NCL_vector_4.pyΒΆ

Plot U & V vectors globally, colored according to temperature

This script illustrates the following concepts:
  • Coloring vectors based on temperature data

  • Changing the scale of the vectors on the plot

See following URLs to see the reproduced NCL plot & script:
import cartopy
import cartopy.crs as ccrs
import geocat.datafiles as gdf

Import packages:

import xarray as xr
from geocat.viz import cmaps as gvcmaps
from geocat.viz import util as gvutil
from matplotlib import pyplot as plt

Read in data:

# Open a netCDF data file using xarray default engine and load the data into xarrays
file_in = xr.open_dataset(gdf.get("netcdf_files/83.nc"))

# Extract slices of lon and lat for first timestamp and 13th lev
ds = file_in.isel(time=0, lev=12, lon=slice(0, -1, 5), lat=slice(2, -1, 3))

Plot:

# Because there is no equivalent to ``CurlyVector`` in ``geocat.viz`` yet,
# this plot does not look as identical as the NCL version.

# Generate figure (set its size (width, height) in inches)
fig, ax = plt.subplots(figsize=(10, 7.25))

# Generate axes using Cartopy projection
ax = plt.axes(projection=ccrs.PlateCarree())

# Import an NCL colormap and truncate it for a range and color levels
plt.cm.register_cmap(
    'BlAqGrYeOrReVi200',
    gvutil.truncate_colormap(gvcmaps.BlAqGrYeOrReVi200,
                             minval=0.03,
                             maxval=0.95,
                             n=16))
cmap = plt.cm.get_cmap('BlAqGrYeOrReVi200', 16)

# Draw vector plot
# (there is no matplotlib equivalent to "CurlyVector" yet)
Q = plt.quiver(ds['lon'],
               ds['lat'],
               ds['U'].data,
               ds['V'].data,
               ds['T'].data,
               cmap=cmap,
               zorder=1,
               pivot="middle",
               width=0.001)
plt.clim(228, 292)

# Draw legend for vector plot
ax.add_patch(
    plt.Rectangle((150, -140),
                  30,
                  30,
                  facecolor='white',
                  edgecolor='black',
                  clip_on=False))
qk = ax.quiverkey(Q,
                  0.93,
                  0.06,
                  10,
                  r'10 $m/s$',
                  labelpos='N',
                  coordinates='figure',
                  color='black')

# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax, labelsize=12)

# 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)

# Set major and minor ticks
plt.xticks(range(-180, 181, 30))
plt.yticks(range(-90, 91, 30))

# Use geocat.viz.util convenience function to add titles to left and right of the plot axis.
gvutil.set_titles_and_labels(ax,
                             maintitle="Vectors colored by a scalar map",
                             lefttitle="Temperature",
                             righttitle="$^{\circ}$K")

cax = plt.axes((0.225, 0.075, 0.55, 0.025))
cbar = fig.colorbar(Q,
                    ax=ax,
                    cax=cax,
                    orientation='horizontal',
                    ticks=range(232, 289, 8),
                    drawedges=True)

# Turn on continent shading
ax.add_feature(cartopy.feature.LAND,
               edgecolor='lightgray',
               facecolor='lightgray',
               zorder=0)

# Generate plot!
plt.tight_layout()
plt.show()
Temperature, Vectors colored by a scalar map, $^{\circ}$K

Out:

/home/docs/checkouts/readthedocs.org/user_builds/geocat-examples/checkouts/latest/Plots/Vectors/NCL_vector_4.py:116: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
  plt.tight_layout()

Total running time of the script: ( 0 minutes 0.439 seconds)

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