nsw-2016-storm-impact/notebooks/01_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data exploration\n",
    "This notebook provides an example how the data has been loaded and accessed for further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-03T23:02:22.927101Z",
     "start_time": "2018-12-03T23:02:22.612233Z"
    }
   },
   "outputs": [],
   "source": [
    "# Enable autoreloading of our modules. \n",
    "# Most of the code will be located in the /src/ folder, \n",
    "# and then called from the notebook.\n",
    "%matplotlib inline\n",
    "%reload_ext autoreload\n",
    "%autoreload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-03T23:02:24.527369Z",
     "start_time": "2018-12-03T23:02:22.929088Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from IPython.core.debugger import set_trace\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "import plotly\n",
    "import plotly.graph_objs as go\n",
    "import plotly.plotly as py\n",
    "import plotly.tools as tls\n",
    "import plotly.figure_factory as ff\n",
    "import plotly.io as pio\n",
    "\n",
    "\n",
    "import matplotlib\n",
    "from matplotlib import cm\n",
    "import colorlover as cl\n",
    "\n",
    "from ipywidgets import widgets, Output\n",
    "from IPython.display import display, clear_output, Image, HTML\n",
    "\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import our data into pandas Dataframes for the analysis. Data files are `.csv` files which are stored in the `./data/interim/` folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-03T23:02:39.868010Z",
     "start_time": "2018-12-03T23:02:24.529339Z"
    },
    "pixiedust": {
     "displayParams": {}
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Importing waves.csv\n",
      "Importing tides.csv\n",
      "Importing profiles.csv\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z5189959\\Desktop\\nsw-2016-storm-impact\\.venv\\lib\\site-packages\\numpy\\lib\\arraysetops.py:522: FutureWarning:\n",
      "\n",
      "elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Importing sites.csv\n",
      "Importing profile_features.csv\n",
      "Importing impacts_forecasted_foreshore_slope_sto06.csv\n",
      "Importing impacts_forecasted_mean_slope_sto06.csv\n",
      "Importing impacts_observed.csv\n",
      "Importing twl_foreshore_slope_sto06.csv\n",
      "Importing twl_mean_slope_sto06.csv\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "def df_from_csv(csv, index_col, data_folder='../data/interim'):\n",
    "    print('Importing {}'.format(csv))\n",
    "    return pd.read_csv(os.path.join(data_folder,csv), index_col=index_col)\n",
    "\n",
    "df_waves = df_from_csv('waves.csv', index_col=[0, 1])\n",
    "df_tides = df_from_csv('tides.csv', index_col=[0, 1])\n",
    "df_profiles = df_from_csv('profiles.csv', index_col=[0, 1, 2])\n",
    "df_sites = df_from_csv('sites.csv', index_col=[0])\n",
    "df_profile_features = df_from_csv('profile_features.csv', index_col=[0])\n",
    "\n",
    "# Note that the forecasted data sets should be in the same order for impacts and twls\n",
    "impacts = {\n",
    "    'forecasted': {\n",
    "        'foreshore_slope_sto06': df_from_csv('impacts_forecasted_foreshore_slope_sto06.csv', index_col=[0]),\n",
    "        'mean_slope_sto06': df_from_csv('impacts_forecasted_mean_slope_sto06.csv', index_col=[0]),\n",
    "        },\n",
    "    'observed': df_from_csv('impacts_observed.csv', index_col=[0])\n",
    "    }\n",
    "\n",
    "\n",
    "twls = {\n",
    "    'forecasted': {\n",
    "        'foreshore_slope_sto06': df_from_csv('twl_foreshore_slope_sto06.csv', index_col=[0, 1]),\n",
    "        'mean_slope_sto06':df_from_csv('twl_mean_slope_sto06.csv', index_col=[0, 1]),\n",
    "    }\n",
    "}\n",
    "print('Done!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-27T23:02:57.631306Z",
     "start_time": "2018-11-27T23:02:57.615263Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": true
   },
   "source": [
    "The following interactive data explorer displays information on a per `site_id` basis. It can be used to examine pre/post storm cross-sections, water level time series and observed/predicted storm impacts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-05T03:57:14.533063Z",
     "start_time": "2018-12-05T03:57:13.745017Z"
    },
    "code_folding": [
     408
    ],
    "hide_input": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eae0e9440a5f45599b2c9b43352d3d13",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(VBox(children=(HTML(value='<b>Filter by observed and predicted impacts:</b>'), HBox(children=(V…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create widgets for filtering by observed and forecasted impacts\n",
    "filter_title = widgets.HTML(\n",
    "    value=\"<b>Filter by observed and predicted impacts:</b>\", )\n",
    "\n",
    "titles = ['Observed Impacts']\n",
    "selectboxes = [\n",
    "    widgets.SelectMultiple(\n",
    "        options=impacts['observed'].storm_regime.dropna().unique().tolist(),\n",
    "        value=impacts['observed'].storm_regime.dropna().unique().tolist(),\n",
    "        disabled=False)\n",
    "]\n",
    "\n",
    "# Iterate through each of our forecasted impacts\n",
    "for forecast in impacts['forecasted']:\n",
    "    selectboxes.append(\n",
    "        widgets.SelectMultiple(\n",
    "            options=impacts['forecasted'][\n",
    "                forecast].storm_regime.dropna().unique().tolist(),\n",
    "            value=impacts['forecasted'][forecast].storm_regime.dropna()\n",
    "            .unique().tolist(),\n",
    "            disabled=False))\n",
    "    titles.append('Forecasted: {}'.format(forecast))\n",
    "\n",
    "titles = [widgets.HTML(value=title) for title in titles]\n",
    "\n",
    "children = widgets.HBox(children=[\n",
    "    widgets.VBox(children=[title, box])\n",
    "    for title, box in zip(titles, selectboxes)\n",
    "])\n",
    "filter_container = widgets.VBox(children=[filter_title, children])\n",
    "\n",
    "# Create widgets for selecting site_id\n",
    "site_id_title = widgets.HTML(value=\"<b>Filter by site_id:</b>\", )\n",
    "\n",
    "site_id_select = widgets.Dropdown(\n",
    "    description='site_id:   ',\n",
    "    value='NARRA0001',\n",
    "    options=df_profiles.index.get_level_values('site_id').unique()\n",
    "    .sort_values().tolist())\n",
    "\n",
    "site_id_impacts = widgets.HTML(value=\"\", )\n",
    "\n",
    "site_id_container = widgets.HBox(children=[\n",
    "    widgets.VBox(\n",
    "        children=[site_id_title,\n",
    "                  widgets.HBox(children=[site_id_select])]), site_id_impacts\n",
    "])\n",
    "\n",
    "# Build colors for each of our forecasts\n",
    "colors = list(\n",
    "    reversed(cl.scales[str(max(len(impacts['forecasted']),\n",
    "                               3))]['seq']['YlGnBu']))\n",
    "\n",
    "# Add panel for pre/post storm profiles\n",
    "trace1 = go.Scatter(\n",
    "    x=[0],\n",
    "    y=[0],\n",
    "    name='Pre Storm Profile',\n",
    "    line=dict(color=('rgb(51,160,44)'), width=2))\n",
    "trace2 = go.Scatter(\n",
    "    x=[0],\n",
    "    y=[0],\n",
    "    name='Post Storm Profile',\n",
    "    line=dict(color=('rgb(255,127,0)'), width=2))\n",
    "trace3 = go.Scatter(\n",
    "    x=[0],\n",
    "    y=[0],\n",
    "    name='Pre-storm dune crest',\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        color='rgba(255,255,255,0)',\n",
    "        size=10,\n",
    "        line=dict(color='rgba(106,61,154, 1)', width=2)),\n",
    ")\n",
    "trace4 = go.Scatter(\n",
    "    x=[0],\n",
    "    y=[0],\n",
    "    name='Pre-storm dune toe',\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        color='rgba(255,255,255,0)',\n",
    "        size=10,\n",
    "        line=dict(color='rgba(202,178,214,1)', width=2)),\n",
    ")\n",
    "\n",
    "trace5 = go.Scatter(\n",
    "    x=[0],\n",
    "    y=[0],\n",
    "    name='Post-storm dune crest',\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        color='rgba(255,255,255,0)',\n",
    "        size=10,\n",
    "        line=dict(color='rgba(106,61,154, 1)', width=2),\n",
    "        symbol='square'),\n",
    ")\n",
    "trace6 = go.Scatter(\n",
    "    x=[0],\n",
    "    y=[0],\n",
    "    name='Post-storm dune toe',\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        color='rgba(255,255,255,0)',\n",
