{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# MHKit Short-term Extreme Loads\n", "\n", "Short-term extreme loads are typically required as part of a long-term extreme load estimate. \n", "A short-term extreme distribution is a probability distribution for the extreme value of a short-term (e.g. 3-hours) time-series. \n", "\n", "MHKiT includes functions adapted from the [WDRT](https://github.com/WEC-Sim/WDRT) for estimating the short-term extreme distribution. \n", "Several methods are included, which can be divided into two main approaches:\n", "\n", "1. Block maxima: approximate the short-term extreme distribution directly by fitting a probability distribution to the maxima from N distinct short-term blocks. \n", "2. Peaks distribution: approximate the short-term extreme distribution from the distribution of peaks from a time-series of arbitrary length. \n", "\n", "The two sets of methods will be demonstrated. \n", "\n", "**NOTE**: This notebook will walk through the two different approaches, and each method in them. The short-term extremes will be obtained step-by-step showing the intermediate outputs. However, a convenience function, the `loads.extreme.short_term_extreme` function, can be be used directly in a single step. For this usage jump to the last section. It is recommended that you go over this entire example to understand what is going on \"under the hood\" prior to using this convenience function. \n", "\n", "The quantity (load) of interest can be anything related to the WEC, such as position (e.g. PTO extension, heave displacement), velocity, structural load, mooring loads, etc. \n", "For this example we will use the wave elevation as the quantity of interest, since such time-series can be easily created without having to run a WEC-modeling software. \n", "The short-term period of interest will be 3-hours.\n", "\n", "An important pre-processing step is making the time-series zero-mean. \n", "In this case, since wave elevation is zero-mean no pre-processing is needed.\n", "\n", "\n", "We will start by importing the `loads.extreme` module which includes these different methods, and the `wave.resource` module to create the response time-series. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from mhkit.loads import extreme \n", "from mhkit.wave.resource import jonswap_spectrum, surface_elevation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We first define the short-term period (3 hours)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# short-term period in seconds\n", "t_st = 3.0 * 60.0 * 60.0 " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The methods based on peaks distributions require a single time series of any length while the methods based on block maxima require N time series of length equal to the short-term time period. \n", "\n", "We will now create N=10 time series of length equal to the short-term period (3 hours). For block maxima methods we will use all 10 and for peaks distribution methods we will only use the first hour of the first time-series. For the block maxima we use N=10 for speed considerations, but note that this would be inadequately small for most real applications. \n", "\n", "For the sake of example we will consider the wave elevation as the quantity of interest (can be thought as a proxy for heave motion in this example). \n", "Wave elevation time-series for a specific sea state can be created quickly without running any external software. \n", "\n", "**NOTE:** The code below is simply creating the synthetic data (wave elevation time series). In a realistic case the time series would be obtained externally, e.g. through simulation software such as WEC-Sim or CFD. For this reason the details of creating the synthetic data are not presented here, instead assume for the quantity of interest there are ten 3-hours time-series available. \n", "\n", "We start by creating the ten 3-hour time-series of the quantity of interest. We do this with a list comprehension and using the `wave.resource.surface_elevation` function. The result is a list of 10 different time-series. Alternatively we could have created a single time-series of length N times the short-term period (30 hours) and used the `extreme.block_maxima` function to split it into blocks and return the block maxima. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# JONSWAP spectrum for creating short-term time-series\n", "T_min = 1 # s\n", "Tp = 8 # s\n", "Hs = 1.5 # m\n", "f0 = 1/t_st\n", "f_max = 1/T_min\n", "Nf = int(f_max/f0)\n", "f = np.linspace(f0, f_max, Nf)\n", "S = jonswap_spectrum(f, Tp, Hs)\n", "\n", "# time in seconds\n", "time = np.linspace(0, t_st, 2*Nf+1)\n", "\n", "# 10 distinct time-series\n", "N = 10\n", "qoi_timeseries = [surface_elevation(\n", " S, time).values.squeeze() for i in range(N)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can view the different time-series. Change value of `i` below to plot a different time-series. Below we plot both the full 3 hours time series and the first 2 minutes to get a better look at the behavior of the response." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "i = 0 # 0–9\n", "\n", "plt.figure()\n", "timeseries = qoi_timeseries[i]\n", "plt.plot(time, timeseries)\n", "plt.title(\"Full 3 hours\")\n", "plt.xlabel('time [s]')\n", "plt.ylabel('elevation [m]')\n", "\n", "plt.figure()\n", "timeseries = qoi_timeseries[i]\n", "plt.plot(time[time <= 120], timeseries[time <= 120])\n", "plt.title(\"First 2 minutes\")\n", "plt.xlabel('time [s]')\n", "plt.ylabel('elevation [m]');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Block Maxima \n", "The short-term extreme distribution can be estimated directly from N time-series of short-term period. \n", "These time-series can be obtained, for instance, from N distinct simulations of the quantity of interest, or alternatively by dividing a long time-series of the quantity of interest into blocks of length equal to the short-term period. In this example we will use N=10 for speed considerations, but this would be inadequately small for most real applications. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Find the maximum of each block.\n", "The first step is to get the maximum in each block, i.e. in each of the ten time series. We do this with a list comprehension. The result is a list of the 10 block maxima." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1.54604643, 1.467073 , 1.57427583, 1.45417816, 1.44266541,\n", " 1.35937156, 1.33657588, 1.41939984, 1.53371198, 1.41075921])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "block_maxima = [np.max(time_series) for time_series in qoi_timeseries]\n", "block_maxima = np.array(block_maxima)\n", "block_maxima" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can view the different time-series and their maxima. Change value of i below to plot a different time-series. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "i = 0 # select: 0-9\n", "\n", "plt.figure()\n", "line, = plt.plot(time, qoi_timeseries[i], alpha=0.5, label='time-series')\n", "plt.plot(time[np.argmax(qoi_timeseries[i])], block_maxima[i],\n", " 'o', color=line.get_color(), label='maximum')\n", "plt.xlabel('time [s]')\n", "plt.ylabel('elevation [m]')\n", "plt.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also look at the 10 block maximas. Each point represent the largest elevation from one of the 10 time-series." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(block_maxima, 'o')\n", "plt.title(\"Block maxima\")\n", "plt.xlabel('time series')\n", "plt.ylabel('maximum elevation [m]')\n", "plt.ylim([0, np.max(block_maxima*1.1)]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Fit a distribution to the block maxima\n", "We can now get the short-term extreme distribution by fitting either a generalized extreme value distribution or a Gumbel (right) distributions to these N=10 maxima. \n", "We will compare both methods. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# generalized extreme value distribution\n", "ste_gev = extreme.ste_block_maxima_gev(block_maxima)\n", "# Gumbel (right) distribution\n", "ste_gum = extreme.ste_block_maxima_gumbel(block_maxima)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The outputs of these functions are probability distributions (`scipy.stats.rv_continuous`). \n", "These objects provide common statistical functions (PDF, CDF, PPF, etc.) and metrics (expected value, median, etc). Here we will plot the CDF and PDF (including the 10 samples represented by the black dots) and print the expected value and 95% interval for both methods. The expected value for both is about 1.49 meters." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GEV:\n", " Expected value: 1.4539484564137006 m\n", " 95% interval: (1.3049051355780483 m, 1.5837252119008833 m)\n", "Gumbel:\n", " Expected value: 1.4562830217935439 m\n", " 95% interval: (1.3303537374571037 m, 1.663587600246415 m)\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# print distribution statistics\n", "print(f'GEV:\\n Expected value: {ste_gev.expect()} m\\n 95% interval: ({ste_gev.ppf(0.025)} m, {ste_gev.ppf(0.975)} m)')\n", "print(f'Gumbel:\\n Expected value: {ste_gum.expect()} m\\n 95% interval: ({ste_gum.ppf(0.025)} m, {ste_gum.ppf(0.975)} m)')\n", "\n", "# plot CDF and PDF\n", "x = np.linspace(0, 3, 1000)\n", "fig, axs = plt.subplots(1,2)\n", "axs[0].plot(x, ste_gev.pdf(x))\n", "axs[0].plot(x, ste_gum.pdf(x))\n", "axs[0].plot(block_maxima, np.zeros(N), 'k.')