# (C) British Crown Copyright 2011 - 2013, Met Office
#
# This file is part of cartopy.
#
# cartopy is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the
# Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# cartopy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with cartopy. If not, see <http://www.gnu.org/licenses/>.
"""
This module defines the :class:`GeoAxes` class, for use with matplotlib.
When a matplotlib figure contains a GeoAxes the plotting commands can transform
plot results from source coordinates to the GeoAxes' target projection.
"""
import warnings
import weakref
import matplotlib
import matplotlib.axes
import matplotlib.collections as mcollections
from matplotlib.image import imread
import matplotlib.transforms as mtransforms
import matplotlib.patches as mpatches
import matplotlib.path as mpath
import numpy
import shapely.geometry
from cartopy import config
import cartopy.crs as ccrs
import cartopy.feature
import cartopy.img_transform
import cartopy.mpl.feature_artist as feature_artist
import cartopy.mpl.patch as patch
assert matplotlib.__version__ >= '1.2', ('Cartopy can only work with '
'matplotlib 1.2 or greater.')
_PATH_TRANSFORM_CACHE = weakref.WeakKeyDictionary()
"""
A nested mapping from path, source CRS, and target projection to the
resulting transformed paths::
{path: {(source_crs, target_projection): list_of_paths}}
Provides a significant performance boost for contours which, at
matplotlib 1.2.0 called transform_path_non_affine twice unnecessarily.
"""
# XXX call this InterCRSTransform
class InterProjectionTransform(mtransforms.Transform):
"""
Transforms coordinates from the source_projection to
the ``target_projection``.
"""
input_dims = 2
output_dims = 2
is_separable = False
has_inverse = True
def __init__(self, source_projection, target_projection):
"""
Create the transform object from the given projections.
Args:
* source_projection - A :class:`~cartopy.crs.CRS`.
* target_projection - A :class:`~cartopy.crs.CRS`.
"""
# assert target_projection is cartopy.crs.Projection
# assert source_projection is cartopy.crs.CRS
self.source_projection = source_projection
self.target_projection = target_projection
mtransforms.Transform.__init__(self)
def __repr__(self):
return ('< {!s} {!s} -> {!s} >'.format(self.__class__.__name__,
self.source_projection,
self.target_projection))
def transform_non_affine(self, xy):
"""
Transforms from source to target coordinates.
Args:
* xy - An (n,2) array of points in source coordinates.
Returns:
* An (n,2) array of transformed points in target coordinates.
"""
prj = self.target_projection
if isinstance(xy, numpy.ndarray):
return prj.transform_points(self.source_projection,
xy[:, 0], xy[:, 1])[:, 0:2]
else:
x, y = xy
x, y = prj.transform_point(x, y, self.source_projection)
return x, y
def transform_path_non_affine(self, src_path):
"""
Transforms from source to target coordinates.
Caches results, so subsequent calls with the same *src_path* argument
(and the same source and target projections) are faster.
Args:
* src_path - A matplotlib :class:`~matplotlib.path.Path` object
with vertices in source coordinates.
Returns
* A matplotlib :class:`~matplotlib.path.Path` with vertices
in target coordinates.
"""
mapping = _PATH_TRANSFORM_CACHE.get(src_path)
if mapping is not None:
key = (self.source_projection, self.target_projection)
result = mapping.get(key)
if result is not None:
return result
bypass = self.source_projection == self.target_projection
if bypass:
projection = self.source_projection
if isinstance(projection, ccrs._CylindricalProjection):
x = src_path.vertices[:, 0]
x_limits = projection.x_limits
bypass = x.min() >= x_limits[0] and x.max() <= x_limits[1]
if bypass:
return src_path
if src_path.vertices.shape == (1, 2):
return mpath.Path(self.transform(src_path.vertices))
transformed_geoms = []
for geom in patch.path_to_geos(src_path):
transformed_geoms.append(
self.target_projection.project_geometry(geom,
self.source_projection)
)
if not transformed_geoms:
result = mpath.Path(numpy.empty([0, 2]))
else:
paths = patch.geos_to_path(transformed_geoms)
if not paths:
return mpath.Path(numpy.empty([0, 2]))
points, codes = zip(*[patch.path_segments(path, curves=False,
simplify=False)
for path in paths])
result = mpath.Path(numpy.concatenate(points, 0),
numpy.concatenate(codes))
# store the result in the cache for future performance boosts
key = (self.source_projection, self.target_projection)
if mapping is None:
_PATH_TRANSFORM_CACHE[src_path] = {key: result}
else:
mapping[key] = result
return result
def inverted(self):
"""
Return a matplotlib :class:`~matplotlib.transforms.Transform`
from target to source coordinates.
"""
return InterProjectionTransform(self.target_projection,
self.source_projection)
[docs]class GeoAxes(matplotlib.axes.Axes):
"""
A subclass of :class:`matplotlib.axes.Axes` which represents a
map :class:`~cartopy.crs.Projection`.