    "        size=10,\n",
    "        line=dict(color='rgba(202,178,214,1)', width=2),\n",
    "        symbol='square'),\n",
    ")\n",
    "\n",
    "\n",
    "forecast_traces = []\n",
    "for forecast, color in zip(impacts['forecasted'], colors):\n",
    "    forecast_traces.append(\n",
    "        go.Scatter(\n",
    "            x=[0],\n",
    "            y=[0],\n",
    "            name='Peak R_high: {}'.format(forecast),\n",
    "            mode='lines',\n",
    "            line=dict(\n",
    "                color=color,\n",
    "                width=4,\n",
    "            )))\n",
    "\n",
    "layout = go.Layout(\n",
    "    title='Bed Profiles',\n",
    "    height=300,\n",
    "    legend=dict(font={'size': 10}),\n",
    "    margin=dict(t=50, b=50, l=50, r=20),\n",
    "    xaxis=dict(\n",
    "        title='x (m)',\n",
    "        autorange=True,\n",
    "        showgrid=True,\n",
    "        zeroline=True,\n",
    "        showline=True,\n",
    "        range=[0, 200]),\n",
    "    yaxis=dict(\n",
    "        title='z (m)',\n",
    "        autorange=False,\n",
    "        showgrid=True,\n",
    "        zeroline=True,\n",
    "        showline=True,\n",
    "        range=[-1, 20]))\n",
    "\n",
    "g_profiles = go.FigureWidget(\n",
    "    data=[trace1, trace2, trace3, trace4, trace5, trace6] + forecast_traces, layout=layout)\n",
    "\n",
    "# Add panel for google maps\n",
    "mapbox_access_token = 'pk.eyJ1IjoiY2hyaXNsZWFtYW4iLCJhIjoiY2pvNTY1MzZpMDc2OTN2bmw5MGsycHp5bCJ9.U2dwFg2c7RFjUNSayERUiw'\n",
    "\n",
    "data = [\n",
    "    go.Scattermapbox(\n",
    "        lat=df_sites['lat'],\n",
    "        lon=df_sites['lon'],\n",
    "        mode='markers',\n",
    "        marker=dict(size=10),\n",
    "        text=df_sites.index.get_level_values('site_id'),\n",
    "    ),\n",
    "    go.Scattermapbox(\n",
    "        lat=[0],\n",
    "        lon=[0],\n",
    "        mode='markers',\n",
    "        marker=dict(\n",
    "            size=20,\n",
    "            color='rgb(255, 0, 0)',\n",
    "            opacity=0.5,\n",
    "        ),\n",
    "        text=df_sites.index.get_level_values('site_id'),\n",
    "    ),\n",
    "]\n",
    "\n",
    "layout = go.Layout(\n",
    "    autosize=True,\n",
    "    height=300,\n",
    "    hovermode='closest',\n",
    "    showlegend=False,\n",
    "    margin=dict(t=50, b=50, l=20, r=20),\n",
    "    mapbox=dict(\n",
    "        accesstoken=mapbox_access_token,\n",
    "        bearing=0,\n",
    "        center=dict(lat=-33.7, lon=151.3),\n",
    "        pitch=0,\n",
    "        zoom=12,\n",
    "        style='satellite-streets'),\n",
    ")\n",
    "\n",
    "fig = dict(data=data, layout=layout)\n",
    "g_map = go.FigureWidget(data=data, layout=layout)\n",
    "\n",
    "subplot = tls.make_subplots(3, 1, print_grid=False, shared_xaxes=True)\n",
    "g_timeseries = go.FigureWidget(subplot)\n",
    "\n",
    "# Add trace for Hs0\n",
    "g_timeseries.add_trace(\n",
    "    go.Scatter(\n",
    "        x=[0, 1],\n",
    "        y=[0, 1],\n",
    "        name='Hs0',\n",
    "    ),\n",
    "    row=3,\n",
    "    col=1,\n",
    ")\n",
    "\n",
    "# Add trace for Tp\n",
    "g_timeseries.add_trace(\n",
    "    go.Scatter(\n",
    "        x=[0, 1],\n",
    "        y=[0, 1],\n",
    "        name='Tp',\n",
    "    ),\n",
    "    row=3,\n",
    "    col=1,\n",
    ")\n",
    "\n",
    "# Add water levels\n",
    "g_timeseries.add_trace(\n",
    "    go.Scatter(\n",
    "        x=[0, 3],\n",
    "        y=[0, 3],\n",
    "        name='Dune Crest',\n",
    "        mode='lines',\n",
    "        line=dict(color=('rgb(214, 117, 14)'), width=2, dash='dot')),\n",
    "    row=1,\n",
    "    col=1)\n",
    "\n",
    "g_timeseries.add_trace(\n",
    "    go.Scatter(\n",
    "        x=[0, 3],\n",
    "        y=[0, 3],\n",
    "        name='Dune Toe',\n",
    "        mode='lines',\n",
    "        line=dict(color=('rgb(142, 77, 8)'), width=2, dash='dash')),\n",
    "    row=1,\n",
    "    col=1)\n",
    "\n",
    "g_timeseries.add_trace(\n",
    "    go.Scatter(\n",
    "        x=[0, 3],\n",
    "        y=[0, 3],\n",
    "        name='Tide+Surge WL',\n",
    "        line=dict(color=('rgb(8,51,137)'), width=2, dash='dot')),\n",
    "    row=1,\n",
    "    col=1)\n",
    "\n",
    "for forecast, color in zip(twls['forecasted'], colors):\n",
    "    g_timeseries.add_trace(\n",
    "        go.Scatter(\n",
    "            x=[0],\n",
    "            y=[0],\n",
    "            name='R_high: {}'.format(forecast),\n",
    "            line=dict(color=color, width=3)),\n",
    "        row=1,\n",
    "        col=1)\n",
    "\n",
    "# Add trace for each forecasted beta term\n",
    "for forecast, color in zip(impacts['forecasted'], colors):\n",
    "    g_timeseries.add_trace(\n",
    "        go.Scatter(\n",
    "            x=[0, 1],\n",
    "            y=[0, 1],\n",
    "            name='Beta: {}'.format(forecast),\n",
    "            line=dict(color=color, width=3)),\n",
    "        row=2,\n",
    "        col=1,\n",
    "    )\n",
    "\n",
    "# Create axis for Tp on same plot as Hs\n",
    "g_timeseries['layout']['yaxis4'] = {'overlaying': 'y3', 'side': 'right'}\n",
    "g_timeseries.data[1]['yaxis'] = 'y4'\n",
    "\n",
    "# Add labels to each axis\n",
    "g_timeseries.layout['xaxis']['title'] = 'datetime'\n",
    "g_timeseries.layout['yaxis1']['title'] = 'z (mAHD)'\n",
    "g_timeseries.layout['yaxis2']['title'] = 'beta (-)'\n",
    "g_timeseries.layout['yaxis3']['title'] = 'Hs0 (m)'\n",
    "g_timeseries.layout['yaxis4']['title'] = 'Tp (s)'\n",
    "\n",
    "# Update figure size\n",
    "g_timeseries['layout'].update(height=400, legend=dict(font={'size': 10}))\n",
    "g_timeseries['layout'].update(margin=dict(t=20, l=50, r=20, b=100))\n",
    "\n",
    "# Add panel for some tables\n",
    "titles = ['observed'] + [forecast for forecast in impacts['forecasted']]\n",
    "titles = [widgets.HTML(value=\"{}\".format(title)) for title in titles]\n",
    "\n",
    "\n",
    "def get_observed_impacts_table(site_id):\n",
    "    display(impacts['observed'].query(\"site_id=='{}'\".format(site_id)).T)\n",
    "\n",
    "\n",
    "def get_forecasted_impacts_table(site_id, forecast):\n",
    "    display(impacts['forecasted'][forecast].query(\n",
    "        \"site_id=='{}'\".format(site_id)).T)\n",
    "\n",
    "\n",
    "impacts_table_observed = widgets.interactive_output(\n",
    "    get_observed_impacts_table, {'site_id': site_id_select})\n",
    "forecasted_impacts_tables = []\n",
    "for forecast, title in zip(impacts['forecasted'], titles[1:]):\n",
    "    forecasted_impacts_tables.append(\n",
    "        widgets.interactive_output(get_forecasted_impacts_table, {\n",
    "            'site_id': site_id_select,\n",
    "            'forecast': title\n",
    "        }))\n",
    "\n",
    "tables = [impacts_table_observed] + forecasted_impacts_tables\n",
    "\n",
    "title_tables = [\n",
    "    widgets.VBox(children=[title, table])\n",
    "    for title, table in zip(titles, tables)\n",
    "]\n",
    "\n",
    "tables_container = widgets.HBox(children=[*title_tables])\n",
    "\n",
    "\n",
    "def update_profile(change):\n",
    "\n",
    "    site_id = site_id_select.value\n",
    "\n",
    "    if site_id is None:\n",
    "        return\n",
    "\n",
    "    site_profile = df_profiles.query('site_id == \"{}\"'.format(site_id))\n",
    "    prestorm_profile = site_profile.query('profile_type == \"prestorm\"')\n",
    "    poststorm_profile = site_profile.query('profile_type == \"poststorm\"')\n",
    "\n",
    "    poststorm_x = poststorm_profile.index.get_level_values('x').tolist()\n",
    "    poststorm_z = poststorm_profile.z.tolist()\n",
    "\n",
    "    prestorm_x = prestorm_profile.index.get_level_values('x').tolist()\n",
    "    prestorm_z = prestorm_profile.z.tolist()\n",
    "\n",
    "    prestorm_site_features = df_profile_features.query(\n",
    "        'site_id == \"{}\" and profile_type==\"prestorm\"'.format(site_id))\n",
    "    prestorm_dune_crest_x = prestorm_site_features.dune_crest_x\n",
    "    prestorm_dune_crest_z = prestorm_site_features.dune_crest_z\n",
    "    prestorm_dune_toe_x = prestorm_site_features.dune_toe_x\n",
    "    prestorm_dune_toe_z = prestorm_site_features.dune_toe_z\n",
    "\n",
    "    \n",
    "    poststorm_site_features = df_profile_features.query(\n",
