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107 lines
2.9 KiB
Python
107 lines
2.9 KiB
Python
import os
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import re
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import numpy as np
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import pandas as pd
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from scipy import stats
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import matplotlib.pyplot as plt
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PLOT = False
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START_YEAR = 2020
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END_YEAR = 2100
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n_runs = 100000
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df = pd.read_csv('cauchy-values.csv', index_col=0)
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years = np.arange(START_YEAR, END_YEAR + 1)
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# Squeeze distribution to zero in 2020
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df.loc[2020, 'scale'] = 0.0001
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df.loc[2020, 'min'] = df.loc[2020, 'loc'] - 0.0001
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df.loc[2020, 'max'] = df.loc[2020, 'loc'] + 0.0001
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# Interpolate intermediate values
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df = df.reindex(years).interpolate(method='cubic')
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# Prepare array for SLR values
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slr = np.zeros([len(years), n_runs], dtype=float)
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for i, (year, row) in enumerate(df.iterrows()):
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# Get probability distribution
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dist = stats.cauchy(loc=row['loc'], scale=row['scale'])
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# Generate random samples
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for factor in range(2, 10):
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s_raw = dist.rvs(n_runs * factor)
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# Take first samples within valid range
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s = s_raw[(s_raw > row['min']) & (s_raw < row['max'])]
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if len(s) > n_runs:
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break # Success
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else:
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continue # We need more samples, so try larger factor
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# Add the requried number of samples
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slr[i] = s[:n_runs]
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# Sort each row to make SLR trajectories smooth
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slr = np.sort(slr, axis=1)
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# Randomise run order (column-wise)
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slr = np.random.permutation(slr.T).T
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# Set first year to zero
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slr[0, :] = df.loc[2020, 'loc']
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# Plot first few trajectories
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if PLOT:
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fig, ax = plt.subplots(1,
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3,
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figsize=(12, 5),
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sharey=True,
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gridspec_kw={
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'wspace': 0.05,
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'width_ratios': [3, 1, 1]
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})
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ax[0].plot(years, slr[:, :100], c='#444444', lw=0.2)
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ax[1].hist(slr[-1, :],
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bins=100,
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fc='#cccccc',
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ec='#aaaaaa',
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orientation='horizontal')
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i = len(years) - 1
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dff = df.T.loc['5':'95', years[i]]
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ax[2].hist(
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slr[i, :],
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bins=100,
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fc='#cccccc',
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ec='#aaaaaa',
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orientation='horizontal',
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cumulative=True,
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)
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ax[2].plot(dff.index.astype(int) / 100 * n_runs,
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dff.values,
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'o',
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c='C3',
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label='IPCC AR6 data')
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ax[0].set_xlim(right=years[i])
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ax[0].set_title(f'SLR trajectories\n(first 100 out of {n_runs:,} runs)')
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ax[1].set_title(f'Probability\ndistribution\nin year {years[i]}')
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ax[2].set_title(f'Cumulative\ndistribution\nin year {years[i]}')
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ax[0].set_ylabel('SLR (m)', labelpad=10)
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ax[2].legend()
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ax[0].spines['top'].set_visible(False)
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ax[0].spines['right'].set_visible(False)
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for a in ax[1:]:
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a.spines['top'].set_visible(False)
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a.spines['right'].set_visible(False)
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a.spines['bottom'].set_visible(False)
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a.xaxis.set_visible(False)
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