This class replaces the matplotlib :class:`~matplotlib.axes.Axes` class
when created with the *projection* keyword. For example::
# Set up a standard map for latlon data.
geo_axes = pyplot.axes(projection=cartopy.crs.PlateCarree())
# Set up an OSGB map.
geo_axes = pyplot.subplot(2, 2, 1, projection=cartopy.crs.OSGB())
When a source projection is provided to one of it's plotting methods,
using the *transform* keyword, the standard matplotlib plot result is
transformed from source coordinates to the target projection. For example::
# Plot latlon data on an OSGB map.
pyplot.axes(projection=cartopy.crs.OSGB())
pyplot.contourf(x, y, data, transform=cartopy.crs.PlateCarree())
"""
def __init__(self, *args, **kwargs):
"""
Create a GeoAxes object using standard matplotlib
:class:`~matplotlib.axes.Axes` args and kwargs.
Kwargs:
* map_projection - The target :class:`~cartopy.crs.Projection` of
this Axes object.
All other args and keywords are passed through to
:class:`matplotlib.axes.Axes`.
"""
self.projection = kwargs.pop('map_projection')
super(GeoAxes, self).__init__(*args, **kwargs)
self._gridliners = []
self.img_factories = []
self._done_img_factory = False
[docs] def add_image(self, factory, *args, **kwargs):
"""
Adds an image "factory" to the Axes.
Any image "factory" added, will be asked to retrieve an image
with associated metadata for a given bounding box at draw time.
The advantage of this approach is that the limits of the map
do not need to be known when adding the image factory, but can
be deferred until everything which can effect the limits has been
added.
Currently an image "factory" is just an object with
a ``image_for_domain`` method. Examples of image factories
are :class:`cartopy.io.img_nest.NestedImageCollection` and
:class:`cartopy.io.image_tiles.GoogleTiles`.
"""
# XXX TODO: Needs working on
self.img_factories.append([factory, args, kwargs])
@matplotlib.axes.allow_rasterization
def draw(self, renderer=None, inframe=False):
"""
Extends the standard behaviour of :func:`matplotlib.axes.Axes.draw`.
Draws grid lines and image factory results before invoking standard
matplotlib drawing. A global range is used if no limits have yet
been set.
"""
# if no data has been added, and no extents set, then make the
# map global
if self.ignore_existing_data_limits and \
self._autoscaleXon and self._autoscaleYon:
self.set_global()
self.ignore_existing_data_limits = True
for gl in self._gridliners:
gl._draw_gridliner(background_patch=self.background_patch)
self._gridliners = []
# XXX This interface needs a tidy up:
# image drawing on pan/zoom;
# caching the resulting image;
# buffering the result by 10%...;
if not self._done_img_factory:
for factory, args, kwargs in self.img_factories:
img, extent, origin = factory.image_for_domain(
self._get_extent_geom(factory.crs), args[0])
self.imshow(img, extent=extent, origin=origin,
transform=factory.crs, *args[1:], **kwargs)
self._done_img_factory = True
return matplotlib.axes.Axes.draw(self, renderer=renderer,
inframe=inframe)
def __str__(self):
return '< GeoAxes: %s >' % self.projection
def cla(self):
"""Clears the current axes and adds boundary lines."""
result = matplotlib.axes.Axes.cla(self)
self.xaxis.set_visible(False)
self.yaxis.set_visible(False)
self.autoscale_view(tight=True)
self.set_aspect('equal')
pre_bounary = self.ignore_existing_data_limits
self._boundary()
self.ignore_existing_data_limits = pre_bounary
# XXX consider a margin - but only when the map is not global...
# self._xmargin = 0.15
# self._ymargin = 0.15
return result
def format_coord(self, x, y):
"""Return a string formatted for the matplotlib GUI status bar."""
lon, lat = ccrs.Geodetic().transform_point(x, y, self.projection)
ns = 'N' if lat >= 0.0 else 'S'
ew = 'E' if lon >= 0.0 else 'W'
return u'%.4g, %.4g (%f\u00b0%s, %f\u00b0%s)' % (x, y, abs(lat),
ns, abs(lon), ew)
[docs] def coastlines(self, resolution='110m', color='black', **kwargs):
"""
Adds coastal **outlines** to the current axes from the Natural Earth
"coastline" shapefile collection.
Kwargs:
* resolution - a named resolution to use from the Natural Earth
dataset. Currently can be one of "110m", "50m", and
"10m".
.. note::
Currently no clipping is done on the coastlines before adding
them to the axes. This means, if very high resolution coastlines
are being used, performance is likely to be severely effected.
This should be resolved transparently by v0.5.
"""
kwargs['edgecolor'] = color
kwargs['facecolor'] = 'none'
feature = cartopy.feature.NaturalEarthFeature('physical', 'coastline',
resolution, **kwargs)
return self.add_feature(feature)
[docs] def natural_earth_shp(self, name='land', resolution='110m',
category='physical', **kwargs):
"""
Adds the geometries from the specified Natural Earth shapefile to the
Axes as a :class:`~matplotlib.collections.PathCollection`.
``**kwargs`` are passed through to the
:class:`~matplotlib.collections.PathCollection` constructor.
Returns the created :class:`~matplotlib.collections.PathCollection`.
.. note::
Currently no clipping is done on the geometries before adding them
to the axes. This means, if very high resolution geometries are
being used, performance is likely to be severely effected. This
should be resolved transparently by v0.5.
"""
warnings.warn('This method has been deprecated.'
' Please use `add_feature` instead.')
kwargs.setdefault('edgecolor', 'face')
kwargs.setdefault('facecolor', cartopy.feature.COLORS['land'])
feature = cartopy.feature.NaturalEarthFeature(category, name,
resolution, **kwargs)
return self.add_feature(feature)
[docs] def add_feature(self, feature, **kwargs):
"""
Adds the given :class:`~cartopy.feature.Feature` instance to the axes.
Args:
* feature:
An instance of :class:`~cartopy.feature.Feature`.
Kwargs:
Keyword arguments to be used when drawing the feature. This allows
standard matplotlib control over aspects such as 'facecolor',
'alpha', etc.
Returns:
* A :class:`cartopy.mpl.feature_artist.FeatureArtist`
instance responsible for drawing the feature.
"""
# Instantiate an artist to draw the feature and add it to the axes.
artist = feature_artist.FeatureArtist(feature, **kwargs)
return self.add_artist(artist)
[docs] def add_geometries(self, geoms, crs, **kwargs):
"""
Add the given shapely geometries (in the given crs) to the axes.
Args:
* geoms:
A collection of shapely geometries.
* crs:
The cartopy CRS in which the provided geometries are defined.
Kwargs:
Keyword arguments to be used when drawing this feature.
Returns:
* A :class:`cartopy.mpl.feature_artist.FeatureArtist`
instance responsible for drawing the geometries.
"""
feature = cartopy.feature.ShapelyFeature(geoms, crs, **kwargs)
return self.add_feature(feature)
[docs] def get_extent(self, crs=None):
"""
Get the extent (x0, x1, y0, y1) of the map in the given coordinate
system.
If no crs is given, the returned extents' coordinate system will be
assumed to be the Geodetic version of this axes' projection.
"""
p = self._get_extent_geom(crs)
r = p.bounds
x1, y1, x2, y2 = r
return x1, x2, y1, y2
def _get_extent_geom(self, crs=None):
# Perform the calculations for get_extent(), which just repackages it.
x1, x2 = self.get_xlim()
y1, y2 = self.get_ylim()
if x1 == 0 and y1 == 0 and x2 == 1 and y2 == 1:
x1, x2 = self.projection.x_limits
y1, y2 = self.projection.y_limits
domain_in_src_proj = shapely.geometry.Polygon([[x1, y1], [x2, y1],
[x2, y2], [x1, y2],
[x1, y1]])
# Determine target projection based on requested CRS.
if crs is None:
proj = self.projection
elif isinstance(crs, ccrs.Projection):
proj = crs
else:
# Attempt to select suitable projection for
# non-projection CRS.
if isinstance(crs, ccrs.RotatedGeodetic):
proj = ccrs.RotatedPole(crs.proj4_params['lon_0'] - 180,
crs.proj4_params['o_lat_p'])
warnings.warn('Approximating coordinate system {!r} with a '
'RotatedPole projection.'.format(crs))
elif hasattr(crs, 'is_geodetic') and crs.is_geodetic():
proj = ccrs.PlateCarree()
warnings.warn('Approximating coordinate system {!r} with the '
'PlateCarree projection.'.format(crs))
else:
raise ValueError('Cannot determine extent in'
' coordinate system {!r}'.format(crs))
# Calculate intersection with boundary and project if necesary.
boundary_poly = shapely.geometry.Polygon(self.projection.boundary)
if proj != self.projection:
# Erode boundary by threshold to avoid transform issues.
# This is a workaround for numerical issues at the boundary.
eroded_boundary = boundary_poly.buffer(-self.projection.threshold)
geom_in_src_proj = eroded_boundary.intersection(
domain_in_src_proj)
geom_in_crs = proj.project_geometry(geom_in_src_proj,
self.projection)
else:
geom_in_crs = boundary_poly.intersection(domain_in_src_proj)
return geom_in_crs
[docs] def set_extent(self, extents, crs=None):
"""
Set the extent (x0, x1, y0, y1) of the map in the given
coordinate system.
If no crs is given, the extents' coordinate system will be assumed
to be the Geodetic version of this axes' projection.
"""
# TODO: Implement the same semantics as plt.xlim and
# plt.ylim - allowing users to set None for a minimum and/or
# maximum value
x1, x2, y1, y2 = extents
domain_in_crs = shapely.geometry.LineString([[x1, y1], [x2, y1],
[x2, y2], [x1, y2],
[x1, y1]])
r = self.projection.project_geometry(domain_in_crs, crs)
x1, y1, x2, y2 = r.bounds
self.set_xlim([x1, x2])
self.set_ylim([y1, y2])
[docs] def set_global(self):
"""
Set the extent of the Axes to the limits of the projection.
.. note::
In some cases where the projection has a limited sensible range
the ``set_global`` method does not actually make the whole globe
visible. Instead, the most appropriate extents will be used (e.g.
Ordnance Survey UK will set the extents to be around the British
Isles.
"""
self.set_xlim(self.projection.x_limits)
self.set_ylim(self.projection.y_limits)
[docs] def set_xticks(self, ticks, minor=False, crs=None):
"""
Set the x ticks.
Args:
* ticks - list of floats denoting the desired position of x ticks.
Kwargs:
* minor - boolean flag indicating whether the ticks should be minor
ticks i.e. small and unlabelled (default is False).
* crs - An instance of :class:`~cartopy.crs.CRS` indicating the
coordinate system of the provided tick values. If no
coordinate system is specified then the values are assumed
to be in the coordinate system of the projection.
.. note::
This method is limited to cylindrical projections.
.. note::
This interface is subject to change whilst functionality is added
to support other map projections.
