{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MHKiT WEC-Sim Example\n",
    "\n",
    "This example loads simulated data from a [WEC-Sim](http://wec-sim.github.io/WEC-Sim/index.html) run for a two-body point absorber [(Reference Model 3)](http://wec-sim.github.io/WEC-Sim/tutorials.html#two-body-point-absorber-rm3) and demonstrates the application of the [MHKiT wave module](https://mhkit-software.github.io/MHKiT/mhkit-python/api.wave.html) to interact with the simulated data. The analysis is broken down into three parts: load WEC-Sim data, view and interact with the WEC-Sim data , and apply the MHKiT wave module. \n",
    "\n",
    "  1. Load WEC-Sim Simulated Data\n",
    "  2. WEC-Sim Simulated Data \n",
    "      - Wave Class Data\n",
    "      - Body Class Data\n",
    "      - PTO Class Data\n",
    "      - Constraint Class Data\n",
    "      - Mooring Class Data\n",
    "  3. Apply MHKiT Wave Module\n",
    " \n",
    "Start by importing MHKiT and the necessary python packages (e.g.`scipy.io` and `matplotlib.pyplot`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from  mhkit import wave\n",
    "import scipy.io as sio\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load WEC-Sim Simulated Data\n",
    "\n",
    "WEC-Sim saves output data as a MATLAB output object, generated by WEC-Sim's [Response Class](http://wec-sim.github.io/WEC-Sim/api.html#response-class). The WEC-Sim output object must be converted to a structure for use in Python. \n",
    "\n",
    "Here we will load the WEC-Sim RM3 data run with a mooring matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "moorDyn class not used\n",
      "ptosim class not used\n",
      "cable class not used\n"
     ]
    }
   ],
   "source": [
    "# Relative location and filename of simulated WEC-Sim data (run with mooring)\n",
    "filename = './data/wave/RM3MooringMatrix_matlabWorkspace_structure.mat' \n",
    "\n",
    "# Load data using the `wecsim.read_output` function which returns a dictionary of dataFrames\n",
    "wecsim_data = wave.io.wecsim.read_output(filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE:** The `mhkit.wave.io.wecsim.read_output` function prints a message letting the user know which WEC-Sim classes were not used. This WEC-Sim example data for the RM3 was not run with MoorDyn, PTO-Sim or a cable. \n",
    "\n",
    "**NOTE**: Conversion of the WEC-Sim object  to a struct must be done *in MATLAB* and can be achieved using the command: `output = struct(output);`.\n",
    "\n",
    "\n",
    "## 2. WEC-Sim Simulated Data\n",
    "\n",
    "This section will investigate the WEC-Sim RM3 data loaded using MHKiT. In the previous section `mhkit.wave.io.wecsim.read_output` returned a [dictonary](https://docs.python.org/3/tutorial/datastructures.html#dictionaries) of [DataFrames](https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#dataframe) in a  format similar to the WEC-Sim output object. To see what data is available we can call the `keys` method on the `wecsim_data` dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['wave', 'bodies', 'ptos', 'constraints', 'mooring', 'moorDyn', 'ptosim', 'cables'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the available WEC-Sim data, a dataFrame for each WEC-Sim Class\n",
    "wecsim_data.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see above that there are eight returned keys. As noted from the `mhkit.wave.io.wecsim.read_output` function the `moorDyn`, `ptosim` and `cables` keys will be empty as the function found the data missing from the output. The following sections will work through each of the 5 other WEC-Sim classes.\n",
    "\n",
    "### Wave Class Data\n",
    "\n",
    "Data from WEC-Sim's [Wave Class](http://wec-sim.github.io/WEC-Sim/code_structure.html#wave-class) includes information about the wave input, including the the wave type, and wave elevation as a function of time. Below we will wave the wave DataFrame as wave and then look at the wave type and the top (head) of the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WEC-Sim wave type: elevationImport\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>elevation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.01</th>\n",
       "      <td>-2.766568e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.02</th>\n",
       "      <td>-1.106006e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.03</th>\n",
       "      <td>-2.486738e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.04</th>\n",
       "      <td>-4.416811e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399.96</th>\n",
       "      <td>-3.480172e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399.97</th>\n",
       "      <td>-3.429291e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399.98</th>\n",
       "      <td>-3.378586e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399.99</th>\n",
       "      <td>-3.326988e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400.00</th>\n",
