NCL_polyg_8_lbar.py

This script illustrates the following concepts:

• Drawing a scatter plot on a map

• Changing the marker color and size in a map plot

• Plotting station locations using markers

• Creating a custom color bar

• Adding text to a plot

• Generating dummy data using “random_uniform”

• Binning data

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

• Original NCL script: https://www.ncl.ucar.edu/Applications/Scripts/polyg_8_lbar.ncl

• Original NCL plot: https://www.ncl.ucar.edu/Applications/Images/polyg_8_lbar_lg.png

Import packages:

import numpy as np
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
from matplotlib import colors, cm
import cmaps

import geocat.viz as gv

Generate dummy data

npts = 100
random = np.random.default_rng(seed=1)

# Create random coordinates to position the markers
lat = random.uniform(low=25, high=50, size=npts)
lon = random.uniform(low=-125, high=-70, size=npts)

# Create random data which the color will be based off of
r = random.uniform(low=-1.2, high=35, size=npts)

Specify bins and sizes and create custom mappable based on NCV_jet colormap

bins = [0, 5, 10, 15, 20, 23, 26]
cmap = cmaps.NCV_jet

# Create the boundaries for your data, this may be larger than bins to
# accommodate colors for data outside of the smallest and largest bins
boundaries = [-1.2, 0, 5, 10, 15, 20, 23, 26, 35]
norm = colors.BoundaryNorm(boundaries, cmap.N)
mappable = cm.ScalarMappable(norm=norm, cmap=cmap)

# Retrieve the list of colors to use for the markers
marker_colors = mappable.to_rgba(boundaries)

# Increasing sizes for the markers in each bin, by using numpy.geomspace the
# size differences are more noticeable
sizes = np.geomspace(10, 250, len(boundaries))

Plot:

plt.figure(figsize=(9, 6))
projection = ccrs.PlateCarree()
ax = plt.axes(projection=projection)
ax.set_extent([-125, -70, 25, 50], crs=projection)

# Draw land
ax.add_feature(cfeature.LAND, color='silver', zorder=0)
ax.add_feature(cfeature.LAKES, color='white', zorder=0)

# Use geocat.viz.util convenience function to set axes tick values
gv.set_axes_limits_and_ticks(ax,
                             xticks=np.linspace(-120, -70, 6),
                             yticks=np.linspace(25, 50, 6))

# Use geocat.viz.util convenience function to make latitude and longitude tick
# labels
gv.add_lat_lon_ticklabels(ax)

# Use geocat.viz.util convenience function to add minor and major tick lines
gv.add_major_minor_ticks(ax,
                         x_minor_per_major=1,
                         y_minor_per_major=1,
                         labelsize=12)

# Remove ticks on the top and right sides of the plot
ax.tick_params(axis='both', which='both', top=False, right=False)

# Use geocat.viz.util convenience function to add titles
gv.set_titles_and_labels(
    ax,
    maintitlefontsize=16,
    maintitle=
    "Dummy station data colored and\nsized according to range of values")

# Plot markers with values less than first bin value
masked_lon = np.where(r < bins[0], lon, np.nan)
masked_lat = np.where(r < bins[0], lat, np.nan)
plt.scatter(masked_lon,
            masked_lat,
            s=sizes[0],
            color=marker_colors[0],
            zorder=1)

# Plot all other markers but those in the last bin
for x in range(1, len(bins)):
    masked_lon = np.where(bins[x - 1] <= r, lon, np.nan)
    masked_lon = np.where(r < bins[x], masked_lon, np.nan)
    masked_lat = np.where(bins[x - 1] <= r, lat, np.nan)
    masked_lat = np.where(r < bins[x], masked_lat, np.nan)
    plt.scatter(masked_lon,
                masked_lat,
                s=sizes[x],
                color=marker_colors[x],
                zorder=1)

# Plot markers with values greater than or equal to last bin value
masked_lon = np.where(r >= bins[-1], lon, np.nan)
masked_lat = np.where(r >= bins[-1], lat, np.nan)
plt.scatter(masked_lon,
            masked_lat,
            s=sizes[-1],
            color=marker_colors[-1],
            zorder=1)

# Create colorbar
plt.colorbar(mappable=mappable,
             ax=ax,
             orientation='horizontal',
             drawedges=True,
             format='%.2f',
             ticks=bins)

plt.show()