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# Copyright Cartopy Contributors
#
# This file is part of Cartopy and is released under the LGPL license.
# See COPYING and COPYING.LESSER in the root of the repository for full
# licensing details.
"""
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.
"""
from __future__ import (absolute_import, division, print_function)
import six
import collections
import contextlib
import functools
import warnings
import weakref
if not six.PY2:
import collections.abc as collections_abc
else:
import collections as collections_abc
import matplotlib as mpl
import matplotlib.artist
import matplotlib.axes
import matplotlib.contour
from matplotlib.image import imread
import matplotlib.transforms as mtransforms
import matplotlib.patches as mpatches
import matplotlib.path as mpath
import matplotlib.spines as mspines
import numpy as np
import numpy.ma as ma
import shapely.geometry as sgeom
from cartopy import config
import cartopy.crs as ccrs
import cartopy.feature
import cartopy.img_transform
import cartopy.mpl.contour
import cartopy.mpl.feature_artist as feature_artist
import cartopy.mpl.patch as cpatch
from cartopy.mpl.slippy_image_artist import SlippyImageArtist
from cartopy.vector_transform import vector_scalar_to_grid
assert mpl.__version__ >= '1.5.1', ('Cartopy is only supported with '
'Matplotlib 1.5.1 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.
"""
_BACKG_IMG_CACHE = {}
"""
A dictionary of pre-loaded images for large background images, kept as a
dictionary so that large images are loaded only once.
"""
_USER_BG_IMGS = {}
"""
A dictionary of background images in the directory specified by the
CARTOPY_USER_BACKGROUNDS environment variable.
"""
# XXX call this InterCRSTransform
class InterProjectionTransform(mtransforms.Transform):
"""
Transform 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.
Parameters
----------
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 __eq__(self, other):
if not isinstance(other, self.__class__):
result = NotImplemented
else:
result = (self.source_projection == other.source_projection and
self.target_projection == other.target_projection)
return result
def __ne__(self, other):
return not self == other
def transform_non_affine(self, xy):
"""
Transform from source to target coordinates.
Parameters
----------
xy
An (n,2) array of points in source coordinates.
Returns
-------
x, y
An (n,2) array of transformed points in target coordinates.
"""
prj = self.target_projection
if isinstance(xy, np.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):
"""
Transform from source to target coordinates.
Cache results, so subsequent calls with the same *src_path* argument
(and the same source and target projections) are faster.
Parameters
----------
src_path
A Matplotlib :class:`~matplotlib.path.Path` object
with vertices in source coordinates.
Returns
-------
result
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
# Allow the vertices to be quickly transformed, if
# quick_vertices_transform allows it.
new_vertices = self.target_projection.quick_vertices_transform(
src_path.vertices, self.source_projection)
if new_vertices is not None:
if new_vertices is src_path.vertices:
return src_path
else:
return mpath.Path(new_vertices, src_path.codes)
if src_path.vertices.shape == (1, 2):
return mpath.Path(self.transform(src_path.vertices))
transformed_geoms = []
# Check whether this transform has the "force_path_ccw" attribute set.
# This is a cartopy extension to the Transform API to allow finer
# control of Path orientation handling (Path ordering is not important
# in matplotlib, but is in Cartopy).
geoms = cpatch.path_to_geos(src_path,
getattr(self, 'force_path_ccw', False))
for geom in geoms:
proj_geom = self.target_projection.project_geometry(
geom, self.source_projection)
transformed_geoms.append(proj_geom)
if not transformed_geoms:
result = mpath.Path(np.empty([0, 2]))
else:
paths = cpatch.geos_to_path(transformed_geoms)
if not paths:
return mpath.Path(np.empty([0, 2]))
points, codes = list(zip(*[cpatch.path_segments(path,
curves=False,
simplify=False)
for path in paths]))
result = mpath.Path(np.concatenate(points, 0),
np.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):
"""
Returns
-------
InterProjectionTransform
A Matplotlib :class:`~matplotlib.transforms.Transform`
from target to source coordinates.
"""
return InterProjectionTransform(self.target_projection,
self.source_projection)
class _ViewClippedPathPatch(mpatches.PathPatch):
def __init__(self, axes, **kwargs):
self._original_path = mpath.Path(np.empty((0, 2)))
super(_ViewClippedPathPatch, self).__init__(self._original_path,
**kwargs)
self._axes = axes
def set_boundary(self, path, transform):
self._original_path = path
self.set_transform(transform)
self.stale = True
def _adjust_location(self):
if self.stale:
self._path = self._original_path.clip_to_bbox(self.axes.viewLim)
@matplotlib.artist.allow_rasterization
def draw(self, renderer, *args, **kwargs):
self._adjust_location()
super(_ViewClippedPathPatch, self).draw(renderer, *args, **kwargs)
class GeoSpine(mspines.Spine):
def __init__(self, axes, **kwargs):
self._original_path = mpath.Path(np.empty((0, 2)))
kwargs.setdefault('clip_on', False)
super(GeoSpine, self).__init__(axes, 'geo', self._original_path,
**kwargs)
self.set_capstyle('butt')
def set_boundary(self, path, transform):
self._original_path = path
self.set_transform(transform)
self.stale = True
def _adjust_location(self):
if self.stale:
self._path = self._original_path.clip_to_bbox(self.axes.viewLim)
def get_window_extent(self, renderer=None):
# make sure the location is updated so that transforms etc are
# correct:
self._adjust_location()
return super(GeoSpine, self).get_window_extent(renderer=renderer)
@matplotlib.artist.allow_rasterization
def draw(self, renderer):
self._adjust_location()
ret = super(GeoSpine, self).draw(renderer)
self.stale = False
return ret
def set_position(self, position):
raise NotImplementedError(
'GeoSpine does not support changing its position.')
def _add_transform(func):
"""A decorator that adds and validates the transform keyword argument."""
@functools.wraps(func)
def wrapper(self, *args, **kwargs):
transform = kwargs.get('transform', None)
if transform is None:
transform = self.projection
if (isinstance(transform, ccrs.CRS) and
not isinstance(transform, ccrs.Projection)):
raise ValueError('Invalid transform: Spherical {} '
'is not supported - consider using '
'PlateCarree/RotatedPole.'.format(func.__name__))
kwargs['transform'] = transform
return func(self, *args, **kwargs)
return wrapper
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 = plt.axes(projection=cartopy.crs.PlateCarree())
# Set up an OSGB map.
geo_axes = plt.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.
plt.axes(projection=cartopy.crs.OSGB())
plt.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.
Parameters
----------
map_projection: optional
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')
"""The :class:`cartopy.crs.Projection` of this GeoAxes."""
super(GeoAxes, self).__init__(*args, **kwargs)
self._gridliners = []
self.img_factories = []
self._done_img_factory = False
@property
def outline_patch(self):
"""
DEPRECATED. The patch that provides the line bordering the projection.
