diff --git a/notebooks/01_exploration.ipynb b/notebooks/01_exploration.ipynb
index 7d3e2d5..0430e6f 100644
--- a/notebooks/01_exploration.ipynb
+++ b/notebooks/01_exploration.ipynb
@@ -17,13 +17,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-19T02:55:24.823837Z",
-     "start_time": "2018-12-19T02:55:16.373122Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Enable autoreloading of our modules. \n",
@@ -36,14 +31,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-19T02:55:24.844241Z",
-     "start_time": "2018-12-19T02:55:24.823837Z"
-    },
-    "scrolled": true
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from IPython.core.debugger import set_trace\n",
@@ -80,36 +69,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-19T02:57:41.855174Z",
-     "start_time": "2018-12-19T02:57:13.770371Z"
-    },
     "pixiedust": {
      "displayParams": {}
-    },
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Importing waves.csv\n",
-      "Importing tides.csv\n",
-      "Importing profiles.csv\n",
-      "Importing sites.csv\n",
-      "Importing profile_features_crest_toes.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"
-     ]
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "def df_from_csv(csv, index_col, data_folder='../data/interim'):\n",
     "    print('Importing {}'.format(csv))\n",
@@ -142,12 +108,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-11-27T23:02:57.631306Z",
-     "start_time": "2018-11-27T23:02:57.615263Z"
-    }
-   },
+   "metadata": {},
    "source": [
     "## Profile/timeseries dashboard"
    ]
@@ -163,33 +124,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-19T02:59:16.919983Z",
-     "start_time": "2018-12-19T02:59:15.659950Z"
-    },
     "code_folding": [
      408
     ],
     "hide_input": false
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a209da7c5b98416990efed34f38e82f0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(VBox(children=(HTML(value='<b>Filter by observed and predicted impacts:</b>'), HBox(children=(V…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Create widgets for filtering by observed and forecasted impacts\n",
     "filter_title = widgets.HTML(\n",
@@ -665,13 +607,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-18T21:44:39.877356Z",
-     "start_time": "2018-12-18T21:44:39.864319Z"
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "df_sites.site_no.to_csv('temp.csv')"
@@ -680,12 +617,7 @@
   {
    "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
+    "hide_input": true
    },
    "source": [
     "## Confusion matrix\n",
@@ -694,32 +626,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-19T03:23:53.913428Z",
-     "start_time": "2018-12-19T03:23:52.722678Z"
-    },
     "code_folding": [],
-    "hide_input": false,
-    "scrolled": false
+    "hide_input": false
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6693731b472f4a9987be65ae4d528530",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(VBox(children=(HTML(value='<b>Filter by beach:</b>'), SelectMultiple(index=(0, 1, 2, 3, 4, 5, 6…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Create colorscale\n",
     "rdylgr_cmap = matplotlib.cm.get_cmap('RdYlGn')\n",
@@ -823,12 +735,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-11T22:17:29.215527Z",
-     "start_time": "2018-12-11T22:16:56.023Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# To output to file\n",
@@ -841,12 +748,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-03T23:02:47.179180Z",
-     "start_time": "2018-12-03T23:02:46.367273Z"
-    }
-   },
+   "metadata": {},
    "source": [
     "## Identify sites with no results"
    ]
@@ -861,26 +763,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-13T02:09:10.914191Z",
-     "start_time": "2018-12-13T02:09:09.913622Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The following sites have no slope defined in the twl csv file:\n",
-      "['ENTRA0078', 'ENTRA0079', 'MANNING0109']\n",
-      "\n",
-      "The following sites have no R_high defined in the twl csv file:\n",
-      "['ENTRA0078', 'ENTRA0079', 'MANNING0109']\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df_twls = twls['forecasted']['mean_slope_sto06']\n",
     "\n",
@@ -904,1062 +789,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-13T02:09:14.785919Z",
-     "start_time": "2018-12-13T02:09:14.520986Z"
-    },
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>prestorm_swash_vol</th>\n",
-       "      <th>poststorm_swash_vol</th>\n",
-       "      <th>swash_vol_change</th>\n",
-       "      <th>swash_pct_change</th>\n",
-       "      <th>prestorm_dune_face_vol</th>\n",
-       "      <th>poststorm_dune_face_vol</th>\n",
-       "      <th>dune_face_vol_change</th>\n",
-       "      <th>dune_face_pct_change</th>\n",
-       "      <th>storm_regime</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>site_id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>AVOCAn0009</th>\n",
-       "      <td>4.5783</td>\n",
-       "      <td>0.1110</td>\n",
-       "      <td>4.4673</td>\n",
-       "      <td>97.5750</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>AVOCAs0001</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>AVOCAs0002</th>\n",
-       "      <td>97.9463</td>\n",
-       "      <td>26.6638</td>\n",
-       "      <td>71.2825</td>\n",
-       "      <td>72.7771</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>AVOCAs0003</th>\n",
-       "      <td>70.7306</td>\n",
-       "      <td>40.2020</td>\n",
-       "      <td>30.7232</td>\n",
-       "      <td>43.4369</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>AVOCAs0004</th>\n",
-       "      <td>98.2859</td>\n",
-       "      <td>45.4986</td>\n",
-       "      <td>52.6330</td>\n",
-       "      <td>53.5509</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>AVOCAs0005</th>\n",
-       "      <td>95.5841</td>\n",
-       "      <td>54.9753</td>\n",
-       "      <td>40.5733</td>\n",
-       "      <td>42.4478</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>AVOCAs0006</th>\n",
-       "      <td>113.0441</td>\n",
-       "      <td>67.8912</td>\n",
-       "      <td>45.2582</td>\n",
-       "      <td>40.0359</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>AVOCAs0007</th>\n",
-       "      <td>65.3283</td>\n",
-       "      <td>44.2821</td>\n",
-       "      <td>21.4544</td>\n",
-       "      <td>32.8409</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>AVOCAs0008</th>\n",
-       "      <td>52.3933</td>\n",
-       "      <td>45.2243</td>\n",
-       "      <td>7.1728</td>\n",
-       "      <td>13.6904</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BILG0001</th>\n",
-       "      <td>20.3405</td>\n",
-       "      <td>7.6207</td>\n",
-       "      <td>12.7198</td>\n",
-       "      <td>62.5344</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BILG0002</th>\n",
-       "      <td>156.4205</td>\n",
-       "      <td>98.1716</td>\n",
-       "      <td>58.1659</td>\n",
-       "      <td>37.1856</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BOAT0001</th>\n",
-       "      <td>23.8361</td>\n",
-       "      <td>23.6865</td>\n",
-       "      <td>-0.0926</td>\n",
-       "      <td>-0.3885</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BOAT0002</th>\n",
-       "      <td>38.8398</td>\n",
-       "      <td>14.0819</td>\n",
-       "      <td>24.7579</td>\n",
-       "      <td>63.7436</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BOAT0003</th>\n",
-       "      <td>73.6809</td>\n",
-       "      <td>17.8545</td>\n",
-       "      <td>55.8264</td>\n",
-       "      <td>75.7678</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BOAT0004</th>\n",
-       "      <td>73.1954</td>\n",
-       "      <td>23.1583</td>\n",
-       "      <td>50.0372</td>\n",
-       "      <td>68.3610</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BOAT0005</th>\n",
-       "      <td>53.5122</td>\n",
-       "      <td>22.4537</td>\n",
-       "      <td>31.0585</td>\n",
-       "      <td>58.0400</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BOOM0001</th>\n",
-       "      <td>236.4540</td>\n",
-       "      <td>218.4918</td>\n",
-       "      <td>20.8725</td>\n",
-       "      <td>8.8273</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CATHIE0024</th>\n",
-       "      <td>63.6452</td>\n",
-       "      <td>38.4261</td>\n",
-       "      <td>25.2191</td>\n",
-       "      <td>39.6245</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CATHIE0026</th>\n",
-       "      <td>75.1334</td>\n",
-       "      <td>43.7179</td>\n",
-       "      <td>31.0940</td>\n",
-       "      <td>41.3851</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CRESn0069</th>\n",
-       "      <td>37.5896</td>\n",
-       "      <td>8.3495</td>\n",
-       "      <td>29.2401</td>\n",
-       "      <td>77.7877</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DEEWHYn0008</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DEEWHYn0009</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DEEWHYs0005</th>\n",
-       "      <td>62.3514</td>\n",
-       "      <td>24.9797</td>\n",
-       "      <td>37.3716</td>\n",
-       "      <td>59.9372</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DEEWHYs0008</th>\n",
-       "      <td>1.0688</td>\n",
-       "      <td>1.3640</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DIAMONDn0023</th>\n",
-       "      <td>67.9416</td>\n",
-       "      <td>21.1812</td>\n",
-       "      <td>46.7603</td>\n",
-       "      <td>68.8244</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DIAMONDs0006</th>\n",
-       "      <td>74.9357</td>\n",
-       "      <td>42.4382</td>\n",
-       "      <td>32.2536</td>\n",
-       "      <td>43.0416</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DIAMONDs0007</th>\n",
-       "      <td>153.7639</td>\n",
-       "      <td>127.9469</td>\n",
-       "      <td>26.1595</td>\n",
-       "      <td>17.0128</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DUNBn0031</th>\n",
-       "      <td>36.5301</td>\n",
-       "      <td>6.3289</td>\n",
-       "      <td>30.2012</td>\n",
-       "      <td>82.6748</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DUNBn0055</th>\n",
-       "      <td>189.5283</td>\n",
-       "      <td>134.2760</td>\n",
-       "      <td>56.7139</td>\n",
-       "      <td>29.9237</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ELIZA0002</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>STOCNs0170</th>\n",
-       "      <td>198.2785</td>\n",
-       "      <td>216.6368</td>\n",
-       "      <td>-18.6067</td>\n",
-       "      <td>-9.3841</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>STOCNs0175</th>\n",
-       "      <td>136.2126</td>\n",
-       "      <td>114.1715</td>\n",
-       "      <td>22.4984</td>\n",
-       "      <td>16.5171</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>STOCNs0179</th>\n",
-       "      <td>67.7795</td>\n",
-       "      <td>45.3981</td>\n",
-       "      <td>22.3815</td>\n",
-       "      <td>33.0210</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>STOCNs0180</th>\n",
-       "      <td>166.0813</td>\n",
-       "      <td>149.5195</td>\n",
-       "      <td>15.3441</td>\n",
-       "      <td>9.2389</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>STOCNs0181</th>\n",
-       "      <td>90.1147</td>\n",
-       "      <td>98.9808</td>\n",
-       "      <td>-9.1107</td>\n",
-       "      <td>-10.1102</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>STOCNs0182</th>\n",
-       "      <td>67.8622</td>\n",
-       "      <td>86.0118</td>\n",
-       "      <td>-18.1671</td>\n",
-       "      <td>-26.7705</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>STOCNs0183</th>\n",
-       "      <td>125.9085</td>\n",
-       "      <td>137.7342</td>\n",
-       "      <td>-12.6233</td>\n",
-       "      <td>-10.0257</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>STOCNs0184</th>\n",
-       "      <td>146.6586</td>\n",
-       "      <td>123.5371</td>\n",
-       "      <td>23.2603</td>\n",
-       "      <td>15.8602</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>STOCNs0185</th>\n",
-       "      <td>141.5421</td>\n",
-       "      <td>142.7279</td>\n",
-       "      <td>-1.2619</td>\n",
-       "      <td>-0.8915</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>STOCNs0186</th>\n",
-       "      <td>115.9148</td>\n",
-       "      <td>123.1507</td>\n",
-       "      <td>-7.8392</td>\n",
-       "      <td>-6.7629</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>STOCNs0187</th>\n",
-       "      <td>126.5519</td>\n",
-       "      <td>147.4371</td>\n",
-       "      <td>-22.4452</td>\n",
-       "      <td>-17.7359</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>STOCNs0188</th>\n",
-       "      <td>345.5234</td>\n",
-       "      <td>353.8766</td>\n",
-       "      <td>-11.3322</td>\n",
-       "      <td>-3.2797</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>STOCNs0189</th>\n",
-       "      <td>171.6354</td>\n",
-       "      <td>134.9192</td>\n",
-       "      <td>35.8697</td>\n",
-       "      <td>20.8988</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>STOCNs0190</th>\n",
-       "      <td>151.4113</td>\n",
-       "      <td>116.6381</td>\n",
-       "      <td>35.0161</td>\n",
-       "      <td>23.1264</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>STOCS0014</th>\n",
-       "      <td>98.4991</td>\n",
-       "      <td>57.3495</td>\n",
-       "      <td>40.9006</td>\n",
-       "      <td>41.5238</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>STOCS0043</th>\n",
-       "      <td>36.4256</td>\n",
-       "      <td>11.7208</td>\n",
-       "      <td>24.7048</td>\n",
-       "      <td>67.8225</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WAMBE0005</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WAMBE0015</th>\n",
-       "      <td>56.2724</td>\n",
-       "      <td>16.0428</td>\n",
-       "      <td>40.2296</td>\n",
-       "      <td>71.4908</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WAMBE0016</th>\n",
-       "      <td>97.8849</td>\n",
-       "      <td>39.8432</td>\n",
-       "      <td>58.0417</td>\n",
-       "      <td>59.2958</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WAMBE0017</th>\n",
-       "      <td>36.5683</td>\n",
-       "      <td>8.5380</td>\n",
-       "      <td>28.0303</td>\n",
-       "      <td>76.6520</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WAMBE0018</th>\n",
-       "      <td>42.1423</td>\n",
-       "      <td>10.5498</td>\n",
-       "      <td>31.5925</td>\n",
-       "      <td>74.9662</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WAMBE0019</th>\n",
-       "      <td>39.6097</td>\n",
-       "      <td>9.2404</td>\n",
-       "      <td>30.3693</td>\n",
-       "      <td>76.6714</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WAMBE0020</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WAMBE0021</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WAMBE0022</th>\n",
-       "      <td>1.1034</td>\n",
-       "      <td>0.4478</td>\n",
-       "      <td>0.6556</td>\n",
-       "      <td>59.4166</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WAMBE0023</th>\n",
-       "      <td>4.4796</td>\n",
-       "      <td>0.3356</td>\n",
-       "      <td>4.1440</td>\n",
-       "      <td>92.5081</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WAMBE0024</th>\n",
-       "      <td>61.8478</td>\n",
-       "      <td>31.3007</td>\n",
-       "      <td>30.5470</td>\n",
-       "      <td>49.3907</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WAMBE0025</th>\n",