"""
if not isinstance(self.projection, (ccrs._RectangularProjection,
ccrs._CylindricalProjection,
ccrs.OSGB)):
raise RuntimeError('Cannot set xticks for not-cylindrical '
'coordinate systems.')
# Switch on drawing of x axis
self.xaxis.set_visible(True)
# Project ticks if crs differs from axes' projection
if crs is not None and crs != self.projection:
proj_xyz = self.projection.transform_points(crs,
numpy.asarray(ticks),
numpy.zeros(len(ticks))
)
xticks = proj_xyz[..., 0]
else:
xticks = ticks
return super(GeoAxes, self).set_xticks(xticks, minor)
[docs] def set_yticks(self, ticks, minor=False, crs=None):
"""
Set the y ticks.
Args:
* ticks - list of floats denoting the desired position of y ticks.
Kwargs:
* minor - boolean flag indicating whether the ticks should be minor
ticks i.e. small and unlabelled (default is False).
* crs - An instance of :class:`~cartopy.crs.CRS` indicating the
coordinate system of the provided tick values. If no
coordinate system is specified then the values are assumed
to be in the coordinate system of the projection.
.. note::
This method is limited to cylindrical projections.
.. note::
This interface is subject to change whilst functionality is added
to support other map projections.
"""
if not isinstance(self.projection, (ccrs._RectangularProjection,
ccrs._CylindricalProjection,
ccrs.OSGB)):
raise RuntimeError('Cannot set yticks for non-cylindrical '
'coordinate systems.')
# Switch on drawing of y axis
self.yaxis.set_visible(True)
# Project ticks if crs differs from axes' projection
if crs is not None and crs != self.projection:
prj = self.projection
proj_xyz = prj.transform_points(crs,
numpy.zeros(len(ticks)),
numpy.asarray(ticks))
yticks = proj_xyz[..., 1]
else:
yticks = ticks
return super(GeoAxes, self).set_yticks(yticks, minor)
# def geod_circle_meters(self, lon_0, lat_0, radius, npts=80, **kwargs):
# # radius is in meters
# geod = self.projection.as_geodetic()
#
# az = numpy.linspace(0, 360, npts)
# lats = numpy.zeros(npts) + lat_0
# lons = numpy.zeros(npts) + lon_0
# distances = numpy.zeros(npts) + radius
#
# lons, lats, _reverse_az = geod.fwd(lons, lats, az, distances,
# radians=False)
# ll = numpy.concatenate([lons[:, None], lats[:, None]], 1)
# from matplotlib.patches import Polygon
# poly = Polygon(ll, transform=cartopy.prj.PlateCarree(), **kwargs)
# self.add_patch(poly)
# return poly
#
# def gshhs_line(self, outline_color='k', domain=None,
# resolution='low', **kwargs):
# # domain is a shapely geometry (Polygon or MultiPolygon)
# import cartopy.gshhs as gshhs
## import cartopy.spherical as spherical
# from matplotlib.collections import PatchCollection, LineCollection
#
# paths = []
#
# projection = self.projection
#
# if domain is None:
# domain = self.map_domain(ccrs.PlateCarree())
#
# for points in gshhs.read_gshhc(gshhs.fnames[resolution],
# poly=False, domain=domain):
# paths.extend(patch.geos_to_path(
# shapely.geometry.LineString(points))
# )
#
## slinestring = shapely.geometry.LineString(points)
## projected = projection.project_geometry(slinestring)
## paths.extend(patch.geos_to_path(projected))
#
# collection = PatchCollection([mpatches.PathPatch(pth)
# for pth in paths],
# edgecolor=outline_color, facecolor='none',
# transform=ccrs.PlateCarree(),
# **kwargs
# )
#
# self.add_collection(collection, autolim=False)
[docs] def stock_img(self, name='ne_shaded'):
"""
Add a standard image to the map.
Currently, the only (and default) option is a downsampled version of
the Natural Earth shaded relief raster.
"""
if name == 'ne_shaded':
import os
source_proj = ccrs.PlateCarree()
fname = os.path.join(config["repo_data_dir"],
'raster', 'natural_earth',
'50-natural-earth-1-downsampled.png')
img_origin = 'lower'
img = imread(fname)
img = img[::-1]
return self.imshow(img, origin=img_origin, transform=source_proj,
extent=[-180, 180, -90, 90])
else:
raise ValueError('Unknown stock image %r.' % name)
def imshow(self, img, *args, **kwargs):
"""
Add the "transform" keyword to :func:`~matplotlib.pyplot.imshow'.
Extra kwargs:
transform - a :class:`~cartopy.crs.Projection`.
regrid_shape - default is (750, 375). But may be changed to "auto"
in the future...
extent = (left, right, bottom, top) - transform coordinates for
the extent of the source
image.
target_extent = (left, right, bottom, top) - native coordinates
for the extent of
the desired image.
