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:
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt

Import packages:

import numpy as np
from geocat.viz import cmaps as gvcmap
from geocat.viz import util as gvutil
from matplotlib import cm, colors

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 = gvcmap.NCV_jet

# Create the boundaries for your data, this may be larger than bins to
# accomodate 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)

# Retreive 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
gvutil.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
gvutil.add_lat_lon_ticklabels(ax)

# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.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
gvutil.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()
Dummy station data colored and sized according to range of values

Total running time of the script: ( 0 minutes 0.240 seconds)

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