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JYP's map room
You will find below a gallery of maps made with cartopy.
This page is under construction and its content may change drastically. The source codes are on LSCE servers, that you can hopefully access (too bad otherwise…)
PFT maps
Summary: plot two PFT maps on a page
Authors: Camille Contoux & Jean-Yves Peterschmitt
Date: September 2019
Script: ~jypeter/CDAT/Progs/Devel/contoux/plot_PFT_plio_pi.py
Load the NetCDF model data with cdms2
Optionally reduce the number of PFTs before plotting
Use matplotlib
+cartopy
to create the plot
Create two plots with a PlateCarree projection and a common colorbar
Use an A4 portrait (figsize=(8.3, 11.7)
) figure size and subplots_adjust
in order to improve the page layout
Use a listed colormap (and the associated norm), and pcolormesh
to plot the discrete PFT values
Use a trick to make sure that the black plot border is correctly plotted above everything (top_plot.outline_patch.set_zorder(50)
)
Megabiome maps
Summary: Plot two megabiome maps based on 2 versions of the IPSL model (CM5 and CM6), and compare them to a map with observations
Authors: Raj Rani & Jean-Yves Peterschmitt
Date: September 2019
Script: ~jypeter/CDAT/Progs/Devel/pasb/PMIP4/Press_Conference_1909/mh_CM6_CM5_megabiome.py
Load the NetCDF model data with cdms2
, and the csv observations' data with pandas
Use matplotlib
+cartopy
to create the plot
Create three plots on a 2×2 layout with an Orthographic projection (centered on Europe) and a common colorbar. Explicitly make the 4th plot invisible in order to make space for plotting the colorbar (colorbar_plot.background_patch.set_visible(False)
and colorbar_plot.outline_patch.set_visible(False)
)
Use a square (figsize=(8, 8)
) figure size in order to improve the page layout
Use the same color scale for both the model data (plotted with pcolormesh
) and the observation data (plotted with scatter
)
Use a dictionary trick for easily using the same gridlines parameters for all the maps
Use a common listed colormap to plot the discrete megabiome values (with a norm associated with the megabiome values) and the observations (with a different norm associated with the observations)
The three plots have the same colormap, but we create a standalone colormap (created with mpl.colorbar.ColorbarBase
) in the Bottom Left quadrant. We use UR_plot.get_position().bounds
to determine the exact location of the Upper Right and Lower Left plots, and combine the coordinates to create the axis (with my_page.add_axes
) where the colorbar will be plotted
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