-       "      <td>45.9707</td>\n",
-       "      <td>14.6125</td>\n",
-       "      <td>31.3582</td>\n",
-       "      <td>68.2134</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WAMBE0026</th>\n",
-       "      <td>32.8591</td>\n",
-       "      <td>12.9479</td>\n",
-       "      <td>19.9112</td>\n",
-       "      <td>60.5957</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WAMBE0027</th>\n",
-       "      <td>26.4132</td>\n",
-       "      <td>18.7142</td>\n",
-       "      <td>7.6990</td>\n",
-       "      <td>29.1484</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>242 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              prestorm_swash_vol  poststorm_swash_vol  swash_vol_change  \\\n",
-       "site_id                                                                   \n",
-       "AVOCAn0009                4.5783               0.1110            4.4673   \n",
-       "AVOCAs0001                   NaN                  NaN               NaN   \n",
-       "AVOCAs0002               97.9463              26.6638           71.2825   \n",
-       "AVOCAs0003               70.7306              40.2020           30.7232   \n",
-       "AVOCAs0004               98.2859              45.4986           52.6330   \n",
-       "AVOCAs0005               95.5841              54.9753           40.5733   \n",
-       "AVOCAs0006              113.0441              67.8912           45.2582   \n",
-       "AVOCAs0007               65.3283              44.2821           21.4544   \n",
-       "AVOCAs0008               52.3933              45.2243            7.1728   \n",
-       "BILG0001                 20.3405               7.6207           12.7198   \n",
-       "BILG0002                156.4205              98.1716           58.1659   \n",
-       "BOAT0001                 23.8361              23.6865           -0.0926   \n",
-       "BOAT0002                 38.8398              14.0819           24.7579   \n",
-       "BOAT0003                 73.6809              17.8545           55.8264   \n",
-       "BOAT0004                 73.1954              23.1583           50.0372   \n",
-       "BOAT0005                 53.5122              22.4537           31.0585   \n",
-       "BOOM0001                236.4540             218.4918           20.8725   \n",
-       "CATHIE0024               63.6452              38.4261           25.2191   \n",
-       "CATHIE0026               75.1334              43.7179           31.0940   \n",
-       "CRESn0069                37.5896               8.3495           29.2401   \n",
-       "DEEWHYn0008                  NaN                  NaN               NaN   \n",
-       "DEEWHYn0009                  NaN                  NaN               NaN   \n",
-       "DEEWHYs0005              62.3514              24.9797           37.3716   \n",
-       "DEEWHYs0008               1.0688               1.3640            0.0000   \n",
-       "DIAMONDn0023             67.9416              21.1812           46.7603   \n",
-       "DIAMONDs0006             74.9357              42.4382           32.2536   \n",
-       "DIAMONDs0007            153.7639             127.9469           26.1595   \n",
-       "DUNBn0031                36.5301               6.3289           30.2012   \n",
-       "DUNBn0055               189.5283             134.2760           56.7139   \n",
-       "ELIZA0002                    NaN                  NaN               NaN   \n",
-       "...                          ...                  ...               ...   \n",
-       "STOCNs0170              198.2785             216.6368          -18.6067   \n",
-       "STOCNs0175              136.2126             114.1715           22.4984   \n",
-       "STOCNs0179               67.7795              45.3981           22.3815   \n",
-       "STOCNs0180              166.0813             149.5195           15.3441   \n",
-       "STOCNs0181               90.1147              98.9808           -9.1107   \n",
-       "STOCNs0182               67.8622              86.0118          -18.1671   \n",
-       "STOCNs0183              125.9085             137.7342          -12.6233   \n",
-       "STOCNs0184              146.6586             123.5371           23.2603   \n",
-       "STOCNs0185              141.5421             142.7279           -1.2619   \n",
-       "STOCNs0186              115.9148             123.1507           -7.8392   \n",
-       "STOCNs0187              126.5519             147.4371          -22.4452   \n",
-       "STOCNs0188              345.5234             353.8766          -11.3322   \n",
-       "STOCNs0189              171.6354             134.9192           35.8697   \n",
-       "STOCNs0190              151.4113             116.6381           35.0161   \n",
-       "STOCS0014                98.4991              57.3495           40.9006   \n",
-       "STOCS0043                36.4256              11.7208           24.7048   \n",
-       "WAMBE0005                    NaN                  NaN               NaN   \n",
-       "WAMBE0015                56.2724              16.0428           40.2296   \n",
-       "WAMBE0016                97.8849              39.8432           58.0417   \n",
-       "WAMBE0017                36.5683               8.5380           28.0303   \n",
-       "WAMBE0018                42.1423              10.5498           31.5925   \n",
-       "WAMBE0019                39.6097               9.2404           30.3693   \n",
-       "WAMBE0020                    NaN                  NaN               NaN   \n",
-       "WAMBE0021                    NaN                  NaN               NaN   \n",
-       "WAMBE0022                 1.1034               0.4478            0.6556   \n",
-       "WAMBE0023                 4.4796               0.3356            4.1440   \n",
-       "WAMBE0024                61.8478              31.3007           30.5470   \n",
-       "WAMBE0025                45.9707              14.6125           31.3582   \n",
-       "WAMBE0026                32.8591              12.9479           19.9112   \n",
-       "WAMBE0027                26.4132              18.7142            7.6990   \n",
-       "\n",
-       "              swash_pct_change  prestorm_dune_face_vol  \\\n",
-       "site_id                                                  \n",
-       "AVOCAn0009             97.5750                     NaN   \n",
-       "AVOCAs0001                 NaN                     NaN   \n",
-       "AVOCAs0002             72.7771                     NaN   \n",
-       "AVOCAs0003             43.4369                     NaN   \n",
-       "AVOCAs0004             53.5509                     NaN   \n",
-       "AVOCAs0005             42.4478                     NaN   \n",
-       "AVOCAs0006             40.0359                     NaN   \n",
-       "AVOCAs0007             32.8409                     NaN   \n",
-       "AVOCAs0008             13.6904                     NaN   \n",
-       "BILG0001               62.5344                     NaN   \n",
-       "BILG0002               37.1856                     NaN   \n",
-       "BOAT0001               -0.3885                     NaN   \n",
-       "BOAT0002               63.7436                     NaN   \n",
-       "BOAT0003               75.7678                     NaN   \n",
-       "BOAT0004               68.3610                     NaN   \n",
-       "BOAT0005               58.0400                     NaN   \n",
-       "BOOM0001                8.8273                     NaN   \n",
-       "CATHIE0024             39.6245                     0.0   \n",
-       "CATHIE0026             41.3851                     NaN   \n",
-       "CRESn0069              77.7877                     NaN   \n",
-       "DEEWHYn0008                NaN                     NaN   \n",
-       "DEEWHYn0009                NaN                     NaN   \n",
-       "DEEWHYs0005            59.9372                     NaN   \n",
-       "DEEWHYs0008             0.0000                     NaN   \n",
-       "DIAMONDn0023           68.8244                     NaN   \n",
-       "DIAMONDs0006           43.0416                     NaN   \n",
-       "DIAMONDs0007           17.0128                     NaN   \n",
-       "DUNBn0031              82.6748                     NaN   \n",
-       "DUNBn0055              29.9237                     NaN   \n",
-       "ELIZA0002                  NaN                     NaN   \n",
-       "...                        ...                     ...   \n",
-       "STOCNs0170             -9.3841                     NaN   \n",
-       "STOCNs0175             16.5171                     NaN   \n",
-       "STOCNs0179             33.0210                     NaN   \n",
-       "STOCNs0180              9.2389                     NaN   \n",
-       "STOCNs0181            -10.1102                     NaN   \n",
-       "STOCNs0182            -26.7705                     NaN   \n",
-       "STOCNs0183            -10.0257                     NaN   \n",
-       "STOCNs0184             15.8602                     NaN   \n",
-       "STOCNs0185             -0.8915                     NaN   \n",
-       "STOCNs0186             -6.7629                     NaN   \n",
-       "STOCNs0187            -17.7359                     NaN   \n",
-       "STOCNs0188             -3.2797                     NaN   \n",
-       "STOCNs0189             20.8988                     NaN   \n",
-       "STOCNs0190             23.1264                     NaN   \n",
-       "STOCS0014              41.5238                     NaN   \n",
-       "STOCS0043              67.8225                     NaN   \n",
-       "WAMBE0005                  NaN                     NaN   \n",
-       "WAMBE0015              71.4908                     NaN   \n",
-       "WAMBE0016              59.2958                     NaN   \n",
-       "WAMBE0017              76.6520                     NaN   \n",
-       "WAMBE0018              74.9662                     NaN   \n",
-       "WAMBE0019              76.6714                     NaN   \n",
-       "WAMBE0020                  NaN                     NaN   \n",
-       "WAMBE0021                  NaN                     NaN   \n",
-       "WAMBE0022              59.4166                     NaN   \n",
-       "WAMBE0023              92.5081                     NaN   \n",
-       "WAMBE0024              49.3907                     NaN   \n",
-       "WAMBE0025              68.2134                     NaN   \n",
-       "WAMBE0026              60.5957                     NaN   \n",
-       "WAMBE0027              29.1484                     NaN   \n",
-       "\n",
-       "              poststorm_dune_face_vol  dune_face_vol_change  \\\n",
-       "site_id                                                       \n",
-       "AVOCAn0009                        NaN                   NaN   \n",
-       "AVOCAs0001                        NaN                   NaN   \n",
-       "AVOCAs0002                        NaN                   NaN   \n",
-       "AVOCAs0003                        NaN                   NaN   \n",
-       "AVOCAs0004                        NaN                   NaN   \n",
-       "AVOCAs0005                        NaN                   NaN   \n",
-       "AVOCAs0006                        NaN                   NaN   \n",
-       "AVOCAs0007                        NaN                   NaN   \n",
-       "AVOCAs0008                        NaN                   NaN   \n",
-       "BILG0001                          NaN                   NaN   \n",
-       "BILG0002                          NaN                   NaN   \n",
-       "BOAT0001                          NaN                   NaN   \n",
-       "BOAT0002                          NaN                   NaN   \n",
-       "BOAT0003                          NaN                   NaN   \n",
-       "BOAT0004                          NaN                   NaN   \n",
-       "BOAT0005                          NaN                   NaN   \n",
-       "BOOM0001                          NaN                   NaN   \n",
-       "CATHIE0024                        0.0                   0.0   \n",
-       "CATHIE0026                        NaN                   NaN   \n",
-       "CRESn0069                         NaN                   NaN   \n",
-       "DEEWHYn0008                       NaN                   NaN   \n",
-       "DEEWHYn0009                       NaN                   NaN   \n",
-       "DEEWHYs0005                       NaN                   NaN   \n",
-       "DEEWHYs0008                       NaN                   NaN   \n",
-       "DIAMONDn0023                      NaN                   NaN   \n",
-       "DIAMONDs0006                      NaN                   NaN   \n",
-       "DIAMONDs0007                      NaN                   NaN   \n",
-       "DUNBn0031                         NaN                   NaN   \n",
-       "DUNBn0055                         NaN                   NaN   \n",
-       "ELIZA0002                         NaN                   NaN   \n",
-       "...                               ...                   ...   \n",
-       "STOCNs0170                        NaN                   NaN   \n",
-       "STOCNs0175                        NaN                   NaN   \n",
-       "STOCNs0179                        NaN                   NaN   \n",
-       "STOCNs0180                        NaN                   NaN   \n",
-       "STOCNs0181                        NaN                   NaN   \n",
-       "STOCNs0182                        NaN                   NaN   \n",
-       "STOCNs0183                        NaN                   NaN   \n",
-       "STOCNs0184                        NaN                   NaN   \n",
-       "STOCNs0185                        NaN                   NaN   \n",
-       "STOCNs0186                        NaN                   NaN   \n",
-       "STOCNs0187                        NaN                   NaN   \n",
-       "STOCNs0188                        NaN                   NaN   \n",
-       "STOCNs0189                        NaN                   NaN   \n",
-       "STOCNs0190                        NaN                   NaN   \n",
-       "STOCS0014                         NaN                   NaN   \n",
-       "STOCS0043                         NaN                   NaN   \n",
-       "WAMBE0005                         NaN                   NaN   \n",
-       "WAMBE0015                         NaN                   NaN   \n",
-       "WAMBE0016                         NaN                   NaN   \n",
-       "WAMBE0017                         NaN                   NaN   \n",
-       "WAMBE0018                         NaN                   NaN   \n",
-       "WAMBE0019                         NaN                   NaN   \n",
-       "WAMBE0020                         NaN                   NaN   \n",
-       "WAMBE0021                         NaN                   NaN   \n",
-       "WAMBE0022                         NaN                   NaN   \n",
-       "WAMBE0023                         NaN                   NaN   \n",
-       "WAMBE0024                         NaN                   NaN   \n",
-       "WAMBE0025                         NaN                   NaN   \n",
-       "WAMBE0026                         NaN                   NaN   \n",
-       "WAMBE0027                         NaN                   NaN   \n",
-       "\n",
-       "              dune_face_pct_change storm_regime  \n",
-       "site_id                                          \n",
-       "AVOCAn0009                     NaN          NaN  \n",
-       "AVOCAs0001                     NaN          NaN  \n",
-       "AVOCAs0002                     NaN          NaN  \n",
-       "AVOCAs0003                     NaN          NaN  \n",
-       "AVOCAs0004                     NaN          NaN  \n",
-       "AVOCAs0005                     NaN          NaN  \n",
-       "AVOCAs0006                     NaN          NaN  \n",
-       "AVOCAs0007                     NaN          NaN  \n",
-       "AVOCAs0008                     NaN          NaN  \n",
-       "BILG0001                       NaN          NaN  \n",
-       "BILG0002                       NaN          NaN  \n",
-       "BOAT0001                       NaN          NaN  \n",
-       "BOAT0002                       NaN          NaN  \n",
-       "BOAT0003                       NaN          NaN  \n",
-       "BOAT0004                       NaN          NaN  \n",
-       "BOAT0005                       NaN          NaN  \n",
-       "BOOM0001                       NaN          NaN  \n",
-       "CATHIE0024                     NaN          NaN  \n",
-       "CATHIE0026                     NaN          NaN  \n",
-       "CRESn0069                      NaN          NaN  \n",
-       "DEEWHYn0008                    NaN          NaN  \n",
-       "DEEWHYn0009                    NaN          NaN  \n",
-       "DEEWHYs0005                    NaN          NaN  \n",
-       "DEEWHYs0008                    NaN          NaN  \n",
-       "DIAMONDn0023                   NaN          NaN  \n",
-       "DIAMONDs0006                   NaN          NaN  \n",
-       "DIAMONDs0007                   NaN          NaN  \n",
-       "DUNBn0031                      NaN          NaN  \n",
-       "DUNBn0055                      NaN          NaN  \n",
-       "ELIZA0002                      NaN          NaN  \n",
-       "...                            ...          ...  \n",
-       "STOCNs0170                     NaN          NaN  \n",
-       "STOCNs0175                     NaN          NaN  \n",
-       "STOCNs0179                     NaN          NaN  \n",
-       "STOCNs0180                     NaN          NaN  \n",
-       "STOCNs0181                     NaN          NaN  \n",
-       "STOCNs0182                     NaN          NaN  \n",
-       "STOCNs0183                     NaN          NaN  \n",
-       "STOCNs0184                     NaN          NaN  \n",
-       "STOCNs0185                     NaN          NaN  \n",
-       "STOCNs0186                     NaN          NaN  \n",
-       "STOCNs0187                     NaN          NaN  \n",
-       "STOCNs0188                     NaN          NaN  \n",
-       "STOCNs0189                     NaN          NaN  \n",
-       "STOCNs0190                     NaN          NaN  \n",
-       "STOCS0014                      NaN          NaN  \n",
-       "STOCS0043                      NaN          NaN  \n",
-       "WAMBE0005                      NaN          NaN  \n",
-       "WAMBE0015                      NaN          NaN  \n",
-       "WAMBE0016                      NaN          NaN  \n",
-       "WAMBE0017                      NaN          NaN  \n",
-       "WAMBE0018                      NaN          NaN  \n",
-       "WAMBE0019                      NaN          NaN  \n",
-       "WAMBE0020                      NaN          NaN  \n",
-       "WAMBE0021                      NaN          NaN  \n",
-       "WAMBE0022                      NaN          NaN  \n",
-       "WAMBE0023                      NaN          NaN  \n",
-       "WAMBE0024                      NaN          NaN  \n",
-       "WAMBE0025                      NaN          NaN  \n",
-       "WAMBE0026                      NaN          NaN  \n",
-       "WAMBE0027                      NaN          NaN  \n",
-       "\n",
-       "[242 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df_impacts = impacts['observed']\n",
     "df_no_obs_impacts = df_impacts[df_impacts.storm_regime.isnull()]\n",
@@ -2000,253 +832,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-13T02:37:07.225965Z",
-     "start_time": "2018-12-13T02:37:07.213921Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "231 sites have no dune crests:\n",
-      "AVOCAn0009\n",
-      "AVOCAs0001\n",
-      "AVOCAs0002\n",
-      "AVOCAs0003\n",
-      "AVOCAs0004\n",
-      "AVOCAs0005\n",
-      "AVOCAs0006\n",
-      "AVOCAs0007\n",
-      "AVOCAs0008\n",
-      "BILG0001\n",
-      "BILG0002\n",
-      "BOAT0001\n",
-      "BOAT0002\n",
-      "BOAT0003\n",
-      "BOAT0004\n",
-      "BOAT0005\n",
-      "BOOM0001\n",
-      "CATHIE0026\n",
-      "CRESn0069\n",
-      "DEEWHYn0008\n",
-      "DEEWHYn0009\n",
-      "DEEWHYs0005\n",
-      "DEEWHYs0008\n",
-      "DIAMONDs0006\n",
-      "DIAMONDs0007\n",
-      "ENTRA0005\n",
-      "ENTRA0006\n",
-      "ENTRA0077\n",
-      "ENTRA0078\n",
-      "ENTRA0079\n",
-      "FOST0003\n",
-      "GRANTSn0004\n",
-      "GRANTSn0005\n",
-      "GRANTSn0006\n",
-      "GRANTSn0007\n",
-      "GRANTSn0008\n",
-      "GRANTSn0009\n",
-      "GRANTSn0021\n",
-      "GRANTSs0014\n",
-      "HARGs0003\n",
-      "HARGs0004\n",
-      "HARGs0005\n",
-      "HARR0056\n",
-      "LHOUSE0001\n",
-      "LHOUSE0002\n",
-      "LHOUSE0003\n",
-      "LHOUSE0004\n",
-      "LHOUSE0012\n",
-      "LHOUSE0013\n",
-      "LHOUSEs0015\n",
-      "MACM0008\n",
-      "MACM0012\n",
-      "MACM0013\n",
-      "MACM0014\n",
-      "MACM0015\n",
-      "MACM0016\n",
-      "MANNING0001\n",
-      "MANNING0002\n",
-      "MANNING0003\n",
-      "MANNING0004\n",
-      "MANNING0005\n",
-      "MANNING0101\n",
-      "MANNING0102\n",
-      "MANNING0103\n",
-      "MANNING0104\n",
-      "MANNING0105\n",
-      "MANNING0106\n",
-      "MANNING0107\n",
-      "MANNING0108\n",
-      "MANNING0109\n",
-      "MONA0001\n",
-      "MONA0002\n",
-      "MONA0003\n",
-      "MONA0014\n",
-      "MONA0015\n",
-      "MONA0016\n",
-      "MONA0017\n",
-      "MONA0018\n",
-      "MONA0019\n",
-      "MONA0020\n",
-      "MONA0021\n",
-      "NAMB0027\n",
-      "NAMB0041\n",
-      "NARRA0001\n",
-      "NARRA0028\n",
-      "NARRA0035\n",
-      "NINEMn0050\n",
-      "OLDBAR0035\n",
-      "PEARLn0001\n",
-      "PEARLn0002\n",
-      "PEARLn0003\n",
-      "PEARLn0004\n",
-      "PEARLs0003\n",
-      "PEARLs0004\n",
-      "PEARLs0005\n",
-      "STOCNn0012\n",
-      "STOCNn0013\n",
-      "STOCNn0014\n",
-      "STOCNn0015\n",
-      "STOCNn0016\n",
-      "STOCNn0017\n",
-      "STOCNn0018\n",
-      "STOCNn0019\n",
-      "STOCNn0020\n",
-      "STOCNn0021\n",
-      "STOCNn0022\n",
-      "STOCNn0023\n",
-      "STOCNn0024\n",
-      "STOCNn0025\n",
-      "STOCNn0026\n",
-      "STOCNn0027\n",
-      "STOCNn0028\n",
-      "STOCNn0029\n",
-      "STOCNn0030\n",
-      "STOCNn0031\n",
-      "STOCNn0032\n",
-      "STOCNn0033\n",
-      "STOCNn0034\n",
-      "STOCNn0035\n",
-      "STOCNn0036\n",
-      "STOCNn0037\n",
-      "STOCNn0038\n",
-      "STOCNn0039\n",
-      "STOCNn0044\n",
-      "STOCNn0059\n",
-      "STOCNn0062\n",
-      "STOCNn0063\n",
-      "STOCNn0064\n",
-      "STOCNn0065\n",
-      "STOCNs0022\n",
-      "STOCNs0025\n",
-      "STOCNs0026\n",
-      "STOCNs0031\n",
-      "STOCNs0045\n",
-      "STOCNs0048\n",
-      "STOCNs0049\n",
-      "STOCNs0053\n",
-      "STOCNs0055\n",
-      "STOCNs0056\n",
-      "STOCNs0057\n",
-      "STOCNs0058\n",
-      "STOCNs0059\n",
-      "STOCNs0060\n",
-      "STOCNs0061\n",
-      "STOCNs0062\n",
-      "STOCNs0073\n",
-      "STOCNs0079\n",
-      "STOCNs0088\n",
-      "STOCNs0089\n",
-      "STOCNs0090\n",
-      "STOCNs0091\n",
-      "STOCNs0092\n",
-      "STOCNs0093\n",
-      "STOCNs0094\n",
-      "STOCNs0095\n",
-      "STOCNs0096\n",
-      "STOCNs0097\n",
-      "STOCNs0098\n",
-      "STOCNs0099\n",
-      "STOCNs0100\n",
-      "STOCNs0101\n",
-      "STOCNs0102\n",
-      "STOCNs0103\n",
-      "STOCNs0104\n",
-      "STOCNs0105\n",
-      "STOCNs0106\n",
-      "STOCNs0107\n",
-      "STOCNs0108\n",
-      "STOCNs0109\n",
-      "STOCNs0110\n",
-      "STOCNs0111\n",
-      "STOCNs0112\n",
-      "STOCNs0113\n",
-      "STOCNs0114\n",
-      "STOCNs0115\n",
-      "STOCNs0116\n",
-      "STOCNs0117\n",
-      "STOCNs0118\n",
-      "STOCNs0119\n",
-      "STOCNs0120\n",
-      "STOCNs0121\n",
-      "STOCNs0122\n",
-      "STOCNs0123\n",
-      "STOCNs0124\n",
-      "STOCNs0125\n",
-      "STOCNs0126\n",
-      "STOCNs0127\n",
-      "STOCNs0128\n",
-      "STOCNs0137\n",
-      "STOCNs0141\n",
-      "STOCNs0144\n",
-      "STOCNs0150\n",
-      "STOCNs0155\n",
-      "STOCNs0156\n",
-      "STOCNs0157\n",
-      "STOCNs0158\n",
-      "STOCNs0159\n",
-      "STOCNs0160\n",
-      "STOCNs0167\n",
-      "STOCNs0168\n",
-      "STOCNs0169\n",
-      "STOCNs0170\n",
-      "STOCNs0175\n",
-      "STOCNs0179\n",
-      "STOCNs0180\n",
-      "STOCNs0181\n",
-      "STOCNs0182\n",
-      "STOCNs0183\n",
-      "STOCNs0184\n",
-      "STOCNs0185\n",
-      "STOCNs0186\n",
-      "STOCNs0187\n",
-      "STOCNs0188\n",
-      "STOCNs0189\n",
-      "STOCNs0190\n",
-      "STOCS0014\n",
-      "STOCS0043\n",
-      "WAMBE0005\n",
-      "WAMBE0015\n",
-      "WAMBE0016\n",
-      "WAMBE0017\n",
-      "WAMBE0018\n",
-      "WAMBE0019\n",
-      "WAMBE0020\n",
-      "WAMBE0021\n",
-      "WAMBE0022\n",
-      "WAMBE0023\n",
-      "WAMBE0024\n",
-      "WAMBE0025\n",
-      "WAMBE0026\n",
-      "WAMBE0027\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df_no_crests = df_profile_features_crest_toes.query('profile_type==\"prestorm\" & (dune_crest_x != dune_crest_x)')\n",
     "print('{} sites have no dune crests:'.format(len(df_no_crests)))\n",
diff --git a/notebooks/04_profile_picker.ipynb b/notebooks/04_profile_picker.ipynb
deleted file mode 100644
index ce31898..0000000
--- a/notebooks/04_profile_picker.ipynb
+++ /dev/null
@@ -1,743 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-05T00:54:50.235522Z",
-     "start_time": "2018-12-05T00:54:42.731587Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import os\n",
-    "import numpy.ma as ma\n",
-    "\n",
-    "import numpy\n",
-    "from pyearth import Earth\n",
-    "from matplotlib import pyplot\n",
-    "\n",
-    "np.random.seed(2017)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-05T00:54:54.936556Z",
-     "start_time": "2018-12-05T00:54:50.271465Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Importing profiles.csv\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\z5189959\\Desktop\\nsw-2016-storm-impact\\.venv\\lib\\site-packages\\numpy\\lib\\arraysetops.py:522: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
-      "  mask |= (ar1 == a)\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_profiles = df_from_csv('profiles.csv', index_col=[0, 1, 2])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-04T23:49:04.770025Z",
-     "start_time": "2018-12-04T23:49:04.265699Z"
-    }
-   },
-   "source": [
-    "## Try using pyearth"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-05T01:54:00.320555Z",
-     "start_time": "2018-12-05T01:53:58.905803Z"
-    },
-    "code_folding": [
-     5,
-     20,
-     31,
-     40
-    ],
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
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-     "metadata": {
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-     "output_type": "display_data"
-    },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[   {   'slope': 0.0008328773436106309,\n",
-      "        'type': 'berm',\n",
-      "        'x_end': 58.0,\n",
-      "        'x_start': 37.0,\n",
-      "        'z_end': 1.8572595562110041,\n",
-      "        'z_mean': 1.8943805115420846,\n",
-      "        'z_start': 1.910394386927169},\n",
-      "    {   'slope': -0.021220543877635122,\n",
-      "        'type': 'berm',\n",
-      "        'x_end': 138.0,\n",
-      "        'x_start': 58.0,\n",
-      "        'z_end': 3.5977328867797485,\n",
-      "        'z_mean': 2.7247048500781106,\n",
-      "        'z_start': 1.8468598845719795},\n",
-      "    {   'slope': -0.06381773295732189,\n",
-      "        'x_end': 142.0,\n",
-      "        'x_start': 138.0,\n",
-      "        'z_end': 3.8359998018824712,\n",
-      "        'z_mean': 3.741723519647647,\n",
-      "        'z_start': 3.6479493664613014},\n",
-      "    {   'slope': -0.016157748520191067,\n",
-      "        'x_end': 157.0,\n",
-      "        'x_start': 142.0,\n",
-      "        'z_end': 4.169920934351783,\n",
-      "        'z_mean': 4.013083138991948,\n",
-      "        'z_start': 3.8843177342116757},\n",
-      "    {   'slope': -0.1322171985012303,\n",
-      "        'x_end': 177.0,\n",
-      "        'x_start': 157.0,\n",
-      "        'z_end': 6.427354838550462,\n",
-      "        'z_mean': 5.4360212510513115,\n",
-      "        'z_start': 4.223480595415142},\n",
-      "    {   'slope': 0.34782985777944603,\n",
-      "        'x_end': 193.0,\n",
-      "        'x_start': 177.0,\n",
-      "        'z_end': 1.3683244048034364,\n",
-      "        'z_mean': 3.876447007928749,\n",
-      "        'z_start': 6.295019014555152},\n",
-      "    {   'slope': 0.06019958288565148,\n",
-      "        'type': 'foreshore',\n",
-      "        'x_end': 213.5,\n",
-      "        'x_start': 193.0,\n",
-      "        'z_end': 0.06618164722605435,\n",
-      "        'z_mean': 0.6722026822899186,\n",
-      "        'z_start': 1.1980470061124415}]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from scipy.signal import savgol_filter\n",
-    "import re\n",
-    "from scipy.stats import linregress\n",
-    "import warnings\n",
-    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
-    "\n",
-    "def get_breakpoints(model, min_distance=20):\n",
-    "    # Get breakpoints\n",
-    "    breakpoints = []\n",
-    "    for line in model.summary().split('\\n'):\n",
-    "        # Get unpruned lines\n",
-    "        if 'No' in line and 'None' not in line:\n",
-    "            # Get break points\n",
-    "            m = re.search(\"h\\(x0-(\\d*\\.?\\d+)\\)\", line)\n",
-    "            if m:\n",
-    "                breakpoints.append(float(m.groups()[0]))\n",
-    "            m = re.search(\"h\\((\\d*\\.?\\d+)-x0\\)\", line)\n",
-    "            if m:\n",
-    "                breakpoints.append(float(m.groups()[0]))\n",
-    "    return sorted(list(set(breakpoints)))\n",
-    "    \n",
-    "def get_segments(breakpoints, x_min, x_max):\n",
-    "    segments = []\n",
-    "    breakpoints = [x_min] + breakpoints + [x_max]\n",
-    "\n",
-    "    for x1, x2 in zip(breakpoints, breakpoints[1:]):\n",
-    "        segments.append({\n",
-    "            'x_start': x1,\n",
-    "            'x_end': x2\n",
-    "        })\n",
-    "    return segments            \n",
-    "\n",
-    "def get_segment_slopes(segments, x, z):\n",
-    "    for segment in segments:\n",
-    "        mask = ma.masked_where((segment['x_start'] < x) & (x < segment['x_end']),x ).mask\n",
-    "        segment['z_mean'] = np.mean(z[mask])\n",
-    "        segment['z_start'] = np.mean(z[mask][0])\n",
-    "        segment['z_end'] = np.mean(z[mask][-1])\n",
-    "        segment['slope'] = -linregress(x[mask], z[mask]).slope\n",
-    "    return segments\n",
-    "    \n",
-    "def classify_segments(segments, x,z):\n",
-    "    \n",
-    "    # Most seaward slope must be foreshore\n",
-    "    segments[-1]['type'] = 'foreshore'\n",
-    "    \n",
-    "    # Most landward slope must be land\n",
-    "    segments[0]['type'] = 'land'\n",
-    "    \n",
-    "    # Segments with really high slopes must be structures\n",
-    "    for seg in segments:\n",
-    "        if seg['slope'] > 2.0:\n",
-    "            seg['type'] = 'structure'\n",
-    "    \n",
-    "    # Segments with large change of slope and \n",
-    "    # Segment with max slope should be dune face\n",
