{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extreme Conditions Modeling - Full Sea State Approach\n",
    "Extreme conditions modeling consists of identifying the expected extreme (e.g. 100-year) response of some quantity of interest, such as WEC motions or mooring loads. \n",
    "Three different methods of estimating extreme conditions were adapted from [WDRT](https://github.com/WEC-Sim/WDRT): full sea state approach, contour approach, and MLER design wave. \n",
    "This noteboook presents the full sea state approach. \n",
    "\n",
    "The full sea state approach consists of the following steps: \n",
    "1. Take $N$ samples to represent the sea state. Each sample represents a small area of the sea state and consists of a representative $(H_{s}, T_{e})_i$ pair and a weight $W_i$ associated with the probability of that sea state area. \n",
    "2. For each sample $(H_{s}, T_{e})_i$ calculate the short-term (e.g. 3-hours) extreme for the quantity of interest (e.g. WEC motions or mooring tension).\n",
    "3. Integrate over the entire sea state to obtain the long-term extreme. This is a sum of the products of the weight of each sea state times the short-term extreme. \n",
    "\n",
    "See more details and equations in\n",
    "\n",
    "> [1] Coe, Ryan G., Carlos A. Michelén Ströfer, Aubrey Eckert-Gallup, and Cédric Sallaberry. 2018. “Full Long-Term Design Response Analysis of a Wave Energy Converter.” Renewable Energy 116: 356–66.\n",
    "\n",
    "**NOTE:** Prior to running this example it is recommended to become familiar with `environmental_contours_example.ipynb` and `short_term_extremes_example.ipynb` since some code blocks are adapted from those examples and used here without the additional description. \n",
    "\n",
    "We start by importing the relevant modules, including `waves.contours` submodule which includes the sampling function, and `loads.extreme` which inlcudes the short-term extreme and full sea state integration functions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mhkit.wave import resource, contours, graphics\n",
    "from mhkit.loads import extreme\n",
    "import matplotlib.pyplot as plt\n",
    "from mhkit.wave.io import ndbc\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Obtain and Process NDBC Buouy Data\n",
    "The first step will be obtaining the environmental data and creating the contours.\n",
    "See `environmental_contours_example.ipynb` for more details and explanations of how this is being done in the following code block. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameter = 'swden'\n",
    "buoy_number = '46022'\n",
    "ndbc_available_data = ndbc.available_data(parameter, buoy_number)\n",
    "\n",
    "years_of_interest = ndbc_available_data[ndbc_available_data.year < 2013]\n",
    "\n",
    "filenames = years_of_interest['filename']\n",
    "ndbc_requested_data = ndbc.request_data(parameter, filenames)\n",
    "\n",
    "ndbc_data = {}\n",
    "for year in ndbc_requested_data:\n",
    "    year_data = ndbc_requested_data[year]\n",
    "    ndbc_data[year] = ndbc.to_datetime_index(parameter, year_data)\n",
    "\n",
    "Hm0_list = []\n",
    "Te_list = []\n",
    "\n",
    "# Iterate over each year and save the result in the initalized dictionary\n",
    "for year in ndbc_data:\n",
    "    year_data = ndbc_data[year]\n",
    "    Hm0_list.append(resource.significant_wave_height(year_data.T))\n",
    "    Te_list.append(resource.energy_period(year_data.T))\n",
    "\n",
    "# Concatenate list of Series into a single DataFrame\n",
    "Te = pd.concat(Te_list, axis=0)\n",
    "Hm0 = pd.concat(Hm0_list, axis=0)\n",
    "Hm0_Te = pd.concat([Hm0, Te], axis=1)\n",
    "\n",
    "# Drop any NaNs created from the calculation of Hm0 or Te\n",
    "Hm0_Te.dropna(inplace=True)\n",
    "# Sort the DateTime index\n",
    "Hm0_Te.sort_index(inplace=True)\n",
    "\n",
    "Hm0_Te_clean = Hm0_Te[Hm0_Te.Hm0 < 20]\n",
    "\n",
    "Hm0 = Hm0_Te_clean.Hm0.values\n",
    "Te = Hm0_Te_clean.Te.values\n",
    "\n",
    "dt = (Hm0_Te_clean.index[2]-Hm0_Te_clean.index[1]).seconds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Sampling\n",
    "The first step is sampling the sea state to get samples $(H_s, T_e)_i$ and associtated weights. \n",
