{
 "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": "2018-12-03T02:14:51.434299Z",
     "start_time": "2018-12-03T02:14:51.048281Z"
    }
   },
   "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": "2018-12-03T02:14:56.153167Z",
     "start_time": "2018-12-03T02:14:51.435303Z"
    },
    "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\n",
    "\n",
    "import ruptures as rpt"
   ]
  },
  {
   "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": "2018-12-03T02:15:19.403132Z",
     "start_time": "2018-12-03T02:14:56.154127Z"
    },
    "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\\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"
     ]
    },
    {
     "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": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-03T02:42:30.072305Z",
     "start_time": "2018-12-03T02:42:26.548191Z"
    },
    "code_folding": [
     408
    ],
    "hide_input": false,
    "scrolled": false
   },
   "outputs": [
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       "              'name': 'Post-storm dune toe',\n",
       "              'type': 'scatter',\n",
       "              'uid': 'efec8ac2-fedc-4ee0-9849-0e72846641ff',\n",
       "              'x': [0],\n",
       "              'y': [0]},\n",
       "             {'line': {'color': 'rgb(44,127,184)', 'width': 4},\n",
       "              'mode': 'lines',\n",
       "              'name': 'Peak R_high: foreshore_slope_sto06',\n",
       "              'type': 'scatter',\n",
       "              'uid': 'f67daafe-c7e5-4c97-a57a-6a24d115b4f6',\n",
       "              'x': [0],\n",
       "              'y': [0]},\n",
       "             {'line': {'color': 'rgb(127,205,187)', 'width': 4},\n",
       "              'mode': 'lines',\n",
       "              'name': 'Peak R_high: mean_slope_sto06',\n",
       "              'type': 'scatter',\n",
       "              'uid': '96dc2836-4d14-447f-ba9d-67f9901d88e4',\n",
       "              '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([-33.45779575, -33.4584035 , -33.45911131, ..., -33.44589876,\n",
       "                            -33.44665247, -33.44732817]),\n",
       "              'lon': array([151.44105489, 151.44024676, 151.43957804, ..., 151.44420973,\n",
       "                            151.44479881, 151.44550951]),\n",
       "              'marker': {'size': 10},\n",
       "              'mode': 'markers',\n",
       "              'text': array(['AVOCAn0001', 'AVOCAn0002', 'AVOCAn0003', ..., 'WAMBE0025', 'WAMBE0026',\n",
       "                             'WAMBE0027'], dtype='<U12'),\n",
       "              'type': 'scattermapbox',\n",
       "              'uid': '570c8855-f1c5-4e96-9aba-4b7d22a27a3e'},\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': 'ea15e404-eb95-4a60-84a4-b836dca7540a'}],\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': [{'name': 'Hs0',\n",
       "              'type': 'scatter',\n",
       "              'uid': 'c651ac70-6488-4092-8d05-94711360d983',\n",
       "              'x': [0, 1],\n",
       "              'xaxis': 'x',\n",
       "              'y': [0, 1],\n",
       "              'yaxis': 'y3'},\n",
       "             {'name': 'Tp',\n",
       "              'type': 'scatter',\n",
       "              'uid': 'e41de225-8da2-43fe-a017-8d20cf87eabf',\n",
       "              'x': [0, 1],\n",
       "              'xaxis': 'x',\n",
       "              'y': [0, 1],\n",
       "              'yaxis': 'y4'},\n",
       "             {'line': {'color': 'rgb(214, 117, 14)', 'dash': 'dot', 'width': 2},\n",
       "              'mode': 'lines',\n",
       "              'name': 'Dune Crest',\n",
       "              'type': 'scatter',\n",
       "              'uid': 'cef80970-266f-4fda-95d9-086df66f1378',\n",
       "              'x': [0, 3],\n",
       "              'xaxis': 'x',\n",
       "              'y': [0, 3],\n",
       "              'yaxis': 'y'},\n",
       "             {'line': {'color': 'rgb(142, 77, 8)', 'dash': 'dash', 'width': 2},\n",
       "              'mode': 'lines',\n",
       "              'name': 'Dune Toe',\n",
       "              'type': 'scatter',\n",
       "              'uid': '862e890d-df27-495a-9b35-9088bceb63a7',\n",
       "              'x': [0, 3],\n",
       "              'xaxis': 'x',\n",
       "              'y': [0, 3],\n",
       "              'yaxis': 'y'},\n",
       "             {'line': {'color': 'rgb(8,51,137)', 'dash': 'dot', 'width': 2},\n",
       "              'name': 'Tide+Surge WL',\n",
       "              'type': 'scatter',\n",
       "              'uid': 'b192f254-90d8-4a9d-9f85-3893cf345d35',\n",
       "              'x': [0, 3],\n",
       "              'xaxis': 'x',\n",
       "              'y': [0, 3],\n",
       "              'yaxis': 'y'},\n",
       "             {'line': {'color': 'rgb(44,127,184)', 'width': 3},\n",
       "              'name': 'R_high: foreshore_slope_sto06',\n",
       "              'type': 'scatter',\n",
       "              'uid': 'd3bfad9e-ca15-4397-a45b-894faa84fa87',\n",
       "              'x': [0],\n",
       "              'xaxis': 'x',\n",
