# Copyright Crown and Cartopy Contributors
#
# This file is part of Cartopy and is released under the BSD 3-clause license.
# See LICENSE in the root of the repository for full licensing details.
"""
This module defines the :class:`cartopy.mpl.geoaxes.GeoAxes` class, an extension of
matplotlib which adds a `transform` keyword argument to many plotting methods to enable
geographic projections and boundary wrapping to occur on the axes.
When a Matplotlib figure contains a GeoAxes the plotting commands can transform
plot results from source coordinates to the GeoAxes' target projection.
"""
import collections
import contextlib
import functools
import json
import os
from pathlib import Path
import warnings
import weakref
import matplotlib as mpl
import matplotlib.artist
import matplotlib.axes
import matplotlib.contour
from matplotlib.image import imread
import matplotlib.patches as mpatches
import matplotlib.path as mpath
import matplotlib.spines as mspines
import matplotlib.transforms as mtransforms
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
from cartopy.mpl import _MPL_38
import cartopy.mpl.contour
import cartopy.mpl.feature_artist as feature_artist
import cartopy.mpl.geocollection
import cartopy.mpl.patch as cpatch
from cartopy.mpl.slippy_image_artist import SlippyImageArtist
# 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.
_PATH_TRANSFORM_CACHE = weakref.WeakKeyDictionary()
# A dictionary of pre-loaded images for large background images, kept as a
# dictionary so that large images are loaded only once.
_BACKG_IMG_CACHE = {}
# A dictionary of background images in the directory specified by the
# CARTOPY_USER_BACKGROUNDS environment variable.
_USER_BG_IMGS = {}
# XXX call this InterCRSTransform
class _ViewClippedPathPatch(mpatches.PathPatch):
def __init__(self, axes, **kwargs):
self._original_path = mpath.Path(np.empty((0, 2)))
super().__init__(self._original_path, **kwargs)
self._axes = axes
# We need to use a TransformWrapper as our transform so that we can
# update the transform without breaking others' references to this one.
self._trans_wrap = mtransforms.TransformWrapper(self.get_transform())
def set_transform(self, transform):
self._trans_wrap.set(transform)
super().set_transform(self._trans_wrap)
def set_boundary(self, path, transform):
self._original_path = path
self.set_transform(transform)
self.stale = True
# Can remove and use matplotlib's once we support only >= 3.2
def set_path(self, path):
self._path = path
def _adjust_location(self):
if self.stale:
self.set_path(self._original_path.clip_to_bbox(self.axes.viewLim))
# Some places in matplotlib's transform stack cache the actual
# path so we trigger an update by invalidating the transform.
self._trans_wrap.invalidate()
@matplotlib.artist.allow_rasterization
def draw(self, renderer, *args, **kwargs):
self._adjust_location()
super().draw(renderer, *args, **kwargs)
[docs]
class GeoSpine(mspines.Spine):
def __init__(self, axes, **kwargs):
self._original_path = mpath.Path(np.empty((0, 2)))
kwargs.setdefault('clip_on', False)
super().__init__(axes, 'geo', self._original_path, **kwargs)
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)
self._path = mpath.Path(self._path.vertices, closed=True)
[docs]
def get_window_extent(self, renderer=None):
# make sure the location is updated so that transforms etc are
# correct:
self._adjust_location()
return super().get_window_extent(renderer=renderer)
[docs]
@matplotlib.artist.allow_rasterization
def draw(self, renderer):
self._adjust_location()
ret = super().draw(renderer)
self.stale = False
return ret
[docs]
def set_position(self, position):
"""GeoSpine does not support changing its 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
# Raise an error if any of these functions try to use
# a spherical source CRS.
non_spherical_funcs = ['contour', 'contourf', 'pcolormesh', 'pcolor',
'quiver', 'barbs', 'streamplot']
if (func.__name__ in non_spherical_funcs and
isinstance(transform, ccrs.CRS) and
not isinstance(transform, ccrs.Projection)):
raise ValueError(f'Invalid transform: Spherical {func.__name__} '
'is not supported - consider using '
'PlateCarree/RotatedPole.')
kwargs['transform'] = transform
return func(self, *args, **kwargs)
return wrapper
def _add_transform_first(func):
"""
A decorator that adds and validates the transform_first keyword argument.