-    "#     dune_face_idx = [n for n, seg in enumerate(segments) if seg['slope']==max(x['slope'] for x in segments)][0]\n",
-    "#     segments[dune_face_idx]['type'] = 'dune_face'\n",
-    "    \n",
-    "    # Pick out berms \n",
-    "    for seg in segments:\n",
-    "        if (-0.03 < seg['slope'] < 0.03  # berms should be relatively flat\n",
-    "            and 0 < seg['z_mean'] < 4  # berms should be located between 0-4 m AHD\n",
-    "            ):  # berms should be seaward of dune face\n",
-    "            seg['type'] = 'berm'\n",
-    "            \n",
-    "#     slope = None\n",
-    "#     for seg in reversed(segments):\n",
-    "#         if slope is None:\n",
-    "#             continue\n",
-    "#         elif slope - 0.03 < seg['slope'] < slope + 0.03:\n",
-    "#             seg['type'] = 'foreshore'\n",
-    "#         else:\n",
-    "#             break\n",
-    "    \n",
-    "    return segments\n",
-    "\n",
-    "def get_piecewise_linear_model(x,z):\n",
-    "    #Fit an Earth model\n",
-    "    model = Earth(penalty=3,thresh=0.0005)\n",
-    "    model.fit(x,z)\n",
-    "    return model\n",
-    "\n",
-    "def plot_profile_classification(site_id, profile_type):\n",
-    "    df_profile = df_profiles.query(\"site_id == '{}' and profile_type == '{}'\".format(site_id, profile_type))\n",
-    "    x = np.array(df_profile.index.get_level_values('x').tolist())\n",
-    "    z = np.array(df_profile.z.tolist())    \n",
-    "    \n",
-    "    nan_mask = ma.masked_invalid(z).mask\n",
-    "    x = x[~nan_mask]\n",
-    "    z_unfiltered = z[~nan_mask]\n",
-    "    z = savgol_filter(z_unfiltered, 51, 3)\n",
-    "    \n",
-    "    model = get_piecewise_linear_model(x,z)\n",
-    "    breakpoints = get_breakpoints(model)\n",
-    "    segments = get_segments(breakpoints, x_min=x.min(), x_max=x.max())\n",
-    "    segments = get_segment_slopes(segments, x=x, z=z)\n",
-    "#     segments = merge_similar_segments(segments)\n",
-    "    segments = classify_segments(segments, x=x, z=z)\n",
-    "    \n",
-    "    pyplot.figure()\n",
-    "    pyplot.plot(x,z_unfiltered, color='0.5',marker='.', alpha=.2, ms=10,linestyle=\"None\")\n",
-    "\n",
-    "    # Plot different segments\n",
-    "    foreshore_segments = [x for x in segments if x.get('type') == 'foreshore']\n",
-    "    for seg in foreshore_segments:\n",
-    "        pyplot.plot([seg['x_start'], seg['x_end']],\n",
-    "                   [seg['z_start'], seg['z_end']],\n",
-    "                   linewidth=4, \n",
-    "                    color='b')\n",
-    "\n",
-    "    land_segments = [x for x in segments if x.get('type') == 'land']\n",
-    "    for seg in land_segments:\n",
-    "        pyplot.plot([seg['x_start'], seg['x_end']],\n",
-    "                   [seg['z_start'], seg['z_end']],\n",
-    "                   linewidth=4, \n",
-    "                    color='g')\n",
-    "\n",
-    "    berm_segments = [x for x in segments if x.get('type') == 'berm']\n",
-    "    for seg in berm_segments:\n",
-    "        pyplot.plot([seg['x_start'], seg['x_end']],\n",
-    "                   [seg['z_start'], seg['z_end']],\n",
-    "                    linewidth=4, \n",
-    "                    color='y')\n",
-    "\n",
-    "    dune_face_segments = [x for x in segments if x.get('type') == 'dune_face']\n",
-    "    for seg in dune_face_segments:\n",
-    "        pyplot.plot([seg['x_start'], seg['x_end']],\n",
-    "                   [seg['z_start'], seg['z_end']],\n",
-    "                    linewidth=4, \n",
-    "                    color='r')\n",
-    "        \n",
-    "    structure_segments = [x for x in segments if x.get('type') == 'structure']\n",
-    "    for seg in structure_segments:\n",
-    "        pyplot.plot([seg['x_start'], seg['x_end']],\n",
-    "                   [seg['z_start'], seg['z_end']],\n",
-    "                    linewidth=4, \n",
-    "                    color='m')\n",
-    "        \n",
-    "    unclassified_segments = [x for x in segments if x.get('type') is None]\n",
-    "    for seg in unclassified_segments:\n",
-    "        pyplot.plot([seg['x_start'], seg['x_end']],\n",
-    "                   [seg['z_start'], seg['z_end']],\n",
-    "                   linewidth=4, \n",
-    "                    color='0.4')\n",
-    "\n",
-    "    pyplot.xlabel('x (m)')\n",
-    "    pyplot.ylabel('z (m AHD)')\n",
-    "    pyplot.title('{} profile at {}'.format(profile_type, site_id))\n",
-    "    pyplot.show()\n",
-    "\n",
-    "    import pprint\n",
-    "    pp = pprint.PrettyPrinter(indent=4)\n",
-    "    pp.pprint(segments)\n",
-    "\n",
-    "plot_profile_classification('NARRA0018', 'prestorm')\n",
-    "plot_profile_classification('NARRA0019', 'prestorm')\n",
-    "plot_profile_classification('CRESn0017', 'poststorm')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "heading_collapsed": true
-   },
-   "source": [
-    "## Try lmfit"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hidden": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-05T01:13:41.773484Z",
-     "start_time": "2018-12-05T01:13:27.230746Z"
-    },
-    "code_folding": [
-     0
-    ],
-    "hidden": true,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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xeBbtH+SlWwScO3eOVCqFy+WyZ7rHjh3D4XDQ2dmJz+cjnU7b+8NUVFTYM8WhoSFWrVpFeXn5wgyurAy2bi18XEprIgMDnHvqKbKnT6N7e/GNjeEdH6dyaorW06dxXdKhk/D5iFRUEKuqYramhpmGBhIbNlC3axcV27eTd7mu+0U0m81iWZa9EKx4szKdTtsz72KZxOv14nK5KC8vJ5/PU19fj8PhsEtpWmsSiQTd3d1MTEyQTCbJ5/MEAgGqqqrsMlgwGCSXy9krfePxuL15WzKZJBwOU1lZKW2NYtGt+EBPp9MkEgnq6+uJRCKYplkIQsvidY89RltPD8//+q/zwiteQZ3Xi2maNDQ0EAwGl3rol7lwpl5czAKF0Cq+Wzh8+DCRSIStW7fas8vi9gG5XM4uD8Tj8YUL9GuwtGY4keCEz4fv7rtJ79hBPp8nm81SU1NDMpEg1tuLZ3SUwMQEgclJyqanKZuepnp4mLajR3Hk8xd9zUxlJan6ehLNzbg7OnCuW4dz/XpUW1vhZu4FNe9ijX5yctIugeRyOZxOp/3i7nK5qKmpoa6uDtM0icViuN1uUqmUXTopzvAnJycZHR21Z/aGYZDL5XC5XLS2tmIYBlVVVZSXlzMyMsLhw4fx+XxkMhm8Xq/9rqo4BiEW04oP9Gw2a4d0KpVidHSUXDjMr37uczSfOsXxP/gDRl/1KlqAzZs3U1tbe9EpRMtNcaa+adMmYrEYmUyGRCKB2+22ywVTU1MMDAywfv16u5wwOztrd5UUZ4rFnSJvFcuy6O3t5fjx43Yger1eHA4HdXV1dHZ2UlFRQXd3N93d3URzOXsb4WLHkM7nCcbjlM/M4B8fpyIcpiIapWJmhuC+fYV9dC4IfK0Uubo6so2N5FtasJqbqfH5wO8nB2QDAfJz90UMw6Curo5NmzbR1tZmv9MZHx/n9OnT9Pf3E41G7a2MoTBLL9bZtda4XC4sy6KsrMzu1inWxrPZLKFQiEwmY99sLZb6ijdGhVhMKz7Qi4FW7Ouu93io/Yu/oPzMGV5473sZvPde3A4H7e3ttLe3L9sgv5BSijVr1hCJRDhy5Ig9ZqfTaZcBTpw4wezsLKZpkkwm7Vmn2+1m1apVdkngSv3VyWSSqakpu+5eLG9UVFRcd6+41prBwUEOHjxod5C45xZm1dTUUFFRQTAYxOFwsGHDBmKxGMPDw2SzWTKZjP1OQivFbFkZs2Vl6Lk94S88si+bTBKMxSifnqYyGiU0M0PZzAwV4TDlvb0Ew2HKtWY9hfq9ZRgkKitJ1NaiW1sJbd+OK5NBRaPQ2oqqrGR2dpZ0Om2Xs4r18eKLX7Hl1eVy2TdQN2/eTFVV1UVloEAgQEVFBdFolEQiARTOoV2zZg2rVq2SG6Ji0a34QHe73ZSXlzMxMYGanKTt934Pz9mzpB97jMZ776Xuap0fy1yxndHhcHDo0CE7iIs3fKemphgaGsLtdtvBVFdXR2NjI06nk1QqdVmg5/N5ent7OXr0KGNjYyQSCXtv9mAwSDAYZNWqVWzYsMHupy6GUvGUpeLNwnw+z+nTp5mensY0TUzTtG/gJhIJ1q5da89Qiwd9GIaB1ppwOAy8WP8u1p+LQVrcAEtrjeFyMVtZSTQU4vxcyBdn06ZpYqVSlMdi1MzO0mxZNKRSeIaHCc3M4Dx4EPWjH130fXU7nWysq2NVVRXRykomAwGmgkFmQiGilZXM+v2YDof9DiKXy7FmzRoaGhou+/vj8XhobW1lbGyMqakpEokEoVAI0zSZmpqSLhex6FZ8oBc5xsZoffe7cY2MEP7ylwm96U00rvB/TKZp0tnZCcCxY8eIRCJks1lcLhdaa/tUpWJnz+TkpN0bXWzha25utvctGRkZYXBwkGg0apeqALtHvDiL7uvro6Wlhba2Nqqrq5menubkyZOcP3+eRCJBIpGwV+EahoHP57M7SgBWr15NY2PjRatv6+rqKC8vp6Ojwy4PxeNxzp8/z9DQEJlMxn5RKM7eTdO0v+aFp0cBdhuhcrlINzZyXilmfD6mGhsLs+mmpsLjU6lCS+ZcZ07+zBmS3d04+/poPneOddHoRd/zrMNBtLKSeG0tqfp6yu+4g2qHAyOTKbRpVlZetC1ycQsBrTWrV6+272WMj48TDAbxer239O+IEBda8YGeTqdJ9PSw9j//Z4zpaaa++lVmOjvxXqHcsBKZpsmWLVsIBoM8//zzJBIJ+6ZnsY0OCjcHo3PhlEwmSafTDA8P093dbYdhcXVj8SZqsS2vOEsu1tyLrX/nzp3D7/fbu1QWb/RdWKJwOBz2i0zxXkZbW9tls1mlFF6vF6/XS21t4cRCrTUbNmzgyJEjnD59+qK6fzEYL5y5X6i4yKfYwVLcFTMYDFJeXv5i+Hs80NFR+ABMrUlfsFHb7NgYruFhyufKOKFIhOpYjMpwGN/evZhPPnnxDyQQuGxbZFd9PSGfj3htLVO5nL24yefzsWbNGpmli0Wz4gM939ND81vfipFOM/XNb5K94w6MRKKkWsYMw6ChoYHGxkb6+vrs5evwYrBduLd6sZZdPBWpGI7FxwN2gF/638XTlJLJJLlcjqmpKXw+30XvCi4skSil7BeJUChER0fHdc9KlVL4/X7uuusuOjo6mJyctGv609PT9PX12XuvXHjjsvg9KV67w+GwFwoVx3qt56yrq6OsrOyyrZSL7za8gQCOYBDD43mxB/9Kq2x/8hOYncULrJ77+tlAgGR9PfHqaqzVq8nefTeuhx8GmamLRbCiAv2ynRSPHsX7mteQA/q+8AXMDRtwWtYNn/25Eng8HtavX08ymbR3+yuWPYqzQafTaZctirPCC4O3+FGsT+dyObscU5xRF7eFLXZ3QKEmfmFIFl8I3G43Ho8H0zSprq5my5YtV9+e4BoMw6CiooKKigr7c62trWzcuPGy0C2+gEQiEc6fP08sFrOvr7y8nIaGhpfsLlFK4fP5ru9kqPJyuOOOwseltIa5k62Gf/ELpvbvp3xmBu/oKMHhYbwHD2J+4xuFx/7RH93It0SIm7JiAr24xHtiYgKtNd4DB2h973vJ+nz89OMfJ5bJ4Dt6lNraWlavXl1yLWNKKerr6ykvL78o5HK5nL0z47lz5zh8+DDZbNZ+0Su21MGLHUFutxuv18uqVatYtWoVU1NTDA8P2y18gB2SxfAvbkeQn2shdDqddpmjpqaGTZs20dzcvGA3nl8qdC88sCSZTOLz+aioqLip1abzGCRUVaGqqnCtXs3Ypk1MzG2sBhCLRnnDJz6B52tfQ0mgi0WwYgI9lUrR399PLpcjuGcPrR/5CLPl5fyf97+ffEUF7rl/xE6nk7KyspKsW75UyBUXEp05cwbDMIjH43Y5xuFwYJom5eXlBAIBOjs76ejosGfokUiEvr4+BgYGGBoasmf9+XyeVCpl92JXVFTQ0NBAVVUVpmnarXuLGqTX8b1YbIFAAJ/PZy9wKm7d0Ld9OxsffxxrZgbjgncgQtwKKybQZ2dniUQi1Dz7LJv+8i8J19by1Xe8A8PnI0RhP5fiDHSht7ldKRwOBzt27GDt2rXMzMzY7YXFnQWdTicej4e6ujqamprs2XSx5BEKhVi/fj179uzhzJkz+Hw+u7RiGAYbNmygra1t0cN7JfB4PDQ0NBCNRpmamrLLQ0ONjWwERn7wAxre9rYV1TorVp4VEeiWZRW2wf3Xf2XrP/0T483NfOOhh5h1uXDNvd0uboFbvKl2uzIMg1AoRCgUsj93vacIFW9S7tq1y95CwePx2OWV9vZ2acO7CqUUDQ0N9g6Nxb+DE6sLt0tjTz+N9cpX0lRspxTiFlj2ga61LuwT/s//zD2f/SxD7e18/53vJKUUzrmbfLOzszgcDgKBALW1tSVXP5+vG91ozOfzsWHDBiYmJuwbpjU1NSXTNXSreDweqqqq6O/vR2td2N+lpoZYfT2VZ88yMj0t30dxSy37QE8mk2QeeYQtn/0s57du5V/f+lYypolrrl0tn8/bO+ht27ZNZkALoLhgpnhi01KfDbpSKKVobm5mdHSUkZER+53NzLp11J04wcRcz74EurhVlnWga60ZHBjAPH2agV27eO4978GdTJKLx+3VicV2tc2bN19xQYu4OYu9fXCpKG5zADA6OlrYXnfjRtzPPIMxNoajrW2JRyhK2bIO9HQ6TWx2luH3vAfTMHBksziyWXv/llAohM/no7W19aKbfEIslQsXLg0ODnL+/Hlm5w4OMQ8cILp580V75AixkJZ1oGez2cKpMoFA4fdzhws3NTWxceNGvF4vfr9f/oGIZaXYUtnc3Ew8HoeuLrRpUtPXx3g4LAdJi1tmWU9pizXIuro6AoEAfr+fsrIyNmzYQFNTE9XV1dJCJ5YlrbVdSx+aniba3EzuuefsfXKEuBWWdaAX9+h2uVxUVVURCoXsMJcQF8tZOp0mEonYG4xNr11L2alTDA0O2nvWCLHQ5lVyUUr9MfAuQANHgN/RWqcWYmBzX5+amhrKysqk20KsKMXtid1uN+FwmOn2dtY8+SQ1sRixWOyKh48IMV83PUNXSjUCfwh0aa07ARN4cKEGdsHz4PF47JNjJMzFSlDcZtjtdlNWVkZ6bnOvhsFBnE4n2UsOzhZiIcy35OIAvEopB+ADhuc/JCFWPrfbTU1NDS6Xi1QqxURtLXmXi8Dx46TTaftwESEW0k0Hutb6PPA/gQFgBIhorX986eOUUg8rpfYppfZNTEzc/EiFWEGKi7O2b9/Opk2bMFwuZlpb8XR3Mzw8TH9//2WHdggxX/MpuVQAbwDWAKsAv1LqbZc+Tmv9qNa6S2vdVVNTc/MjFWKFKbYv1tfX4/F4yGzfTkVfH1Xl5QwNDdknTAmxUOZTcrkPOKe1ntBaZ4HvAC9bmGEJUTqSySQul4vo+vWY6TS6p4fZ2VmGh4el20UsqPkE+gCwWynlU4U7la8Gji/MsIQoHT6fj0wmw3hrKwCh06fJ5/PMzs7ah2EIsRDmU0PfA3wLOEChZdEAHl2gcQlRMoLBINXV1YwFAmT8fhyHDuFyuYjH44yOjsosXSyYefWha63/DPizBRqLECXJMAw6OjrIZrNEOzpoHBoi0dJCMpkkHo9LT7pYMMt6pagQpcLr9VJTU8Psxo34enuZGR6211RkMpklHp0oFRLoQiyC4i6Mic5OVD6P2d3N+Pg4fX19hMNhKbuIBSGBLsQiUUrZW+nW9PfjdrsL3S/RqGzYJRbEst4+V4hSksvl8KxZQ7qmhpreXhLV1eRyOSzLkpOMxIKQGboQi6S4v8vsxo0Ejh8nl8uRTqftA86FmC8JdCEWidvtprq6mpm1a/EMDjJ24gTRaBSHw4HL5Vrq4YkSIIEuxCIpnn9r3n03AHdks4V9XgxDOl3EgpBAF2IR5XI59I4dAJSdPIllWeTzeQl0sSAk0IVYRE6nk6zfT7q1Ff3CC0xOTjI2NkYkEpHWRTFvEuhCLCK3243P52Nm7VoCx44BUFNTQyKRkNZFMW8S6EIsomIdXXd14ZqcJBSPY1kWqVRKAl3MmwS6EIvM5XLZR9LFnn6a/v5+Tp8+LYdeiHmTQBdikbndbqytW7FMk8qzZ3G5XNTW1jI1NUUsFlvq4YkVTAJdiEWmlMITCjHb1kZVby/l5eW43W601sTj8aUenljBJNCFWAJ+v59YRwf+48eJzMwQDoeZnp4mmUxKt4u4aRLoQiyBYDCI1dWFc3YW18AA6XSahoYGezsAIW6GBLoQS8AwDEL33w9A29QUbW1t1NbWyiIjMS8S6EIsEefWrVheL/5jx5idnWVqakoWGYl5kUAXYom4/X4yW7bgPHjQ/pwsMhLzIYEuxBJRSqHuuovg2bOUeb2UlZXh9/sxDINsNrvUwxMrkAS6EEtI3XUXRjpN9uBBYrEYo6OjzMzM4HDI2TPixkmgC7GErDvvBCBw/PgSj0SUApkGCLGEsk1NOEMhavv6SAeDAGityWazeL3eJR6dWGkk0IVYQk6Xi+SWLTgOHSIajQKQTCYpKysjGAyilFriEYqVREouQiwht9tNfscOPGfOYCSTgHS6iJsngS7EElJK4XzZy1CWxaqxMerr66moqMA0Tel0ETfnpDn+AAAb30lEQVRMAl2IJWbs2gVA8Phx+7Boy7JwOp1LOSyxAs0r0JVSIaXUt5RSJ5RSx5VSdy/UwIS4XbhbWsg3NaH27yeRSBCPxwmFQrjd7qUemlhh5ntT9G+Bf9da/z9KKRfgW4AxCXFbUUph7NpF4OBBVF0dTqcTt9stN0TFDbvpGbpSqgz4ZeBfALTWGa11eKEGJsRtZedOjN5emJpa6pGIFWw+JZc2YAL4glLqoFLqc0op/6UPUko9rJTap5TaNzExMY+nE6I0aa0Jr1sHQOwnP2FgYICJiQnZoEvcsPkEugPYAXxWa70diAMfvvRBWutHtdZdWuuumpqaeTydEKUpnU4z1dqKVorAsWP4/X7C4bC0LYobNp9AHwKGtNZ75v77WxQCXghxA7LZLJSVkVu7Ftfhw4WaumzQJW7CTQe61noUGFRKdcx96tXAsQUZlRC3EafTiWVZZO+4A+ehQ2jLkrZFcVPm24f+B8BXlVLdwDbgr+Y/JCFuL263m1AoRGzTJsyJCdJnz0rborgp8wp0rfWhufr4Vq31G7XWMws1MCFuF0opampqCN13HwBNIyPU1NRI26K4YbJSVIhlQCmF+667wOnEdeiQhLm4KRLoQiwXbjfccQfs3bvUIxErlAS6EMuE1prcnXei9+0jlUhIH7q4YRLoQiwDWmsmJiaYbG1FRaOM/Md/yOIiccMk0IVYBtLpNOFwGHXXXQCUnzoli4vEDZNAF2IZyGazGIZBft06LL8f1+HDsrhI3DAJdCGWgeLiIm0YZLduxXXokCwuEjdMAl2IZaC4uCgej5PYvBlHTw8hn08WF4kbIoEuxDJQXFzU3NyMuXs3RjZLWX//Ug9LrDAS6EIsI7FYjMm2NgAiTzwhnS7ihkigC7FMFDtd3OvWka+qInDihHS6iBsigS7EMlHsdFGGQXbbNlyHDkmni7ghEuhCLBN2p4vWZLZtw3H6NDoWk04Xcd0k0IVYJtxuN+Xl5czMzDDd1oayLKr6+6XTRVw3CXQhlqH4pk0AOA8eXOKRiJXEsdQDEEIUpNNpIpEIoVCIXCBAtqkJa88eUqkUXq93qYcnVgCZoQuxTGSzWZRShMNhBgcHmVm3Dtfhw4yMjEjrorguEuhCLBNOp5NUKsXIyAizs7NMrVmDd3SU0SNHSCaTSz08sQJIoAuxTLjdblwuF5FIhFQqxfnGRgCyzz5Lb28vlmUt8QjFcieBLsQyoZSisrKSQCCAaZqkNm1CGwah06fp7+/n/PnzUnoR1ySBLsQyEggE8Pl8WJZFXCmijY3UDQxQVlbG9PS0rBoV1ySBLsQy4vF4aG1txel0Ypom4fXrqTxzBm1ZmKYpq0bFNUmgC7GMKKVoamqio6MD0zSZaW/HFYmgBgdJJBI4HNJpLK5OAl2IZcYwDFatWkVdXR3ZHTsAqDh9mmw2KzV0cU0S6EIsQ/l8nurqalw7dmA5nYTmAn1sbExCXVyVBLoQy5DT6SSTyZDI5Uh2dFB+8iQA4XCYVCq1xKMTy5UEuhDLkNvtxu/3k0qlmG5vx3v8OLm5rQFGR0dlli6uaN6BrpQylVIHlVL/thADEkIUbo7W19cTCoWIbdyII5mkZnoawzCYnZ2V9kVxRQsxQ/8j4PgCfB0hxAU8Hg/l5eWMr14NgLVnD5lMhlQqRSaTWeLRieVoXoGulGoCXgd8bmGGI4QoKq4cza5ZQ87vp2FwEL/fTzgclm0AxBXNd4b+/wEfAq76t0sp9bBSap9Sat/ExMQ8n06I24thGIQqK4msW4e7u5uRkRESiQRTU1NSRxeXuelAV0q9HhjXWu+/1uO01o9qrbu01l01NTU3+3RC3JZcLhdut5vw+vWUnzuHJ50mm80yNDQkOzCKy8xnhn4P8J+UUn3A14FXKaW+siCjEkIAhW4Xt9tN/9atGPk8lT/9KUopRkZGZAdGcZmbDnSt9Ue01k1a61bgQeBprfXbFmxkQgi7jp7Zvp3ZVavY9bnPsf0zn4HhYc6ePcvQ0JCUXoRNNoYQYpkLBAL4AgH+46/+ijVf/SodTz9Ny89/zonXvpaTuRzBYJBQKIRSaqmHKpaYWsxX966uLr1v375Fez4hSoHWmsHBQQ4ePMj09DTBiQl2fPvbtL3wAslAgNNvehP87u9S39JCMBjE4/FIuJcYpdR+rXXXSz5OAl2I5c+yLI4ePcqRI0fIZrMkk0kqe3vZ9d3vsvrMGSLV1Zx6+9vRv/mbrF6zhtraWgn1EnK9gS5L/4VYAQzDoL29nYaGBnsL3ZHGRv73ww/znXe/m4zLxc5PfYqOt7+d6W9/m2g0KrX125AEuhArhNfrpb29nUAggGEYKKVwud0MbNrEV/74j/m/b3kLxuQkG9/zHvQDDzD9059KqN9m5KaoECtE8fCLTCZDIpEgk8mQz+fJ5XKYpkn3tm2c2raNHc8+y84nnsD56leTefBBXJ/8JGpu+wBR2mSGLsQKYhgGbW1tdHV1UVtbSyAQwOl0ks/nsSwL5fVy4FWv4kt/8id033cfjm99Czo60B/8IMzMLPXwxS0mN0WFWIEsy2JoaIihoSFGRkaIxWL2PunJZBLTNNFa02RZ3P2jH9Hw1FMQCqE++lF473vB41niKxA3Qm6KClHCDMOgubmZ7du309nZSW1tLT6fj3Q6jWmaWJaFx+MhXlVF9/vfz8nHH8fauRM++EHo6IAvfxny+aW+DLHAJNCFWKGUUni9XtatW2eHusfjwTAMHA4HhmGQSCQYHx9noKKCqcceQz/5JNTUwEMPwY4d8O//DnLjtGRIoAuxwhVn67t27WLdunXU1dVhGAb5fJ5MJkMmk2FgYIAjR44wtnkzes8e+NrXIBaDBx6A+++H/dfcY++KtNakUim73CMdNUtPulyEKAFKKUKhEJs3b6a/vx/LsohEIrjdbgzDwDAMxsfHcTgclJWV4Xvzm+E3fgP+6Z/gv/036OqCN78Z/vt/hzVrrvlclmURDoc5e/Yss7OzOJ1OLMsiEAjQ2NhIWVmZrFZdInJTVIgSorUmGo1y/PhxJicnmZmZwTRNEokEpmliGAY7duxg8+bNGMbcG/RIBP7mb+DTn4ZcDn7/9+HjH4fqaizLIhqNMjMzQzqdJpVKMTo6yvDwMLOzs+RyOSzLwuVy2RuJtba20t7eTl1dnYT6Arnem6IyQxeihCilKCsro7GxkampKSzLIpFIYFkW6XQarTU9PT14vV7a2toKoV5eXpiZ//7vw5//Ofrv/x6+8AUi/+W/cOCXf5mxWIxYLEY0GiWfz5Ofu5lanIUXSzsej4doNEp/fz+5XK7wTsDnW+LvyO1FauhClBilFI2NjXR0dOByuchms+TzeZRSGIZBNBpl7969nDhxwu5fj0QinAf6P/5xjn3964ysX0/of/wPdr71rVR897vEZmbs2bZpmiilyGazZLNZO9SLi5ySySTnz59ncHBQ6uqLTEouQpQoy7Lo6enh4MGDWJZFMpm0Z9OmaeJ2u2lpaSmciBQOk0gkiEQiZLNZcrkcTefO8Yof/YhVAwNM1tbyHw88QO+mTVhak8/ncTgcaK3tQzY8c73tXq8XwzBYtWoV99xzj2ztuwBkt0UhBIlEgr179zI0NEQkEiGdTgOFWns6nS6sLlUKp9OJ1hqlFLlcDofDgVIKK59n3ZEj3Pvv/07l1BSDra0882u/xsCqVWitMQzDnrHn83lcLpe9eZjD4aC5uZnOzk6am5tfrNmLGyaBLoRAa83Y2Bjd3d2cPXuWTCYDYJdHirNrpRRKqctKJA6Hg2w2C9ks2/bt4+VPP41/dpaz27dz6qGHqNi1i7q6OgYHB+0j8VKpFEopu64eDAbZtm0bHR0dmKa56N+DUiCBLoQACqEeDod5/vnnGRwcJJ/Pk06nyeVydm29ODsv5oFpmvZM3eVyUVdXR3NzM0GlqPvKV6j+4hdR6TS8+92oP/9zEsEge/bssbtfijX14tfz+/1s3ryZrq4uewYvrp90uQghgBd71Ldt24ZlWfbNymJ/umEY9qzasiy8Xi9KKaqqqqipqaGlpYX29nZ8Pl+hFn733fCnfwqf+AT8r/8Fjz2G9/3vZ+2DDxKNRkkmkwDkcjkA+0COgwcPkk6n6erqevFriQUlM3QhbhNaaxKJBGfPnuX06dOEw2GSySTZbBaXy4XL5aKqqoqqqiqCweD1LRI6dQo++lH49rfRdXVM/N7v8UxHB5ORiH0Tttj/blkWTqeTtrY2Nm3aJHX1GyAlFyHEFWmtSSaTTE9PE4/HAXA6nfj9fjweD263G7fbfWMz6Oefhw99CJ55hmxbG0cefJBn6+vJz9XoAXvfdo/HQ1lZmdTVb4AEuhBicWkN//Zv6A9/GHXsGNFNm3j6Na/hfFsb6XQawzDsUg+Az+dj/fr13Hnnnfj9finBXIMEuhBiaeRy8KUvof/0T1HDw/TfcQfPvPa1TNfX27P0Yi+8Uor6+nrWr19PU1OT7ANzFRLoQoillUhgffrT6EceQcXjnHzZy/j5/fcTDQTQWmOaJtlsFq01LpeLUChk34itqKggGAxSUVFh36S9nUmgCyGWBWt8nNmPfITAl79MXikO3nsv+1/9alJud6HHHeytBIrB7fP5CAQClJeX09jYSHl5OblcDsMw7D8LBoO3zWxeAl0IsWxorUkePUrsfe+j7qmnSPr9PHffffTccw/JudWqWmv7wzAMO+SLpRmlFKZp4nA4qKiooL6+nubmZlavXl3ys3gJdCHEspPL5Tj1+ONU/PVf03D0KJHKSn72q7/Kqe3byV8Q6BeGs2VZ9o3U4lmpSilcLhc+n4+WlhY6OjpKug1SAl0IsSxZlkV4Zoahz3+e5n/4ByoGBhhtbORnr3sdfe3t9v4yF65cvXQ1q1LKPmbP4/EQCoW48847aW9vL8lQv+WHRCulmpVSP1FKHVdKHVVK/dHNfi0hxO3DMAwqq6rofP/74cAB+j7xCYLpNL/16KP81he+QO3ICFAo0xRD+1KWZWFZFvl8nlQqxfj4OM8995y9JfDt6qZn6EqpBqBBa31AKRUE9gNv1Fofu9r/IzN0IcSltNYkZ2bIfPrTBP/+7zGiUYZe8Qr2vP71jHs8pNNpuxum+PjiDB0KZZh8Po/T6aS8vJzOzk62bdtWUguWFr3kopT6HvAPWusnrvYYCXQhxDXNzKA/+Un4u78DIPLbv834u95FvqyMTCZDLBZjenqakZERZmdn7S0FijN5n8+HaZps376dzs7Okgn1RQ10pVQr8DOgU2sdveTPHgYeBmhpabmzv79/3s8nhChxAwOFDcC+/OXCEXkf/Sj8wR+g3W6SySRnzpzh6NGjhMNhMpnMRTV3pRRer5fOzs6S2d3xltfQL3iiAPBt4H2XhjmA1vpRrXWX1rqrpqZmvk8nhLgdtLTAF78Ihw4Vdnf80Idg/XrUY4/hc7vp7OzkZS97GatXr7YP1cjn8xiGYR+Nd/jwYfbt23db1dTnFehKKSeFMP+q1vo7CzMkIYSYs3Ur/OhH8PTTUFcHDz0EO3Zg/PjHtDQ3c++997J161bcbre9pYDb7QYKh3j09PTQ09Nz24T6fLpcFPAvwHGt9acWbkhCCHGJV74S9uyBr38dZmfhgQdQ99+P/8QJdu/eTVdXF16v1z7PtHiGavEIvr1799r7s5ey+czQ7wF+G3iVUurQ3MdrF2hcQghxMcOAN70Jjh+Hv/1b6O6Gri7Mt72NTp+Pzs5Oe0fHVCpln7p0O5VfbjrQtdY/11orrfVWrfW2uY8fLeTghBDiMi4X/OEfwtmz8LGPwfe+h7l5M3d99avc2dKCy+XC6XRiWdZtV34pvSVVQojbQ1kZ/OVfwpkz8I53YHzmM9z5pjfxukOHCJrmReWXVCpFMplk//79PP/888Tj8csOxC4FsvRfCFEajh+Hj3wEvvc9MtXVPPea13DsrrtIZrP2jL3Yr97a2rqijsFbtLZFIYRYFjZuhH/9V3jmGZxr13LvV77Cg5/8JOtPnoS5/vR8Pk8mk2FgYIB9+/bZB2aXCgl0IURp+aVfQj37LPlvfQuvy8XrH32U3/yHf6C+rw8o7AMDMDExwYEDBxgbG7M/t9JJyUUIUbLyqRRDf/EX1H7mM3ijUU5v2cKeN76RycpKsnOlmOrqatatW0dnZyc+n29Z7qsu2+cKIQSFDpdTBw6QfuQRNv3whziyWbp37eKFBx4gHgjYXTHt7e1s3ryZurq6ZRfq1xvoK3+TAyGEuAbTNOm4807OPvII/3bffaz7xjfY8swzbNq/n/2veAVHfuVXyCnF4OAgAMFgEL/fv8SjvjkS6EKIkmcYBu3t7Sil6K6r4+irX83O736Xu3/8Y+549lmevf9+zr7qVZw6dQqlFLt27Vq25ZdrkUAXQtwWDMOgra0Np9NJt9PJEzU17Dl+nJf/4Afc993vcufPf86zr3sdJ+eOwVuu5ZdrkUAXQtw2DMOgpaWF6upqTp48yWG3m++tXcvqnh7u/v73ed0Xv8jYT3/KwTe/me58np07dxIKhVZMqMtNUSHEbcmyLE6cOMHevXvJZDKkEwk69+/nzu9/n2AkQt+WLfT/7u/S+rrXLfkCJFlYJIQQ12AYBh0dHXR2duJyucA06e7q4rGPf5xfvP71NJw+zcvf+15yv/M79P3iFyuiV11m6EKI21o+n+fkyZMcPHiQeDxOIpHAsiy8iQS7n3qKrc88gzZNwg89RMUjj2BWVi76GGWGLoQQ18E0TTZs2MCuXbuorKzE4/EUTkAKhfj5b/wGn//QhzizZQvV//zP6LY2rE99CtLppR72FUmgCyFue8UOmG3btlFeXo5SCsuyyGQyxGtr+fHb3853PvYxxpuaMN7/fvTGjfC1r8EyK8NIoAshBC+GeldXFzU1NTidTkzTRGtNPp9nqLaWbz38ME984AOkXS5461th50546qmlHrpNAl0IIeYUFyDdfffdrF69GqfTiWEYGIaBaZpks1lOtLTw+Ac/yKk/+ROsyUm47z54zWvg8OGlHr4EuhBCXKjYq37vvfeyceNGe6+XdDqN2+3GsiziySRPNTTwxN/9HdMf+xj6hRdg+3Z4+9uhv3/pxr5kzyyEEMuUUgq/38+uXbtYv349Pp8Pn8+HYRjk83ny+TzpdJre4WF+3NlJ749/jP7AB+Cb34T16+EDH4Dp6UUftwS6EEJchc/nY/PmzTQ3N6OUQmttLzDK5/PkcjkmJyfZc+oUZx9+GOvkSXjLW+BTn4L2dvibv4FkctHGK4EuhBBXoZSirq6O3bt309nZidfrtU8+Kt40zefzRKNRDh06RG82i/Uv/1Kop7/sZfBf/yt0dMAXvwiLcDi1BLoQQlxDsfyye/dudu/eTWVlJU6nE6UU6XTaPoR6amqKPXv2cOLECfKbNsEPfwg/+QnU18Pv/A4cOHDLxyqbcwkhxHUoLkByOp3s37+fcDhsbwdgGAapVIpMJsNzzz1HNBqlq6sLxyteAXv2wM9/XmhxvMUk0IUQ4joV2xpdLhdHjhyxD5nOZDJ2J0wqleLwXAvjzp07MU0TXv7yRRmfBLoQQtyAYltjWVkZ+/btY3h4GIBMJlMIbwo3THt6evB6vXR2dtqfv+VjW5RnEUKIEqKUIhQKsWXLFpqamuzPFfdNT6VSJJNJ9u/fz/PPP088HmcxNkKUGboQQtyEYgdMMBjE4XBw7NgxtNakUimcTieWZZFMJunu7iYajbJp06Zbvq/6vAJdKfUa4G8BE/ic1vqRBRmVEEKsAMUOmHvuuQe3201PTw+WZaG1ttsbc7kcAwMDJBIJAFpaWm7ZCUg3/VKhlDKBfwQeADYBb1ZKbVqogQkhxErhcDjYuXMnO3fuJBAIXHTAtNYah8NBLBbj7NmzpFKpWzaO+cz97wLOaK17tdYZ4OvAGxZmWEIIsbKYpklnZydbtmzB4XBgWRb5fB6/34/b7cbpdJJKpYjH47dsDPMJ9EZg8IL/Hpr73EWUUg8rpfYppfZNTEzM4+mEEGJ5M02Tbdu2sXPnTiorK3G73TgcDpRSeL3ewlF3t9B8auhXKgJddhtXa/0o8CgUjqCbx/MJIcSyd+ECpEOHDqG1xuVy4fP5KCsrw+/337Lnnk+gDwHNF/x3EzA8v+EIIcTKV1yA5Ha7GR8fRymF2+2mpqYGj8dzy553PoG+F1inlFoDnAceBN6yIKMSQogVzjAMmpubqa2tJZvN4nQ6cbvdt6zDBeYR6FrrnFLqvcD/pdC2+Hmt9dEFG5kQQqxwSik8Hs8tnZVfaF596FrrHwE/WqCxCCGEmAdZ+i+EECVCAl0IIUqEBLoQQpQICXQhhCgRajG2dLSfTKkJoH+Rnq4amFyk51pO5LpvL3Ldt4fVWuual3rQogb6YlJK7dNady31OBabXPftRa5bXEhKLkIIUSIk0IUQokSUcqA/utQDWCJy3bcXuW5hK9kauhBC3G5KeYYuhBC3lZIIdKVUn1LqiFLqkFJq39znKpVSTyilTs/9WrHU41wISqnPK6XGlVI9F3zuiteqCv5OKXVGKdWtlNqxdCOfn6tc958rpc7P/dwPKaVee8GffWTuuk8qpX51aUY9f0qpZqXUT5RSx5VSR5VSfzT3+ZL+mV/jukv+Zz4vWusV/wH0AdWXfO5vgA/P/f7DwF8v9TgX6Fp/GdgB9LzUtQKvBf4PhcNIdgN7lnr8C3zdfw584AqP3QQcBtzAGuAsYC71NdzkdTcAO+Z+HwROzV1fSf/Mr3HdJf8zn89HSczQr+INwJfmfv8l4I1LOJYFo7X+GTB9yaevdq1vAL6sC54HQkqphsUZ6cK6ynVfzRuAr2ut01rrc8AZCmfgrjha6xGt9YG538eA4xSOeizpn/k1rvtqSuZnPh+lEuga+LFSar9S6uG5z9VprUeg8JcDqF2y0d16V7vW6zr3dYV771xp4fMXlNVK8rqVUq3AdmAPt9HP/JLrhtvoZ36jSiXQ79Fa7wAeAN6jlPrlpR7QMnFd576uYJ8F2oFtwAjw/859vuSuWykVAL4NvE9rHb3WQ6/wuRV77Ve47tvmZ34zSiLQtdbDc7+OA9+l8FZrrPhWc+7X8aUb4S13tWst6XNftdZjWuu81toC/pkX32KX1HUrpZwUQu2rWuvvzH265H/mV7ru2+VnfrNWfKArpfxKqWDx98CvAD3A94GH5h72EPC9pRnhorjatX4fePtc58NuIFJ8m14KLqkN/zqFnzsUrvtBpZR77szbdcALiz2+haAKB1D+C3Bca/2pC/6opH/mV7vu2+FnPi9LfVd2vh9AG4W724eBo8DH5j5fBTwFnJ77tXKpx7pA1/s4hbeaWQqzknde7VopvA39Rwp3/I8AXUs9/gW+7sfmrqubwj/ohgse/7G56z4JPLDU45/Hdf8ShdJBN3Bo7uO1pf4zv8Z1l/zPfD4fslJUCCFKxIovuQghhCiQQBdCiBIhgS6EECVCAl0IIUqEBLoQQpQICXQhhCgREuhCCFEiJNCFEKJE/P8siopkmiN3twAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[Model]]\n",
-      "    Model(piecewise_linear)\n",
-      "[[Fit Statistics]]\n",
-      "    # fitting method   = ampgo, with L-BFGS-B as local solver\n",
-      "    # function evals   = 79866\n",
-      "    # data points      = 491\n",
-      "    # variables        = 8\n",
-      "    chi-square         = 65.4379204\n",
-      "    reduced chi-square = 0.13548224\n",
-      "    Akaike info crit   = -973.533027\n",
-      "    Bayesian info crit = -939.961474\n",
-      "[[Variables]]\n",
-      "    x0:  62.1499306 (init = 40.5)\n",
-      "    x1:  181.207436 (init = 40.5)\n",
-      "    x2:  196.301872 (init = 40.5)\n",
-      "    b:   5.81709441 (init = 0)\n",
-      "    k1:  0.06544372 (init = 0)\n",
-      "    k2: -0.07421739 (init = 0)\n",
-      "    k3: -0.32199700 (init = 0)\n",
-      "    k4:  0.29325377 (init = 0)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from lmfit import Model, Parameters\n",
-    "\n",
-    "def get_data():\n",
-    "    site_id='NARRA0018'\n",
-    "    profile_type='prestorm'\n",
-    "    df_profile = df_profiles.query(\"site_id == '{}' and profile_type == '{}'\".format(site_id, profile_type))\n",
-    "    x = np.array(df_profile.index.get_level_values('x').tolist())\n",
-    "    z = np.array(df_profile.z.tolist())    \n",
-    "\n",
-    "    nan_mask = ma.masked_invalid(z).mask\n",
-    "    x = x[~nan_mask]\n",
-    "    z = z[~nan_mask]\n",
-    "    return x,z\n",
-    "\n",
-    "# def piecewise_linear(x, x0, x1, b, k1, k2, k3):\n",
-    "#     condlist = [x < x0, (x >= x0) & (x < x1), x >= x1]\n",
-    "#     funclist = [lambda x: k1*x + b, lambda x: k1*x + b + k2*(x-x0), lambda x: k1*x + b + k2*(x-x0) + k3*(x - x1)]\n",
-    "#     return np.piecewise(x, condlist, funclist)\n",
-    "\n",
-    "# x,z = get_data()\n",
-    "\n",
-    "# fmodel = Model(piecewise_linear)\n",
-    "# params = Parameters()\n",
-    "# params.add('x0', value=0, vary=True, min=min(x), max=max(x))\n",
-    "# params.add('x1', value=0, vary=True, min=min(x), max=max(x))\n",
-    "# params.add('b', value=0, vary=True)\n",
-    "# params.add('k1', value=0, vary=True, min=-0.01, max=0.01)\n",
-    "# params.add('k2', value=0, vary=True, min=-0.1, max=-0.5)\n",
-    "# params.add('k3', value=0, vary=True, min=0.1, max=0.5)\n",
-    "\n",
-    "def piecewise_linear(x, x0, x1, x2, b, k1, k2, k3,k4):\n",
-    "    condlist = [x < x0, (x >= x0) & (x < x1), (x >= x1) & (x < x2), x >= x2]\n",
-    "    funclist = [lambda x: k1*x + b, lambda x: k1*x + b + k2*(x-x0), lambda x: k1*x + b + k2*(x-x0) + k3*(x - x1), lambda x: k1*x + b + k2*(x-x0) + k3*(x - x1) +k4*(x-x2)]\n",
-    "    return np.piecewise(x, condlist, funclist)\n",
-    "\n",
-    "x,z = get_data()\n",
-    "\n",
-    "fmodel = Model(piecewise_linear)\n",
-    "params = Parameters()\n",
-    "params.add('x0', value=0, vary=True, min=min(x), max=max(x))\n",
-    "params.add('x1', value=0, vary=True, min=min(x), max=max(x))\n",
-    "params.add('x2', value=0, vary=True, min=min(x), max=max(x))\n",
-    "params.add('b', value=0, vary=True)\n",
-    "params.add('k1', value=0, vary=True, min=-0.5, max=0.5)\n",
-    "params.add('k2', value=0, vary=True, min=-0.5, max=0.5)\n",
-    "params.add('k3', value=0, vary=True, min=-0.5, max=0.5)\n",
-    "params.add('k4', value=0, vary=True, min=-0.5, max=0.5)\n",
-    "\n",
-    "\n",
-    "result = fmodel.fit(z, params, x=x,method='ampgo')\n",
-    "\n",
-    "\n",
-    "pyplot.figure()\n",
-    "pyplot.plot(x,z, color='0.5',marker='.', alpha=.2, ms=10,linestyle=\"None\")\n",
-    "pyplot.plot(x,result.best_fit, color='r')\n",
-    "pyplot.show()\n",
-    "print(result.fit_report())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Try spline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2018-12-05T01:49:30.968871Z",
-     "start_time": "2018-12-05T01:49:30.648994Z"
-    },
-    "code_folding": [
-     2
-    ]
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from scipy.signal import savgol_filter\n",
-    "\n",
-    "def get_data():\n",
-    "    site_id='NARRA0018'\n",
-    "    profile_type='prestorm'\n",
-    "    df_profile = df_profiles.query(\"site_id == '{}' and profile_type == '{}'\".format(site_id, profile_type))\n",
-    "    x = np.array(df_profile.index.get_level_values('x').tolist())\n",
-    "    z = np.array(df_profile.z.tolist())    \n",
-    "\n",
-    "    nan_mask = ma.masked_invalid(z).mask\n",
-    "    x = x[~nan_mask]\n",
-    "    z = z[~nan_mask]\n",
-    "    return x,z\n",
-    "\n",
-    "x,z = get_data()\n",
-    "\n",
-    "z_filtered = savgol_filter(z, 31, 3)\n",
-    "\n",
-    "\n",
-    "pyplot.figure()\n",
-    "pyplot.plot(x,z, color='0.5',marker='.', alpha=.2, ms=10,linestyle=\"None\")\n",
-    "pyplot.plot(x,z_filtered, color='r')\n",
-    "pyplot.show()\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.7"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/04a_profile_picker_superseded.ipynb b/notebooks/04a_profile_picker_superseded.ipynb
new file mode 100644
index 0000000..bbb228c
--- /dev/null
+++ b/notebooks/04a_profile_picker_superseded.ipynb
@@ -0,0 +1,407 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import os\n",
+    "import numpy.ma as ma\n",
+    "\n",
+    "import numpy\n",
+    "from pyearth import Earth\n",
+    "from matplotlib import pyplot\n",
+    "\n",
+    "np.random.seed(2017)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "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_profiles = df_from_csv('profiles.csv', index_col=[0, 1, 2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Try using pyearth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "code_folding": [
+     5,
+     20,
+     31,
+     40
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.signal import savgol_filter\n",
+    "import re\n",
+    "from scipy.stats import linregress\n",
+    "import warnings\n",
+    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
+    "\n",
+    "def get_breakpoints(model, min_distance=20):\n",
+    "    # Get breakpoints\n",
+    "    breakpoints = []\n",
+    "    for line in model.summary().split('\\n'):\n",
+    "        # Get unpruned lines\n",
+    "        if 'No' in line and 'None' not in line:\n",
+    "            # Get break points\n",
+    "            m = re.search(\"h\\(x0-(\\d*\\.?\\d+)\\)\", line)\n",
+    "            if m:\n",
+    "                breakpoints.append(float(m.groups()[0]))\n",
+    "            m = re.search(\"h\\((\\d*\\.?\\d+)-x0\\)\", line)\n",
+    "            if m:\n",
+    "                breakpoints.append(float(m.groups()[0]))\n",
+    "    return sorted(list(set(breakpoints)))\n",
+    "    \n",
+    "def get_segments(breakpoints, x_min, x_max):\n",
+    "    segments = []\n",
+    "    breakpoints = [x_min] + breakpoints + [x_max]\n",
+    "\n",
+    "    for x1, x2 in zip(breakpoints, breakpoints[1:]):\n",
+    "        segments.append({\n",
+    "            'x_start': x1,\n",
+    "            'x_end': x2\n",
+    "        })\n",
+    "    return segments            \n",
+    "\n",
+    "def get_segment_slopes(segments, x, z):\n",
+    "    for segment in segments:\n",
+    "        mask = ma.masked_where((segment['x_start'] < x) & (x < segment['x_end']),x ).mask\n",
+    "        segment['z_mean'] = np.mean(z[mask])\n",
+    "        segment['z_start'] = np.mean(z[mask][0])\n",
+    "        segment['z_end'] = np.mean(z[mask][-1])\n",
+    "        segment['slope'] = -linregress(x[mask], z[mask]).slope\n",
+    "    return segments\n",
+    "    \n",
+    "def classify_segments(segments, x,z):\n",
+    "    \n",
+    "    # Most seaward slope must be foreshore\n",
+    "    segments[-1]['type'] = 'foreshore'\n",
+    "    \n",
+    "    # Most landward slope must be land\n",
+    "    segments[0]['type'] = 'land'\n",
+    "    \n",
+    "    # Segments with really high slopes must be structures\n",
+    "    for seg in segments:\n",
+    "        if seg['slope'] > 2.0:\n",
+    "            seg['type'] = 'structure'\n",
+    "    \n",
+    "    # Segments with large change of slope and \n",
+    "    # Segment with max slope should be dune face\n",
+    "#     dune_face_idx = [n for n, seg in enumerate(segments) if seg['slope']==max(x['slope'] for x in segments)][0]\n",
+    "#     segments[dune_face_idx]['type'] = 'dune_face'\n",
+    "    \n",
+    "    # Pick out berms \n",
+    "    for seg in segments:\n",
+    "        if (-0.03 < seg['slope'] < 0.03  # berms should be relatively flat\n",
+    "            and 0 < seg['z_mean'] < 4  # berms should be located between 0-4 m AHD\n",
+    "            ):  # berms should be seaward of dune face\n",
+    "            seg['type'] = 'berm'\n",
+    "            \n",
+    "#     slope = None\n",
+    "#     for seg in reversed(segments):\n",
+    "#         if slope is None:\n",
+    "#             continue\n",
+    "#         elif slope - 0.03 < seg['slope'] < slope + 0.03:\n",
+    "#             seg['type'] = 'foreshore'\n",
+    "#         else:\n",
+    "#             break\n",
+    "    \n",
+    "    return segments\n",
+    "\n",
+    "def get_piecewise_linear_model(x,z):\n",
+    "    #Fit an Earth model\n",
+    "    model = Earth(penalty=3,thresh=0.0005)\n",
+    "    model.fit(x,z)\n",
+    "    return model\n",
+    "\n",
+    "def plot_profile_classification(site_id, profile_type):\n",
+    "    df_profile = df_profiles.query(\"site_id == '{}' and profile_type == '{}'\".format(site_id, profile_type))\n",
+    "    x = np.array(df_profile.index.get_level_values('x').tolist())\n",
+    "    z = np.array(df_profile.z.tolist())    \n",
+    "    \n",
+    "    nan_mask = ma.masked_invalid(z).mask\n",
+    "    x = x[~nan_mask]\n",
+    "    z_unfiltered = z[~nan_mask]\n",
+    "    z = savgol_filter(z_unfiltered, 51, 3)\n",
+    "    \n",
+    "    model = get_piecewise_linear_model(x,z)\n",
+    "    breakpoints = get_breakpoints(model)\n",
+    "    segments = get_segments(breakpoints, x_min=x.min(), x_max=x.max())\n",
+    "    segments = get_segment_slopes(segments, x=x, z=z)\n",
+    "#     segments = merge_similar_segments(segments)\n",
+    "    segments = classify_segments(segments, x=x, z=z)\n",
+    "    \n",
+    "    pyplot.figure()\n",
+    "    pyplot.plot(x,z_unfiltered, color='0.5',marker='.', alpha=.2, ms=10,linestyle=\"None\")\n",
+    "\n",
+    "    # Plot different segments\n",
+    "    foreshore_segments = [x for x in segments if x.get('type') == 'foreshore']\n",
+    "    for seg in foreshore_segments:\n",
+    "        pyplot.plot([seg['x_start'], seg['x_end']],\n",
+    "                   [seg['z_start'], seg['z_end']],\n",
+    "                   linewidth=4, \n",
+    "                    color='b')\n",
+    "\n",
+    "    land_segments = [x for x in segments if x.get('type') == 'land']\n",
+    "    for seg in land_segments:\n",
+    "        pyplot.plot([seg['x_start'], seg['x_end']],\n",
+    "                   [seg['z_start'], seg['z_end']],\n",
+    "                   linewidth=4, \n",
+    "                    color='g')\n",
+    "\n",
+    "    berm_segments = [x for x in segments if x.get('type') == 'berm']\n",
+    "    for seg in berm_segments:\n",
+    "        pyplot.plot([seg['x_start'], seg['x_end']],\n",
+    "                   [seg['z_start'], seg['z_end']],\n",
+    "                    linewidth=4, \n",
+    "                    color='y')\n",
+    "\n",
+    "    dune_face_segments = [x for x in segments if x.get('type') == 'dune_face']\n",
+    "    for seg in dune_face_segments:\n",
+    "        pyplot.plot([seg['x_start'], seg['x_end']],\n",
+    "                   [seg['z_start'], seg['z_end']],\n",
+    "                    linewidth=4, \n",
+    "                    color='r')\n",
+    "        \n",
+    "    structure_segments = [x for x in segments if x.get('type') == 'structure']\n",
+    "    for seg in structure_segments:\n",
+    "        pyplot.plot([seg['x_start'], seg['x_end']],\n",
+    "                   [seg['z_start'], seg['z_end']],\n",
+    "                    linewidth=4, \n",
+    "                    color='m')\n",
+    "        \n",
+    "    unclassified_segments = [x for x in segments if x.get('type') is None]\n",
+    "    for seg in unclassified_segments:\n",
+    "        pyplot.plot([seg['x_start'], seg['x_end']],\n",
+    "                   [seg['z_start'], seg['z_end']],\n",
+    "                   linewidth=4, \n",
+    "                    color='0.4')\n",
+    "\n",
+    "    pyplot.xlabel('x (m)')\n",
+    "    pyplot.ylabel('z (m AHD)')\n",
+    "    pyplot.title('{} profile at {}'.format(profile_type, site_id))\n",
+    "    pyplot.show()\n",
+    "\n",
+    "    import pprint\n",
+    "    pp = pprint.PrettyPrinter(indent=4)\n",
+    "    pp.pprint(segments)\n",
+    "\n",
+    "plot_profile_classification('NARRA0018', 'prestorm')\n",
+    "plot_profile_classification('NARRA0019', 'prestorm')\n",
+    "plot_profile_classification('CRESn0017', 'poststorm')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## Try lmfit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "code_folding": [
+     0
+    ],
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "from lmfit import Model, Parameters\n",
+    "\n",
+    "def get_data():\n",
+    "    site_id='NARRA0018'\n",
+    "    profile_type='prestorm'\n",
+    "    df_profile = df_profiles.query(\"site_id == '{}' and profile_type == '{}'\".format(site_id, profile_type))\n",
+    "    x = np.array(df_profile.index.get_level_values('x').tolist())\n",
+    "    z = np.array(df_profile.z.tolist())    \n",
+    "\n",
+    "    nan_mask = ma.masked_invalid(z).mask\n",
+    "    x = x[~nan_mask]\n",
+    "    z = z[~nan_mask]\n",
+    "    return x,z\n",
+    "\n",
+    "# def piecewise_linear(x, x0, x1, b, k1, k2, k3):\n",
+    "#     condlist = [x < x0, (x >= x0) & (x < x1), x >= x1]\n",
+    "#     funclist = [lambda x: k1*x + b, lambda x: k1*x + b + k2*(x-x0), lambda x: k1*x + b + k2*(x-x0) + k3*(x - x1)]\n",
+    "#     return np.piecewise(x, condlist, funclist)\n",
+    "\n",
+    "# x,z = get_data()\n",
+    "\n",
+    "# fmodel = Model(piecewise_linear)\n",
+    "# params = Parameters()\n",
+    "# params.add('x0', value=0, vary=True, min=min(x), max=max(x))\n",
+    "# params.add('x1', value=0, vary=True, min=min(x), max=max(x))\n",
+    "# params.add('b', value=0, vary=True)\n",
+    "# params.add('k1', value=0, vary=True, min=-0.01, max=0.01)\n",
+    "# params.add('k2', value=0, vary=True, min=-0.1, max=-0.5)\n",
+    "# params.add('k3', value=0, vary=True, min=0.1, max=0.5)\n",
+    "\n",
+    "def piecewise_linear(x, x0, x1, x2, b, k1, k2, k3,k4):\n",
+    "    condlist = [x < x0, (x >= x0) & (x < x1), (x >= x1) & (x < x2), x >= x2]\n",
+    "    funclist = [lambda x: k1*x + b, lambda x: k1*x + b + k2*(x-x0), lambda x: k1*x + b + k2*(x-x0) + k3*(x - x1), lambda x: k1*x + b + k2*(x-x0) + k3*(x - x1) +k4*(x-x2)]\n",
+    "    return np.piecewise(x, condlist, funclist)\n",
+    "\n",
+    "x,z = get_data()\n",
+    "\n",
+    "fmodel = Model(piecewise_linear)\n",
+    "params = Parameters()\n",
+    "params.add('x0', value=0, vary=True, min=min(x), max=max(x))\n",
+    "params.add('x1', value=0, vary=True, min=min(x), max=max(x))\n",
+    "params.add('x2', value=0, vary=True, min=min(x), max=max(x))\n",
+    "params.add('b', value=0, vary=True)\n",
+    "params.add('k1', value=0, vary=True, min=-0.5, max=0.5)\n",
+    "params.add('k2', value=0, vary=True, min=-0.5, max=0.5)\n",
+    "params.add('k3', value=0, vary=True, min=-0.5, max=0.5)\n",
+    "params.add('k4', value=0, vary=True, min=-0.5, max=0.5)\n",
+    "\n",
+    "\n",
+    "result = fmodel.fit(z, params, x=x,method='ampgo')\n",
+    "\n",
+    "\n",
+    "pyplot.figure()\n",
+    "pyplot.plot(x,z, color='0.5',marker='.', alpha=.2, ms=10,linestyle=\"None\")\n",
+    "pyplot.plot(x,result.best_fit, color='r')\n",
+    "pyplot.show()\n",
+    "print(result.fit_report())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Try spline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "code_folding": [
+     2
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.signal import savgol_filter\n",
+    "\n",
+    "def get_data():\n",
+    "    site_id='NARRA0018'\n",
+    "    profile_type='prestorm'\n",
+    "    df_profile = df_profiles.query(\"site_id == '{}' and profile_type == '{}'\".format(site_id, profile_type))\n",
+    "    x = np.array(df_profile.index.get_level_values('x').tolist())\n",
+    "    z = np.array(df_profile.z.tolist())    \n",
+    "\n",
+    "    nan_mask = ma.masked_invalid(z).mask\n",
+    "    x = x[~nan_mask]\n",
+    "    z = z[~nan_mask]\n",
+    "    return x,z\n",
+    "\n",
+    "x,z = get_data()\n",
+    "\n",
+    "z_filtered = savgol_filter(z, 31, 3)\n",
+    "\n",
+    "\n",
+    "pyplot.figure()\n",
+    "pyplot.plot(x,z, color='0.5',marker='.', alpha=.2, ms=10,linestyle=\"None\")\n",
+    "pyplot.plot(x,z_filtered, color='r')\n",
+    "pyplot.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/04b_profile_picker.ipynb b/notebooks/04b_profile_picker.ipynb
new file mode 100644
index 0000000..160904f
--- /dev/null
+++ b/notebooks/04b_profile_picker.ipynb
@@ -0,0 +1,1374 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Beach profile classifier\n",
+    "Using RANSAC algorithm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## Setup notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hidden": true
+   },
+   "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": null,
+   "metadata": {
+    "hidden": 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",
+    "import decimal\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",
+    "from plotly import tools\n",
+    "import plotly.io as pio\n",
+    "from scipy import stats\n",
+    "import math\n",
+    "import matplotlib\n",
+    "from matplotlib import cm\n",
+    "import colorlover as cl\n",
+    "import numpy.ma as ma\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 numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "from sklearn import linear_model, datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "# Matplot lib default settings\n",
+    "plt.rcParams[\"figure.figsize\"] = (10,3)\n",
+    "plt.rcParams['axes.grid']=True\n",
+    "plt.rcParams['grid.alpha'] = 0.5\n",
+    "plt.rcParams['grid.color'] = \"grey\"\n",
+    "plt.rcParams['grid.linestyle'] = \"--\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "heading_collapsed": true
+   },
+   "source": [
+    "## Import data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "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_crest_toes = df_from_csv('profile_features_crest_toes.csv', index_col=[0,1])\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": "markdown",
+   "metadata": {},
+   "source": [
+    "# Implement algorithm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get profile data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_profile_data(site, profile_type, df_profiles):\n",
+    "    \"\"\"\n",
+    "    Returns a list of x and z coordinates for a given profile.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Get x and z coorindates of profile\n",
+    "    x = df_profiles.loc[(site, profile_type)].index.values\n",
+    "    z = df_profiles.loc[(site, profile_type)].z.values\n",
+    "\n",
+    "    # Get land limit\n",
+    "    land_lim = df_profiles.loc[(site, 'poststorm')].land_lim.loc[0]\n",
+    "\n",
+    "    # Remove nan values\n",
+    "    m = ma.masked_invalid(z)\n",
+    "    x = x[~m.mask]\n",
+    "    z = z[~m.mask]\n",
+    "\n",
+    "#     # Remove landwards of landlim\n",
+    "#     mask = ma.masked_where(x < land_lim, x)\n",
+    "#     x = x[~mask.mask]\n",
+    "#     z = z[~mask.mask]\n",
+    "\n",
+    "    return x, z\n",
+    "\n",
+    "\n",
+    "# Load profile data\n",
+    "# site = 'WAMBE0010'\n",
+    "# site = 'NINEMs0048'\n",
+    "# site = 'NAMB0013'\n",
+    "# site = 'AVOCAn0009'\n",
+    "# site = 'GRANTSs0014'\n",
+    "site = 'NARRA0001'\n",
+    "profile_type = 'prestorm'\n",
+    "x, z = get_profile_data(site, profile_type, df_profiles)\n",
+    "\n",
+    "print('x = {} ...'.format(x[0:5]))\n",
+    "print('z = {} ...'.format(z[0:5]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hide_input": true
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "\n",
+    "# # n_samples = 1000\n",
+    "# # n_outliers = 50\n",
+    "\n",
+    "\n",
+    "# # X, y, coef = datasets.make_regression(n_samples=n_samples, n_features=1,\n",
+    "# #                                       n_informative=1, noise=10,\n",
+    "# #                                       coef=True, random_state=0)\n",
+    "\n",
+    "# # # Add outlier data\n",
+    "# # np.random.seed(0)\n",
+    "# # X[:n_outliers] = 3 + 0.5 * np.random.normal(size=(n_outliers, 1))\n",
+    "# # y[:n_outliers] = -3 + 10 * np.random.normal(size=n_outliers)\n",
+    "\n",
+    "# # # Fit line using all data\n",
+    "# # lr = linear_model.LinearRegression()\n",
+    "# # lr.fit(X, y)\n",
+    "\n",
+    "# # Robustly fit linear model with RANSAC algorithm\n",
+    "# ransac = linear_model.RANSACRegressor()\n",
+    "# ransac.fit(x, z)\n",
+    "# inlier_mask = ransac.inlier_mask_\n",
+    "# outlier_mask = np.logical_not(inlier_mask)\n",
+    "\n",
+    "# # Predict data of estimated models\n",
+    "# line_x = np.arange(x.min(), x.max())[:, np.newaxis]\n",
+    "# line_y_ransac = ransac.predict(line_x)\n",
+    "\n",
+    "# lw = 2\n",
+    "# plt.scatter(x[inlier_mask], z[inlier_mask], color='yellowgreen', marker='.',\n",
+    "#             label='Inliers')\n",
+    "# plt.scatter(x[outlier_mask], z[outlier_mask], color='gold', marker='.',\n",
+    "#             label='Outliers')\n",
+    "# plt.plot(line_x, line_y_ransac, color='cornflowerblue', linewidth=lw,\n",
+    "#          label='RANSAC regressor')\n",
+    "# plt.legend(loc='lower right')\n",
+    "# plt.xlabel(\"Input\")\n",
+    "# plt.ylabel(\"Response\")\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fit linear interpolated spline to profile\n",
+    "- Linear interpolated spline used to simplify the profile.\n",
+    "- Need to do a bit of optimization to calculate the best smoothing parameter for the profile."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "code_folding": [
+     4,
+     11,
+     20,
+     25,
+     30,
+     42,
+     92
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.interpolate import UnivariateSpline\n",
+    "from scipy.interpolate import interp1d\n",
+    "\n",
+    "\n",
+    "def pairwise(iterable):\n",
+    "    \"s -> (s0,s1), (s1,s2), (s2, s3), ...\"\n",
+    "    a, b = itertools.tee(iterable)\n",
+    "    next(b, None)\n",
+    "    return zip(a, b)\n",
+    "\n",
+    "\n",
+    "def xz(x_start, x_end, x, z):\n",
+    "    \"\"\"\n",
+    "    Returns a list of x coordinates and z coorindates which lie between x_start and x_end\n",
+    "    \"\"\"\n",
+    "    x_seg = [val for val in x if x_start <= val <= x_end]\n",
+    "    z_seg = [z_val for x_val, z_val in zip(x, z) if x_start <= x_val <= x_end]\n",
+    "    return x_seg, z_seg\n",
+    "\n",
+    "\n",
+    "def get_slope(x, z):\n",
+    "    slope, intercept, r_value, p_value, std_err = linregress(x, z)\n",
+    "    return slope\n",
+    "\n",
+    "\n",
+    "def get_r_squared(x, z):\n",
+    "    slope, intercept, r_value, p_value, std_err = linregress(x, z)\n",
+    "    return r_value**2\n",
+    "\n",
+    "\n",
+    "def rolling_std(a, w):\n",
+    "    \"\"\"\n",
+    "    Calculates a rolling window standard deviation\n",
+    "    https://stackoverflow.com/a/40773366\n",
+    "    \"\"\"\n",
+    "    a = np.array(a)\n",
+    "    nrows = a.size - w + 1\n",
+    "    n = a.strides[0]\n",
+    "    a2D = np.lib.stride_tricks.as_strided(a, shape=(nrows, w), strides=(n, n))\n",
+    "    return np.std(a2D, 1)\n",
+    "\n",
+    "\n",
+    "def iterate_over_smoothing_params(x, z):\n",
+    "    \"\"\"\n",
+    "    Finds the best smoothing parameter for a linear interpolated spline. \n",
+    "    Checks a number of smoothing factors, and then tries to minimize the \n",
+    "    number of knots (changepoints) while retaining decent match to the \n",
+    "    original profile.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Store the results of our iterations in a list of dictionaries\n",
+    "    results = []\n",
+    "\n",
+    "    # Iterate through different value of smoothing parameters\n",
+    "    s_vals = [x for x in np.arange(0, 10, 0.01)]\n",
+    "\n",
+    "    r2_vals = []\n",
+    "    n_knots = []\n",
+    "    s_vals_to_keep = []\n",
+    "\n",
+    "    # Iterate backwards just to make further calcultions more easier.\n",
+    "    for s_val in s_vals[::-1]:\n",
+    "\n",
+    "        # Fit spline to profile using the s_val for this iteration\n",
+    "        # k=1 is used to force linear line segments between knots\n",
+    "        s = UnivariateSpline(x, z, s=s_val, k=1)\n",
+    "        xs = np.linspace(x[0], x[-1], 1000)\n",
+    "        zs = s(xs)\n",
+    "\n",
+    "        # Find knots\n",
+    "        x_knots = s.get_knots()\n",
+    "        z_knots = s(x_knots)\n",
+    "\n",
+    "        # Get number of knots\n",
+    "        n = len(s.get_knots())\n",
+    "\n",
+    "        # If we've already recorded data about this number of knots, just skip.\n",
+    "        # Need to also, only have increasing results for spline interpolation\n",
+    "        if n in [x['n'] for x in results\n",
+    "                 ] or n < max([x['n'] for x in results], default=0):\n",
+    "            continue\n",
+    "\n",
+    "        # Get r2, how well does the simplification fit the original profile?\n",
+    "        # Create interp model from the knots at our original points.\n",
+    "        f = interp1d(x_knots, z_knots)\n",
+    "        z_lin = f(x)\n",
+    "        r2 = get_r_squared(z, z_lin)\n",
+    "\n",
+    "        results.append({'n': n, 's': s_val, 'r2': r2})\n",
+    "    return results\n",
+    "\n",
+    "\n",
+    "def find_best_smoothing_param(results, min_std=0.0002):\n",
+    "    \"\"\"\n",
+    "    Given a list of smoothing parameters and their fits, determine what the best smoothing parameter is.\n",
