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

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 "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": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-21T05:09:17.883914Z",
     "start_time": "2018-11-21T05:09:16.981157Z"
    }
   },
   "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",
    "\n",
    "%reload_ext autoreload\n",
    "%autoreload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-21T05:09:17.891936Z",
     "start_time": "2018-11-21T05:09:17.884916Z"
    },
    "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",
    "\n",
    "from ipywidgets import widgets, Output\n",
    "from IPython.display import display, clear_output, Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-21T05:26:30.254784Z",
     "start_time": "2018-11-21T05:26:13.523566Z"
    },
    "pixiedust": {
     "displayParams": {}
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\numpy\\lib\\arraysetops.py:472: FutureWarning:\n",
      "\n",
      "elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def df_from_csv(csv, index_col, data_folder='../data/interim'):\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",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-21T04:02:25.614132Z",
     "start_time": "2018-11-21T04:02:25.609119Z"
    }
   },
   "outputs": [],
   "source": []
  },
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   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-21T06:07:17.024328Z",
     "start_time": "2018-11-21T06:07:14.488829Z"
    },
    "code_folding": [],
    "scrolled": false
   },
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       "    'data': [{'name': 'Pre Storm Profile',\n",
       "              'type': 'scatter',\n",
       "              'uid': '391027af-be5a-4413-863d-53385a59391d',\n",
       "              'x': [0],\n",
       "              'y': [0]},\n",
       "             {'name': 'Post Storm Profile',\n",
       "              'type': 'scatter',\n",
       "              'uid': 'a5d7d2e9-d3af-462c-9466-51c2b6f09aa4',\n",
       "              'x': [0],\n",
       "              'y': [0]},\n",
       "             {'marker': {'color': 'rgb(17, 157, 255)', 'size': 20},\n",
       "              'mode': 'markers',\n",
       "              'name': 'Pre-storm dune crest',\n",
       "              'type': 'scatter',\n",
       "              'uid': '2eba8b17-45a7-4bcd-b988-31bbb487fb1f',\n",
       "              'x': [0],\n",
       "              'y': [0]},\n",
       "             {'marker': {'color': 'rgb(231, 99, 250)', 'size': 20},\n",
       "              'mode': 'markers',\n",
       "              'name': 'Pre-storm dune toe',\n",
       "              'type': 'scatter',\n",
       "              'uid': '022f5c93-fc10-4d1d-ac97-e30b8efd28d1',\n",
       "              'x': [0],\n",
       "              'y': [0]},\n",
       "             {'name': 'Peak R_high: foreshore_slope_sto06',\n",
       "              'type': 'scatter',\n",
       "              'uid': '0c93ba4e-2213-4db9-9b13-1227d17f5074',\n",
       "              'x': [0],\n",
       "              'y': [0]},\n",
       "             {'name': 'Peak R_high: mean_slope_sto06',\n",
       "              'type': 'scatter',\n",
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       "              'x': [0],\n",
       "              'y': [0]}],\n",
       "    'layout': {'height': 300,\n",
       "               'legend': {'font': {'size': 10}},\n",
       "               'margin': {'b': 50, 'l': 50, 'r': 20, 't': 50},\n",
       "               'title': 'Bed Profiles',\n",
       "               'xaxis': {'autorange': True,\n",
       "                         'range': [0, 200],\n",
       "                         'showgrid': True,\n",
       "                         'showline': True,\n",
       "                         'title': 'x (m)',\n",
       "                         'zeroline': True},\n",
       "               'yaxis': {'autorange': False,\n",