This handles a fast-path optimization that projects the points before
creating any patches or lines. This means that the lines/patches will be
calculated in projected-space, not data-space. It requires the first
three arguments to be x, y, and z and all must be two-dimensional to use
the fast-path option.
This should be added after the _add_transform wrapper so that a transform
is guaranteed to be present.
"""
@functools.wraps(func)
def wrapper(self, *args, **kwargs):
if kwargs.pop('transform_first', False):
if len(args) < 3:
# For the fast-path we need X and Y input points
raise ValueError("The X and Y arguments must be provided to "
"use the transform_first=True fast-path.")
x, y, z = (np.array(i) for i in args[:3])
if not (x.ndim == y.ndim == 2):
raise ValueError("The X and Y arguments must be gridded "
"2-dimensional arrays")
# Remove the transform from the keyword arguments
t = kwargs.pop('transform')
# Transform all of the x and y points
pts = self.projection.transform_points(t, x, y)
x = pts[..., 0].reshape(x.shape)
y = pts[..., 1].reshape(y.shape)
# The x coordinates could be wrapped, but matplotlib expects
# them to be sorted, so we will reorganize the arrays based on x
ind = np.argsort(x, axis=1)
x = np.take_along_axis(x, ind, axis=1)
y = np.take_along_axis(y, ind, axis=1)
z = np.take_along_axis(z, ind, axis=1)
# Use the new points as the input arguments
args = (x, y, z) + args[3:]
return func(self, *args, **kwargs)
return wrapper
[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 = plt.axes(projection=cartopy.crs.PlateCarree())
# Set up a standard map for latlon data for multiple subplots
fig, geo_axes = plt.subplots(nrows=2, ncols=2,
subplot_kw={'projection': ccrs.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())
"""
name = 'cartopy.geoaxes'
def __init__(self, *args, **kwargs):
"""
Create a GeoAxes object using standard matplotlib
:class:`~matplotlib.axes.Axes` args and kwargs.
Parameters
----------
projection : cartopy.crs.Projection
The target projection of this Axes.
"""
if "map_projection" in kwargs:
warnings.warn("The `map_projection` keyword argument is "
"deprecated, use `projection` to instantiate a "
"GeoAxes instead.")
projection = kwargs.pop("map_projection")
else:
projection = kwargs.pop("projection")
# The :class:`cartopy.crs.Projection` of this GeoAxes.
if not isinstance(projection, ccrs.Projection):
raise ValueError("A GeoAxes can only be created with a "
"projection of type cartopy.crs.Projection")
self.projection = projection
super().__init__(*args, **kwargs)
self.img_factories = []
self._done_img_factory = False
[docs]
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.img_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().add_image(image)
return image
[docs]
@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.
"""
with contextlib.ExitStack() as stack:
if hold:
stack.callback(self.dataLim.set_points,
self.dataLim.frozen().get_points())
stack.callback(self.viewLim.set_points,
self.viewLim.frozen().get_points())
stack.callback(setattr, self, 'ignore_existing_data_limits',
self.ignore_existing_data_limits)
stack.callback(self.set_autoscalex_on,
self.get_autoscalex_on())
stack.callback(self.set_autoscaley_on,
self.get_autoscaley_on())
yield
def _draw_preprocess(self, renderer):
"""
Perform pre-processing steps shared between :func:`GeoAxes.draw`
and :func:`GeoAxes.get_tightbbox`.
"""
# 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()
# apply_aspect may change the x or y data limits, so must be called
# before the patch is updated.
self.apply_aspect()
# Adjust location of background patch so that new gridlines generated
# by `draw` or `get_tightbbox` are positioned and clipped correctly.
self.patch._adjust_location()
[docs]
def get_tightbbox(self, renderer, *args, **kwargs):
"""
Extend the standard behaviour of
:func:`matplotlib.axes.Axes.get_tightbbox`.
Adjust the axes aspect ratio and background patch location before
calculating the tight bounding box.
