data = data.sortby('longitude') data = data.sortby('latitude') #data.to_netcdf('/home/hanna/TEMP_MODELS/{}_L5.nc'.format(fold)) #print('stored nc files') vals = data['mae_test'].values.transpose() # constant is num hour 2014+-2018 cntours = axes[-1].contourf(vals, levels=100, cmap='hot_r') # Removes white lines for c in cntours.collections: c.set_edgecolor("face") #fig.colorbar(cntours, ax=axes[-1], label = '{}'.format(var)) axes[-1].set_title('AR-B-L5', fontsize = 14) #plt.xlabel('Longitude') #ax.set_xlabel('Longitude') axes[-1].set_ylabel('Latitude') axes[-1] = add_ticks(axes[-1]) #################### Plot ConvLSTM data = xr.open_dataset(os.path.join('/home/hanna/MS-thesis/python_figs/','mae_convlstm_best_model.nc')) vals = data[var].values/43680 print(np.mean(vals)) cntours = axes[1].contourf(vals, levels=levels_contourplot, cmap='hot_r') # Removes white lines for c in cntours.collections: c.set_edgecolor("face") #a = sns.heatmap(vals, ax = ax, cbar = True, cmap = 'viridis', linewidths=0) axes[1].set_title('ConvLSTM-B10-SL24-32-3x3-32-3x3', fontsize = 14) axes[1].set_ylabel('Latitude')
p_vals = prediction[var].values cntours = axes[1].contourf(p_vals, levels=levels_contourplot, cmap='Blues_r') axes[1].set_title('Prediction') # Removes white lines for c in cntours.collections: c.set_edgecolor("face") print('Warming the colorbar is made based on the last subplot---') #cb_ax = fig.add_axes([0.83, 0.1, 0.02, 0.8]) fig.colorbar(cntours, ax=axes, anchor=(1.0, 0.0), label='{} [{}]'.format( var, UNITS[var])) # ax=axes, orientation="horizontal", #a = sns.heatmap(vals, ax = ax, cbar = True, cmap = 'viridis', linewidths=0) axes[0].set_ylabel('Latitude') axes[1].set_ylabel('Latitude') axes[1].set_xlabel('Longitude') axes[0] = add_ticks(axes[0], x_num_tikz=9, y_num_tikz=5) axes[1] = add_ticks(axes[1], x_num_tikz=9, y_num_tikz=5) plt.subplots_adjust(left=0.1, bottom=0.2, right=0.8, top=0.9, wspace=0.1, hspace=0.3) plt.savefig(path_python_figures + 'target_prediction_plot_vertical.pdf')
fig.set_size_inches(w = TEXT_WIDTH_IN, h = TEXT_HEIGHT_IN - 1 - 2) # minus to for title fig.suptitle('Correlation to Cloud Fractional Cover', fontsize = 16) counter = 0 for var, ax in zip(VARIABLES, axes): #if var != 'tcc': #print('Warning this duplicates the RH in plot for tcc') #vals = data[var].values vals = data[var].values min = np.min(vals) max = np.max(vals) val = np.max( [max, abs(min)] ) cntours = ax.contourf(vals, levels=levels_contourplot, cmap='PiYG', vmin = -val, vmax = val) counter += 1 # Removes white lines for c in cntours.collections: c.set_edgecolor("face") fig.colorbar(cntours, ax=ax) #a = sns.heatmap(vals, ax = ax, cbar = True, cmap = 'viridis', linewidths=0) ax.set_title(LONGNAME[var], fontsize = 14) ax.set_ylabel('Latitude') ax = add_ticks(ax, x_num_tikz = 9, y_num_tikz = 5) #a.legend() plt.xlabel('Longitude') plt.subplots_adjust(wspace = 0.2, hspace = 0.3, top=0.9, bottom=0.1, left = 0.14, right = .95) plt.savefig(path_python_figures + 'correlation_figure.pdf')
#ax.tick_params(labelbottom=False, labeltop=False, labelleft=False, labelright=False, #top=False, bottom=False, left=False, right=False) test = data.isel(time=i) vals = np.flipud(test[var].values) cntours = ax.contourf(vals, levels=levels_contourplot, cmap='Blues_r', vmin=0.0, vmax=1.0) # Removes white lines for c in cntours.collections: c.set_edgecolor("face") ax.set_xlabel('Longitude') ax.set_ylabel('Latitude') ax = add_ticks(ax) cmap = mpl.cm.get_cmap('Blues_r') norm = mpl.colors.Normalize(vmin=0.0, vmax=1.0) mappable = mpl.cm.ScalarMappable(norm=norm, cmap=cmap) fig.colorbar(mappable, ax=ax, label='cfc [1]') plt.subplots_adjust(left=0.1, bottom=0.15, right=.99, top=0.92, wspace=0.2, hspace=0.3) plt.savefig( os.path.join(path_store_figures, '{}_{}_{}.png'.format(i, model, start_date)))