diff --git a/climada/engine/impact.py b/climada/engine/impact.py index e8042afef..149a7c359 100644 --- a/climada/engine/impact.py +++ b/climada/engine/impact.py @@ -2318,6 +2318,8 @@ def interpolate( min_impact=0, bin_decimals=None, y_asymptotic=0.0, + threshold_percentile=90, + min_sample_size=20, ): """Interpolate and extrapolate impact frequency curve using different methods. @@ -2328,9 +2330,12 @@ def interpolate( method : str, optional Method to interpolate to new return periods. Currently available are "interpolate", - "extrapolate", "extrapolate_constant" and "stepfunction". If set to "interpolate", - return periods outside the range of the Impact object's observed return periods - will be assigned NaN. If set to "extrapolate_constant" or "stepfunction", + "fit_GPD", "extrapolate", "extrapolate_constant", and "stepfunction". If set to + "interpolate", return periods outside the range of the Impact object's observed return + periods will be assigned NaN. If set to "fit_GPD", a generalized Pareto distribution + is fitted to the tail of the data. The tail is defined by the percentile value + corresponding to threshold_percentile after first filtering out all impacts below + min_impact. If set to "extrapolate_constant" or "stepfunction", return periods larger than the Impact object's observed return periods will be assigned the largest impact, and return periods smaller than the Impact object's observed return periods will be assigned 0. If set to "extrapolate", @@ -2354,6 +2359,13 @@ def interpolate( Has no effect if method is "interpolate". Else, if data size < 2 or if method is set to "extrapolate_constant" or "stepfunction", it provides return value for exceeded impact for return periods smaller than the data range. Defaults to 0. + threshold_percentile : float, optional + Has only effect if method is "fit_GPD". Defined percentile to set threshold in + GPD peak-over-threshold, after first filtering out all impacts below min_impact. + Defaults to 90. + min_sample_size : int, optinal + Has only effect if method is "fit_GPD". Minimal sample size to require for fitting + GPD to the tail. Defaults to 20. Returns ------- @@ -2364,6 +2376,8 @@ def interpolate( -------- util.interpolation.preprocess_and_interpolate_ev : inter- and extrapolation method + util.interpolation.fit_tail_GPD : + method to fit a GPD distribution to the tail """ exceedance_frequency = 1 / np.array(return_periods) @@ -2372,18 +2386,31 @@ def interpolate( impacts = np.squeeze(np.array(self.impact)[sorted_idxs]) rps = np.asarray(self.return_per)[sorted_idxs] frequency = np.diff(1 / np.array(rps)[::-1], prepend=0)[::-1] - impact_interpolated = u_interp.preprocess_and_interpolate_ev( - exceedance_frequency, - None, - frequency, - impacts, - log_frequency=log_frequency, - log_values=log_impact, - value_threshold=min_impact, - method=method, - y_asymptotic=y_asymptotic, - bin_decimals=bin_decimals, - ) + + if method == "fit_GPD": + _, impact_interpolated, _ = u_interp.fit_tail_GPD( + exceedance_frequency, + None, + frequency, + impacts, + value_threshold=min_impact, + min_sample_size=min_sample_size, + threshold_percentile=threshold_percentile, + ) + + else: + impact_interpolated = u_interp.preprocess_and_interpolate_ev( + exceedance_frequency, + None, + frequency, + impacts, + log_frequency=log_frequency, + log_values=log_impact, + value_threshold=min_impact, + method=method, + y_asymptotic=y_asymptotic, + bin_decimals=bin_decimals, + ) return ImpactFreqCurve( return_per=return_periods, diff --git a/climada/util/interpolation.py b/climada/util/interpolation.py index bf64f37cd..bc1b9ae58 100644 --- a/climada/util/interpolation.py +++ b/climada/util/interpolation.py @@ -24,6 +24,8 @@ import numpy as np from scipy import interpolate +from scipy.optimize import minimize +from scipy.stats import genextreme, genpareto LOGGER = logging.getLogger(__name__) @@ -405,3 +407,159 @@ def _group_frequency(frequency, value, bin_decimals): return