{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MHKiT SWAN Example\n",
    "\n",
    "This example notebook demonstrates the input and plotting of output data from the software [Simulating WAves Nearshore (SWAN)](http://swanmodel.sourceforge.net/) using MHKiT. In this example the [SNL-SWAN](https://github.com/SNL-WaterPower/SNL-SWAN) tutorial was run for a wave energy converter. The output was written in ASCII and binary (*.mat) files. This MHKiT example notebook demonstrates how to import these different files into MHKiT and plot the output data. First, we will import the MHKiT SWAN package and the other Python packages needed for this example. Secondly, we will create an operating system independent path to the folder housing the SWAN data used in this example `swan_data_folder` using the `join` funtion. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mhkit.wave.io import swan\n",
    "import matplotlib.pyplot as plt\n",
    "from os.path import join\n",
    "import pandas as pd\n",
    "\n",
    "swan_data_folder = join('data','wave','swan')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supported SWAN Output Files\n",
    "\n",
    "MHKiT currenlty supports block and table SWAN output files in ASCII or binary (*.mat) files. Detailed descriptions of these file types may be found in the [SWAN User Manual](http://swanmodel.sourceforge.net/download/zip/swanuse.pdf). In the following cells, SWAN table and block data will be imported, discussed, and plotted. Three SWAN output files will be imported:\n",
    " 1. An ASCII table file ('SWANOUT.DAT'), \n",
    " 2. An ASCII block file ('SWANOUTBlock.DAT') \n",
    " 3. A binary block file ('SWANOUT.mat')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "swan_table_file = join(swan_data_folder, 'SWANOUT.DAT')\n",
    "swan_block_file = join(swan_data_folder, 'SWANOUTBlock.DAT')\n",
    "swan_block_mat_file = join(swan_data_folder, 'SWANOUT.mat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load SWAN Files with MHKiT\n",
    "\n",
    "To load a supported SWAN file simply call the `swan.read_table` or `swan.read_block` as appropriate for the swan output. The MHKiT function will read in the SWAN output and return the data as a DataFrame for table data or a dictionary of DataFrames for block data with multiple quantities of interest written to the file. The MHKiT SWAN read function will also return any metadata that the file may contain which will vary based on the file type and options specified in the SWAN run. MHKiT requires that for block data written in ASCII format that the file was written with headers. The `swan.read_block` function accepts both binary and ASCII format by assuming that any non-'.mat' extension is ASCII format."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SWAN Table Data and Metadata\n",
    "\n",
    "The SWAN output table is parsed from the MHKiT funtion `swan.read_table` into a DataFrame that is displayed below. The DataFrame columns contain a series of x-points ('Xp'), y-points ('Yp'), and keyword values at a given (x,y) point. The keywords are specified in the SWAN user manual and here can be seen as: 'Hsig' (significant wave height), 'Dir' (average wave direction), 'RTpeak' (Relative peak period), 'TDir' (direction of the energy transport). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Xp</th>\n",
       "      <th>Yp</th>\n",
       "      <th>Hsig</th>\n",
       "      <th>Dir</th>\n",
       "      <th>RTpeak</th>\n",
       "      <th>TDir</th>\n",
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       "  </thead>\n",
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       "      <td>9.5726</td>\n",
       "      <td>0.000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00106</td>\n",
       "      <td>0.000</td>\n",
       "      <td>9.5726</td>\n",
       "      <td>0.000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0.000</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>1.00091</td>\n",
       "      <td>359.990</td>\n",
       "      <td>9.5726</td>\n",
       "      <td>359.991</td>\n",
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       "      <td>1000.0</td>\n",
       "      <td>1.00089</td>\n",
       "      <td>359.988</td>\n",
       "      <td>9.5726</td>\n",
       "      <td>359.989</td>\n",
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       "    <tr>\n",
       "      <th>10198</th>\n",
       "      <td>980.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.00086</td>\n",
       "      <td>359.986</td>\n",
       "      <td>9.5726</td>\n",
       "      <td>359.987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10199</th>\n",
       "      <td>990.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.00083</td>\n",
       "      <td>359.984</td>\n",
       "      <td>9.5726</td>\n",
       "      <td>359.985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10200</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.00079</td>\n",
       "      <td>359.981</td>\n",
       "      <td>9.5726</td>\n",
       "      <td>359.982</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10201 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Xp      Yp     Hsig      Dir  RTpeak     TDir\n",
       "0         0.0     0.0  1.00106    0.000  9.5726    0.000\n",
       "1        10.0     0.0  1.00106    0.000  9.5726    0.000\n",
       "2        20.0     0.0  1.00106    0.000  9.5726    0.000\n",
       "3        30.0     0.0  1.00106    0.000  9.5726    0.000\n",
       "4        40.0     0.0  1.00106    0.000  9.5726    0.000\n",
       "...       ...     ...      ...      ...     ...      ...\n",
       "10196   960.0  1000.0  1.00091  359.990  9.5726  359.991\n",
       "10197   970.0  1000.0  1.00089  359.988  9.5726  359.989\n",
       "10198   980.0  1000.0  1.00086  359.986  9.5726  359.987\n",
       "10199   990.0  1000.0  1.00083  359.984  9.5726  359.985\n",
       "10200  1000.0  1000.0  1.00079  359.981  9.5726  359.982\n",
       "\n",
       "[10201 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "swan_table, metadata_table = swan.read_table(swan_table_file)\n",
    "swan_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the cell below, metadata is written to screen and can be seen to be a dictionary of keywords which contains the SWAN run name, the type of table written, the version of SWAN run, the column headers, and the associated units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Run': 'TEST',\n",
       " 'Table': ['COMPGRID'],\n",
       " 'version': '41.20',\n",
       " 'header': ['Xp', 'Yp', 'Hsig', 'Dir', 'RTpeak', 'TDir'],\n",
       " 'units': ['m', 'm', 'm', 'degr', 'sec', 'degr']}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SWAN Block (ASCII) Data and Metadata\n",
    "\n",
    "MHKiT will read in block data as a Dictionary of DataFrames for each quantity of interest in the file. The Dictionary `swan_block` (shown below) is read using `swan.read_block` on the ASCII block data, and has the same four keys from the table data shown previously. In the cell below the DataFrame for the 'Significant wave height' is shown by accessing the Dictionary using the specified key. This DataFrame has indices and columns referring to a point on the grid and a value of significant wave height at each point. In the last code block, the metadata Dictionary is written to screen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['Significant wave height', 'Average wave direction', 'Relative peak period', 'direction of the energy transport'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "swan_block, metadata_block = swan.read_block(swan_block_file)\n",
    "swan_block.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    3    4    5    6    7    8    9    ...  91   92   93   \\\n",
       "100  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
       "99   1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
       "98   1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
       "97   1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
       "96   1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "4    1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
       "3    1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
       "2    1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
       "1    1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
       "0    1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
       "\n",
       "     94   95   96   97   98   99   100  \n",
       "100  1.0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
       "99   1.0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
       "98   1.0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
       "97   1.0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
       "96   1.0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
       "..   ...  ...  ...  ...  ...  ...  ...  \n",
       "4    1.0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
       "3    1.0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
       "2    1.0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
       "1    1.0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
       "0    1.0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
       "\n",
       "[101 rows x 101 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "swan_block['Significant wave height']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Significant wave height': {'Run': 'TEST',\n",
       "  'Frame': 'COMPGRID',\n",
       "  'Unit': '0.1000E-01 m',\n",
       "  'unitMultiplier': 0.01},\n",
       " 'Average wave direction': {'Run': 'TEST',\n",
       "  'Frame': 'COMPGRID',\n",
       "  'Unit': '0.1000E+00 degr',\n",
       "  'unitMultiplier': 0.1},\n",
       " 'Relative peak period': {'Run': 'TEST',\n",
       "  'Frame': 'COMPGRID',\n",
       "  'Unit': '0.1000E+00 sec',\n",
       "  'unitMultiplier': 0.1},\n",
       " 'direction of the energy transport': {'Run': 'TEST',\n",
       "  'Frame': 'COMPGRID',\n",
       "  'Unit': '0.1000E+00 degr',\n",
       "  'unitMultiplier': 0.1}}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata_block"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SWAN Block (.mat) Data and Metadata\n",
    "\n",
    "The Block \"SWANOUT.mat\" file is a binary output from SWAN containing the same data as was shown for the ASCII block file above. The Dictionary `swan_block_mat` (shown below) is read using `swan.read_block` on the binary block data, and has the same four keys from the Table data shown previously. Looking at the first code block below it can be seen that the returned Dictionary keys are the SWAN variable names ('Hsig', 'Dir', 'RTpeak', 'TDir'). Looking at the DataFrame for the significant wave height ('Hsig') we can see that the indices and columns are the same as the previous block ASCII DataFrame but the values now contain six decimal places. One consideration for working with binary data is shown in the last cell block of the section where there is no metadata letting the user know the units of the data. For binary data, the user would need to check the run's SWAN input file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['Hsig', 'Dir', 'RTpeak', 'TDir'])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "swan_block_mat, metadata_block_mat = swan.read_block(swan_block_mat_file)\n",
    "swan_block_mat.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "      <th>100</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.001056</td>\n",
       "      <td>1.001056</td>\n",
       "      <td>1.001055</td>\n",
       "      <td>1.001055</td>\n",
       "      <td>1.001055</td>\n",
       "      <td>1.001055</td>\n",
       "      <td>1.001054</td>\n",
       "      <td>1.001054</td>\n",
       "      <td>1.001054</td>\n",
       "      <td>1.001053</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000756</td>\n",
       "      <td>1.000708</td>\n",
       "      <td>1.000654</td>\n",
       "      <td>1.000595</td>\n",
       "      <td>1.000529</td>\n",
       "      <td>1.000456</td>\n",
       "      <td>1.000376</td>\n",
       "      <td>1.000289</td>\n",
       "      <td>1.000195</td>\n",
       "      <td>1.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.001056</td>\n",
       "      <td>1.001055</td>\n",
       "      <td>1.001055</td>\n",