    "        'site_id == \"{}\" and profile_type==\"poststorm\"'.format(site_id))\n",
    "    poststorm_dune_crest_x = poststorm_site_features.dune_crest_x\n",
    "    poststorm_dune_crest_z = poststorm_site_features.dune_crest_z\n",
    "    poststorm_dune_toe_x = poststorm_site_features.dune_toe_x\n",
    "    poststorm_dune_toe_z = poststorm_site_features.dune_toe_z\n",
    "    \n",
    "    # Update beach profile section plots\n",
    "    with g_profiles.batch_update():\n",
    "        g_profiles.data[0].x = prestorm_x\n",
    "        g_profiles.data[0].y = prestorm_z\n",
    "        g_profiles.data[1].x = poststorm_x\n",
    "        g_profiles.data[1].y = poststorm_z\n",
    "        g_profiles.data[2].x = prestorm_dune_crest_x\n",
    "        g_profiles.data[2].y = prestorm_dune_crest_z\n",
    "        g_profiles.data[3].x = prestorm_dune_toe_x\n",
    "        g_profiles.data[3].y = prestorm_dune_toe_z\n",
    "        g_profiles.data[4].x = poststorm_dune_crest_x\n",
    "        g_profiles.data[4].y = poststorm_dune_crest_z\n",
    "        g_profiles.data[5].x = poststorm_dune_toe_x\n",
    "        g_profiles.data[5].y = poststorm_dune_toe_z\n",
    "    \n",
    "        for n, forecast in enumerate(impacts['forecasted']):\n",
    "            R_high = max(impacts['forecasted'][forecast].query(\n",
    "                \"site_id=='{}'\".format(site_id)).R_high)\n",
    "            g_profiles.data[6 + n].x = [200, 400]\n",
    "            g_profiles.data[6 + n].y = [R_high, R_high]\n",
    "\n",
    "    # Relocate plan of satellite imagery\n",
    "    site_coords = df_sites.query('site_id == \"{}\"'.format(site_id))\n",
    "    with g_map.batch_update():\n",
    "        g_map.layout.mapbox['center'] = {\n",
    "            'lat': site_coords['lat'].values[0],\n",
    "            'lon': site_coords['lon'].values[0]\n",
    "        }\n",
    "        g_map.layout.mapbox['zoom'] = 15\n",
    "        g_map.data[1].lat = [site_coords['lat'].values[0]]\n",
    "        g_map.data[1].lon = [site_coords['lon'].values[0]]\n",
    "        g_map.data[1].text = site_coords['lon'].index.get_level_values(\n",
    "            'site_id').tolist()\n",
    "\n",
    "    # Update time series plots\n",
    "    df_waves_site = df_waves.query(\"site_id=='{}'\".format(site_id))\n",
    "    times = df_waves_site.index.get_level_values('datetime').tolist()\n",
    "    Hs0s = df_waves_site.Hs0.tolist()\n",
    "    Tps = df_waves_site.Tp.tolist()\n",
    "\n",
    "    df_tide_site = df_tides.query(\"site_id=='{}'\".format(site_id))\n",
    "    mask = (df_tide_site.index.get_level_values('datetime') >= min(times)) & (\n",
    "        df_tide_site.index.get_level_values('datetime') <= max(times))\n",
    "    df_tide_site = df_tide_site.loc[mask]\n",
    "\n",
    "    with g_timeseries.batch_update():\n",
    "        g_timeseries.data[0].x = times\n",
    "        g_timeseries.data[0].y = Hs0s\n",
    "        g_timeseries.data[1].x = times\n",
    "        g_timeseries.data[1].y = Tps\n",
    "\n",
    "        # Update beta values\n",
    "        idx_betas = [\n",
    "            n for n, x in enumerate(g_timeseries.data) if 'Beta' in x.name\n",
    "        ]\n",
    "        for i, forecast in zip(idx_betas, twls['forecasted']):\n",
    "            df_twl = twls['forecasted'][forecast].query(\n",
    "                \"site_id=='{}'\".format(site_id))\n",
    "            times = df_twl.index.get_level_values('datetime').tolist()\n",
    "            beta = df_twl.beta.tolist()\n",
    "            g_timeseries.data[i].x = times\n",
    "            g_timeseries.data[i].y = beta\n",
    "\n",
    "        g_timeseries.data[2].x = [min(times), max(times)]\n",
    "        g_timeseries.data[3].x = [min(times), max(times)]\n",
    "        g_timeseries.data[4].x = df_tide_site.index.get_level_values(\n",
    "            'datetime')\n",
    "        g_timeseries.data[2].y = prestorm_dune_crest_z.tolist()[\n",
    "            0], prestorm_dune_crest_z.tolist()[0],\n",
    "        g_timeseries.data[3].y = prestorm_dune_toe_z.tolist()[0], prestorm_dune_toe_z.tolist()[\n",
    "            0],\n",
    "        g_timeseries.data[4].y = df_tide_site.tide.tolist()\n",
    "\n",
    "        # Update rhigh values\n",
    "        idx_betas = [\n",
    "            n for n, x in enumerate(g_timeseries.data) if 'R_high' in x.name\n",
    "        ]\n",
    "        for i, forecast in zip(idx_betas, twls['forecasted']):\n",
    "            df_twl = twls['forecasted'][forecast].query(\n",
    "                \"site_id=='{}'\".format(site_id))\n",
    "            times = df_twl.index.get_level_values('datetime').tolist()\n",
    "            R_high = df_twl.R_high.tolist()\n",
    "            g_timeseries.data[i].x = times\n",
    "            g_timeseries.data[i].y = R_high\n",
    "\n",
    "    # Update site id impacts\n",
    "    observed_regime = impacts['observed'].query(\n",
    "        \"site_id=='{}'\".format(site_id)).storm_regime.values[0]\n",
    "    site_id_impacts.value = \"Observed: <b>{}</b><br>\".format(\n",
    "        observed_regime)\n",
    "\n",
    "    for forecast in impacts['forecasted']:\n",
    "        regime = impacts['forecasted'][forecast].query(\n",
    "            \"site_id=='{}'\".format(site_id)).storm_regime.values[0]\n",
    "        site_id_impacts.value += '{}: <b>{}</b><br>'.format(\n",
    "            forecast, regime)\n",
    "\n",
    "\n",
    "site_id_select.observe(update_profile, names=\"value\")\n",
    "\n",
    "\n",
    "def update_filter(change):\n",
    "\n",
    "    # Iterate through each box, only keeping site_ids which are not filtered out by each box\n",
    "    valid_site_ids = impacts['observed'].index.tolist()\n",
    "    dfs = [impacts['observed']\n",
    "           ] + [impacts['forecasted'][key] for key in impacts['forecasted']]\n",
    "\n",
    "    for box, df in zip(selectboxes, dfs):\n",
    "        valid_site_ids = list(\n",
    "            set(valid_site_ids).intersection(\n",
    "                set(df[df.storm_regime.isin(box.value)].index.tolist())))\n",
    "    site_id_select.options = sorted(valid_site_ids)\n",
    "\n",
    "    # TODO Update options in selectboxes with number of observations?\n",
    "\n",
    "\n",
    "# Update the filter if any of the boxes changes\n",
    "for box in selectboxes:\n",
    "    box.observe(update_filter, names=\"value\")\n",
    "\n",
    "# Display our widgets!\n",
    "widgets.VBox([\n",
    "    filter_container, site_id_container,\n",
    "    widgets.HBox([g_profiles, g_map]), g_timeseries, tables_container\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-27T23:06:31.686277Z",
     "start_time": "2018-11-27T23:06:31.665206Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-22T22:52:36.039701Z",
     "start_time": "2018-11-22T22:52:36.035189Z"
    },
    "hide_input": true,
    "scrolled": true
   },
   "source": [
    "This visualization looks at how well the storm impact predictions performed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-03T23:02:42.021445Z",
     "start_time": "2018-12-03T23:02:41.468637Z"
    },
    "code_folding": [],
    "hide_input": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a2b52c5ad861454db1756a427f13b55d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(VBox(children=(HTML(value='<b>Filter by beach:</b>'), SelectMultiple(index=(0, 1, 2, 3, 4, 5, 6…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create colorscale\n",
    "rdylgr_cmap = matplotlib.cm.get_cmap('RdYlGn')\n",
    "\n",
    "norm = matplotlib.colors.Normalize(vmin=0, vmax=255)\n",
    "\n",
    "def matplotlib_to_plotly(cmap, pl_entries):\n",
    "    h = 1.0/(pl_entries-1)\n",
    "    pl_colorscale = []\n",
    "\n",
    "    for k in range(pl_entries):\n",
    "        C = list(map(np.uint8, np.array(cmap(k*h)[:3])*255))\n",
    "        pl_colorscale.append([k*h, 'rgb'+str((C[0], C[1], C[2]))])\n",
    "\n",
    "    return pl_colorscale\n",
    "\n",
    "rdylgr = matplotlib_to_plotly(rdylgr_cmap, 255)\n",
    "\n",
    "\n",
    "\n",
    "# Create widget for list of beaches.\n",
    "beaches = df_sites.beach.unique().tolist()\n",
    "\n",
    "beach_title = widgets.HTML(value=\"<b>Filter by beach:</b>\", )\n",
    "\n",
    "beach_select = widgets.SelectMultiple(\n",
    "    options=beaches, value=beaches, disabled=False)\n",
    "\n",