       "                         'range': [-1, 20],\n",
       "                         'showgrid': True,\n",
       "                         'showline': True,\n",
       "                         'title': 'z (m)',\n",
       "                         'zeroline': True}}\n",
       "}), FigureWidget({\n",
       "    'data': [{'lat': array([-33.46381539, -33.46301835, -33.46221051, ..., -33.4279646 ,\n",
       "                            -33.42732743, -33.42671036]),\n",
       "              'lon': array([151.43639576, 151.43690633, 151.43738179, ..., 151.4501613 ,\n",
       "                            151.45092222, 151.45170635]),\n",
       "              'marker': {'size': 10},\n",
       "              'mode': 'markers',\n",
       "              'text': array(['AVOCAn0001', 'AVOCAn0002', 'AVOCAn0003', ..., 'WAMBE0025', 'WAMBE0026',\n",
       "                             'WAMBE0027'], dtype='<U12'),\n",
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       "              'uid': 'a3b0c913-a478-4496-a607-3da5b8e48bcf'},\n",
       "             {'lat': [0],\n",
       "              'lon': [0],\n",
       "              'marker': {'color': 'rgb(255, 0, 0)', 'opacity': 0.5, 'size': 20},\n",
       "              'mode': 'markers',\n",
       "              'text': array(['AVOCAn0001', 'AVOCAn0002', 'AVOCAn0003', ..., 'WAMBE0025', 'WAMBE0026',\n",
       "                             'WAMBE0027'], dtype='<U12'),\n",
       "              'type': 'scattermapbox',\n",
       "              'uid': '0af9db05-d087-4eea-94f5-973734e364b3'}],\n",
       "    'layout': {'autosize': True,\n",
       "               'height': 300,\n",
       "               'hovermode': 'closest',\n",
       "               'mapbox': {'accesstoken': ('pk.eyJ1IjoiY2hyaXNsZWFtYW4iLCJ' ... 'Hp5bCJ9.U2dwFg2c7RFjUNSayERUiw'),\n",
       "                          'bearing': 0,\n",
       "                          'center': {'lat': -33.7, 'lon': 151.3},\n",
       "                          'pitch': 0,\n",
       "                          'style': 'satellite-streets',\n",
       "                          'zoom': 12},\n",
       "               'margin': {'b': 50, 'l': 20, 'r': 20, 't': 50},\n",
       "               'showlegend': False}\n",
       "}))), FigureWidget({\n",
       "    'data': [{'line': {'color': 'rgb(214, 117, 14)', 'dash': 'dot', 'width': 2},\n",
       "              'name': 'Dune Crest',\n",
       "              'type': 'scatter',\n",
       "              'uid': '8dbd0ac0-321f-42c9-b401-a2bcd5d66867',\n",
       "              'x': [0, 3],\n",
       "              'y': [0, 3]},\n",
       "             {'line': {'color': 'rgb(142, 77, 8)', 'dash': 'dash', 'width': 2},\n",
       "              'name': 'Dune Toe',\n",
       "              'type': 'scatter',\n",
       "              'uid': '3938e7cf-2a0c-4c56-b258-459480b42c5c',\n",
       "              'x': [0, 3],\n",
       "              'y': [0, 3]},\n",
       "             {'line': {'color': 'rgb(8,51,137)', 'dash': 'dot', 'width': 2},\n",
       "              'name': 'Tide+Surge WL',\n",
       "              'type': 'scatter',\n",
       "              'uid': '15bce5f1-f7b7-4555-b19f-1d9c70041e60',\n",
       "              'x': [0, 4],\n",
       "              'y': [0, 4]},\n",
       "             {'name': 'R_high: foreshore_slope_sto06',\n",
       "              'type': 'scatter',\n",
       "              'uid': 'fedcd3c9-f6b7-4e68-abc9-f65e6f4075fe',\n",
       "              'x': [0],\n",
       "              'y': [0]},\n",
       "             {'name': 'R_high: mean_slope_sto06',\n",
       "              'type': 'scatter',\n",
       "              'uid': '6f7e80b2-a994-4f1a-ade0-08ec5921e54d',\n",
       "              'x': [0],\n",
       "              'y': [0]}],\n",
       "    'layout': {'height': 200,\n",
       "               'margin': {'b': 50, 'l': 50, 'r': 50, 't': 50},\n",
       "               'title': 'Water Level & Dune Toe/Crest',\n",
