def plot_cartopy(lons, lats, size, color, labels=None, projection='global', resolution='110m', continent_fill_color='0.8', water_fill_color='1.0', colormap=None, colorbar=None, marker="o", title=None, colorbar_ticklabel_format=None, show=True, proj_kwargs=None, **kwargs): # @UnusedVariable """ Creates a Cartopy plot with a data point scatter plot. :type lons: list/tuple of floats :param lons: Longitudes of the data points. :type lats: list/tuple of floats :param lats: Latitudes of the data points. :type size: float or list/tuple of floats :param size: Size of the individual points in the scatter plot. :type color: list/tuple of floats (or objects that can be converted to floats, like e.g. :class:`~obspy.core.utcdatetime.UTCDateTime`) :param color: Color information of the individual data points to be used in the specified color map (e.g. origin depths, origin times). :type labels: list/tuple of str :param labels: Annotations for the individual data points. :type projection: str, optional :param projection: The map projection. Currently supported are: * ``"global"`` (Will plot the whole world using :class:`~cartopy.crs.Mollweide`.) * ``"ortho"`` (Will center around the mean lat/long using :class:`~cartopy.crs.Orthographic`.) * ``"local"`` (Will plot around local events using :class:`~cartopy.crs.AlbersEqualArea`.) * Any other Cartopy :class:`~cartopy.crs.Projection`. An instance of this class will be created using the supplied ``proj_kwargs``. Defaults to "global" :type resolution: str, optional :param resolution: Resolution of the boundary database to use. Will be passed directly to the Cartopy module. Possible values are: * ``"110m"`` * ``"50m"`` * ``"10m"`` Defaults to ``"110m"``. For compatibility, you may also specify any of the Basemap resolutions defined in :func:`plot_basemap`. :type continent_fill_color: Valid matplotlib color, optional :param continent_fill_color: Color of the continents. Defaults to ``"0.9"`` which is a light gray. :type water_fill_color: Valid matplotlib color, optional :param water_fill_color: Color of all water bodies. Defaults to ``"white"``. :type colormap: str, any matplotlib colormap, optional :param colormap: The colormap for color-coding the events as provided in `color` kwarg. The event with the smallest `color` property will have the color of one end of the colormap and the event with the highest `color` property the color of the other end with all other events in between. Defaults to None which will use the default matplotlib colormap. :type colorbar: bool, optional :param colorbar: When left `None`, a colorbar is plotted if more than one object is plotted. Using `True`/`False` the colorbar can be forced on/off. :type title: str :param title: Title above plot. :type colorbar_ticklabel_format: str or function or subclass of :class:`matplotlib.ticker.Formatter` :param colorbar_ticklabel_format: Format string or Formatter used to format colorbar tick labels. :type show: bool :param show: Whether to show the figure after plotting or not. Can be used to do further customization of the plot before showing it. :type proj_kwargs: dict :param proj_kwargs: Keyword arguments to pass to the Cartopy :class:`~cartopy.ccrs.Projection`. In this dictionary, you may specify ``central_longitude='auto'`` or ``central_latitude='auto'`` to have this function calculate the latitude or longitude as it would for other projections. Some arguments may be ignored if you choose one of the built-in ``projection`` choices. """ import matplotlib.pyplot as plt if isinstance(color[0], (datetime.datetime, UTCDateTime)): datetimeplot = True color = [date2num(getattr(t, 'datetime', t)) for t in color] else: datetimeplot = False fig = plt.figure() # The colorbar should only be