NCL_polyg_4.py

This script illustrates the following concepts:
  • Drawing a cylindrical equidistant map

  • Zooming in on a particular area on a cylindrical equidistant map

  • Attaching an outlined box to a map plot

  • Attaching filled polygons to a map plot

  • Filling in polygons with a shaded pattern

  • Changing the color and thickness of polylines

  • Changing the color of a filled polygon

  • Labeling the lines in a polyline

  • Changing the density of a fill pattern

  • Adding text to a plot

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

Import packages:

import numpy as np
import xarray as xr
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, choosing the 2nd timestamp
ds = xr.open_dataset(gdf.get("netcdf_files/uv300.nc")).isel(time=1)

Utility Function: Make Base Plot:

# Define a utility function to create the basic contour plot, which will get used twice to create two slightly
# different plots
def make_base_plot():

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

    # Add continents
    continents = cartopy.feature.NaturalEarthFeature(
        name="coastline",
        category="physical",
        scale="50m",
        edgecolor="None",
        facecolor="lightgray",
    )
    ax.add_feature(continents)

    # Set map extent
    ax.set_extent([-130, 0, -20, 40], crs=ccrs.PlateCarree())

    # Define the contour levels. The top range value of 44 is not included in the levels.
    levels = np.arange(-12, 44, 4)

    # Using a dictionary prevents repeating the same keyword arguments twice for the contours.
    kwargs = dict(
        levels=levels,  # contour levels specified outside this function
        xticks=[-120, -90, -60, -30, 0],  # nice x ticks
        yticks=[-20, 0, 20, 40],  # nice y ticks
        transform=ccrs.PlateCarree(),  # ds projection
        add_colorbar=False,  # don't add individual colorbars for each plot call
        add_labels=False,  # turn off xarray's automatic Lat, lon labels
        colors="gray",  # note plurals in this and following kwargs
        linestyles="-",
        linewidths=0.5,
    )

    # Contourf-plot data (for filled contours)
    hdl = ds.U.plot.contour(x="lon", y="lat", ax=ax, **kwargs)

    # Add contour labels.   Default contour labels are sparsely placed, so we specify label locations manually.
    # Label locations only need to be approximate; the nearest contour will be selected.
    label_locations = [
        (-123, 35),
        (-116, 17),
        (-94, 4),
        (-85, -6),
        (-95, -10),
        (-85, -15),
        (-70, 35),
        (-42, 28),
        (-54, 7),
        (-53, -5),
        (-39, -11),
        (-28, 11),
        (-16, -1),
        (-8, -9),  # Python allows trailing list separators.
    ]
    ax.clabel(
        hdl,
        np.arange(-8, 24, 8),  # Only label these contour levels: [-8, 0, 8, 16]
        fontsize="small",
        colors="black",
        fmt="%.0f",  # Turn off decimal points
        manual=label_locations,  # Manual label locations
        inline=False)  # Don't remove the contour line where labels are located.

    # Create a rectangle patch, to color the border of the rectangle a different color.
    # Specify the rectangle as a corner point with width and height, to help place border text more easily.
    left, width = -90, 45
    bottom, height = 0, 30
    right = left + width
    top = bottom + height

    # Draw rectangle patch on the plot
    p = plt.Rectangle(
        (left, bottom),
        width,
        height,
        fill=False,
        zorder=3,  # Plot on top of the purple box border.
        edgecolor='red',
        alpha=0.5)  # Lower color intensity.
    ax.add_patch(p)

    # Draw text labels around the box.
    # Change the default padding around a text box to zero, making it a "tight" box.
    # Create "text_args" to keep from repeating code when drawing text.
    text_shared_args = dict(
        fontsize=8,
        bbox=dict(boxstyle='square, pad=0',
                  facecolor='white',
                  edgecolor='white'),
    )

    # Draw top text
    ax.text(left + 0.6 * width,
            top,
            'test',
            horizontalalignment='right',
            verticalalignment='center',
            **text_shared_args)