    "beach_container = widgets.VBox([beach_title, beach_select])\n",
    "\n",
    "# Create confusion matrix for each forecasted impact data set\n",
    "heatmaps = []\n",
    "for forecast in impacts['forecasted']:\n",
    "\n",
    "    z = [[.1, .3, .5, 2], [1.0, .8, .6, 1], [1.4, .28, 1.6, .21],\n",
    "         [.6, .4, .2, 3]]\n",
    "\n",
    "    x = ['swash', 'collision', 'overwash', 'inundation']\n",
    "    y = list(reversed(x))\n",
    "\n",
    "    z_text = z\n",
    "\n",
    "    fig = ff.create_annotated_heatmap(z, x=x, y=y, annotation_text=z_text, colorscale=rdylgr)\n",
    "    heatmap = go.FigureWidget(data=fig.data, layout=fig.layout)\n",
    "\n",
    "    heatmap.layout.update(\n",
    "        height=300, margin=go.layout.Margin(l=100, r=100, b=40, t=40, pad=0))\n",
    "    heatmap.layout.xaxis.update(title='Predicted')\n",
    "    heatmap.layout.yaxis.update(title='Observed')\n",
    "    heatmap_title = widgets.HTML(value=\"<b>{}</b>\".format(forecast) )\n",
    "    heatmaps.append(widgets.VBox([heatmap_title, heatmap]))\n",
    "\n",
    "    \n",
    "def update_heatmaps(change):\n",
    "    \n",
    "    for forecast, heatmap in zip(impacts['forecasted'], heatmaps):\n",
    "        selected_site_ids = df_sites[df_sites.beach.isin(beach_select.value)].index.tolist()\n",
    "\n",
    "        df_ob = impacts['observed']\n",
    "        df_fo = impacts['forecasted'][forecast]\n",
    "\n",
    "        observed_regimes = df_ob[df_ob.index.isin(selected_site_ids)].storm_regime.dropna().rename(\"observed_regime\")\n",
    "        forecasted_regimes = df_fo[df_fo.index.isin(selected_site_ids)].storm_regime.dropna().rename(\"forecasted_regime\")\n",
    "\n",
    "        if any([observed_regimes.empty, forecasted_regimes.empty]):\n",
    "            return\n",
    "        \n",
    "        df_compare = pd.concat([observed_regimes, forecasted_regimes], axis='columns', names=['a','b'], sort=True)\n",
    "        df_compare.dropna(axis='index',inplace=True)\n",
    "\n",
    "        z = confusion_matrix(df_compare.observed_regime.tolist(), df_compare.forecasted_regime.tolist(), labels = ['swash','collision','overwash','inundation'])\n",
    "        z = np.flip(z,axis=0)\n",
    "        z_list = list(reversed(z.tolist()))\n",
    "        \n",
    "        # Make incorrect values negative, so they get assigned a different color.\n",
    "        # Better for visualization\n",
    "        z_neg_incorrect = np.flip(np.identity(4),axis=0)\n",
    "        z_neg_incorrect[z_neg_incorrect==0]= -1\n",
    "        z_neg_incorrect = (z * z_neg_incorrect).tolist()\n",
    "        \n",
    "        # Also want to display percentages\n",
    "        z_with_pct = []\n",
    "        for row in z:\n",
    "            new_row = []\n",
    "            for val in row:\n",
    "                new_row.append('{}<br>({}%)'.format(val, np.around(val/np.sum(z)*100,1)))\n",
    "            z_with_pct.append(new_row)\n",
    "        \n",
    "        fig = ff.create_annotated_heatmap(z_neg_incorrect, x=x, y=y, annotation_text=z_with_pct)\n",
    "        heatmap.children[1].data[0].z = z_neg_incorrect\n",
    "        heatmap.children[1].layout.annotations = fig.layout.annotations\n",
    "\n",
    "# Hook changes to beach filter to update confusion heatmaps\n",
    "beach_select.observe(update_heatmaps, names=\"value\")\n",
    "\n",
    "# Display our widgets\n",
    "widgets.VBox([beach_container, widgets.VBox(heatmaps)])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-03T23:03:48.457193Z",
     "start_time": "2018-12-03T23:03:48.274709Z"
    }
   },
   "outputs": [],
   "source": [
    "# To output to file\n",
    "# fig = heatmaps[1].children[1]\n",
    "# img_bytes = pio.write_image(fig, 'fig1.png',format='png', width=600, height=400, scale=5)\n",
    "\n",
    "# fig = g_profiles\n",
    "# img_bytes = pio.write_image(fig, 'fig1.png',format='png', width=600, height=200, scale=5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-03T23:02:47.179180Z",
     "start_time": "2018-12-03T23:02:46.367273Z"
    }
   },
   "source": [
    "### Look at time dependance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-03T23:49:16.581105Z",
     "start_time": "2018-12-03T23:49:16.274275Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8a366d02e4564347a5950b0f24c86363",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(VBox(children=(HTML(value='<b>Filter by site_id:</b>'), HBox(children=(Dropdown(…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create widgets for selecting site_id\n",
    "site_id_title = widgets.HTML(value=\"<b>Filter by site_id:</b>\", )\n",
    "\n",
    "site_id_select = widgets.Dropdown(\n",
    "    description='site_id:   ',\n",
    "    value='NARRA0001',\n",
    "    options=df_profiles.index.get_level_values('site_id').unique()\n",
    "    .sort_values().tolist())\n",
    "\n",
    "site_id_impacts = widgets.HTML(value=\"\", )\n",
    "\n",
    "site_id_container = widgets.HBox(children=[\n",
    "    widgets.VBox(\n",
    "        children=[site_id_title,\n",
    "                  widgets.HBox(children=[site_id_select])]), site_id_impacts\n",
    "])\n",
    "\n",
    "\n",
    "# Plot profiles\n",
    "\n",
    "# Add panel for pre/post storm profiles\n",
    "trace1 = go.Scatter(\n",
    "    x=[0],\n",
    "    y=[0],\n",
    "    name='Pre Storm Profile',\n",
    "    line=dict(color=('rgb(51,160,44)'), width=2))\n",
    "trace2 = go.Scatter(\n",
    "    x=[0],\n",
    "    y=[0],\n",
    "    name='Post Storm Profile',\n",
    "    line=dict(color=('rgb(255,127,0)'), width=2))\n",
    "trace3 = go.Scatter(\n",
    "    x=[0],\n",
    "    y=[0],\n",
    "    name='Pre-storm dune crest',\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        color='rgba(255,255,255,0)',\n",
    "        size=10,\n",
    "        line=dict(color='rgba(106,61,154, 1)', width=2)),\n",
    ")\n",
    "trace4 = go.Scatter(\n",
    "    x=[0],\n",
    "    y=[0],\n",
    "    name='Pre-storm dune toe',\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        color='rgba(255,255,255,0)',\n",
    "        size=10,\n",
    "        line=dict(color='rgba(202,178,214,1)', width=2)),\n",
    ")\n",
    "\n",
    "trace5 = go.Scatter(\n",
    "    x=[0],\n",
    "    y=[0],\n",
    "    name='Post-storm dune crest',\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        color='rgba(255,255,255,0)',\n",
    "        size=10,\n",
    "        line=dict(color='rgba(106,61,154, 1)', width=2),\n",
    "        symbol='square'),\n",
    ")\n",
    "trace6 = go.Scatter(\n",
    "    x=[0],\n",
    "    y=[0],\n",
    "    name='Post-storm dune toe',\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        color='rgba(255,255,255,0)',\n",
    "        size=10,\n",
    "        line=dict(color='rgba(202,178,214,1)', width=2),\n",
    "        symbol='square'),\n",
    ")\n",
    "\n",
    "\n",
    "forecast_traces = []\n",
    "for forecast, color in zip(impacts['forecasted'], colors):\n",
    "    forecast_traces.append(\n",
    "        go.Scatter(\n",
    "            x=[0],\n",
    "            y=[0],\n",
    "            name='Peak R_high: {}'.format(forecast),\n",
    "            mode='lines',\n",
    "            line=dict(\n",
    "                color=color,\n",
    "                width=4,\n",
    "            )))\n",
    "\n",
    "layout = go.Layout(\n",
    "    title='Bed Profiles',\n",
    "    height=300,\n",
    "    legend=dict(font={'size': 10}),\n",
    "    margin=dict(t=50, b=50, l=50, r=20),\n",
    "    xaxis=dict(\n",
    "        title='x (m)',\n",
    "        autorange=True,\n",
    "        showgrid=True,\n",
    "        zeroline=True,\n",
    "        showline=True,\n",
    "        range=[0, 200]),\n",
    "    yaxis=dict(\n",
    "        title='z (m)',\n",
    "        autorange=False,\n",
    "        showgrid=True,\n",
    "        zeroline=True,\n",
    "        showline=True,\n",
    "        range=[-1, 20]))\n",
    "\n",
    "g_profiles = go.FigureWidget(\n",
    "    data=[trace1, trace2, trace3, trace4, trace5, trace6] + forecast_traces, layout=layout)\n",
    "\n",
    "widgets.VBox([\n",
    "   site_id_container,\n",
    "    g_profiles\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-07T05:08:12.117885Z",
     "start_time": "2018-12-07T05:08:12.078780Z"
    }
   },
   "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>prestorm_swash_vol</th>\n",