frequency, value_unique return frequency, value + + +def _gpd_distribution(values, xi, beta, lambda_u, threshold): + """ + Survival function (1-CDF) using a generalized Pareto distribution for the conditional + distribution of the tail and a probability of threhsold exceedance (lambda_u). + See https://en.wikipedia.org/wiki/Generalized_Pareto_distribution + """ + values = np.asarray(values) + return lambda_u * (1 + xi * (values - threshold) / beta) ** (-1 / xi) + + +def _gpd_inverse_distribution(lambdas, xi, beta, lambda_u, threshold): + """ + Inverse survival function (1-CDF) using a generalized Pareto distribution for the conditional + distribution of the tail and a probability of threhsold exceedance (lambda_u). + See https://en.wikipedia.org/wiki/Generalized_Pareto_distribution + """ + lambdas = np.asarray(lambdas) + return threshold + (beta / xi) * ((lambdas / lambda_u) ** (-xi) - 1) + + +def fit_tail_GPD( + test_frequency=None, + test_values=None, + frequency=None, + values=None, + value_threshold=0, + threshold_percentile=90, + min_sample_size=30, +): + """ + Fit a generalized Pareto distribution (GPD) to the tail of the exceedance data + and extrapolate to test points. + + Extreme value theory provides one principal solution for fitting exceedance values + using a peaks-over-threshold approach leading to the GPD, see e.g. + https://en.wikipedia.org/wiki/Generalized_Pareto_distribution and + (Coles, 2001, Chapter 4, https://doi.org/10.1007/978-1-4471-3675-0). + The selection of the threshold is a crucial choice because lower thresholds result in + more data points to fit, while higher thresholds ensure that data points actually + correspond to extreme values. + + Parameters + ---------- + test_frequency : array_like, optional + Exceedance frequencies for which to compute values. If given, test_values must be None. + test_values : array_like, optional + Values for which to compute exceedance frequencies. If given, test_frequency must be None. + frequency : array_like + Frequencies of the observed values. + values : array_like + Observed values. + value_threshold : float, optional + Value threshold to filter values before the threshold percentile is calculated. + Defaults to 0, filtering out all values less or equal to zero. + threshold_percentile : float, optional + Percentile for the tail threshold. Defaults to 90. + min_sample_size : int, optional + Minimum number of points above the threshold. Defaults to 30. + + Returns + ------- + tuple + (test_frequency, values) if test_frequency is given, else (exceedance_frequencies, test_values) + """ + + # Validate inputs + if (test_frequency is not None and test_values is not None) or ( + test_frequency is None and test_values is None + ): + raise ValueError("Provide exactly one of test_frequency or test_values") + + # Filter by value_threshold before computing threshold with percentile + mask_filter = values > value_threshold + frequency = frequency[mask_filter] + values = values[mask_filter] + + # compute percentile threshold and select tail + threshold = np.percentile(values, threshold_percentile) + mask = values > threshold + values_tail = values[mask] + frequency_tail = frequency[mask] + if sum(mask) < min_sample_size: + raise ValueError( + f"Not enough data points above the threshold for fitting the GPD. You can try to " + f"choose a smaller threshold_percentile={threshold_percentile} or a smaller " + f"min_sample_size={min_sample_size}." + ) + + # Sort values and frequencies + sorted_idxs = np.argsort(values_tail) + values_tail = np.squeeze(values_tail[sorted_idxs]) + frequency_tail = frequency_tail[sorted_idxs] + ex_freq = np.cumsum(frequency_tail[::-1])[::-1] + lambda_u = ex_freq[0] # estimated frequency for exceeding the threshold + + def exceedance_negerror(params): + """function to be optimized: summed squared of log errors""" + _, beta = params + if beta <= 0: + return np.inf + model = _gpd_distribution(values_tail, *params, lambda_u, threshold) + return