Version 2.2.4
"""
An experimental support for curvilinear grid.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from six.moves import zip
# 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 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(FixedAxisArtistHelper, self).__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_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 = d/3.14159*180.
d2 = d2/3.14159*180.
#_mod = (d2-d1+180)%360
#if _mod < 180:
# d1 += 180
##_div, _mod = divmod(d2-d1, 360)
yield [x, y], d1, d2, lab
#, d2/3.14159*180.+da)
return f1(), iter([])
[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(GridHelperCurveLinear, self).__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)
_floatingaxes_classes = {}
[docs]def floatingaxes_class_factory(axes_class):
new_class = _floatingaxes_classes.get(axes_class)
if new_class is None:
new_class = type(str("Floating %s" % (axes_class.__name__)),
(FloatingAxesBase, axes_class),
{'_axes_class_floating': axes_class})
_floatingaxes_classes[axes_class] = new_class
return new_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)