Use GeoAxes.spines['geo'] or default Axes properties instead.
"""
warnings.warn("The outline_patch property is deprecated. Use "
"GeoAxes.spines['geo'] or the default Axes properties "
"instead.",
DeprecationWarning,
stacklevel=2)
return self.spines['geo']
@property
def background_patch(self):
"""
DEPRECATED. The patch that provides the filled background of the
projection.
"""
warnings.warn('The background_patch property is deprecated. '
'Use GeoAxes.patch instead.',
DeprecationWarning,
stacklevel=2)
return self.patch
def add_image(self, factory, *args, **kwargs):
"""
Add 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.
Parameters
----------
factory
Currently an image "factory" is just an object with
an ``image_for_domain`` method. Examples of image factories
are :class:`cartopy.io.img_nest.NestedImageCollection` and
:class:`cartopy.io.image_tiles.GoogleTiles`.
"""
if hasattr(factory, 'image_for_domain'):
# XXX TODO: Needs deprecating.
self.img_factories.append([factory, args, kwargs])
else:
# Args and kwargs not allowed.
assert not bool(args) and not bool(kwargs)
image = factory
super(GeoAxes, self).add_image(image)
return image
@contextlib.contextmanager
def hold_limits(self, hold=True):
"""
Keep track of the original view and data limits for the life of this
context manager, optionally reverting any changes back to the original
values after the manager exits.
Parameters
----------
hold: bool, optional
Whether to revert the data and view limits after the
context manager exits. Defaults to True.
"""
data_lim = self.dataLim.frozen().get_points()
view_lim = self.viewLim.frozen().get_points()
other = (self.ignore_existing_data_limits,
self._autoscaleXon, self._autoscaleYon)
try:
yield
finally:
if hold:
self.dataLim.set_points(data_lim)
self.viewLim.set_points(view_lim)
(self.ignore_existing_data_limits,
self._autoscaleXon, self._autoscaleYon) = other
@matplotlib.artist.allow_rasterization
def draw(self, renderer=None, **kwargs):
"""
Extend the standard behaviour of :func:`matplotlib.axes.Axes.draw`.
Draw grid lines and image factory results before invoking standard
Matplotlib drawing. A global range is used if no limits have yet
been set.
"""
# If data has been added (i.e. autoscale hasn't been turned off)
# then we should autoscale the view.
if self.get_autoscale_on() and self.ignore_existing_data_limits:
self.autoscale_view()
# Adjust location of background patch so that new gridlines below are
# clipped correctly.
self.patch._adjust_location()
self.apply_aspect()
for gl in self._gridliners:
gl._draw_gridliner(renderer=renderer)
# 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, **kwargs)
def _update_title_position(self, renderer):
matplotlib.axes.Axes._update_title_position(self, renderer)
if not self._gridliners:
return
if self._autotitlepos is not None and not self._autotitlepos:
return
# Get the max ymax of all top labels
top = -1
for gl in self._gridliners:
if gl.has_labels():
for label in (gl.top_label_artists +
gl.left_label_artists +
gl.right_label_artists):
# we skip bottom labels because they are usually
# not at the top
bb = label.get_tightbbox(renderer)
top = max(top, bb.ymax)
if top < 0:
# nothing to do if no label found
return
yn = self.transAxes.inverted().transform((0., top))[1]
if yn <= 1:
# nothing to do if the upper bounds of labels is below
# the top of the axes
return
# Loop on titles to adjust
titles = (self.title, self._left_title, self._right_title)
for title in titles:
x, y0 = title.get_position()
y = max(1.0, yn)
title.set_position((x, y))
def __str__(self):
return '< GeoAxes: %s >' % self.projection
def cla(self):
"""Clear the current axes and adds boundary lines."""
result = matplotlib.axes.Axes.cla(self)
self.xaxis.set_visible(False)
self.yaxis.set_visible(False)
# Enable tight autoscaling.
self._tight = True
self.set_aspect('equal')
self._boundary()
# XXX consider a margin - but only when the map is not global...
# self._xmargin = 0.15
# self._ymargin = 0.15
self.dataLim.intervalx = self.projection.x_limits
self.dataLim.intervaly = self.projection.y_limits
return result
def format_coord(self, x, y):
"""
Returns
-------
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)
def coastlines(self, resolution='auto', color='black', **kwargs):
"""
Add coastal **outlines** to the current axes from the Natural Earth
"coastline" shapefile collection.
Parameters
----------
resolution : str or :class:`cartopy.feature.Scaler`, optional
A named resolution to use from the Natural Earth
dataset. Currently can be one of "auto" (default), "110m", "50m",
and "10m", or a Scaler object.
"""
kwargs['edgecolor'] = color
kwargs['facecolor'] = 'none'
feature = cartopy.feature.COASTLINE
# The coastline feature is automatically scaled by default, but for
# anything else, including custom scaler instances, create a new
# feature which derives from the default one.
if resolution != 'auto':
feature = feature.with_scale(resolution)
return self.add_feature(feature, **kwargs)
def tissot(self, rad_km=500, lons=None, lats=None, n_samples=80, **kwargs):
"""
Add Tissot's indicatrices to the axes.
Parameters
----------
rad_km
The radius in km of the the circles to be drawn.
lons
A numpy.ndarray, list or tuple of longitude values that
locate the centre of each circle. Specifying more than one
dimension allows individual points to be drawn whereas a
1D array produces a grid of points.
lats
A numpy.ndarray, list or tuple of latitude values that
that locate the centre of each circle. See lons.
n_samples
Integer number of points sampled around the circumference of
each circle.
``**kwargs`` are passed through to `class:ShapelyFeature`.
"""
from cartopy import geodesic
geod = geodesic.Geodesic()
geoms = []
if lons is None:
lons = np.linspace(-180, 180, 6, endpoint=False)
else:
lons = np.asarray(lons)
if lats is None:
lats = np.linspace(-80, 80, 6)
else:
lats = np.asarray(lats)
if lons.ndim == 1 or lats.ndim == 1:
lons, lats = np.meshgrid(lons, lats)
lons, lats = lons.flatten(), lats.flatten()
if lons.shape != lats.shape:
raise ValueError('lons and lats must have the same shape.')
for lon, lat in zip(lons, lats):
circle = geod.circle(lon, lat, rad_km*1e3, n_samples=n_samples)
geoms.append(sgeom.Polygon(circle))
feature = cartopy.feature.ShapelyFeature(geoms, ccrs.Geodetic(),
**kwargs)
return self.add_feature(feature)
def natural_earth_shp(self, name='land', resolution='110m',
category='physical', **kwargs):
"""
Add the geometries from the specified Natural Earth shapefile to the
Axes as a :class:`~matplotlib.collections.PathCollection`.
Parameters
----------
name: optional
Name of the shapefile geometry to add. Defaults to 'land'.
resolution: optional
Resolution of shapefile geometry to add. Defaults to '110m'.
category: optional
Category of shapefile geometry to add. Defaults to 'physical'.