plotted if more then one event is # present. if colorbar is not None: show_colorbar = colorbar else: if len(lons) > 1 and hasattr(color, "__len__") and \ not isinstance(color, (str, native_str)): show_colorbar = True else: show_colorbar = False if projection == "local": ax_x0, ax_width = 0.10, 0.80 elif projection == "global": ax_x0, ax_width = 0.01, 0.98 else: ax_x0, ax_width = 0.05, 0.90 proj_kwargs = proj_kwargs or {} if projection == 'global': proj_kwargs['central_longitude'] = np.mean(lons) proj = ccrs.Mollweide(**proj_kwargs) elif projection == 'ortho': proj_kwargs['central_latitude'] = np.mean(lats) proj_kwargs['central_longitude'] = mean_longitude(lons) proj = ccrs.Orthographic(**proj_kwargs) elif projection == 'local': if min(lons) < -150 and max(lons) > 150: max_lons = max(np.array(lons) % 360) min_lons = min(np.array(lons) % 360) else: max_lons = max(lons) min_lons = min(lons) lat_0 = max(lats) / 2. + min(lats) / 2. lon_0 = max_lons / 2. + min_lons / 2. if lon_0 > 180: lon_0 -= 360 deg2m_lat = 2 * np.pi * 6371 * 1000 / 360 deg2m_lon = deg2m_lat * np.cos(lat_0 / 180 * np.pi) if len(lats) > 1: height = (max(lats) - min(lats)) * deg2m_lat width = (max_lons - min_lons) * deg2m_lon margin = 0.2 * (width + height) height += margin width += margin else: height = 2.0 * deg2m_lat width = 5.0 * deg2m_lon # Do intelligent aspect calculation for local projection # adjust to figure dimensions w, h = fig.get_size_inches() aspect = w / h if show_colorbar: aspect *= 1.2 if width / height < aspect: width = height * aspect else: height = width / aspect proj_kwargs['central_latitude'] = lat_0 proj_kwargs['central_longitude'] = lon_0 proj_kwargs['standard_parallels'] = [lat_0, lat_0] proj = ccrs.AlbersEqualArea(**proj_kwargs) # User-supplied projection. elif isinstance(projection, type): if 'central_longitude' in proj_kwargs: if proj_kwargs['central_longitude'] == 'auto': proj_kwargs['central_longitude'] = mean_longitude(lons) if 'central_latitude' in proj_kwargs: if proj_kwargs['central_latitude'] == 'auto': proj_kwargs['central_latitude'] = np.mean(lats) if 'pole_longitude' in proj_kwargs: if proj_kwargs['pole_longitude'] == 'auto': proj_kwargs['pole_longitude'] = np.mean(lons) if 'pole_latitude' in proj_kwargs: if proj_kwargs['pole_latitude'] == 'auto': proj_kwargs['pole_latitude'] = np.mean(lats) proj = projection(**proj_kwargs) else: msg = "Projection '%s' not supported." % projection raise ValueError(msg) if show_colorbar: map_ax = fig.add_axes([ax_x0, 0.13, ax_width, 0.77], projection=proj) cm_ax = fig.add_axes([ax_x0, 0.05, ax_width, 0.05]) plt.sca(map_ax) else: ax_y0, ax_height = 0.05, 0.85 if projection == "local": ax_y0 += 0.05 ax_height -= 0.05 map_ax = fig.add_axes([ax_x0, ax_y0, ax_width, ax_height], projection=proj) if projection == 'local': x0, y0 = proj.transform_point(lon_0, lat_0, proj.as_geodetic()) map_ax.set_xlim(x0 - width / 2, x0 + width / 2) map_ax.set_ylim(y0 - height / 2, y0 + height / 2) else: map_ax.set_global() # Pick features at specified resolution. resolution = _CARTOPY_RESOLUTIONS[resolution] try: borders, land, ocean = _CARTOPY_FEATURES[resolution] except KeyError: borders = cfeature.NaturalEarthFeature(cfeature.BORDERS.category, cfeature.BORDERS.name, resolution, edgecolor='none', facecolor='none') land = cfeature.NaturalEarthFeature(cfeature.LAND.category, cfeature.LAND.name, resolution, edgecolor='face', facecolor='none') ocean = cfeature.NaturalEarthFeature(cfeature.OCEAN.category, cfeature.OCEAN.name, resolution, edgecolor='face', facecolor='none') _CARTOPY_FEATURES[resolution] = (borders, land, ocean) # Draw coast lines, country boundaries, fill continents. if MATPLOTLIB_VERSION >= [2, 0, 0]: map_ax.set_facecolor(water_fill_color) else: map_ax.set_axis_bgcolor(water_fill_color) map_ax.add_feature(ocean, facecolor=water_fill_color) map_ax.add_feature(land, facecolor=continent_fill_color) map_ax.add_feature(borders, edgecolor='0.75') map_ax.coastlines(resolution=resolution, color='0.4') # Draw grid lines - TODO: draw_labels=True doesn't work yet. if projection == 'local': map_ax.gridlines() else: # Draw lat/lon grid lines every 30 degrees. map_ax.gridlines(xlocs=range(-180, 181, 30), ylocs=range(-90, 91, 30)) # Plot labels if labels and len(lons) > 0: with map_ax.hold_limits(): for name, xpt, ypt, _colorpt in zip(labels, lons, lats, color): map_ax.text(xpt, ypt, name, weight="heavy", color="k", zorder=100, transform=ccrs.Geodetic(), path_effects=[ patheffects.withStroke(linewidth=3, foreground="white") ]) scatter = map_ax.scatter(lons, lats, marker=marker, s=size, c=color, zorder=10, cmap=colormap, transform=ccrs.Geodetic()) if title: plt.suptitle(title) # Only show the colorbar for more than one event. if show_colorbar: if colorbar_ticklabel_format is not None: if isinstance(colorbar_ticklabel_format, (str, native_str)): formatter = FormatStrFormatter(colorbar_ticklabel_format) elif hasattr(colorbar_ticklabel_format, '__call__'): formatter = FuncFormatter(colorbar_ticklabel_format) elif isinstance(colorbar_ticklabel_format, Formatter): formatter = colorbar_ticklabel_format locator = MaxNLocator(5) else: if datetimeplot: locator = AutoDateLocator() formatter = AutoDateFormatter(locator) # Compat with old matplotlib versions. if hasattr(formatter, "scaled"): formatter.scaled[1 / (24. * 60.)] = '%H:%M:%S' else: locator = None formatter = None cb = Colorbar(cm_ax, scatter, cmap=colormap, orientation='horizontal', ticks=locator, format=formatter) # Compat with old matplotlib versions. if hasattr(cb, "update_ticks"): cb.update_ticks() if show: plt.show() return fig
def plot_cartopy(lons, lats, size, color, labels=None, projection='global', resolution='110m', continent_fill_color='0.8', water_fill_color='1.0', colormap=None, colorbar=None, marker="o", title=None, colorbar_ticklabel_format=None, show=True, proj_kwargs=None, **kwargs): # @UnusedVariable """ Creates a Cartopy plot with a data point scatter plot. :type lons: list/tuple of floats :param lons: Longitudes of the data points. :type lats: list/tuple of floats :param lats: Latitudes of the data points. :type size: float or list/tuple of floats :param size: Size of the individual points in the scatter plot. :type color: list/tuple of floats (or objects that can be converted to floats, like e.g. :class:`~obspy.core.utcdatetime.UTCDateTime`) :param color: Color information of the individual data points to be used in the specified color map (e.g. origin depths, origin times). :type labels: list/tuple of str :param labels: Annotations for the individual data points. :type projection: str, optional :param projection: The map projection. Currently supported are: * ``"global"`` (Will plot the whole world using :class:`~cartopy.crs.Mollweide`.) * ``"ortho"`` (Will center around the mean lat/long using :class:`~cartopy.crs.Orthographic`.) * ``"local"`` (Will plot around local events using :class:`~cartopy.crs.AlbersEqualArea`.) * Any other Cartopy :class:`~cartopy.crs.Projection`. An instance of this class will be created using the supplied ``proj_kwargs``. Defaults to "global" :type resolution: str, optional :param resolution: Resolution of the boundary database to use. Will be passed directly to the Cartopy module. Possible values are: * ``"110m"`` * ``"50m"`` * ``"10m"`` Defaults to ``"110m"``. For compatibility, you may also specify any of the Basemap resolutions defined in :func:`plot_basemap`. :type continent_fill_color: Valid matplotlib color, optional :param continent_fill_color: Color of the continents. Defaults to ``"0.9"`` which is a light gray. :type water_fill_color: Valid matplotlib color, optional :param water_fill_color: Color of all water bodies. Defaults to ``"white"``. :type colormap: str, any matplotlib colormap, optional :param colormap: The colormap for color-coding the events as provided in `color` kwarg. The event with the smallest `color` property will have the color of one end of the colormap and the event with the highest `color` property the color of the other end with all other events in between. Defaults to None which will use the default matplotlib colormap. :type colorbar: bool, optional :param colorbar: When left `None`, a colorbar is plotted if more than one object is plotted. Using `True`/`False` the colorbar can be forced on/off. :type title: str :param title: Title above plot. :type colorbar_ticklabel_format: str or function or subclass of :class:`matplotlib.ticker.Formatter` :param colorbar_ticklabel_format: Format string or Formatter used to format colorbar tick labels. :type show: bool :param show: Whether to show the figure after plotting or not. Can be used to do further customization of the plot before showing it. :type proj_kwargs: dict :param proj_kwargs: Keyword arguments to pass to the Cartopy :class:`~cartopy.ccrs.Projection`. In this dictionary, you may specify ``central_longitude='auto'`` or ``central_latitude='auto'`` to have this function calculate the latitude or longitude as it would for other projections. Some arguments may be ignored if you choose one of the built-in ``projection`` choices. """ import matplotlib.pyplot as plt if isinstance(color[0], (datetime.datetime, UTCDateTime)): datetimeplot = True color = [date2num(getattr(t, 'datetime', t)) for t in color] else: datetimeplot = False fig = plt.figure() # The colorbar should only be plotted if more then one event is # present. if colorbar is not None: show_colorbar = colorbar else: if len(lons) > 1 and hasattr(color, "__len__") and \ not isinstance(color, (str, native_str)): show_colorbar = True else: show_colorbar = False if projection == "local": ax_x0, ax_width = 0.10, 0.80 elif projection == "global": ax_x0, ax_width = 0.01, 0.98 else: ax_x0, ax_width = 0.05, 0.90 proj_kwargs = proj_kwargs or {} if projection == 'global': proj_kwargs['central_longitude'] = np.mean(lons) proj = ccrs.Mollweide(**proj_kwargs) elif projection == 'ortho': proj_kwargs['central_latitude'] = np.mean(lats) proj_kwargs['central_longitude'] = mean_longitude(lons) proj = ccrs.Orthographic(**proj_kwargs) elif projection == 'local': if min(lons) < -150 and max(lons) > 150: max_lons = max(np.array(lons) % 360) min_lons = min(np.array(lons) % 360) else: max_lons = max(lons) min_lons = min(lons) lat_0 = max(lats) / 2. + min(lats) / 2. lon_0 = max_lons / 2. + min_lons / 2. if lon_0 > 180: lon_0 -= 360 deg2m_lat = 2 * np.pi * 6371 * 1000 / 360 deg2m_lon = deg2m_lat * np.cos(lat_0 / 180 * np.pi) if len(lats) > 1: height = (max(lats) - min(lats)) * deg2m_lat width = (max_lons - min_lons) * deg2m_lon margin = 0.2 * (width + height) height += margin width += margin else: height = 2.0 * deg2m_lat width = 5.0 * deg2m_lon # Do intelligent aspect calculation for local projection # adjust to figure dimensions w, h = fig.get_size_inches() aspect = w / h if show_colorbar: aspect *= 1.2 if width / height < aspect: width = height * aspect else: height = width / aspect proj_kwargs['central_latitude'] = lat_0 proj_kwargs['central_longitude'] = lon_0 proj_kwargs['standard_parallels'] = [lat_0, lat_0] proj = ccrs.AlbersEqualArea(**proj_kwargs) # User-supplied projection. elif isinstance(projection, type): if 'central_longitude' in proj_kwargs: if proj_kwargs['central_longitude'] == 'auto': proj_kwargs['central_longitude'] = mean_longitude(lons) if 'central_latitude' in proj_kwargs: if proj_kwargs['central_latitude'] == 'auto': proj_kwargs['central_latitude'] = np.mean(lats) if 'pole_longitude' in proj_kwargs: if proj_kwargs['pole_longitude'] == 'auto': proj_kwargs['pole_longitude'] = np.mean(lons) if 'pole_latitude' in proj_kwargs: if proj_kwargs['pole_latitude'] == 'auto': proj_kwargs['pole_latitude'] = np.mean(lats) proj = projection(**proj_kwargs) else: msg = "Projection '%s' not supported." % projection raise ValueError(msg) if show_colorbar: map_ax = fig.add_axes([ax_x0, 0.13, ax_width, 0.77], projection=proj) cm_ax = fig.add_axes([ax_x0, 0.05, ax_width, 0.05]) plt.sca(map_ax) else: ax_y0, ax_height = 0.05, 0.85 if projection == "local": ax_y0 += 0.05 ax_height -= 0.05 map_ax = fig.add_axes([ax_x0, ax_y0, ax_width, ax_height], projection=proj) if projection == 'local': x0, y0 = proj.transform_point(lon_0, lat_0, proj.as_geodetic()) map_ax.set_xlim(x0 - width / 2, x0 + width / 2) map_ax.set_ylim(y0 - height / 2, y0 + height / 2) else: map_ax.set_global() # Pick features at specified resolution. resolution = _CARTOPY_RESOLUTIONS[resolution] try: borders, land, ocean = _CARTOPY_FEATURES[resolution] except KeyError: borders = cfeature.NaturalEarthFeature(cfeature.BORDERS.category, cfeature.BORDERS.name, resolution, edgecolor='none', facecolor='none') land = cfeature.NaturalEarthFeature(cfeature.LAND.category, cfeature.LAND.name, resolution, edgecolor='face', facecolor='none') ocean = cfeature.NaturalEarthFeature(cfeature.OCEAN.category, cfeature.OCEAN.name, resolution, edgecolor='face', facecolor='none') _CARTOPY_FEATURES[resolution] = (borders, land, ocean) # Draw coast lines, country boundaries, fill continents. if MATPLOTLIB_VERSION >= [2, 0, 0]: map_ax.set_facecolor(water_fill_color) else: map_ax.set_axis_bgcolor(water_fill_color) map_ax.add_feature(ocean, facecolor=water_fill_color) map_ax.add_feature(land, facecolor=continent_fill_color) map_ax.add_feature(borders, edgecolor='0.75') map_ax.coastlines(resolution=resolution, color='0.4') # Draw grid lines - TODO: draw_labels=True doesn't work yet. if projection == 'local': map_ax.gridlines() else: # Draw lat/lon grid lines every 30 degrees. map_ax.gridlines(xlocs=range(-180, 181, 30), ylocs=range(-90, 91, 30)) # Plot labels if labels and len(lons) > 0: with map_ax.hold_limits(): for name, xpt, ypt, _colorpt in zip(labels, lons, lats, color): map_ax.text(xpt, ypt, name, weight="heavy", color="k", zorder=100, transform=ccrs.Geodetic(), path_effects=[ PathEffects.withStroke(linewidth=3, foreground="white")]) scatter = map_ax.scatter(lons, lats, marker=marker, s=size, c=color, zorder=10, cmap=colormap, transform=ccrs.Geodetic()) if title: plt.suptitle(title) # Only show the colorbar for more than one event. if show_colorbar: if colorbar_ticklabel_format is not None: if isinstance(colorbar_ticklabel_format, (str, native_str)): formatter = FormatStrFormatter(colorbar_ticklabel_format) elif hasattr(colorbar_ticklabel_format, '__call__'): formatter = FuncFormatter(colorbar_ticklabel_format) elif isinstance(colorbar_ticklabel_format, Formatter): formatter = colorbar_ticklabel_format locator = MaxNLocator(5) else: if datetimeplot: locator = AutoDateLocator() formatter = AutoDateFormatter(locator) # Compat with old matplotlib versions. if hasattr(formatter, "scaled"): formatter.scaled[1 / (24. * 60.)] = '%H:%M:%S' else: locator = None formatter = None cb = Colorbar(cm_ax, scatter, cmap=colormap, orientation='horizontal', ticks=locator, format=formatter) # Compat with old matplotlib versions. if hasattr(cb, "update_ticks"): cb.update_ticks() if show: plt.show() return fig