"""
# Shared processing steps
self._draw_preprocess(renderer)
return super().get_tightbbox(renderer, *args, **kwargs)
[docs]
@matplotlib.artist.allow_rasterization
def draw(self, renderer=None, **kwargs):
"""
Extend the standard behaviour of :func:`matplotlib.axes.Axes.draw`.
Draw image factory results before invoking standard Matplotlib drawing.
A global range is used if no limits have yet been set.
"""
# Shared processing steps
self._draw_preprocess(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, factory_args, factory_kwargs in self.img_factories:
img, extent, origin = factory.image_for_domain(
self._get_extent_geom(factory.crs), factory_args[0])
self.imshow(img, extent=extent, origin=origin,
transform=factory.crs, *factory_args[1:],
**factory_kwargs)
self._done_img_factory = True
return super().draw(renderer=renderer, **kwargs)
def _update_title_position(self, renderer):
super()._update_title_position(renderer)
if self._autotitlepos is not None and not self._autotitlepos:
return
from cartopy.mpl.gridliner import Gridliner
gridliners = [a for a in self.artists if isinstance(a, Gridliner)]
if not gridliners:
return
# Get the max ymax of all top labels
top = -1
for gl in gridliners:
if gl.has_labels():
# Both top and geo labels can appear at the top of the axes
for label in (gl.top_label_artists +
gl.geo_label_artists):
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 __clear(self):
"""Clear the current axes and add boundary lines."""
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
if mpl.__version__ >= '3.6':
[docs]
def clear(self):
"""Clear the current Axes and add boundary lines."""
result = super().clear()
self.__clear()
return result
else:
def cla(self):
"""Clear the current Axes and add boundary lines."""
result = super().cla()
self.__clear()
return result
[docs]
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. If "auto" is selected, the
resolution is defined by `~cartopy.feature.auto_scaler`.
"""
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)
[docs]
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 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:`cartopy.feature.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)
[docs]
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_collection(artist)
[docs]
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)
[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
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(f'Approximating coordinate system {crs!r} with '
'a RotatedPole projection.')
elif hasattr(crs, 'is_geodetic') and crs.is_geodetic():
proj = ccrs.PlateCarree(globe=crs.globe)
warnings.warn(f'Approximating coordinate system {crs!r} with '
'the PlateCarree projection.')
else:
raise ValueError('Cannot determine extent in'
f' coordinate system {crs!r}')
# 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
[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.
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:
raise ValueError(
'Failed to determine the required bounds in projection '
'coordinates. Check that the values provided are within the '
f'valid range (x_limits={self.projection.x_limits}, '
f'y_limits={self.projection.y_limits}).')
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 autoscale_view(self, tight=None, scalex=True, scaley=True):
"""
Autoscale the view limits using the data limits, taking into
account the projection of the geoaxes.
See :meth:`~matplotlib.axes.Axes.imshow()` for more details.
"""
super().autoscale_view(tight=tight, scalex=scalex, scaley=scaley)
# Limit the resulting bounds to valid area.
if scalex and self.get_autoscalex_on():
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.get_autoscaley_on():
bounds = self.get_ybound()
self.set_ybound(max(bounds[0], self.projection.y_limits[0]),
min(bounds[1], self.projection.y_limits[1]))
[docs]
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().set_xticks(xticks, minor=minor)
[docs]
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().set_yticks(yticks, minor=minor)
[docs]
def stock_img(self, name='ne_shaded', **kwargs):
"""
Add a standard image to the map.
Currently, the only (and default) option for image is a downsampled
version of the Natural Earth shaded relief raster. Other options
(e.g., alpha) will be passed to :func:`GeoAxes.imshow`.