       "      <td>-3.274562e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40001 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           elevation\n",
       "time                \n",
       "0.00   -0.000000e+00\n",
       "0.01   -2.766568e-07\n",
       "0.02   -1.106006e-06\n",
       "0.03   -2.486738e-06\n",
       "0.04   -4.416811e-06\n",
       "...              ...\n",
       "399.96 -3.480172e-01\n",
       "399.97 -3.429291e-01\n",
       "399.98 -3.378586e-01\n",
       "399.99 -3.326988e-01\n",
       "400.00 -3.274562e-01\n",
       "\n",
       "[40001 rows x 1 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store WEC-Sim output from the Wave Class to a new dataFrame, called `wave_data`\n",
    "wave_data = wecsim_data['wave']\n",
    "\n",
    "# Display the wave type from the WEC-Sim Wave Class\n",
    "wave_type = wave_data.name\n",
    "print(\"WEC-Sim wave type:\", wave_type)\n",
    "\n",
    "# View the WEC-Sim output dataFrame for the Wave Class \n",
    "wave_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The WEC-Sim wave type refers to the type of wave used to run the WEC-Sim simulation. Data from this WEC-Sim run was generated used '[etaImport](http://wec-sim.github.io/WEC-Sim/code_structure.html#etaimport)', meaning a user-defined wave surface elevation was defined. For more information about the wave types used by WEC-Sim, refer to the [Wave Class](http://wec-sim.github.io/WEC-Sim/code_structure.html#wave-class) documentation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot the Wave Elevation Data\n",
    "\n",
    "We can use the pandas DataFrame plot method to quickly view the Data. The plot method will set the index (time) on the x-axis and any columns (in this case only wave elevation) on the y-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Wave Surface Elevation [m]')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot WEC-Sim output from the Wave Class\n",
    "wave_data.plot()\n",
    "plt.xlabel(\"Time [s]\")\n",
    "plt.ylabel(\"Wave Surface Elevation [m]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Body Class Data\n",
    "\n",
    "Data from WEC-Sim's [Body Class](http://wec-sim.github.io/WEC-Sim/code_structure.html#body-class) includes information about each body, including the body's position, velocity, acceleration, forces acting on the body, and the body's name. For the RM3 example there will be 2 boides a float and a spar. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['body1', 'body2'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store WEC-Sim output from the Body Class to a new dictionary of dataFrames, i.e. 'bodies'. \n",
    "bodies = wecsim_data['bodies']\n",
    "\n",
    "# Data fron each body is stored as its own dataFrame, i.e. 'body1' and 'body2'.\n",
    "bodies.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Body Class Data for Body 1\n",
    "\n",
    "Let us determine which body 'body1' is by requesting the body's name. In this this case the body name is 'float'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name of Body 1: float\n"
     ]
    }
   ],
   "source": [
    "# Store Body Class dataFrame for Body 1 as `body1`. \n",
    "body1 = bodies['body1']\n",
    "\n",
    "# Display the name of Body 1 from the WEC-Sim Body Class\n",
    "print(\"Name of Body 1:\", body1.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a given body WEC-Sim returns simulated parameters (the number of simulated parameters varies by the options choosen) for each of the 6 degrees of freedom (DOF). We can view the unique simulated parameters by filtering the columns by a single DOF. For this example we look at surge data (DOF 1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['position_dof1',\n",
       " 'velocity_dof1',\n",
       " 'acceleration_dof1',\n",
       " 'forceTotal_dof1',\n",
       " 'forceExcitation_dof1',\n",
       " 'forceRadiationDamping_dof1',\n",
       " 'forceAddedMass_dof1',\n",
       " 'forceRestoring_dof1',\n",
       " 'forceMorisonAndViscous_dof1',\n",
       " 'forceLinearDamping_dof1']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print a list of Body 1 columns that end with 'dof1'\n",
    "[col for col in body1 if col.endswith('dof1')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Plot heave position data for Body 1\n",
    "\n",
    "The RM3 device creates power in the heave direction (DOF 3). Let us consider the float's (body 1) position as a function of time by plotting `postion_dof3`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body 1 max heave position = -0.09745294999054366 [m]\n",
      "Body 1 min heave position = -1.3264489287377081 [m]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use Pandas to plot Body 1 position in heave (DOF 3)\n",
    "body1.position_dof3.plot()\n",
    "plt.xlabel(\"Time [s]\")\n",
    "plt.ylabel(\"Heave Position [m]\")\n",
    "plt.title('Body 1')\n",
    "\n",
    "# Use Pandas to calculate the maximum and minimum heave position of Body 1 \n",