       "               'xaxis': {'domain': [0.0, 0.95], 'title': 'time', 'zeroline': False},\n",
       "               'yaxis': {'title': 'Water Level (m)'}}\n",
       "}), FigureWidget({\n",
       "    'data': [{'name': 'Hs0', 'type': 'scatter', 'uid': 'ba107c82-0112-4674-a471-c08d69d6f900', 'x': [0, 1], 'y': [0, 1]},\n",
       "             {'name': 'Tp',\n",
       "              'type': 'scatter',\n",
       "              'uid': '6e348dfe-5a22-49d6-912b-d6dacacdeb02',\n",
       "              'x': [0, 2],\n",
       "              'y': [0, 2],\n",
       "              'yaxis': 'y2'},\n",
       "             {'name': 'Beta: foreshore_slope_sto06',\n",
       "              'type': 'scatter',\n",
       "              'uid': '39364e19-0edc-4243-976d-d7c8e3fbdfb7',\n",
       "              'x': [0],\n",
       "              'y': [0],\n",
       "              'yaxis': 'y3'},\n",
       "             {'name': 'Beta: mean_slope_sto06',\n",
       "              'type': 'scatter',\n",
       "              'uid': '83696b2e-a5b6-4682-ab40-7c41ae8b7f73',\n",
       "              'x': [0],\n",
       "              'y': [0],\n",
       "              'yaxis': 'y3'}],\n",
       "    'layout': {'height': 200,\n",
       "               'margin': {'b': 50, 'l': 50, 'r': 50, 't': 50},\n",
       "               'title': 'Hydro/Morpho Parameters',\n",
       "               'xaxis': {'domain': [0.0, 0.9], 'title': 'time', 'zeroline': False},\n",
       "               'yaxis': {'title': 'Hs0 (m)'},\n",
       "               'yaxis2': {'overlaying': 'y', 'side': 'right', 'title': 'Tp (s)'},\n",
       "               'yaxis3': {'overlaying': 'y', 'position': 0.97, 'side': 'right', 'title': 'beta (-)'}}\n",
       "})))"
      ]
     },
     "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",
    "\n",
    "titles = ['Observed Impacts']\n",
    "selectboxes = [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",
    "# Iterate through each of our forecasted impacts \n",
    "for forecast in impacts['forecasted']:\n",
    "    selectboxes.append(\n",
    "        widgets.SelectMultiple(\n",
    "            options=impacts['forecasted'][forecast].storm_regime.dropna().unique().tolist(),\n",
    "            value=impacts['forecasted'][forecast].storm_regime.dropna().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=[widgets.VBox(children=[title, box]) for title, box in zip(titles, selectboxes)])\n",
    "filter_container = widgets.VBox(children=[filter_title,children])\n",
    "\n",
    "\n",
    "\n",
    "# Create widgets for selecting site_id\n",
    "site_id_title = widgets.HTML(\n",
    "    value=\"<b>Filter by site_id:</b>\",\n",
    ")\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().sort_values().tolist()\n",
    ")\n",
    "site_id_container = widgets.VBox(children=[site_id_title,widgets.HBox(children=[site_id_select])])\n",
    "\n",
    "\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",
    ")\n",
    "trace2 = go.Scatter(\n",
    "    x = [0],\n",
    "    y = [0],\n",
    "    name='Post Storm Profile'\n",
    ")\n",
    "trace3 = go.Scatter(\n",
    "    x = [0],\n",
    "    y = [0],\n",
    "    name='Pre-storm dune crest',\n",
    "    mode = 'markers',\n",
    "    marker = dict(\n",
    "          color = 'rgb(17, 157, 255)',\n",
    "          size = 20,\n",
    "        ),\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 = 'rgb(231, 99, 250)',\n",
    "          size = 20,\n",
    "         ),\n",
    ")\n",
    "\n",
    "forecast_traces = []\n",
    "for forecast in impacts['forecasted']:\n",
    "    forecast_traces.append(go.Scatter(\n",
    "        x = [0],\n",
    "        y = [0],\n",
    "        name = 'Peak R_high: {}'.format(forecast)\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",
    "    ),\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",
    ")\n",
    "\n",
    "g_profiles = go.FigureWidget(data=[trace1, trace2, trace3, trace4]+forecast_traces,\n",
    "                    layout=layout)\n",
