More advanced mapping with cartopy and matplotlib ================================================= From the outset, cartopy's purpose has been to simplify and improve the quality of mapping visualisations available for scientific data. Contour plots ------------- .. plot:: :include-source: import matplotlib.pyplot as plt from scipy.io import netcdf from cartopy import config import cartopy.crs as ccrs # get the path of the file. It can be found in the repo data directory. fname = config["repo_data_dir"] / 'netcdf' / 'HadISST1_SST_update.nc' dataset = netcdf.netcdf_file(fname, maskandscale=True, mmap=False) sst = dataset.variables['sst'][0, :, :] lats = dataset.variables['lat'][:] lons = dataset.variables['lon'][:] ax = plt.axes(projection=ccrs.PlateCarree()) plt.contourf(lons, lats, sst, 60, transform=ccrs.PlateCarree()) ax.coastlines() plt.show() Images ------ .. plot:: :include-source: import os import matplotlib.pyplot as plt from cartopy import config import cartopy.crs as ccrs fig = plt.figure(figsize=(8, 12)) # get the path of the file. It can be found in the repo data directory. fname = config["repo_data_dir"] / 'raster' / 'sample' / 'Miriam.A2012270.2050.2km.jpg' img_extent = (-120.67660000000001, -106.32104523100001, 13.2301484511245, 30.766899999999502) img = plt.imread(fname) ax = plt.axes(projection=ccrs.PlateCarree()) plt.title('Hurricane Miriam from the Aqua/MODIS satellite\n' '2012 09/26/2012 20:50 UTC') ax.use_sticky_edges = False # set a margin around the data ax.set_xmargin(0.05) ax.set_ymargin(0.10) # add the image. Because this image was a tif, the "origin" of the image is in the # upper left corner ax.imshow(img, origin='upper', extent=img_extent, transform=ccrs.PlateCarree()) ax.coastlines(resolution='50m', color='black', linewidth=1) # mark a known place to help us geo-locate ourselves ax.plot(-117.1625, 32.715, 'bo', markersize=7, transform=ccrs.Geodetic()) ax.text(-117, 33, 'San Diego', transform=ccrs.Geodetic()) plt.show() .. _vector_plotting: Vector plotting --------------- Cartopy comes with powerful vector field plotting functionality. There are 3 distinct options for visualising vector fields: :meth:`quivers ` (:ref:`example `), :meth:`barbs ` (:ref:`example `) and :meth:`streamplots ` (:ref:`example `) each with their own benefits for displaying certain vector field forms. .. figure:: ../gallery/vector_data/images/sphx_glr_arrows_001.png :target: ../gallery/vector_data/arrows.html :align: center :scale: 50 Since both :meth:`~cartopy.mpl.geoaxes.GeoAxes.quiver` and :meth:`~cartopy.mpl.geoaxes.GeoAxes.barbs` are visualisations which draw every vector supplied, there is an additional option to "regrid" the vector field into a regular grid on the target projection (done via :func:`cartopy.vector_transform.vector_scalar_to_grid`). This is enabled with the ``regrid_shape`` keyword and can have a massive impact on the effectiveness of the visualisation: .. figure:: ../gallery/vector_data/images/sphx_glr_regridding_arrows_001.png :target: ../gallery/vector_data/regridding_arrows.html :align: center :scale: 50