``**kwargs`` are passed through to the
:class:`~matplotlib.collections.PathCollection` constructor.
Returns
-------
The created :class:`~matplotlib.collections.PathCollection`.
"""
warnings.warn('This method has been deprecated.'
' Please use `add_feature` instead.',
DeprecationWarning,
stacklevel=2)
kwargs.setdefault('edgecolor', 'face')
kwargs.setdefault('facecolor', cartopy.feature.COLORS['land'])
feature = cartopy.feature.NaturalEarthFeature(category, name,
resolution, **kwargs)
return self.add_feature(feature)
def add_feature(self, feature, **kwargs):
"""
Add the given :class:`~cartopy.feature.Feature` instance to the axes.
Parameters
----------
feature
An instance of :class:`~cartopy.feature.Feature`.
Returns
-------
A :class:`cartopy.mpl.feature_artist.FeatureArtist` instance
The instance responsible for drawing the feature.
Note
----
Matplotlib keyword arguments can be used when drawing the feature.
This allows standard Matplotlib control over aspects such as
'facecolor', 'alpha', etc.
"""
# Instantiate an artist to draw the feature and add it to the axes.
artist = feature_artist.FeatureArtist(feature, **kwargs)
return self.add_artist(artist)
def add_geometries(self, geoms, crs, **kwargs):
"""
Add the given shapely geometries (in the given crs) to the axes.
Parameters
----------
geoms
A collection of shapely geometries.
crs
The cartopy CRS in which the provided geometries are defined.
styler
A callable that returns matplotlib patch styling given a geometry.
Returns
-------
A :class:`cartopy.mpl.feature_artist.FeatureArtist` instance
The instance responsible for drawing the feature.
Note
----
Matplotlib keyword arguments can be used when drawing the feature.
This allows standard Matplotlib control over aspects such as
'facecolor', 'alpha', etc.
"""
styler = kwargs.pop('styler', None)
feature = cartopy.feature.ShapelyFeature(geoms, crs, **kwargs)
return self.add_feature(feature, styler=styler)
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
the CRS of this Axes.
"""
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.
with self.hold_limits():
if self.get_autoscale_on():
self.autoscale_view()
[x1, y1], [x2, y2] = self.viewLim.get_points()
domain_in_src_proj = sgeom.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(crs.globe)
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 necessary.
boundary_poly = sgeom.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
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.
Parameters
----------
extents
Tuple of floats representing the required extent (x0, x1, y0, y1).
"""
# 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 = sgeom.polygon.LineString([[x1, y1], [x2, y1],
[x2, y2], [x1, y2],
[x1, y1]])
projected = None
# Sometimes numerical issues cause the projected vertices of the
# requested extents to appear outside the projection domain.
# This results in an empty geometry, which has an empty `bounds`
# tuple, which causes an unpack error.
# This workaround avoids using the projection when the requested
# extents are obviously the same as the projection domain.
try_workaround = ((crs is None and
isinstance(self.projection, ccrs.PlateCarree)) or
crs == self.projection)
if try_workaround:
boundary = self.projection.boundary
if boundary.equals(domain_in_crs):
projected = boundary
if projected is None:
projected = self.projection.project_geometry(domain_in_crs, crs)
try:
# This might fail with an unhelpful error message ('need more
# than 0 values to unpack') if the specified extents fall outside
# the projection extents, so try and give a better error message.
x1, y1, x2, y2 = projected.bounds
except ValueError:
msg = ('Failed to determine the required bounds in projection '
'coordinates. Check that the values provided are within '
'the valid range (x_limits=[{xlim[0]}, {xlim[1]}], '
'y_limits=[{ylim[0]}, {ylim[1]}]).')
raise ValueError(msg.format(xlim=self.projection.x_limits,
ylim=self.projection.y_limits))
self.set_xlim([x1, x2])
self.set_ylim([y1, y2])
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)
def autoscale_view(self, tight=None, scalex=True, scaley=True):
matplotlib.axes.Axes.autoscale_view(self, tight=tight,
scalex=scalex, scaley=scaley)
# Limit the resulting bounds to valid area.
if scalex and self._autoscaleXon:
bounds = self.get_xbound()
self.set_xbound(max(bounds[0], self.projection.x_limits[0]),
min(bounds[1], self.projection.x_limits[1]))
if scaley and self._autoscaleYon:
bounds = self.get_ybound()
self.set_ybound(max(bounds[0], self.projection.y_limits[0]),
min(bounds[1], self.projection.y_limits[1]))
autoscale_view.__doc__ = matplotlib.axes.Axes.autoscale_view.__doc__
def set_xticks(self, ticks, minor=False, crs=None):
"""
Set the x ticks.
Parameters
----------
ticks
List of floats denoting the desired position of x ticks.
minor: optional
flag indicating whether the ticks should be minor
ticks i.e. small and unlabelled (defaults to False).
crs: optional
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.
Only transformations from one rectangular coordinate system
to another rectangular coordinate system are supported (defaults
to None).
Note
----
This interface is subject to change whilst functionality is added
to support other map projections.
"""
# Project ticks if crs differs from axes' projection
if crs is not None and crs != self.projection:
if not isinstance(crs, (ccrs._RectangularProjection,
ccrs.Mercator)) or \
not isinstance(self.projection,
(ccrs._RectangularProjection,
ccrs.Mercator)):
raise RuntimeError('Cannot handle non-rectangular coordinate '
'systems.')
proj_xyz = self.projection.transform_points(crs,
np.asarray(ticks),
np.zeros(len(ticks)))
xticks = proj_xyz[..., 0]
else:
xticks = ticks
# Switch on drawing of x axis
self.xaxis.set_visible(True)
return super(GeoAxes, self).set_xticks(xticks, minor=minor)
def set_yticks(self, ticks, minor=False, crs=None):
"""
Set the y ticks.
Parameters
----------
ticks
List of floats denoting the desired position of y ticks.
minor: optional
flag indicating whether the ticks should be minor
ticks i.e. small and unlabelled (defaults to False).
crs: optional
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.
Only transformations from one rectangular coordinate system
to another rectangular coordinate system are supported (defaults
to None).
Note
----
This interface is subject to change whilst functionality is added
to support other map projections.
"""
# Project ticks if crs differs from axes' projection
if crs is not None and crs != self.projection:
if not isinstance(crs, (ccrs._RectangularProjection,
ccrs.Mercator)) or \
not isinstance(self.projection,
(ccrs._RectangularProjection,
ccrs.Mercator)):
raise RuntimeError('Cannot handle non-rectangular coordinate '
'systems.')
proj_xyz = self.projection.transform_points(crs,
np.zeros(len(ticks)),
np.asarray(ticks))
yticks = proj_xyz[..., 1]
else:
yticks = ticks
# Switch on drawing of y axis
self.yaxis.set_visible(True)
return super(GeoAxes, self).set_yticks(yticks, minor=minor)
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')
return self.imshow(imread(fname), origin='upper',
transform=source_proj,
extent=[-180, 180, -90, 90])
else:
raise ValueError('Unknown stock image %r.' % name)
def background_img(self, name='ne_shaded', resolution='low', extent=None,
cache=False):
"""
Add a background image to the map, from a selection of pre-prepared
images held in a directory specified by the CARTOPY_USER_BACKGROUNDS
environment variable. That directory is checked with
func:`self.read_user_background_images` and needs to contain a JSON
file which defines for the image metadata.
Parameters
----------
name: optional
The name of the image to read according to the contents
of the JSON file. A typical file might have, for instance:
'ne_shaded' : Natural Earth Shaded Relief
'ne_grey' : Natural Earth Grey Earth.
resolution: optional
The resolution of the image to read, according to
the contents of the JSON file. A typical file might
have the following for each name of the image:
'low', 'med', 'high', 'vhigh', 'full'.
extent: optional
Using a high resolution background image zoomed into
a small area will take a very long time to render as
the image is prepared globally, even though only a small
area is used. Adding the extent will only render a
particular geographic region. Specified as
[longitude start, longitude end,
latitude start, latitude end].
e.g. [-11, 3, 48, 60] for the UK
or [167.0, 193.0, 47.0, 68.0] to cross the date line.
cache: optional
Logical flag as to whether or not to cache the loaded
images into memory. The images are stored before the
extent is used.
"""
# read in the user's background image directory:
if len(_USER_BG_IMGS) == 0:
self.read_user_background_images()
import os
bgdir = os.getenv('CARTOPY_USER_BACKGROUNDS')
if bgdir is None:
bgdir = os.path.join(config["repo_data_dir"],
'raster', 'natural_earth')
# now get the filename we want to use:
try:
fname = _USER_BG_IMGS[name][resolution]
except KeyError:
msg = ('Image "{}" and resolution "{}" are not present in '
'the user background image metadata in directory "{}"')
raise ValueError(msg.format(name, resolution, bgdir))
# Now obtain the image data from file or cache:
fpath = os.path.join(bgdir, fname)
if cache:
if fname in _BACKG_IMG_CACHE:
img = _BACKG_IMG_CACHE[fname]
else:
img = imread(fpath)
_BACKG_IMG_CACHE[fname] = img
else:
img = imread(fpath)
if len(img.shape) == 2:
# greyscale images are only 2-dimensional, so need replicating
# to 3 colour channels:
img = np.repeat(img[:, :, np.newaxis], 3, axis=2)
# now get the projection from the metadata:
if _USER_BG_IMGS[name]['__projection__'] == 'PlateCarree':
# currently only PlateCarree is defined:
source_proj = ccrs.PlateCarree()
else:
raise NotImplementedError('Background image projection undefined')
if extent is None:
# not specifying an extent, so return all of it:
return self.imshow(img, origin='upper',
transform=source_proj,
extent=[-180, 180, -90, 90])
else:
# return only a subset of the image:
# set up coordinate arrays:
d_lat = 180.0 / img.shape[0]
d_lon = 360.0 / img.shape[1]
# latitude starts at 90N for this image:
lat_pts = (np.arange(img.shape[0]) * -d_lat - (d_lat / 2.0)) + 90.0
lon_pts = (np.arange(img.shape[1]) * d_lon + (d_lon / 2.0)) - 180.0
# which points are in range:
lat_in_range = np.logical_and(lat_pts >= extent[2],
lat_pts <= extent[3])
if extent[0] < 180 and extent[1] > 180:
# we have a region crossing the dateline
# this is the westerly side of the input image:
lon_in_range1 = np.logical_and(lon_pts >= extent[0],
lon_pts <= 180.0)
img_subset1 = img[lat_in_range, :, :][:, lon_in_range1, :]
# and the eastward half:
lon_in_range2 = lon_pts + 360. <= extent[1]
img_subset2 = img[lat_in_range, :, :][:, lon_in_range2, :]
# now join them up:
img_subset = np.concatenate((img_subset1, img_subset2), axis=1)
# now define the extent for output that matches those points:
ret_extent = [lon_pts[lon_in_range1][0] - d_lon / 2.0,
lon_pts[lon_in_range2][-1] + d_lon / 2.0 + 360,
lat_pts[lat_in_range][-1] - d_lat / 2.0,
lat_pts[lat_in_range][0] + d_lat / 2.0]
else:
# not crossing the dateline, so just find the region:
lon_in_range = np.logical_and(lon_pts >= extent[0],
lon_pts <= extent[1])
img_subset = img[lat_in_range, :, :][:, lon_in_range, :]
# now define the extent for output that matches those points:
ret_extent = [lon_pts[lon_in_range][0] - d_lon / 2.0,
lon_pts[lon_in_range][-1] + d_lon / 2.0,
lat_pts[lat_in_range][-1] - d_lat / 2.0,
lat_pts[lat_in_range][0] + d_lat / 2.0]
return self.imshow(img_subset, origin='upper',
transform=source_proj,
extent=ret_extent)
def read_user_background_images(self, verify=True):
"""
Read the metadata in the specified CARTOPY_USER_BACKGROUNDS
environment variable to populate the dictionaries for background_img.
If CARTOPY_USER_BACKGROUNDS is not set then by default the image in
lib/cartopy/data/raster/natural_earth/ will be made available.
The metadata should be a standard JSON file which specifies a two
level dictionary. The first level is the image type.
For each image type there must be the fields:
__comment__, __source__ and __projection__
and then an element giving the filename for each resolution.
An example JSON file can be found at:
lib/cartopy/data/raster/natural_earth/images.json
"""
import os
import json
bgdir = os.getenv('CARTOPY_USER_BACKGROUNDS')
if bgdir is None:
bgdir = os.path.join(config["repo_data_dir"],
'raster', 'natural_earth')
json_file = os.path.join(bgdir, 'images.json')
with open(json_file, 'r') as js_obj:
dict_in = json.load(js_obj)
for img_type in dict_in:
_USER_BG_IMGS[img_type] = dict_in[img_type]
if verify:
required_info = ['__comment__', '__source__', '__projection__']
for img_type in _USER_BG_IMGS:
if img_type == '__comment__':
# the top level comment doesn't need verifying:
pass
else:
# check that this image type has the required info:
for required in required_info:
if required not in _USER_BG_IMGS[img_type]:
msg = ('User background metadata file "{}", '
'image type "{}", does not specify '
'metadata item "{}"')
raise ValueError(msg.format(json_file, img_type,
required))
for resln in _USER_BG_IMGS[img_type]:
# the required_info items are not resolutions:
if resln not in required_info:
img_it_r = _USER_BG_IMGS[img_type][resln]
test_file = os.path.join(bgdir, img_it_r)
if not os.path.isfile(test_file):
msg = 'File "{}" not found'
raise ValueError(msg.format(test_file))
def add_raster(self, raster_source, **slippy_image_kwargs):
"""
Add the given raster source to the GeoAxes.