origin - default is changed to 'lower'
update_datalim - flag whether the image should affect the data
limits (default: True)
"""
transform = kwargs.pop('transform', None)
regrid_shape = kwargs.pop('regrid_shape', (750, 375))
update_datalim = kwargs.pop('update_datalim', True)
kwargs.setdefault('origin', 'lower')
same_projection = (isinstance(transform, ccrs.Projection) and
self.projection == transform)
if not update_datalim:
data_lim = self.dataLim.frozen().get_points()
view_lim = self.viewLim.frozen().get_points()
if transform is None or transform == self.transData or same_projection:
if isinstance(transform, ccrs.Projection):
transform = transform._as_mpl_transform(self)
result = matplotlib.axes.Axes.imshow(self, img, *args, **kwargs)
else:
extent = kwargs.pop('extent', None)
if not isinstance(transform, ccrs.Projection):
raise ValueError('Expected a projection subclass. Cannot '
'handle a %s in imshow.' % type(transform))
warp_array = cartopy.img_transform.warp_array
target_extent = self.get_extent(self.projection)
# XXX adaptive resolution depending on incoming img?
img, extent = warp_array(img,
source_proj=transform,
source_extent=extent,
target_proj=self.projection,
target_res=regrid_shape,
target_extent=target_extent,
)
# as a workaround to a matplotlib limitation, turn any images
# which are RGB with a mask into RGBA images with an alpha
# channel.
if (isinstance(img, numpy.ma.MaskedArray) and
img.shape[2:3] == (3, ) and
img.mask is not False):
old_img = img
img = numpy.zeros(img.shape[:2] + (4, ))
img[:, :, 0:3] = old_img
# put an alpha channel in if the image was masked
img[:, :, 3] = ~ numpy.any(old_img.mask, axis=2)
result = matplotlib.axes.Axes.imshow(self, img, *args,
extent=extent, **kwargs)
# clip the image. This does not work as the patch moves with mouse
# movement, but the clip path doesn't
# This could definitely be fixed in matplotlib
# if result.get_clip_path() in [None, self.patch]:
# # image does not already have clipping set, clip to axes patch
# result.set_clip_path(self.outline_patch)
if not update_datalim:
self.dataLim.set_points(data_lim)
self.viewLim.set_points(view_lim)
return result
[docs] def gridlines(self, crs=None, draw_labels=False, **kwargs):
"""
Automatically adds gridlines to the axes, in the given coordinate
system, at draw time.
Kwargs:
* crs
The :class:`cartopy._crs.CRS` defining the coordinate system in
which gridlines are drawn.
Default is :class:`cartopy.crs.PlateCarree`.
* draw_labels
Label gridlines like axis ticks, around the edge.
Returns:
A :class:`cartopy.mpl.gridliner.Gridliner` instance.
All other keywords control line properties. These are passed through
to :class:`matplotlib.collections.Collection`.
"""
if crs is None:
crs = ccrs.PlateCarree()
from cartopy.mpl.gridliner import Gridliner
gl = Gridliner(
self, crs=crs, draw_labels=draw_labels, collection_kwargs=kwargs)
self._gridliners.append(gl)
return gl
def _gen_axes_spines(self, locations=None, offset=0.0, units='inches'):
# generate some axes spines, as some Axes super class machinery
# requires them. Just make them invisible
spines = matplotlib.axes.Axes._gen_axes_spines(self,
locations=locations,
offset=offset,
units=units)
for spine in spines.itervalues():
spine.set_visible(False)
return spines
def _boundary(self):
"""
Adds the map's boundary.
Note:
The boundary is not the axes.patch, which provides rectilinear
clipping for all of the map's artists.
The axes.patch will have its visibility set to False inside
GeoAxes.gca()
"""
import cartopy.mpl.patch as p
path, = p.geos_to_path(self.projection.boundary)
# from matplotlib.collections import PatchCollection
sct = SimpleClippedTransform(self.transScale + self.transLimits,
self.transAxes)
# XXX Should be exactly one path...
collection = mpatches.PathPatch(path,
facecolor='none', edgecolor='k',
zorder=1000,
# transform=self.transData,
transform=sct, clip_on=False,
)
self.outline_patch = collection
# XXX autolim = False
self.add_patch(collection)
# put a color patch for background color
# XXX Should be exactly one path...
collection = mpatches.PathPatch(path,
facecolor='w', edgecolor='none',
zorder=-1, transform=sct,
clip_on=False,
)
self.background_patch = collection
# XXX autolim = False
self.add_patch(collection)
self.patch.set_facecolor((1, 1, 1, 0))
self.patch.set_edgecolor((0.5, 0.5, 0.5))
self.patch.set_linewidth(0.0)
# mpl 1.2.0rc2 compatibility. To be removed once 1.2 is released
def contour(self, *args, **kwargs):
"""
Add the "transform" keyword to :func:`~matplotlib.pyplot.contour'.
Extra kwargs:
transform - a :class:`~cartopy.crs.Projection`.
"""
t = kwargs.get('transform', None)
# Keep this bit - even at mpl v1.2
if t is None:
t = self.projection
if isinstance(t, ccrs.CRS) and not isinstance(t, ccrs.Projection):
raise ValueError('invalid transform:'
' Spherical contouring is not supported - '
' consider using PlateCarree/RotatedPole.')
if isinstance(t, ccrs.Projection):
kwargs['transform'] = t._as_mpl_transform(self)
else:
kwargs['transform'] = t
return matplotlib.axes.Axes.contour(self, *args, **kwargs)
# mpl 1.2.0rc2 compatibility. To be removed once 1.2 is released
def contourf(self, *args, **kwargs):
"""
Add the "transform" keyword to :func:`~matplotlib.pyplot.contourf'.
Extra kwargs:
transform - a :class:`~cartopy.crs.Projection`.