       "      <th>poststorm_swash_vol</th>\n",
       "      <th>swash_vol_change</th>\n",
       "      <th>swash_pct_change</th>\n",
       "      <th>prestorm_dune_face_vol</th>\n",
       "      <th>poststorm_dune_face_vol</th>\n",
       "      <th>dune_face_vol_change</th>\n",
       "      <th>dune_face_pct_change</th>\n",
       "      <th>storm_regime</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>site_id</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>AVOCAn0001</th>\n",
       "      <td>113.9091</td>\n",
       "      <td>77.9830</td>\n",
       "      <td>35.6103</td>\n",
       "      <td>31.2620</td>\n",
       "      <td>165.4760</td>\n",
       "      <td>166.7296</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>swash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVOCAn0002</th>\n",
       "      <td>106.8959</td>\n",
       "      <td>67.0913</td>\n",
       "      <td>39.6373</td>\n",
       "      <td>37.0803</td>\n",
       "      <td>256.4137</td>\n",
       "      <td>258.9174</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>swash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVOCAn0003</th>\n",
       "      <td>99.0484</td>\n",
       "      <td>53.6563</td>\n",
       "      <td>45.1621</td>\n",
       "      <td>45.5960</td>\n",
       "      <td>372.7031</td>\n",
       "      <td>373.9198</td>\n",
       "      <td>-0.3147</td>\n",
       "      <td>-0.0844</td>\n",
       "      <td>swash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVOCAn0004</th>\n",
       "      <td>74.7543</td>\n",
       "      <td>29.3280</td>\n",
       "      <td>45.4262</td>\n",
       "      <td>60.7674</td>\n",
       "      <td>275.1689</td>\n",
       "      <td>276.0476</td>\n",
       "      <td>0.4104</td>\n",
       "      <td>0.1492</td>\n",
       "      <td>swash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVOCAn0005</th>\n",
       "      <td>70.5968</td>\n",
       "      <td>24.1071</td>\n",
       "      <td>46.4897</td>\n",
       "      <td>65.8524</td>\n",
       "      <td>268.5194</td>\n",
       "      <td>263.4262</td>\n",
       "      <td>7.4196</td>\n",
       "      <td>2.7631</td>\n",
       "      <td>collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVOCAn0006</th>\n",
       "      <td>68.7583</td>\n",
       "      <td>23.9665</td>\n",
       "      <td>44.7918</td>\n",
       "      <td>65.1438</td>\n",
       "      <td>202.6770</td>\n",
       "      <td>198.2397</td>\n",
       "      <td>4.7944</td>\n",
       "      <td>2.3655</td>\n",
       "      <td>collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVOCAn0007</th>\n",
       "      <td>75.7895</td>\n",
       "      <td>27.2715</td>\n",
       "      <td>48.5180</td>\n",
       "      <td>64.0168</td>\n",
       "      <td>149.8479</td>\n",
       "      <td>143.1312</td>\n",
       "      <td>7.1323</td>\n",
       "      <td>4.7597</td>\n",
       "      <td>collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVOCAn0008</th>\n",
       "      <td>93.3107</td>\n",
       "      <td>42.4968</td>\n",
       "      <td>50.8139</td>\n",
       "      <td>54.4567</td>\n",
       "      <td>187.9201</td>\n",
       "      <td>187.3459</td>\n",
       "      <td>2.8297</td>\n",
       "      <td>1.5058</td>\n",
       "      <td>collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVOCAn0009</th>\n",
       "      <td>3.6955</td>\n",
       "      <td>0.1038</td>\n",
       "      <td>3.5917</td>\n",
       "      <td>97.1908</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVOCAs0001</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVOCAs0002</th>\n",
       "      <td>97.3864</td>\n",
       "      <td>26.6619</td>\n",
       "      <td>70.7246</td>\n",
       "      <td>72.6226</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVOCAs0003</th>\n",
       "      <td>70.7401</td>\n",
       "      <td>40.0608</td>\n",
       "      <td>30.7919</td>\n",
       "      <td>43.5282</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVOCAs0004</th>\n",
       "      <td>97.8389</td>\n",
       "      <td>45.4845</td>\n",
       "      <td>52.2157</td>\n",
       "      <td>53.3691</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVOCAs0005</th>\n",
       "      <td>95.1711</td>\n",
       "      <td>54.9722</td>\n",
       "      <td>40.1706</td>\n",
       "      <td>42.2088</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVOCAs0006</th>\n",
       "      <td>112.5818</td>\n",
       "      <td>67.8718</td>\n",
       "      <td>44.8252</td>\n",
       "      <td>39.8157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVOCAs0007</th>\n",
       "      <td>65.3531</td>\n",
       "      <td>44.1537</td>\n",
       "      <td>21.5228</td>\n",
       "      <td>32.9331</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVOCAs0008</th>\n",
       "      <td>52.3940</td>\n",
       "      <td>44.9152</td>\n",
       "      <td>7.4803</td>\n",
       "      <td>14.2770</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILG0001</th>\n",
       "      <td>19.4177</td>\n",
       "      <td>7.5746</td>\n",
       "      <td>11.8431</td>\n",
       "      <td>60.9913</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILG0002</th>\n",
       "      <td>155.6998</td>\n",
       "      <td>98.1693</td>\n",
       "      <td>57.4340</td>\n",
       "      <td>36.8876</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILG0003</th>\n",
       "      <td>83.5219</td>\n",
       "      <td>52.8059</td>\n",
       "      <td>30.5537</td>\n",
       "      <td>36.5817</td>\n",
       "      <td>41.1469</td>\n",
       "      <td>40.6081</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>swash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILG0004</th>\n",
       "      <td>158.6283</td>\n",
       "      <td>116.6189</td>\n",
       "      <td>42.1178</td>\n",
       "      <td>26.5512</td>\n",
       "      <td>11.2211</td>\n",
       "      <td>11.0892</td>\n",
       "      <td>-0.0132</td>\n",
       "      <td>-0.1179</td>\n",
       "      <td>swash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BILG0005</th>\n",
       "      <td>212.8478</td>\n",
       "      <td>164.0044</td>\n",
       "      <td>48.4312</td>\n",
       "      <td>22.7539</td>\n",
       "      <td>613.6156</td>\n",
       "      <td>606.2766</td>\n",
       "      <td>5.7738</td>\n",
       "      <td>0.9410</td>\n",
       "      <td>collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLUEYS0001</th>\n",
       "      <td>65.4628</td>\n",
       "      <td>19.2938</td>\n",
       "      <td>46.1690</td>\n",
       "      <td>70.5270</td>\n",
       "      <td>130.7447</td>\n",
       "      <td>120.5446</td>\n",
       "      <td>9.5601</td>\n",
       "      <td>7.3121</td>\n",
       "      <td>collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLUEYS0002</th>\n",
       "      <td>50.2084</td>\n",
       "      <td>10.3009</td>\n",
       "      <td>39.9074</td>\n",
       "      <td>79.4836</td>\n",
       "      <td>512.0154</td>\n",
       "      <td>477.1774</td>\n",
       "      <td>33.2825</td>\n",
       "      <td>6.5003</td>\n",
       "      <td>collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLUEYS0003</th>\n",
       "      <td>50.6308</td>\n",
       "      <td>11.1682</td>\n",
       "      <td>39.4625</td>\n",
       "      <td>77.9418</td>\n",
       "      <td>443.0853</td>\n",
       "      <td>414.3901</td>\n",
       "      <td>24.8870</td>\n",
       "      <td>5.6167</td>\n",
       "      <td>collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLUEYS0004</th>\n",
       "      <td>95.1608</td>\n",
       "      <td>31.3330</td>\n",
       "      <td>63.8279</td>\n",
       "      <td>67.0737</td>\n",
       "      <td>287.5805</td>\n",
       "      <td>272.4267</td>\n",
       "      <td>12.9641</td>\n",
       "      <td>4.5080</td>\n",
       "      <td>collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLUEYS0005</th>\n",
       "      <td>141.0643</td>\n",
       "      <td>58.2545</td>\n",
       "      <td>82.8098</td>\n",
       "      <td>58.7036</td>\n",
       "      <td>539.3864</td>\n",
       "      <td>520.0732</td>\n",
       "      <td>12.0470</td>\n",
       "      <td>2.2335</td>\n",
       "      <td>collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLUEYS0006</th>\n",
       "      <td>88.4207</td>\n",
