NCL_panel_10.pyΒΆ

This script illustrates the following concepts:
  • Drawing Hovmueller plots

  • Attaching plots along the Y axis

  • Using a blue-white-red color map

  • Drawing zonal average plots

  • Paneling attached plots

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

import geocat.datafiles as gdf
import geocat.viz.util as gvutil
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt

Import packages:

import numpy as np
import xarray as xr
from cartopy.mpl.gridliner import LatitudeFormatter, LongitudeFormatter
from geocat.viz import cmaps as gvcmaps
from mpl_toolkits.axes_grid1.inset_locator import inset_axes

Read in data:

# Open a netCDF data file using xarray default engine
# and load the data into xarrays
filename = 'chi200_ud_smooth.nc'
ds = xr.open_dataset(gdf.get('netcdf_files/' + filename))

lon = ds.lon
times = ds.time
scale = 1000000
chi = ds.CHI
chi = chi / scale

# Calculate zonal mean
with warnings.catch_warnings(
):  # This is not needed but is supressing a warning thrown by numpy checking for NaN values
    warnings.simplefilter("ignore")
    mean = chi.mean(dim='lon')

Create Single Plot:

fig, (ax1, ax2) = plt.subplots(nrows=1,
                               ncols=2,
                               sharey=True,
                               figsize=(12, 9),
                               gridspec_kw=dict(wspace=0,
                                                width_ratios=[0.75, 0.25],
                                                left=0.15,
                                                right=0.85,
                                                top=0.85,
                                                bottom=0.15))
# Create inset axes for color bar
cax1 = inset_axes(ax1,
                  width='100%',
                  height='7%',
                  loc='lower left',
                  bbox_to_anchor=(0, -0.15, 1, 1),
                  bbox_transform=ax1.transAxes,
                  borderpad=0)

# Draw contour lines
ax1.contour(lon,
            times,
            chi,
            levels=np.arange(-12, 13, 2),
            colors='black',
            linestyles='solid',
            linewidths=.5)

# Draw filled contours
cf = ax1.contourf(lon,
                  times,
                  chi,
                  levels=np.arange(-12, 13, 2),
                  cmap=gvcmaps.BlWhRe)

# Draw colorbar with larger tick labels
cbar = plt.colorbar(cf,
                    cax=cax1,
                    orientation='horizontal',
                    ticks=np.arange(-10, 11, 2))
cbar.ax.tick_params(labelsize=12)

# Use geocat.viz.util convenience function to set axes limits & tick values
gvutil.set_axes_limits_and_ticks(ax1,
                                 xlim=[100, 220],
                                 ylim=[0, 1.55 * 1e16],
                                 xticks=[135, 180],
                                 yticks=np.linspace(0, 1.55 * 1e16, 7),
                                 xticklabels=['135E', '180'],
                                 yticklabels=np.arange(0, 181, 30))

# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax1,
                             x_minor_per_major=3,
                             y_minor_per_major=3,
                             labelsize=12)

# Remove tick marks on right side of ax1
ax1.tick_params('y', which='both', right=False)

# Use geocat.viz.util convenience function to add titles
gvutil.set_titles_and_labels(ax1,
                             maintitle="Pacific Region",
                             lefttitle="Velocity Potential",
                             righttitle="m2/s",
                             ylabel="elapsed time")

# Format axes for zonal average plot
# Use geocat.viz.util convenience function to set axes limits & tick values
gvutil.set_axes_limits_and_ticks(ax2,
                                 xlim=[-0.6, 0.9],
                                 ylim=[0, 1.55 * 1e16],
                                 xticks=np.arange(-0.3, 0.7, 0.3),
                                 yticks=np.linspace(0, 1.55 * 1e16, 7),
                                 xticklabels=['-0.30', '', '0.30', ''],
                                 yticklabels=np.arange(0, 181, 30))

# Use geocat.viz.util convenience function to add minor and major tick lines
gvutil.add_major_minor_ticks(ax2,
                             x_minor_per_major=1,
                             y_minor_per_major=3,
                             labelsize=12)

# Remove tick marks on left side of ax2
ax2.tick_params('y', which='both', left=False)

# Use geocat.viz.util convenience function to add titles
gvutil.set_titles_and_labels(ax2, maintitle="Zonal Ave", maintitlefontsize=12)

# Plot zonal average
ax2.plot(mean, times, linewidth=0.5, color='black')

plt.show()
Velocity Potential, Pacific Region, m2/s, Zonal Ave

Define helper function to create the four subplots

def make_subplot(fig, gridspec, xlim):
    # Create axes for the contour plot and the zonal average plot
    ax1 = fig.add_subplot(gridspec[0])
    ax2 = fig.add_subplot(gridspec[1])

    # Draw contour lines
    ax1.contour(lon,
                times,
                chi,
                levels=np.arange(-12, 13, 2),
                colors='black',
                linestyles='solid',
                linewidths=.5)

    # Draw filled contours, save the mappable to create colorbar later
    color_mappable = ax1.contourf(lon,
                                  times,
                                  chi,
                                  levels=np.arange(-12, 13, 2),
                                  cmap=gvcmaps.BlWhRe)

    # Use geocat.viz.util convenience function to add longitude tick labels
    gvutil.add_lat_lon_ticklabels(ax1)

    # Remove degree symbol from tick labels
    ax1.xaxis.set_major_formatter(LongitudeFormatter(degree_symbol=''))

    # Use geocat.viz.util convenience function to set axes limits & tick values
    gvutil.set_axes_limits_and_ticks(ax1,
                                     xlim=xlim,
                                     ylim=[0, 1.55 * 1e16],
                                     xticks=np.arange(xlim[0], xlim[1], 30),
                                     yticks=np.linspace(0, 1.55 * 1e16, 7),
                                     yticklabels=[])

    # Use geocat.viz.util convenience function to add minor and major tick lines
    gvutil.add_major_minor_ticks(ax1,
                                 x_minor_per_major=2,
                                 y_minor_per_major=3,
                                 labelsize=10)

    # Remove tick marks on right side of ax1
    ax1.tick_params('y', which='both', right=False)

    # Use geocat.viz.util convenience function to add titles
    gvutil.set_titles_and_labels(ax1,
                                 lefttitle="Velocity Potential",
                                 righttitle="m2/s",
                                 lefttitlefontsize=10,
                                 righttitlefontsize=10)

    # Format axes for zonal average plot
    # Use geocat.viz.util convenience function to set axes limits & tick values
    gvutil.set_axes_limits_and_ticks(ax2,
                                     xlim=[-0.6, 0.9],
                                     ylim=[0, 1.55 * 1e16],
                                     xticks=np.arange(-0.3, 0.7, 0.3),
                                     yticks=np.linspace(0, 1.55 * 1e16, 7),
                                     xticklabels=['-0.30', '', '0.30', ''],
                                     yticklabels=[])

    # Use geocat.viz.util convenience function to add minor and major tick lines
    gvutil.add_major_minor_ticks(ax2,
                                 x_minor_per_major=1,
                                 y_minor_per_major=3,
                                 labelsize=8)

    # Remove tick marks on left side of ax2
    ax2.tick_params('y', which='both', left=False)

    # Use geocat.viz.util convenience function to add titles
    gvutil.set_titles_and_labels(ax2,
                                 maintitle="Zonal Ave",
                                 maintitlefontsize=8)

    # Plot zonal average
    ax2.plot(mean, times, linewidth=0.5, color='black')
    return color_mappable

Create the four panel plot

fig = plt.figure(figsize=(10, 10))

# Create a three by two grid to hold the four plots and the colorbar
outer_grid = gridspec.GridSpec(3,
                               2,
                               figure=fig,
                               hspace=0.35,
                               height_ratios=[0.475, 0.475, 0.05])

# Create an array to hold the internal gridspecs
inner_grids = np.empty(4, dtype=gridspec.GridSpec)

# Create the gridspecs for each of the four plots
for i in range(0, 4):
    inner_grids[i] = gridspec.GridSpecFromSubplotSpec(
        1, 2, subplot_spec=outer_grid[i], wspace=0, width_ratios=[0.75, 0.25])
make_subplot(fig, inner_grids[0], [0, 90])
make_subplot(fig, inner_grids[1], [90, 180])
make_subplot(fig, inner_grids[2], [180, 270])
color_mappable = make_subplot(fig, inner_grids[3], [270, 360])

# Create axes for colorbar and then draw colorbar
cax = fig.add_subplot(outer_grid[2, :])
plt.colorbar(color_mappable,
             cax=cax,
             ticks=np.arange(-10, 12, 2),
             orientation='horizontal',
             drawedges=True)

# Add figure title
fig.suptitle(filename, fontsize=18, y=0.95)
plt.show()
chi200_ud_smooth.nc, Velocity Potential, m2/s, Zonal Ave, Velocity Potential, m2/s, Zonal Ave, Velocity Potential, m2/s, Zonal Ave, Velocity Potential, m2/s, Zonal Ave

Total running time of the script: ( 0 minutes 1.107 seconds)

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