\n", "axs[1].plot(x, ste_gev.cdf(x), label='GEV')\n", "axs[1].plot(x, ste_gum.cdf(x), label='Gumbel')\n", "axs[0].set_ylabel('PDF')\n", "axs[1].set_ylabel('CDF')\n", "axs[1].legend()\n", "axs[0].set_xlabel('elevation [m]')\n", "axs[1].set_xlabel('elevation [m]');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Peaks Distribution\n", "The block maxima approach is the most direct way to approximate the short-term extreme but it requires a lot of data (N timeseries of length equal to the short-term period). \n", "An alternative approach approximates the short-term extreme from a timeseries of arbitrary length, even shorter than the short-term period, by fitting a distribution to the peaks of the timeseries. \n", "\n", "To demonstrate these methods we will approximate the 3-hour short-term extreme distribution from a single 1-hour timeseries. \n", "\n", "We start by generating this 1-hour response timeseries." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "t_end = 1.0 * 60.0 * 60.0\n", "timeseries_1hr = qoi_timeseries[0][time" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(time_1hr, timeseries_1hr)\n", "plt.xlabel('time [s]')\n", "plt.ylabel('elevation [m]')\n", "plt.xlim([0, t_end]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Find the global peaks\n", "The global peaks are the maximum between succesive zero-upcrossings. \n", "We use the `extreme.global_peaks` function to find these peaks. " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "t_peaks, qoi_peaks = extreme.global_peaks(time_1hr, timeseries_1hr)\n", "npeaks = len(qoi_peaks)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will plot the entire time-series and the global peaks." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "\n", "plt.plot([0, t_end], [0, 0], color='tab:grey')\n", "plt.plot(time_1hr, timeseries_1hr, color='tab:blue')\n", "plt.plot(t_peaks, qoi_peaks, 'o', color='tab:green')\n", "plt.xlabel('time [s]')\n", "plt.ylabel('elevation [m]');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will also plot the first 2 minutes to get a better look at what is going on." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "\n", "plt.plot([0, t_end], [0, 0], color='tab:grey')\n", "plt.plot(time_1hr, timeseries_1hr, color='tab:blue')\n", "plt.plot(t_peaks, qoi_peaks, 'o', color='tab:green')\n", "plt.xlabel('time [s]')\n", "plt.ylabel('elevation [m]')\n", "plt.xlim([0, 120]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Fit a distribution to the peaks\n", "\n", "MHKiT has three methods for doing this. \n", "\n", "1. Fit a Weibull distribution to all the peaks. \n", "2. Weibull tail fit: seven different weibull distributions are fit to successively smaller subsets of the highest peaks, and the peak distribution is taken as the average of these seven. \n", "3. Peaks over threshold: a generalized Pareto distribution is fit to all peaks above a certain threshold, typically taken as 1.4 standard deviations above the mean. \n", "\n", "We will now compare the results from these three methods. For the peaks over threshold we will take the threshold (variable `thresh` below) to be 1.4 standard deviations above the mean." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# Weibull\n", "peaks_w = extreme.peaks_distribution_weibull(qoi_peaks)\n", "\n", "# Weibull tail fit\n", "peaks_wtf = extreme.peaks_distribution_weibull_tail_fit(qoi_peaks)\n", "\n", "# peaks over threshold\n", "thresh = np.mean(qoi_peaks) + 1.4 * np.std(qoi_peaks)\n", "peaks_pot = extreme.peaks_distribution_peaks_over_threshold(qoi_peaks, thresh)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The outputs are again `stats.rv_continuous` distributions. \n", "We will plot both the PDF (with the raw peak data shown as black dots) and CDF of the peaks distribution, and obtain the expected value and 95% interval for each distribution. Note however that for the peaks over threshold method the distribution is only defined for values over the threshold, and many of the functionalities that require the entire distribution (e.g. expected value) are therefor not available. " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Weibull:\n", " Expected value: 0.42994843847996356\n", " 95% interval: (0.04643388017403304, 1.0964882950671226)\n", "Weibull tail fit:\n", " Expected value: 0.4488922633713867\n", " 95% interval: (0.07565935519410608, 0.9939086430385311)\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print(f'Weibull:\\n Expected value: {peaks_w.expect()}\\n 95% interval: ({peaks_w.ppf(0.025)}, {peaks_w.ppf(0.975)})')\n", "print(f'Weibull tail fit:\\n Expected value: {peaks_wtf.expect()}\\n 95% interval: ({peaks_wtf.ppf(0.025)}, {peaks_wtf.ppf(0.975)})')\n", "\n", "fig, axs = plt.subplots(1,2)\n", "axs[0].plot(x, peaks_w.pdf(x))\n", "axs[0].plot(x, peaks_wtf.pdf(x))\n", "axs[0].plot(x, peaks_pot.pdf(x))\n", "axs[0].plot(qoi_peaks, np.zeros(npeaks), 'k.')