       "      <td>51.6205</td>\n",
       "      <td>36.2553</td>\n",
       "      <td>41.0032</td>\n",
       "      <td>271.6036</td>\n",
       "      <td>267.1954</td>\n",
       "      <td>3.6045</td>\n",
       "      <td>1.3271</td>\n",
       "      <td>collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOAT0001</th>\n",
       "      <td>23.8510</td>\n",
       "      <td>23.5660</td>\n",
       "      <td>-0.0264</td>\n",
       "      <td>-0.1108</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOAT0002</th>\n",
       "      <td>37.6524</td>\n",
       "      <td>14.0209</td>\n",
       "      <td>23.6316</td>\n",
       "      <td>62.7624</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TREACH0014</th>\n",
       "      <td>97.5323</td>\n",
       "      <td>46.2994</td>\n",
       "      <td>51.0816</td>\n",
       "      <td>52.3740</td>\n",
       "      <td>508.7400</td>\n",
       "      <td>505.7877</td>\n",
       "      <td>0.4254</td>\n",
       "      <td>0.0836</td>\n",
       "      <td>swash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TREACH0015</th>\n",
       "      <td>96.8327</td>\n",
       "      <td>45.1962</td>\n",
       "      <td>51.6364</td>\n",
       "      <td>53.3254</td>\n",
       "      <td>690.8275</td>\n",
       "      <td>683.4458</td>\n",
       "      <td>1.5086</td>\n",
       "      <td>0.2184</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TREACH0016</th>\n",
       "      <td>106.9083</td>\n",
       "      <td>66.0567</td>\n",
       "      <td>40.3629</td>\n",
       "      <td>37.7547</td>\n",
       "      <td>508.0014</td>\n",
       "      <td>499.6315</td>\n",
       "      <td>0.3386</td>\n",
       "      <td>0.0667</td>\n",
       "      <td>swash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0001</th>\n",
       "      <td>132.3413</td>\n",
       "      <td>143.4459</td>\n",
       "      <td>-9.7255</td>\n",
       "      <td>-7.3488</td>\n",
       "      <td>665.9898</td>\n",
       "      <td>667.5923</td>\n",
       "      <td>0.0410</td>\n",
       "      <td>0.0062</td>\n",
       "      <td>swash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0002</th>\n",
       "      <td>151.1833</td>\n",
       "      <td>126.9844</td>\n",
       "      <td>23.9548</td>\n",
       "      <td>15.8449</td>\n",
       "      <td>385.8467</td>\n",
       "      <td>386.7284</td>\n",
       "      <td>-0.0449</td>\n",
       "      <td>-0.0116</td>\n",
       "      <td>swash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0003</th>\n",
       "      <td>154.1788</td>\n",
       "      <td>117.9441</td>\n",
       "      <td>36.2425</td>\n",
       "      <td>23.5068</td>\n",
       "      <td>694.2226</td>\n",
       "      <td>700.5105</td>\n",
       "      <td>-4.2136</td>\n",
       "      <td>-0.6070</td>\n",
       "      <td>swash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0004</th>\n",
       "      <td>137.8449</td>\n",
       "      <td>76.6007</td>\n",
       "      <td>61.2725</td>\n",
       "      <td>44.4503</td>\n",
       "      <td>559.5485</td>\n",
       "      <td>569.8591</td>\n",
       "      <td>-4.4590</td>\n",
       "      <td>-0.7969</td>\n",
       "      <td>swash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0005</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0006</th>\n",
       "      <td>205.8453</td>\n",
       "      <td>186.0784</td>\n",
       "      <td>22.5892</td>\n",
       "      <td>10.9739</td>\n",
       "      <td>55.0898</td>\n",
       "      <td>55.8919</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>swash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0007</th>\n",
       "      <td>80.4674</td>\n",
       "      <td>35.4614</td>\n",
       "      <td>45.0059</td>\n",
       "      <td>55.9307</td>\n",
       "      <td>178.1005</td>\n",
       "      <td>178.5439</td>\n",
       "      <td>0.4727</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>swash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0008</th>\n",
       "      <td>88.4574</td>\n",
       "      <td>40.3200</td>\n",
       "      <td>48.1374</td>\n",
       "      <td>54.4187</td>\n",
       "      <td>258.7513</td>\n",
       "      <td>258.3849</td>\n",
       "      <td>-1.2073</td>\n",
       "      <td>-0.4666</td>\n",
       "      <td>swash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0009</th>\n",
       "      <td>70.9159</td>\n",
       "      <td>26.1742</td>\n",
       "      <td>44.7418</td>\n",
       "      <td>63.0913</td>\n",
       "      <td>267.3725</td>\n",
       "      <td>258.3720</td>\n",
       "      <td>9.8041</td>\n",
       "      <td>3.6668</td>\n",
       "      <td>collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0010</th>\n",
       "      <td>58.6604</td>\n",
       "      <td>18.0418</td>\n",
       "      <td>40.6186</td>\n",
       "      <td>69.2437</td>\n",
       "      <td>187.5259</td>\n",
       "      <td>161.9748</td>\n",
       "      <td>25.3087</td>\n",
       "      <td>13.4961</td>\n",
       "      <td>collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0011</th>\n",
       "      <td>59.2415</td>\n",
       "      <td>16.3165</td>\n",
       "      <td>42.9250</td>\n",
       "      <td>72.4577</td>\n",
       "      <td>197.0129</td>\n",
       "      <td>175.2512</td>\n",
       "      <td>21.9882</td>\n",
       "      <td>11.1608</td>\n",
       "      <td>collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0012</th>\n",
       "      <td>74.4189</td>\n",
       "      <td>23.0232</td>\n",
       "      <td>51.3957</td>\n",
       "      <td>69.0627</td>\n",
       "      <td>178.4783</td>\n",
       "      <td>168.3475</td>\n",
       "      <td>10.0386</td>\n",
       "      <td>5.6246</td>\n",
       "      <td>collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0013</th>\n",
       "      <td>70.4964</td>\n",
       "      <td>22.7546</td>\n",
       "      <td>47.7419</td>\n",
       "      <td>67.7224</td>\n",
       "      <td>231.1513</td>\n",
       "      <td>195.2581</td>\n",
       "      <td>35.8072</td>\n",
       "      <td>15.4908</td>\n",
       "      <td>collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0014</th>\n",
       "      <td>68.0896</td>\n",
       "      <td>24.1853</td>\n",
       "      <td>43.9043</td>\n",
       "      <td>64.4802</td>\n",
       "      <td>82.4268</td>\n",
       "      <td>61.2601</td>\n",
       "      <td>21.1718</td>\n",
       "      <td>25.6856</td>\n",
       "      <td>collision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0015</th>\n",
       "      <td>55.0780</td>\n",
       "      <td>16.0119</td>\n",
       "      <td>39.0660</td>\n",
       "      <td>70.9286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0016</th>\n",
       "      <td>96.7687</td>\n",
       "      <td>39.8224</td>\n",
       "      <td>56.9463</td>\n",
       "      <td>58.8479</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0017</th>\n",
       "      <td>35.2987</td>\n",
       "      <td>8.5140</td>\n",
       "      <td>26.7847</td>\n",
       "      <td>75.8801</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0018</th>\n",
       "      <td>40.9407</td>\n",
       "      <td>10.5147</td>\n",
       "      <td>30.4260</td>\n",
       "      <td>74.3173</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0019</th>\n",
       "      <td>38.2838</td>\n",
       "      <td>9.2156</td>\n",
       "      <td>29.0682</td>\n",
       "      <td>75.9282</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0020</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0021</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0022</th>\n",
       "      <td>0.5516</td>\n",
       "      <td>0.2840</td>\n",
       "      <td>0.2675</td>\n",
       "      <td>48.5063</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0023</th>\n",
       "      <td>3.3761</td>\n",
       "      <td>0.3020</td>\n",
       "      <td>3.0741</td>\n",
       "      <td>91.0554</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0024</th>\n",
       "      <td>60.8648</td>\n",
       "      <td>31.2794</td>\n",
       "      <td>29.5854</td>\n",
       "      <td>48.6084</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0025</th>\n",
       "      <td>45.1055</td>\n",
       "      <td>14.6028</td>\n",
       "      <td>30.5028</td>\n",