       "      <td>1.001054</td>\n",
       "      <td>1.001054</td>\n",
       "      <td>1.001054</td>\n",
       "      <td>1.001053</td>\n",
       "      <td>1.001053</td>\n",
       "      <td>1.001052</td>\n",
       "      <td>1.001052</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000698</td>\n",
       "      <td>1.000641</td>\n",
       "      <td>1.000579</td>\n",
       "      <td>1.000509</td>\n",
       "      <td>1.000432</td>\n",
       "      <td>1.000348</td>\n",
       "      <td>1.000256</td>\n",
       "      <td>1.000156</td>\n",
       "      <td>1.000047</td>\n",
       "      <td>0.999916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.001056</td>\n",
       "      <td>1.001055</td>\n",
       "      <td>1.001055</td>\n",
       "      <td>1.001054</td>\n",
       "      <td>1.001054</td>\n",
       "      <td>1.001053</td>\n",
       "      <td>1.001052</td>\n",
       "      <td>1.001052</td>\n",
       "      <td>1.001051</td>\n",
       "      <td>1.001051</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000615</td>\n",
       "      <td>1.000548</td>\n",
       "      <td>1.000473</td>\n",
       "      <td>1.000390</td>\n",
       "      <td>1.000299</td>\n",
       "      <td>1.000200</td>\n",
       "      <td>1.000092</td>\n",
       "      <td>0.999976</td>\n",
       "      <td>0.999850</td>\n",
       "      <td>0.999699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.001056</td>\n",
       "      <td>1.001055</td>\n",
       "      <td>1.001055</td>\n",
       "      <td>1.001054</td>\n",
       "      <td>1.001054</td>\n",
       "      <td>1.001053</td>\n",
       "      <td>1.001052</td>\n",
       "      <td>1.001052</td>\n",
       "      <td>1.001051</td>\n",
       "      <td>1.001051</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000515</td>\n",
       "      <td>1.000434</td>\n",
       "      <td>1.000345</td>\n",
       "      <td>1.000247</td>\n",
       "      <td>1.000140</td>\n",
       "      <td>1.000023</td>\n",
       "      <td>0.999897</td>\n",
       "      <td>0.999762</td>\n",
       "      <td>0.999616</td>\n",
       "      <td>0.999444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.001056</td>\n",
       "      <td>1.001055</td>\n",
       "      <td>1.001055</td>\n",
       "      <td>1.001054</td>\n",
       "      <td>1.001054</td>\n",
       "      <td>1.001053</td>\n",
       "      <td>1.001052</td>\n",
       "      <td>1.001052</td>\n",
       "      <td>1.001051</td>\n",
       "      <td>1.001050</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000392</td>\n",
       "      <td>1.000295</td>\n",
       "      <td>1.000189</td>\n",
       "      <td>1.000073</td>\n",
       "      <td>0.999947</td>\n",
       "      <td>0.999811</td>\n",
       "      <td>0.999665</td>\n",
       "      <td>0.999508</td>\n",
       "      <td>0.999340</td>\n",
       "      <td>0.999143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1.001056</td>\n",
       "      <td>1.001055</td>\n",
       "      <td>1.001055</td>\n",
       "      <td>1.001054</td>\n",
       "      <td>1.001054</td>\n",
       "      <td>1.001053</td>\n",
       "      <td>1.001052</td>\n",
       "      <td>1.001052</td>\n",
       "      <td>1.001051</td>\n",
       "      <td>1.001050</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000917</td>\n",
       "      <td>1.000893</td>\n",
       "      <td>1.000865</td>\n",
       "      <td>1.000833</td>\n",
       "      <td>1.000795</td>\n",
       "      <td>1.000752</td>\n",
       "      <td>1.000703</td>\n",
       "      <td>1.000648</td>\n",
       "      <td>1.000586</td>\n",
       "      <td>1.000506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1.001056</td>\n",
       "      <td>1.001055</td>\n",
       "      <td>1.001055</td>\n",
       "      <td>1.001054</td>\n",
       "      <td>1.001054</td>\n",
       "      <td>1.001053</td>\n",
       "      <td>1.001052</td>\n",
       "      <td>1.001052</td>\n",
       "      <td>1.001051</td>\n",
       "      <td>1.001051</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000941</td>\n",
       "      <td>1.000921</td>\n",
       "      <td>1.000899</td>\n",
       "      <td>1.000872</td>\n",
       "      <td>1.000841</td>\n",
       "      <td>1.000806</td>\n",
       "      <td>1.000765</td>\n",
       "      <td>1.000720</td>\n",
       "      <td>1.000668</td>\n",
       "      <td>1.000599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1.001056</td>\n",
       "      <td>1.001055</td>\n",
       "      <td>1.001055</td>\n",
       "      <td>1.001054</td>\n",
       "      <td>1.001054</td>\n",
       "      <td>1.001053</td>\n",
       "      <td>1.001052</td>\n",
       "      <td>1.001052</td>\n",
       "      <td>1.001051</td>\n",
       "      <td>1.001051</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000960</td>\n",
       "      <td>1.000945</td>\n",
       "      <td>1.000926</td>\n",
       "      <td>1.000905</td>\n",
       "      <td>1.000879</td>\n",
       "      <td>1.000850</td>\n",
       "      <td>1.000817</td>\n",
       "      <td>1.000779</td>\n",
       "      <td>1.000735</td>\n",
       "      <td>1.000678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1.001056</td>\n",
       "      <td>1.001055</td>\n",
       "      <td>1.001055</td>\n",
       "      <td>1.001054</td>\n",
       "      <td>1.001054</td>\n",
       "      <td>1.001053</td>\n",
       "      <td>1.001053</td>\n",
       "      <td>1.001052</td>\n",
       "      <td>1.001052</td>\n",
       "      <td>1.001052</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000976</td>\n",
       "      <td>1.000964</td>\n",
       "      <td>1.000949</td>\n",
       "      <td>1.000931</td>\n",
       "      <td>1.000911</td>\n",
       "      <td>1.000887</td>\n",
       "      <td>1.000859</td>\n",
       "      <td>1.000827</td>\n",
       "      <td>1.000791</td>\n",
       "      <td>1.000743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1.001056</td>\n",
       "      <td>1.001055</td>\n",
       "      <td>1.001055</td>\n",
       "      <td>1.001055</td>\n",
       "      <td>1.001055</td>\n",