Parameters
----------
raster_source:
:class:`cartopy.io.RasterSource` like instance
``raster_source`` may be any object which
implements the RasterSource interface, including
instances of objects such as
:class:`~cartopy.io.ogc_clients.WMSRasterSource`
and
:class:`~cartopy.io.ogc_clients.WMTSRasterSource`.
Note that image retrievals are done at draw time,
not at creation time.
"""
# Allow a fail-fast error if the raster source cannot provide
# images in the current projection.
raster_source.validate_projection(self.projection)
img = SlippyImageArtist(self, raster_source, **slippy_image_kwargs)
with self.hold_limits():
self.add_image(img)
return img
def _regrid_shape_aspect(self, regrid_shape, target_extent):
"""
Helper for setting regridding shape which is used in several
plotting methods.
"""
if not isinstance(regrid_shape, collections_abc.Sequence):
target_size = int(regrid_shape)
x_range, y_range = np.diff(target_extent)[::2]
desired_aspect = x_range / y_range
if x_range >= y_range:
regrid_shape = (int(target_size * desired_aspect), target_size)
else:
regrid_shape = (target_size, int(target_size / desired_aspect))
return regrid_shape
@_add_transform
def imshow(self, img, *args, **kwargs):
"""
Add the "transform" keyword to :func:`~matplotlib.pyplot.imshow'.
Parameters
----------
img
The image to be displayed.
Other Parameters
----------------
transform: :class:`~cartopy.crs.Projection` or matplotlib transform
The coordinate system in which the given image is
rectangular.
regrid_shape: int or pair of ints
The shape of the desired image if it needs to be
transformed. If a single integer is given then
that will be used as the minimum length dimension,
while the other dimension will be scaled up
according to the target extent's aspect ratio.
The default is for the minimum dimension of a
transformed image to have length 750, so for an
image being transformed into a global PlateCarree
projection the resulting transformed image would
have a shape of ``(750, 1500)``.
extent: tuple
The corner coordinates of the image in the form
``(left, right, bottom, top)``. The coordinates should
be in the coordinate system passed to the transform
keyword.
origin: {'lower', 'upper'}
The origin of the vertical pixels. See
:func:`matplotlib.pyplot.imshow` for further details.
Default is ``'upper'``. Prior to 0.18, it was ``'lower'``.
"""
if 'update_datalim' in kwargs:
raise ValueError('The update_datalim keyword has been removed in '
'imshow. To hold the data and view limits see '
'GeoAxes.hold_limits.')
transform = kwargs.pop('transform')
extent = kwargs.get('extent', None)
kwargs.setdefault('origin', 'upper')
same_projection = (isinstance(transform, ccrs.Projection) and
self.projection == transform)
# Only take the shortcut path if the image is within the current
# bounds (+/- threshold) of the projection
x0, x1 = self.projection.x_limits
y0, y1 = self.projection.y_limits
eps = self.projection.threshold
inside_bounds = (extent is None or
(x0 - eps <= extent[0] <= x1 + eps and
x0 - eps <= extent[1] <= x1 + eps and
y0 - eps <= extent[2] <= y1 + eps and
y0 - eps <= extent[3] <= y1 + eps))
if (transform is None or transform == self.transData or
same_projection and inside_bounds):
result = matplotlib.axes.Axes.imshow(self, img, *args, **kwargs)
else:
extent = kwargs.pop('extent', None)
img = np.asanyarray(img)
if kwargs['origin'] == 'upper':
# It is implicitly assumed by the regridding operation that the
# origin of the image is 'lower', so simply adjust for that
# here.
img = img[::-1]
kwargs['origin'] = 'lower'
if not isinstance(transform, ccrs.Projection):
raise ValueError('Expected a projection subclass. Cannot '
'handle a %s in imshow.' % type(transform))
target_extent = self.get_extent(self.projection)
regrid_shape = kwargs.pop('regrid_shape', 750)
regrid_shape = self._regrid_shape_aspect(regrid_shape,
target_extent)
warp_array = cartopy.img_transform.warp_array
img, extent = warp_array(img,
source_proj=transform,
source_extent=extent,
target_proj=self.projection,
target_res=regrid_shape,
target_extent=target_extent,
mask_extrapolated=True,
)
# 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, np.ma.MaskedArray) and
img.shape[2:3] == (3, ) and
img.mask is not False):
old_img = img
img = np.zeros(img.shape[:2] + (4, ), dtype=img.dtype)
img[:, :, 0:3] = old_img
# Put an alpha channel in if the image was masked.
img[:, :, 3] = ~ np.any(old_img.mask, axis=2)
if img.dtype.kind == 'u':
img[:, :, 3] *= 255
result = matplotlib.axes.Axes.imshow(self, img, *args,
extent=extent, **kwargs)
return result
def gridlines(self, crs=None, draw_labels=False,
xlocs=None, ylocs=None, dms=False,
x_inline=None, y_inline=None, auto_inline=True,
xformatter=None, yformatter=None,
**kwargs):
"""
Automatically add gridlines to the axes, in the given coordinate
system, at draw time.
Parameters
----------
crs: optional
The :class:`cartopy._crs.CRS` defining the coordinate system in
which gridlines are drawn.
Defaults to :class:`cartopy.crs.PlateCarree`.
draw_labels: optional
Label gridlines like axis ticks, around the edge.
xlocs: optional
An iterable of gridline locations or a
:class:`matplotlib.ticker.Locator` instance which will be
used to determine the locations of the gridlines in the
x-coordinate of the given CRS. Defaults to None, which
implies automatic locating of the gridlines.
ylocs: optional
An iterable of gridline locations or a
:class:`matplotlib.ticker.Locator` instance which will be
used to determine the locations of the gridlines in the
y-coordinate of the given CRS. Defaults to None, which
implies automatic locating of the gridlines.
dms: bool
When default longitude and latitude locators and formatters are
used, ticks are able to stop on minutes and seconds if minutes is
set to True, and not fraction of degrees. This keyword is passed
to :class:`~cartopy.mpl.gridliner.Gridliner` and has no effect
if xlocs and ylocs are explicitly set.
x_inline: optional
Toggle whether the x labels drawn should be inline.
y_inline: optional
Toggle whether the y labels drawn should be inline.
auto_inline: optional
Set x_inline and y_inline automatically based on projection
xformatter: optional
A :class:`matplotlib.ticker.Formatter` instance to format labels
for x-coordinate gridlines. It defaults to None, which implies the
use of a :class:`cartopy.mpl.ticker.LongitudeFormatter` initiated
with the ``dms`` argument, if the crs is of
:class:`~cartopy.crs.PlateCarree` type.
yformatter: optional
A :class:`matplotlib.ticker.Formatter` instance to format labels
for y-coordinate gridlines. It defaults to None, which implies the
use of a :class:`cartopy.mpl.ticker.LatitudeFormatter` initiated
with the ``dms`` argument, if the crs is of
:class:`~cartopy.crs.PlateCarree` type.