    "For this we will use the `waves.contours.samples_full_seastate` function. \n",
    "We will sample 20 points between each return level, for 10 levels ranging from 0.001—100 years return periods. \n",
    "For more details on the sampling approach see\n",
    "\n",
    "> [1] Coe, Ryan G., Carlos A. Michelén Ströfer, Aubrey Eckert-Gallup, and Cédric Sallaberry. 2018. “Full Long-Term Design Response Analysis of a Wave Energy Converter.” Renewable Energy 116: 356–66.\n",
    "\n",
    "> [2] Eckert-Gallup, Aubrey C., Cédric J. Sallaberry, Ann R. Dallman, and Vincent S. Neary. 2016. “Application of Principal Component Analysis (PCA) and Improved Joint Probability Distributions to the Inverse First-Order Reliability Method (I-FORM) for Predicting Extreme Sea States.” Ocean Engineering 112 (January): 307–19."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# return levels\n",
    "levels = np.array([0.001, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50, 100])\n",
    "\n",
    "# points per return level interval\n",
    "npoints = 20\n",
    "\n",
    "# Create samples\n",
    "sample_hs, sample_te, sample_weights = contours.samples_full_seastate(\n",
    "    Hm0, Te, npoints, levels, dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now plot the samples alongside the contours. \n",
    "First we will create the different contours using `contours.environmental_contours`. \n",
    "See `environmental_contours_example.ipynb` for more details on using this function. \n",
    "There are 20 samples, randomly distributed, between each set of return levels. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the contours\n",
    "Te_contours = []\n",
    "Hm0_contours = []\n",
    "\n",
    "for period in levels:\n",
    "    copula = contours.environmental_contours(\n",
    "        Hm0, Te, dt, period, 'PCA', return_PCA=True)\n",
    "    Hm0_contours.append(copula['PCA_x1'])\n",
    "    Te_contours.append(copula['PCA_x2'])\n",
    "\n",
    "# plot\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "\n",
    "labels = [f\"{period}-year Contour\" for period in levels]\n",
    "\n",
    "ax = graphics.plot_environmental_contour(\n",
    "    sample_te, sample_hs, Te_contours, Hm0_contours,\n",
    "    data_label='Samples', contour_label=labels,\n",
    "    x_label='Energy Period, $Te$ [s]',\n",
    "    y_label='Sig. wave height, $Hm0$ [m]', ax=ax)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Short-Term Extreme Distributions\n",
    "Many different methods for short-term extremes were adapted from WDRT, and a summary and examples can be found in `short_term_extremes_example.ipynb`. \n",
    "The response quantity of interest is typically related to the WEC itself, e.g. maximum heave displacement, PTO extension, or load on the mooring lines. \n",
    "This requires running a simulation (e.g. WEC-Sim) for each of the 200 sampled sea states $(H_s, T_e)_i$. \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 majority of the for loop below is simply creating the synthetic data (wave elevation time series). In a realistic case the variables `time` and `data` describing each 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 each sea state there is time-series data available. \n",
    "\n",
    "The last lines of the for-loop create the short-term extreme distribution from the time-series using the `loads.extreme.short_term_extreme` function. The short-term period will be 3-hours and we will use 1-hour \"simulations\" and the Weibul-tail-fitting method to estimate the 3-hour short-term extreme distributions for each of the 200 samples.\n",
    "\n",
    "For more details on short-term extreme distributions see `short_term_extremes_example.ipynb` and \n",
    "\n",
    "> [3] Michelén Ströfer, Carlos A., and Ryan Coe. 2015. “Comparison of Methods for Estimating Short-Term Extreme Response of Wave Energy Converters.” In OCEANS 2015 - MTS/IEEE Washington, 1–6. IEEE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sea state 1/200. (Hs, Te) = (2.7476690431712387 m, 9.898396804782426 s). Tp = 10.953763434059923 s\n",
      "Sea state 2/200. (Hs, Te) = (3.531570115440943 m, 11.072854690290276 s). Tp = 12.253441967345596 s\n",
      "Sea state 3/200. (Hs, Te) = (3.5631169008261416 m, 10.529043453084103 s). Tp = 11.651649600094585 s\n",
