Gfrktyl

Im trying to run a short climate model in Python, but get the error: TypeError: super(type, obj): obj must be an instance or subtype of type. can somebody advise please?

# Import libraries

import netCDF4 as nc

import numpy as np

import matplotlib.pyplot as plt



# import the coordinate reference system from cartopyy

import cartopy.crs as ccrs



# import the mutils library

# import mplotutils as mp





#--------------------------------------------------------------------------

# Load in netcdf files from simulation

#--------------------------------------------------------------------------



fN='control2_latent_heat.nc'

print(fN)



# opening the dataset

ncf = nc.Dataset(fN)



# print all variables

print(ncf.variables.keys())



# get a variable 

ncf.variables['lon']



# check the units of the variable (in the netCDF4.variable structure)

ncf.variables['time'].units



# load actual data (as a numpy array): longitude and latitude

lon = ncf.variables['lon'][:]

lat = ncf.variables['lat'][:]



# example to convert the masked array to a np array

lon_nm = np.asarray(lon)

lat_nm = np.asarray(lat)



# load evaporation

var_orig = ncf.variables['variables'][:]





#--------------------------------------------------------------------------

# Manipulate variables

#--------------------------------------------------------------------------



# remove first dimension of variable (to have 2D var)

rows = var_orig.shape[1]

cols = var_orig.shape[2]

var_masked = np.reshape(var_orig,(rows,cols))





# extract the mask from the variable and convert to a nan mask

mask_bool = np.logical_not(np.ma.getmask(var_masked))

mask = 1*mask_bool.astype(float)

mask[~mask_bool] = np.nan



# convert the masked variable to a numpy array and apply the nan mask

var_nomask =  np.asarray(var_masked)

var = var_nomask*mask 







#-----------------------------------------------------------------------------

# Plot the netCDF file (map)

# ----------------------------------------------------------------------------



# define the projection

projection = ccrs.PlateCarree()



# initiate the figure 

ax = plt.axes(projection=projection)



# add the coastlines to the plot

ax.coastlines()



# the original cesm lon and lat assumes center of gridcel. pcolormesh assumes border. 

# this function transforms lon and lat to represent gridcel edges.

# only necessary for the cesm grid! (not for the observational data)



LON = np.arange(0, 361, 2.5)

LAT = np.arange(-90, 91, 1.9)





# add the data to the map (more info on colormaps: https://matplotlib.org/users/colormaps.html)

h = ax.pcolormesh(LON,LAT,var, cmap='GnBu',rasterized=True)



# set the extent of the cartopy geoAxes to "global"

ax.set_global()



# or alternatively, if you want to plot a certain region, use (example: Europe)

#ax.set_extent([-13, 43, 35, 70], ccrs.PlateCarree())



# plot the colorbar

cbar = plt.colorbar(h, ax=ax, label='label', orientation='horizontal', extend='max')



# adjust colorbar extent to axes extent

ax.set_aspect('auto')



# add title

plt.title('your title')



# save the figure, adjusting the resolution 

plt.savefig('yourfigure.png', dpi=300)