    "\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(\n",
    "            size=10\n",
    "        ),\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(\n",
    "            lat=-33.7,\n",
    "            lon=151.3\n",
    "        ),\n",
    "        pitch=0,\n",
    "        zoom=12,\n",
    "        style='satellite-streets'\n",
    "    ),\n",
    ")\n",
    "\n",
    "fig = dict(data=data, layout=layout)\n",
    "g_map = go.FigureWidget(data=data,layout=layout)\n",
    "\n",
    "\n",
    "# Add panel for time series\n",
    "\n",
    "trace_Hs0 = go.Scatter(\n",
    "    x = [0,1],\n",
    "    y = [0,1],\n",
    "    name='Hs0'\n",
    ")\n",
    "trace_Tp = go.Scatter(\n",
    "    x = [0,2],\n",
    "    y = [0,2],\n",
    "    name='Tp',\n",
    "    yaxis='y2'\n",
    ")\n",
    "\n",
    "forecast_traces = []\n",
    "for forecast in impacts['forecasted']:\n",
    "    forecast_traces.append(go.Scatter(\n",
    "        x = [0],\n",
    "        y = [0],\n",
    "        name = 'Beta: {}'.format(forecast),\n",
    "        yaxis='y3'\n",
    "    ))\n",
    "\n",
    "    \n",
    "data=[trace_Hs0, trace_Tp] + forecast_traces\n",
    "\n",
    "layout = go.Layout(\n",
    "    title = 'Hydro/Morpho Parameters',\n",
    "    height=200,\n",
    "    margin=dict(t=50,b=50,l=50,r=50),\n",
    "    xaxis=dict(\n",
    "        title='time',\n",
    "        domain=[0.0, 0.9],\n",
    "        zeroline=False,\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title = 'Hs0 (m)',\n",
    "    ),\n",
    "    yaxis2=dict(\n",
    "        title='Tp (s)',\n",
    "        overlaying='y',\n",
    "        side='right'\n",
    "    ),\n",
    "    yaxis3=dict(\n",
    "        title='beta (-)',\n",
    "        overlaying='y',\n",
    "        side='right',\n",
    "        position=0.97\n",
    "    )\n",
    ")\n",
    "\n",
    "g_params = go.FigureWidget(data=data, layout=layout)\n",
    "\n",
    "\n",
    "# Add panel for water level\n",
    "trace_dune_crest = go.Scatter(\n",
    "    x = [0,3],\n",
    "    y = [0,3],\n",
    "    name='Dune Crest',\n",
    "    line = dict(\n",
    "        color = ('rgb(214, 117, 14)'),\n",
    "        width = 2,\n",
    "        dash = 'dot')\n",
    ")\n",
    "trace_dune_toe = go.Scatter(\n",
    "    x = [0,3],\n",
    "    y = [0,3],\n",
    "    name='Dune Toe',\n",
    "    line = dict(\n",
    "        color = ('rgb(142, 77, 8)'),\n",
    "        width = 2,\n",
    "        dash = 'dash')\n",
    ")\n",
    "trace_tide = go.Scatter(\n",
    "    x = [0,4],\n",
    "    y = [0,4],\n",
    "    name='Tide+Surge WL',\n",
    "    line = dict(\n",
    "        color = ('rgb(8,51,137)'),\n",
    "        width = 2,\n",
    "        dash = 'dot')\n",
    ")\n",
    "\n",
    "forecast_traces = []\n",
    "for forecast in twls['forecasted']:\n",
    "    forecast_traces.append(go.Scatter(\n",
    "        x = [0],\n",
    "        y = [0],\n",
    "        name = 'R_high: {}'.format(forecast),\n",
    "    ))\n",
    "    \n",
    "data=[trace_dune_crest, trace_dune_toe,trace_tide] + forecast_traces\n",
    "\n",
    "layout = go.Layout(\n",
    "    title = 'Water Level & Dune Toe/Crest',\n",
    "    height=200,\n",
    "    margin=dict(t=50,b=50,l=50,r=50),\n",
    "    xaxis=dict(\n",
    "        title='time',\n",
    "        domain=[0.0, 0.95],\n",
    "        zeroline=False,\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title = 'Water Level (m)',\n",
    "    ),\n",
    ")\n",
    "\n",
    "g_twls = go.FigureWidget(data=data, layout=layout)\n",
    "\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",
    "    site_features = df_profile_features.query('site_id == \"{}\"'.format(site_id))\n",
    "    dune_crest_x = site_features.dune_crest_x\n",
    "    dune_crest_z = site_features.dune_crest_z\n",
    "    dune_toe_x = site_features.dune_toe_x\n",
    "    dune_toe_z = 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 = dune_crest_x\n",
    "        g_profiles.data[2].y = dune_crest_z\n",