       "              'y': [0],\n",
       "              'yaxis': 'y'},\n",
       "             {'line': {'color': 'rgb(127,205,187)', 'width': 3},\n",
       "              'name': 'R_high: mean_slope_sto06',\n",
       "              'type': 'scatter',\n",
       "              'uid': 'd775dc35-f973-49fe-9b00-a5d31e78a3f1',\n",
       "              'x': [0],\n",
       "              'xaxis': 'x',\n",
       "              'y': [0],\n",
       "              'yaxis': 'y'},\n",
       "             {'line': {'color': 'rgb(44,127,184)', 'width': 3},\n",
       "              'name': 'Beta: foreshore_slope_sto06',\n",
       "              'type': 'scatter',\n",
       "              'uid': '792ba8ba-6863-42b6-ac2d-0e5fd82b23f5',\n",
       "              'x': [0, 1],\n",
       "              'xaxis': 'x',\n",
       "              'y': [0, 1],\n",
       "              'yaxis': 'y2'},\n",
       "             {'line': {'color': 'rgb(127,205,187)', 'width': 3},\n",
       "              'name': 'Beta: mean_slope_sto06',\n",
       "              'type': 'scatter',\n",
       "              'uid': '85b18217-d1e6-43d2-ac74-9282f4e7c139',\n",
       "              'x': [0, 1],\n",
       "              'xaxis': 'x',\n",
       "              'y': [0, 1],\n",
       "              'yaxis': 'y2'}],\n",
       "    'layout': {'height': 400,\n",
       "               'legend': {'font': {'size': 10}},\n",
       "               'margin': {'b': 100, 'l': 50, 'r': 20, 't': 20},\n",
       "               'xaxis': {'anchor': 'y3', 'domain': [0.0, 1.0], 'title': 'datetime'},\n",
       "               'yaxis': {'anchor': 'free', 'domain': [0.7333333333333333, 1.0], 'position': 0.0, 'title': 'z (mAHD)'},\n",
       "               'yaxis2': {'anchor': 'free',\n",
       "                          'domain': [0.36666666666666664, 0.6333333333333333],\n",
       "                          'position': 0.0,\n",
       "                          'title': 'beta (-)'},\n",
       "               'yaxis3': {'anchor': 'x', 'domain': [0.0, 0.26666666666666666], 'title': 'Hs0 (m)'},\n",
       "               'yaxis4': {'overlaying': 'y3', 'side': 'right', 'title': 'Tp (s)'}}\n",
       "}), HBox(children=(VBox(children=(HTML(value='observed'), Output())), VBox(children=(HTML(value='foreshore_slope_sto06'), Output())), VBox(children=(HTML(value='mean_slope_sto06'), Output()))))))"
      ]
     },
     "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": "2018-12-03T02:15:25.538827Z",
     "start_time": "2018-12-03T02:15:24.506209Z"
    },
    "code_folding": [],
    "hide_input": false,
    "scrolled": false
   },
   "outputs": [
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       "                                'text': '1.6',\n",
       "                                'x': 'overwash',\n",
       "                                'xref': 'x',\n",
       "                                'y': 'collision',\n",
       "                                'yref': 'y'},\n",
       "                               {'font': {'color': '#FFFFFF'},\n",
       "                                'showarrow': False,\n",
       "                                'text': '0.21',\n",
       "                                'x': 'inundation',\n",
       "                                'xref': 'x',\n",
       "                                'y': 'collision',\n",
       "                                'yref': 'y'},\n",
       "                               {'font': {'color': '#FFFFFF'},\n",
       "                                'showarrow': False,\n",
       "                                'text': '0.6',\n",
       "                                'x': 'swash',\n",
       "                                'xref': 'x',\n",
       "                                'y': 'swash',\n",
       "                                'yref': 'y'},\n",
       "                               {'font': {'color': '#FFFFFF'},\n",
       "                                'showarrow': False,\n",
       "                                'text': '0.4',\n",
       "                                'x': 'collision',\n",
       "                                'xref': 'x',\n",
       "                                'y': 'swash',\n",
       "                                'yref': 'y'},\n",
       "                               {'font': {'color': '#FFFFFF'},\n",
       "                                'showarrow': False,\n",
       "                                'text': '0.2',\n",
       "                                'x': 'overwash',\n",
       "                                'xref': 'x',\n",
       "                                'y': 'swash',\n",
       "                                'yref': 'y'},\n",
       "                               {'font': {'color': '#FFFFFF'},\n",
       "                                'showarrow': False,\n",
       "                                'text': '3',\n",
       "                                'x': 'inundation',\n",
       "                                'xref': 'x',\n",
       "                                'y': 'swash',\n",
       "                                'yref': 'y'}],\n",
       "               'height': 300,\n",
       "               'margin': {'b': 40, 'l': 100, 'pad': 0, 'r': 100, 't': 40},\n",
       "               'xaxis': {'dtick': 1, 'gridcolor': 'rgb(0, 0, 0)', 'side': 'top', 'ticks': '', 'title': 'Predicted'},\n",
       "               'yaxis': {'dtick': 1, 'ticks': '', 'ticksuffix': '  ', 'title': 'Observed'}}\n",
       "})))))))"
      ]
     },
     "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": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-03T02:15:40.575569Z",
     "start_time": "2018-12-03T02:15:25.540804Z"
    }
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "\nFor some reason plotly.py was unable to communicate with the\nlocal orca server process, even though the server process seems to be running.\n\nPlease review the process and connection information below:\n\norca status\n-----------\n    state: running\n    executable: C:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\orca.CMD\n    version: 1.1.1\n    port: 59780\n    pid: 9652\n    command: ['C:\\\\Users\\\\z5189959\\\\AppData\\\\Local\\\\Continuum\\\\anaconda3\\\\orca.CMD', 'serve', '-p', '59780', '--plotly', 'C:\\\\Users\\\\z5189959\\\\AppData\\\\Local\\\\Continuum\\\\anaconda3\\\\lib\\\\site-packages\\\\plotly\\\\package_data\\\\plotly.min.js', '--graph-only', '--mathjax', 'https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js']\n    \n\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mConnectionRefusedError\u001b[0m                    Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    140\u001b[0m             conn = connection.create_connection(\n\u001b[1;32m--> 141\u001b[1;33m                 (self.host, self.port), self.timeout, **extra_kw)\n\u001b[0m\u001b[0;32m    142\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\urllib3\\util\\connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m     82\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 83\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     84\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\urllib3\\util\\connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m     72\u001b[0m                 \u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 73\u001b[1;33m             \u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msa\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     74\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0msock\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mConnectionRefusedError\u001b[0m: [WinError 10061] No connection could be made because the target machine actively refused it",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mNewConnectionError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    600\u001b[0m                                                   \u001b[0mbody\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 601\u001b[1;33m                                                   chunked=chunked)\n\u001b[0m\u001b[0;32m    602\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m    356\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 357\u001b[1;33m             \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mhttplib_request_kw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    358\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1238\u001b[0m         \u001b[1;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1239\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1240\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_send_request\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1284\u001b[0m             \u001b[0mbody\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'body'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1285\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1286\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mendheaders\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1233\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1234\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1235\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_send_output\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1025\u001b[0m         \u001b[1;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1026\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1027\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    963\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 964\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    965\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    165\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 166\u001b[1;33m         \u001b[0mconn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_new_conn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    167\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_prepare_conn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    149\u001b[0m             raise NewConnectionError(\n\u001b[1;32m--> 150\u001b[1;33m                 self, \"Failed to establish a new connection: %s\" % e)\n\u001b[0m\u001b[0;32m    151\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNewConnectionError\u001b[0m: <urllib3.connection.HTTPConnection object at 0x0000020C1E2B7E48>: Failed to establish a new connection: [WinError 