       "      <td>1.001054</td>\n",
       "      <td>1.001054</td>\n",
       "      <td>1.001054</td>\n",
       "      <td>1.001053</td>\n",
       "      <td>1.001053</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000988</td>\n",
       "      <td>1.000978</td>\n",
       "      <td>1.000965</td>\n",
       "      <td>1.000951</td>\n",
       "      <td>1.000933</td>\n",
       "      <td>1.000913</td>\n",
       "      <td>1.000889</td>\n",
       "      <td>1.000862</td>\n",
       "      <td>1.000832</td>\n",
       "      <td>1.000793</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6    \\\n",
       "0    1.001056  1.001056  1.001055  1.001055  1.001055  1.001055  1.001054   \n",
       "1    1.001056  1.001055  1.001055  1.001054  1.001054  1.001054  1.001053   \n",
       "2    1.001056  1.001055  1.001055  1.001054  1.001054  1.001053  1.001052   \n",
       "3    1.001056  1.001055  1.001055  1.001054  1.001054  1.001053  1.001052   \n",
       "4    1.001056  1.001055  1.001055  1.001054  1.001054  1.001053  1.001052   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "96   1.001056  1.001055  1.001055  1.001054  1.001054  1.001053  1.001052   \n",
       "97   1.001056  1.001055  1.001055  1.001054  1.001054  1.001053  1.001052   \n",
       "98   1.001056  1.001055  1.001055  1.001054  1.001054  1.001053  1.001052   \n",
       "99   1.001056  1.001055  1.001055  1.001054  1.001054  1.001053  1.001053   \n",
       "100  1.001056  1.001055  1.001055  1.001055  1.001055  1.001054  1.001054   \n",
       "\n",
       "          7         8         9    ...       91        92        93   \\\n",
       "0    1.001054  1.001054  1.001053  ...  1.000756  1.000708  1.000654   \n",
       "1    1.001053  1.001052  1.001052  ...  1.000698  1.000641  1.000579   \n",
       "2    1.001052  1.001051  1.001051  ...  1.000615  1.000548  1.000473   \n",
       "3    1.001052  1.001051  1.001051  ...  1.000515  1.000434  1.000345   \n",
       "4    1.001052  1.001051  1.001050  ...  1.000392  1.000295  1.000189   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "96   1.001052  1.001051  1.001050  ...  1.000917  1.000893  1.000865   \n",
       "97   1.001052  1.001051  1.001051  ...  1.000941  1.000921  1.000899   \n",
       "98   1.001052  1.001051  1.001051  ...  1.000960  1.000945  1.000926   \n",
       "99   1.001052  1.001052  1.001052  ...  1.000976  1.000964  1.000949   \n",
       "100  1.001054  1.001053  1.001053  ...  1.000988  1.000978  1.000965   \n",
       "\n",
       "          94        95        96        97        98        99        100  \n",
       "0    1.000595  1.000529  1.000456  1.000376  1.000289  1.000195  1.000088  \n",
       "1    1.000509  1.000432  1.000348  1.000256  1.000156  1.000047  0.999916  \n",
       "2    1.000390  1.000299  1.000200  1.000092  0.999976  0.999850  0.999699  \n",
       "3    1.000247  1.000140  1.000023  0.999897  0.999762  0.999616  0.999444  \n",
       "4    1.000073  0.999947  0.999811  0.999665  0.999508  0.999340  0.999143  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "96   1.000833  1.000795  1.000752  1.000703  1.000648  1.000586  1.000506  \n",
       "97   1.000872  1.000841  1.000806  1.000765  1.000720  1.000668  1.000599  \n",
       "98   1.000905  1.000879  1.000850  1.000817  1.000779  1.000735  1.000678  \n",
       "99   1.000931  1.000911  1.000887  1.000859  1.000827  1.000791  1.000743  \n",
       "100  1.000951  1.000933  1.000913  1.000889  1.000862  1.000832  1.000793  \n",
       "\n",
       "[101 rows x 101 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "swan_block_mat['Hsig']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'filetype': 'mat', 'variables': ['Hsig', 'Dir', 'RTpeak', 'TDir']}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata_block_mat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Block to Table\n",
    "\n",
    "MHKiT provides functionality to convert SWAN block Dictionaries to table DataFrame format. This provides the user with the ability to easily process and manipulate data across multiple data types. The function converts each key to a column in a single DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>Significant wave height</th>\n",
       "      <th>Average wave direction</th>\n",
       "      <th>Relative peak period</th>\n",
       "      <th>direction of the energy transport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10104</th>\n",
       "      <td>100</td>\n",
       "      <td>96</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10103</th>\n",
       "      <td>100</td>\n",
       "      <td>97</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10102</th>\n",
       "      <td>100</td>\n",
       "      <td>98</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10101</th>\n",
       "      <td>100</td>\n",
       "      <td>99</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10100</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10201 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         x    y  Significant wave height  Average wave direction  \\\n",
       "100      0    0                      1.0                     0.0   \n",
       "99       0    1                      1.0                     0.0   \n",
       "98       0    2                      1.0                     0.0   \n",
       "97       0    3                      1.0                     0.0   \n",
       "96       0    4                      1.0                     0.0   \n",
       "...    ...  ...                      ...                     ...   \n",
       "10104  100   96                      1.0                     NaN   \n",
       "10103  100   97                      1.0                     NaN   \n",
       "10102  100   98                      1.0                     NaN   \n",
       "10101  100   99                      1.0                     NaN   \n",
       "10100  100  100                      1.0                     NaN   \n",
       "\n",
       "       Relative peak period  direction of the energy transport  \n",
       "100                     9.6                                0.0  \n",