Keyword Parameters
------------------
**kwargs
All other keywords control line properties. These are passed
through to :class:`matplotlib.collections.Collection`.
Returns
-------
gridliner
A :class:`cartopy.mpl.gridliner.Gridliner` instance.
Notes
-----
The "x" and "y" for locations and inline settings do not necessarily
correspond to X and Y, but to the first and second coordinates of the
specified CRS. For the common case of PlateCarree gridlines, these
correspond to longitudes and latitudes. Depending on the projection
used for the map, meridians and parallels can cross both the X axis and
the Y axis.
"""
if crs is None:
crs = ccrs.PlateCarree()
from cartopy.mpl.gridliner import Gridliner
gl = Gridliner(
self, crs=crs, draw_labels=draw_labels, xlocator=xlocs,
ylocator=ylocs, collection_kwargs=kwargs, dms=dms,
x_inline=x_inline, y_inline=y_inline, auto_inline=auto_inline,
xformatter=xformatter, yformatter=yformatter)
self._gridliners.append(gl)
return gl
def _gen_axes_patch(self):
return _ViewClippedPathPatch(self)
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.values():
spine.set_visible(False)
spines['geo'] = GeoSpine(self)
return spines
def _boundary(self):
"""
Add the map's boundary to this GeoAxes.
The :data:`.patch` and :data:`.spines['geo']` are updated to match.
"""
path, = cpatch.geos_to_path(self.projection.boundary)
# Get the outline path in terms of self.transData
proj_to_data = self.projection._as_mpl_transform(self) - self.transData
trans_path = proj_to_data.transform_path(path)
# Set the boundary - we can make use of the rectangular clipping.
self.set_boundary(trans_path)
# Attach callback events for when the xlim or ylim are changed. This
# is what triggers the patches to be re-clipped at draw time.
self.callbacks.connect('xlim_changed', _trigger_patch_reclip)
self.callbacks.connect('ylim_changed', _trigger_patch_reclip)
def set_boundary(self, path, transform=None, use_as_clip_path=None):
"""
Given a path, update :data:`.spines['geo']` and :data:`.patch`.
Parameters
----------
path: :class:`matplotlib.path.Path`
The path of the desired boundary.
transform: None or :class:`matplotlib.transforms.Transform`, optional
The coordinate system of the given path. Currently
this must be convertible to data coordinates, and
therefore cannot extend beyond the limits of the
axes' projection.
"""
if use_as_clip_path is not None:
warnings.warn(
'Passing use_as_clip_path to set_boundary is deprecated.',
DeprecationWarning,
stacklevel=2)
if transform is None:
transform = self.transData
if isinstance(transform, cartopy.crs.CRS):
transform = transform._as_mpl_transform(self)
# Attach the original path to the patches. This will be used each time
# a new clipped path is calculated.
self.patch.set_boundary(path, transform)
self.spines['geo'].set_boundary(path, transform)
@_add_transform
def contour(self, *args, **kwargs):
"""
Add the "transform" keyword to :func:`~matplotlib.pyplot.contour'.
Other Parameters
----------------
transform
A :class:`~cartopy.crs.Projection`.
"""
result = matplotlib.axes.Axes.contour(self, *args, **kwargs)
self.autoscale_view()
# Re-cast the contour as a GeoContourSet.
if isinstance(result, matplotlib.contour.QuadContourSet):
result.__class__ = cartopy.mpl.contour.GeoContourSet
return result
@_add_transform
def contourf(self, *args, **kwargs):
"""
Add the "transform" keyword to :func:`~matplotlib.pyplot.contourf'.
Other Parameters
----------------
transform
A :class:`~cartopy.crs.Projection`.
"""
t = kwargs['transform']
if isinstance(t, ccrs.Projection):
kwargs['transform'] = t = t._as_mpl_transform(self)
# Set flag to indicate correcting orientation of paths if not ccw
if isinstance(t, mtransforms.Transform):
for sub_trans, _ in t._iter_break_from_left_to_right():
if isinstance(sub_trans, InterProjectionTransform):
if not hasattr(sub_trans, 'force_path_ccw'):
sub_trans.force_path_ccw = True
result = matplotlib.axes.Axes.contourf(self, *args, **kwargs)
# We need to compute the dataLim correctly for contours.
bboxes = [col.get_datalim(self.transData)
for col in result.collections
if col.get_paths()]
if bboxes:
extent = mtransforms.Bbox.union(bboxes)
self.dataLim.update_from_data_xy(extent.get_points())
self.autoscale_view()
# Re-cast the contour as a GeoContourSet.
if isinstance(result, matplotlib.contour.QuadContourSet):
result.__class__ = cartopy.mpl.contour.GeoContourSet
return result
@_add_transform
def scatter(self, *args, **kwargs):
"""
Add the "transform" keyword to :func:`~matplotlib.pyplot.scatter'.
Other Parameters
----------------
transform
A :class:`~cartopy.crs.Projection`.
"""
# exclude Geodetic as a valid source CS
if (isinstance(kwargs['transform'],
InterProjectionTransform) and
kwargs['transform'].source_projection.is_geodetic()):
raise ValueError('Cartopy cannot currently do spherical '
'scatter. The source CRS cannot be a '
'geodetic, consider using the cyllindrical form '
'(PlateCarree or RotatedPole).')
result = matplotlib.axes.Axes.scatter(self, *args, **kwargs)
self.autoscale_view()
return result
@_add_transform
def pcolormesh(self, *args, **kwargs):
"""
Add the "transform" keyword to :func:`~matplotlib.pyplot.pcolormesh'.
Other Parameters
----------------
transform
A :class:`~cartopy.crs.Projection`.
"""
result = self._pcolormesh_patched(*args, **kwargs)
self.autoscale_view()
return result
def _pcolormesh_patched(self, *args, **kwargs):
"""
A modified duplicate of :func:`~matplotlib.pyplot.pcolormesh'.
This function contains patches for Cartopy-specific behaviour, such as
using the transform for limit checks, applying longitude wrapping, etc.
See PATCH comments below.