+    "    Larger min_std = more smoothing\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Get the 2nd derivate of the n_knots vs r2_vals.\n",
+    "    y_spl = UnivariateSpline([x['n'] for x in results],\n",
+    "                             [x['r2'] for x in results],\n",
+    "                             s=0)\n",
+    "    y_spl_2d = y_spl.derivative(n=2)\n",
+    "    y_spl_2d_vals = y_spl_2d([x['n'] for x in results])\n",
+    "\n",
+    "    # Get a rolling standard deviation of the derivative\n",
+    "    std = rolling_std(y_spl_2d_vals, w=3)\n",
+    "\n",
+    "    # Best smoothing parameter will be when the standard deviation stops changing\n",
+    "    best_i = np.argmax(std < min_std)\n",
+    "    best_s = results[best_i]['s']\n",
+    "    return best_s\n",
+    "\n",
+    "\n",
+    "def simplify_profile(x, z, site):\n",
+    "    results = iterate_over_smoothing_params(x, z)\n",
+    "    best_s = find_best_smoothing_param(results)\n",
+    "\n",
+    "    # Fit spline to profile\n",
+    "    s = UnivariateSpline(x, z, s=best_s, k=1)\n",
+    "    xs = np.linspace(x[0], x[-1], 1000)\n",
+    "    zs = s(xs)\n",
+    "\n",
+    "    # Find knots\n",
+    "    x_knots = s.get_knots()\n",
+    "    z_knots = s(x_knots)\n",
+    "\n",
+    "    # Plot for checking\n",
+    "    plt.title('{}: Linear spline simplification'.format(site))\n",
+    "    plt.xlabel('Distance (m)')\n",
+    "    plt.ylabel('Elevation (m AHD)')\n",
+    "    plt.plot(x, z, 'k.-', label='Raw profile')\n",
+    "    plt.plot(\n",
+    "        xs, zs, 'r-', label='Interpolated profile (s={:.2f})'.format(best_s))\n",
+    "    plt.plot(\n",
+    "        x_knots,\n",
+    "        z_knots,\n",
+    "        'ro',\n",
+    "        markersize=8,\n",
+    "        markeredgewidth=1.5,\n",
+    "        markeredgecolor='orange',\n",
+    "        label='Interpolated knots (n={})'.format(len(x_knots)))\n",
+    "    plt.legend(loc='best')\n",
+    "#     plt.show()\n",
+    "\n",
+    "    return x_knots, z_knots, best_s\n",
+    "\n",
+    "\n",
+    "x_knots, z_knots, best_s = simplify_profile(x, z, site)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "code_folding": [
+     0
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "def get_x_z_from_knots(x_knots, z_knots):\n",
+    "    # Fit spline to profile\n",
+    "    s = UnivariateSpline(x_knots, z_knots, s=0, k=1)\n",
+    "    xs = np.linspace(x_knots[0], x_knots[-1], 1000)\n",
+    "    zs = s(xs)\n",
+    "    return xs, zs\n",
+    "\n",
+    "def simplify_segments_with_same_slopes(x_knots, x, z, site, best_s):\n",
+    "    \"\"\"\n",
+    "    Removes knots which have similar slopes on either side. \n",
+    "    This is an extra simplifcation as the linear splines may still include\n",
+    "    some nodes that aren't required.\n",
+    "    \"\"\"\n",
+    "    removed_x_knots = []\n",
+    "    while True:\n",
+    "        for x_knot1, x_knot2, x_knot3 in zip(x_knots[0:-2], x_knots[1:-1], x_knots[2:]):\n",
+    "\n",
+    "            # Get slope of segment behind point at x_knot2\n",
+    "            x_seg, z_seg = xz(x_knot1, x_knot2, x, z)\n",
+    "            slope1 = get_slope(x_seg, z_seg)\n",
+    "\n",
+    "            # Get slope of line in front of point at x_knot2\n",
+    "            x_seg, z_seg = xz(x_knot2, x_knot3, x, z)\n",
+    "            slope2 = get_slope(x_seg, z_seg)\n",
+    "\n",
+    "            slope_diff = abs(slope1 - slope2)\n",
+    "\n",
+    "            # Also check if points are too close together\n",
+    "            if x_knot3 - x_knot1 < 3: # m\n",
+    "                too_close = True\n",
+    "            else:\n",
+    "                too_close = False\n",
+    "            \n",
+    "            # Good, the slopes are different on each side, keep checking\n",
+    "            if slope_diff > 0.01 and too_close == False:\n",
+    "                continue\n",
+    "            # Else bad, slopes are the same so we have to remove this point\n",
+    "            else:\n",
+    "                print('Knot at x={} removed (slope_diff={:.3f})'.format(\n",
+    "                    x_knot2, slope_diff))\n",
+    "                x_knots = np.delete(x_knots, np.where(x_knots == x_knot2), axis=0)\n",
+    "                removed_x_knots.append(x_knot2)\n",
+    "                break\n",
+    "\n",
+    "        else:\n",
+    "            print(\"All segments have different slopes\")\n",
+    "            x_knots_simplified = x_knots\n",
+    "            z_knots_simplified = [\n",
+    "                z_val for x_val, z_val in zip(x, z) if x_val in x_knots_simplified\n",
+    "            ]\n",
+    "            break\n",
+    "    \n",
+    "    # Find z location of our removed knots\n",
+    "    removed_z_knots = [\n",
+    "                z_val for x_val, z_val in zip(x, z) if x_val in removed_x_knots\n",
+    "            ]\n",
+    "    \n",
+    "    # Get the new interpolated spline from our simplified knots\n",
+    "    xs, zs = get_x_z_from_knots(x_knots_simplified, z_knots_simplified)\n",
+    "    \n",
+    "    # Plot for checking\n",
+    "#     plt.title('{}: Linear spline simplification by comparing slopes'.format(site))\n",
+    "#     plt.xlabel('Distance (m)')\n",
+    "#     plt.ylabel('Elevation (m AHD)')\n",
+    "#     plt.plot(x, z, 'k.-', label='Raw profile')\n",
+    "#     plt.plot(\n",
+    "#         xs, zs, 'r-', label='Interpolated profile (s={:.2f})'.format(best_s))\n",
+    "#     plt.plot(\n",
+    "#         x_knots_simplified,\n",
+    "#         z_knots_simplified,\n",
+    "#         'ro',\n",
+    "#         markersize=8,\n",
+    "#         markeredgewidth=1.5,\n",
+    "#         markeredgecolor='orange',\n",
+    "#         label='Interpolated knots (n={})'.format(len(x_knots)))\n",
+    "#     plt.plot(\n",
+    "#         removed_x_knots,\n",
+    "#         removed_z_knots,\n",
+    "#         'ro',\n",
+    "#         markersize=10,\n",
+    "#         markeredgewidth=2.5,\n",
+    "#         markerfacecolor=\"None\",\n",
+    "#         markeredgecolor='orange',\n",
+    "#         label='Removed knots (n={})'.format(len(removed_x_knots)))\n",
+    "#     plt.legend(loc='best')\n",
+    "#     plt.show()\n",
+    "\n",
+    "    return x_knots_simplified,z_knots_simplified\n",
+    "\n",
+    "x_knots, z_knots = simplify_segments_with_same_slopes(x_knots, x, z, site, best_s)\n",
+    "xs, zs = get_x_z_from_knots(x_knots, z_knots)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "code_folding": [
+     1
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "## BACKUP\n",
+    "def find_crest_and_toes(x,z,x_knots,best_s):\n",
+    "\n",
+    "    # Get a cubic spline so we can have continuous second order deriviates for our profile\n",
+    "#     spl = interpolate.splrep(x, z, k=4,t=x_knots[1:-1])\n",
+    "    spl = interpolate.splrep(x, z, k=3,s=best_s)\n",
+    "\n",
+    "    xx = np.linspace(x[0], x[-1], 1000)\n",
+    "    zz = interpolate.splev(xx, spl)\n",
+    "    zz_d1 = interpolate.splev(xx, spl, der=1)\n",
+    "    zz_d2 = interpolate.splev(xx, spl, der=2)\n",
+    "    zz_d3 = interpolate.splev(xx, spl, der=3)\n",
+    "\n",
+    "    try:\n",
+    "        # Find dune crest by looking at elevation\n",
+    "        peaks_crest, props_crest = find_peaks(zz, height=3, prominence=0.5)\n",
+    "        \n",
+    "        # Save potential crests\n",
+    "        x_potential_crests = xx[peaks_crest]\n",
+    "        z_potential_crests = zz[peaks_crest]\n",
+    "\n",
+    "        # Take most seaward peak as dune crest\n",
+    "        i_crest = peaks_crest[-1]\n",
+    "        x_crest = xx[i_crest]\n",
+    "        z_crest = zz[i_crest]\n",
+    "\n",
+    "        print('Found {} potential dune crests from elevation'.format(len(peaks_crest)))\n",
+    "\n",
+    "    except:\n",
+    "        print('No crests found from elevation')\n",
+    "        x_crest = np.nan\n",
+    "        z_crest = np.nan\n",
+    "        pass\n",
+    "\n",
+    "    try:\n",
+    "        if np.isnan(x_crest) and np.isnan(x_crest):\n",
+    "            # Find dune crest by looking at 2nd deriviate\n",
+    "            # Multiply by -1 since crest will have a negative 2nd derivative and we want to find peaks (i.e. positive)\n",
+    "            peaks_crest, props_crest = find_peaks(zz_d2*-1, height=0.02,width=0)\n",
+    "            \n",
+    "            \n",
+    "            # Save potential crests\n",
+    "            x_potential_crests = xx[peaks_crest]\n",
+    "            z_potential_crests = zz[peaks_crest]\n",
+    "            print('Found {} potential dune crests from 2nd derivitive'.format(len(peaks_crest)))\n",
+    "\n",
+    "            # Take peak with biggest height\n",
+    "            i_biggest_crest = np.argmax(props_crest['peak_heights'])\n",
+    "            i_crest = peaks_crest[i_biggest_crest]\n",
+    "#             x_crest = xx[i_crest]\n",
+    "#             z_crest = zz[i_crest]\n",
+    "\n",
+    "            # Then take the left base of that peak\n",
+    "            x_crest = xx[props_crest['left_bases'][i_biggest_crest]]\n",
+    "            z_crest= zz[props_crest['left_bases'][i_biggest_crest]]\n",
+    "\n",
+    "\n",
+    "    except:\n",
+    "        # Take crest as location with maximum elevation\n",
+    "        i_crest = np.argmax(zz)\n",
+    "        x_crest = xx[i_crest]\n",
+    "        z_crest = zz[i_crest]\n",
+    "        print('No crest found, taking maximum elevation as crest')\n",
+    "\n",
+    "    try:\n",
+    "        # Find due toe\n",
+    "        peaks_toe, props_toe = find_peaks(zz_d2, height=0, prominence=0.02)\n",
+    "#         peaks_toe, props_toe = find_peaks(zz_d2, height=-3, prominence=0.00)\n",
+    "\n",
+    "        # Save potential crests\n",
+    "        x_potential_toes = xx[peaks_toe]\n",
+    "        z_potential_toes = zz[peaks_toe]\n",
+    "\n",
+    "        # Remove toes which are behind dune crest\n",
+    "        heights_toe = props_toe['peak_heights'][peaks_toe > i_crest]\n",
+    "        peaks_toe = peaks_toe[peaks_toe > i_crest]\n",
+    "\n",
+    "        # Remove toes that are less than 0.5m in height from dune crest\n",
+    "\n",
+    "        mask = z_crest-0.5>zz[peaks_toe]\n",
+    "        heights_toe = heights_toe[mask]\n",
+    "        peaks_toe = peaks_toe[mask]\n",
+    "\n",
+    "        # Take toe with biggest 2nd derivative height\n",
+    "        i_toe = peaks_toe[np.argmax(heights_toe)]\n",
+    "        x_toe = xx[i_toe]\n",
+    "        z_toe = zz[i_toe]\n",
+    "        print('Found {} potential dune toes from 2nd derivitive'.format(len(peaks_toe)))\n",
+    "\n",
+    "#         # The above method usually results in a toe value which is slightly too high up.\n",
+    "#         # We can improve this by moving the toe seaward to the next negative peak in the third\n",
+    "#         # derivative of elevation\n",
+    "#         peaks_toe_d3, props_toe_d3 = find_peaks(zz_d3*-1, height=0.03, prominence=0.02)\n",
+    "\n",
+    "#         # Filter everything landward of the previous toe position, then take the first (most landward) peak\n",
+    "#         mask = xx[peaks_toe_d3] > x_toe\n",
+    "#         peaks_toe_d3 = peaks_toe_d3[mask]\n",
+    "\n",
+    "#         if peaks_toe_d3.size != 0:\n",
+    "#             print('Adjusting toe position based on d3')\n",
+    "#             x_toe = xx[peaks_toe_d3[0]]\n",
+    "#             z_toe = zz[peaks_toe_d3[0]]\n",
+    "\n",
+    "#         # Move toe forward to based on the third derivative of elevation\n",
+    "#         print('Adjusting toe position based on d3')\n",
+    "#         mask = (zz_d3 > -0.0008) & (xx > x_toe) & (np.r_[np.diff(zz_d3)[0],np.diff(zz_d3)] > 0)\n",
+    "#         x_toe = xx[mask][1]\n",
+    "#         z_toe = zz[mask][1]\n",
+    "\n",
+    "    except:\n",
+    "        x_toe = np.nan\n",
+    "        z_toe = np.nan\n",
+    "        print('No toe found')\n",
+    "    \n",
+    "    return xx,zz, zz_d1,zz_d2,zz_d3,x_crest,z_crest, x_toe,z_toe, x_potential_crests,z_potential_crests, x_potential_toes,z_potential_toes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "code_folding": [
+     126
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "def find_crest_and_toes(x,z,x_knots,best_s):\n",
+    "\n",
+    "    # Get a cubic spline so we can have continuous second order deriviates for our profile\n",
+    "#     spl = interpolate.splrep(x, z, k=4,t=x_knots[1:-1])\n",
+    "    spl = interpolate.splrep(x, z, k=3,s=best_s)\n",
+    "\n",
+    "    xx = np.linspace(x[0], x[-1], 1000)\n",
+    "    zz = interpolate.splev(xx, spl)\n",
+    "    zz_d1 = interpolate.splev(xx, spl, der=1)\n",
+    "    zz_d2 = interpolate.splev(xx, spl, der=2)\n",
+    "    zz_d3 = interpolate.splev(xx, spl, der=3)\n",
+    "\n",
+    "    # Find the biggest slopes\n",
+    "    peaks, props = find_peaks(zz_d1*-1, height=0.15, width=0,prominence=0.1)\n",
+    "\n",
+    "    if len(peaks) != 0:\n",
+    "\n",
+    "        x_potential_crests =[xx[val] for val in props['left_bases']]\n",
+    "        x_potential_toes =[xx[val] for val in props['right_bases']]\n",
+    "        x_potential_heights = [val for val in props['peak_heights']]\n",
+    "        x_potential_face_center = xx[peaks]\n",
+    "\n",
+    "        # Put a limit on how when a slope ends.\n",
+    "        # Dune faces has to be steeper than this value\n",
+    "#         min_slope = -0.10\n",
+    "\n",
+    "        x_new_potential_crests = []\n",
+    "        x_new_potential_toes = []\n",
+    "        z_new_potential_crests = []\n",
+    "        z_new_potential_toes = []\n",
+    "        dune_heights = []\n",
+    "        slopes = []\n",
+    "\n",
+    "        for x_crest, x_center, x_toe,height in zip(x_potential_crests, x_potential_face_center, x_potential_toes,x_potential_heights):\n",
+    "            print('Checking x_crest={:.2f}m,x_center={:.2f}m,x_toe={:.2f}m'.format(x_crest, x_center, x_toe))\n",
+    "\n",
+    "            min_slope = height/-6\n",
+    "            # Check the land side of the dune face\n",
+    "            land_mask = ((x_crest<=xx)&(xx<=x_center))\n",
+    "            land_slope_okay = zz_d1[land_mask]<min_slope\n",
+    "            x_land_slope_okay = xx[land_mask][~land_slope_okay]\n",
+    "            if len(x_land_slope_okay) == 0:\n",
+    "                x_new_crest = x_crest\n",
+    "            else:\n",
+    "                x_new_crest = x_land_slope_okay[-1]\n",
+    "            idx_new_crest = np.where(x_new_crest==xx)[0]\n",
+    "            z_new_crest = zz[idx_new_crest][0]\n",
+    "\n",
+    "    #         # TODO Check, for now, keep crest as detected level\n",
+    "    #         # Maybe we don't need to change\n",
+    "    #         x_new_crest = x_crest\n",
+    "    #         idx_new_crest = np.where(x_new_crest==xx)[0]\n",
+    "    #         z_new_crest = zz[idx_new_crest][0]\n",
+    "    #         ###\n",
+    "\n",
+    "            # Check the sea side of the dune face\n",
+    "            sea_mask = ((x_center<=xx)&(xx<=x_toe))\n",
+    "            sea_slope_okay = zz_d1[sea_mask]<min_slope\n",
+    "            x_sea_slope_okay = xx[sea_mask][~sea_slope_okay]\n",
+    "            if len(x_sea_slope_okay) == 0:\n",
+    "                x_new_toe = x_toe\n",
+    "            else:\n",
+    "                x_new_toe = x_sea_slope_okay[0]\n",
+    "            idx_new_toe = np.where(x_new_toe==xx)[0]\n",
+    "            z_new_toe = zz[idx_new_toe][0]\n",
+    "\n",
+    "            slopes.append((z_new_crest - z_new_toe)/(x_new_crest - x_new_toe))\n",
+    "            dune_heights.append(z_new_crest - z_new_toe)\n",
