Version 3.0.0
matplotlib
Fork me on GitHub

Source code for mpl_toolkits.axisartist.floating_axes

"""
An experimental support for curvilinear grid.
"""

import functools

# TODO :
# see if tick_iterator method can be simplified by reusing the parent method.

import numpy as np

from matplotlib.transforms import Affine2D, IdentityTransform
from . import grid_helper_curvelinear
from .axislines import AxisArtistHelper, GridHelperBase
from .axis_artist import AxisArtist
from .grid_finder import GridFinder


[docs]class FloatingAxisArtistHelper(grid_helper_curvelinear.FloatingAxisArtistHelper): pass
[docs]class FixedAxisArtistHelper(grid_helper_curvelinear.FloatingAxisArtistHelper): def __init__(self, grid_helper, side, nth_coord_ticks=None): """ nth_coord = along which coordinate value varies. nth_coord = 0 -> x axis, nth_coord = 1 -> y axis """ value, nth_coord = grid_helper.get_data_boundary(side) # return v= 0 , nth=1, extremes of the other coordinate. super().__init__(grid_helper, nth_coord, value, axis_direction=side) #self.grid_helper = grid_helper if nth_coord_ticks is None: nth_coord_ticks = nth_coord self.nth_coord_ticks = nth_coord_ticks self.value = value self.grid_helper = grid_helper self._side = side
[docs] def update_lim(self, axes): self.grid_helper.update_lim(axes) self.grid_info = self.grid_helper.grid_info
[docs] def get_axislabel_pos_angle(self, axes): extremes = self.grid_info["extremes"] if self.nth_coord == 0: xx0 = self.value yy0 = (extremes[2]+extremes[3])/2. dxx, dyy = 0., abs(extremes[2]-extremes[3])/1000. elif self.nth_coord == 1: xx0 = (extremes[0]+extremes[1])/2. yy0 = self.value dxx, dyy = abs(extremes[0]-extremes[1])/1000., 0. grid_finder = self.grid_helper.grid_finder xx1, yy1 = grid_finder.transform_xy([xx0], [yy0]) trans_passingthrough_point = axes.transData + axes.transAxes.inverted() p = trans_passingthrough_point.transform_point([xx1[0], yy1[0]]) if 0 <= p[0] <= 1 and 0 <= p[1] <= 1: xx1c, yy1c = axes.transData.transform_point([xx1[0], yy1[0]]) xx2, yy2 = grid_finder.transform_xy([xx0+dxx], [yy0+dyy]) xx2c, yy2c = axes.transData.transform_point([xx2[0], yy2[0]]) return (xx1c, yy1c), np.arctan2(yy2c-yy1c, xx2c-xx1c)/np.pi*180. else: return None, None
[docs] def get_tick_transform(self, axes): return IdentityTransform() #axes.transData
[docs] def get_tick_iterators(self, axes): """tick_loc, tick_angle, tick_label, (optionally) tick_label""" grid_finder = self.grid_helper.grid_finder lat_levs, lat_n, lat_factor = self.grid_info["lat_info"] lon_levs, lon_n, lon_factor = self.grid_info["lon_info"] lon_levs, lat_levs = np.asarray(lon_levs), np.asarray(lat_levs) if lat_factor is not None: yy0 = lat_levs / lat_factor dy = 0.001 / lat_factor else: yy0 = lat_levs dy = 0.001 if lon_factor is not None: xx0 = lon_levs / lon_factor dx = 0.001 / lon_factor else: xx0 = lon_levs dx = 0.001 _extremes = self.grid_helper._extremes xmin, xmax = sorted(_extremes[:2]) ymin, ymax = sorted(_extremes[2:]) if self.nth_coord == 0: mask = (ymin <= yy0) & (yy0 <= ymax) yy0 = yy0[mask] elif self.nth_coord == 1: mask = (xmin <= xx0) & (xx0 <= xmax) xx0 = xx0[mask] def