inputs/distributions.py

@@ -1,126 +0,0 @@
-"""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
-df = pd.read_excel('IPCC AR6.xlsx', 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()