"""Calculate probability distributions for IPCC sea level rise forecasts.

This will calculate the values required to generate triangular distributions,
i.e. 'min', 'mode', and 'max' in the `numpy.random.triang()` function.

The values are written to 'triang-values.csv'

D. Howe
d.howe@wrl.unsw.edu.au
2022-05-05
"""
import os
import re
import numpy as np
import pandas as pd
from scipy import stats, optimize
import matplotlib.pyplot as plt

PLOT = False


def norm_cdf(x, loc, scale):
    """Calculate cumulative density function, using normal distribution."""
    return stats.norm(loc=loc, scale=scale).cdf(x)


def triang_cdf(x, loc, scale, c):
    """Calculate cumulative density function, using triangular distribution."""
    return stats.triang(loc=loc, scale=scale, c=c).cdf(x)


# Read data
xlsx_path = '20220502_Probabilistic_Erosion_Parameters_1st_DRAFT_FOR_DISCUSSION.xlsx'

df = pd.read_excel(xlsx_path, sheet_name='IPCC AR6', index_col=[0, 1, 2, 3, 4])
df = df.sort_index()
dff = df.loc[838, 'total', 'medium', 'ssp585'].T
dff.index.name = 'year'
percentiles = dff.columns.to_numpy() / 100

# Make SLR relative to 2020 level (at the 50th percentile)
dff -= dff.loc[2020, 50]

for i, row in dff.iterrows():
    values = row.to_numpy()

    # Fit normal distribution
    loc, scale = optimize.curve_fit(norm_cdf, values, percentiles)[0]
    p_norm = {'loc': loc, 'scale': scale}

    # Fit triangular distribution
    loc, scale, c = optimize.curve_fit(triang_cdf,
                                       values,
                                       percentiles,
                                       p0=[values[0] - 0.1, 0.5, 0.5])[0]
    p_triang = {'loc': loc, 'scale': scale, 'c': c}

    # Get triangular distribution parameters
    left = p_triang['loc']
    centre = p_triang['loc'] + p_triang['scale'] * p_triang['c']
    right = p_triang['loc'] + p_triang['scale']

    dff.loc[i, 'min'] = left
    dff.loc[i, 'mode'] = centre
    dff.loc[i, 'max'] = right

    if PLOT:
        fig, ax = plt.subplots(1, 2, figsize=(10, 3))

        x_min = stats.triang.ppf(0.01, **p_triang) - 0.2
        x_max = stats.triang.ppf(0.99, **p_triang) + 0.2
        x = np.linspace(x_min, x_max, num=1000)

        ax[0].plot(x, 100 * stats.norm.cdf(x, **p_norm))
        ax[0].plot(x, 100 * stats.triang.cdf(x, **p_triang))
        ax[0].plot(values, 100 * percentiles, '.', c='#444444')

        ax[1].plot(x, stats.norm.pdf(x, **p_norm), label='Normal')
        ax[1].plot(x, stats.triang.pdf(x, **p_triang), label='Triangular')
        ax[1].plot([], [], '.', c='#444444', label='IPCC data')
        ax[1].legend()

        ax[1].axvline(x=left, c='C3')
        ax[1].axvline(x=centre, c='C3')
        ax[1].axvline(x=right, c='C3')

        ax[0].set_ylabel('Percentile', labelpad=10)
        ax[0].set_title('Cumulative distribution')
        ax[1].set_title('Probability density')

        ax[0].annotate(i, (-0.3, 1),
                       xycoords='axes fraction',
                       clip_on=False,
                       size=14)
        for a in ax:

            a.set_xlabel('SLR (m)', labelpad=10)
            a.spines['top'].set_visible(False)
            a.spines['right'].set_visible(False)

        plt.show()

# Save distribution parameters
dff[['min', 'mode', 'max']].to_csv('triang-values.csv', float_format='%0.3f')

if PLOT:
    # Plot all triangular distributions
    fig, ax = plt.subplots(1, 1, figsize=(8, 4))

    cmap = plt.cm.get_cmap('RdBu_r', len(dff))
    c = list(cmap(range(cmap.N)))

    j = -1
    for i, row in dff.iterrows():
        j += 1
        ax.plot(row[['min', 'mode', 'max']], [0, 1, 0], c=c[j])
        if j % 2 == 0:
            ax.annotate(f' {i}', (row['mode'], 1),
                        ha='center',
                        va='bottom',
                        rotation=90)

    ax.set_xlabel('SLR (m)', labelpad=10)
    ax.set_ylabel('Probability density (-)', labelpad=10)

    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    plt.show()