      "Sea state 4/200. (Hs, Te) = (3.3136880458617415 m, 9.477416257821945 s). Tp = 10.48789795981279 s\n",
      "Sea state 5/200. (Hs, Te) = (3.172687890319087 m, 9.360633157328573 s). Tp = 10.35866345031299 s\n",
      "Sea state 6/200. (Hs, Te) = (3.0630002153170452 m, 8.59765613746172 s). Tp = 9.514337854353085 s\n",
      "Sea state 7/200. (Hs, Te) = (2.7069002802048345 m, 8.619396416083681 s). Tp = 9.538396080519615 s\n",
      "Sea state 8/200. (Hs, Te) = (2.4473757676516734 m, 8.436380373858702 s). Tp = 9.335866875971888 s\n",
      "Sea state 9/200. (Hs, Te) = (2.046394958916393 m, 7.376850430979894 s). Tp = 8.163369897472288 s\n",
      "Sea state 10/200. (Hs, Te) = (2.293289736769183 m, 8.853033813999282 s). Tp = 9.796943887461476 s\n",
      "Sea state 11/200. (Hs, Te) = (2.2020532526799967 m, 8.69872911280172 s). Tp = 9.626187225850904 s\n",
      "Sea state 12/200. (Hs, Te) = (2.0876480479410366 m, 8.539262027183852 s). Tp = 9.449717766621593 s\n",
      "Sea state 13/200. (Hs, Te) = (1.8902057494339104 m, 8.529332396785021 s). Tp = 9.438729439469068 s\n",
      "Sea state 14/200. (Hs, Te) = (2.308096327590195 m, 9.112272664224243 s). Tp = 10.083822772424941 s\n",
      "Sea state 15/200. (Hs, Te) = (1.9622893666119121 m, 9.151949676311485 s). Tp = 10.127730145785039 s\n",
      "Sea state 16/200. (Hs, Te) = (2.305667961346008 m, 9.200023294046321 s). Tp = 10.18092937051529 s\n",
      "Sea state 17/200. (Hs, Te) = (1.9701858425767147 m, 10.185262006644049 s). Tp = 11.27121419104896 s\n",
      "Sea state 18/200. (Hs, Te) = (2.292470528080541 m, 9.616889178090641 s). Tp = 10.642241476668234 s\n",
      "Sea state 19/200. (Hs, Te) = (2.6097974565855493 m, 10.607583411714911 s). Tp = 11.738563485638863 s\n",
      "Sea state 20/200. (Hs, Te) = (2.7555697594812547 m, 10.323372655443912 s). Tp = 11.42405019111185 s\n",
      "Sea state 21/200. (Hs, Te) = (3.682633905384873 m, 12.402475369156193 s). Tp = 13.724826744150525 s\n",
      "Sea state 22/200. (Hs, Te) = (4.212665972583244 m, 12.185046415308522 s). Tp = 13.48421552486582 s\n",
      "Sea state 23/200. (Hs, Te) = (4.05026799253212 m, 11.113905814149078 s). Tp = 12.298869960213526 s\n",
      "Sea state 24/200. (Hs, Te) = (5.303297566743566 m, 10.639559673240361 s). Tp = 11.773949054753066 s\n",
      "Sea state 25/200. (Hs, Te) = (4.924134073730911 m, 9.785109089934313 s). Tp = 10.828396988068192 s\n",
      "Sea state 26/200. (Hs, Te) = (3.5985615413148957 m, 8.581846021873012 s). Tp = 9.496842064939978 s\n",
      "Sea state 27/200. (Hs, Te) = (3.135645469719277 m, 7.8511911960291645 s). Tp = 8.688284853899408 s\n",
      "Sea state 28/200. (Hs, Te) = (2.431810006062661 m, 7.116680274464138 s). Tp = 7.875460410382518 s\n",
      "Sea state 29/200. (Hs, Te) = (2.0838410893707966 m, 6.298801092171736 s). Tp = 6.970378985868918 s\n",
      "Sea state 30/200. (Hs, Te) = (1.6423455509793814 m, 6.523674804404985 s). Tp = 7.219228723348807 s\n",
      "Sea state 31/200. (Hs, Te) = (1.4428843954801813 m, 7.031455517574027 s). Tp = 7.7811489936843605 s\n",
      "Sea state 32/200. (Hs, Te) = (1.0660804027836115 m, 7.003369546884362 s). Tp = 7.750068498042693 s\n",
      "Sea state 33/200. (Hs, Te) = (0.6706525629195497 m, 7.391577956604073 s). Tp = 8.179667671226758 s\n",
      "Sea state 34/200. (Hs, Te) = (0.9453680651508931 m, 8.514256864842773 s). Tp = 9.422046554978298 s\n",
      "Sea state 35/200. (Hs, Te) = (0.8606454052847035 m, 9.19445876440277 s). Tp = 10.1747715509674 s\n",
      "Sea state 36/200. (Hs, Te) = (1.446046535282041 m, 9.890985020002242 s). Tp = 10.94556140511448 s\n",
      "Sea state 37/200. (Hs, Te) = (1.814284454479686 m, 10.884111327310288 s). Tp = 12.044574795357422 s\n",
      "Sea state 38/200. (Hs, Te) = (1.8369294990041127 m, 14.071484897068036 s). Tp = 15.571785994067179 s\n",
      "Sea state 39/200. (Hs, Te) = (2.4279375035930384 m, 11.721321793811422 s). Tp = 12.971048604746592 s\n",
      "Sea state 40/200. (Hs, Te) = (3.4366878484585097 m, 13.500507507743283 s). Tp = 14.939931020760929 s\n",
      "Sea state 41/200. (Hs, Te) = (4.881749805564816 m, 15.102009111828629 s). Tp = 16.712184655000232 s\n",
      "Sea state 42/200. (Hs, Te) = (5.929650654049805 m, 14.041374740462938 s). Tp = 15.538465493897355 s\n",
