This script illustrates the following concepts:
  • Customizing a colorbar for a contour plot

  • Making the colorbar be horizontal

  • Setting the fontsize of colorbar labels

  • Changing the levels of the colorbar labels

  • Changing the aspect ratio and size of a colorbar

  • Changing the spaces (padding) between ticklabels and axes

  • Using a different color scheme to follow best practices for visualizations

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

Import packages:

import numpy as np
import xarray as xr
import as ccrs
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/"))
u = ds.u.isel(time=4)


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

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

# Set contour levels
levels = np.arange(0, 11, 1)

# Plot contour lines
lines = u.plot.contour(ax=ax, levels=levels, linewidths=0.5, add_labels=False)

# Draw contour labels and set bounding boxes for the labels
ax.clabel(lines, np.array([0]), colors='black', fmt="%.0f", fontsize=18)
        dict(mutation_aspect=0.8, facecolor='white', edgecolor='none', pad=2))
    for txt in lines.labelTexts

# Plot filled contour
colors = u.plot.contourf(ax=ax,

# Add colorbar
cbar = plt.colorbar(
    shrink=0.65,  # fraction of the size of the colorbar
    pad=0.13,  # fraction of original axes between colorbar and new image axes
    extendrect=True,  # set colorbar shape to be rectangular
    aspect=11,  # aspect ratio
    ticks=levels[:-1:2])  # set colorbar levels

# Set colorbar label size, labelsize=24, pad=12)

# Use geocat.viz.util convenience function to set axes limits & tick values without calling several matplotlib functions
                             xlim=(0, 49),
                             ylim=(0, 29),
                             xticks=np.linspace(0, 40, 5),
                             yticks=np.linspace(0, 25, 6))

# Use geocat.viz.util convenience function to add minor and major tick lines
gv.add_major_minor_ticks(ax, x_minor_per_major=5, y_minor_per_major=5)

# Use geocat.viz.util convenience function to add titles to left and right of the plot axis.
gv.set_titles_and_labels(ax, maintitle='Cone amplitude', maintitlefontsize=32)

# Set both major and minor ticks to point inwards
ax.tick_params(which='both', direction='in')

# Set different tick font sizes and padding for X and Y axis
ax.tick_params(axis='x', labelsize=24, pad=18)
ax.tick_params(axis='y', labelsize=16, pad=12)

# Set major and minor tick length
ax.tick_params(which='major', length=16)
ax.tick_params(which='minor', length=10)

# Show plot
Cone amplitude

Total running time of the script: (0 minutes 0.299 seconds)

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