GeometryCollection' object is not subscriptable gfs.t1xz.pgrb2.0p25.f00x

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Error ( TypeError: 'GeometryCollection' object is not subscriptable ) when creating the figure with global GFS data gfs.t1xz.pgrb2.0p25.f00x. With some files I don't get this error.

The error is in the next line of the code.

    contourf_mcs = ax.contourf(ds['longitude'], ds['latitude'], masked_mcs_pwat, levels=[-1.15, -0.12, 1.58, 2.74], cmap=cmap, transform=ccrs.PlateCarree(),extend= 'both')

I use python3.11 in Anaconda environment, matplotlib 3.7.2 metpy 1.5.1 cartopy 0.22.0

Code fragment:

    Pyhton
        import pygrib
        import xarray as xr
        import metpy.calc as mpcalc
        import matplotlib.pyplot as plt
        import matplotlib as mpl
        import numpy as np
        import cartopy.crs as ccrs
        import cartopy.feature as cfeature
            
        def read_f00x_data (file):
            indices = []
            variable_mapping = {}
            
            grbs = pygrib.open(file)
            ds = xr.Dataset()
        
            for idx in grbs:
                # buscar los nombres de las variables
                if idx.name == 'U component of wind' and idx.level == 500:        
                    indices.append(idx.messagenumber)
                    variable_mapping[idx.messagenumber] = 'u500hPa'
                if idx.name == 'V component of wind' and idx.level == 500:        
                    indices.append(idx.messagenumber)
                    variable_mapping[idx.messagenumber] = 'v500hPa'
                if idx.name == 'Temperature' and idx.level == 750:        
                    indices.append(idx.messagenumber)
                    variable_mapping[idx.messagenumber] = 't750hPa'
                if idx.name == 'U component of wind' and idx.level == 750:        
                    indices.append(idx.messagenumber)
                    variable_mapping[idx.messagenumber] = 'u750hPa'
                if idx.name == 'V component of wind' and idx.level == 750:        
                    indices.append(idx.messagenumber)
                    variable_mapping[idx.messagenumber] = 'v750hPa'
                if idx.name == 'U component of wind' and idx.level ==1000:        
                    indices.append(idx.messagenumber)
                    variable_mapping[idx.messagenumber] = 'u1000hPa'
                if idx.name == 'V component of wind' and idx.level == 1000:        
                    indices.append(idx.messagenumber)
                    variable_mapping[idx.messagenumber] = 'v1000hPa'
                if idx.name == 'Vertical velocity' and idx.level == 800:        
                    indices.append(idx.messagenumber)
                    variable_mapping[idx.messagenumber] = 'w800hPa'   
                if idx.name == 'Precipitable water':        
                    indices.append(idx.messagenumber)
                    variable_mapping[idx.messagenumber] = 'pwatcapa'
                if idx.name == 'Best (4-layer) lifted index':        
                    indices.append(idx.messagenumber)
                    variable_mapping[idx.messagenumber] = 'lftx4'
        
            grbs.close()
        
            grbs = pygrib.open(file)
            ds = xr.Dataset()
        
            for idx in indices:
                grb = grbs.message(idx)
                variable_short_name = variable_mapping.get(idx, grb.shortName)
                unit_of_measure = grb.units
                START_DATE = grb.dataDate
                
                latitudes, longitudes = grb.latlons()
                values = grb.values
                
                data = xr.DataArray(values, coords={'latitude': latitudes[:, 0], 'longitude': longitudes[0, :]},
                                    dims=['latitude', 'longitude'], attrs={'units': unit_of_measure, 'long_name': grb.name, 'short_name': variable_short_name, 'START_DATE': START_DATE})
                ds[variable_short_name] = data
        
            grbs.close()
            ds.metpy.parse_cf()
            return ds
        
        def calculate_mcs_index(ds):
            u_500hPa = ds.u500hPa
            v_500hPa = ds.v500hPa
            u_1000hPa = ds.u1000hPa
            v_1000hPa = ds.v1000hPa
            u_750hPa = ds.u750hPa
            v_750hPa = ds.v750hPa
            t_750hPa = ds.t750hPa
            w_800hPa = ds.w800hPa
            pli = ds.lftx4
            pwat = ds.pwatcapa
            
            mag500 = mpcalc.wind_speed(u_500hPa, v_500hPa)
            mag1000 = mpcalc.wind_speed(u_1000hPa, v_1000hPa)
            shear = mag500 - mag1000
            gradiente_horizontal = mpcalc.advection(t_750hPa, u_750hPa, v_750hPa)
            
            mcs = ((shear.values - 13.66) / 5.5) + ((gradiente_horizontal.values - 4.28e-5) / 5.19e-5) + (-(w_800hPa + 0.269) / 0.286) + (-(pli + 2.142) / 2.175)
            return mcs
        
        def create_mcs_map(ds, mcs, pwat, pli):
            fig = plt.figure(figsize=(16, 14))
            ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
            ax.add_feature(cfeature.COASTLINE)
            ax.add_feature(cfeature.BORDERS)
            # ax.add_feature(cfeature.STATES)
            ax.gridlines(draw_labels=True, linewidth=0.5, color='gray', alpha=0.5, linestyle='--')
            extent = [-75, -45, -40, -16]
            ax.set_extent(extent, crs=ccrs.PlateCarree())
            ax.set_title(f"Parcel MCS index {fecha_actual}.{archivo[20:]} {fecha_actual_title}.{hora_local}", fontsize=16, fontweight='bold')
            
            masked_mcs_pwat = np.where((pwat < 27) & (pli > 0.9), np.nan, mcs)
            
            cmap = (mpl.colors.ListedColormap(['green', 'yellow', 'orange'])
                    .with_extremes(over='red', under='white'))
            contourf_mcs = ax.contourf(ds['longitude'], ds['latitude'], masked_mcs_pwat, levels=[-1.15, -0.12, 1.58, 2.74], cmap=cmap, transform=ccrs.PlateCarree(),extend='both')
            
            bounds = [-1.15, -0.12, 1.58, 2.74]
            norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
            cbar_temp = plt.colorbar(
                mpl.cm.ScalarMappable(cmap=cmap, norm=norm),
                ax=ax,
                extend='both',
                extendfrac='auto',
                ticks=bounds,
                spacing='uniform',
                orientation='horizontal',
                label='Parcel MCS index',
                aspect=30,
                pad=0.02,
                shrink=0.8,
                fraction=0.1,
                drawedges=True
            )
            cbar_temp.set_ticklabels([-1.15, -0.12, 1.58, '2.74'])
            cbar_temp.set_label('Parcel MCS index', fontsize=14)
        
        ds = read_f00x_data(archivo)
        mcs = calculate_mcs_index(ds)
        create_mcs_map(ds, mcs, ds.pwatcapa, ds.lftx4)
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