{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# NCL_panel_3.py\nThis script illustrates the following concepts:\n - Two panel (subplot) image with shared colorbar and title\n - Adding a common title to paneled plots\n - Adding a common colorbar to paneled plots\n - Importing and truncating a NCL colormap\n\nSee following URLs to see the reproduced NCL plot & script:\n - Original NCL script: https://www.ncl.ucar.edu/Applications/Scripts/panel_3.ncl\n - Original NCL plot: https://www.ncl.ucar.edu/Applications/Images/panel_3_lg.png\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import packages:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import cartopy.crs as ccrs\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport xarray as xr\n\nimport geocat.datafiles as gdf\nfrom geocat.viz import cmaps as gvcmaps\nimport geocat.viz.util as gvutil" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Read in data:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Open a netCDF data file using xarray default engine and load the data into xarrays, choosing the 2nd timestamp\nds = xr.open_dataset(gdf.get(\"netcdf_files/uv300.nc\")).isel(time=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Utility Function: Labelled Filled Contour Plot:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Define a utility plotting function in order not to repeat many lines of codes since we need to make the same figure\n# with two different variables.\n\n\ndef plot_labelled_filled_contours(data, ax=None):\n \"\"\"\n A utility function for convenience that plots labelled, filled contours with black contours\n marking each level.It will return a dictionary containing three objects corresponding to the\n filled contours, the black contours, and the contour labels.\n \"\"\"\n\n # Import an NCL colormap, truncating it by using geocat.viz.util convenience function\n newcmp = gvutil.truncate_colormap(gvcmaps.gui_default,\n minval=0.03,\n maxval=0.9)\n\n handles = dict()\n handles[\"filled\"] = data.plot.contourf(\n ax=ax, # this is the axes we want to plot to\n cmap=newcmp, # our special colormap\n levels=levels, # contour levels specified outside this function\n xticks=np.arange(-180, 181, 30), # nice x ticks\n yticks=np.arange(-90, 91, 30), # nice y ticks\n transform=projection, # data projection\n add_colorbar=False, # don't add individual colorbars for each plot call\n add_labels=False, # turn off xarray's automatic Lat, lon labels\n )\n\n # matplotlib's contourf doesn't let you specify the \"edgecolors\" (MATLAB terminology)\n # instead we plot black contours on top of the filled contours\n handles[\"contour\"] = data.plot.contour(\n ax=ax,\n levels=levels,\n colors=\"black\", # note plurals in this and following kwargs\n linestyles=\"-\",\n linewidths=0.5,\n add_labels=False, # again turn off automatic labels\n )\n\n # Label the contours\n ax.clabel(\n handles[\"contour\"],\n fontsize=8,\n fmt=\"%.0f\", # Turn off decimal points\n )\n\n # Add coastlines\n ax.coastlines(linewidth=0.5)\n\n # Use geocat.viz.util convenience function to add minor and major tick lines\n gvutil.add_major_minor_ticks(ax)\n\n # Use geocat.viz.util convenience function to make plots look like NCL plots by using latitude, longitude tick labels\n gvutil.add_lat_lon_ticklabels(ax)\n\n # Use geocat.viz.util convenience function to add main title as well as titles to left and right of the plot axes.\n gvutil.set_titles_and_labels(ax,\n lefttitle=data.attrs['long_name'],\n lefttitlefontsize=10,\n righttitle=data.attrs['units'],\n righttitlefontsize=10)\n\n return handles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Make two panels (i.e. subplots in matplotlib)\n# Specify ``constrained_layout=True`` to automatically layout panels, colorbars and axes decorations nicely.\n# See https://matplotlib.org/tutorials/intermediate/constrainedlayout_guide.html\n# Generate figure and axes using Cartopy projection\nprojection = ccrs.PlateCarree()\nfig, ax = plt.subplots(2,\n 1,\n constrained_layout=True,\n subplot_kw={\"projection\": projection})\n\n# Set figure size (width, height) in inches\nfig.set_size_inches((8, 8.2))\n\n# Define the contour levels\nlevels = np.linspace(-10, 50, 13)\n\n# Contour-plot U data, save \"handles\" to add a colorbar later\nhandles = plot_labelled_filled_contours(ds.U, ax=ax[0])\n\n# Set a common title\nax[0].set_title(\"A plot with a common colorbar\", fontsize=14, y=1.15)\n\n# Contour-plot V data\nplot_labelled_filled_contours(ds.V, ax=ax[1])\n\n# Add horizontal colorbar\ncbar = plt.colorbar(handles[\"filled\"],\n ax=ax,\n orientation=\"horizontal\",\n ticks=levels[:-1],\n drawedges=True,\n aspect=30)\ncbar.ax.tick_params(labelsize=10)\n\n# Show the plot\nplt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }