I finally moved into python (using pycharm) after years with MATLAB (thanks to my classes during college, where they mostly used MATLAB). I work with netcdf data such as Outgoing Longwave Radiation. As much as I like a lot more the language, I noticed that the time to compile plots takes almost 2 minutes (which, for my previous work, seems like "a lot"). I would like to ask you if there is some way in which I am making my code slower to compile? Or is it just the way it works? This is my code, I use cartopy to plot a total of 4 subplots in a for loop: # Rangos de contornos clevs=np.arange(-25,26,2.5) nrows=4 ncols=1 # Define the figure and each axis for the 3 rows and 3 columns fig, axs = plt.subplots(nrows=nrows,ncols=ncols, subplot_kw={'projection': ccrs.PlateCarree(central_longitude=-180)}, figsize=(6,20)) # getting the original colormap using cm.get_cmap() function orig_map=cmaps.NEO_div_vegetation_c # reversing the original colormap using reversed() function reversed_map = orig_map.reversed() # axs is a 2 dimensional array of `GeoAxes`. We will flatten it into a 1-D array axs=axs.flatten() #Loop over all of the models for i,olrms in enumerate(F_mjo): # Select the week 1 forecast from the specified model data=PCP_MJO # Add the cyclic point data,lons=add_cyclic_point(data,coord=ds['lon']) # Contour plot cs=axs.contourf(lons,ds['lat'],data,clevs, transform = ccrs.PlateCarree(), cmap=reversed_map,extend='both') # Draw the coastines for each subplot axs.coastlines() gl = axs.gridlines(color='black',alpha=0.2) axs.set_extent([50,-60, -35, 35], crs=ccrs.PlateCarree(central_longitude=-180)) # Longitude labels if i == 3: axs.set_xticks(np.arange(50,301,50), crs=ccrs.PlateCarree()) lon_formatter = cticker.LongitudeFormatter() axs.xaxis.set_major_formatter(lon_formatter) else: axs.set_xticks(np.arange(50,301,50), crs=ccrs.PlateCarree()) axs.set_xticklabels([]) # Latitude labels axs.set_yticks(np.arange(-35,36,20), crs=ccrs.PlateCarree()) lat_formatter = cticker.LatitudeFormatter() axs.yaxis.set_major_formatter(lat_formatter) # Title each subplot with the name of the model axs.set_ylabel(olrms) axs.set_title(str(days)+' days',loc='right',fontsize=10) # Adjust the location of the subplots on the page to make room for the colorbar fig.subplots_adjust(bottom=0.05, top=0.4, left=0.1, right=0.9, wspace=0.01, hspace=0.3) # Add a colorbar axis at the bottom of the graph cbar_ax = fig.add_axes([0.2, 0.005, 0.6, 0.015]) # Draw the colorbar rango= np.arange(-25,26,5) cbar=fig.colorbar(cs, cax=cbar_ax,orientation='horizontal',ticks=rango) #plt.title('OLR ($W/m^{2}$)') cbar.set_label('OLR ($W/m^{2}$)') I would be glad to read your answers Thanks Continue reading...