"""
if name == 'ne_shaded':
source_proj = ccrs.PlateCarree()
fname = (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],
**kwargs)
else:
raise ValueError('Unknown stock image %r.' % name)
[docs]
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()
bgdir = Path(os.getenv(
'CARTOPY_USER_BACKGROUNDS',
config["repo_data_dir"] / 'raster' / 'natural_earth'))
# now get the filename we want to use:
try:
fname = _USER_BG_IMGS[name][resolution]
except KeyError:
raise ValueError(
f'Image {name!r} and resolution {resolution!r} are not '
f'present in the user background image metadata in directory '
f'{bgdir!r}')
# Now obtain the image data from file or cache:
fpath = 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 / img.shape[0]
d_lon = 360 / img.shape[1]
# latitude starts at 90N for this image:
lat_pts = (np.arange(img.shape[0]) * -d_lat - (d_lat / 2)) + 90
lon_pts = (np.arange(img.shape[1]) * d_lon + (d_lon / 2)) - 180
# 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,
lon_pts[lon_in_range2][-1] + d_lon / 2 + 360,
lat_pts[lat_in_range][-1] - d_lat / 2,
lat_pts[lat_in_range][0] + d_lat / 2]
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)
[docs]
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
"""
bgdir = Path(os.getenv(
'CARTOPY_USER_BACKGROUNDS',
config["repo_data_dir"] / 'raster' / 'natural_earth'))
json_file = bgdir / 'images.json'
with open(json_file) 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]:
raise ValueError(
f'User background metadata file {json_file!r},'
f' image type {img_type!r}, does not specify'
f' metadata item {required!r}')
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 = bgdir / img_it_r
if not test_file.is_file():
raise ValueError(
f'File "{test_file}" not found')
[docs]
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
[docs]
@_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 = super().imshow(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)
# Lazy import because scipy/pykdtree in img_transform are only
# optional dependencies
from cartopy.img_transform import warp_array
original_extent = extent
img, extent = warp_array(img,
source_proj=transform,
source_extent=original_extent,
target_proj=self.projection,
target_res=regrid_shape,
target_extent=target_extent,
mask_extrapolated=True,
)
alpha = kwargs.pop('alpha', None)
if np.array(alpha).ndim == 2:
alpha, _ = warp_array(alpha,
source_proj=transform,
source_extent=original_extent,
target_proj=self.projection,
target_res=regrid_shape,
target_extent=target_extent,
mask_extrapolated=True,
)
kwargs['alpha'] = alpha
# As a workaround to a matplotlib limitation, turn any images
# which are masked array RGB(A) into RGBA images
if np.ma.is_masked(img) and len(img.shape) > 2:
# transform RGB(A) into RGBA
old_img = img
img = np.ones(old_img.shape[:2] + (4, ),
dtype=old_img.dtype)
img[:, :, :3] = old_img[:, :, :3]
# if img is RGBA, save alpha channel
if old_img.shape[-1] == 4:
img[:, :, 3] = old_img[:, :, 3]
elif img.dtype.kind == 'u':
img[:, :, 3] *= 255
# apply the mask to the A channel
img[np.any(old_img[:, :, :3].mask, axis=2), 3] = 0
result = super().imshow(img, *args, extent=extent, **kwargs)
return result
[docs]
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, xlim=None, ylim=None,
rotate_labels=None, xlabel_style=None, ylabel_style=None,
labels_bbox_style=None, xpadding=5, ypadding=5,
offset_angle=25, auto_update=None, formatter_kwargs=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
Toggle whether to draw labels. For finer control, attributes of
:class:`Gridliner` may be modified individually. Defaults to False.
- string: "x" or "y" to only draw labels of the respective
coordinate in the CRS.
- list: Can contain the side identifiers and/or coordinate
types to select which ones to draw.
For all labels one would use
`["x", "y", "top", "bottom", "left", "right", "geo"]`.
- dict: The keys are the side identifiers
("top", "bottom", "left", "right") and the values are the
coordinates ("x", "y"); this way you can precisely
decide what kind of label to draw and where.
For x labels on the bottom and y labels on the right you
could pass in `{"bottom": "x", "left": "y"}`.
Note that, by default, x and y labels are not drawn on left/right
and top/bottom edges respectively unless explicitly requested.
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.
xlim: optional
Set a limit for the gridlines so that they do not go all the
way to the edge of the boundary. xlim can be a single number or
a (min, max) tuple. If a single number, the limits will be
(-xlim, +xlim).
ylim: optional
Set a limit for the gridlines so that they do not go all the
way to the edge of the boundary. ylim can be a single number or
a (min, max) tuple. If a single number, the limits will be
(-ylim, +ylim).
rotate_labels: optional, bool, str
Allow the rotation of non-inline labels.
