.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "gallery/Station/NCL_station_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_Station_NCL_station_2.py: NCL_station_2.py ================ This script illustrates the following concepts: - Drawing markers on a map indicating the locations of station data - Generating dummy data using "random_uniform" - Drawing markers of different sizes and colors on a map - Drawing a custom legend outside of a map plot - Attaching a custom colorbar to a plot See following URLs to see the reproduced NCL plot & script: - Original NCL script: https://www.ncl.ucar.edu/Applications/Scripts/station_2.ncl - Original NCL plot: https://www.ncl.ucar.edu/Applications/Images/station_2_1_lg.png and https://www.ncl.ucar.edu/Applications/Images/station_2_2_lg.png .. GENERATED FROM PYTHON SOURCE LINES 17-18 Import packages: .. GENERATED FROM PYTHON SOURCE LINES 18-27 .. code-block:: Python import numpy as np import matplotlib as mpl from matplotlib import pyplot as plt import cartopy import cartopy.crs as ccrs import geocat.viz as gv .. GENERATED FROM PYTHON SOURCE LINES 28-30 Generate random data: --------------------- .. GENERATED FROM PYTHON SOURCE LINES 30-41 .. code-block:: Python # Set up random values npts = 100 np.random.seed(20200127) # Lat between 25 N and 50 N, lon between 125 W and 70 W lat = np.random.uniform(25, 50, npts) lon = np.random.uniform(235, 290, npts) - 360 dummy_data = np.random.uniform(-1.2, 35, npts) .. GENERATED FROM PYTHON SOURCE LINES 42-44 Define colormap for plotting: ----------------------------- .. GENERATED FROM PYTHON SOURCE LINES 44-62 .. code-block:: Python # Set up colormap: # Need to define boundaries for each color map as well as colors for each bin # Note that len(colors) = len(bin_bounds) + 1 # color[0] => dummy_data < bin_bounds[0] # color[-1] => dummy_data => bin_bounds[-1] # color[j] => bin_bounds[j-1] <= dummy_data < bin_bounds[j] bin_bounds = [0.0, 5.0, 10.0, 15.0, 20.0, 23.0, 26.0] colors = ['purple', 'darkblue', 'blue', 'lightblue', 'yellow', 'orange', 'red', 'pink'] nbins = len(colors) # One bin for each color # Define colormap and norm for plotting based on these colors cmap = mpl.colors.ListedColormap(colors) norm = mpl.colors.BoundaryNorm([-1.2] + bin_bounds + [35], len(colors)) .. GENERATED FROM PYTHON SOURCE LINES 63-65 Utility Function: Make Shared Plot: ----------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 65-105 .. code-block:: Python # Define a utility function to create the basic contour plot that will be used twice to create two slightly # different plots, both of which rely on same base figure def make_shared_plot(title): # Generate figure (set its size (width, height) in inches) and axes using Cartopy projection plt.figure(figsize=(10, 5.5)) ax = plt.axes(projection=ccrs.PlateCarree()) # Use geocat.viz.util convenience function to add minor and major tick lines gv.add_major_minor_ticks(ax, x_minor_per_major=4, y_minor_per_major=5, labelsize=14) # Use geocat.viz.util convenience function to make plots look like NCL plots by using latitude, longitude tick labels gv.add_lat_lon_ticklabels(ax) # Use geocat.viz.util convenience function to set axes limits & tick values without calling several matplotlib functions gv.set_axes_limits_and_ticks( ax, xlim=(-125, -70), ylim=(25, 50), xticks=range(-120, -75, 20), yticks=range(30, 51, 10), ) # Turn on continent shading ax.add_feature( cartopy.feature.LAND, edgecolor='lightgray', facecolor='lightgray', zorder=0 ) ax.add_feature( cartopy.feature.LAKES, edgecolor='white', facecolor='white', zorder=0 ) # Scatter-plot the location data on the map scatter = plt.scatter(lon, lat, c=dummy_data, cmap=cmap, norm=norm, zorder=1) plt.title(title, fontsize=16, y=1.04) return scatter, ax .. GENERATED FROM PYTHON SOURCE LINES 106-108 Plot 1 (with a legend outside, i.e. station_2_1.png) ---------------------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 108-141 .. code-block:: Python # Draw the base plot scatter1, ax = make_shared_plot( "Dummy station data colored according to range of values" ) # Add a legend to the bottom outside of the plot # Given how we generated the plot, adding a legend is a little kludgy. Basically, we draw a second plot where no data # is in frame but the legend for that plot is drawn where we want it lax = plt.axes((0, 0, 1, 0.1), frameon=False) # Plotting window is [0,1] x [0,1] plt.xlim(0, 1) plt.ylim(0, 1) plt.axis('off') for n, color in enumerate(colors): if n == 0: label = f'x < {bin_bounds[0]:.0f}' elif n == nbins - 1: label = f'x >= {bin_bounds[-1]:.0f}' else: label = f'{bin_bounds[n - 1]:.0f} <= x < {bin_bounds[n]:.0f}' # Plotting data at (-10, -10) which is not in the plotting window scatter = plt.scatter(-10, -10, color=color, label=label) # The legend for this second plot is what we are actually interested in # We want large font, no frame around the legend, and 4 columns of labels lax.legend(loc='center', fontsize='large', frameon=False, ncol=4) plt.show() .. image-sg:: /gallery/Station/images/sphx_glr_NCL_station_2_001.png :alt: Dummy station data colored according to range of values :srcset: /gallery/Station/images/sphx_glr_NCL_station_2_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 142-144 Plot 2 (with a colorbar, i.e. station_2_2.png) ---------------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 144-161 .. code-block:: Python # Draw the base plot scatter2 = make_shared_plot("Dummy station data colored according to range of values") # Add a horizontal colorbar cax = plt.axes((0.225, 0.05, 0.55, 0.025)) mpl.colorbar.ColorbarBase( cax, cmap=cmap, orientation='horizontal', norm=norm, boundaries=[-1.2] + bin_bounds + [35], ticks=bin_bounds, ) # Show the plot plt.show() .. image-sg:: /gallery/Station/images/sphx_glr_NCL_station_2_002.png :alt: Dummy station data colored according to range of values :srcset: /gallery/Station/images/sphx_glr_NCL_station_2_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.259 seconds) .. _sphx_glr_download_gallery_Station_NCL_station_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_station_2.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: NCL_station_2.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: NCL_station_2.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_