to these # transforms are in display space in x and axes space in y. # Therefore, the input values will be in range (-xmin, 0), # (xmax, 1). The goal of these transforms is to go from that # space to display space. The tick labels will be offset 4 # pixels from the equator. self._xaxis_pretransform = \ Affine2D() \ .scale(1.0, self._longitude_cap * 2.0) \ .translate(0.0, -self._longitude_cap) self._xaxis_transform = \ self._xaxis_pretransform + \ self.transData self._xaxis_text1_transform = \ Affine2D().scale(1.0, 0.0) + \ self.transData + \ Affine2D().translate(0.0, 4.0) self._xaxis_text2_transform = \ Affine2D().scale(1.0, 0.0) + \ self.transData + \ Affine2D().translate(0.0, -4.0) # Now set up the transforms for the latitude ticks. The input to # these transforms are in axes space in x and display space in # y. Therefore, the input values will be in range (0, -ymin), # (1, ymax). The goal of these transforms is to go from that # space to display space. The tick labels will be offset 4 # pixels from the edge of the axes ellipse. yaxis_stretch = Affine2D().scale(np.pi*2, 1).translate(-np.pi, 0) yaxis_space = Affine2D().scale(1.0, 1.1) self._yaxis_transform = \ yaxis_stretch + \ self.transData yaxis_text_base = \ yaxis_stretch + \ self.transProjection + \ (yaxis_space + self.transAffine + self.transAxes) self._yaxis_text1_transform = \ yaxis_text_base + \ Affine2D().translate(-8.0, 0.0) self._yaxis_text2_transform = \ yaxis_text_base + \ Affine2D().translate(8.0, 0.0) def _get_affine_transform(self): transform = self._get_core_transform(1) xscale, _ = transform.transform((np.pi, 0)) _, yscale = transform.transform((0, np.pi/2)) return Affine2D() \ .scale(0.5 / xscale, 0.5 / yscale) \ .translate(0.5, 0.5) def get_xaxis_transform(self, which='grid'): """ Override this method to provide a transformation for the x-axis tick labels. Returns a tuple of the form (transform, valign, halign) """ if which not in ['tick1', 'tick2', 'grid']: raise ValueError( "'which' must be one of 'tick1', 'tick2', or 'grid'") return self._xaxis_transform def get_xaxis_text1_transform(self, pad): return self._xaxis_text1_transform, 'bottom', 'center' def get_xaxis_text2_transform(self, pad): """ Override this method to provide a transformation for the secondary x-axis tick labels. Returns a tuple of the form (transform, valign, halign) """ return self._xaxis_text2_transform, 'top', 'center' def get_yaxis_transform(self, which='grid'): """ Override this method to provide a transformation for the y-axis grid and ticks. """ i