NCL_lb_2.pyΒΆ

This script illustrates the following concepts:
  • Making a vertical colorbar

  • Changing the colorbar labels

  • Setting color maps using the new standard

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

Import packages:

import numpy as np
import xarray as xr
import cartopy.crs as ccrs
import matplotlib.pyplot as plt

import geocat.datafiles as gdf
from geocat.viz import cmaps as gvcmaps
from geocat.viz import util as gvutil

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)
# Extract variable
v = ds.V.isel(time=0, lev=3)

# Fix the artifact of not-shown-data around 0 and 360-degree longitudes
wrap_v = gvutil.xr_add_cyclic_longitudes(v, "lon")

Plot:

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

# Generate axes using Cartopy and draw coastlines
ax = plt.axes(projection=ccrs.PlateCarree())
ax.coastlines(linewidths=0.5)

# Import an NCL colormap
newcmp = gvcmaps.wgne15

# Contourf-plot data (for filled contours)
a = wrap_v.plot.contourf(levels=14,
                         cmap=newcmp,
                         add_colorbar=False,
                         add_labels=False)
# Contour-plot data (for borderlines)
wrap_v.plot.contour(levels=14, linewidths=0.5, cmap='black', add_labels=False)

# Add vertical colorbar
clabels = [
    "-70", "-50", "-30", "-10", "10", "30", "50", "70", "90", "110", "130",
    "150"
]
cbar = fig.colorbar(a, label='', ticks=np.linspace(-24, 24, 12), shrink=0.4)
cbar.ax.set_yticklabels(clabels)

# Use geocat.viz.util convenience function to set axes limits & tick values without calling several matplotlib functions
gvutil.set_axes_limits_and_ticks(ax,
                                 ylim=(-90, 90),
                                 xticks=np.linspace(-180, 180, 13),
                                 yticks=np.linspace(-90, 90, 7))

# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax, labelsize=10)

# Use geocat.viz.util convenience function to make plots look like NCL plots by using latitude, longitude tick labels
gvutil.add_lat_lon_ticklabels(ax)

# Use geocat.viz.util convenience function to add titles to left and right of the plot axis.
gvutil.set_titles_and_labels(ax,
                             lefttitle="meridional wind component",
                             lefttitlefontsize=14,
                             righttitle="m/s",
                             righttitlefontsize=14,
                             xlabel="",
                             ylabel="")

# Show the plot
plt.show()
meridional wind component, m/s

Total running time of the script: ( 0 minutes 0.715 seconds)

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