    "print(\"Body 1 max heave position =\", body1.position_dof3.max(),\"[m]\")\n",
    "print(\"Body 1 min heave position =\", body1.position_dof3.min(),\"[m]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Plot Body 1 position data for all DOFs\n",
    "As an example we could plot multiple postion DOFs by calling a plot on only the position columns. To do that we would first create a list of stings (`filter_col`) which include the string `'position'`. Then slicing the DataFrame on that list of strings and calling plot will plot the position over time of all 6 degrees of freedom for body1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x204dea0fdf0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a list of Body 1 data columns that start with 'position'\n",
    "filter_col = [col for col in body1 if col.startswith('position')]\n",
    "\n",
    "# Plot filtered 'position' data for Body 1\n",
    "body1[filter_col].plot()\n",
    "plt.xlabel('Time [s]')\n",
    "plt.ylabel('Position [m or rad]')\n",
    "plt.title('Body 1')\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Body Class Data for Body 2\n",
    "\n",
    "We can look at the name of body 2 to see that is the 'sapr' of RM3. Looking at the top of the body2 DataFrame we see the same available data as body1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name of Body 2: spar\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>position_dof1</th>\n",
       "      <th>velocity_dof1</th>\n",
       "      <th>acceleration_dof1</th>\n",
       "      <th>forceTotal_dof1</th>\n",
       "      <th>forceExcitation_dof1</th>\n",
       "      <th>forceRadiationDamping_dof1</th>\n",
       "      <th>forceAddedMass_dof1</th>\n",
       "      <th>forceRestoring_dof1</th>\n",
       "      <th>forceMorisonAndViscous_dof1</th>\n",
       "      <th>forceLinearDamping_dof1</th>\n",
       "      <th>...</th>\n",
       "      <th>position_dof6</th>\n",
       "      <th>velocity_dof6</th>\n",
       "      <th>acceleration_dof6</th>\n",
       "      <th>forceTotal_dof6</th>\n",
       "      <th>forceExcitation_dof6</th>\n",
       "      <th>forceRadiationDamping_dof6</th>\n",
       "      <th>forceAddedMass_dof6</th>\n",
       "      <th>forceRestoring_dof6</th>\n",
       "      <th>forceMorisonAndViscous_dof6</th>\n",
       "      <th>forceLinearDamping_dof6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.755268e-11</td>\n",
       "      <td>0.002335</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.002335</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000032</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.01</th>\n",
       "      <td>1.591394e-14</td>\n",
       "      <td>3.896569e-12</td>\n",
       "      <td>4.658216e-10</td>\n",
       "      <td>0.003717</td>\n",
       "      <td>0.000114</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.003603</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000049</td>\n",
       "      <td>3.542912e-11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.02</th>\n",
       "      <td>1.060269e-13</td>\n",
       "      <td>1.600503e-11</td>\n",
       "      <td>1.593218e-09</td>\n",
       "      <td>0.005343</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.004872</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000068</td>\n",
       "      <td>1.419347e-10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.03</th>\n",
       "      <td>3.850573e-13</td>\n",
       "      <td>4.276107e-11</td>\n",
       "      <td>3.369237e-09</td>\n",
       "      <td>0.006388</td>\n",
       "      <td>0.001097</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000079</td>\n",
       "      <td>3.198299e-10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.04</th>\n",
       "      <td>1.024419e-12</td>\n",
       "      <td>8.867535e-11</td>\n",
       "      <td>5.507643e-09</td>\n",
       "      <td>0.006671</td>\n",
       "      <td>0.002016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.004656</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000081</td>\n",
       "      <td>5.694090e-10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      position_dof1  velocity_dof1  acceleration_dof1  forceTotal_dof1  \\\n",
       "time                                                                     \n",
       "0.00   0.000000e+00   0.000000e+00       3.755268e-11         0.002335   \n",
       "0.01   1.591394e-14   3.896569e-12       4.658216e-10         0.003717   \n",
       "0.02   1.060269e-13   1.600503e-11       1.593218e-09         0.005343   \n",
       "0.03   3.850573e-13   4.276107e-11       3.369237e-09         0.006388   \n",
       "0.04   1.024419e-12   8.867535e-11       5.507643e-09         0.006671   \n",
       "\n",
       "      forceExcitation_dof1  forceRadiationDamping_dof1  forceAddedMass_dof1  \\\n",
       "time                                                                          \n",
