.. _sphx_glr_examples_Four_Panel_Map.py: Four Panel Map =============== By reading model output data from a netCDF file, we can create a four panel plot showing: * 300 hPa heights and winds * 500 hPa heights and absolute vorticity * Surface temperatures * Precipitable water .. code-block:: python import cartopy.crs as ccrs import cartopy.feature as cfeature import matplotlib.gridspec as gridspec import matplotlib.pyplot as plt import netCDF4 import numpy as np import scipy.ndimage as ndimage from metpy.cbook import get_test_data from metpy.plots import add_metpy_logo from metpy.units import units .. code-block:: python # Make state boundaries feature states_provinces = cfeature.NaturalEarthFeature(category='cultural', name='admin_1_states_provinces_lines', scale='50m', facecolor='none') # Make country borders feature country_borders = cfeature.NaturalEarthFeature(category='cultural', name='admin_0_countries', scale='50m', facecolor='none') crs = ccrs.LambertConformal(central_longitude=-100.0, central_latitude=45.0) .. code-block:: python # Function used to create the map subplots def plot_background(ax): ax.set_extent([235., 290., 20., 55.]) ax.coastlines('50m', edgecolor='black', linewidth=0.5) ax.add_feature(states_provinces, edgecolor='black', linewidth=0.5) ax.add_feature(country_borders, edgecolor='black', linewidth=0.5) return ax .. code-block:: python # Open the example netCDF data ds = netCDF4.Dataset(get_test_data('gfs_output.nc', False)) print(ds) .. rst-class:: sphx-glr-script-out Out:: root group (NETCDF4 data model, file format HDF5): title: Test GFS Output Data subtitle: For MetPy examples and tests dimensions(sizes): lat(201), lon(361), time(1) variables(dimensions): float32 lat(lat), float32 lon(lon), float64 time(time), float64 temp(time,lat,lon), float64 precip_water(time,lat,lon), float64 heights_300(time,lat,lon), float64 heights_500(time,lat,lon), float64 vort_500(time,lat,lon), float64 winds_300(time,lat,lon) groups: .. code-block:: python # Convert number of hours since the reference time into an actual date time_vals = netCDF4.num2date(ds.variables['time'][:].squeeze(), ds.variables['time'].units) .. code-block:: python # Combine 1D latitude and longitudes into a 2D grid of locations lon_2d, lat_2d = np.meshgrid(ds.variables['lon'][:], ds.variables['lat'][:]) .. code-block:: python # Assign units vort_500 = ds.variables['vort_500'][0] * units(ds.variables['vort_500'].units) surface_temp = ds.variables['temp'][0] * units(ds.variables['temp'].units) precip_water = ds.variables['precip_water'][0] * units(ds.variables['precip_water'].units) winds_300 = ds.variables['winds_300'][0] * units(ds.variables['winds_300'].units) .. code-block:: python # Do unit conversions to what we wish to plot vort_500 = vort_500 * 1e5 surface_temp = surface_temp.to('degF') precip_water = precip_water.to('inches') winds_300 = winds_300.to('knots') .. code-block:: python # Smooth the height data heights_300 = ndimage.gaussian_filter(ds.variables['heights_300'][0], sigma=1.5, order=0) heights_500 = ndimage.gaussian_filter(ds.variables['heights_500'][0], sigma=1.5, order=0) .. code-block:: python # Create the figure fig = plt.figure(figsize=(20, 15)) add_metpy_logo(fig, 80, 100, size='large') gs = gridspec.GridSpec(5, 2, height_ratios=[1, .05, 1, .05, 0], bottom=.05, top=.95, wspace=.1) # Upper left plot - 300-hPa winds and geopotential heights ax1 = plt.subplot(gs[0, 0], projection=crs) plot_background(ax1) cf1 = ax1.contourf(lon_2d, lat_2d, winds_300, cmap='cool', transform=ccrs.PlateCarree()) c1 = ax1.contour(lon_2d, lat_2d, heights_300, colors='black', linewidths=2, transform=ccrs.PlateCarree()) plt.clabel(c1, fontsize=10, inline=1, inline_spacing=1, fmt='%i', rightside_up=True) ax2 = plt.subplot(gs[1, 0]) cb1 = plt.colorbar(cf1, cax=ax2, orientation='horizontal') cb1.set_label('knots', size='x-large') ax1.set_title('300-hPa Wind Speeds and Heights', fontsize=16) # Upper right plot - 500mb absolute vorticity and geopotential heights ax3 = plt.subplot(gs[0, 1], projection=crs) plot_background(ax3) cf2 = ax3.contourf(lon_2d, lat_2d, vort_500, cmap='BrBG', transform=ccrs.PlateCarree(), zorder=0, norm=plt.Normalize(-32, 32)) c2 = ax3.contour(lon_2d, lat_2d, heights_500, colors='k', linewidths=2, transform=ccrs.PlateCarree()) plt.clabel(c2, fontsize=10, inline=1, inline_spacing=1, fmt='%i', rightside_up=True) ax4 = plt.subplot(gs[1, 1]) cb2 = plt.colorbar(cf2, cax=ax4, orientation='horizontal') cb2.set_label(r'$10^{-5}$ s$^{-1}$', size='x-large') ax3.set_title('500-hPa Absolute Vorticity and Heights', fontsize=16) # Lower left plot - surface temperatures ax5 = plt.subplot(gs[2, 0], projection=crs) plot_background(ax5) cf3 = ax5.contourf(lon_2d, lat_2d, surface_temp, cmap='YlOrRd', transform=ccrs.PlateCarree(), zorder=0) ax6 = plt.subplot(gs[3, 0]) cb3 = plt.colorbar(cf3, cax=ax6, orientation='horizontal') cb3.set_label(u'\N{DEGREE FAHRENHEIT}', size='x-large') ax5.set_title('Surface Temperatures', fontsize=16) # Lower right plot - precipitable water entire atmosphere ax7 = plt.subplot(gs[2, 1], projection=crs) plot_background(ax7) cf4 = plt.contourf(lon_2d, lat_2d, precip_water, cmap='Greens', transform=ccrs.PlateCarree(), zorder=0) ax8 = plt.subplot(gs[3, 1]) cb4 = plt.colorbar(cf4, cax=ax8, orientation='horizontal') cb4.set_label('in.', size='x-large') ax7.set_title('Precipitable Water', fontsize=16) fig.suptitle('{0:%d %B %Y %H:%MZ}'.format(time_vals), fontsize=24) # Display the plot plt.show() .. image:: /examples/images/sphx_glr_Four_Panel_Map_001.png :align: center **Total running time of the script:** ( 0 minutes 8.811 seconds) .. only :: html .. container:: sphx-glr-footer .. container:: sphx-glr-download :download:`Download Python source code: Four_Panel_Map.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: Four_Panel_Map.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_