def _plot_basemap_into_axes(ax, lons, lats, size, color, bmap=None, labels=None, projection='global', resolution='l', continent_fill_color='0.8', water_fill_color='1.0', colormap=None, marker="o", title=None, adjust_aspect_to_colorbar=False, **kwargs): # @UnusedVariable """ Creates a (or adds to existing) basemap plot with a data point scatter plot in given axes. See :func:`plot_basemap` for details on most args/kwargs. :type ax: :class:`matplotlib.axes.Axes` :param ax: Existing matplotlib axes instance, optionally with previous basemap plot (see `bmap` kwarg). :type bmap: :class:`mpl_toolkits.basemap.Basemap` :param bmap: Basemap instance in provided matplotlib Axes `ax` to reuse. If specified, any kwargs regarding the basemap plot setup will be ignored (i.e. `projection`, `resolution`, `continent_fill_color`, `water_fill_color`). :rtype: :class:`matplotlib.collections.PathCollection` :returns: Matplotlib path collection (e.g. to reuse for colorbars). """ fig = ax.figure if bmap is None: if projection == 'global': bmap = Basemap(projection='moll', lon_0=round(np.mean(lons), 4), resolution=_BASEMAP_RESOLUTIONS[resolution], ax=ax) elif projection == 'ortho': bmap = Basemap(projection='ortho', resolution=_BASEMAP_RESOLUTIONS[resolution], area_thresh=1000.0, lat_0=round(np.mean(lats), 4), lon_0=round(mean_longitude(lons), 4), ax=ax) elif projection == 'local': if min(lons) < -150 and max(lons) > 150: max_lons = max(np.array(lons) % 360) min_lons = min(np.array(lons) % 360) else: max_lons = max(lons) min_lons = min(lons) lat_0 = max(lats) / 2. + min(lats) / 2. lon_0 = max_lons / 2. + min_lons / 2. if lon_0 > 180: lon_0 -= 360 deg2m_lat = 2 * np.pi * 6371 * 1000 / 360 deg2m_lon = deg2m_lat * np.cos(lat_0 / 180 * np.pi) if len(lats) > 1: height = (max(lats) - min(lats)) * deg2m_lat width = (max_lons - min_lons) * deg2m_lon margin = 0.2 * (width + height) height += margin width += margin else: height = 2.0 * deg2m_lat width = 5.0 * deg2m_lon # do intelligent aspect calculation for local projection # adjust to figure dimensions w, h = fig.get_size_inches() ax_bbox = ax.get_position() aspect = (w * ax_bbox.width) / (h * ax_bbox.height) if adjust_aspect_to_colorbar: aspect *= 1.2 if width / height < aspect: width = height * aspect else: height = width / aspect bmap = Basemap(projection='aea', resolution=_BASEMAP_RESOLUTIONS[resolution], area_thresh=1000.0, lat_0=round(lat_0, 4), lon_0=round(lon_0, 4), width=width, height=height, ax=ax) # not most elegant way to calculate some round lats/lons def linspace2(val1, val2, n): """ returns around n 'nice' values between val1 and val2 """ dval = val2 - val1 round_pos = int(round(-np.log10(1. * dval / n))) # Fake negative rounding as not supported by future as of now. if round_pos < 0: factor = 10**(abs(round_pos)) delta = round(2. * dval / n / factor) * factor / 2 else: delta = round(2. * dval / n, round_pos) / 2 new_val1 = np.ceil(val1 / delta) * delta new_val2 = np.floor(val2 / delta) * delta n = int((new_val2 - new_val1) / delta + 1) return np.linspace(new_val1, new_val2, n) n_1 = int(np.ceil(height / max(width, height) * 8)) n_2 = int(np.ceil(width / max(width, height) * 8)) parallels = linspace2(lat_0 - height / 2 / deg2m_lat, lat_0 + height / 2 / deg2m_lat, n_1) # Old basemap versions have problems with non-integer parallels. try: bmap.drawparallels(parallels, labels=[0, 1, 1, 0]) except KeyError: parallels = sorted(list(set(map(int, parallels)))) bmap.drawparallels(parallels, labels=[0, 1, 1, 0]) if min(lons) < -150 and max(lons) > 150: lon_0 %= 360 meridians = linspace2(lon_0 - width / 2 / deg2m_lon, lon_0 + width / 2 / deg2m_lon, n_2) meridians[meridians > 180] -= 360 bmap.drawmeridians(meridians, labels=[1, 0, 0, 1]) else: msg = "Projection '%s' not supported." % projection raise