\n", "axs[1].plot(x, peaks_w.cdf(x), label='Weibull')\n", "axs[1].plot(x, peaks_wtf.cdf(x), label='Weibull tail fit')\n", "axs[1].plot(x, peaks_pot.cdf(x), label='peaks over threshold')\n", "axs[0].set_ylabel('PDF')\n", "axs[1].set_ylabel('CDF')\n", "axs[1].legend()\n", "axs[0].set_xlabel('QOI [units]')\n", "axs[1].set_xlabel('QOI [units]');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Obtain the short-term extreme distribution from the peaks distribution\n", "\n", "We do this using the peaks distribution and an approximation for the number of peaks per short-term period. \n", "We use the `extreme.ste_peaks` function to obtain an approximation for the number of peaks in a short-term period. " ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Approximate number of peaks in a (3-hours) short-term period: 1851.0\n" ] } ], "source": [ "N_st = extreme.number_of_short_term_peaks(npeaks, t_end, t_st)\n", "print(f\"Approximate number of peaks in a (3-hours) short-term period: {N_st}\")\n", "\n", "ste_w = extreme.ste_peaks(peaks_w, N_st)\n", "ste_wtf = extreme.ste_peaks(peaks_wtf, N_st)\n", "ste_pot = extreme.ste_peaks(peaks_pot, N_st)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again the outputs are `stats.rv_continuous` distributions for the short-term extreme. We will again plot the PDF and CDF and obtain the expected values and confidence intervals for each method. " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\hivanov\\AppData\\Local\\Continuum\\anaconda3\\envs\\MHKdev\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:2738: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n", " vals = integrate.quad(fun, lb, ub, **kwds)[0] / invfac\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Weibull:\n", " Expected value: 1.8015746515403694\n", " 95% interval: (1.5274922894251206, 2.218869399911306)\n", "Weibull tail fit:\n", " Expected value: 1.4882943596679838\n", " 95% interval: (1.3020212588619686, 1.764801679403271)\n", "Peaks over threshold:\n", " Expected value: 1.6890855491151695\n", " 95% interval: (1.4147174341282949, 2.1125268120706844)\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print(f'Weibull:\\n Expected value: {ste_w.expect()}\\n 95% interval: ({ste_w.ppf(0.025)}, {ste_w.ppf(0.975)})')\n", "print(f'Weibull tail fit:\\n Expected value: {ste_wtf.expect()}\\n 95% interval: ({ste_wtf.ppf(0.025)}, {ste_wtf.ppf(0.975)})')\n", "print(f'Peaks over threshold:\\n Expected value: {ste_pot.expect()}\\n 95% interval: ({ste_pot.ppf(0.025)}, {ste_pot.ppf(0.975)})')\n", "\n", "fig, axs = plt.subplots(1,2)\n", "axs[0].plot(x, ste_w.pdf(x))\n", "axs[0].plot(x, ste_wtf.pdf(x))\n", "axs[0].plot(x, ste_pot.pdf(x))\n", "axs[1].plot(x, ste_w.cdf(x), label='Weibull')\n", "axs[1].plot(x, ste_wtf.cdf(x), label='Weibull tail fit')\n", "axs[1].plot(x, ste_pot.cdf(x), label='peaks over threshold')\n", "axs[0].set_ylabel('PDF')\n", "axs[1].set_ylabel('CDF')\n", "axs[1].legend()\n", "axs[0].set_xlabel('QOI [units]')\n", "axs[1].set_xlabel('QOI [units]');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The `loads.extreme.short_term_extreme` function\n", "\n", "This example showed step-by-step how to estimate the short-term extreme using different methods. \n", "However for most use cases the results from the intermediate steps are not of interest. \n", "The estimated short-term extreme distribution can be obtained directly from a time-series of the response using the `loads.extreme.short_term_extreme` function. \n", "For the methods based on fitting the peaks the time-series can be of arbitrary length, while for methods based on block maxima it should be many times longer than the short-term period. \n", "\n", "**Note:** This function is provided for convenience but the user should be aware of what is going on \"under the hood\" and ensure the method they choose is appropriate. For instance if using a block maxima method it is good practice to have at least 20 short-term periods worth of data. \n", "\n", "As an example the Weibull tail fit approximation of the short-term extreme distribution can be obtained in a single step. \n", "The function takes the time series (time, response), the desired short-term period, and the method as inputs." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "ste_wtf = extreme.short_term_extreme(time_1hr, timeseries_1hr, t_st, method='peaks_weibull')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "interpreter": { "hash": "86b838398ea3043e858ca5cb30e94c5903c7b3039d84354953dbfa9ff6edead2" }, "kernelspec": { "display_name": "Python 3.8.8 64-bit ('base': conda)", "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": "3.8.10" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }