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NCL_panel_10.py#
- 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:
Original NCL script: https://www.ncl.ucar.edu/Applications/Scripts/panel_10.ncl and https://www.ncl.ucar.edu/Applications/Scripts/panel_attach_10.ncl
Original NCL plot: https://www.ncl.ucar.edu/Applications/Images/panel_10_lg.png and https://www.ncl.ucar.edu/Applications/Images/panel_attach_10_lg.png
Import packages:
import numpy as np
import xarray as xr
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import matplotlib.gridspec as gridspec
from cartopy.mpl.gridliner import LongitudeFormatter
import warnings
import cmaps
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
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=cmaps.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
gv.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
gv.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
gv.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
gv.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
gv.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
gv.set_titles_and_labels(ax2, maintitle="Zonal Ave", maintitlefontsize=12)
# Plot zonal average
ax2.plot(mean, times, linewidth=0.5, color='black')
plt.show()
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=cmaps.BlWhRe)
# Use geocat.viz.util convenience function to add longitude tick labels
gv.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
gv.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
gv.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
gv.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
gv.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
gv.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
gv.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()
Total running time of the script: (0 minutes 0.785 seconds)