      "Sea state 43/200. (Hs, Te) = (6.733647106251836 m, 12.756124450005649 s). Tp = 14.116181874349593 s\n",
      "Sea state 44/200. (Hs, Te) = (5.823482465094092 m, 10.757719220728456 s). Tp = 11.904706767965251 s\n",
      "Sea state 45/200. (Hs, Te) = (4.522240943762454 m, 8.448078861366799 s). Tp = 9.348812655700389 s\n",
      "Sea state 46/200. (Hs, Te) = (3.9632342495762476 m, 7.763746279892604 s). Tp = 8.591516564674196 s\n",
      "Sea state 47/200. (Hs, Te) = (3.48351682452572 m, 7.324368618974508 s). Tp = 8.105292476993444 s\n",
      "Sea state 48/200. (Hs, Te) = (2.563456346797842 m, 6.056216781584966 s). Tp = 6.701930346822829 s\n",
      "Sea state 49/200. (Hs, Te) = (1.995627635392273 m, 5.300300446310389 s). Tp = 5.865418245333961 s\n",
      "Sea state 50/200. (Hs, Te) = (1.2482525200731613 m, 5.00092407195376 s). Tp = 5.534122375192174 s\n",
      "Sea state 51/200. (Hs, Te) = (1.0112713778368576 m, 5.4894639018048546 s). Tp = 6.0747503000819165 s\n",
      "Sea state 52/200. (Hs, Te) = (0.7225874968126063 m, 5.797180698365338 s). Tp = 6.415275847873832 s\n",
      "Sea state 53/200. (Hs, Te) = (0.3412147422072267 m, 6.444682361990287 s). Tp = 7.131814110219623 s\n",
      "Sea state 54/200. (Hs, Te) = (0.37629800539801495 m, 7.278127327491898 s). Tp = 8.054120941059342 s\n",
      "Sea state 55/200. (Hs, Te) = (0.2593556848203602 m, 9.772429376897422 s). Tp = 10.814365364588443 s\n",
      "Sea state 56/200. (Hs, Te) = (0.22439382449464995 m, 11.838773644910981 s). Tp = 13.101023167012615 s\n",
      "Sea state 57/200. (Hs, Te) = (0.7231170863341507 m, 13.331652990941636 s). Tp = 14.75307324285046 s\n",
      "Sea state 58/200. (Hs, Te) = (1.0944677391961881 m, 14.740489201708725 s). Tp = 16.31211950806222 s\n",
      "Sea state 59/200. (Hs, Te) = (2.2544246940776325 m, 15.00832277152429 s). Tp = 16.608509481238684 s\n",
      "Sea state 60/200. (Hs, Te) = (4.221697213400411 m, 15.06617516629942 s). Tp = 16.672530095784513 s\n",
      "Sea state 61/200. (Hs, Te) = (0.42919942735341365 m, 14.389743813203152 s). Tp = 15.923977661756238 s\n",
      "Sea state 62/200. (Hs, Te) = (0.8066477894744781 m, 14.929114433172016 s). Tp = 16.520855953356616 s\n",
      "Sea state 63/200. (Hs, Te) = (2.18706772725983 m, 16.553280263601604 s). Tp = 18.31819027274999 s\n",
      "Sea state 64/200. (Hs, Te) = (4.058781607387172 m, 17.104385307921103 s). Tp = 18.92805411250597 s\n",
      "Sea state 65/200. (Hs, Te) = (5.280990480119309 m, 17.091896795314423 s). Tp = 18.914234075237836 s\n",
      "Sea state 66/200. (Hs, Te) = (6.67877937615683 m, 15.499740904199125 s). Tp = 17.152322593471858 s\n",
      "Sea state 67/200. (Hs, Te) = (6.927635065873248 m, 14.740354537612657 s). Tp = 16.31197048608619 s\n",
      "Sea state 68/200. (Hs, Te) = (6.837998607790748 m, 12.158743171888053 s). Tp = 13.455107830795898 s\n",
      "Sea state 69/200. (Hs, Te) = (6.515875893176208 m, 10.937669435895 s). Tp = 12.103843267109248 s\n",
      "Sea state 70/200. (Hs, Te) = (5.304001413051129 m, 8.964849111642947 s). Tp = 9.92068092719158 s\n",
      "Sea state 71/200. (Hs, Te) = (4.21625929476447 m, 7.632380539272374 s). Tp = 8.446144614602561 s\n",
      "Sea state 72/200. (Hs, Te) = (3.90275894328102 m, 7.0180531016534715 s). Tp = 7.766317612771473 s\n",
      "Sea state 73/200. (Hs, Te) = (3.092131576145537 m, 6.263479592630679 s). Tp = 6.931291509609928 s\n",
      "Sea state 74/200. (Hs, Te) = (2.061691984692526 m, 5.2597539358137295 s). Tp = 5.8205486676825124 s\n",
      "Sea state 75/200. (Hs, Te) = (1.7477973238002613 m, 4.952976645409529 s). Tp = 5.481062796151686 s\n",
      "Sea state 76/200. (Hs, Te) = (1.188105496243663 m, 4.789426304957664 s). Tp = 5.300074725638678 s\n",
      "Sea state 77/200. (Hs, Te) = (0.8909185568988089 m, 4.865417933018925 s). Tp = 5.384168577720861 s\n",
      "Sea state 78/200. (Hs, Te) = (0.5868809150201082 m, 5.342702431380711 s). Tp = 5.912341128176003 s\n",
      "Sea state 79/200. (Hs, Te) = (0.3249029161852639 m, 5.8652556732843655 s). Tp = 6.490608973606103 s\n",
      "Sea state 80/200. (Hs, Te) = (0.19854447978643908 m, 6.682309275317205 s). Tp = 7.394776794529401 s\n",
      "Sea state 81/200. (Hs, Te) = (1.1378533719939599 m, 16.29937669010103 s). Tp = 18.037215511479157 s\n",
      "Sea state 82/200. (Hs, Te) = (1.7207753935819574 m, 18.18049776867239 s). Tp = 20.11890163619976 s\n",
      "Sea state 83/200. (Hs, Te) = (3.045435393397856 m, 18.2722028817323 s). Tp = 20.220384344355757 s\n",
      "Sea state 84/200. (Hs, Te) = (5.9410240832717705 m, 18.20434585099714 s). Tp = 20.14529240000659 s\n",
      "Sea state 85/200. (Hs, Te) = (6.900385063891696 m, 16.965929376112204 s). Tp = 18.774836015374902 s\n",
      "Sea state 86/200. (Hs, Te) = (7.5413039919231215 m, 16.07885944382636 s). Tp = 17.79318672616556 s\n",
      "Sea state 87/200. (Hs, Te) = (8.33324739000868 m, 14.60855477544739 s). Tp = 16.16611823911159 s\n",
      "Sea state 88/200. (Hs, Te) = (8.073493320987428 m, 13.666423534396822 s). Tp = 15.123537006833741 s\n",
      "Sea state 89/200. (Hs, Te) = (7.742808676245161 m, 11.899449249557978 s). Tp = 13.168168001941908 s\n",
      "Sea state 90/200. (Hs, Te) = (6.04122128102763 m, 9.145483011133436 s). Tp = 10.120574005051886 s\n",
      "Sea state 91/200. (Hs, Te) = (5.307838900691543 m, 8.532751345737658 s). Tp = 9.44251291660773 s\n",
      "Sea state 92/200. (Hs, Te) = (3.5538843391472863 m, 6.449345501593802 s). Tp = 7.1369744335613 s\n",
      "Sea state 93/200. (Hs, Te) = (3.2727441707177842 m, 6.178574531557655 s). Tp = 6.837333874682727 s\n",
      "Sea state 94/200. (Hs, Te) = (2.396435845611073 m, 5.2840531245047435 s). Tp = 5.847438634796415 s\n",
      "Sea state 95/200. (Hs, Te) = (1.7925351881694949 m, 4.859872189983066 s). Tp = 5.378031547807942 s\n",
      "Sea state 96/200. (Hs, Te) = (1.3712158702331128 m, 4.614584181856148 s). Tp = 5.106590943109626 s\n",
      "Sea state 97/200. (Hs, Te) = (1.0020762104765706 m, 4.4051257091119895 s). Tp = 4.874800017270961 s\n",
      "Sea state 98/200. (Hs, Te) = (0.6840846016235075 m, 4.6709868121298745 s). Tp = 5.169007219327068 s\n",
      "Sea state 99/200. (Hs, Te) = (0.4400318176169047 m, 5.264806535859035 s). Tp = 5.826139975721048 s\n",
      "Sea state 100/200. (Hs, Te) = (0.2950455607439133 m, 5.368907174671332 s). Tp = 5.941339820036593 s\n",
      "Sea state 101/200. (Hs, Te) = (0.2538207006543258 m, 17.12463819459854 s). Tp = 18.950466361064674 s\n",
      "Sea state 102/200. (Hs, Te) = (2.401827718374254 m, 19.74909028958335 s). Tp = 21.854737422273 s\n",
      "Sea state 103/200. (Hs, Te) = (4.771083170894034 m, 20.076537695823838 s). Tp = 22.217097256475835 s\n",
      "Sea state 104/200. (Hs, Te) = (6.672059784308026 m, 18.96224243886321 s). Tp = 20.98399588853062 s\n",
      "Sea state 105/200. (Hs, Te) = (7.266218886285855 m, 18.517848875220015 s). Tp = 20.492221102798194 s\n",
      "Sea state 106/200. (Hs, Te) = (8.704119700269523 m, 16.896830852172723 s). Tp = 18.698370209870912 s\n",
      "Sea state 107/200. (Hs, Te) = (9.025791407684109 m, 15.924773336852542 s). Tp = 17.622671965285193 s\n",
      "Sea state 108/200. (Hs, Te) = (8.335871487107436 m, 12.413322380618183 s). Tp = 13.736830263494564 s\n",
      "Sea state 109/200. (Hs, Te) = (7.4617419694961065 m, 10.850802026111152 s). Tp = 12.00771405793852 s\n",
      "Sea state 110/200. (Hs, Te) = (6.0882272377996465 m, 9.069127068362327 s). Tp = 10.036076995041613 s\n",
      "Sea state 111/200. (Hs, Te) = (4.971882552682233 m, 7.629443455087086 s). Tp = 8.442894378631417 s\n",
      "Sea state 112/200. (Hs, Te) = (3.9137147533928953 m, 6.396097294409882 s). Tp = 7.078048904883903 s\n",
      "Sea state 113/200. (Hs, Te) = (3.06161156988711 m, 5.531224857642791 s). Tp = 6.120963807183185 s\n",
      "Sea state 114/200. (Hs, Te) = (2.7343619821293204 m, 5.293654205328456 s). Tp = 5.85806338243266 s\n",
      "Sea state 115/200. (Hs, Te) = (2.0584231261728334 m, 4.660229583463185 s). Tp = 5.157103055415991 s\n",
      "Sea state 116/200. (Hs, Te) = (1.3721475288257854 m, 4.323265832156141 s). Tp = 4.784212243856746 s\n",
      "Sea state 117/200. (Hs, Te) = (0.8599939026300656 m, 4.213959963822748 s). Tp = 4.663252188678921 s\n",
      "Sea state 118/200. (Hs, Te) = (0.5419078120521271 m, 4.519931230768316 s). Tp = 5.001846098565626 s\n",
      "Sea state 119/200. (Hs, Te) = (0.3868003926473669 m, 4.617757194340929 s). Tp = 5.110102262045115 s\n",
      "Sea state 120/200. (Hs, Te) = (0.07629861562822371 m, 5.12934517057858 s). Tp = 5.676235725669164 s\n",
      "Sea state 121/200. (Hs, Te) = (0.34633798237637237 m, 19.72005237767673 s). Tp = 21.822603489483953 s\n",
      "Sea state 122/200. (Hs, Te) = (2.3276352644630043 m, 20.988388061461745 s). Tp = 23.22616906774459 s\n",
      "Sea state 123/200. (Hs, Te) = (4.4025116313558925 m, 20.635956263518874 s). Tp = 22.836161007101737 s\n",
      "Sea state 124/200. (Hs, Te) = (6.356747543266486 m, 20.26518087818985 s). Tp = 22.425853566597404 s\n",
      "Sea state 125/200. (Hs, Te) = (8.350325561657009 m, 18.84467767246754 s). Tp = 20.853896371944614 s\n",
      "Sea state 126/200. (Hs, Te) = (10.507095756225086 m, 16.726452821049758 s). Tp = 18.509826480609608 s\n",
      "Sea state 127/200. (Hs, Te) = (10.051931328368415 m, 15.526444557776568 s). Tp = 17.181873389411972 s\n",
      "Sea state 128/200. (Hs, Te) = (9.569671582265315 m, 13.484451946197973 s). Tp = 14.922163615954188 s\n",
      "Sea state 129/200. (Hs, Te) = (7.670417919717313 m, 10.688834805882522 s). Tp = 11.828477899856887 s\n",
      "Sea state 130/200. (Hs, Te) = (6.695773472447784 m, 9.28833992655812 s). Tp = 10.27866231847709 s\n",
      "Sea state 131/200. (Hs, Te) = (5.2124365852315915 m, 7.762126130889369 s). Tp = 8.589723675455936 s\n",
      "Sea state 132/200. (Hs, Te) = (4.332304023671216 m, 6.700456908860995 s). Tp = 7.414859327958517 s\n",
      "Sea state 133/200. (Hs, Te) = (3.6845002818825954 m, 6.05544401874487 s). Tp = 6.701075192042921 s\n",
      "Sea state 134/200. (Hs, Te) = (2.651930188462395 m, 4.882016850087267 s). Tp = 5.402537270592454 s\n",
      "Sea state 135/200. (Hs, Te) = (1.761425029748757 m, 4.320829327277171 s). Tp = 4.781515958935407 s\n",
      "Sea state 136/200. (Hs, Te) = (1.6715886684994976 m, 4.2515655747998595 s). Tp = 4.704867308234196 s\n",
      "Sea state 137/200. (Hs, Te) = (0.982745350372394 m, 4.044748867057365 s). Tp = 4.475999812264762 s\n",
      "Sea state 138/200. (Hs, Te) = (0.6319038698634393 m, 3.9844552342473887 s). Tp = 4.4092776749928495 s\n",
      "Sea state 139/200. (Hs, Te) = (0.43826897160129663 m, 4.309987462686959 s). Tp = 4.769518135222388 s\n",
      "Sea state 140/200. (Hs, Te) = (0.12296019879881004 m, 4.824883587782545 s). Tp = 5.339312462389205 s\n",
      "Sea state 141/200. (Hs, Te) = (1.7008854788623395 m, 21.591483415345785 s). Tp = 23.893566421570075 s\n",
      "Sea state 142/200. (Hs, Te) = (1.958403888846231 m, 22.399914218070567 s). Tp = 24.78819208070469 s\n",
      "Sea state 143/200. (Hs, Te) = (6.654571320228031 m, 21.834806379665565 s). Tp = 24.1628324695775 s\n",
      "Sea state 144/200. (Hs, Te) = (7.417015230376994 m, 22.004251917947876 s). Tp = 24.350344283652746 s\n",
      "Sea state 145/200. (Hs, Te) = (10.015094697709129 m, 19.80808526300293 s). Tp = 21.920022437147782 s\n",
      "Sea state 146/200. (Hs, Te) = (10.935487969680494 m, 18.194709889967456 s). Tp = 20.134629053238356 s\n",
      "Sea state 147/200. (Hs, Te) = (10.740817257849123 m, 16.312190381294457 s). Tp = 18.051395397860563 s\n",
      "Sea state 148/200. (Hs, Te) = (10.318979128904427 m, 13.810844960534954 s). Tp = 15.283356639035425 s\n",
      "Sea state 149/200. (Hs, Te) = (8.926396179491384 m, 11.686930863038155 s). Tp = 12.932990914456232 s\n",
      "Sea state 150/200. (Hs, Te) = (6.360592705852408 m, 8.544681430031973 s). Tp = 9.455714985961547 s\n",
      "Sea state 151/200. (Hs, Te) = (5.079095551882201 m, 7.2036675682669244 s). Tp = 7.97172228560098 s\n",
      "Sea state 152/200. (Hs, Te) = (4.139472783285571 m, 6.228329733752621 s). Tp = 6.89239397433383 s\n",
      "Sea state 153/200. (Hs, Te) = (3.4483306755718233 m, 5.481984054027074 s). Tp = 6.0664729512650855 s\n",
      "Sea state 154/200. (Hs, Te) = (2.8921184163756575 m, 4.935956882773301 s). Tp = 5.462228387176384 s\n",
      "Sea state 155/200. (Hs, Te) = (2.1073029243263735 m, 4.3654137834767255 s). Tp = 4.830854007881896 s\n",
      "Sea state 156/200. (Hs, Te) = (1.6474602839139243 m, 4.092507633924886 s). Tp = 4.528850616742159 s\n",
      "Sea state 157/200. (Hs, Te) = (1.0572722867269018 m, 3.8421613140275808 s). Tp = 4.2518123832963495 s\n",
      "Sea state 158/200. (Hs, Te) = (0.6406099130872815 m, 3.8713841750793403 s). Tp = 4.284150984500205 s\n",
      "Sea state 159/200. (Hs, Te) = (0.4054145619344566 m, 4.049398568043829 s). Tp = 4.481145264164633 s\n",
      "Sea state 160/200. (Hs, Te) = (0.04052982291891705 m, 4.528167327323754 s). Tp = 5.010960327371321 s\n",
      "Sea state 161/200. (Hs, Te) = (0.9321525156202579 m, 23.34548077630066 s). Tp = 25.83457490352772 s\n",
      "Sea state 162/200. (Hs, Te) = (3.967681748224935 m, 24.333091362041877 s). Tp = 26.92748448621447 s\n",
      "Sea state 163/200. (Hs, Te) = (6.784028064639531 m, 23.110281041500112 s). Tp = 25.57429818341138 s\n",
      "Sea state 164/200. (Hs, Te) = (9.44189240558023 m, 22.593599747148144 s). Tp = 25.002528352321065 s\n",
      "Sea state 165/200. (Hs, Te) = (11.240531608432693 m, 19.575407738491727 s). Tp = 21.662536845270218 s\n",
      "Sea state 166/200. (Hs, Te) = (12.004540248825101 m, 17.882918927035586 s). Tp = 19.789594951636836 s\n",
      "Sea state 167/200. (Hs, Te) = (12.024225448635484 m, 16.671252728627874 s). Tp = 18.448740956776554 s\n",
      "Sea state 168/200. (Hs, Te) = (11.357641718079881 m, 14.223019851712502 s). Tp = 15.739477598869739 s\n",
      "Sea state 169/200. (Hs, Te) = (8.821707924875701 m, 11.009149800345794 s). Tp = 12.18294486485446 s\n",
      "Sea state 170/200. (Hs, Te) = (7.106361798567036 m, 9.224265100430067 s). Tp = 10.207755837222862 s\n",
      "Sea state 171/200. (Hs, Te) = (5.211295520287843 m, 7.015787616178823 s). Tp = 7.7638105813357745 s\n",
      "Sea state 172/200. (Hs, Te) = (4.8195217091534825 m, 6.637986810245572 s). Tp = 7.3457286701932984 s\n",
      "Sea state 173/200. (Hs, Te) = (3.3902610822019144 m, 5.164748265935532 s). Tp = 5.7154134974863275 s\n",
      "Sea state 174/200. (Hs, Te) = (2.6095653202369826 m, 4.621018348933888 s). Tp = 5.113711120796454 s\n",
      "Sea state 175/200. (Hs, Te) = (1.8697727603224317 m, 4.0452987797603335 s). Tp = 4.4766083566357855 s\n",
      "Sea state 176/200. (Hs, Te) = (1.5454032632552606 m, 3.901895461469463 s). Tp = 4.317915382894982 s\n",
      "Sea state 177/200. (Hs, Te) = (1.1162594384590447 m, 3.7807872241095453 s). Tp = 4.1838945906273315 s\n",
      "Sea state 178/200. (Hs, Te) = (0.9028634682006197 m, 3.7222798357613645 s). Tp = 4.119149147122681 s\n",
      "Sea state 179/200. (Hs, Te) = (0.32249025006568055 m, 3.8987959672401518 s). Tp = 4.314485420728108 s\n",
      "Sea state 180/200. (Hs, Te) = (0.3250987448403442 m, 3.9671561761459007 s). Tp = 4.390134192082142 s\n",
      "Sea state 181/200. (Hs, Te) = (1.0306696654071352 m, 23.938975875960722 s). Tp = 26.49134842444872 s\n",
      "Sea state 182/200. (Hs, Te) = (2.603264286267793 m, 25.13838407710226 s). Tp = 27.81863747491678 s\n",
      "Sea state 183/200. (Hs, Te) = (5.858754709021494 m, 25.063693808326345 s). Tp = 27.735983732989357 s\n",
      "Sea state 184/200. (Hs, Te) = (10.155435864907151 m, 23.211633456341872 s). Tp = 25.686456788238267 s\n",
      "Sea state 185/200. (Hs, Te) = (12.361550008947113 m, 20.139407734957956 s). Tp = 22.286670496400152 s\n",
      "Sea state 186/200. (Hs, Te) = (12.732660337947125 m, 18.002340099850258 s). Tp = 19.92174880461209 s\n",
      "Sea state 187/200. (Hs, Te) = (12.471212243449607 m, 16.22100142577672 s). Tp = 17.950483879941046 s\n",
      "Sea state 188/200. (Hs, Te) = (11.30029974478813 m, 13.819219928253016 s). Tp = 15.292624545440956 s\n",
      "Sea state 189/200. (Hs, Te) = (9.231594696755867 m, 11.08794558713383 s). Tp = 12.270141854942771 s\n",
      "Sea state 190/200. (Hs, Te) = (7.4957968275525655 m, 9.146029757697058 s). Tp = 10.121179045709924 s\n",
      "Sea state 191/200. (Hs, Te) = (6.174952841653434 m, 7.83613931929022 s). Tp = 8.67162814672867 s\n",
      "Sea state 192/200. (Hs, Te) = (4.681623492075341 m, 6.339937017001209 s). Tp = 7.01590082118326 s\n",
      "Sea state 193/200. (Hs, Te) = (3.900851734182826 m, 5.570871321782711 s). Tp = 6.164837375575169 s\n",
      "Sea state 194/200. (Hs, Te) = (3.1189685428910567 m, 4.886108588584802 s). Tp = 5.407065270067518 s\n",
      "Sea state 195/200. (Hs, Te) = (1.9193980219283122 m, 3.8820797075687836 s). Tp = 4.295986874190348 s\n",
      "Sea state 196/200. (Hs, Te) = (1.5120152039662609 m, 3.6534201969813007 s). Tp = 4.042947696703235 s\n",
      "Sea state 197/200. (Hs, Te) = (1.0490506632626337 m, 3.48025912982143 s). Tp = 3.8513242042259317 s\n",