10061] No connection could be made because the target machine actively refused it",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mMaxRetryError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    439\u001b[0m                     \u001b[0mretries\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 440\u001b[1;33m                     \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    441\u001b[0m                 )\n",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    638\u001b[0m             retries = retries.increment(method, url, error=e, _pool=self,\n\u001b[1;32m--> 639\u001b[1;33m                                         _stacktrace=sys.exc_info()[2])\n\u001b[0m\u001b[0;32m    640\u001b[0m             \u001b[0mretries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\urllib3\\util\\retry.py\u001b[0m in \u001b[0;36mincrement\u001b[1;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[0;32m    387\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mnew_retry\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_exhausted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 388\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mMaxRetryError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_pool\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mResponseError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcause\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    389\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMaxRetryError\u001b[0m: HTTPConnectionPool(host='localhost', port=59780): Max retries exceeded with url: / (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x0000020C1E2B7E48>: Failed to establish a new connection: [WinError 10061] No connection could be made because the target machine actively refused it',))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mConnectionError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\plotly\\io\\_orca.py\u001b[0m in \u001b[0;36mto_image\u001b[1;34m(fig, format, width, height, scale, validate)\u001b[0m\n\u001b[0;32m   1300\u001b[0m             \u001b[0mwidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mwidth\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1301\u001b[1;33m             height=height)\n\u001b[0m\u001b[0;32m   1302\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\retrying.py\u001b[0m in \u001b[0;36mwrapped_f\u001b[1;34m(*args, **kw)\u001b[0m\n\u001b[0;32m     48\u001b[0m             \u001b[1;32mdef\u001b[0m \u001b[0mwrapped_f\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 49\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mRetrying\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mdargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mdkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\retrying.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m    211\u001b[0m                     \u001b[1;31m# get() on an attempt with an exception should cause it to be raised, but raise just in case\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 212\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mattempt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    213\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\retrying.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, wrap_exception)\u001b[0m\n\u001b[0;32m    246\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 247\u001b[1;33m                 \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    248\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\six.py\u001b[0m in \u001b[0;36mreraise\u001b[1;34m(tp, value, tb)\u001b[0m\n\u001b[0;32m    692\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 693\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    694\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\retrying.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m    199\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 200\u001b[1;33m                 \u001b[0mattempt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAttempt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattempt_number\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    201\u001b[0m             \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\plotly\\io\\_orca.py\u001b[0m in \u001b[0;36mrequest_image_with_retrying\u001b[1;34m(**kwargs)\u001b[0m\n\u001b[0;32m   1207\u001b[0m     \u001b[0mjson_str\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrequest_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mplotly\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPlotlyJSONEncoder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1208\u001b[1;33m     \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpost\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mserver_url\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'/'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mjson_str\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1209\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mpost\u001b[1;34m(url, data, json, **kwargs)\u001b[0m\n\u001b[0;32m    111\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 