    # Draw bottom text.   Change text background to match the map.
    ax.text(
        left + 0.5 * width,
        bottom,
        'test',
        horizontalalignment='right',
        verticalalignment='center',
        fontsize=8,
        bbox=dict(boxstyle='square, pad=0',
                  facecolor='lightgrey',
                  edgecolor='lightgrey'),
    )

    # Draw left text
    ax.text(left,
            top,
            'test',
            horizontalalignment='center',
            verticalalignment='top',
            rotation=90,
            **text_shared_args)

    # Draw right text
    ax.text(right,
            bottom,
            'test',
            horizontalalignment='center',
            verticalalignment='bottom',
            rotation=-90,
            **text_shared_args)

    # Add lower text box.  Box appears off-center, but this is to leave room
    # for lower-case letters that drop lower.
    ax.text(1.0,
            -0.20,
            "CONTOUR FROM -12 TO 40 BY 4",
            fontname='Helvetica',
            horizontalalignment='right',
            transform=ax.transAxes,
            bbox=dict(boxstyle='square, pad=0.15',
                      facecolor='white',
                      edgecolor='black'))

    # Use geocat.viz.util convenience function to add main title as well as titles to left and right of the plot axes.
    gvutil.set_titles_and_labels(ax,
                                 lefttitle="Zonal Wind",
                                 lefttitlefontsize=12,
                                 righttitle="m/s",
                                 righttitlefontsize=12)

    # Use geocat.viz.util convenience function to add minor and major tick lines
    gvutil.add_major_minor_ticks(ax, y_minor_per_major=4)

    # 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)

    return ax

Plot 1 (Text inside a box):

# Create the base figure
ax = make_base_plot()

# Draw text inside of box
ax.text(-60.0, 15.0, "sample", fontsize=11, horizontalalignment='center')

# Show the plot
plt.show()
Zonal Wind, m/s

Utility Function: Draw Hatch Polygon:

# Define a utility function that draws a polygon and then erases its border with another polygon.
def draw_hatch_polygon(xvals, yvals, hatchcolor, hatchpattern):
    """Draw a polygon filled with a hatch pattern, but with no edges on the
    polygon."""
    ax.fill(
        xvals,
        yvals,
        edgecolor=hatchcolor,
        zorder=
        -1,  # Place underneath contour map (larger zorder is closer to viewer).
        fill=False,
        linewidth=0.5,
        hatch=hatchpattern,
        alpha=0.3  # Reduce color intensity
    )

    # Hatch color and polygon edge color are tied together, so we have to draw a white polygon edge
    # on top of the original polygon to remove the edge.
    ax.fill(
        xvals,
        yvals,
        edgecolor='white',
        zorder=
        0,  # Place on top of other polygon (larger zorder is closer to viewer).
        fill=False,
        linewidth=1  # Slightly larger linewidth removes ghost edges.
    )

Plot 2 (Polygons with hatch patterns):

# Make this figure the thumbnail image on the HTML page.
# sphinx_gallery_thumbnail_number = 2

# Create the base figure
ax = make_base_plot()

# Plot the hatch pattern "underneath" the red box, to hide the purple border that is unavoidably attached to producing
# the hatch pattern.
x_points = [-90.0, -45.0, -45.0, -90.0, -90.0]
y_points = [30.0, 30.0, 0.0, 0.0, 30.0]

ax.fill(
    x_points,
    y_points,
    edgecolor='purple',  # Box hatch pattern is purple.
    zorder=2,  # Place on top of map (larger zorder is closer to viewer).
    fill=False,
    hatch='...',  # Adding more or fewer dots to '...' will change hatch density.
    linewidth=0.5,  # Make each dot smaller
    alpha=0.2  # Make hatch semi-transparent using alpha level in range [0, 1].
)

# Draw some triangles with various hatch pattern densities.
x_tri = np.array([-125, -115, -120])
y_tri = np.array([-15, -10, 5])

draw_hatch_polygon(x_tri, y_tri, 'brown', '++++')

draw_hatch_polygon(x_tri + 10, y_tri, 'blue', '+++')

draw_hatch_polygon(x_tri + 20, y_tri, 'forestgreen', '++')

# Show the plot
plt.show()
Zonal Wind, m/s

Total running time of the script: ( 0 minutes 0.528 seconds)

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