      "Sea state 198/200. (Hs, Te) = (0.5803416777834116 m, 3.454649334283839 s). Tp = 3.8229838934214713 s\n",
      "Sea state 199/200. (Hs, Te) = (0.49309280206832695 m, 3.543799112476096 s). Tp = 3.9216388170193093 s\n",
      "Sea state 200/200. (Hs, Te) = (0.08514972661072973 m, 3.7868424627988535 s). Tp = 4.190595438597723 s\n"
     ]
    }
   ],
   "source": [
    "# create the short-term extreme distribution for each sample sea state\n",
    "t_st = 3.0 * 60.0 * 60.0\n",
    "gamma = 3.3 \n",
    "t_sim = 1.0 * 60.0 * 60.0\n",
    "\n",
    "ste_all = []\n",
    "i = 0\n",
    "n = len(sample_hs)\n",
    "for hs, te in zip(sample_hs, sample_te):\n",
    "    tp = te / (0.8255 + 0.03852*gamma - 0.005537*gamma**2 + 0.0003154*gamma**3)\n",
    "    i += 1\n",
    "    print(f\"Sea state {i}/{n}. (Hs, Te) = ({hs} m, {te} s). Tp = {tp} s\")\n",
    "    # time & frequency arrays\n",
    "    f0 = 1.0/t_sim\n",
    "    T_min = tp/10.0  # s\n",
    "    f_max = 1.0/T_min\n",
    "    Nf = int(f_max/f0)\n",
    "    time = np.linspace(0, t_sim, 2*Nf+1)\n",
    "    f = np.linspace(f0, f_max, Nf)\n",
    "    # spectrum\n",
    "    S = resource.jonswap_spectrum(f, tp, hs, gamma)\n",
    "    # 1-hour elevation time-series\n",
    "    data = resource.surface_elevation(S, time).values.squeeze()\n",
    "    # 3-hour extreme distribution\n",
    "    ste = extreme.short_term_extreme(time, data, t_st, 'peaks_weibull_tail_fit')\n",
    "    ste_all.append(ste)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Long-Term Extreme Distribution\n",
    "Finally we integrate the weighted short-term extreme distributions over the entire sea state space to obtain the extreme distribution, assuming a 3-hour sea state coherence. \n",
    "For this we use the `loads.extreme.full_seastate_long_term_extreme` function.\n",
    "The integral reduces to a sum over the 200 bins of the weighted short-term extreme distributions.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "lte = extreme.full_seastate_long_term_extreme(ste_all, sample_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the short-term extreme functions, the output of long-term extreme function is a probability distribution (`scipy.stats.rv_continuous`). \n",
    "This object provides common statistical functions (PDF, CDF, PPF, etc.) and metrics (expected value, median, etc). \n",
    "Here, we will look at the survival function and the 100-year return level. \n",
    "\n",
    "The value of the survival function at a given return level (e.g. 100-years) \n",
    "may be calculated using the `return_year_value` function. This gives a 100-year wave of about 11.1 meters.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100-year elevation: 11.139864671857818 m\n"
     ]
    }
   ],
   "source": [
    "t_st_hr = t_st/(60.0*60.0)\n",
    "t_return_yr = 100.0\n",
    "x_t = extreme.return_year_value(lte.ppf, t_return_yr, t_st_hr)\n",
    "\n",
    "print(f\"100-year elevation: {x_t} m\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we plot the survival function and show the 100-year return level (dashed grey lines). The 100-year value is about 11.1 m (where the grey line intersects the x-axis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a63e0d8460>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 20, 1000)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# plot survival function\n",
    "ax.semilogy(x, lte.sf(x))\n",
    "\n",
    "# format plot\n",
    "plt.grid(True, which=\"major\", linestyle=\":\")\n",
    "ax.tick_params(axis=\"both\", which=\"major\", direction=\"in\")\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "ax.yaxis.set_ticks_position('both') \n",
    "plt.minorticks_off()\n",
    "ax.set_xticks([0, 5, 10, 15, 20])\n",
    "ax.set_yticks(1.0*10.0**(-1*np.arange(11)))\n",
    "ax.set_xlabel(\"elevation [m]\")\n",
    "ax.set_ylabel(\"survival function (1-cdf)\")\n",
    "ax.set_xlim([0, x[-1]])\n",
    "ylim = [1e-10, 1]\n",
    "ax.set_ylim(ylim)\n",
    "\n",
    "# 100-year return level\n",
    "ax.plot([0, x[-1]], [s_t, s_t], '--', color=\"0.5\", linewidth=1)\n",
    "ax.plot([x_t, x_t], ylim, '--', color=\"0.5\", linewidth=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}