plot_4subplots.py

# NOTE : This module is used to plot the ETo values averaged over time
#        for one GCM. This creates four subplots, for ETo values calculated for 
#        (i) 2021-2030 (without considering the effect of CO2),
#        (ii) 2021-2030 (considering the effect of CO2),
#        (iii) 2091-2100 (without considering the effect of CO2),
#        (iv) 2091-2100 (considering the effect of CO2)


from netCDF4 import Dataset
import matplotlib.pyplot as plt
import numpy as np
import cartopy.crs as ccrs
import cartopy.feature
import tkinter as tk
from tkinter import filedialog

# Define the paths to your four NetCDF files to be plotted
file_paths = [""] * 4 
file_list = ['2021-2030 without CO2','2021-2030 with CO2', '2091-2100 without CO2', '2091-2100 with CO2']

def browse_file(file_index):
    file_path = filedialog.askopenfilename()
    if file_path:
        file_paths[file_index] = file_path
        labels[file_index].config(
            text=f" {file_list[file_index]} : {file_path}")

def finish():
    root.destroy()
root = tk.Tk()
root.title("File Browser")
root.geometry("800x500")  # Set the window size to 800x300

labels = []
buttons = []

for i in range(len(file_paths)):

    button = tk.Button(
        root, text=f"Browse File for {file_list[i]}", command=lambda i=i: browse_file(i))
    button.pack(pady=5)
    buttons.append(button)

    label = tk.Label(root, text=f"{file_list[i]}: ")
    label.pack(pady=5)
    labels.append(label)

# Create a button to finish selecting files and close the window
finish_button = tk.Button(root, text="Finish Selecting Files", command=finish)
finish_button.pack(pady=5)
root.mainloop()
# Create an empty list to store the mean data for each dataset
mean_et = []

# Loop through the NetCDF files to extract and calculate mean data
for file_path in file_paths:
    data = Dataset(file_path, 'r')
    et = data.variables['evapotranspiration'][:]
    mean_et.append(np.mean(et, axis=0))

# Find the maximum value in all datasets to normalize them
max_value = max(np.max(mean_et[0]), np.max(mean_et[1]), np.max(mean_et[2]), np.max(mean_et[3]))
data1 = Dataset(file_paths[0], 'r')
lat = data1.variables['latitude'][:]
lon = data1.variables['longitude'][:]
time = data1.variables['time'][:]

# Create a 2x2 grid of subplots
fig, axes = plt.subplots(2, 2, subplot_kw={'projection': ccrs.PlateCarree()}, figsize=(16, 10))

# Create subplots and add data
for i, ax in enumerate(axes.flat):
    plot = ax.contourf(lon, lat, mean_et[i], cmap='RdYlBu_r', transform=ccrs.PlateCarree(), levels=np.linspace(0, max_value, 11))
    ax.set_extent([67.75, 97.75, 7.25, 37.25])  # regional map (x0, x1, y0, y1)
    ax.coastlines()
    ax.gridlines(draw_labels=False)
    ax.add_feature(cartopy.feature.OCEAN)
    ax.add_feature(cartopy.feature.LAND, edgecolor='black')
    titles = ['Evapotranspiration considering CO2 2091-2100','Evapotranspiration without considering CO2 2091-2100', 'Evapotranspiration considering CO2 2021-2030', 'Evapotranspiration without considering CO2 2021-2030']
    ax.set_title(titles[i])

# Create colorbars for each subplot
cax0 = fig.add_axes([0.92, 0.55, 0.02, 0.3])  # Position of colorbar for subplot 1
cax1 = fig.add_axes([0.92, 0.15, 0.02, 0.3])  # Position of colorbar for subplot 2
cax2 = fig.add_axes([0.47, 0.55, 0.02, 0.3])  # Position of colorbar for subplot 3
cax3 = fig.add_axes([0.47, 0.15, 0.02, 0.3])  # Position of colorbar for subplot 4

cbar0 = plt.colorbar(plot, cax=cax0, orientation='vertical', label='Evapotranspiration (in mm/day)', extend='both')
cbar1 = plt.colorbar(plot, cax=cax1, orientation='vertical', label='Evapotranspiration (in mm/day)', extend='both')
cbar2 = plt.colorbar(plot, cax=cax2, orientation='vertical', label='Evapotranspiration (in mm/day)', extend='both')
cbar3 = plt.colorbar(plot, cax=cax3, orientation='vertical', label='Evapotranspiration (in mm/day)', extend='both')

plt.suptitle('Comparison of Mean Evapotranspiration'.format(max_value),fontsize=20)
plt.tight_layout(rect=[0, 0, 1, 0.95])
plt.show()