- False: Do not rotate the labels.
- True: Rotate the labels parallel to the gridlines.
- None: no rotation except for some projections (default).
- A float: Rotate labels by this value in degrees.
xlabel_style: dict
A dictionary passed through to ``ax.text`` on x label creation
for styling of the text labels.
ylabel_style: dict
A dictionary passed through to ``ax.text`` on y label creation
for styling of the text labels.
labels_bbox_style: dict
bbox style for all text labels.
xpadding: float
Padding for x labels. If negative, the labels are
drawn inside the map.
ypadding: float
Padding for y labels. If negative, the labels are
drawn inside the map.
offset_angle: float
Difference of angle in degrees from 90 to define when
a label must be flipped to be more readable.
For example, a value of 10 makes a vertical top label to be
flipped only at 100 degrees.
auto_update: bool, default=True
Whether to update the gridlines and labels when the plot is
refreshed.
.. deprecated:: 0.23
In future the gridlines and labels will always be updated.
formatter_kwargs: dict, optional
Options passed to the default formatters.
See :class:`~cartopy.mpl.ticker.LongitudeFormatter` and
:class:`~cartopy.mpl.ticker.LatitudeFormatter`
Keyword Parameters
------------------
**kwargs: dict
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(globe=self.projection.globe)
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,
xlim=xlim, ylim=ylim, rotate_labels=rotate_labels,
xlabel_style=xlabel_style, ylabel_style=ylabel_style,
labels_bbox_style=labels_bbox_style,
xpadding=xpadding, ypadding=ypadding, offset_angle=offset_angle,
auto_update=auto_update, formatter_kwargs=formatter_kwargs)
self.add_artist(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 = super()._gen_axes_spines(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)
[docs]
def set_boundary(self, path, transform=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 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)
[docs]
@_add_transform
@_add_transform_first
def contour(self, *args, **kwargs):
"""
Add the "transform" keyword to :func:`~matplotlib.pyplot.contour`.
Other Parameters
----------------
transform
A :class:`~cartopy.crs.Projection`.
transform_first : bool, optional
If True, this will transform the input arguments into
projection-space before computing the contours, which is much
faster than computing the contours in data-space and projecting
the filled polygons. Using this method does not handle wrapped
coordinates as well and can produce misleading contours in the
middle of the domain. To use the projection-space method the input
arguments X and Y must be provided and be 2-dimensional.
The default is False, to compute the contours in data-space.
"""
result = super().contour(*args, **kwargs)
# We need to compute the dataLim correctly for contours.
if not _MPL_38:
bboxes = [col.get_datalim(self.transData)
for col in result.collections
if col.get_paths()]
if bboxes:
extent = mtransforms.Bbox.union(bboxes)
self.update_datalim(extent.get_points())
else:
self.update_datalim(result.get_datalim(self.transData))
self.autoscale_view()
# Re-cast the contour as a GeoContourSet.
if isinstance(result, matplotlib.contour.QuadContourSet):
result.__class__ = cartopy.mpl.contour.GeoContourSet
return result
[docs]
@_add_transform
@_add_transform_first
def contourf(self, *args, **kwargs):
"""
Add the "transform" keyword to :func:`~matplotlib.pyplot.contourf`.
Other Parameters
----------------
transform
A :class:`~cartopy.crs.Projection`.
transform_first : bool, optional
If True, this will transform the input arguments into
projection-space before computing the contours, which is much
faster than computing the contours in data-space and projecting
the filled polygons. Using this method does not handle wrapped
coordinates as well and can produce misleading contours in the
middle of the domain. To use the projection-space method the input
arguments X and Y must be provided and be 2-dimensional.
The default is False, to compute the contours in data-space.