       "0.00              0.000000                         0.0            -0.002335   \n",
       "0.01              0.000114                         0.0            -0.003603   \n",
       "0.02              0.000471                         0.0            -0.004872   \n",
       "0.03              0.001097                         0.0            -0.005291   \n",
       "0.04              0.002016                         0.0            -0.004656   \n",
       "\n",
       "      forceRestoring_dof1  forceMorisonAndViscous_dof1  \\\n",
       "time                                                     \n",
       "0.00                  0.0                            0   \n",
       "0.01                  0.0                            0   \n",
       "0.02                  0.0                            0   \n",
       "0.03                  0.0                            0   \n",
       "0.04                  0.0                            0   \n",
       "\n",
       "      forceLinearDamping_dof1  ...  position_dof6  velocity_dof6  \\\n",
       "time                           ...                                 \n",
       "0.00                        0  ...            0.0            0.0   \n",
       "0.01                        0  ...            0.0            0.0   \n",
       "0.02                        0  ...            0.0            0.0   \n",
       "0.03                        0  ...            0.0            0.0   \n",
       "0.04                        0  ...            0.0            0.0   \n",
       "\n",
       "      acceleration_dof6  forceTotal_dof6  forceExcitation_dof6  \\\n",
       "time                                                             \n",
       "0.00                0.0        -0.000032          0.000000e+00   \n",
       "0.01                0.0        -0.000049          3.542912e-11   \n",
       "0.02                0.0        -0.000068          1.419347e-10   \n",
       "0.03                0.0        -0.000079          3.198299e-10   \n",
       "0.04                0.0        -0.000081          5.694090e-10   \n",
       "\n",
       "      forceRadiationDamping_dof6  forceAddedMass_dof6  forceRestoring_dof6  \\\n",
       "time                                                                         \n",
       "0.00                         0.0             0.000032                  0.0   \n",
       "0.01                         0.0             0.000049                  0.0   \n",
       "0.02                         0.0             0.000068                  0.0   \n",
       "0.03                         0.0             0.000079                  0.0   \n",
       "0.04                         0.0             0.000081                  0.0   \n",
       "\n",
       "      forceMorisonAndViscous_dof6  forceLinearDamping_dof6  \n",
       "time                                                        \n",
       "0.00                            0                        0  \n",
       "0.01                            0                        0  \n",
       "0.02                            0                        0  \n",
       "0.03                            0                        0  \n",
       "0.04                            0                        0  \n",
       "\n",
       "[5 rows x 60 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store Body Class dataFrame for Body 2 as `body2` \n",
    "body2 = bodies['body2']\n",
    "\n",
    "# Display the name of Body 2 from the WEC-Sim Body Class\n",
    "print(\"Name of Body 2:\", body2.name)\n",
    "\n",
    "# View the Body Class dataFrame for Body 2\n",
    "body2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PTO Class Data\n",
    "\n",
    "Data from WEC-Sim's [PTO Class](http://wec-sim.github.io/WEC-Sim/code_structure.html#pto-class) includes information about each PTO, including the PTO's position, velocity, acceleration, forces acting on the PTO, and the PTO's name. In this case there is only 1 PTO."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name of PTO: PTO1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['position_dof1',\n",
       " 'velocity_dof1',\n",
       " 'acceleration_dof1',\n",
       " 'forceTotal_dof1',\n",
       " 'forceActuation_dof1',\n",
       " 'forceConstraint_dof1',\n",
       " 'forceInternalMechanics_dof1',\n",
       " 'powerInternalMechanics_dof1']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store WEC-Sim output from the PTO Class to a DataFrame, called `ptos`\n",
    "ptos = wecsim_data['ptos']\n",
    "\n",
    "# Display the name of the PTO from the WEC-Sim PTO Class\n",
    "print(\"Name of PTO:\", ptos.name)\n",
    "\n",
    "# Print a list of available columns that end with 'dof1'\n",
    "[col for col in ptos if col.endswith('dof1')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'PTO')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use Pandas to plot pto internal power in heave (DOF 3)\n",
    "# NOTE: WEC-Sim requires a negative sign to convert internal power to generated power\n",
    "(-1*ptos.powerInternalMechanics_dof3/1000).plot()\n",
    "plt.xlabel(\"Time [s]\")\n",
    "plt.ylabel(\"Power Generated [kW]\")\n",
    "plt.title('PTO')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constraint Class Data\n",
    "Data from WEC-Sim's [Constraint Class](http://wec-sim.github.io/WEC-Sim/code_structure.html#constraint-class) includes  information about each constraint, including the constraint's position, velocity, acceleration, forces acting on the constraint, and the constraint's name. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name of Constraint: Constraint1\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>position_dof1</th>\n",
       "      <th>velocity_dof1</th>\n",
       "      <th>acceleration_dof1</th>\n",
       "      <th>forceConstraint_dof1</th>\n",
       "      <th>position_dof2</th>\n",
       "      <th>velocity_dof2</th>\n",
       "      <th>acceleration_dof2</th>\n",
       "      <th>forceConstraint_dof2</th>\n",
       "      <th>position_dof3</th>\n",
       "      <th>velocity_dof3</th>\n",
       "      <th>...</th>\n",
       "      <th>acceleration_dof4</th>\n",
       "      <th>forceConstraint_dof4</th>\n",
       "      <th>position_dof5</th>\n",
       "      <th>velocity_dof5</th>\n",
       "      <th>acceleration_dof5</th>\n",
       "      <th>forceConstraint_dof5</th>\n",
       "      <th>position_dof6</th>\n",
       "      <th>velocity_dof6</th>\n",
       "      <th>acceleration_dof6</th>\n",
       "      <th>forceConstraint_dof6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-2.382256e-10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.615587e-27</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.636025</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-1.282689e-11</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-7.703720e-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.01</th>\n",
       "      <td>-7.759428e-14</td>\n",
       "      <td>-1.900049e-11</td>\n",
       "      <td>-2.327210e-09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.198363e-03</td>\n",
       "      <td>1.828372e-07</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.325180</td>\n",
       "      <td>-4.349220e-15</td>\n",
       "      <td>-1.064980e-12</td>\n",
       "      <td>-1.299084e-10</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.168962e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.02</th>\n",
       "      <td>-4.942732e-13</td>\n",
       "      <td>-7.203815e-11</td>\n",
       "      <td>-6.948927e-09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.493818e-03</td>\n",
       "      <td>8.204028e-07</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.984545</td>\n",
       "      <td>-2.792094e-14</td>\n",
       "      <td>-4.095032e-12</td>\n",
       "      <td>-3.973091e-10</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.653122e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.03</th>\n",
       "      <td>-1.677273e-12</td>\n",
       "      <td>-1.739122e-10</td>\n",
       "      <td>-1.255916e-08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.245134e-03</td>\n",
       "      <td>2.036966e-06</td>\n",
       "      <td>0.000154</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.781700</td>\n",
       "      <td>-9.592234e-14</td>\n",
       "      <td>-1.007783e-11</td>\n",
       "      <td>-7.408555e-10</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.740994e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.04</th>\n",
       "      <td>-4.117573e-12</td>\n",
       "      <td>-3.216348e-10</td>\n",
       "      <td>-1.705139e-08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.345214e-03</td>\n",
       "      <td>3.906281e-06</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.746425</td>\n",
       "      <td>-2.391624e-13</td>\n",
       "      <td>-1.908419e-11</td>\n",
       "      <td>-1.049258e-09</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.123989e-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      position_dof1  velocity_dof1  acceleration_dof1  forceConstraint_dof1  \\\n",
       "time                                                                          \n",
       "0.00   0.000000e+00   0.000000e+00      -2.382256e-10                   0.0   \n",
       "0.01  -7.759428e-14  -1.900049e-11      -2.327210e-09                   0.0   \n",
       "0.02  -4.942732e-13  -7.203815e-11      -6.948927e-09                   0.0   \n",
       "0.03  -1.677273e-12  -1.739122e-10      -1.255916e-08                   0.0   \n",
       "0.04  -4.117573e-12  -3.216348e-10      -1.705139e-08                   0.0   \n",
       "\n",
       "      position_dof2  velocity_dof2  acceleration_dof2  forceConstraint_dof2  \\\n",
       "time                                                                          \n",
       "0.00            0.0            0.0                0.0          1.615587e-27   \n",
       "0.01            0.0            0.0                0.0          1.198363e-03   \n",
       "0.02            0.0            0.0                0.0          2.493818e-03   \n",
       "0.03            0.0            0.0                0.0          3.245134e-03   \n",
       "0.04            0.0            0.0                0.0          3.345214e-03   \n",
       "\n",
       "      position_dof3  velocity_dof3  ...  acceleration_dof4  \\\n",
       "time                                ...                      \n",
       "0.00   0.000000e+00       0.000000  ...                0.0   \n",
       "0.01   1.828372e-07       0.000039  ...                0.0   \n",
       "0.02   8.204028e-07       0.000091  ...                0.0   \n",
       "0.03   2.036966e-06       0.000154  ...                0.0   \n",
       "0.04   3.906281e-06       0.000220  ...                0.0   \n",
       "\n",
       "      forceConstraint_dof4  position_dof5  velocity_dof5  acceleration_dof5  \\\n",
       "time                                                                          \n",
       "0.00             -3.636025  -0.000000e+00  -0.000000e+00      -1.282689e-11   \n",
       "0.01             -3.325180  -4.349220e-15  -1.064980e-12      -1.299084e-10   \n",
       "0.02             -2.984545  -2.792094e-14  -4.095032e-12      -3.973091e-10   \n",
       "0.03             -2.781700  -9.592234e-14  -1.007783e-11      -7.408555e-10   \n",
       "0.04             -2.746425  -2.391624e-13  -1.908419e-11      -1.049258e-09   \n",
       "\n",
       "      forceConstraint_dof5  position_dof6  velocity_dof6  acceleration_dof6  \\\n",
       "time                                                                          \n",
       "0.00                  -0.0            0.0            0.0                0.0   \n",
       "0.01                  -0.0            0.0            0.0                0.0   \n",
       "0.02                  -0.0            0.0            0.0                0.0   \n",
       "0.03                  -0.0            0.0            0.0                0.0   \n",
       "0.04                  -0.0            0.0            0.0                0.0   \n",
       "\n",
       "      forceConstraint_dof6  \n",
       "time                        \n",
       "0.00         -7.703720e-34  \n",
       "0.01          3.168962e-05  \n",
       "0.02          6.653122e-05  \n",
       "0.03          8.740994e-05  \n",
       "0.04          9.123989e-05  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store WEC-Sim output from the Constraint Class to a new dataFrame, called `constraints`\n",
    "constraints = wecsim_data['constraints']\n",
    "\n",
    "# Display the name of the Constraint from the WEC-Sim Constraint Class\n",
    "print(\"Name of Constraint:\", constraints.name)\n",
    "\n",
    "# View the Constraint Class dataFrame\n",
    "constraints.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mooring Class Data\n",
    "Data from WEC-Sim's [Mooring Class](http://wec-sim.github.io/WEC-Sim/code_structure.html#mooring-class) includes relevant information about the mooring, including the mooring's position, velocity, mooring force, and the mooring's name. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>position_dof1</th>\n",
       "      <th>velocity_dof1</th>\n",
       "      <th>forceMooring_dof1</th>\n",
       "      <th>position_dof2</th>\n",
       "      <th>velocity_dof2</th>\n",
       "      <th>forceMooring_dof2</th>\n",
       "      <th>position_dof3</th>\n",
       "      <th>velocity_dof3</th>\n",
       "      <th>forceMooring_dof3</th>\n",
       "      <th>position_dof4</th>\n",
       "      <th>velocity_dof4</th>\n",
       "      <th>forceMooring_dof4</th>\n",
       "      <th>position_dof5</th>\n",
       "      <th>velocity_dof5</th>\n",
       "      <th>forceMooring_dof5</th>\n",
       "      <th>position_dof6</th>\n",
       "      <th>velocity_dof6</th>\n",
       "      <th>forceMooring_dof6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.01</th>\n",
       "      <td>-7.759428e-14</td>\n",
       "      <td>-1.900049e-11</td>\n",
       "      <td>7.759428e-09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.828372e-07</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.349220e-15</td>\n",
       "      <td>-1.064980e-12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.02</th>\n",
       "      <td>-4.942732e-13</td>\n",
       "      <td>-7.203815e-11</td>\n",
       "      <td>4.942732e-08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.204028e-07</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.792094e-14</td>\n",
       "      <td>-4.095032e-12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.03</th>\n",
       "      <td>-1.677273e-12</td>\n",
       "      <td>-1.739122e-10</td>\n",
       "      <td>1.677273e-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.036966e-06</td>\n",
       "      <td>0.000154</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-9.592234e-14</td>\n",
