# 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. """ import collections import contextlib import functools import warnings import weakref 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.mpl.contour import cartopy.mpl.geocollection import cartopy.mpl.feature_artist as feature_artist import cartopy.mpl.patch as cpatch from cartopy.mpl.slippy_image_artist import SlippyImageArtist assert mpl.__version__ >= '3.1', \ 'Cartopy is only supported with Matplotlib 3.1 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 (f'< {self.__class__.__name__!s} {self.source_projection!s} ' f'-> {self.target_projection!s} >') 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().__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 tranform 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) 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) 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().get_window_extent(renderer=renderer) @matplotlib.artist.allow_rasterization def draw(self, renderer): self._adjust_location() ret = super().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 # 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 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._gridliners = [] self.img_factories = [] self._done_img_factory = False 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().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. """ 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() # 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) def get_tightbbox(self, renderer, *args, **kwargs): """ Extend the standard behaviour of :func:`matplotlib.axes.Axes.get_tightbbox`. Adjust the axes aspect ratio, background patch location, and add gridliners before calculating the tight bounding box. """ # Shared processing steps self._draw_preprocess(renderer) return super().get_tightbbox(renderer, *args, **kwargs) @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. """ # 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 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 __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': 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 def format_coord(self, x, y): """ Returns ------- A string formatted for the Matplotlib GUI status bar. """ lon, lat = self.projection.as_geodetic().transform_point( x, y, self.projection, ) ns = 'N' if lat >= 0.0 else 'S' ew = 'E' if lon >= 0.0 else 'W' return ( f'{x:.4g}, {y:.4g} ' f'({abs(lat):f}°{ns}, {abs(lon):f}°{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:`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) 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(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 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]) 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): """ 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])) 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) 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) 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: 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 = 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 / 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) 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) 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 = os.path.join(bgdir, img_it_r) if not os.path.isfile(test_file): raise ValueError( f'File "{test_file}" not found') 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 = 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 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=False, 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 Whether to update the grilines and labels when the plot is refreshed. 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() 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._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 = 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) 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) @_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. 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(), ignore=False) 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 @_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. """ t = kwargs.get('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 = super().contourf(*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(), ignore=False) 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 = super().scatter(*args, **kwargs) self.autoscale_view() return result @_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 @_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 @_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. """ # The shading keyword argument was added in MPL 3.3, so keep # this default updating until we only support MPL>=3.3 default_shading = mpl.rcParams.get('pcolor.shading', 'auto') 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 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 else: data_shape = Ny - 1, Nx - 1 # data array C = collection.get_array().reshape(data_shape) 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) # The original data mask (regardless of wrapped cells) C_mask = getattr(C, 'mask', None) # create the masked array to be used with this pcolormesh full_mask = mask if C_mask is None else mask | C_mask pcolormesh_data = np.ma.array(C, mask=full_mask) collection.set_array(pcolormesh_data.ravel()) # 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. # 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_data = np.ma.array(np.zeros(C.shape), mask=~mask) pcolor_col = self.pcolor(coords[..., 0], coords[..., 1], pcolor_data, zorder=zorder, **kwargs) # Now add back in the masked data if there was any full_mask = ~mask if C_mask is None else ~mask | C_mask pcolor_data = np.ma.array(C, mask=full_mask) # The pcolor_col is now possibly shorter than the # actual collection, so grab the masked cells pcolor_col.set_array(pcolor_data[mask].ravel()) 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.ravel() collection._wrapped_collection_fix = pcolor_col 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`. """ # Add in an argument checker to handle Matplotlib's potential # interpolation when coordinate wraps are involved args, kwargs = self._wrap_args(*args, **kwargs) if matplotlib.__version__ < "3.3": # MPL 3.3 introduced the shading option, and it isn't # handled before that for pcolor calls. kwargs.pop('shading', None) 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 @_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) @_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) @_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 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