Example: contour plot with colors and labeled lines

In this example, we’ll plot the Meridional Overturning (MOC) streamfunction from the CESM-POP ocean component model.

%matplotlib inline
import xarray as xr
import numpy as np
import matplotlib.pyplot as plt

Load the dataset

ds = xr.open_zarr('../data/moc.zarr')
ds

<xarray.Dataset>
Dimensions:       (lat_aux_grid: 395, moc_z: 61)
Coordinates:
  * lat_aux_grid  (lat_aux_grid) float32 -79.48815 -78.952896 ... 89.47441 90.0
  * moc_z         (moc_z) float32 0.0 1000.0 2000.0 ... 525000.94 549999.06
Data variables:
    MOC           (moc_z, lat_aux_grid) float32 dask.array<shape=(61, 395), chunksize=(61, 395)>
Attributes:
    note:         Total global MOC from b.e21.B1850.f09_g17.CMIP6-piControl.001
    time_period:  1000-1099

Make a “quick-look” plot

Use the xarray plot method to take a quick look at the data.

h = ds.MOC.plot(yincrease=False)

png

Make a publication-quality plot

Set the contour levels.

lo = -40.
hi = 40.
dc = 4.
cnlevels = np.arange(lo, hi+dc, dc)
cnlevels

array([-40., -36., -32., -28., -24., -20., -16., -12.,  -8.,  -4.,   0.,
         4.,   8.,  12.,  16.,  20.,  24.,  28.,  32.,  36.,  40.])

Generate the figure

Create a two-panel plot with refined resolution in the upper ocean.

# create a figure object
fig = plt.figure(figsize=(7.2, 4.8))

# add two axes
ax1 = fig.add_axes([0.1, 0.51, 0.7, 0.35]) # top 1000 m
ax2 = fig.add_axes([0.1, 0.1, 0.7, 0.4]) # deep ocean

# set the background color where 
ax1.set_facecolor('darkgray')
ax2.set_facecolor('darkgray')

# plot the field by looping over axes
cs = [None]*2 # dimension lists
mesh = [None]*2
for i, ax in enumerate([ax1, ax2]):
    # contour lines
    cs[i] = ax.contour(ds.lat_aux_grid, ds.moc_z*1e-2, ds.MOC,
                       colors='k',
                       linewidths=0.5,
                       levels=cnlevels)

    # contour colors
    mesh[i] = ax.contourf(ds.lat_aux_grid, ds.moc_z*1e-2, ds.MOC,
                          levels=cnlevels,
                          cmap='PRGn',
                          extend='both')

# set axis limits, note the reversed limits reverse the y-axis
ax1.set_ylim([1000., 0.])
ax2.set_ylim([5500., 1000.])
ax1.set_xlim([-90, 90])
ax2.set_xlim([-90, 90])

# add contour line labels after axis limits have been set
for csi in cs:
    lb = plt.clabel(csi, fontsize=8, inline=True, fmt='%.0f')    

# set tick properties top axis
ax1.set_xticklabels([])
ax1.set_yticklabels(np.arange(0, 1000, 200))
ax1.minorticks_on()
ax1.xaxis.set_ticks_position('top')

# set tick properties bottom axis
ax2.minorticks_on()
ax2.set_xlabel('Latitude')
ax2.xaxis.set_ticks_position('bottom')

# axis label
ax2.set_ylabel('Depth [m]')
ax2.yaxis.set_label_coords(-0.12, 1.05)

# title
ax1.set_title('MOC [Sv]',loc='left')
ax1.set_title('Global',loc='right');

png

Save figure to file

fig.savefig('moc-plot.png', dpi=300, bbox_inches='tight')