       "99                      9.6                                0.0  \n",
       "98                      9.6                                0.0  \n",
       "97                      9.6                                0.0  \n",
       "96                      9.6                                0.0  \n",
       "...                     ...                                ...  \n",
       "10104                   9.6                                NaN  \n",
       "10103                   9.6                                NaN  \n",
       "10102                   9.6                                NaN  \n",
       "10101                   9.6                                NaN  \n",
       "10100                   9.6                                NaN  \n",
       "\n",
       "[10201 rows x 6 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "swan_block_as_table = swan.dictionary_of_block_to_table(swan_block)\n",
    "swan_block_mat_as_table = swan.dictionary_of_block_to_table(swan_block_mat)\n",
    "swan_block_as_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example Plots from SWAN Data\n",
    "\n",
    "This last section shows a couple of plots for the significant wave height using each of the imported results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XmtTj4uA4C2KcKCxSEOYVg0ULQZ0IzCMAaWsBapG2M1ppPfnT0nlxLoYBJzepxMXBcVqmDVFoUxDmFYMmQtCGCIyNLUww/lmLsYfNHnMws4nLcTfBxcFxWmJaUZhHENoSg0lC0MQbmEYEaruUxhj/Joa/ra6lfG2lrYCkl1ck3w5cbmZXTCrv4uA4czKNKLQlCOO6itoUgyZC0FQE6gRgkvFvYvhb61ayEHfYIpwQj/8bz58BXAn8uqQPmNkbxhV2cXCcGakThUlewjhBmMU7KC9IN1S31V8rCsEsItCGAMzTlTSou8WA9BT33QQcCTzKzO4CkPQa4O+BxxOGt7o4OE6bNBWFcV5CE0GYVgzGeQXTCMGsIlAlALN0I4W6mhn89uc5tDcJbgPwQxRGKQErwP3N7G5J+2rK9HFxcJyGNBGFtgRhWjGYRQgmiUATAZjW+E8y+k2N/YotznRtoW6l9wCXSvpoPP/PwHslHQhcM6mwi4Nzj6KjhHSGFZGrhKFOFKYRhCrvYBoxqBOCcd7AJBFoIgDVwjHbTOZpDH26Bm/1m320Uo6Z/aGkf2CwRMevx+U3AH5hUnkXB2fL0lG1QZpGIMqiMK7rKDf0VYLQ1DuoEoNJXkFTb6Bo0GcRgDrjX2f4Jxn9poZ+EUtk1GG2+cVB0sFmdoek+wDXxyO/dh8zu7VJPS4OzpaiThCmYR5BmNY7yI1yU6+gTghmEYEmxr/KMNcZ/UnGvqmRz9Y5ILwFYg7vJYxMupwQY1fp0xfec+4ZTCsI47yGbKhLqFoUmngIZe9gGjGo6h6qEoI6ERjXbVQWgLLBrjL8dUZ/8ppHzX6XtfQMmtBWzEHSKcCbCUt2n29mry9d/zMgn7C2HfghMztE0g8Df0vYxrkH/IWZvS2WOQP47wQj/y3gF83s5uH22zPi5zHztN/Fwdm0zOIl1AlD1jf44+MIZQ9hWjGYRgiq3vLrRKBOACYZ/yrDXz+pbdzy2Q0mqrXwRt7mTOg62uhWktQBzgV+hrDX82WSdsRluuN97GWF/C8BHhlPvw081sz2SToI+LKkHYQd4t4MHG9mN0t6A3AWYS/qqjaIEFs4JsYfHgQ8wMw+3+QZXBycTcesXUdVwlDlKUwShSaCMEkM6oSgyhuoFolmAlA2yKOB6rr5BzXpDQznPAZ8vecYGGor5nAisMvMrgeQdAFwGvWjhM4AXgNgZvsL6cvQ/4MqHgdKuoWwGdCuMW34X4T/LE8G/pCwE9yHgJ9s8gBziYOkQ4DzCZtPGPDLwFeA9wNHA98Anm1mP4gq9mbC3qZ7CLseNVod0HGgPVFoIghVHkKVGFR5BtMKQVkEqryAorGuM/6TDH+Vwa8z9uMM/LQGfFMtgW2QZY3be5iknYXz88zsvPj9COCGwrUbgcdUVRK7kY4BPlFIO4owYe1Y4JVm9q2Y/hvAVcBu4DrgxWPa9xgze5SkLwJEO7zU9OHm9RzeDHzMzJ4Vb7qd0B92iZm9XtLZwNnA7wJPA46Lx2OAt1Lzx3KcIvMEmeuEoSwKRQ+hThDqPIMqMagSgjoRqPICqgRgOBhdb/irDH7lBLWx6xmt4ZIVG4wpPIebzeyEydkmcjrwQTPrb8hjZjcAD5P0QOAjkj4I3Ar8BqH76XrC/tGvAv6opt6V2L1lAJLux+jE+1pmFgdJ9yZMw34B9F2h/ZJOA54Qs70T+BRBHE4D3hX3SP2cpEMkHW5m3561Dc7WZVGCUOUhVMUPqsSg2EVUJwRV3kBZBMoCUPX2nw2NVKo2/CMjkZoOPR1j1FesU3utsq4NFkxug5YC0jcBRxXOj4xpVZxOjQdgZt+S9GXgPwLfjGlfA5B0IeHlu463EALbPyTpdcCzgN9r+gDzeA7HAN8H/lrSwwnDpl5KmJ6dG/zvAPeP36vcrCMIwZc+ks4EzgQ46ojp/kN1tgZtCUOVKJS7jHLvoNxVVCUGxe6hshAMewj1IjDO+NcZ/rGjjSoMfZWBb2LE2xhCOu3qqJ0JGyGtNUZr8xwuA46TdAxBFE4HnlvOJOkhwKHAZwtpRwK3xGUuDgV+Gvgz4BbgeEn3M7PvE4Ld19Y+i9l7JF0OPIkQq3immdXmLzOPOHSBRwEvMbNLJb2ZkoqZmUma6tePfXbnATz64csb678cZ6GME4UkN6o1XnEuCuMEoegh7B/qOhqIQZVXkAtB2RsoisA4ASgb/yrDP9S1VDDSZUPfPKDcIHC8AbqFppmrnmj6me1TY0AL4mBmq5LOAi4mDGV9h5ldLekcYKeZ7YhZTwcuiD0qOT8O/Gm0nQL+xMyuApD0B8CnJa0QPIkXTGjKdcAdRFsv6UFm9u9NnmEecbgRuNHMLo3nHySIw3fz7iJJhxOGX8F0bpZzD6OJMJQpewnFOEJREIrewUAMNCQGVUJQ9gZyESjGAIoCUDb+I91JFUZ/fBxh2EjNKgQbQQTqmMbgpwWhXKTH0dY8BzO7CLiolPbq0vlrK8p9HHhYTZ1vA97W5P5xeOxrgO8CKYNJcJV1l5lZHMzsO5JukPRjZvYVgutyTTyeD7w+fn40FtkBnBWHdD2GsPm1xxucEWEoewllbyG1rDKwXBSElTFisJ9OpRBUiUDRCygLQNH459/LRn+4+0gjacX0nCpjPm/weL1IJhjxqufqNBCMjAV6Elunv+KlwI+Z2S2zFJ53tNJLgPfEkUrXAy8kjMm9UNKLCG7Ps2PeiwjDWHcRhrK+cM57O1uQSV5C7iHUeQcrNhCDvdbpewUrlowIwQqdIREoCkDx7T83/kXDP/AYhg1+lQBMGzMI5ZsZ/GkDyJPoKZ2cqYakoge5qrZJxj+LzzRJWBZDa/McNgI3EHZ+m4m5xCFuNVc1lOtJFXmN8WNyHafvCVR1GdV5BysxZrDXutE76MTP4nnwBvZbZ0QEygIwEIqBoR/6Pqa7qGjUy4a7ejjp7IaoLCBVxnla8jZ3GtaVFL26miJlwclKf5e6duel6sRkYSKyyT2Hwvag1wOfkvT3FPZ1MLM3NanHZ0g7G47UMlZJ+wHlvZYOxQ1WECsm9lqXFeuw3zqs0BnyBnIR2G+dvgCsWGfE+A99FoPMNhiiuhLrGLQvF4rqbiKojwMsYlG3vnGd0qiN67dfZXK3TSIjpd5zyQVmX8XfZ5yojBOTpt7JzGyBVVmBe8XPf4/HUjymwsXB2TCsWNr3EFbI+t7BisGKid3WGxKDFeuw15bCeRSKFeuwN+v1BaDvRWTdvneQmYYMfmqhG2q0a0hDhn+0S6hislmLwd9p+tQnvelXvV2vTrqPJWO9kU5N5Davb5xnUxaVYvuLYjIkIjAkJPN0gY1lk3sOZvYHbdTj4uCsO6ll3GX72WMpe7IQKN5tPfZm3b4I7M6W2WtLQwJQFIQVC57DvqwbPQUNRCAa+SAOw2/7q3kXUUkI8jRoL+DbtPtjnEGuMuR1+bs1Rn+kjvjcVQKTjBGAqr9LIhsaVQQDD6W8jWmxHUUhqRKRqrbts2RUPNpg83sOreDi4Kw7d9g+dmcZeyzhtmyZ3dkyd2bb2J0tsycee63LStZlX9Zln/VYyYJXsJp1yBArWYfV6BVk+Se5R6BCF9CwwV/NqgVhHOOMdzepMcgVwlCup6re0bTOaLmaulcp9+3nbevU3q9KUKoEqVMlDFilAJRFJL9v3dDUvI4q8Si3OaXTOD7SmE3uObSFi4OzIfj5q17I7bsPIMtEutLBUtFZyugtrSIZaZqQpgmS0e1mSIaZyOdYlr/ns4ckq8xLvJYjjRqevKxKdjCRjQRJ8zxJLFPMW75X8T65Ya+7XjwvCk9ZEJIG5ZsIy+pQPfVi0kREOrLheEHJgxgy/owa/9Q6I3GRzBY8Gc7Ami+8t6VxcXDWlQzjpz/3a9x7x0E84AcpK9sTLIHVbSJdFukyZD1IOtDtQNYF60AWDwTWsdAzIiCxfq+AJeG8v9Bx4Roy+tMrEgOFfH0jnX9XNPwFocjzKLH+O3FZSIoiMfgcGL9cXIr5i3mLRrKcVjToVfkniUmdkCSy/ltz+J7Ull2lM2Kkk8J8la6yIS8iUdaP5eRv+lWjjcrGP0OjHs5IXKdlsdginoOkHyUscHp/M3uopIcBp5pZ3UJ9Q7g4OOvKjWkYYdfbbWz/xh3YUpdsqUO23GH1gA5ZT6RLCdmSSHuQdYV1Ie0FkTABich64bt1hCXxWvxE0Z7I+umVIpKAyfp5i8KQi4xU/ozXYUg8lMQ0BsIR8gQDu0JnJB3Gi0dT4Uhjet5lllSU6wuHDYzzWNHIvxeFohQnKHoE+63kbVjS9zbKnkOKhrqGMuuUPJqS8S+/2Lc9A3zrxBz+N/BK4K8AzOxKSe+lfhXXIVwcnHUlM9i3e4nu7hTdcjvqdkh6Peh26C33yJa6WC8JgrGUkHVF1hNZLyHrQdZR/9MKnkUuAvk5GuQJYhBFJAmeB9KwaChPpy8MFq9JQUTIPYpkem9DJUGBaGDz/njlXV/NvI2UfGhprNsG1/Nle/rXqA4A54IRYgeDbp7a74W3+sxU6DbqFNIHIrDfSt1aJcEY8kQK9nlqsZiTtkMY68h2M/u8hvtFV+syl3FxcNaVFEEmOisZtnsPdBLU7cLSErp7L51eD1vq0ekI63WCZ9FLsE5CupxgnYJYdIniMSoWWQeSLiNeRfA+BEneTaV+N5VSYUmIXlgSDDt9ryJek/oiQUE0FPOMiEU2EIPcw8j/6WYVHkaeDkEwqj0MDQSLgQDk50VRyI1sLhiKBj+RsT8dftvvJtlADBjkA/rf5xELgNXC/JGiMZpLLObB2DLdSsDNkn6EwX4Oz6K0CvY4XBycDYFSw/bvR50OlmawfwV1OtDdH8Sik6CkA70uyVIH63bp9DrYUicIw1ISvIil3KuwcN4ddEX1hSIB65ZEIgHlXsWqglAkhiW5MAx6LywRJIakgVchRS8ifObnFgWi73GgGcRCQ2KhaIglI9OwQa8Vi2jM0xiY7weAZxCLgdGvFot+2pyexexiMQ/aSt1KLyascP0QSTcBXyfsKd0IFwdnw2D790OnEw4JkgRWO9CJQtHrweoqSnuok2LLXSztoLSL0oSkm5BmCUkK2WqIUSgLL/VZFjyBrAt0w8qblkZhyAqfUSwSCyIgFbqekmjYzcKIllwUOtHAC8gmCwUKr3KK5QddUECmQtwCMPW7oXJByLsJinGI3J5l6vQDy4NuKMU8w55FWSySXEQYpANDYlHugioa/ZyiQAxRtLlDl4sxgxqvQllpHsogX6cgdq3Qks5IOoWwW2YHON/MXl+R59nAa+Ndv2Rmz43pDyJswXxUvPZ0M/tG3G75j4CfJziFbzWzt9Q04Ztm9mRJBwKJmd05TftdHJyNg5K+MChJYpeNkJIgFBJ0krzPpX/dymNN8+osikNK38grC0d/BFMaDGtlDcbAbplQEgPapmC487iDKQbGw5F3KZkN7iMNC0X//v2AtvpCYXVCAag4JDevl4FQmA0LRX6toywafEa8iCqvoigUMNpxUyUUUxnVCUKRKBvqslq1aqFI4nO1SgviELfnPJewIc+NwGWSdpjZNYU8xxG2+Xxc3N/5hwpVvAt4nZl9XNJBDH6CFxAE4yFmlpXKlPm6pI8B76ewP3VTXBycdaO4VLdJJNuWodPpdyMhQbcbxrD2ethyFzodrJuEQHVX4XsvdClZHqzuBg/BEvXjDfkopqzYnURhNJMY9DfHQwRvQhBGMmVCxLjDkGdhwb6bsIxRkejHInIvoXDP2NWkvmDkcYzB237QkzxAPSoU6hv2UaHIjX2/Kyd6FGWhyC1P/ov0xSCPcUSDWfQocvJRUUMTAMcY2KpYRbi3FbqnqkUCBrPayyLRCga0M8/hRGCXmV0PELcqOI2wpUHOrwLnmtkPAMzsezHv8UA37uuAmd1VKPMbwHPNwsJjeZkaHgI8g9C99HZJf0fYWOhfmjyAi4Oz5oxs7CPDukEI1AvBaLod6HaxpR70OlivQ9brhJFL3YSsp/C9E2MKnYEgZP3Ac3FEEoPv8S3fCsdguCujbkQuGNGYKovDXaMBVBaD031BiUqjPL4wXiSQhViEDafl9wtlC5P5cgOdDJ8XxSA3+kA/vS8IUQI6/f0yBvlz418rElh/EmHZHBdFIkOVo56qRj8NxShGygyLRLG7anVotFN7w1mresRqOEzSzsL5eXEnS6jeFvkxpfI/CiDp/xG6nl5rZh+L6bdJ+jBhO+Z/As42sxT4EeA5kv4LYZvm3zSz66oaZ2Z7gAsJWygcSuji+mcYs1piARcHZ02o2+mtEy1qtpSg+903xBE6QQzS7V2y5c5g+Gr8zA1/1o2jkuKbetYtvI3nhh7i/If4PRkWASt85l5ETl80qgSDYERyO1IpEEn/KjEU3ReBpiIhWbhHHtEOLkq1SOTtrhGJeHW8SMTUiZ5E3oYKo1/2JIY9ggliQV3AO6n0IhZC86pvNrOqLQua0gWOA55A2Bnz05J+Iqb/R+CRhFVV30/oTno7sAzsNbMTJP1X4B0xbyWS/hPwHOAUYCeD/XUaNc5xFsq4LUABOksp+w/qsXzv7aTbu6TbOqTbElYPSOIkOAZdRfnwUwgjjopiAMNGPz+n4jwZPu/nKeSzqrpkw6JRQHl//ySBGPxf1IG8Tyv/jF1NWP+Tfg4VbFcs07fkVmpU8Y280M7oZQyCuAkdZVFU8iAzsWyeY/i83N3Uz1cw+qtZ0siLGBnxNEEgiunFLqYNRpNtkW8ELjWzFUJ84KsEsbgRuKLQJfUR4CSCONwIfDiW/1vgr+saIOkbwBcJ3sMrzWz3NA/g4uAshEmCkNOT0e2lfP+RCd997IGYIFkRSiFbNrKlLFigTGglegkdGxjCVQ0CzIU3vv6LpWn4TTC3wYXzvlEf19XcF4jh0UHFa6G7ygZpGuQfyhO7jFSss1SfKtLqKK4bVZdeNLJ5ei4GEPavGBEIWW3ddZSNflkgqvLUCQRUza8YFQhgKGA9Ly05JZcBx0k6hiAKpwPPLeX5CHAG8NeSDiN0J10P3AYcIul+ZvZ94GTCW39e5omEYan/CfjqmDY8zMzumPUBXByc1hknDOX9oQFe87C/Z+9Dw14Nt6fbuX31AO5Kl9mdLpNg7Mu63LW6zP60w57VHmkWlt9eSTvsW+2SZmI17ZBlcQXWNCHLFN7cLbzBh+9RKCyOJIrfVRaPsktQNhZlQ10SAIpVFIx8Pl9uSAwKZUfWdSrUWbVMR/97YamOwXUblGN42Ossaz01pWqpjro8G5YWRj+Z2aqks4CLCX387zCzqyWdA+w0sx3x2lMkXUMYlvrKfL9nSb8NXBKHrl5OWAoD4PWErZlfBtwF/MqYZuyX9GLgPwDbCm375SbP4OLgtEoTYSjyoO6B3Gf7t9hjKXdmcFu2zF7rcVu2nTvSbeyzHnemB7AnW2JPusRd6TJ3p0vsy7rsWV1if9Zhf9Zh72qXlaxDmiWspgkrWRCL1TQhy4JYZFnYH9iKwlEQjIFwFIxX0QOpo8rYFY16MU/NuYZEpCgSw/lnFQYK14faVUHdIoDjFgCso24J83K7Kq+vx1Rlo7UJ12Z2EXBRKe3Vhe8GvDwe5bIfBx5WkX4b8LMNm/Bu4N+ApwLnECbAXduwrIuDs3ZkpX91qWVkGD0l9MjoKeOQZB97bZWeVtmufeyxZbYn+9mTLbG7s8xB2RJ70mXuSpc5sLuPu9Mee9Me+7td9qcd9qZdVtKwt8Nq2mF/2iHNRJYl/c+yUBRFwnJPIheD/vfByJ9JDBneOlGIaaMewqgXQsVbfNUbfVXaOA+gzmuYlhHRqLpXxeJ+5XYM1VG7imxWmd5mzGGjOzZTcKyZ/byk08zsnXHRvc80Lezi4LRG2Wuo6kKCIAoh3cjI2Gthu8dtyvul07DmUgKdmDds6jn4V9tRxl3ptvjdSFIjwcJM3iRj72q335WSZgmrmZFkIstsSCQsCUIhi6N8LA7V7H9CHF4ERJvdxHhUCUQhvUoEhr2H4bLFN/7iIn55FVVdRkP11lwrswivoclGR3Vpk9oJCwhGbx1xWImft0l6KPAdYNykuSFcHJyFkZGNCMFKPM/3iM6AFCM1WEGsWBL3ik7Yaz32Wo/9+b7R8cgsIbVkZGZsoiAOXWV0kyxsGDTUrZEP0kyQMqQQo5CiF1HwHIaFAvLho32mFQhKb+81IjFOFIbOS3tJVOUd1520KK9hkSx0k5+IDDbewKeZOS/Ob/h9YAdwUPzeCBcHZ2Hk3Ub7bJUUI8NYMSPFWDHYawkrlpAS9nreb8E/2G8dVgj7RBf3jd6TLQ/tFV2kqyxuQBOMVL5IXJaFCWSmuHtbEgUiS/paEWZLh/WSgkBETyF6EEAcnroogShmmCwKxWrHCcMk0Wi64dA0XsPIkhoV1yalTXN9IbS9HMc6YWbnx6//DDx42vIuDk5r7LOVoa6kFQtdRhmw34x9JvZah73WHQiA9VjJz61LamLFuqxYuJZF8diXhdFMK1lnaA/p/VkIRO9NeyHOkIX9o/PPnPLe0Pks4H6yKQ6ZBciHeloh9pB/lozVONtVYWMq4xGU4wnDaWVBGLpWY/jbFobKx1sDr6F2Eb9FskW6lSR9DfgcIc7wGTO7epryLg5Oq9ye7et3Fe3JxB22FLqKsmX22HL0BoKhz79nlvS9g9SCUd9nPVKLQ1OjQOzPujEtYTXm2591B4IQxWE1SzATGSLNwkqtQL/uIioGEaJAKHoRA5EAlI/5LxQ2VQpAJSUDVyUGxfT+BkLUewnFstOKwriyVcJQ9hrGBbiH0hrGG+rKrIfnsIUC0scTluz4j8AbJf0YcKWZ/ZcmhV0cnNb457334s70AHZny9yZbWNPtkRPaX90UUbCvrTLiiVksWtof9ZhNevExeE0NDGqfxQscL40Q34N6AtBZiF2MPwJK1ln0DtUupaf5/Yg31ze+nUzdB5Opux2GDPKqOwlwGyeQpM8VfGFpl1JMCoMVaOMqvatrhyNVJFWLDOaZ/GjlPpsHXFICUHplOATfy8ejXBxcFrji3uO5u0fP5lkBTCRbcvItmVYx1Am6GV9i6dOFgxuJvKtNsNS2NY/gPh2HvrPk5hucRhqnlbGoD9hupi3f71g3HOBGK1kNK1uGKtiaKIyqDuh62gkvSamUPw+yVuoytNkkluTGMO4YatVo5MmikHFPaYVhVYD1balPIc7gKuANwH/O59g1xQXB6ddDB78gbvItnVY3d6NayR1SJdFutQlXQprImUdyHr010rKV1IdWi01btdJAllipDFG3F8+Iz/XYBnrwV4J8V94xZv5pK6gkS6TqsBx/9qwwW4iADDeOyh+H/eW30QMqq41EQSY3VMoXt80olBk64jDGcBPA/8N+BVJ/wp82swuaVLYxcFplWS/6Ny+h86dorN9ieyAJbKlhHQpbuVZWF01X1AvF4d8VdVcNEgGezAgDZbe7lhMC9+BwZpLuTCIuMdzfLMviwZQuYZR4Vr/a83w0qFr5fSSRzOLCEyTd5wYFMtOSquKKUzrJdSVabP7qJiv0/ar/hYRBzP7KPBRSQ8Bngb8FvA7wAFNyrs4OK1x++oBdO8G7rgTsozkriWSXg+6nbAUd7eL5fs+R8EI+zOItCeyHkE8OsOiYdGr6O8BHa/192zo5F6GoucRDX7HwnBVxWsKfT/9dYpiV1adSBRFoW5oKcznBdTlHZd/XOygXMc4bwHGi0H5e1NBGPu9gSiM3HeMt9C6MLB1upUkfQh4OPA14NPA84BLm5Z3cXBaIyMJE4iyDNu/AmmK9u6D5SW0Nwk7vPW62HKXpNeh04lC0YteRd+jSLCu9ZfpzrrBy0i6YXqCCt1Rg0ODrqh8jwcbdFXJIF/a25KwEHaYD1ewBBYNffQ2yusZTTvXAKqN8zQCMC5PnUGfJARj81SIQbnstIIwvnwzUSjnLYvCuqzDtPH5n8AX4yZBU+Pi4LRPZtjdd4ctPpMEVlehk6BOB5IE9XpBKKJHkRQ9il5Cuhw8inQp74KCZCV6FV3IVmPcou9JFEQiE1kGdC2suNqJAewE6NDfI6EvEAbkGzAUYwvRWygvcieqxWCcCCxaAMrXmohA+bxOCEbqnlMMwnl11xGMFwRYA1Ew0EymdONhZjsn56rHxcFZCJZZWH4iGwxEjS/vsLLSz6d8v+QESIU6IlkxMhlJ3NJTHUjSaNClEHzOwj9iS+jv52AdBqNN8rQ42qmSPD0KQxAAhoShLAp1gjDLvIK8bJEmwgD1AlDOO2J8ZxCBSefj6gnns3kH0EwMWg9Mt6Q3kk4hbM3ZAc43s9eXrr8AeCODTYD+Mp/VLOmPGay++odm9v5S2bcAv2xmB7XT2lFcHJyFoETBU+h0Qj//Um/gSeReQ6872B86eg3WS0h7CdmSQndTj+BBxFhD1iuMdurvFU1/b+ihz2L8IYkxhiR8VyFdiY0IQz5stigKuSBoSBTC8yay2uGied66tHI6NDf+5fPyshWTDfeU5w09gqrrk4QAJovBpHvMS3nMwsz1SB3gXOBnCLu3XSZph5ldU8r6fjM7q1T2Z4FHAY8gbAv6KUn/kG/cI+kE4ND5WzkeFwenNRKy2H2ToAMOCF1HnQR6vXh0saUgGNlSNwSjC4JgvTwwrUG8oaehrqOhIbBJ+J57DXnMIevSD0xbQuhiSix4HlEA+qKQxxiSZqJQPIfgGVQNFW06Kqh4vZyn6nzRxr9ZnfOJQFUQeRYR6CwqztBOtScCuwpbfV4AnAaUxaGK4wlDTleBVUlXEvaAvjCKzhsJu8qNneks6RIze9KktDrmFofY2J3ATWb2jLgt3gXAfQk7GP2Sme2XtAy8C3g0cAvwHDP7xrz3dzYWwRh3US4I0UOwbV1sqRvEoJuQLnei4VfwDPJ4QheyOAJpSASKcYWit5B/71pfHPpDXKOXkIuCoveQdxXViUKSZLWCUPQOil5ElTCMmzxW/g7Nu3yqzhvnabCcRdUb/SK8gKpyTUVgkXMcWnJGjgBuKJzfSFjKoszPSXo8YbvPl5nZDcCXgNdI+lNgO/BEBqJyFrDDzL6tmqV0JW2L5Q6Lq7LmGQ+O7WpEG57DSwm7Cx0cz/8Y+DMzu0DS24AXAW+Nnz8ws2MlnR7zPaeF+zsbCQH3OpBs21I/yJwe0AkT4ZYGXkG6NBiGmvYKw1U7kE+E6w9jVUjLOgwMfx5jkIVupG4UA412HUmGOuO7jiYJQpUYFL/XTRibZihok/PatAliEtKqDWpV3qplKZoIQFVbqsrOIwKVZdvsXmpe1WGSikHf88zsvCnu9H+B95nZPkm/BrwTONnM/lHSTwL/Cnwf+CyQSnog8PPAEybU+2uEOQ0PJLyg5+JwB/CXTRs3lzhIOpIQNHkd8PK43+nJDDbSfifwWoI4nBa/A3wQ+EtJMmuyt5azGTios4+Vg4x9D7w36fYOq9sSVg8Qq9vihLde9AaS4dFGQ15A1/pikBv+/kzowiQ3y7uHNBh+mi/D0R92WvAM8rQkGTX+k8RgnBCURaAsAPOO9KnLE9KaGU6oX4OoTjDqjO00AeFpuoKmeZZqQWp3CY2G3GxmJ9Rcuwk4qnB+JIPAc7jN8HIW5wNvKFx7HcGuEndw+yrwSOBYYFf0GrZL2mVmx5bqfTPwZkkvMbO/aPw0Jeb1HP6cMOPuXvH8vsBtsa8MgiuVuzF9Nytuvn17zH9zsUJJZwJnAhx1RGfO5jlrySvu+yXu87S7+JNDn4qtgpZX6C6vkiRGmiZ0eymWiTRNsEx0uhlJJwvbKmSiU1yJVDa0IF7+dj+4PljPqGoYKUCS2FBXUF6uHy9IstE3fWzE6NcZ/LrPYt5yejivH8qZM25BuXFdKk3eoMfvxzDeyI4LAI+LAUwjIDD+OVoVggpackIuA46L3ew3AaczeGkO95EON7Nvx9NTifs7x676Q8zsFkkPI+wl/Y/Rrj6gUP6usjAUMbO/kPRTwNEUbL2ZvavJA8wsDpKeAXzPzC6X9IRZ6ykT3bLzAB798GX3KjYZz7rXv/H0J1/L99Nt7LYl7szCKq17siV2Z8vsy3rsjXsz3J0t9fdlCEtud0jjMtz5Ut35qqzFVVjLn8DQyq1lph23XxUzGFybfnkHmO5NfFw94/NO/89lmqBu07ZMakczAau/V2/RExFasDrxBfgs4GLCUNZ3mNnVks4BdprZDuA3JZ0KrAK3Ai+IxXvAZ6J3cAfwi4UX7sZIejfwI8AVhJVZITzdYsUBeBxwqqSnA9sIMYc3A4dI6saHKbpSuZt1o6QucG9CYNrZYmyXuF9nL4fYPvYme0gRu7Ml9lgQiOLmPvkOb/lucCtZlwz1N/Up7+mQf4cgCGnhfNWSkbaU92+oY1zAddqx99N0kfTrnNIitRGQnXUY6DT9+03f8psY/LaHrVZitDbPwcwuAi4qpb268P1VwKsqyu0ljFiaVP+kOQ4nAMfP2nU/szgUHyx6Dr9tZr8g6QPAswgjlp4PfDQW2RHPPxuvf8LjDVuLLh0OTUJX4L1IWbEsbg+asWJ72Gu7WYm7we2nw4olrFiXvdZlry2FbUItGRKOlXyHuCge+f7RQRiCZ7EStxcF+mIC9EUDIC15Fk1Eo7bLY8ZumabGbRHrBfXb0GKXzKxv8NMY+U4DEWx7lvRaaNAa8WVCN9S3J2WsYhHzHH4XuEDSHwFfBN4e098OvFvSLoILdfoC7u2sIx0V3twNugpCkWFkyli2IBj3IiVllRWDFRO7rUtK3E/aElboxG1Cl/rbhqYk/e+5MKxYh5Sk4FUUPoe8i0G7iqKwYtUxrVxoqkjI+tenNbQL7w4psSZv2gWaGPIqpjXus96nKQuufi05DLhG0ueBfXmimZ3apHAr4mBmnwI+Fb9fT5gAUs6zlzAMy7kHUBSKTvz/hIQs/stbsYxUFsVilf1mZASxSAneRVkwco8hF42BMAThyBj2KoriAQPvoe48J63onioyLr4xjnvi4nDzGvKFzWcYx9b5mV47T2GfIe0sjIT87T38A++pA3RILaOrTt+jWLGMZRkZxooZKRnbLRsSi5XoFQSDv3dEMIKQdCtFI7RhIBwwEIDce0gpehf13VHl602pqscZZdZZz22JiNqbBLfumNk/z1PexcFpjdSyIY8hq+h2SUj6U3I6hBhBooGIrBDjFDJSMzJBSkYaxSJ0Rw3EIghBEJBg/JPC92HRAEqexLB4hGcYdEcNnmtYDKq6o9IxXVEwm6AM6m5XWOZpS5u06RW0upTGFhEHSScBfwH8OLBE+Ce328wOHlsw4uLgtEpZIIok/b76pC8cHSVRJDISOpVCAQzEAkhVFAuRGUPexYhgKOkb2KJoACPCAQyJRyhTHDKbsI3BqrJl4ajqckptIIh11MU/IIxrHKpvghDVkT9XZ6usSb0gtornQJgNfTrwAcLIpecBP9q0sIuD0zp1AlHlSeR0lESByK1owrIGIrJiGUnseuqhOAoKUoyUEPTepmwgFopiIRWMvsJ59DAAUq3ENhe6nArCEa4Np4e64ncNp6cVo6CyGrEsCktVsLpJbGNSfKTICvNPKp1VmDYVW0ccMLNdkjpxw5+/lvRFKobPVuHi4CyEcR5EHeUgdu5NZFilUEAQhb53wRixQCCqBSNeGxINxDat9N/6y8IR2jH459MXBY0a0JGAd7xeFoQqYenXUWOU8zqaiMS0weGqOnvM7nWM847mpdWRYFtHHPZIWgKukPQGwpDWxv8oXRychZHaoOtoFqq8CaAvFCuW0UFDQpEY9DQQil5BLFJET+lALIAVCl1OKgkGha6mgmjk5IYyi+JSzA8F8egbcMVyhb8RRe+BeP+qv1c69q190rDaILOTKQpC0tDgNh29teghqK2whQLSwC8R/tGcBbyMMAn555oWdnFwFs48IjE8d6JoXEK3E1DrUfQQqRmdvuG2+BmuA/RkZJZfHwhGfl4UjGKe8H1YOMrXi2/ZRQEpls0peiEorfQiehXlcjpjhCMloTPhjT+/36QgcZUH09QfmKYLbF3ZOuJwM7A/TiP4g7hm03LTwi4OzpoxrydRJPcmMqwf4O4poQd9jyLFSKS+EOTr+nU0EIrELHYbEb0M9Q1pMPQpK7FrKUVDBrqnLIpG2heFHgMj2FNaKxxJaXGAkbfqiu4pKIlIgbTGc0jjX6eKoofSqXn5L7ehTmTGdYnljBOecRMP15rN4OA05BLgycBd8fwA4B+Bn2pS2MXBWXNmFYli/ryOcpcTDLpn6oSiw7BQwHixyLuiQr6CYMAE0QglmgjHoO4BZQEJ7R21XClJ/5mLrFi30pjnhryum6coCFXCUtXt1VRc6hjn2TQRnjbZQt1K28wsFwbM7C5J25sWdnFw1o200E00i1AUy5c9ifA9eBNAqUuKIaFIzUgQGQOvIqQPZjVnyu8T718wZv3uqIIxTwu7dCUFA1kcj19+20+Ky3yQjAw5DV7NMKklJCXDmscAOtpfKp8L03AdZW+kbKjLxrksKtPEQmaZY1EWnoWOmGpx4b0NwG5JjzKzLwBIejRwd9PCLg7OhmAWb6IsEFAtEjDqTQCVHgVUexWDa8NikaK+gFQLRjLUTVU0dMX2ZaYhMSgLRxCBAbnXMVwmigLD5N5KUUSKQeQ6EckpiklVt9YkMRnKO4Ww1FEUnIVM6Ns64vBbwAckfYsQ8XoAU+y+6eLgbCjm8SaKFEUinI96E3mX0whDM7gHXkVoU41nQVFMxgtGuHe9aIQ4Qay33wU0XE+nYCAHxn9AsctqkoCUA8V1IhLqGghJnWGfJCZD9TE8kmtaOspa9SREe91Kkk4hbGPQAc43s9eXrr8AeCODbQ3+0szOl/QIwu6ZBxPGT7zOzN4fy7yHMKFtBfg88GtmtkIFZnaZpIcAPxaTvlKXtwoXB2fDMsmbKHsNVSQF41bnTQAj3U59SkKR1zksBgOxCNetJCYapPe9koEw5ENb8zjGONHI64F64Qh1jIpHKJv/LcaLR/9a4fs0IjKoe/9Q2+vIf4tJYlJ5D6pHd81MC+IQRwadC/wMYUfMyyTtMLNrSlnfb2ZnldL2AM8zs+vivtGXS7rYzG4D3gP8Ysz3XuBXCEJSvPfJZvYJSf+1VO+PSsLMPtzkGVwcnA1PExFoQjDqxa08K2ITMFEoQh4buVAcBVUWi3CfYe8iv1YlGMBY0WCorqLxHxaOqnpDXeEZRw0+sa1VQefRegb1UVnfoN7CnJAxNBWTMlUB+ZkwUNaK63AisCuuUo2kC4DTgLI4jDbB7KuF79+S9D3gfoQtmPubB8WluI+sqOLxwCeA/1xVPeDi4Dhlyt1NIW14vsTEbicYEooqrwKGxWKQL7/nsHcB1YIRrqdDBjnETYpB7grjP4dwhDqrxSPUUz/0tNzWkes1dY7eI515afR5maJb6TBJOwvn58VtjgGOAG4oXLsReExFHT8n6fHAV4GXmVmxDJJOJCya97VSeo8wye2lFXX+IH6+3cz+penDlHFxcO6RjBOJkD7sTYwVCqj1KopiEfJVi0UsOLimYYNdJRhQnnQ3LBrhHqPGulMxhyLUX+ER1Bj7SeIB4wWk3PbKfDX1L5zm4nCzmZ0wx53+L/A+M9sn6deAdwIn5xclHQ68G3i+2Yg7+7+AT5vZZyrqfSEh1vEW4FGzNs7FwblHUyUSIb3am4BRoUixodjGiM2r6IIanNaLRdG7gFHBGK272pjPIxr9dtT06Y/1FCYa2QaGfx1mpLUUkL6JsFxFzpEMAs8AmNkthdPzgTf02yAdDPw98D/M7HND7ZNeQ+hm+rWae18r6TrggZKuLBYNt7WHNXkAFwfHYbJIhGvTC0WG0SnMeSgb83FiEfKX2zlZMMpeRqh31Mg2FY3BfWqMeY0xndzFlDXwDJp5Dq3teWGt6dFlwHGSjiGIwunAc4sZJB1uZvn+zqcC18b0JeBvgXeZ2QdLZX4FeCrwpApvIjyC2RmSHgBcHOudCRcHxylQJxLh2rA3AcPxCWAqoQjDZMuMF4swfLZMhXVWA8GAaktYa7DrAvVjDPikt/BJlrjxW3yL3U8teA5mtirpLIKB7gDvMLOrJZ0D7DSzHcBvSjoVWAVuBV4Qiz+bEFS+bxzuCvACM7sCeBvwTeCzCr/xh83snIr7fwd4+DzP4OLgOBU0EYlwvdqbgPFCAYzowCxiAdQYs8mCEbJVLdFR88ZfIxp13ka4Z73Rzkxjd3ALQ3knv8aHCYItbRNKe/Mc4siii0ppry58fxUVeyuY2f8B/k9NnRNttqQLzezZkq5i+D8E71ZynLYYJxLhekJ5E6MmQlGsO6+/UzLeZbHISvXkdY0sL1HhXYSyzQQjs7q81QY4TOirs6hjjPakSY4TjPRgSG/LcYkKwdxk5COYnjFPJS4OjtOAaT2JnHK3EzQTCmBiF1RTsQAqDe00ghHqqDGaM4hGeZb36PWkv/x6FZO8jnnY7Avv5XEMM/vmPPW4ODjOFDTxJHKqYhMwv1DAdGIRyg/nrY5dVAtGf/5FhWbUeRlhNdtZPI35hWMuttDCe3GG9B8DP0TsMSN0Kx3cpLyLg+PMwCSRCHlGA9gwu1AU71f2KkJeSnlHxSKvu0os6uuof/ufRjDyMuNEgwn3C9cXO7x1C+3n8AbgP5vZtbMUdnFwnDmYRySgutsJqoWieL/iPau8ipB3mP6M7IZdUeO8i1BPtcdQJxh1ZfJys4pGXr41tojnAHx3VmEAFwfHaYVpRCLka+ZNwHRCAe2IRahnOH+ddzFcV3PBgPGi0V9jasyrfBPhmJbNHnMosFPS+4GPAPvyRF94z3HWgSYiEfJN9iZgNqEo37+qCyqUGWZasQj1Vdc1XF+94Z/Vy8gZJxwz0d7CexuBgwkrvD6lkOYL7znOejKtSIS87QhF8f7lNtR5FaHMMJPEItQ3XGZ4+Y/q+kKd0wkGTBaNNhBbJ+ZgZi+cp7yLg+MskKYiEfLWexPQjlCU29KGWBTvOat3EeqtFwwY72W0htlWmOcAgKS3VCTfTpih/dFJ5V0cHGcNmEUkQv75hAKaexVQ3wUVyg0ot6ot76Jc9yyCMS9bKOawDXgI8IF4/nPA14GHS3qimf3WuMIuDo6zhkwjEiH/eG8CxgsFrI1XEcpW37N833HeRbO6F2y9t444PAx4nJmlAJLeCnwG+GngqkmFXRwcZx2YVSRCmclCUSUSMF4oiu2qats4sQhlB0zjWYS6h/OnpaZNEow22UKew6HAQYSuJIADgfuYWSppX32xgIuD46wj04pEKDO/NwHzCQW061mU2zDJu6i7x9wYCwhkrBtvAK6Q9ClCrP3xwP8n6UDgnyYVXodtlhzHKZOg0VVbJ5YZ/G8cPSX9o44O6h/j2jeunR2pf1TXMTgmtaGqHR2NHgvBGh4TkHSKpK9I2iXp7DH5fk6SSTohnh8t6W5JV8TjbYW8j5Z0VazzLVLdQlhgZm8Hfoowz+FvgZ82s/PNbLeZvXJS+2cWB0lHSfqkpGskXS3ppTH9PpI+Lum6+HloTFd8mF2SrpQ08/Z1jrNVmUUkQrnJIgHtCEWxnU2EokoskopjXDvq2rIIoZA1O8bWIXWAc4GnAccDZ0g6viLfvQirqF5auvQ1M3tEPH69kP5W4FeB4+JxSkWdD4mfjwIOJ+xlfQPwgGns7jzdSqvAK8zsC/EBL5f0ccKGFZeY2eujWp4N/C7hj5Q/0GMID1m14bbj3OOZpbsplGsWm4Dpu55gcvdTuO90XVCDeoaZFLeoa8/ctDOU9URgl5ldDyDpAuA04JpSvj8kLI438U0+7il9cL5tqKR3Ac8E/qGU9eXAmcCfVlRjFPapHsfM4hCXhc2Xhr1T0rXAEYQ/wBNitncCnyKIw2mEbe8M+JykQ0rb5DmOU2JWkQhlJ8cmcpoIBUyOU4T7jo9VQDtiUW5PW0wRkD5M0s7C+Xlmdl78fgThbT3nRkovw/Et/igz+3tJZXE4RtIXgTuA3zOzz8Q6byzVeUS5UWZ2Zvx8YuMnqaCVgLSko4FHElyj+xcM/neA+8fvVX+sI4gCU6jrTILqcdQRnTaa5zibnjZEIpRfP6EI958sFlVCEeoaZhGjlTTd8hk3m9kJM91HSoA3MdgatMi3gQeZ2S2SHg18RNJ/mKLunwRuiFuFIul5hDkO3wRea2a3Nqln7oC0pIOADwG/ZWZ3FK9FL2Gq/5rN7DwzO8HMTrjffV0cHKfIrDGJQflmsYmcJjEKaBanCPcfH6uAyfGKQV2Tg9wzkTU8xnMTcFTh/MiYlnMv4KHApyR9AzgJ2CHpBDPbZ2a3AJjZ5cDXgB+N5Y8cU2fOXwH7ASQ9Hng98C7CkNbzKvJXMtffVFKPIAzvKaz0993YN5b3kX0vpk/6YzmO05C2RGIWoZjELELRhli0QvQcmhwTuAw4TtIxkpaA04Ed/duY3W5mh5nZ0WZ2NPA54FQz2ynpfjGgjaQHE+K018cemTsknRRHKT0PqFoGo1PwDp5D6O76kJn9PnBs0z/FPKOVBLwduNbM3lS4tAN4fvz+fAaN3wE8L45aOgm43eMNjjMfTYzr5DqmE4qm3gQ0G3E0aEezZ1msWNhgfaVJx7hazFaBs4CLgWuBC83saknnSDp1QiMeD1wp6Qrgg8CvF4z9fwPOB3YRPIpyMBqgIykPGTwJ+EThWuNQwjwxh8cBvwRcFR8C4L8TXJgLJb2I0Mf17HjtIuDphIfaA8y1YqDjOMPME5cY1NE8iA3N4xM5TeIUoR3NYhUwGtyel7ZmSJvZRQS7V0x7dU3eJxS+f4jQI1OVbyehO2oc7wP+WdLNwN2EJTOQdCyD2dITmWe00r9ArcQ/qSK/AS+e9X6O4zSjTZEI9ayvUIT2TB4B1RqbfFVWM3udpEsIcxz+MdpeCD1FL2lajy+f4ThblDZEItQznTcBaycUoV0tGnPbGvs55HMhSmlfnaYOFwfH2eK0LRKhrrURCpheLOZmk3sObeHi4Dj3ENoSiVDX2ggFTOdVtIJrA+Di4Dj3ONoUiVDf9N1OsHGFQu45AC4OjnOPZVEiEepcW6FoDWN0bfB7KC4OjnMPp22RCHXO5k3AbELRFsLcc4i4ODiOAyxmuOg83gSsk1C4OAAuDo7jVLBIbyLUu4GFwsUBcHFwHGcMixCJUO/s3U6wQKEwFrc59SbDxcFxnIksWiRC3fMLRRsoc3UAFwfHcaZgUSIR6p5fKOZn8qJ69xRcHBzHmZpFikSof75up5kxXBwi7fpjjuPco5h3ufDJ9U+/78TctLPZD5JOkfQVSbsknV1x/dclXSXpCkn/Iun4mP4LMS0/MkmPiNfOiGWulPQxSYe189CjuDg4jjM3bewrMfkeayMUyrJGx9g6wmY95wJPA44HzsiNf4H3mtlPmNkjgDcQtg3FzN5jZo+I6b8EfN3Mroh7NLwZeKKZPQy4krBnxEJwcXAcp1UWLRLhHgsSCQMya3aM50Rgl5ldb2b7gQuA04ZuNbyt8oFUr+p0RiwLYYsEAQfGzdYOBr419TM2xGMOjuMshEXHJcI92haIqQLSh0naWTg/z8zyPZqPAG4oXLsReEy5AkkvBl4OLAEnV9zjOURRMbMVSb8BXAXsBq5jgXvkuOfgOM5CWQtPolWabxN6s5mdUDjOm1T16K3sXDP7EeB3gd8rXpP0GGCPmX05nveA3wAeCTyQ0K30qvketh4XB8dx1oRNIxIt7CEN3AQcVTg/MqbVcQHwzFLa6YQtP3MeEZpnX4u7u10I/NTkB5oNFwfHcdaUDS0S7cUcLgOOk3SMpCWCod9RzCDpuMLpzxK6ifJrCfBsBvEGCOJyvKT7xfOfAa6d5TGb4DEHx3HWhTXdF7oxBi0sx2Fmq5LOAi4GOsA7zOxqSecAO81sB3CWpCcDK8APgOcXqng8cIOZXV+o81uS/gD4tKQV4JvAC+ZubA0uDo7jrDtrEbxuTEuT4MzsIuCiUtqrC99fOqbsp4CTKtLfBrytlQZOwMXBcZwNw7qLRN6t5Lg4OI6z8VhXkfDlMwAXB8dxNjBrLhJmsLq6Nvfa4Lg4OI6z4VnT4LV7DoCLg+M4m4yFexMuDoCLg+M4m5TFiESjOQz3CFwcHMfZ1LQ6oc7AFrk/9SbCxcFxHKeIew6Ai4PjOM4wHnMAXBwcx3EGmMGEjXzuKbg4OI7jFHHPAXBxcBzHGcLccwBcHBzHcQpMtRPclsbFwXEcJ8eANF3vVmwI1nyzH0mnSPqKpF2Szl7r+zuO49RhgGXW6JjEJFsn6dclXSXpCkn/Iun4wrVXxXJfkfTUQvohkj4o6d8kXSvpsW09e5k19RwkdYBzCTsY3QhcJmmHmV2zlu1wHMepxAxrwXNoaOveG/dnQNKpwJuAU6JInA78B8Je0f8k6UfNLAXeDHzMzJ4Vd5jbPndja1hrz+FEYJeZXW9m+wlb4J22xm1wHMepx7Jmx3gm2jozu6NweiD01wE5DbjAzPaZ2deBXcCJku5N2CHu7bH8fjO7bd7HrWOtYw5HADcUzm8EHlPMIOlM4Mx4um/bA7/+5TVq21pyGHDzejeiZbbiM4E/12bix+at4E5+cPE/2QcPa5h9m6SdhfPzzOy8+H2irQOQ9GLg5cAScHKh7OdKZY8A7ga+D/y1pIcDlwMvNbPdDds7FRsuIB3/uOcBSNppZiesc5NaZys+11Z8JvDn2kyUDPVMmNkpbbRlivudC5wr6bnA7zG8j3SZLvAo4CVmdqmkNwNnA7+/iLatdbfSTcBRhfMjY5rjOM5WYlpbdwHwzAllbwRuNLNLY/oHCWKxENZaHC4DjpN0TAymnA7sWOM2OI7jLJqJtk7ScYXTnwWui993AKdLWpZ0DHAc8Hkz+w5wg6S8++xJwMIG86xpt5KZrUo6C7gY6ADvMLOrxxQ5b8y1zcxWfK6t+Ezgz7WZ2DDPVGfrJJ0D7DSzHcBZkp4MrAA/IHYpxXwXEgz/KvDiOFIJ4CXAe6LgXA+8cFHPIPPZgI7jOE6JNZ8E5ziO42x8XBwcx3GcETasOGzWZTYkHSXpk5KukXS1pJfG9PtI+rik6+LnoTFdkt4Sn/NKSQsbfdAGkjqSvijp7+L5MZIuje1/f+wLJQbT3h/TL5V09Lo2vIaq5Qi2wm8l6WXxv78vS3qfpG2b8beS9A5J35P05ULa1L+PpOfH/NdJGjdc1IlsSHEoTD1/GnA8cIYK645scFaBV5jZ8cBJwItj288GLjGz44BL4jmEZzwuHmcCb137Jk/FS4FrC+d/DPyZmR1LCKq9KKa/CPhBTP+zmG8jki9H8BDg4YRn29S/laQjgN8ETjCzhxICoqezOX+rvwHKcw+m+n0k3Qd4DWES2onAa3JBccZgZhvuAB4LXFw4fxXwqvVu14zP8lHC+ipfAQ6PaYcDX4nf/wo4o5C/n2+jHYTx1pcQZnL+HSDCLNtu+XcjjNJ4bPzejfm03s9Qep57A18vt2uz/1YMZufeJ/7t/w546mb9rYCjgS/P+vsAZwB/VUgfyudH9bEhPQeqp54fsU5tmZnonj8SuBS4v5l9O176DnD/+H0zPeufA78D5AvL3Be4zcxW43mx7f3nitdvj/k3EscwWI7gi5LOl3Qgm/y3MrObgD8B/h34NuFvfzmb+7cqMu3vsyl+t43GRhWHTY+kg4APAb9lwwtsYeH1ZVONIZb0DOB7Znb5erelRfLlCN5qZo8EdjPoogA27W91KGHxtmMIq3oeyGjXzJZgM/4+m4WNKg6bepkNST2CMLzHzD4ck78r6fB4/XDgezF9szzr44BTJX2DMNX/ZEJ//SGS8smUxbb3nytevzdwy1o2uAF1yxFs9t/qycDXzez7ZrYCfJjw+23m36rItL/PZvndNhQbVRw27TIbkkRYUvdaM3tT4dIOBotqPZ8Qi8jTnxdHWpwE3F5wmTcMZvYqMzvSzI4m/B6fMLNfAD4JPCtmKz9X/rzPivk31Bue1S9HsKl/K0J30kmStsf/HvPn2rS/VYlpf5+LgadIOjR6VU+Jac441jvoUXcATwe+CnwN+B/r3Z4p2v3TBDf3SuCKeDyd0Id7CWH9lH8C7hPzizAy62vAVYQRJuv+HBOe8QnA38XvDwY+T1hz/gPAckzfFs93xesPXu921zzLI4Cd8ff6CHDoVvitgD8A/g34MvBuYHkz/lbA+whxkxWCp/eiWX4f4Jfj8+0CXrjez7UZDl8+w3Ecxxlho3YrOY7jOOuIi4PjOI4zgouD4ziOM4KLg+M4jjOCi4PjOI4zgouD4ziOM4KLg+M4jjPC/w8TKOD/ef3tUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.tricontourf(swan_table.Xp, swan_table.Yp, \n",
    "                swan_table.Hsig, levels=256)\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_label('Significant wave height [m]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.tricontourf(swan_block_mat_as_table.x, swan_block_mat_as_table.y, \n",
    "                swan_block_mat_as_table.Hsig,\n",
    "                levels=256, cmap='viridis')\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_label('Significant wave height [m]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.tricontourf(swan_block_as_table.x, swan_block_as_table.y, \n",
    "                swan_block_as_table['Significant wave height'], \n",
    "                levels=256, cmap='viridis')\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_label('Significant wave height [m]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Block Data\n",
    "\n",
    "Lastly significant wave height is plotted from the block data using `imshow` with a reversed y-axis to show the same plot achieved above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(swan_block_mat['Hsig'])\n",
    "plt.gca().invert_yaxis()\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_label('Significant wave height [m]')"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}