"""
t = kwargs.get('transform', None)
# Keep this bit - even at mpl v1.2
if t is None:
t = self.projection
if isinstance(t, ccrs.CRS) and not isinstance(t, ccrs.Projection):
raise ValueError('invalid transform:'
' Spherical contouring is not supported - '
' consider using PlateCarree/RotatedPole.')
if isinstance(t, ccrs.Projection):
kwargs['transform'] = t._as_mpl_transform(self)
else:
kwargs['transform'] = t
return matplotlib.axes.Axes.contourf(self, *args, **kwargs)
# mpl 1.2.0rc2 compatibility. To be removed once 1.2 is released
def scatter(self, *args, **kwargs):
"""
Add the "transform" keyword to :func:`~matplotlib.pyplot.scatter'.
Extra kwargs:
transform - a :class:`~cartopy.crs.Projection`.
"""
t = kwargs.get('transform', None)
# Keep this bit - even at mpl v1.2
if t is None:
t = self.projection
if hasattr(t, '_as_mpl_transform'):
kwargs['transform'] = t._as_mpl_transform(self)
# exclude Geodetic as a vaild source CS
if (isinstance(kwargs.get('transform', None),
InterProjectionTransform) and
kwargs['transform'].source_projection.is_geodetic()):
raise ValueError('Cartopy cannot currently do spherical '
'contouring. The source CRS cannot be a '
'geodetic, consider using the cyllindrical form '
'(PlateCarree or RotatedPole).')
return matplotlib.axes.Axes.scatter(self, *args, **kwargs)
def pcolormesh(self, *args, **kwargs):
"""
Add the "transform" keyword to :func:`~matplotlib.pyplot.pcolormesh'.
Extra kwargs:
transform - a :class:`~cartopy.crs.Projection`.
"""
t = kwargs.get('transform', None)
if t is None:
t = self.projection
if isinstance(t, ccrs.CRS) and not isinstance(t, ccrs.Projection):
raise ValueError('invalid transform:'
' Spherical pcolormesh is not supported - '
' consider using PlateCarree/RotatedPole.')
kwargs.setdefault('transform', t)
return self._pcolormesh_patched(*args, **kwargs)
# mpl 1.2.0rc2 compatibility. To be removed once 1.2 is released
def _pcolormesh_patched(self, *args, **kwargs):
"""
A temporary, modified duplicate of
:func:`~matplotlib.pyplot.pcolormesh'.
This function contains a workaround for a matplotlib issue
and will be removed once the issue has been resolved.
https://github.com/matplotlib/matplotlib/pull/1314
"""
import warnings
import numpy as np
import numpy.ma as ma
import matplotlib as mpl
import matplotlib.cbook as cbook
import matplotlib.colors as mcolors
import matplotlib.cm as cm
from matplotlib import docstring
import matplotlib.transforms as transforms
import matplotlib.artist as artist
from matplotlib.artist import allow_rasterization
import matplotlib.backend_bases as backend_bases
import matplotlib.path as mpath
import matplotlib.mlab as mlab
import matplotlib.collections as mcoll
if not self._hold:
self.cla()
alpha = kwargs.pop('alpha', None)
norm = kwargs.pop('norm', None)
cmap = kwargs.pop('cmap', None)
vmin = kwargs.pop('vmin', None)
vmax = kwargs.pop('vmax', None)
shading = kwargs.pop('shading', 'flat').lower()
antialiased = kwargs.pop('antialiased', False)
kwargs.setdefault('edgecolors', 'None')
X, Y, C = self._pcolorargs('pcolormesh', *args)
Ny, Nx = X.shape
# convert to one dimensional arrays
if shading != 'gouraud':
# data point in each cell is value at lower left corner
C = ma.ravel(C[0:Ny - 1, 0:Nx - 1])
else:
C = C.ravel()
X = X.ravel()
Y = Y.ravel()
coords = np.zeros(((Nx * Ny), 2), dtype=float)
coords[:, 0] = X
coords[:, 1] = Y
collection = mcoll.QuadMesh(
Nx - 1, Ny - 1, coords,
antialiased=antialiased, shading=shading, **kwargs)
collection.set_alpha(alpha)
collection.set_array(C)
if norm is not None:
assert(isinstance(norm, mcolors.Normalize))
collection.set_cmap(cmap)
collection.set_norm(norm)
collection.set_clim(vmin, vmax)
collection.autoscale_None()
self.grid(False)
########################
# PATCH FOR MPL 1.2.0rc2
# Transform from native to data coordinates?
t = collection._transform
if (not isinstance(t, mtransforms.Transform)
and hasattr(t, '_as_mpl_transform')):
t = t._as_mpl_transform(self.axes)
if t and any(t.contains_branch_seperately(self.transData)):
trans_to_data = t - self.transData
pts = np.vstack([X, Y]).T.astype(np.float)
transformed_pts = trans_to_data.transform(pts)
X = transformed_pts[..., 0]
Y = transformed_pts[..., 1]
# XXX Not a mpl 1.2 thing...
no_inf = (X != np.inf) & (Y != np.inf)
X = X[no_inf]
Y = Y[no_inf]
# END OF PATCH
##############
minx = np.amin(X)
maxx = np.amax(X)
miny = np.amin(Y)
maxy = np.amax(Y)
corners = (minx, miny), (maxx, maxy)
self.update_datalim(corners)
self.autoscale_view()
self.add_collection(collection)