       "      <td>67.6253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0026</th>\n",
       "      <td>32.1502</td>\n",
       "      <td>12.9335</td>\n",
       "      <td>19.2167</td>\n",
       "      <td>59.7716</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAMBE0027</th>\n",
       "      <td>26.2310</td>\n",
       "      <td>18.6828</td>\n",
       "      <td>7.5482</td>\n",
       "      <td>28.7759</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1768 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            prestorm_swash_vol  poststorm_swash_vol  swash_vol_change  \\\n",
       "site_id                                                                 \n",
       "AVOCAn0001            113.9091              77.9830           35.6103   \n",
       "AVOCAn0002            106.8959              67.0913           39.6373   \n",
       "AVOCAn0003             99.0484              53.6563           45.1621   \n",
       "AVOCAn0004             74.7543              29.3280           45.4262   \n",
       "AVOCAn0005             70.5968              24.1071           46.4897   \n",
       "AVOCAn0006             68.7583              23.9665           44.7918   \n",
       "AVOCAn0007             75.7895              27.2715           48.5180   \n",
       "AVOCAn0008             93.3107              42.4968           50.8139   \n",
       "AVOCAn0009              3.6955               0.1038            3.5917   \n",
       "AVOCAs0001                 NaN                  NaN               NaN   \n",
       "AVOCAs0002             97.3864              26.6619           70.7246   \n",
       "AVOCAs0003             70.7401              40.0608           30.7919   \n",
       "AVOCAs0004             97.8389              45.4845           52.2157   \n",
       "AVOCAs0005             95.1711              54.9722           40.1706   \n",
       "AVOCAs0006            112.5818              67.8718           44.8252   \n",
       "AVOCAs0007             65.3531              44.1537           21.5228   \n",
       "AVOCAs0008             52.3940              44.9152            7.4803   \n",
       "BILG0001               19.4177               7.5746           11.8431   \n",
       "BILG0002              155.6998              98.1693           57.4340   \n",
       "BILG0003               83.5219              52.8059           30.5537   \n",
       "BILG0004              158.6283             116.6189           42.1178   \n",
       "BILG0005              212.8478             164.0044           48.4312   \n",
       "BLUEYS0001             65.4628              19.2938           46.1690   \n",
       "BLUEYS0002             50.2084              10.3009           39.9074   \n",
       "BLUEYS0003             50.6308              11.1682           39.4625   \n",
       "BLUEYS0004             95.1608              31.3330           63.8279   \n",
       "BLUEYS0005            141.0643              58.2545           82.8098   \n",
       "BLUEYS0006             88.4207              51.6205           36.2553   \n",
       "BOAT0001               23.8510              23.5660           -0.0264   \n",
       "BOAT0002               37.6524              14.0209           23.6316   \n",
       "...                        ...                  ...               ...   \n",
       "TREACH0014             97.5323              46.2994           51.0816   \n",
       "TREACH0015             96.8327              45.1962           51.6364   \n",
       "TREACH0016            106.9083              66.0567           40.3629   \n",
       "WAMBE0001             132.3413             143.4459           -9.7255   \n",
       "WAMBE0002             151.1833             126.9844           23.9548   \n",
       "WAMBE0003             154.1788             117.9441           36.2425   \n",
       "WAMBE0004             137.8449              76.6007           61.2725   \n",
       "WAMBE0005                  NaN                  NaN               NaN   \n",
       "WAMBE0006             205.8453             186.0784           22.5892   \n",
       "WAMBE0007              80.4674              35.4614           45.0059   \n",
       "WAMBE0008              88.4574              40.3200           48.1374   \n",
       "WAMBE0009              70.9159              26.1742           44.7418   \n",
       "WAMBE0010              58.6604              18.0418           40.6186   \n",
       "WAMBE0011              59.2415              16.3165           42.9250   \n",
       "WAMBE0012              74.4189              23.0232           51.3957   \n",
       "WAMBE0013              70.4964              22.7546           47.7419   \n",
       "WAMBE0014              68.0896              24.1853           43.9043   \n",
       "WAMBE0015              55.0780              16.0119           39.0660   \n",
       "WAMBE0016              96.7687              39.8224           56.9463   \n",
       "WAMBE0017              35.2987               8.5140           26.7847   \n",
       "WAMBE0018              40.9407              10.5147           30.4260   \n",
       "WAMBE0019              38.2838               9.2156           29.0682   \n",
       "WAMBE0020                  NaN                  NaN               NaN   \n",
       "WAMBE0021                  NaN                  NaN               NaN   \n",
       "WAMBE0022               0.5516               0.2840            0.2675   \n",
       "WAMBE0023               3.3761               0.3020            3.0741   \n",
       "WAMBE0024              60.8648              31.2794           29.5854   \n",
       "WAMBE0025              45.1055              14.6028           30.5028   \n",
       "WAMBE0026              32.1502              12.9335           19.2167   \n",
       "WAMBE0027              26.2310              18.6828            7.5482   \n",
       "\n",
       "            swash_pct_change  prestorm_dune_face_vol  poststorm_dune_face_vol  \\\n",
       "site_id                                                                         \n",
       "AVOCAn0001           31.2620                165.4760                 166.7296   \n",
       "AVOCAn0002           37.0803                256.4137                 258.9174   \n",
       "AVOCAn0003           45.5960                372.7031                 373.9198   \n",
       "AVOCAn0004           60.7674                275.1689                 276.0476   \n",
       "AVOCAn0005           65.8524                268.5194                 263.4262   \n",
       "AVOCAn0006           65.1438                202.6770                 198.2397   \n",
       "AVOCAn0007           64.0168                149.8479                 143.1312   \n",
       "AVOCAn0008           54.4567                187.9201                 187.3459   \n",
       "AVOCAn0009           97.1908                     NaN                      NaN   \n",
       "AVOCAs0001               NaN                     NaN                      NaN   \n",
       "AVOCAs0002           72.6226                     NaN                      NaN   \n",
       "AVOCAs0003           43.5282                     NaN                      NaN   \n",
       "AVOCAs0004           53.3691                     NaN                      NaN   \n",
       "AVOCAs0005           42.2088                     NaN                      NaN   \n",
       "AVOCAs0006           39.8157                     NaN                      NaN   \n",
       "AVOCAs0007           32.9331                     NaN                      NaN   \n",
       "AVOCAs0008           14.2770                     NaN                      NaN   \n",
       "BILG0001             60.9913                     NaN                      NaN   \n",
       "BILG0002             36.8876                     NaN                      NaN   \n",
       "BILG0003             36.5817                 41.1469                  40.6081   \n",
       "BILG0004             26.5512                 11.2211                  11.0892   \n",
       "BILG0005             22.7539                613.6156                 606.2766   \n",
       "BLUEYS0001           70.5270                130.7447                 120.5446   \n",
       "BLUEYS0002           79.4836                512.0154                 477.1774   \n",
       "BLUEYS0003           77.9418                443.0853                 414.3901   \n",
       "BLUEYS0004           67.0737                287.5805                 272.4267   \n",
