Plot a timestep WRF output

labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
sizes = [15, 30, 45, 10]
explode = (0, 0.1, 0, 0)  # only "explode" the 2nd slice (i.e. 'Hogs')

fig1, ax1 = plt.subplots()
ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',
        shadow=True, startangle=90)
ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.

plt.show()

import xarray as xr
import pandas as pd
import xwrf
from pyproj import Transformer, CRS
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import proplot as pplt
import numpy as np
from scipy.stats import pearsonr  
import xskillscore as xs
from sklearn.metrics import mean_squared_error
import math
from datetime import datetime
import proplot as pplt

from dask.distributed import Client, LocalCluster
cluster = LocalCluster()
client = Client(cluster)

import warnings
warnings.filterwarnings("ignore")

client
from datetime import datetime, timedelta

wrfData=xr.open_dataset('/blue/dhingmire/ROMS_Input/ERA5/1992/d2/wrfout_d02_1993-01-02_00:00:00')
fig,axes = pplt.subplots(nrows=6,ncols=4,proj='cyl')
axes.format(lonlim=(-160,-100), latlim=(65,30),
              labels=True, coast=True )
lons=wrfData.SWDOWN[16,:,:].XLONG
lats=wrfData.SWDOWN[16,:,:].XLAT

#inData.T2[0,:,:].plot()
for i in range(0,24):
    #print(i)
   
    con=axes[i].contourf(lons,lats,wrfData.SWDOWN[i,:,:], 
                    extend='both',cmap = 'terrain_r',levels=np.arange(0,600,50))
    axes[i].set_title(wrfData.SWDOWN[i,:,:].XTIME.values)
    #axes[i].colorbar(con)
#con = axes[0,0].contourf(inData.T2[0,:,:], 
#                      extend='both',cmap = 'coolwarm',levels=np.arange(268,294,2))
    
fig.colorbar(con)
#bar = fig.colorbar(con, loc='b', label='Surface pressure (Pa)') 
fig.format(coast=True,suptitle=wrfData.SWDOWN[16,:,:].description)#,toplabels=['WRF Output','ERA5'])