# XXX Non-standard matplotlib 1.2 thing.
# Handle a possible wrap around for rectangular projections.
t = kwargs.get('transform', None)
if isinstance(t, ccrs.CRS):
if isinstance(t, ccrs._RectangularProjection) and \
isinstance(self.projection, ccrs._RectangularProjection):
C = C.reshape((Ny - 1, Nx - 1))
transformed_pts = transformed_pts.reshape((Ny, Nx, 2))
# compute the vertical line angles of the pcolor in
# transformed coordinates
with numpy.errstate(invalid='ignore'):
horizontal_vert_angles = numpy.arctan2(
numpy.diff(transformed_pts[..., 0], axis=1),
numpy.diff(transformed_pts[..., 1], axis=1)
)
# if the change in angle is greater than 90 degrees (absolute),
# then mark it for masking later on.
dx_horizontal = numpy.diff(horizontal_vert_angles)
to_mask = ((numpy.abs(dx_horizontal) > numpy.pi / 2) |
numpy.isnan(dx_horizontal))
if numpy.any(to_mask):
# at this point C has a shape of (Ny-1, Nx-1), to_mask has
# a shape of (Ny, Nx-2) and pts has a shape of (Ny*Nx, 2)
mask = numpy.zeros(C.shape, dtype=numpy.bool)
# mask out the neighbouring cells if there was a cell
# found with an angle change of more than pi/2 . NB.
# Masking too much only has a detrimental impact on
# performance.
to_mask_y_shift = to_mask[:-1, :]
mask[:, :-1][to_mask_y_shift] = True
mask[:, 1:][to_mask_y_shift] = True
to_mask_x_shift = to_mask[1:, :]
mask[:, :-1][to_mask_x_shift] = True
mask[:, 1:][to_mask_x_shift] = True
C_mask = getattr(C, 'mask', None)
if C_mask is not None:
dmask = mask | C_mask
else:
dmask = mask
# print 'Ratio of masked data: ',
# print numpy.sum(mask) / float(numpy.product(mask.shape))
# create the masked array to be used with this pcolormesh
pcolormesh_data = numpy.ma.array(C, mask=mask)
collection.set_array(pcolormesh_data.ravel())
# now that the pcolormesh has masked the bad values,
# create a pcolor with just those values that were masked
pcolor_data = pcolormesh_data.copy()
# invert the mask
pcolor_data.mask = ~pcolor_data.mask
# remember to re-apply the original data mask to the array
if C_mask is not None:
pcolor_data.mask = pcolor_data.mask | C_mask
pts = pts.reshape((Ny, Nx, 2))
pcolor_col = self.pcolor(pts[..., 0], pts[..., 1],
pcolor_data, **kwargs)
pcolor_col.set_cmap(cmap)
pcolor_col.set_norm(norm)
pcolor_col.set_clim(vmin, vmax)
# scale the data according to the *original* data
pcolor_col.norm.autoscale_None(C)
# put the pcolor_col on the pcolormesh collection so that
# if really necessary, users can do things post this method
collection._wrapped_collection_fix = pcolor_col
return collection
def pcolor(self, *args, **kwargs):
"""
Add the "transform" keyword to :func:`~matplotlib.pyplot.pcolor'.
Extra kwargs:
transform - a :class:`~cartopy.crs.Projection`.
"""
t = kwargs.get('transform', None)
if t is None:
t = self.projection
if isinstance(t, ccrs.CRS) and not isinstance(t, ccrs.Projection):
raise ValueError('invalid transform:'
' Spherical pcolor is not supported - '
' consider using PlateCarree/RotatedPole.')
kwargs.setdefault('transform', t)
return self._pcolor_patched(*args, **kwargs)
# mpl 1.2.0rc2 compatibility. To be removed once 1.2 is released
def _pcolor_patched(self, *args, **kwargs):
"""
A temporary, modified duplicate of :func:`~matplotlib.pyplot.pcolor'.
This function contains a workaround for a matplotlib issue
and will be removed once the issue has been resolved.
https://github.com/matplotlib/matplotlib/pull/1314
"""
import warnings
import numpy as np
import numpy.ma as ma
import matplotlib as mpl
import matplotlib.cbook as cbook
import matplotlib.colors as mcolors
import matplotlib.cm as cm
from matplotlib import docstring
import matplotlib.transforms as transforms
import matplotlib.artist as artist
from matplotlib.artist import allow_rasterization
import matplotlib.backend_bases as backend_bases
import matplotlib.path as mpath
import matplotlib.mlab as mlab
import matplotlib.collections as mcoll
if not self._hold:
self.cla()
alpha = kwargs.pop('alpha', None)
norm = kwargs.pop('norm', None)
cmap = kwargs.pop('cmap', None)
vmin = kwargs.pop('vmin', None)
vmax = kwargs.pop('vmax', None)
shading = kwargs.pop('shading', 'flat')
X, Y, C = self._pcolorargs('pcolor', *args)