"""
result = super().contourf(*args, **kwargs)
# We need to compute the dataLim correctly for contours.
if not _MPL_38:
bboxes = [col.get_datalim(self.transData)
for col in result.collections
if col.get_paths()]
if bboxes:
extent = mtransforms.Bbox.union(bboxes)
self.update_datalim(extent.get_points())
else:
self.update_datalim(result.get_datalim(self.transData))
self.autoscale_view()
# Re-cast the contour as a GeoContourSet.
if isinstance(result, matplotlib.contour.QuadContourSet):
result.__class__ = cartopy.mpl.contour.GeoContourSet
return result
[docs]
@_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 cylindrical form '
'(PlateCarree or RotatedPole).')
result = super().scatter(*args, **kwargs)
self.autoscale_view()
return result
[docs]
@_add_transform
def annotate(self, text, xy, xytext=None, xycoords='data', textcoords=None,
*args, **kwargs):
"""
Add the "transform" keyword to :func:`~matplotlib.pyplot.annotate`.
Other Parameters
----------------
transform
A :class:`~cartopy.crs.Projection`.
"""
transform = kwargs.pop('transform', None)
is_transform_crs = isinstance(transform, ccrs.CRS)
# convert CRS to mpl transform for default 'data' setup
if is_transform_crs and xycoords == 'data':
xycoords = transform._as_mpl_transform(self)
# textcoords = xycoords by default but complains if xytext is empty
if textcoords is None and xytext is not None:
textcoords = xycoords
# use transform if textcoords is data and xytext is provided
if is_transform_crs and xytext is not None and textcoords == 'data':
textcoords = transform._as_mpl_transform(self)
# convert to mpl_transform if CRS passed to xycoords
if isinstance(xycoords, ccrs.CRS):
xycoords = xycoords._as_mpl_transform(self)
# convert to mpl_transform if CRS passed to textcoords
if isinstance(textcoords, ccrs.CRS):
textcoords = textcoords._as_mpl_transform(self)
result = super().annotate(text, xy, xytext, xycoords=xycoords,
textcoords=textcoords, *args, **kwargs)
self.autoscale_view()
return result
[docs]
@_add_transform
def hexbin(self, x, y, *args, **kwargs):
"""
Add the "transform" keyword to :func:`~matplotlib.pyplot.hexbin`.
The points are first transformed into the projection of the axes and
then the hexbin algorithm is computed using the data in the axes
projection.
Other Parameters
----------------
transform
A :class:`~cartopy.crs.Projection`.
"""
t = kwargs.pop('transform')
pairs = self.projection.transform_points(
t,
np.asarray(x),
np.asarray(y),
)
x = pairs[:, 0]
y = pairs[:, 1]
result = super().hexbin(x, y, *args, **kwargs)
self.autoscale_view()
return result
[docs]
@_add_transform
def pcolormesh(self, *args, **kwargs):
"""
Add the "transform" keyword to :func:`~matplotlib.pyplot.pcolormesh`.
Other Parameters
----------------
transform
A :class:`~cartopy.crs.Projection`.
"""
# Add in an argument checker to handle Matplotlib's potential
# interpolation when coordinate wraps are involved
args, kwargs = self._wrap_args(*args, **kwargs)
result = super().pcolormesh(*args, **kwargs)
# Wrap the quadrilaterals if necessary
result = self._wrap_quadmesh(result, **kwargs)
# Re-cast the QuadMesh as a GeoQuadMesh to enable future wrapping
# updates to the collection as well.
result.__class__ = cartopy.mpl.geocollection.GeoQuadMesh
self.autoscale_view()
return result
def _wrap_args(self, *args, **kwargs):
"""
Handle the interpolation when a wrap could be involved with
the data coordinates before passing on to Matplotlib.