       "      <td>-1.007783e-11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.04</th>\n",
       "      <td>-4.117573e-12</td>\n",
       "      <td>-3.216348e-10</td>\n",
       "      <td>4.117573e-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.906281e-06</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.391624e-13</td>\n",
       "      <td>-1.908419e-11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      position_dof1  velocity_dof1  forceMooring_dof1  position_dof2  \\\n",
       "time                                                                   \n",
       "0.00   0.000000e+00   0.000000e+00       0.000000e+00            0.0   \n",
       "0.01  -7.759428e-14  -1.900049e-11       7.759428e-09            0.0   \n",
       "0.02  -4.942732e-13  -7.203815e-11       4.942732e-08            0.0   \n",
       "0.03  -1.677273e-12  -1.739122e-10       1.677273e-07            0.0   \n",
       "0.04  -4.117573e-12  -3.216348e-10       4.117573e-07            0.0   \n",
       "\n",
       "      velocity_dof2  forceMooring_dof2  position_dof3  velocity_dof3  \\\n",
       "time                                                                   \n",
       "0.00            0.0                0.0   0.000000e+00       0.000000   \n",
       "0.01            0.0                0.0   1.828372e-07       0.000039   \n",
       "0.02            0.0                0.0   8.204028e-07       0.000091   \n",
       "0.03            0.0                0.0   2.036966e-06       0.000154   \n",
       "0.04            0.0                0.0   3.906281e-06       0.000220   \n",
       "\n",
       "      forceMooring_dof3  position_dof4  velocity_dof4  forceMooring_dof4  \\\n",
       "time                                                                       \n",
       "0.00                0.0            0.0            0.0                0.0   \n",
       "0.01                0.0            0.0            0.0                0.0   \n",
       "0.02                0.0            0.0            0.0                0.0   \n",
       "0.03                0.0            0.0            0.0                0.0   \n",
       "0.04                0.0            0.0            0.0                0.0   \n",
       "\n",
       "      position_dof5  velocity_dof5  forceMooring_dof5  position_dof6  \\\n",
       "time                                                                   \n",
       "0.00  -0.000000e+00  -0.000000e+00                0.0            0.0   \n",
       "0.01  -4.349220e-15  -1.064980e-12                0.0            0.0   \n",
       "0.02  -2.792094e-14  -4.095032e-12                0.0            0.0   \n",
       "0.03  -9.592234e-14  -1.007783e-11                0.0            0.0   \n",
       "0.04  -2.391624e-13  -1.908419e-11                0.0            0.0   \n",
       "\n",
       "      velocity_dof6  forceMooring_dof6  \n",
       "time                                    \n",
       "0.00            0.0                0.0  \n",
       "0.01            0.0                0.0  \n",
       "0.02            0.0                0.0  \n",
       "0.03            0.0                0.0  \n",
       "0.04            0.0                0.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Store WEC-Sim output from the Mooring Class to a new dataFrame, called `mooring`\n",
    "mooring = wecsim_data['mooring']\n",
    "\n",
    "# View the PTO Class dataFrame\n",
    "mooring.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Apply MHKiT Wave Module\n",
    "\n",
    "In the example below we will calculate a spectrum from the wecsim wave elevation timeseries data using the MHKiT `elevation_spectrum` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the MHKiT Wave Module to calculate the wave spectrum from the WEC-Sim Wave Class Data\n",
    "sample_rate=60\n",
    "nnft=1000        # Number of bins in the Fast Fourier Transform\n",
    "ws_spectrum = wave.resource.elevation_spectrum(wave_data, sample_rate, nnft)\n",
    "\n",
    "# Plot calculated wave spectrum\n",
    "spect_plot = wave.graphics.plot_spectrum(ws_spectrum)\n",
    "spect_plot = spect_plot.set_xlim([0, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>elevation</th>\n",
       "      <td>8.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Tp\n",
       "elevation  8.333333"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hm0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>elevation</th>\n",
       "      <td>1.785851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Hm0\n",
       "elevation  1.785851"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate Peak Wave Period (Tp) and Significant Wave Height (Hm0)\n",
    "Tp = wave.resource.peak_period(ws_spectrum)\n",
    "Hm0 = wave.resource.significant_wave_height(ws_spectrum)\n",
    "\n",
    "# Display calculated Peak Wave Period (Tp) and Significant Wave Height (Hm0)\n",
    "display(Tp,Hm0)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}