Ny, Nx = X.shape
# convert to MA, if necessary.
C = ma.asarray(C)
X = ma.asarray(X)
Y = ma.asarray(Y)
mask = ma.getmaskarray(X) + ma.getmaskarray(Y)
xymask = mask[0:-1, 0:-1] + mask[1:, 1:] + mask[0:-1, 1:] + \
mask[1:, 0:-1]
# don't plot if C or any of the surrounding vertices are masked.
mask = ma.getmaskarray(C)[0:Ny - 1, 0:Nx - 1] + xymask
newaxis = np.newaxis
compress = np.compress
ravelmask = (mask == 0).ravel()
X1 = compress(ravelmask, ma.filled(X[0:-1, 0:-1]).ravel())
Y1 = compress(ravelmask, ma.filled(Y[0:-1, 0:-1]).ravel())
X2 = compress(ravelmask, ma.filled(X[1:, 0:-1]).ravel())
Y2 = compress(ravelmask, ma.filled(Y[1:, 0:-1]).ravel())
X3 = compress(ravelmask, ma.filled(X[1:, 1:]).ravel())
Y3 = compress(ravelmask, ma.filled(Y[1:, 1:]).ravel())
X4 = compress(ravelmask, ma.filled(X[0:-1, 1:]).ravel())
Y4 = compress(ravelmask, ma.filled(Y[0:-1, 1:]).ravel())
npoly = len(X1)
xy = np.concatenate((X1[:, newaxis], Y1[:, newaxis],
X2[:, newaxis], Y2[:, newaxis],
X3[:, newaxis], Y3[:, newaxis],
X4[:, newaxis], Y4[:, newaxis],
X1[:, newaxis], Y1[:, newaxis]),
axis=1)
verts = xy.reshape((npoly, 5, 2))
C = compress(ravelmask, ma.filled(C[0:Ny - 1, 0:Nx - 1]).ravel())
linewidths = (0.25,)
if 'linewidth' in kwargs:
kwargs['linewidths'] = kwargs.pop('linewidth')
kwargs.setdefault('linewidths', linewidths)
if shading == 'faceted':
edgecolors = 'k',
else:
edgecolors = 'none'
if 'edgecolor' in kwargs:
kwargs['edgecolors'] = kwargs.pop('edgecolor')
ec = kwargs.setdefault('edgecolors', edgecolors)
# aa setting will default via collections to patch.antialiased
# unless the boundary is not stroked, in which case the
# default will be False; with unstroked boundaries, aa
# makes artifacts that are often disturbing.
if 'antialiased' in kwargs:
kwargs['antialiaseds'] = kwargs.pop('antialiased')
if 'antialiaseds' not in kwargs and ec.lower() == "none":
kwargs['antialiaseds'] = False
collection = mcoll.PolyCollection(verts, **kwargs)
collection.set_alpha(alpha)
collection.set_array(C)
if norm is not None:
assert(isinstance(norm, mcolors.Normalize))
collection.set_cmap(cmap)
collection.set_norm(norm)
collection.set_clim(vmin, vmax)
collection.autoscale_None()
self.grid(False)
x = X.compressed()
y = Y.compressed()
########################
# PATCH FOR MPL 1.2.0rc2
# Transform from native to data coordinates?
t = collection._transform
if (not isinstance(t, mtransforms.Transform) and
hasattr(t, '_as_mpl_transform')):
t = t._as_mpl_transform(self.axes)
if t and any(t.contains_branch_seperately(self.transData)):
trans_to_data = t - self.transData
pts = np.vstack([x, y]).T.astype(np.float)
transformed_pts = trans_to_data.transform(pts)
x = transformed_pts[..., 0]
y = transformed_pts[..., 1]
# XXX Not a mpl 1.2 thing...
no_inf = (x != np.inf) & (y != np.inf)
x = x[no_inf]
y = y[no_inf]
# END OF PATCH
##############
minx = np.amin(x)
maxx = np.amax(x)
miny = np.amin(y)
maxy = np.amax(y)
corners = (minx, miny), (maxx, maxy)
self.update_datalim(corners)
self.autoscale_view()
self.add_collection(collection)
return collection
class SimpleClippedTransform(mtransforms.Transform):
"""
Transforms the values using a pre transform, clips them, then post
transforms them.
This transform should not be widely used, but is useful for transforming
a background patch and clipping the patch to a desired extent.
"""
input_dims = 2
output_dims = 2
has_inverse = True
def __init__(self, pre_clip_transform, post_clip_transform,
xclip=(0, 1), yclip=(0, 1)):
"""
Create the transform.
Args:
* pre_clip_transform - A :class:`matplotlib.transforms.Transform`.
* post_clip_transform - A :class:`matplotlib.transforms.Transform`.
* xclip - Defaults to (0,1).
* yclip - Defaults to (0,1).
"""
mtransforms.Transform.__init__(self)
self.pre_clip_transform = pre_clip_transform
self.post_clip_transform = post_clip_transform
self.x_clips = xclip
self.y_clips = yclip
def transform_non_affine(self, values):
"""
Transforms from source to target coordinates.
Args:
* value - An (n,2) array of points in source coordinates.
Returns:
* An (n,2) array of transformed points in target coordinates.
"""
new_vals = self.pre_clip_transform.transform(values)
x, y = new_vals[:, 0:1], new_vals[:, 1:2]
numpy.clip(x, self.x_clips[0], self.x_clips[1], x)
numpy.clip(y, self.y_clips[0], self.y_clips[1], y)
# XXX support ma's?
return self.post_clip_transform.transform(new_vals)
def inverted(self):
"""
Return a matplotlib :class:`~matplotlib.transforms.Transform` from
target to source coordinates.
"""
return (self.pre_clip_transform + self.post_clip_transform).inverted()
