plot_climatology_table.py

"""
Plot ARM Climatologies Tables
-----------------------------

Process for plotting up a table of climatology values on
a month (x) by year (y) grid

Author: Adam Theisen

"""
import act
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import xarray as xr


ds_dict = {
        'nsametC1.b1': {'variables': ['temp_mean', 'rh_mean'], 'averaging': ['M']},
        'nsa60noaacrnX1.b1': {'variables': ['temperature', 'precipitation'], 'averaging': ['M'],},
        'sgpmetE13.b1': {'variables': ['temp_mean', 'rh_mean', 'tbrg_precip_total'], 'averaging': ['M']},
}

for ds in ds_dict:
    for i, variable in enumerate(ds_dict[ds]['variables']):
        for averaging in ds_dict[ds]['averaging']:
            filename = './results/' + ds + '_' + variable + '_' + averaging + '.csv'
            names = ['time', 'mean', 'n_samples']
            obj = act.io.read_csv(filename, column_names=names, index_col=0, parse_dates=['time'])

            # Set Up Plot
            if averaging == 'M':
                title = 'Monthly Averages of ' + variable + ' in '+ ds
                if 'precip' in variable:
                    title = 'Monthly Totals of ' + variable + ' in '+ ds

            # Exclude data when there's less than 25 days of data in a month
            #obj = obj.where(obj['n_samples'] >= 25 * 24 * 60)
            if 'nsa60noaa' in ds:
                obj = obj.where(obj['n_samples'] >= 25 * 24)
            else:
                obj = obj.where(obj['n_samples'] >= 25 * 24 * 60)

            # Group data by year and run through each month/year
            # selecting the exact match in time and entering nan
            # if not available.
            group = obj.groupby('time.year')
            obj = xr.decode_cf(obj)
            years = []
            data = []
            for y in group.groups:
                y_data = []
                for m in range(1,13):
                    date = str(y) + '-' + str(m).zfill(2) + '-01T00:00:00.000000000'
                    try:
                        y_data.append(str(np.around(obj['mean'].sel(time=date).values, decimals=1)))
                    except:
                        y_data.append('nan')

                data.append(y_data)
                years.append(y)

            # Set up table and plot it
            months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
            fig, ax = plt.subplots(figsize=(10,8))
            ax.axis('off')
            ax.axis('tight')

            table = plt.table(data, rowLabels=years, colLabels=months, bbox=[0, 0, 1, 1])
            table.auto_set_font_size(False)
            table.set_fontsize(12)
            if 'precip' in variable:
                plt.title('Grid of Monthly Totals for ' + ds + ' ' + variable + '\n' + 'Months with less than 25 days of samples have been removed')
            else:
                plt.title('Grid of Monthly Averages for ' + ds + ' ' + variable + '\n' + 'Months with less than 25 days of samples have been removed')

            imagename = './images/' + ds + '_' + variable + '_table' + '.png'
            plt.tight_layout()
            plt.savefig(imagename)