ValueError(msg) # draw coast lines, country boundaries, fill continents. if MATPLOTLIB_VERSION >= [2, 0, 0]: ax.set_facecolor(water_fill_color) else: ax.set_axis_bgcolor(water_fill_color) bmap.drawcoastlines(color="0.4") bmap.drawcountries(color="0.75") bmap.fillcontinents(color=continent_fill_color, lake_color=water_fill_color) # draw the edge of the bmap projection region (the projection limb) bmap.drawmapboundary(fill_color=water_fill_color) # draw lat/lon grid lines every 30 degrees. bmap.drawmeridians(np.arange(-180, 180, 30)) bmap.drawparallels(np.arange(-90, 90, 30)) fig.bmap = bmap # compute the native bmap projection coordinates for events. x, y = bmap(lons, lats) # plot labels if labels: if 100 > len(lons) > 1: for name, xpt, ypt, _colorpt in zip(labels, x, y, color): # Check if the point can actually be seen with the current bmap # projection. The bmap object will set the coordinates to very # large values if it cannot project a point. if xpt > 1e25: continue ax.text(xpt, ypt, name, weight="heavy", color="k", zorder=100, path_effects=[ patheffects.withStroke(linewidth=3, foreground="white") ]) elif len(lons) == 1: ax.text(x[0], y[0], labels[0], weight="heavy", color="k", path_effects=[ patheffects.withStroke(linewidth=3, foreground="white") ]) # scatter plot is removing valid x/y points with invalid color value, # so we plot those points separately. try: nan_points = np.isnan(np.array(color, dtype=np.float)) except ValueError: # `color' was not a list of values, but a list of colors. pass else: if nan_points.any(): x_ = np.array(x)[nan_points] y_ = np.array(y)[nan_points] size_ = np.array(size)[nan_points] bmap.scatter(x_, y_, marker=marker, s=size_, c="0.3", zorder=10, cmap=None) scatter = bmap.scatter(x, y, marker=marker, s=size, c=color, zorder=10, cmap=colormap) if title: ax.set_title(title) return scatter
def _plot_basemap_into_axes( ax, lons, lats, size, color, bmap=None, labels=None, projection='global', resolution='l', continent_fill_color='0.8', water_fill_color='1.0', colormap=None, marker="o", title=None, adjust_aspect_to_colorbar=False, **kwargs): # @UnusedVariable """ Creates a (or adds to existing) basemap plot with a data point scatter plot in given axes. See :func:`plot_basemap` for details on most args/kwargs. :type ax: :class:`matplotlib.axes.Axes` :param ax: Existing matplotlib axes instance, optionally with previous basemap plot (see `bmap` kwarg). :type bmap: :class:`mpl_toolkits.basemap.Basemap` :param bmap: Basemap instance in provided matplotlib Axes `ax` to reuse. If specified, any kwargs regarding the basemap plot setup will be ignored (i.e. `projection`, `resolution`, `continent_fill_color`, `water_fill_color`). :rtype: :class:`matplotlib.collections.PathCollection` :returns: Matplotlib path collection (e.g. to reuse for colorbars). """ fig = ax.figure if bmap is None: if projection == 'global': bmap = Basemap(projection='moll', lon_0=round(np.mean(lons), 4), resolution=_BASEMAP_RESOLUTIONS[resolution], ax=ax) elif projection == 'ortho': bmap = Basemap(projection='ortho', resolution=_BASEMAP_RESOLUTIONS[resolution], area_thresh=1000.0, lat_0=round(np.mean(lats), 4), lon_0=round(mean_longitude(lons), 4), ax=ax) elif projection == 'local': if min(lons) < -150 and max(lons) > 150: max_lons = max(np.array(lons) % 360) min_lons = min(np.array(lons) % 360) else: max_lons = max(lons) min_lons = min(lons) lat_0 = max(lats) / 2. + min(lats) / 2. lon_0 = max_lons / 2. + min_lons / 2. if lon_0 > 180: lon_0 -= 360 deg2m_lat = 2 * np.pi * 6371 * 1000 / 360 deg2m_lon = deg2m_lat * np.cos(lat_0 / 180 * np.pi) if len(lats) > 1: height = (max(lats) - min(lats)) * deg2m_lat width = (max_lons - min_lons) * deg2m_lon margin = 0.2 * (width + height) height += margin