.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "gallery/Polygons/NCL_polyg_2.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_gallery_Polygons_NCL_polyg_2.py: NCL_polyg_2.py ============== Concepts illustrated: - Drawing a Lambert Conformal U.S. map color-coded by climate divisions - Color-coding climate divisions based on precipitation values - Drawing the climate divisions of the U.S. - Zooming in on a particular area on a Lambert Conformal map - Drawing a border around filled polygons - Masking the ocean in a map plot - Masking land in a map plot - Increasing the font size of text - Adding text to a plot - Drawing a custom labelbar on a map - Creating a red-yellow-blue color map See following URLs to see the reproduced NCL plot & script: - Original NCL script: https://www.ncl.ucar.edu/Applications/Scripts/polyg_2.ncl - Original NCL plot: https://www.ncl.ucar.edu/Applications/Images/polyg_2_lg.png .. GENERATED FROM PYTHON SOURCE LINES 23-24 Import packages: .. GENERATED FROM PYTHON SOURCE LINES 24-37 .. code-block:: Python import xarray as xr import cartopy.crs as ccrs import matplotlib.pyplot as plt import matplotlib.colors as colors import matplotlib.cm as cm import geocat.viz as gv import geocat.datafiles as gdf import cartopy.io.shapereader as shpreader import shapely.geometry as sgeom from mpl_toolkits.axes_grid1.inset_locator import inset_axes .. GENERATED FROM PYTHON SOURCE LINES 38-39 Read in data .. GENERATED FROM PYTHON SOURCE LINES 39-45 .. code-block:: Python # Open climate division datafile and add to xarray ds = xr.open_dataset( gdf.get("netcdf_files/climdiv_prcp_1899-1999.nc"), decode_times=False ) .. GENERATED FROM PYTHON SOURCE LINES 46-47 Initialize color map and bounds for each color .. GENERATED FROM PYTHON SOURCE LINES 47-70 .. code-block:: Python colormap = colors.ListedColormap( [ 'mediumpurple', 'mediumblue', 'royalblue', 'cornflowerblue', 'lightblue', 'lightseagreen', 'yellowgreen', 'green', 'wheat', 'tan', 'gold', 'orange', 'red', 'firebrick', ] ) # Values represent average number of inches of rain colorbounds = [0, 5, 10, 15, 20, 25, 30, 35, 40, 50, 60, 70, 80, 90, 100] .. GENERATED FROM PYTHON SOURCE LINES 71-72 Define helper function to determine which color to fill the divisions based on precipitation data .. GENERATED FROM PYTHON SOURCE LINES 72-85 .. code-block:: Python def findDivColor(colorbounds, pdata): for x in range(len(colorbounds)): if pdata >= colorbounds[len(colorbounds) - 1]: return colormap.colors[x - 1] if pdata >= colorbounds[x]: continue else: # Index is 'x-1' because colorbounds is one item longer than colormap return colormap.colors[x - 1] .. GENERATED FROM PYTHON SOURCE LINES 86-87 Create plot .. GENERATED FROM PYTHON SOURCE LINES 87-172 .. code-block:: Python # Create plot figure fig = plt.figure(figsize=(8, 8)) # Add axes for lambert conformal map # Set dimensions of axes with [X0, Y0, width, height] argument. Each value is a fraction of total figure size. ax = plt.axes([0.05, -0.05, 0.9, 1], projection=ccrs.LambertConformal(), frameon=False) # Set latitude and longitude extent of map ax.set_extent([-119, -74, 18, 50], ccrs.Geodetic()) # Set shape name of map (which depicts the United States) shapename = 'admin_1_states_provinces_lakes' states_shp = shpreader.natural_earth( resolution='110m', category='cultural', name=shapename ) # Set title and title fontsize of plot using gv function instead of matplotlib function call gv.set_titles_and_labels( ax, maintitle="Average Annual Precipiation \n Computed for the period 1899-1999 \n NCDC climate division data \n", maintitlefontsize=18, ) # Add outlines of each state within the United States for state in shpreader.Reader(states_shp).geometries(): facecolor = 'white' edgecolor = 'black' ax.add_geometries( [state], ccrs.PlateCarree(), facecolor=facecolor, edgecolor=edgecolor ) # For each variable (climate division) in data set, create outline on map and fill with random color for varname, da in ds.data_vars.items(): # This condition is included because first item in xarray only has one attribute, 'current date' if hasattr(da, 'state_name'): # Get number of years of data by dividing number of months recorded (length of array) by 12 (12 months per year) numYears = len(da.values) / 12 # Get precipitation data for each climate division: # Rather than looping through the whole array to find the sum of each 12 values (a year's worth of data), # adding each sum to an array, and then finding the average of the values in the array, as seen in the NCL # script, the one-line python method involves summing the dataset values and then dividing it by numYears (calculated in line 99) precipitationdata = sum(da.values) / numYears # Get borders of each climate division lat = da.lat lon = da.lon # Get color of climate division color = findDivColor(colorbounds, precipitationdata) # Use "shapely geometry" module to create division outlines from lat/lon coordinates track = sgeom.LineString(zip(lon, lat)) # Add division outlines to map im = ax.add_geometries( [track], ccrs.PlateCarree(), facecolor=color, edgecolor='black', linewidths=0.5, ) # Create and plot colorbar # Map colors to bounds norm = colors.BoundaryNorm(colorbounds, colormap.N) # Add inset axes (axes within pre-existing axes) to hold colorbar axins1 = inset_axes(ax, width="75%", height="3%", loc='lower center') # Add colorbar to plot cb = fig.colorbar( cm.ScalarMappable(cmap=colormap, norm=norm), cax=axins1, boundaries=colorbounds, ticks=colorbounds, spacing='uniform', orientation='horizontal', label='inches', ) plt.show() .. image-sg:: /gallery/Polygons/images/sphx_glr_NCL_polyg_2_001.png :alt: Average Annual Precipiation Computed for the period 1899-1999 NCDC climate division data :srcset: /gallery/Polygons/images/sphx_glr_NCL_polyg_2_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none /home/docs/checkouts/readthedocs.org/user_builds/geocat-examples/conda/latest/lib/python3.14/site-packages/cartopy/io/__init__.py:242: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_cultural/ne_110m_admin_1_states_provinces_lakes.zip warnings.warn(f'Downloading: {url}', DownloadWarning) .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 2.861 seconds) .. _sphx_glr_download_gallery_Polygons_NCL_polyg_2.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: NCL_polyg_2.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: NCL_polyg_2.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: NCL_polyg_2.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_