====== ENVI-met powered by python ======

Visualizing netCDF outputs in python offers a quick and flexible solution for exploring ENVI-met's model output files.
By using python libraries such as matplotlib, you can seamlessly load and display netCDF files. 

Here is some sample code:

<code python[enable_line_numbers="true"]>
import matplotlib.pyplot as plt
import netCDF4

def plot_map(netCDFfile, varName):
    nc = netCDF4.Dataset(netCDFfile)        # path to your ENVI-met netCDF output file e.g. "T:/enviprojects/Berlin/output/NetCDF/Berlin_12.5_AT_2021-07-07_06.00.01.nc"

    print(nc.variables.keys())                      # shows all available variables e.g. "Potential_Air_Temperature", "Relative_Humidity"
    print(len(nc.variables['Time']))                # shows how many timesteps are inside the netCDF output

    dataAr = nc.variables[varName][5][1][:][:]      # first index is the output timestep (here 5), second index is the height level (here 1)
    gridJAr = nc.variables['GridsJ'][:]
    gridIAr = nc.variables['GridsI'][:]

    fig, ax = plt.subplots(figsize=(10*len(gridIAr) / len(gridJAr), 10))                # determines the resolution of the figure
    ax.set_aspect(1)                                                                    # scales the aspect ratio
    contour = plt.contourf(gridIAr, gridJAr, dataAr, cmap=twilight_shifted, levels=100)        # cmap defines the colorramp, levels defines the number of color gradients

    plt.colorbar(contour, ax=ax, label=varName, location='right', shrink=0.4)           # defines the legend
    
    plt.title('ENVI-met powered by python')                                             # adds a title
    plt.xlabel('meters')                                                                # adds the x label
    plt.ylabel('meters')                                                                # adds the y label

    #plt.figtext(0.5, 0.01, 'footnote center', ha='center', va='center', fontsize='small')      # option to add a centered footnote
    #plt.figtext(0.98, 0.02, 'footnote right', ha='right', va='bottom', fontsize='small')       # option to add a footnote on the lower right corner
    
    # Show the plot
    plt.show()              # shows the plot

    #plt.savefig('D:/_images/' + varName + '.png', bbox_inches=0)           # option to save the figure
    #plt.close()

</code>