transform_xy(x, y): x1, y1 = grid_finder.transform_xy(x, y) x2y2 = axes.transData.transform(np.array([x1, y1]).transpose()) x2, y2 = x2y2.transpose() return x2, y2 # find angles if self.nth_coord == 0: xx0 = np.empty_like(yy0) xx0.fill(self.value) #yy0_ = yy0.copy() xx1, yy1 = transform_xy(xx0, yy0) xx00 = xx0.astype(float, copy=True) xx00[xx0+dx>xmax] -= dx xx1a, yy1a = transform_xy(xx00, yy0) xx1b, yy1b = transform_xy(xx00+dx, yy0) yy00 = yy0.astype(float, copy=True) yy00[yy0+dy>ymax] -= dy xx2a, yy2a = transform_xy(xx0, yy00) xx2b, yy2b = transform_xy(xx0, yy00+dy) labels = self.grid_info["lat_labels"] labels = [l for l, m in zip(labels, mask) if m] elif self.nth_coord == 1: yy0 = np.empty_like(xx0) yy0.fill(self.value) #xx0_ = xx0.copy() xx1, yy1 = transform_xy(xx0, yy0) yy00 = yy0.astype(float, copy=True) yy00[yy0+dy>ymax] -= dy xx1a, yy1a = transform_xy(xx0, yy00) xx1b, yy1b = transform_xy(xx0, yy00+dy) xx00 = xx0.astype(float, copy=True) xx00[xx0+dx>xmax] -= dx xx2a, yy2a = transform_xy(xx00, yy0) xx2b, yy2b = transform_xy(xx00+dx, yy0) labels = self.grid_info["lon_labels"] labels = [l for l, m in zip(labels, mask) if m] def f1(): dd = np.arctan2(yy1b-yy1a, xx1b-xx1a) # angle normal dd2 = np.arctan2(yy2b-yy2a, xx2b-xx2a) # angle tangent mm = ((yy1b-yy1a)==0.) & ((xx1b-xx1a)==0.) # mask where dd1 is not defined dd[mm] = dd2[mm] + np.pi / 2 #dd += np.pi #dd = np.arctan2(xx2-xx1, angle_tangent-yy1) trans_tick = self.get_tick_transform(axes) tr2ax = trans_tick + axes.transAxes.inverted() for x, y, d, d2, lab in zip(xx1, yy1, dd, dd2, labels): c2 = tr2ax.transform_point((x, y)) delta=0.00001 if 0-delta <= c2[0] <= 1+delta and 0-delta <= c2[1] <= 1+delta: d1, d2 = np.rad2deg([d, d2]) yield [x, y], d1, d2, lab return f1(), iter([])
[docs] def get_line_transform(self, axes): return axes.transData
[docs] def get_line(self, axes): self.update_lim(axes) from matplotlib.path import Path k, v = dict(left=("lon_lines0", 0), right=("lon_lines0", 1), bottom=("lat_lines0", 0), top=("lat_lines0", 1))[self._side] xx, yy = self.grid_info[k][v] return Path(np.column_stack([xx, yy]))
from .grid_finder import ExtremeFinderSimple
[docs]class ExtremeFinderFixed(ExtremeFinderSimple): def __init__(self, extremes): self._extremes = extremes def __call__(self, transform_xy, x1, y1, x2, y2): """ get extreme values. x1, y1, x2, y2 in image coordinates (0-based) nx, ny : number of division in each axis """ #lon_min, lon_max, lat_min, lat_max = self._extremes return self._extremes
[docs]class GridHelperCurveLinear(grid_helper_curvelinear.GridHelperCurveLinear): def __init__(self, aux_trans, extremes, grid_locator1=None, grid_locator2=None, tick_formatter1=None, tick_formatter2=None): """ aux_trans : a transform from the source (curved) coordinate to target (rectilinear) coordinate. An instance of MPL's Transform (inverse transform should be defined) or a tuple of two callable objects which defines the transform and its inverse. The callables need take two arguments of array of source coordinates and should return two target coordinates: e.g., *x2, y2 = trans(x1, y1)* """ self._old_values = None self._extremes = extremes extreme_finder = ExtremeFinderFixed(extremes) super().