"""
import matplotlib.colors as mcolors
import matplotlib.collections as mcoll
# Remove this check when only MPL >= 3.0 is supported.
if not getattr(self, '_hold', True):
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)
antialiased = kwargs.pop('antialiased', False)
kwargs.setdefault('edgecolors', 'None')
if matplotlib.__version__ < "3.3":
shading = kwargs.pop('shading', 'flat')
allmatch = (shading == 'gouraud')
X, Y, C = self._pcolorargs('pcolormesh', *args, allmatch=allmatch)
else:
shading = kwargs.pop('shading', 'auto')
if shading is None:
shading = 'auto'
X, Y, C, shading = self._pcolorargs('pcolormesh', *args,
shading=shading)
Ny, Nx = X.shape
# convert to one dimensional arrays
C = C.ravel()
coords = np.column_stack((X.flat, Y.flat)).astype(float, copy=False)
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)
# 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)
if t and any(t.contains_branch_seperately(self.transData)):
trans_to_data = t - self.transData
########################
# PATCH
# XXX Non-standard Matplotlib thing:
# * Check for point existence after transform
# * Save non-transformed coords for later work
transformed_pts = trans_to_data.transform(coords)
no_inf = ~np.any(np.isinf(transformed_pts), axis=1)
if np.any(no_inf):
minx, miny = np.min(transformed_pts[no_inf], axis=0)
maxx, maxy = np.max(transformed_pts[no_inf], axis=0)
else:
minx = maxx = miny = maxy = np.nan
else:
transformed_pts = coords
minx, miny = np.min(coords, axis=0)
maxx, maxy = np.max(coords, axis=0)
# END OF PATCH
##############
corners = (minx, miny), (maxx, maxy)
########################
# PATCH
# XXX Non-standard matplotlib thing.
collection._corners = mtransforms.Bbox(corners)
collection.get_datalim = lambda transData: collection._corners
# END OF PATCH
##############
self.update_datalim(corners)
self.add_collection(collection)
self.autoscale_view()
########################
# PATCH
# XXX Non-standard matplotlib thing.
# Handle a possible wrap around for rectangular projections.
t = kwargs.get('transform', None)
if isinstance(t, ccrs.CRS):
wrap_proj_types = (ccrs._RectangularProjection,
ccrs._WarpedRectangularProjection,
ccrs.InterruptedGoodeHomolosine,
ccrs.Mercator)
if isinstance(t, wrap_proj_types) and \
isinstance(self.projection, wrap_proj_types):
C = C.reshape((Ny - 1, Nx - 1))
transformed_pts = transformed_pts.reshape((Ny, Nx, 2))
# Compute the length of edges in transformed coordinates
with np.errstate(invalid='ignore'):
edge_lengths = np.hypot(
np.diff(transformed_pts[..., 0], axis=1),
np.diff(transformed_pts[..., 1], axis=1)
)
to_mask = (
(edge_lengths > abs(self.projection.x_limits[1] -
self.projection.x_limits[0]) / 2) |
np.isnan(edge_lengths)
)
if np.any(to_mask):
if collection.get_cmap()._rgba_bad[3] != 0.0:
warnings.warn("The colormap's 'bad' has been set, but "
"in order to wrap pcolormesh across the "
"map it must be fully transparent.",
stacklevel=3)
# at this point C has a shape of (Ny-1, Nx-1), to_mask has
# a shape of (Ny, Nx-1) and pts has a shape of (Ny*Nx, 2)
mask = np.zeros(C.shape, dtype=np.bool)
# Mask out the neighbouring cells if there was an edge
# found with a large length. NB. Masking too much only has
# a detrimental impact on performance.
mask[to_mask[:-1, :]] = True # Edges above a cell.
mask[to_mask[1:, :]] = True # Edges below a cell.
C_mask = getattr(C, 'mask', None)
# create the masked array to be used with this pcolormesh
if C_mask is not None:
pcolormesh_data = np.ma.array(C, mask=mask | C_mask)
else:
pcolormesh_data = np.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
if C_mask is not None:
# remember to re-apply the original data mask
pcolor_data = np.ma.array(C, mask=~mask | C_mask)
else:
pcolor_data = np.ma.array(C, mask=~mask)
pts = coords.reshape((Ny, Nx, 2))
if np.any(~pcolor_data.mask):
# plot with slightly lower zorder to avoid odd issue
# where the main plot is obscured
zorder = collection.zorder - .1
kwargs.pop('zorder', None)
kwargs.setdefault('snap', False)
pcolor_col = self.pcolor(pts[..., 0], pts[..., 1],
pcolor_data, zorder=zorder,
**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
# END OF PATCH
##############
return collection
@_add_transform
def pcolor(self, *args, **kwargs):
"""
Add the "transform" keyword to :func:`~matplotlib.pyplot.pcolor'.
Other Parameters
----------------
transform
A :class:`~cartopy.crs.Projection`.
"""
result = matplotlib.axes.Axes.pcolor(self, *args, **kwargs)
# Update the datalim for this pcolor.
limits = result.get_datalim(self.transData)
self.update_datalim(limits)
self.autoscale_view()
return result
@_add_transform
def quiver(self, x, y, u, v, *args, **kwargs):
"""
Plot a field of arrows.
Parameters
----------
x
An array containing the x-positions of data points.
y
An array containing the y-positions of data points.
u
An array of vector data in the u-direction.
v
An array of vector data in the v-direction.
Other Parameters
----------------
transform: :class:`cartopy.crs.Projection` or Matplotlib transform
The coordinate system in which the vectors are defined.
regrid_shape: int or 2-tuple of ints
If given, specifies that the points where the arrows are
located will be interpolated onto a regular grid in
projection space. If a single integer is given then that
will be used as the minimum grid length dimension, while the
other dimension will be scaled up according to the target
extent's aspect ratio. If a pair of ints are given they
determine the grid length in the x and y directions
respectively.
target_extent: 4-tuple
If given, specifies the extent in the target CRS that the
regular grid defined by *regrid_shape* will have. Defaults
to the current extent of the map projection.
See :func:`matplotlib.pyplot.quiver` for details on arguments
and other keyword arguments.
Note
----
The vector components must be defined as grid eastward and
grid northward.
"""
t = kwargs['transform']
regrid_shape = kwargs.pop('regrid_shape', None)
target_extent = kwargs.pop('target_extent',
self.get_extent(self.projection))
if regrid_shape is not None:
# If regridding is required then we'll be handling transforms
# manually and plotting in native coordinates.
regrid_shape = self._regrid_shape_aspect(regrid_shape,
target_extent)
if args:
# Interpolate color array as well as vector components.
x, y, u, v, c = vector_scalar_to_grid(
t, self.projection, regrid_shape, x, y, u, v, args[0],
target_extent=target_extent)
args = (c,) + args[1:]
else:
x, y, u, v = vector_scalar_to_grid(
t, self.projection, regrid_shape, x, y, u, v,
target_extent=target_extent)
kwargs.pop('transform', None)
elif t != self.projection:
# Transform the vectors if the projection is not the same as the
# data transform.
if (x.ndim == 1 and y.ndim == 1) and (x.shape != u.shape):
x, y = np.meshgrid(x, y)
u, v = self.projection.transform_vectors(t, x, y, u, v)
return matplotlib.axes.Axes.quiver(self, x, y, u, v, *args, **kwargs)
@_add_transform
def barbs(self, x, y, u, v, *args, **kwargs):
"""
Plot a field of barbs.