    "        g_profiles.data[3].x = dune_toe_x\n",
    "        g_profiles.data[3].y = dune_toe_z\n",
    "        \n",
    "        for n, forecast in enumerate(impacts['forecasted']):\n",
    "            R_high = max(impacts['forecasted'][forecast].query(\"site_id=='{}'\".format(site_id)).R_high)\n",
    "            g_profiles.data[4+n].x=[200,400]\n",
    "            g_profiles.data[4+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('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",
    "    with g_params.batch_update():\n",
    "        g_params.data[0].x = times\n",
    "        g_params.data[0].y = Hs0s\n",
    "        g_params.data[1].x = times\n",
    "        g_params.data[1].y = Tps\n",
    "        \n",
    "        for n, forecast in enumerate(twls['forecasted']):\n",
    "            df_twl = twls['forecasted'][forecast].query(\"site_id=='{}'\".format(site_id))\n",
    "            times = df_twl.index.get_level_values('datetime').tolist()\n",
    "            beta = df_twl.beta.tolist()\n",
    "            g_params.data[2+n].x= times\n",
    "            g_params.data[2+n].y= beta\n",
    "\n",
    "\n",
    "    # Update water levels plot\n",
    "    df_tide_site = df_tides.query(\"site_id=='{}'\".format(site_id))\n",
    "    mask = (df_tide_site.index.get_level_values('datetime') >= min(times)) & (df_tide_site.index.get_level_values('datetime') <= max(times))\n",
    "    df_tide_site = df_tide_site.loc[mask]\n",
    "\n",
    "    with g_twls.batch_update():\n",
    "        g_twls.data[0].x = [min(times), max(times)]\n",
    "        g_twls.data[1].x = [min(times), max(times)]\n",
    "        g_twls.data[2].x = df_tide_site.index.get_level_values('datetime')\n",
    "        g_twls.data[0].y = dune_crest_z.tolist()[0], dune_crest_z.tolist()[0],\n",
    "        g_twls.data[1].y = dune_toe_z.tolist()[0], dune_toe_z.tolist()[0],\n",
    "        g_twls.data[2].y = df_tide_site.tide.tolist()\n",
    "        \n",
    "        for n, forecast in enumerate(twls['forecasted']):\n",
    "            df_twl = twls['forecasted'][forecast].query(\"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_twls.data[3+n].x= times\n",
    "            g_twls.data[3+n].y= R_high\n",
    "    \n",
    "    \n",
    "site_id_select.observe(update_profile, names=\"value\")\n",
    "        \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']] + [impacts['forecasted'][key] for key in impacts['forecasted']]\n",
    "    \n",
    "    for box, df in zip(selectboxes, dfs):\n",
    "        valid_site_ids = list(set(valid_site_ids).intersection(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",
    "# 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([filter_container,site_id_container,widgets.HBox([g_profiles,g_map]),g_twls,g_params])\n",
    "\n",
    "\n",
    "\n",
    "# For table\n",
    "# out = Output()\n",
    "# with out:\n",
    "#     display(df_waves.head(3))\n",
    "\n",
    "# widgets.VBox([filter_container,site_id_container, out])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-21T06:04:27.672922Z",
     "start_time": "2018-11-21T06:04:26.098269Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(max(impacts['forecasted']['foreshore_slope_sto06'].query(\"site_id=='{}'\".format('NARRA0018')).R_high))\n",
    "# print(max(impacts['forecasted']['mean_slope_sto06'].query(\"site_id=='{}'\".format('NARRA0018')).R_high))\n",
    "\n",
    "# df_twl = twls['forecasted']['foreshore_slope_sto06'].query(\"site_id=='{}'\".format('NARRA0018'))\n",
    "\n",
    "df_waves_site = df_waves.query(\"site_id=='{}'\".format(\"NARRA0016\"))\n",
    "times = df_waves_site.index.get_level_values('datetime').tolist()\n",
    "\n",
    "\n",
    "df_tides_site = df_tides.query(\"{} <= datetime <= {}\".format(min(times), max(times)))\n",
    "mask = (df_tides.index.get_level_values('datetime') >= min(times)) & (df_waves_site.index.get_level_values('datetime') <= max(times))"
   ]
  }
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