__init__(aux_trans, extreme_finder, grid_locator1=grid_locator1, grid_locator2=grid_locator2, tick_formatter1=tick_formatter1, tick_formatter2=tick_formatter2) # def update_grid_finder(self, aux_trans=None, **kw): # if aux_trans is not None: # self.grid_finder.update_transform(aux_trans) # self.grid_finder.update(**kw) # self.invalidate() # def _update(self, x1, x2, y1, y2): # "bbox in 0-based image coordinates" # # update wcsgrid # if self.valid() and self._old_values == (x1, x2, y1, y2): # return # self._update_grid(x1, y1, x2, y2) # self._old_values = (x1, x2, y1, y2) # self._force_update = False
[docs] def get_data_boundary(self, side): """ return v= 0 , nth=1 """ lon1, lon2, lat1, lat2 = self._extremes return dict(left=(lon1, 0), right=(lon2, 0), bottom=(lat1, 1), top=(lat2, 1))[side]
[docs] def new_fixed_axis(self, loc, nth_coord=None, axis_direction=None, offset=None, axes=None): if axes is None: axes = self.axes if axis_direction is None: axis_direction = loc _helper = FixedAxisArtistHelper(self, loc, nth_coord_ticks=nth_coord) axisline = AxisArtist(axes, _helper, axis_direction=axis_direction) axisline.line.set_clip_on(True) axisline.line.set_clip_box(axisline.axes.bbox) return axisline
# new_floating_axis will inherit the grid_helper's extremes. # def new_floating_axis(self, nth_coord, # value, # axes=None, # axis_direction="bottom" # ): # axis = super(GridHelperCurveLinear, # self).new_floating_axis(nth_coord, # value, axes=axes, # axis_direction=axis_direction) # # set extreme values of the axis helper # if nth_coord == 1: # axis.get_helper().set_extremes(*self._extremes[:2]) # elif nth_coord == 0: # axis.get_helper().set_extremes(*self._extremes[2:]) # return axis def _update_grid(self, x1, y1, x2, y2): #self.grid_info = self.grid_finder.get_grid_info(x1, y1, x2, y2) if self.grid_info is None: self.grid_info = dict() grid_info = self.grid_info grid_finder = self.grid_finder extremes = grid_finder.extreme_finder(grid_finder.inv_transform_xy, x1, y1, x2, y2) lon_min, lon_max = sorted(extremes[:2]) lat_min, lat_max = sorted(extremes[2:]) lon_levs, lon_n, lon_factor = \ grid_finder.grid_locator1(lon_min, lon_max) lat_levs, lat_n, lat_factor = \ grid_finder.grid_locator2(lat_min, lat_max) grid_info["extremes"] = lon_min, lon_max, lat_min, lat_max #extremes grid_info["lon_info"] = lon_levs, lon_n, lon_factor grid_info["lat_info"] = lat_levs, lat_n, lat_factor grid_info["lon_labels"] = grid_finder.tick_formatter1("bottom", lon_factor, lon_levs) grid_info["lat_labels"] = grid_finder.tick_formatter2("bottom", lat_factor, lat_levs) if lon_factor is None: lon_values = np.asarray(lon_levs[:lon_n]) else: lon_values = np.asarray(lon_levs[:lon_n]/lon_factor) if lat_factor is None: lat_values = np.asarray(lat_levs[:lat_n]) else: lat_values = np.asarray(lat_levs[:lat_n]/lat_factor) lon_values0 = lon_values[(lon_min<lon_values) & (lon_values<lon_max)] lat_values0 = lat_values[(lat_min<lat_values) & (lat_values<lat_max)] lon_lines, lat_lines = grid_finder._get_raw_grid_lines(lon_values0, lat_values0, lon_min, lon_max, lat_min, lat_max) grid_info["lon_lines"] = lon_lines grid_info["lat_lines"] = lat_lines lon_lines, lat_lines = grid_finder._get_raw_grid_lines(extremes[:2], extremes[2:], *extremes) #lon_min, lon_max, # lat_min, lat_max) grid_info["lon_lines0"] = lon_lines grid_info["lat_lines0"] = lat_lines
[docs] def get_gridlines(self, which="major", axis="both"): grid_lines = [] if axis in ["both", "x"]: for gl in self.grid_info["lon_lines"]: grid_lines.extend([gl]) if axis in ["both", "y"]: for gl in self.grid_info["lat_lines"]: grid_lines.extend([gl]) return grid_lines
[docs] def get_boundary(self): """ return Nx2 array of x,y coordinate of the boundary """ x0, x1, y0, y1 = self._extremes tr = self._aux_trans xx = np.linspace(x0, x1, 100) yy0, yy1 = np.empty_like(xx), np.empty_like(xx) yy0.fill(y0) yy1.fill(y1) yy = np.linspace(y0, y1, 100) xx0, xx1 = np.empty_like(yy), np.empty_like(yy) xx0.fill(x0) xx1.fill(x1) xxx = np.concatenate([xx[:-1], xx1[:-1], xx[-1:0:-1], xx0]) yyy = np.concatenate([yy0[:-1], yy[:-1], yy1[:-1], yy[::-1]]) t = tr.transform(np.array([xxx, yyy]).transpose()) return t
[docs]class FloatingAxesBase(object): def __init__(self, *kl, **kwargs): grid_helper = kwargs.get("grid_helper", None) if grid_helper is None: raise ValueError("FloatingAxes requires grid_helper argument") if not hasattr(grid_helper, "get_boundary"): raise ValueError("grid_helper must implement get_boundary method") self._axes_class_floating.__init__(self, *kl, **kwargs) self.set_aspect(1.) self.adjust_axes_lim() def _gen_axes_patch(self): """ Returns the patch used to draw the background of the axes. It is also used as the clipping path for any data elements on the axes. In the standard axes, this is a rectangle, but in other projections it may not be. .. note:: Intended to be overridden by new projection types. """ import matplotlib.patches as mpatches grid_helper = self.get_grid_helper() t = grid_helper.get_boundary() return mpatches.Polygon(t)
[docs] def cla(self): self._axes_class_floating.cla(self) #HostAxes.cla(self) self.patch.set_transform(self.transData) patch = self._axes_class_floating._gen_axes_patch(self) patch.set_figure(self.figure) patch.set_visible(False) patch.set_transform(self.transAxes) self.patch.set_clip_path(patch) self.gridlines.set_clip_path(patch) self._original_patch = patch
[docs] def adjust_axes_lim(self): #t = self.get_boundary() grid_helper = self.get_grid_helper() t = grid_helper.get_boundary() x, y = t[:,0], t[:,1] xmin, xmax = min(x), max(x) ymin, ymax = min(y), max(y) dx = (xmax-xmin)/100. dy = (ymax-ymin)/100. self.set_xlim(xmin-dx, xmax+dx) self.set_ylim(ymin-dy, ymax+dy)
@functools.lru_cache(None) def floatingaxes_class_factory(axes_class): return type("Floating %s" % axes_class.__name__, (FloatingAxesBase, axes_class), {'_axes_class_floating': axes_class}) from .axislines import Axes from mpl_toolkits.axes_grid1.parasite_axes import host_axes_class_factory FloatingAxes = floatingaxes_class_factory(host_axes_class_factory(Axes)) import matplotlib.axes as maxes FloatingSubplot = maxes.subplot_class_factory(FloatingAxes)