np.sum((np.log(ex_freq) - np.log(model)) ** 2) + + res = minimize( + exceedance_negerror, + [0.1, np.std(values_tail - threshold)], + bounds=[(-1, None), (1e-6, None)], + ) + xi_hat, beta_hat = res.x + fit_result = {"xi": xi_hat, "beta": beta_hat} + LOGGER.info( + "Fitted GPD parameters using %.3g points: xi=%.3g, beta=%.3g.", + sum(mask), + xi_hat, + beta_hat, + ) + + # Compute some goodness-of-fit metrics + # Recompute model on tail with fitted params + model_tail = _gpd_distribution(values_tail, xi_hat, beta_hat, lambda_u, threshold) + # Avoid log(0) issues + eps = 1e-12 + lambda_tail_safe = np.maximum(ex_freq, eps) + model_tail_safe = np.maximum(model_tail, eps) + # Root mean-squared error (log-survival space) + residuals = np.log(lambda_tail_safe) - np.log(model_tail_safe) + rmse_log = np.sqrt(np.mean(residuals**2)) + + # Kolmogorov–Smirnov distance on normalized survival function + empirical_prob = lambda_tail_safe / lambda_u + model_prob = model_tail_safe / lambda_u + ks_distance = np.max(np.abs(empirical_prob - model_prob)) + + fit_result.update({"rmse_log": rmse_log, "ks_distance": ks_distance}) + LOGGER.info( + "GOF metrics: RMSE_log=%.3g, KS=%.3g", + rmse_log, + ks_distance, + ) + + if test_values is not None: + mask_tail = test_values > threshold + lambda_dist = _gpd_distribution( + test_values, xi_hat, beta_hat, lambda_u, threshold + ) + lambda_dist[~mask_tail] = np.nan + return (lambda_dist, test_values, fit_result) + else: + vals = _gpd_inverse_distribution( + test_frequency, xi_hat, beta_hat, lambda_u, threshold + ) + mask_tail = vals > threshold + vals[~mask_tail] = np.nan + return (test_frequency, vals, fit_result) diff --git a/climada/util/test/test_interpolation.py b/climada/util/test/test_interpolation.py index 033ce0539..10be45bfd 100644 --- a/climada/util/test/test_interpolation.py +++ b/climada/util/test/test_interpolation.py @@ -22,6 +22,7 @@ import unittest import numpy as np +from scipy.stats import genextreme, genpareto import climada.util.interpolation as u_interp @@ -282,6 +283,60 @@ def test_preprocess_and_interpolate_ev(self): with self.assertRaises(ValueError): u_interp.preprocess_and_interpolate_ev(None, None, frequency, values) + def test_fit_tail_gpd(self): + """Test GPD fitting with synthetic data""" + rng = np.random.default_rng(42) + xi_true = 0.35 + beta_true = 7 + n = 5000 + threshold_percentile = 90 + values = genpareto.rvs(c=xi_true, scale=beta_true, size=n, random_state=rng) + frequency = np.ones(n) / n + + threshold = np.percentile(values, threshold_percentile) + lambda_u = np.sum( + frequency[values >= threshold] + ) # frequency of exceeding threhold + test_freq = lambda_u * np.array([1.2, 0.8, 0.5]) + freq_out, val_out, fit_result = u_interp.fit_tail_GPD( + test_frequency=test_freq, + frequency=frequency, + values=values, + threshold_percentile=threshold_percentile, + ) + # test shapes + np.testing.assert_equal(len(freq_out), len(test_freq)) + np.testing.assert_equal(len(val_out), len(test_freq)) + + # test fitted parameters + # changing the threshold does not change xi but changes beta, see Ch. 4 Eq. 4.16 in + # (Coles, 2001, https://doi.org/10.1007/978-1-4471-3675-0) + expected_beta = beta_true + xi_true * threshold + np.testing.assert_allclose(fit_result["xi"], xi_true, rtol=0.15) + np.testing.assert_allclose(fit_result["beta"], expected_beta, rtol=0.15) + + # Test predicted tail + # Ch. 4 Eq. 4.13 in (Coles, 2001, https://doi.org/10.1007/978-1-4471-3675-0) + expected_vals = threshold + (expected_beta / xi_true) * ( + (test_freq / lambda_u) ** (-xi_true) - 1.0 + ) + expected_vals = np.where(expected_vals >= threshold, expected_vals, np.nan) + np.testing.assert_allclose(val_out, expected_vals, rtol=0.15) + + # testing the inverse of the distribution + test_values = np.array([0.9 * threshold, 1.1 * threshold, 1.5 * threshold]) + freq_out, val_out, fit_result = u_interp.fit_tail_GPD( + test_values=test_values, + frequency=frequency, + values=values, + threshold_percentile=threshold_percentile, + ) + expected_freqs = lambda_u * ( + 1 + xi_true / expected_beta * (test_values - threshold) + ) ** (-1 / xi_true) + expected_freqs = np.where(test_values >= threshold, expected_freqs, np.nan) + np.testing.assert_allclose(freq_out, expected_freqs, rtol=0.15) + # Execute Tests if __name__ == "__main__": diff --git a/doc/user-guide/climada_util_frequency_curve.ipynb b/doc/user-guide/climada_util_frequency_curve.ipynb new file mode 100644 index 000000000..8a5107b2d --- /dev/null +++ b/doc/user-guide/climada_util_frequency_curve.ipynb @@ -0,0 +1,410 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c2b5139a", + "metadata": {}, + "source": [ + "\n", + "# Frequency curves and tail distributions\n", + "\n", + "short tutorial about frequency curves and fitting a tail distribution\n", + "\n", + "### Example 1: Tropical cyclone impacts in Haiti" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "619292e6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-05-06 15:32:02,162 - climada.entity.exposures.base - INFO - Reading /Users/vgebhart/climada/data/exposures/litpop/LitPop_150arcsec_HTI/v3/LitPop_150arcsec_HTI.hdf5\n", + "2026-05-06 15:32:07,338 - climada.hazard.io - INFO - Reading /Users/vgebhart/climada/data/hazard/tropical_cyclone/tropical_cyclone_10synth_tracks_150arcsec_HTI_1980_2020/v2/tropical_cyclone_10synth_tracks_150arcsec_HTI_1980_2020.hdf5\n", + "2026-05-06 15:32:07,366 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard TC. Using the anonymous 'impf_' column.\n", + "2026-05-06 15:32:07,367 - climada.entity.exposures.base - INFO - Matching 1329 exposures with 1332 centroids.\n", + "2026-05-06 15:32:07,370 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 0.08333333333331439 degree\n", + "2026-05-06 15:32:07,372 - climada.engine.impact_calc - INFO - Calculating impact for 1329 assets (>0) and 43560 events.\n" + ] + } + ], + "source": [ + "from climada.entity import ImpactFunc, ImpactFuncSet, ImpfTropCyclone\n", + "from climada.util.api_client import Client\n", + "from climada.engine import ImpactCalc\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "client = Client()\n", + "\n", + "impf_set = ImpactFuncSet([ImpfTropCyclone.from_emanuel_usa()])\n", + "\n", + "exp = client.get_litpop(country=\"Haiti\")\n", + "\n", + "haz = client.get_hazard(\n", + " \"tropical_cyclone\",\n", + " properties={\n", + " \"country_name\": \"Haiti\",\n", + " \"climate_scenario\": \"historical\",\n", + " \"nb_synth_tracks\": \"10\",\n", + " },\n", + ")\n", + "\n", + "impact = ImpactCalc(exp, impf_set, haz).impact()" + ] + }, + { + "cell_type": "markdown", + "id": "aab2799c", + "metadata": {}, + "source": [ + "Standard frequency curve based to the impact data points" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1f623124", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "freq_curve = impact.calc_freq_curve()\n", + "_, ax = plt.subplots(1, 1, figsize=(6, 4))\n", + "freq_curve.plot(axis=ax, label=\"current freq curve\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "id": "dfd22b19", + "metadata": {}, + "source": [ + "Extrapolation options" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "ba369c07", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-05-06 15:27:09,624 - climada.util.interpolation - INFO - Fitted GPD parameters using 14 points: xi=0.223, beta=5.08e+09.\n", + "2026-05-06 15:27:09,625 - climada.util.interpolation - INFO - GOF metrics: RMSE_log=0.117, KS=0.101\n", + "2026-05-06 15:27:09,627 - climada.util.interpolation - INFO - Fitted GPD parameters using 10 points: xi=0.278, beta=5.22e+09.\n", + "2026-05-06 15:27:09,627 - climada.util.interpolation - INFO - GOF metrics: RMSE_log=0.122, KS=0.125\n", + "2026-05-06 15:27:09,629 - climada.util.interpolation - INFO - Fitted GPD parameters using 21 points: xi=0.117, beta=4.83e+09.