width += margin else: height = 2.0 * deg2m_lat width = 5.0 * deg2m_lon # do intelligent aspect calculation for local projection # adjust to figure dimensions w, h = fig.get_size_inches() ax_bbox = ax.get_position() aspect = (w * ax_bbox.width) / (h * ax_bbox.height) if adjust_aspect_to_colorbar: aspect *= 1.2 if width / height < aspect: width = height * aspect else: height = width / aspect bmap = Basemap(projection='aea', resolution=_BASEMAP_RESOLUTIONS[resolution], area_thresh=1000.0, lat_0=round(lat_0, 4), lon_0=round(lon_0, 4), width=width, height=height, ax=ax) # not most elegant way to calculate some round lats/lons def linspace2(val1, val2, n): """ returns around n 'nice' values between val1 and val2 """ dval = val2 - val1 round_pos = int(round(-np.log10(1. * dval / n))) # Fake negative rounding as not supported by future as of now. if round_pos < 0: factor = 10 ** (abs(round_pos)) delta = round(2. * dval / n / factor) * factor / 2 else: delta = round(2. * dval / n, round_pos) / 2 new_val1 = np.ceil(val1 / delta) * delta new_val2 = np.floor(val2 / delta) * delta n = int((new_val2 - new_val1) / delta + 1) return np.linspace(new_val1, new_val2, n) n_1 = int(np.ceil(height / max(width, height) * 8)) n_2 = int(np.ceil(width / max(width, height) * 8)) parallels = linspace2(lat_0 - height / 2 / deg2m_lat, lat_0 + height / 2 / deg2m_lat, n_1) # Old basemap versions have problems with non-integer parallels. try: bmap.drawparallels(parallels, labels=[0, 1, 1, 0]) except KeyError: parallels = sorted(list(set(map(int, parallels)))) bmap.drawparallels(parallels, labels=[0, 1, 1, 0]) if min(lons) < -150 and max(lons) > 150: lon_0 %= 360 meridians = linspace2(lon_0 - width / 2 / deg2m_lon, lon_0 + width / 2 / deg2m_lon, n_2) meridians[meridians > 180] -= 360 bmap.drawmeridians(meridians, labels=[1, 0, 0, 1]) else: msg = "Projection '%s' not supported." % projection raise ValueError(msg) # draw coast lines, country boundaries, fill continents. if MATPLOTLIB_VERSION >= [2, 0, 0]: ax.set_facecolor(water_fill_color) else: ax.set_axis_bgcolor(water_fill_color) bmap.drawcoastlines(color="0.4") bmap.drawcountries(color="0.75") bmap.fillcontinents(color=continent_fill_color, lake_color=water_fill_color) # draw the edge of the bmap projection region (the projection limb) bmap.drawmapboundary(fill_color=water_fill_color) # draw lat/lon grid lines every 30 degrees. bmap.drawmeridians(np.arange(-180, 180, 30)) bmap.drawparallels(np.arange(-90, 90, 30)) fig.bmap = bmap # compute the native bmap projection coordinates for events. x, y = bmap(lons, lats) # plot labels if labels: if 100 > len(lons) > 1: for name, xpt, ypt, _colorpt in zip(labels, x, y, color): # Check if the point can actually be seen with the current bmap # projection. The bmap object will set the coordinates to very # large values if it cannot project a point. if xpt > 1e25: continue ax.text(xpt, ypt, name, weight="heavy", color="k", zorder=100, path_effects=[ PathEffects.withStroke(linewidth=3, foreground="white")]) elif len(lons) == 1: ax.text(x[0], y[0], labels[0], weight="heavy", color="k", path_effects=[ PathEffects.withStroke(linewidth=3, foreground="white")]) # scatter plot is removing valid x/y points with invalid color value, # so we plot those points separately. try: nan_points = np.isnan(np.array(color, dtype=np.float)) except ValueError: # `color' was not a list of values, but a list of colors. pass else: if nan_points.any(): x_ = np.array(x)[nan_points] y_ = np.array(y)[nan_points] size_ = np.array(size)[nan_points] bmap.scatter(x_, y_, marker=marker, s=size_, c="0.3", zorder=10, cmap=None) scatter = bmap.scatter(x, y, marker=marker, s=size, c=color, zorder=10, cmap=colormap) if title: ax.set_title(title) return scatter