112\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'post'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mjson\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    113\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     57\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 58\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     59\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m    507\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 508\u001b[1;33m         \u001b[0mresp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    509\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m    617\u001b[0m         \u001b[1;31m# Send the request\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 618\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    619\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    507\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 508\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    509\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mConnectionError\u001b[0m: HTTPConnectionPool(host='localhost', port=59780): Max retries exceeded with url: / (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x0000020C1E2B7E48>: Failed to establish a new connection: [WinError 10061] No connection could be made because the target machine actively refused it',))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-6b67b6212ab7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# To output to file\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mheatmaps\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mimg_bytes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite_image\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'fig1.png'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m600\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m400\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m# fig = g_profiles\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\plotly\\io\\_orca.py\u001b[0m in \u001b[0;36mwrite_image\u001b[1;34m(fig, file, format, scale, width, height, validate)\u001b[0m\n\u001b[0;32m   1486\u001b[0m                         \u001b[0mwidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mwidth\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1487\u001b[0m                         \u001b[0mheight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mheight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1488\u001b[1;33m                         validate=validate)\n\u001b[0m\u001b[0;32m   1489\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1490\u001b[0m     \u001b[1;31m# Open file\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\plotly\\io\\_orca.py\u001b[0m in \u001b[0;36mto_image\u001b[1;34m(fig, format, width, height, scale, validate)\u001b[0m\n\u001b[0;32m   1316\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1317\u001b[0m \u001b[1;33m{\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1318\u001b[1;33m \"\"\".format(info=status_str))\n\u001b[0m\u001b[0;32m   1319\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1320\u001b[0m             \u001b[1;31m# Reset the status so that if the user tries again, we'll try to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: \nFor some reason plotly.py was unable to communicate with the\nlocal orca server process, even though the server process seems to be running.\n\nPlease review the process and connection information below:\n\norca status\n-----------\n    state: running\n    executable: C:\\Users\\z5189959\\AppData\\Local\\Continuum\\anaconda3\\orca.CMD\n    version: 1.1.1\n    port: 59780\n    pid: 9652\n    command: ['C:\\\\Users\\\\z5189959\\\\AppData\\\\Local\\\\Continuum\\\\anaconda3\\\\orca.CMD', 'serve', '-p', '59780', '--plotly', 'C:\\\\Users\\\\z5189959\\\\AppData\\\\Local\\\\Continuum\\\\anaconda3\\\\lib\\\\site-packages\\\\plotly\\\\package_data\\\\plotly.min.js', '--graph-only', '--mathjax', 'https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js']\n    \n\n"
     ]
    }
   ],
   "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": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-03T02:15:40.576573Z",
     "start_time": "2018-12-03T02:14:51.251Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "site_id_temp = 'NINEMn0003'\n",
    "z_pre = np.array(df_profiles.query(\"site_id=='{}' & profile_type=='prestorm'\".format(site_id_temp)).z.tolist())\n",
    "z_post = np.array(df_profiles.query(\"site_id=='{}' & profile_type=='poststorm'\".format(site_id_temp)).z.tolist())\n",
    "\n",
    "z_diff = z_pre - z_post\n",
    "no_nan=[True if not np.isnan(x) else False for x in z_diff]\n",
    "\n",
    "\n",
    "# algo = rpt.Pelt(model=\"rbf\").fit(z_diff[no_nan])\n",
    "\n",
    "algo = rpt.Binseg(model=\"linear\").fit(signal)\n",
    "\n",
    "result = algo.predict(pen=100)\n",
    "\n",
    "result = [x for x in result if x<len(z_diff[no_nan])]\n",
    "\n",
    "\n",
    "x = np.array(df_profiles.query(\"site_id=='NARRA0018'\").index.get_level_values('x').unique().tolist())\n",
    "x[no_nan][result]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# x[result]\n",
    "\n",
    "\n",
    "# plt.show()"
   ]
  }
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