\n", + "2026-05-06 15:27:09,629 - climada.util.interpolation - INFO - GOF metrics: RMSE_log=0.262, KS=0.213\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "return_periods = np.linspace(0, 1000, 500)\n", + "freq_curve_extrapolated = freq_curve.interpolate(\n", + " return_periods=return_periods, method=\"extrapolate\"\n", + ")\n", + "freq_curve_fit_GPD = freq_curve.interpolate(\n", + " return_periods=return_periods, method=\"fit_GPD\", min_sample_size=10\n", + ")\n", + "freq_curve_fit_GPD_93 = freq_curve.interpolate(\n", + " return_periods=return_periods,\n", + " method=\"fit_GPD\",\n", + " min_sample_size=10,\n", + " threshold_percentile=93,\n", + ")\n", + "freq_curve_fit_GPD_85 = freq_curve.interpolate(\n", + " return_periods=return_periods,\n", + " method=\"fit_GPD\",\n", + " min_sample_size=10,\n", + " threshold_percentile=85,\n", + ")\n", + "\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(10, 8))\n", + "freq_curve.plot(axis=ax, label=\"current freq curve\")\n", + "freq_curve_extrapolated.plot(axis=ax, label=\"extrapolated freq curve\", linestyle=\"--\")\n", + "freq_curve_fit_GPD.plot(\n", + " axis=ax, label=\"GPD tail fit (tail at 90 percentile; default)\", linestyle=\":\"\n", + ")\n", + "freq_curve_fit_GPD_93.plot(\n", + " axis=ax, label=\"GPD tail fit (tail at 93 percentile)\", linestyle=\":\"\n", + ")\n", + "freq_curve_fit_GPD_85.plot(\n", + " axis=ax, label=\"GPD tail fit (tail at 85 percentile)\", linestyle=\":\"\n", + ")\n", + "ax.scatter(freq_curve.return_per, freq_curve.impact, label=\"data points\", s=20)\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "id": "ce0bc707", + "metadata": {}, + "source": [ + "### Example 2: River floods in Italy\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6be46311", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-05-06 15:36:25,998 - climada.entity.exposures.base - INFO - Reading /Users/vgebhart/climada/data/exposures/litpop/LitPop_150arcsec_ITA/v3/LitPop_150arcsec_ITA.hdf5\n", + "2026-05-06 15:36:31,461 - climada.hazard.io - INFO - Reading /Users/vgebhart/climada/data/hazard/river_flood/river_flood_150arcsec_hist_ITA_1980_2000/v2/river_flood_150arcsec_hist_ITA_1980_2000.hdf5\n", + "2026-05-06 15:36:31,480 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard RF. Using the anonymous 'impf_' column.\n", + "2026-05-06 15:36:31,481 - climada.entity.exposures.base - INFO - Matching 19124 exposures with 19093 centroids.\n", + "2026-05-06 15:36:31,508 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 0.08333333333331439 degree\n", + "2026-05-06 15:36:31,521 - climada.util.coordinates - WARNING - Distance to closest centroid is greater than 0.083333 degree for 3 coordinates.\n", + "2026-05-06 15:36:31,523 - climada.engine.impact_calc - INFO - Calculating impact for 19034 assets (>0) and 920 events.\n" + ] + } + ], + "source": [ + "client = Client()\n", + "\n", + "exp_ita = client.get_litpop(country=\"Italy\")\n", + "\n", + "rf = client.get_hazard(\n", + " \"river_flood\",\n", + " properties={\n", + " \"country_name\": \"Italy\",\n", + " \"climate_scenario\": \"historical\",\n", + " },\n", + ")\n", + "\n", + "impf = ImpactFunc()\n", + "# based on iaimip test runs in MATLAB, ca 2017, with impact function 20160929, a dummy FL damage function, a copy of TS\n", + "impf.intensity = np.array(\n", + " [0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 10, 16]\n", + ")\n", + "impf.mdd = np.array(\n", + " [\n", + " 0,\n", + " 0.01,\n", + " 0.02,\n", + " 0.05,\n", + " 0.1,\n", + " 0.2,\n", + " 0.3,\n", + " 0.4,\n", + " 0.5,\n", + " 0.6,\n", + " 0.65,\n", + " 0.675,\n", + " 0.7,\n", + " 0.71,\n", + " 0.72,\n", + " 0.725,\n", + " 0.725,\n", + " 0.725,\n", + " 0.725,\n", + " ]\n", + ")\n", + "impf.paa = np.array(\n", + " [\n", + " 0,\n", + " 0.39346934,\n", + " 0.632120559,\n", + " 0.77686984,\n", + " 0.864664717,\n", + " 0.917915001,\n", + " 0.950212932,\n", + " 0.969802617,\n", + " 0.981684361,\n", + " 0.988891003,\n", + " 0.993262053,\n", + " 0.995913229,\n", + " 0.997521248,\n", + " 0.998496561,\n", + " 