       "BLUEYS0005           58.7036                539.3864                 520.0732   \n",
       "BLUEYS0006           41.0032                271.6036                 267.1954   \n",
       "BOAT0001             -0.1108                     NaN                      NaN   \n",
       "BOAT0002             62.7624                     NaN                      NaN   \n",
       "...                      ...                     ...                      ...   \n",
       "TREACH0014           52.3740                508.7400                 505.7877   \n",
       "TREACH0015           53.3254                690.8275                 683.4458   \n",
       "TREACH0016           37.7547                508.0014                 499.6315   \n",
       "WAMBE0001            -7.3488                665.9898                 667.5923   \n",
       "WAMBE0002            15.8449                385.8467                 386.7284   \n",
       "WAMBE0003            23.5068                694.2226                 700.5105   \n",
       "WAMBE0004            44.4503                559.5485                 569.8591   \n",
       "WAMBE0005                NaN                     NaN                      NaN   \n",
       "WAMBE0006            10.9739                 55.0898                  55.8919   \n",
       "WAMBE0007            55.9307                178.1005                 178.5439   \n",
       "WAMBE0008            54.4187                258.7513                 258.3849   \n",
       "WAMBE0009            63.0913                267.3725                 258.3720   \n",
       "WAMBE0010            69.2437                187.5259                 161.9748   \n",
       "WAMBE0011            72.4577                197.0129                 175.2512   \n",
       "WAMBE0012            69.0627                178.4783                 168.3475   \n",
       "WAMBE0013            67.7224                231.1513                 195.2581   \n",
       "WAMBE0014            64.4802                 82.4268                  61.2601   \n",
       "WAMBE0015            70.9286                     NaN                      NaN   \n",
       "WAMBE0016            58.8479                     NaN                      NaN   \n",
       "WAMBE0017            75.8801                     NaN                      NaN   \n",
       "WAMBE0018            74.3173                     NaN                      NaN   \n",
       "WAMBE0019            75.9282                     NaN                      NaN   \n",
       "WAMBE0020                NaN                     NaN                      NaN   \n",
       "WAMBE0021                NaN                     NaN                      NaN   \n",
       "WAMBE0022            48.5063                     NaN                      NaN   \n",
       "WAMBE0023            91.0554                     NaN                      NaN   \n",
       "WAMBE0024            48.6084                     NaN                      NaN   \n",
       "WAMBE0025            67.6253                     NaN                      NaN   \n",
       "WAMBE0026            59.7716                     NaN                      NaN   \n",
       "WAMBE0027            28.7759                     NaN                      NaN   \n",
       "\n",
       "            dune_face_vol_change  dune_face_pct_change storm_regime  \n",
       "site_id                                                              \n",
       "AVOCAn0001                0.0000                0.0000        swash  \n",
       "AVOCAn0002                0.0000                0.0000        swash  \n",
       "AVOCAn0003               -0.3147               -0.0844        swash  \n",
       "AVOCAn0004                0.4104                0.1492        swash  \n",
       "AVOCAn0005                7.4196                2.7631    collision  \n",
       "AVOCAn0006                4.7944                2.3655    collision  \n",
       "AVOCAn0007                7.1323                4.7597    collision  \n",
       "AVOCAn0008                2.8297                1.5058    collision  \n",
       "AVOCAn0009                   NaN                   NaN          NaN  \n",
       "AVOCAs0001                   NaN                   NaN          NaN  \n",
       "AVOCAs0002                   NaN                   NaN          NaN  \n",
       "AVOCAs0003                   NaN                   NaN          NaN  \n",
       "AVOCAs0004                   NaN                   NaN          NaN  \n",
       "AVOCAs0005                   NaN                   NaN          NaN  \n",
       "AVOCAs0006                   NaN                   NaN          NaN  \n",
       "AVOCAs0007                   NaN                   NaN          NaN  \n",
       "AVOCAs0008                   NaN                   NaN          NaN  \n",
       "BILG0001                     NaN                   NaN          NaN  \n",
       "BILG0002                     NaN                   NaN          NaN  \n",
       "BILG0003                  0.0000                0.0000        swash  \n",
       "BILG0004                 -0.0132               -0.1179        swash  \n",
       "BILG0005                  5.7738                0.9410    collision  \n",
       "BLUEYS0001                9.5601                7.3121    collision  \n",
       "BLUEYS0002               33.2825                6.5003    collision  \n",
       "BLUEYS0003               24.8870                5.6167    collision  \n",
       "BLUEYS0004               12.9641                4.5080    collision  \n",
       "BLUEYS0005               12.0470                2.2335    collision  \n",
       "BLUEYS0006                3.6045                1.3271    collision  \n",
       "BOAT0001                     NaN                   NaN          NaN  \n",
       "BOAT0002                     NaN                   NaN          NaN  \n",
       "...                          ...                   ...          ...  \n",
       "TREACH0014                0.4254                0.0836        swash  \n",
       "TREACH0015                1.5086                0.2184          NaN  \n",
       "TREACH0016                0.3386                0.0667        swash  \n",
       "WAMBE0001                 0.0410                0.0062        swash  \n",
       "WAMBE0002                -0.0449               -0.0116        swash  \n",
       "WAMBE0003                -4.2136               -0.6070        swash  \n",
       "WAMBE0004                -4.4590               -0.7969        swash  \n",
       "WAMBE0005                    NaN                   NaN          NaN  \n",
       "WAMBE0006                 0.0000                0.0000        swash  \n",
       "WAMBE0007                 0.4727                0.2654        swash  \n",
       "WAMBE0008                -1.2073               -0.4666        swash  \n",
       "WAMBE0009                 9.8041                3.6668    collision  \n",
       "WAMBE0010                25.3087               13.4961    collision  \n",
       "WAMBE0011                21.9882               11.1608    collision  \n",
       "WAMBE0012                10.0386                5.6246    collision  \n",
       "WAMBE0013                35.8072               15.4908    collision  \n",
       "WAMBE0014                21.1718               25.6856    collision  \n",
       "WAMBE0015                    NaN                   NaN          NaN  \n",
       "WAMBE0016                    NaN                   NaN          NaN  \n",
       "WAMBE0017                    NaN                   NaN          NaN  \n",
       "WAMBE0018                    NaN                   NaN          NaN  \n",
       "WAMBE0019                    NaN                   NaN          NaN  \n",
       "WAMBE0020                    NaN                   NaN          NaN  \n",
       "WAMBE0021                    NaN                   NaN          NaN  \n",
       "WAMBE0022                    NaN                   NaN          NaN  \n",
       "WAMBE0023                    NaN                   NaN          NaN  \n",
       "WAMBE0024                    NaN                   NaN          NaN  \n",
       "WAMBE0025                    NaN                   NaN          NaN  \n",
       "WAMBE0026                    NaN                   NaN          NaN  \n",
       "WAMBE0027                    NaN                   NaN          NaN  \n",
       "\n",
       "[1768 rows x 9 columns]"
      ]
     },
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