NCL_coneff_11.py

This script illustrates the following concepts:

• Filling contours with multiple styles of hatches

• Changing the color of hatches in a contour plot and colorbar

• Changing the size of hatches in a contour plot

• Changing the density hatches in a contour plot

See following URLs to see the reproduced NCL plot & script:

• Original NCL script: https://www.ncl.ucar.edu/Applications/Scripts/coneff_11.ncl

• Original NCL plots: https://www.ncl.ucar.edu/Applications/Images/coneff_11_1_lg.png

  https://www.ncl.ucar.edu/Applications/Images/coneff_11_2_lg.png

Import packages:

import numpy as np
import xarray as xr
import matplotlib.pyplot as plt

import geocat.datafiles as gdf
import geocat.viz as gv

Read in data

# Open a netCDF data file using xarray default engine and load the data into xarrays
ds = xr.open_dataset(gdf.get('netcdf_files/atmos.nc'), decode_times=False)

# Select meridional wind at lowest pressure level
v = ds.V.isel(time=0, lev=0)

Plot

# Generate figure (set its size (width, height) in inches)
plt.figure(figsize=(8, 10))
ax = plt.axes()

# Choose hatches to fill the contours
hatches = ['/////', '/////', '/////', '/////', None, '..', '.', '.', '.']

# Choose colors for the hatches
colors = [
    'coral', 'palegreen', 'royalblue', 'lemonchiffon', 'white', 'fuchsia',
    'brown', 'cyan', 'mediumblue'
]

# Create a filled contour plot
p = v.plot.contourf(ax=ax,
                    vmin=-45.0,
                    vmax=45,
                    levels=10,
                    add_colorbar=False,
                    hatches=hatches,
                    cmap='white')  # Use white cmap to have a white background

# Set the colors for the hatches
for i, collection in enumerate(p.collections):
    collection.set_edgecolor(colors[i % len(colors)])
    collection.set_linewidth(0.)

# Set linewidth of hatches
plt.rcParams['hatch.linewidth'] = 2.5

# Plot the contour lines
c = v.plot.contour(ax=ax,
                   vmin=-45.0,
                   vmax=45,
                   levels=10,
                   colors='k',
                   linewidths=1,
                   add_colorbar=False,
                   linestyles='solid')

# Add horizontal colorbar
cbar = plt.colorbar(p,
                    orientation='horizontal',
                    shrink=0.97,
                    aspect=10,
                    pad=0.09,
                    drawedges=True)
cbar.ax.tick_params(labelsize=16)
cbar.set_ticks(np.arange(-35, 40, 10))

# Color the hatches in colorbar
# We need to do this manually because matplotlib only uses information
# from the contourf call to create the colorbar.
for i, patch in enumerate(cbar.solids_patches):
    patch.set(edgecolor=colors[i % len(colors)])

# Use geocat-viz utility function to format latitude and longitude labels
gv.add_lat_lon_ticklabels(ax)

# Use geocat-viz utility function to format major and minor ticks
gv.add_major_minor_ticks(ax,
                         x_minor_per_major=2,
                         y_minor_per_major=3,
                         labelsize=16)

# Use geocat-viz utility function to set titles and labels
gv.set_titles_and_labels(ax,
                         maintitle="Patterned Contour Plot",
                         maintitlefontsize=18,
                         lefttitle="meridional wind component",
                         lefttitlefontsize=16,
                         righttitle="m/s",
                         righttitlefontsize=16)

# Remove default x and y labels
ax.set_xlabel(None)
ax.set_ylabel(None)

# Use geocat-viz utility function to set tick marks and tick labels
gv.set_axes_limits_and_ticks(
    ax,
    xticks=np.arange(0, 360, 60),
    yticks=np.arange(-60, 90, 30),
    xticklabels=['0', '60E', '120E', '180', '120W', '60W'],
    yticklabels=['60S', '30S', '0', '30N', '60N'])

plt.show()