"""
default_shading = mpl.rcParams.get('pcolor.shading')
if not (kwargs.get('shading', default_shading) in
('nearest', 'auto') and len(args) == 3 and
getattr(kwargs.get('transform'), '_wrappable', False)):
return args, kwargs
# We have changed the shading from nearest/auto to flat
# due to the addition of an extra coordinate
kwargs['shading'] = 'flat'
X = np.asanyarray(args[0])
Y = np.asanyarray(args[1])
nrows, ncols = np.asanyarray(args[2]).shape[:2]
Nx = X.shape[-1]
Ny = Y.shape[0]
if X.ndim != 2 or X.shape[0] == 1:
X = X.reshape(1, Nx).repeat(Ny, axis=0)
if Y.ndim != 2 or Y.shape[1] == 1:
Y = Y.reshape(Ny, 1).repeat(Nx, axis=1)
def _interp_grid(X, wrap=0):
# helper for below
if np.shape(X)[1] > 1:
dX = np.diff(X, axis=1)
# account for the wrap
if wrap:
dX = (dX + wrap / 2) % wrap - wrap / 2
dX = dX / 2
X = np.hstack((X[:, [0]] - dX[:, [0]],
X[:, :-1] + dX,
X[:, [-1]] + dX[:, [-1]]))
else:
# This is just degenerate, but we can't reliably guess
# a dX if there is just one value.
X = np.hstack((X, X))
return X
t = kwargs.get('transform')
xwrap = abs(t.x_limits[1] - t.x_limits[0])
if ncols == Nx:
X = _interp_grid(X, wrap=xwrap)
Y = _interp_grid(Y)
if nrows == Ny:
X = _interp_grid(X.T, wrap=xwrap).T
Y = _interp_grid(Y.T).T
return (X, Y, args[2]), kwargs
def _wrap_quadmesh(self, collection, **kwargs):
"""
Handles the Quadmesh collection when any of the quadrilaterals
cross the boundary of the projection.
"""
t = kwargs.get('transform', None)
# Get the quadmesh data coordinates
coords = collection._coordinates
Ny, Nx, _ = coords.shape
if kwargs.get('shading') == 'gouraud':
# Gouraud shading has the same shape for coords and data
data_shape = Ny, Nx, -1
else:
data_shape = Ny - 1, Nx - 1, -1
# data array
C = collection.get_array().reshape(data_shape)
if C.shape[-1] == 1:
C = C.squeeze(axis=-1)
transformed_pts = self.projection.transform_points(
t, coords[..., 0], coords[..., 1])
# Compute the length of diagonals in transformed coordinates
# and create a mask where the wrapped cells are of shape (Ny-1, Nx-1)
with np.errstate(invalid='ignore'):
xs, ys = transformed_pts[..., 0], transformed_pts[..., 1]
diagonal0_lengths = np.hypot(xs[1:, 1:] - xs[:-1, :-1],
ys[1:, 1:] - ys[:-1, :-1])
diagonal1_lengths = np.hypot(xs[1:, :-1] - xs[:-1, 1:],
ys[1:, :-1] - ys[:-1, 1:])
# The maximum size of the diagonal of any cell, defined to
# be the projection width divided by 2*sqrt(2)
# TODO: Make this dependent on the boundary of the
# projection which will help with curved boundaries
size_limit = (abs(self.projection.x_limits[1] -
self.projection.x_limits[0]) /
(2 * np.sqrt(2)))
mask = (np.isnan(diagonal0_lengths) |
(diagonal0_lengths > size_limit) |
np.isnan(diagonal1_lengths) |
(diagonal1_lengths > size_limit))
# Update the data limits based on the corners of the mesh
# in transformed coordinates, ignoring nan values
with warnings.catch_warnings():
warnings.filterwarnings('ignore', 'All-NaN slice encountered')
# If we have all nans, that is OK and will be handled by the
# Bbox calculations later, so suppress that warning from the user
corners = ((np.nanmin(xs), np.nanmin(ys)),
(np.nanmax(xs), np.nanmax(ys)))
collection._corners = mtransforms.Bbox(corners)
self.update_datalim(collection._corners)
# We need to keep the transform/projection check after
# update_datalim to make sure we are getting the proper
# datalims on the returned collection
if (not (getattr(t, '_wrappable', False) and
getattr(self.projection, '_wrappable', False)) or
not np.any(mask)):
# If both projections are unwrappable
# or if there aren't any points to wrap
return collection
# Wrapping with gouraud shading is error-prone. We will do our best,
# but pcolor does not handle gouraud shading, so there needs to be
# another way to handle the wrapped cells.
if kwargs.get('shading') == 'gouraud':
warnings.warn("Handling wrapped coordinates with gouraud "
"shading is likely to introduce artifacts. "
"It is recommended to remove the wrap manually "
"before calling pcolormesh.")
# With gouraud shading, we actually want an (Ny, Nx) shaped mask
gmask = np.zeros(data_shape, dtype=bool)
# If any of the cells were wrapped, apply it to all 4 corners
gmask[:-1, :-1] |= mask
gmask[1:, :-1] |= mask
gmask[1:, 1:] |= mask
gmask[:-1, 1:] |= mask
mask = gmask
# We have quadrilaterals that cross the wrap boundary
# Now, we need to update the original collection with
# a mask over those cells and use pcolor to draw those
# cells instead, which will properly handle the wrap.
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)
# Get hold of masked versions of the array to be passed to set_array
# methods of QuadMesh and PolyQuadMesh
pcolormesh_data, pcolor_data, pcolor_mask = \
cartopy.mpl.geocollection._split_wrapped_mesh_data(C, mask)
collection.set_array(pcolormesh_data)
# plot with slightly lower zorder to avoid odd issue
# where the main plot is obscured
zorder = collection.zorder - .1
kwargs.pop('zorder', None)
kwargs.pop('shading', None)
kwargs.setdefault('snap', False)
vmin = kwargs.pop('vmin', None)
vmax = kwargs.pop('vmax', None)
norm = kwargs.pop('norm', None)
cmap = kwargs.pop('cmap', None)
# Plot all of the wrapped cells.
# `pcolor` only draws polygons where the data is not
# masked, so this will only draw a limited subset of
# polygons that were actually wrapped.
if not _MPL_38:
# We will add the original data mask in later to
# make sure that set_array can work in future
# calls on the proper sized array inputs.
# NOTE: we don't use C.data here because C.data could
# contain nan's which would be masked in the
# pcolor routines, which we don't want. We will
# fill in the proper data later with set_array()
# calls.
pcolor_zeros = np.ma.array(np.zeros(C.shape), mask=pcolor_mask)
pcolor_col = self.pcolor(coords[..., 0], coords[..., 1],
pcolor_zeros, zorder=zorder,
**kwargs)
# The pcolor_col is now possibly shorter than the
# actual collection, so grab the masked cells
pcolor_col.set_array(pcolor_data[mask].ravel())
else:
pcolor_col = self.pcolor(coords[..., 0], coords[..., 1],
pcolor_data, zorder=zorder,
**kwargs)
# Currently pcolor_col.get_array() will return a compressed array
# and warn unless we explicitly set the 2D array. This should be
# unnecessary with future matplotlib versions.
pcolor_col.set_array(pcolor_data)
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 and mask on the pcolormesh
# collection so that users can do things post
# this method
collection._wrapped_mask = mask
collection._wrapped_collection_fix = pcolor_col
return collection
[docs]
@_add_transform
def pcolor(self, *args, **kwargs):
"""
Add the "transform" keyword to :func:`~matplotlib.pyplot.pcolor`.
Other Parameters
----------------
transform
A :class:`~cartopy.crs.Projection`.
"""
# Add in an argument checker to handle Matplotlib's potential
# interpolation when coordinate wraps are involved
args, kwargs = self._wrap_args(*args, **kwargs)
result = super().pcolor(*args, **kwargs)
# Update the datalim for this pcolor.
limits = result.get_datalim(self.transData)
self.update_datalim(limits)
self.autoscale_view()
return result
[docs]
@_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)
# Lazy load vector_scalar_to_grid due to the optional
# scipy dependency
from cartopy.vector_transform import vector_scalar_to_grid
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 super().quiver(x, y, u, v, *args, **kwargs)
[docs]
@_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)
# Lazy load vector_scalar_to_grid due to the optional
# scipy dependency
from cartopy.vector_transform import vector_scalar_to_grid
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 super().barbs(x, y, u, v, *args, **kwargs)
[docs]
@_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)
# Lazy load vector_scalar_to_grid due to the optional
# scipy dependency
from cartopy.vector_transform import vector_scalar_to_grid
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 = super().streamplot(x, y, u, v, **kwargs)
return sp
[docs]
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)
[docs]
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