0.999088118,\n", + " 0.999446916,\n", + " 0.999664537,\n", + " 0.9999546,\n", + " 0.999999887,\n", + " ]\n", + ")\n", + "impf.haz_type = \"RF\"\n", + "impf.intensity_unit = \"m\"\n", + "impf.id = 1\n", + "impf_set_rf = ImpactFuncSet([impf])\n", + "\n", + "impact_ita = ImpactCalc(exp_ita, impf_set_rf, rf).impact()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ac4ee5a0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "freq_curve_ita = impact_ita.calc_freq_curve()\n", + "_, ax = plt.subplots(1, 1, figsize=(6, 4))\n", + "freq_curve_ita.plot(axis=ax, label=\"current freq curve\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "id": "d7465e93", + "metadata": {}, + "source": [ + "Extrapolation options" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0cfad9fe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2026-05-06 15:39:14,028 - climada.util.interpolation - INFO - Fitted GPD parameters using 92 points: xi=0.1, beta=1.62e+10.\n", + "2026-05-06 15:39:14,028 - climada.util.interpolation - INFO - GOF metrics: RMSE_log=0.167, KS=0.1\n", + "2026-05-06 15:39:14,030 - climada.util.interpolation - INFO - Fitted GPD parameters using 46 points: xi=0.128, beta=1.38e+10.\n", + "2026-05-06 15:39:14,031 - climada.util.interpolation - INFO - GOF metrics: RMSE_log=0.155, KS=0.144\n", + "2026-05-06 15:39:14,033 - climada.util.interpolation - INFO - Fitted GPD parameters using 19 points: xi=0.262, beta=1.28e+10.\n", + "2026-05-06 15:39:14,033 - climada.util.interpolation - INFO - GOF metrics: RMSE_log=0.207, KS=0.176\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/vgebhart/gitprojects/climada_repo/climada_python/climada/engine/impact.py:2382: RuntimeWarning: divide by zero encountered in divide\n", + " exceedance_frequency = 1 / np.array(return_periods)\n", + "/Users/vgebhart/gitprojects/climada_repo/climada_python/climada/util/interpolation.py:419: RuntimeWarning: invalid value encountered in power\n", + " return lambda_u * (1 + xi * (values - threshold) / beta) ** (-1 / xi)\n", + "/Users/vgebhart/gitprojects/climada_repo/climada_python/climada/util/interpolation.py:513: RuntimeWarning: invalid value encountered in log\n", + " return np.sum((np.log(ex_freq) - np.log(model)) ** 2)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "return_periods_ita = np.linspace(0, 1000, 500)\n", + "freq_curve_extrapolated_ita = freq_curve_ita.interpolate(\n", + " return_periods=return_periods_ita, method=\"extrapolate\"\n", + ")\n", + "freq_curve_fit_GPD_ita = freq_curve_ita.interpolate(\n", + " return_periods=return_periods_ita, method=\"fit_GPD\", min_sample_size=10\n", + ")\n", + "freq_curve_fit_GPD_ita_95 = freq_curve_ita.interpolate(\n", + " return_periods=return_periods_ita,\n", + " method=\"fit_GPD\",\n", + " min_sample_size=10,\n", + " threshold_percentile=95,\n", + ")\n", + "freq_curve_fit_GPD_ita_98 = freq_curve_ita.interpolate(\n", + " return_periods=return_periods_ita,\n", + " method=\"fit_GPD\",\n", + " min_sample_size=10,\n", + " threshold_percentile=98,\n", + ")\n", + "\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(10, 8))\n", + "freq_curve_ita.plot(axis=ax, label=\"current freq curve\")\n", + "freq_curve_extrapolated_ita.plot(\n", + " axis=ax, label=\"extrapolated freq curve\", linestyle=\"--\"\n", + ")\n", + "freq_curve_fit_GPD_ita.plot(\n", + " axis=ax, label=\"GPD tail fit (tail at 90 percentile; default)\", linestyle=\":\"\n", + ")\n", + "freq_curve_fit_GPD_ita_95.plot(\n", + " axis=ax, label=\"GPD tail fit (tail at 95 percentile)\", linestyle=\":\"\n", + ")\n", + "freq_curve_fit_GPD_ita_98.plot(\n", + " axis=ax, label=\"GPD tail fit (tail at 98 percentile)\", linestyle=\":\"\n", + ")\n", + "ax.scatter(freq_curve_ita.return_per, freq_curve_ita.impact, label=\"data points\", s=20)\n", + "ax.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "045cb972", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "climada_dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}