Parameters
----------
x
An array containing the x-positions of data points.
y
An array containing the y-positions of data points.
u
An array of vector data in the u-direction.
v
An array of vector data in the v-direction.
Other Parameters
----------------
transform: :class:`cartopy.crs.Projection` or Matplotlib transform
The coordinate system in which the vectors are defined.
regrid_shape: int or 2-tuple of ints
If given, specifies that the points where the barbs are
located will be interpolated onto a regular grid in
projection space. If a single integer is given then that
will be used as the minimum grid length dimension, while the
other dimension will be scaled up according to the target
extent's aspect ratio. If a pair of ints are given they
determine the grid length in the x and y directions
respectively.
target_extent: 4-tuple
If given, specifies the extent in the target CRS that the
regular grid defined by *regrid_shape* will have. Defaults
to the current extent of the map projection.
See :func:`matplotlib.pyplot.barbs` for details on arguments
and other keyword arguments.
Note
----
The vector components must be defined as grid eastward and
grid northward.
"""
t = kwargs['transform']
regrid_shape = kwargs.pop('regrid_shape', None)
target_extent = kwargs.pop('target_extent',
self.get_extent(self.projection))
if regrid_shape is not None:
# If regridding is required then we'll be handling transforms
# manually and plotting in native coordinates.
regrid_shape = self._regrid_shape_aspect(regrid_shape,
target_extent)
if args:
# Interpolate color array as well as vector components.
x, y, u, v, c = vector_scalar_to_grid(
t, self.projection, regrid_shape, x, y, u, v, args[0],
target_extent=target_extent)
args = (c,) + args[1:]
else:
x, y, u, v = vector_scalar_to_grid(
t, self.projection, regrid_shape, x, y, u, v,
target_extent=target_extent)
kwargs.pop('transform', None)
elif t != self.projection:
# Transform the vectors if the projection is not the same as the
# data transform.
if (x.ndim == 1 and y.ndim == 1) and (x.shape != u.shape):
x, y = np.meshgrid(x, y)
u, v = self.projection.transform_vectors(t, x, y, u, v)
return matplotlib.axes.Axes.barbs(self, x, y, u, v, *args, **kwargs)
@_add_transform
def streamplot(self, x, y, u, v, **kwargs):
"""
Plot streamlines of a vector flow.
Parameters
----------
x
An array containing the x-positions of data points.
y
An array containing the y-positions of data points.
u
An array of vector data in the u-direction.
v
An array of vector data in the v-direction.
Other Parameters
----------------
transform: :class:`cartopy.crs.Projection` or Matplotlib transform.
The coordinate system in which the vector field is defined.
See :func:`matplotlib.pyplot.streamplot` for details on arguments
and keyword arguments.
Note
----
The vector components must be defined as grid eastward and
grid northward.
"""
t = kwargs.pop('transform')
# Regridding is required for streamplot, it must have an evenly spaced
# grid to work correctly. Choose our destination grid based on the
# density keyword. The grid need not be bigger than the grid used by
# the streamplot integrator.
density = kwargs.get('density', 1)
if np.isscalar(density):
regrid_shape = [int(30 * density)] * 2
else:
regrid_shape = [int(25 * d) for d in density]
# The color and linewidth keyword arguments can be arrays so they will
# need to be gridded also.
col = kwargs.get('color', None)
lw = kwargs.get('linewidth', None)
scalars = []
color_array = isinstance(col, np.ndarray)
linewidth_array = isinstance(lw, np.ndarray)
if color_array:
scalars.append(col)
if linewidth_array:
scalars.append(lw)
# Do the regridding including any scalar fields.
target_extent = self.get_extent(self.projection)
gridded = vector_scalar_to_grid(t, self.projection, regrid_shape,
x, y, u, v, *scalars,
target_extent=target_extent)
x, y, u, v = gridded[:4]
# If scalar fields were regridded then replace the appropriate keyword
# arguments with the gridded arrays.
scalars = list(gridded[4:])
if linewidth_array:
kwargs['linewidth'] = scalars.pop()
if color_array:
kwargs['color'] = ma.masked_invalid(scalars.pop())
with warnings.catch_warnings():
# The workaround for nan values in streamplot colors gives rise to
# a warning which is not at all important so it is hidden from the
# user to avoid confusion.
message = 'Warning: converting a masked element to nan.'
warnings.filterwarnings('ignore', message=message,
category=UserWarning)
sp = matplotlib.axes.Axes.streamplot(self, x, y, u, v, **kwargs)
return sp
def add_wmts(self, wmts, layer_name, wmts_kwargs=None, **kwargs):
"""
Add the specified WMTS layer to the axes.
This function requires owslib and PIL to work.
Parameters
----------
wmts
The URL of the WMTS, or an owslib.wmts.WebMapTileService instance.
layer_name
The name of the layer to use.
wmts_kwargs: dict or None, optional
Passed through to the
:class:`~cartopy.io.ogc_clients.WMTSRasterSource` constructor's
``gettile_extra_kwargs`` (e.g. time).
All other keywords are passed through to the construction of the
image artist. See :meth:`~matplotlib.axes.Axes.imshow()` for
more details.
"""
from cartopy.io.ogc_clients import WMTSRasterSource
wmts = WMTSRasterSource(wmts, layer_name,
gettile_extra_kwargs=wmts_kwargs)
return self.add_raster(wmts, **kwargs)
def add_wms(self, wms, layers, wms_kwargs=None, **kwargs):
"""
Add the specified WMS layer to the axes.
This function requires owslib and PIL to work.
Parameters
----------
wms: string or :class:`owslib.wms.WebMapService` instance
The web map service URL or owslib WMS instance to use.
layers: string or iterable of string
The name of the layer(s) to use.
wms_kwargs: dict or None, optional
Passed through to the
:class:`~cartopy.io.ogc_clients.WMSRasterSource`
constructor's ``getmap_extra_kwargs`` for defining
getmap time keyword arguments.
All other keywords are passed through to the construction of the
image artist. See :meth:`~matplotlib.axes.Axes.imshow()` for
more details.
"""
from cartopy.io.ogc_clients import WMSRasterSource
wms = WMSRasterSource(wms, layers, getmap_extra_kwargs=wms_kwargs)
return self.add_raster(wms, **kwargs)
# Define the GeoAxesSubplot class, so that a type(ax) will emanate from
# cartopy.mpl.geoaxes, not matplotlib.axes.
GeoAxesSubplot = matplotlib.axes.subplot_class_factory(GeoAxes)
GeoAxesSubplot.__module__ = GeoAxes.__module__
def _trigger_patch_reclip(event):
"""
Define an event callback for a GeoAxes which forces the background patch to
be re-clipped next time it is drawn.
"""
axes = event.axes
# trigger the outline and background patches to be re-clipped
axes.spines['geo'].stale = True
axes.patch.stale = True
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