plot_x_col_scatter.py

import numpy as np
import pandas as pd

from netCDF4 import Dataset

import matplotlib
matplotlib.use('cairo')

import matplotlib.pyplot as plt

from mpl_toolkits.basemap import Basemap


def plot_x_col_scatter():
    # Open the netCDF file
    dataset = Dataset('data/TROPESS_CrIS-JPSS1_L2_Summary_CH4_20230516_MUSES_R1p20_FS_F0p6.nc', 'r')

    # Read the data from your variables
    latitude = dataset.variables['latitude'][:]
    longitude = dataset.variables['longitude'][:]
    x_col_p = dataset.variables['x_col_p'][:]

    dataset.close()

    # Specify figure size (in inches)
    plt.figure(figsize=(12, 8))  

    # Create a basemap instance
    m = Basemap(projection='cyl', resolution='l', 
                llcrnrlat=-90, urcrnrlat=90,  # set latitude limits to -90 and 90
                llcrnrlon=-180, urcrnrlon=180)  # set longitude limits to -180 and 180

    m.drawcoastlines(linewidth=0.2)
    m.drawcountries(linewidth=0.2)

    # Draw parallels (latitude lines) and meridians (longitude lines)
    parallels = np.arange(-90., 91., 30.)
    m.drawparallels(parallels, labels=[True,False,False,False], linewidth=0.3)

    meridians = np.arange(-180., 181., 60.)
    m.drawmeridians(meridians, labels=[False,False,False,True], linewidth=0.3)    

    # Standard catter plot
    # Transform lat and lon to map projection coordinates
    x, y = m(longitude, latitude)

    # Plot the data using scatter (you may want to choose a different colormap and normalization)
    sc = m.scatter(x, y, c=x_col_p, cmap='jet', s=10)

    # Add a colorbar
    cbar = m.colorbar(sc, location='bottom', pad="10%")
    cbar.set_label('x_col_p')

    # Save figure to PNG file
    plt.savefig('plots/figure_scatter.png', dpi=300, bbox_inches='tight')