+    "            x_new_potential_crests.append(x_new_crest)\n",
+    "            x_new_potential_toes.append(x_new_toe)\n",
+    "            z_new_potential_crests.append(z_new_crest)\n",
+    "            z_new_potential_toes.append(z_new_toe)\n",
+    "\n",
+    "        # Specifiy minimum dune crest elevation and minimum dune height\n",
+    "        min_dune_ele = np.median(zz)/3 # m AHD\n",
+    "        print('min_dune_ele: {:.2f}m'.format(min_dune_ele))\n",
+    "        min_dune_height = 0.5 # m\n",
+    "        mask = (np.array(z_new_potential_crests) > min_dune_ele) & (np.array(dune_heights) > min_dune_height )\n",
+    "\n",
+    "        slopes = np.array(slopes)[mask]\n",
+    "        dune_heights = np.array(dune_heights)[mask]    \n",
+    "        x_potential_crests = np.array(x_new_potential_crests)[mask]\n",
+    "        x_potential_toes = np.array(x_new_potential_toes)[mask]\n",
+    "        z_potential_crests = np.array(z_new_potential_crests)[mask]\n",
+    "        z_potential_toes = np.array(z_new_potential_toes)[mask]\n",
+    "\n",
+    "#         # Select dune by largest dune height\n",
+    "#         i_dune = np.argmax(dune_heights)\n",
+    "\n",
+    "        ## TODO Throws error if x_potential_crests is empty\n",
+    "\n",
+    "        # Select dune by most seaward\n",
+    "        i_dune = np.argmax(x_potential_crests)\n",
+    "\n",
+    "\n",
+    "        \n",
+    "#         # Select dune by biggest slope\n",
+    "#         i_dune =np.argmax(slopes*-1)\n",
+    "        x_crest = x_potential_crests[i_dune]\n",
+    "        x_toe = x_potential_toes[i_dune]\n",
+    "        z_crest = z_potential_crests[i_dune]\n",
+    "        z_toe = z_potential_toes[i_dune]\n",
+    "\n",
+    "    #     try:\n",
+    "    #         # Find dune crest by looking at elevation\n",
+    "    #         peaks_crest, props_crest = find_peaks(zz, height=3, prominence=0.5)\n",
+    "\n",
+    "    #         # Save potential crests\n",
+    "    #         x_potential_crests = xx[peaks_crest]\n",
+    "    #         z_potential_crests = zz[peaks_crest]\n",
+    "\n",
+    "    #         # Take most seaward peak as dune crest\n",
+    "    #         i_crest = peaks_crest[-1]\n",
+    "    #         x_crest = xx[i_crest]\n",
+    "    #         z_crest = zz[i_crest]\n",
+    "\n",
+    "    #         print('Found {} potential dune crests from elevation'.format(len(peaks_crest)))\n",
+    "\n",
+    "    #     except:\n",
+    "    #         print('No crests found from elevation')\n",
+    "    #         x_crest = np.nan\n",
+    "    #         z_crest = np.nan\n",
+    "    #         pass\n",
+    "\n",
+    "    #     try:\n",
+    "    #         if np.isnan(x_crest) and np.isnan(x_crest):\n",
+    "    #             # Find dune crest by looking at 2nd deriviate\n",
+    "    #             # Multiply by -1 since crest will have a negative 2nd derivative and we want to find peaks (i.e. positive)\n",
+    "    #             peaks_crest, props_crest = find_peaks(zz_d2*-1, height=0.02,width=0)\n",
+    "\n",
+    "\n",
+    "    #             # Save potential crests\n",
+    "    #             x_potential_crests = xx[peaks_crest]\n",
+    "    #             z_potential_crests = zz[peaks_crest]\n",
+    "    #             print('Found {} potential dune crests from 2nd derivitive'.format(len(peaks_crest)))\n",
+    "\n",
+    "    #             # Take peak with biggest height\n",
+    "    #             i_biggest_crest = np.argmax(props_crest['peak_heights'])\n",
+    "    #             i_crest = peaks_crest[i_biggest_crest]\n",
+    "    # #             x_crest = xx[i_crest]\n",
+    "    # #             z_crest = zz[i_crest]\n",
+    "\n",
+    "    #             # Then take the left base of that peak\n",
+    "    #             x_crest = xx[props_crest['left_bases'][i_biggest_crest]]\n",
+    "    #             z_crest= zz[props_crest['left_bases'][i_biggest_crest]]\n",
+    "\n",
+    "\n",
+    "    #     except:\n",
+    "    #         # Take crest as location with maximum elevation\n",
+    "    #         i_crest = np.argmax(zz)\n",
+    "    #         x_crest = xx[i_crest]\n",
+    "    #         z_crest = zz[i_crest]\n",
+    "    #         print('No crest found, taking maximum elevation as crest')\n",
+    "\n",
+    "    #     try:\n",
+    "    #         # Find due toe\n",
+    "    #         peaks_toe, props_toe = find_peaks(zz_d2, height=0, prominence=0.02)\n",
+    "    # #         peaks_toe, props_toe = find_peaks(zz_d2, height=-3, prominence=0.00)\n",
+    "\n",
+    "    #         # Save potential crests\n",
+    "    #         x_potential_toes = xx[peaks_toe]\n",
+    "    #         z_potential_toes = zz[peaks_toe]\n",
+    "\n",
+    "    #         # Remove toes which are behind dune crest\n",
+    "    #         heights_toe = props_toe['peak_heights'][peaks_toe > i_crest]\n",
+    "    #         peaks_toe = peaks_toe[peaks_toe > i_crest]\n",
+    "\n",
+    "    #         # Remove toes that are less than 0.5m in height from dune crest\n",
+    "\n",
+    "    #         mask = z_crest-0.5>zz[peaks_toe]\n",
+    "    #         heights_toe = heights_toe[mask]\n",
+    "    #         peaks_toe = peaks_toe[mask]\n",
+    "\n",
+    "    #         # Take toe with biggest 2nd derivative height\n",
+    "    #         i_toe = peaks_toe[np.argmax(heights_toe)]\n",
+    "    #         x_toe = xx[i_toe]\n",
+    "    #         z_toe = zz[i_toe]\n",
+    "    #         print('Found {} potential dune toes from 2nd derivitive'.format(len(peaks_toe)))\n",
+    "\n",
+    "    # #         # The above method usually results in a toe value which is slightly too high up.\n",
+    "    # #         # We can improve this by moving the toe seaward to the next negative peak in the third\n",
+    "    # #         # derivative of elevation\n",
+    "    # #         peaks_toe_d3, props_toe_d3 = find_peaks(zz_d3*-1, height=0.03, prominence=0.02)\n",
+    "\n",
+    "    # #         # Filter everything landward of the previous toe position, then take the first (most landward) peak\n",
+    "    # #         mask = xx[peaks_toe_d3] > x_toe\n",
+    "    # #         peaks_toe_d3 = peaks_toe_d3[mask]\n",
+    "\n",
+    "    # #         if peaks_toe_d3.size != 0:\n",
+    "    # #             print('Adjusting toe position based on d3')\n",
+    "    # #             x_toe = xx[peaks_toe_d3[0]]\n",
+    "    # #             z_toe = zz[peaks_toe_d3[0]]\n",
+    "\n",
+    "    # #         # Move toe forward to based on the third derivative of elevation\n",
+    "    # #         print('Adjusting toe position based on d3')\n",
+    "    # #         mask = (zz_d3 > -0.0008) & (xx > x_toe) & (np.r_[np.diff(zz_d3)[0],np.diff(zz_d3)] > 0)\n",
+    "    # #         x_toe = xx[mask][1]\n",
+    "    # #         z_toe = zz[mask][1]\n",
+    "\n",
+    "    #     except:\n",
+    "    #         x_toe = np.nan\n",
+    "    #         z_toe = np.nan\n",
+    "    #         print('No toe found')\n",
+    "\n",
+    "    \n",
+    "    else:\n",
+    "        # Take crest as location with maximum elevation\n",
+    "        i_crest = np.argmax(zz)\n",
+    "        x_crest = xx[i_crest]\n",
+    "        z_crest = zz[i_crest]\n",
+    "        x_potential_crests = np.nan\n",
+    "        z_potential_crests = np.nan\n",
+    "        print('No crest found, taking maximum elevation as crest')\n",
+    "        \n",
+    "        x_toe = np.nan\n",
+    "        z_toe = np.nan\n",
+    "        x_potential_toes=np.nan\n",
+    "        z_potential_toes=np.nan\n",
+    "        \n",
+    "    # Check if toe is necessary. If slope of the dune face is similar to the median slope seaward of the dune\n",
+    "    # toe, let's remove the dune toe\n",
+    "    try:\n",
+    "        if np.isnan(x_toe) == False:\n",
+    "            dune_slope =(z_crest - z_toe) / (x_crest - x_toe)\n",
+    "            median_foreshore_slope = np.median(zz_d1[xx > x_toe])\n",
+    "            std_foreshore_slope = np.std(zz_d1[xx>x_toe])\n",
+    "            print('foreshore_std:{:.4f}'.format(std_foreshore_slope))\n",
+    "            print('foreshore_med_slope:{:.4f}'.format(median_foreshore_slope))\n",
+    "            print('dune_slope:{:.4f}'.format(dune_slope))\n",
+    "            if abs(dune_slope - median_foreshore_slope) < 0.075 and std_foreshore_slope <0.025:\n",
+    "                x_toe = np.nan\n",
+    "                z_toe = np.nan\n",
+    "    except:\n",
+    "        pass\n",
+    "        \n",
+    "    return xx,zz, zz_d1,zz_d2,zz_d3,x_crest,z_crest, x_toe,z_toe, x_potential_crests,z_potential_crests, x_potential_toes,z_potential_toes \n",
+    "        \n",
+    "        \n",
+    "def plot_profile(site, x,z,  xx,zz, zz_d1,zz_d2,zz_d3,x_crest,z_crest, x_toe,z_toe, x_potential_crests,z_potential_crests, x_potential_toes,z_potential_toes):\n",
+    "    plt.figure(figsize=(10,12))\n",
+    "    plt.subplot(411)\n",
+    "    plt.plot(x, z, label='raw')\n",
+    "    plt.plot(xx,zz,label='interpolated')\n",
+    "    plt.plot(x_potential_crests,z_potential_crests,'rv',markersize=10,fillstyle='none',label='Dune crest (potential)')\n",
+    "    plt.plot(x_potential_toes,z_potential_toes,'r^',markersize=10,fillstyle='none',label='Dune toe (potential)')\n",
+    "    plt.plot(x_crest,z_crest,'rv',markersize=10,label='Dune crest (x={:.2f} m, z={:.2f} m)'.format(x_crest,z_crest))\n",
+    "    plt.plot(x_toe,z_toe,'r^',markersize=10,label='Dune toe (x={:.2f} m, z={:.2f} m)'.format(x_toe,z_toe))\n",
+    "    plt.title('{}: elevations'.format(site))\n",
+    "    plt.legend(loc='best')\n",
+    "\n",
+    "    plt.subplot(412)\n",
+    "    d1 = np.diff(zz) / np.diff(xx)\n",
+    "    plt.plot(xx, zz_d1)\n",
+    "    plt.axvline(x_crest,color='red', label='Dune crest')\n",
+    "    plt.axvline(x_toe,color='red', label='Dune toe')\n",
+    "    # plt.ylim([-0.2,0.2])\n",
+    "    plt.title('first derivative - slope')\n",
+    "\n",
+    "    plt.subplot(413)\n",
+    "    d2 = np.diff(d1) / np.diff(xx[1:])\n",
+    "    plt.plot(xx, zz_d2)\n",
+    "    plt.axvline(x_crest,color='red', label='Dune crest')\n",
+    "    plt.axvline(x_toe,color='red', label='Dune toe')\n",
+    "    plt.title('second derivative - change in slope')\n",
+    "#     plt.ylim([-0.025,0.025])\n",
+    "\n",
+    "    plt.subplot(414)\n",
+    "    plt.plot(xx, zz_d3)\n",
+    "    plt.axvline(x_crest,color='red', label='Dune crest')\n",
+    "    plt.axvline(x_toe,color='red', label='Dune toe')\n",
+    "    plt.title('third derivative')\n",
+    "#     plt.ylim([-0.0025,0.0025])\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "# xx,zz, x_crest,z_crest, x_toe,z_toe, x_potential_crests,z_potential_crests, x_potential_toes,z_potential_toes = find_crest_and_toes(x,z,x_knots)\n",
+    "\n",
+    "# plot_profile(site, x,z, xx,zz, x_crest,z_crest, x_toe,z_toe, x_potential_crests,z_potential_crests, x_potential_toes,z_potential_toes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "code_folding": []
+   },
+   "outputs": [],
+   "source": [
+    "def get_crests_and_toes(site, profile_type, df_profiles):\n",
+    "    x, z = get_profile_data(site, profile_type, df_profiles)\n",
+    "    x_knots, z_knots, best_s = simplify_profile(x, z, site)\n",
+    "    x_knots, z_knots = simplify_segments_with_same_slopes(x_knots, x, z, site, best_s)\n",
+    "#     xs, zs = get_x_z_from_knots(x_knots, z_knots)\n",
+    "    xx,zz, zz_d1,zz_d2,zz_d3,x_crest,z_crest, x_toe,z_toe, x_potential_crests,z_potential_crests, x_potential_toes,z_potential_toes = find_crest_and_toes(x,z,x_knots,best_s)\n",
+    "\n",
+    "    plot_profile(site, x,z,  xx,zz, zz_d1,zz_d2,zz_d3,x_crest,z_crest, x_toe,z_toe, x_potential_crests,z_potential_crests, x_potential_toes,z_potential_toes)\n",
+    "\n",
+    "    \n",
+    "# site = 'WAMBE0010'\n",
+    "# site = 'NINEMs0048'\n",
+    "# site = 'NAMB0013'\n",
+    "# site = 'AVOCAn0009'\n",
+    "# site = 'GRANTSs0014'\n",
+    "site = 'NARRA0001'\n",
+    "\n",
+    "import random\n",
+    "site = random.sample(df_profiles.index.get_level_values('site_id').unique().tolist(), 1)[0]\n",
+    "\n",
+    "print(site)\n",
+    "# site='NSHORE_s0014'\n",
+    "get_crests_and_toes(site,'prestorm', df_profiles)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Find berms \n",
+    "for x_knot1, x_knot2, in zip(x_knots[0:-2], x_knots[1:-1]):\n",
+    "    \n",
+    "    # Berms are only located seaward of the dune toe (but sometimes dune toe is undefined, so use dune crest)\n",
+    "    if x_knot1 < x_crest:\n",
+    "        continue\n",
+    "    \n",
+    "    # Get slope of segment\n",
+    "    x_seg, z_seg = xz(x_knot1, x_knot2, x, z)\n",
+    "    slope = get_slope(x_seg, z_seg)\n",
+    "    z_mean = np.mean(z_seg)\n",
+    "    width = x_knot2 - x_knot1\n",
+    "    \n",
+    "    if slope > -0.01:\n",
+    "        print('{:.1f}m wide berm at x={:.1f}m to x={:.1f}m at z={:.1f}m'.format(width, x_knot1, x_knot2, z_mean))\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hide_input": true
+   },
+   "outputs": [],
+   "source": [
+    "# def reject_outliers(data, m = 5):\n",
+    "#     d = np.abs(data - np.median(data))\n",
+    "#     mdev = np.median(d)\n",
+    "#     s = d/mdev if mdev else 0.\n",
+    "#     return ma.masked_where(s > m, data)\n",
+    "\n",
+    "# def hampel(vals_orig, k=7, t0=3):\n",
+    "#     '''\n",
+    "#     vals: pandas series of values from which to remove outliers\n",
+    "#     k: size of window (including the sample; 7 is equal to 3 on either side of value)\n",
+    "#     '''\n",
+    "#     #Make copy so original not edited\n",
+    "#     vals_orig = pd.Series(vals_orig)\n",
+    "#     vals=vals_orig.copy()    \n",
+    "#     #Hampel Filter\n",
+    "#     L= 1.4826\n",
+    "#     rolling_median=vals.rolling(k).median()\n",
+    "#     difference=np.abs(rolling_median-vals)\n",
+    "#     median_abs_deviation=difference.rolling(k).median()\n",
+    "#     threshold= t0 *L * median_abs_deviation\n",
+    "#     outlier_idx=difference>threshold\n",
+    "#     vals[outlier_idx]=np.nan\n",
+    "#     return(vals.tolist())\n",
+    "\n",
+    "# # Remove segements where slopes are too high, indicative of a structure\n",
+    "# segments = [xz(x1, x2,x,z) for (x1, x2) in pairwise(x_knots_simplified)]\n",
+    "# slopes = np.array([get_slope(x1,x2) for x1,x2 in segments])\n",
+    "# mask = reject_outliers(np.array(slopes))\n",
+    "# x_knots_filtered = x_knots_simplified[np.append(True,~mask.mask)]\n",
+    "# z_knots_filtered = [z_val for x_val, z_val in zip(x,z) if x_val in x_knots_filtered]\n",
+    "\n",
+    "\n",
+    "\n",
+    "# # Hampel filter based on z values\n",
+    "# mask = ma.masked_invalid(hampel(z_knots_filtered))\n",
+    "# x_knots_filtered = x_knots_filtered[~mask.mask]\n",
+    "# z_knots_filtered = [z_val for x_val, z_val in zip(x,z) if x_val in x_knots_filtered]\n",
+    "\n",
+    "\n",
+    "\n",
+    "# # plt.figure(figsize=(10,5))\n",
+    "# # plt.plot(x_knots_simplified, np.append(slopes[0],slopes), '.-')\n",
+    "# # plt.plot(x_knots_simplified[np.append(True,~mask.mask)], np.append(slopes[~mask.mask][0], slopes[~mask.mask]), '.-')\n",
+    "# # # plt.plot(xs, zs)\n",
+    "# # # plt.plot(x_knots_filtered, z_knots_filtered,'.',markersize=15)\n",
+    "# # plt.show()\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "# plt.figure(figsize=(10,5))\n",
+    "# plt.plot(x, z, '.-')\n",
+    "# plt.plot(xs, zs)\n",
+    "# plt.plot(x_knots_filtered, z_knots_filtered,'.',markersize=15)\n",
+    "# plt.show()\n",
+    "# z_knots_filtered"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Find dune crests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "code_folding": [
+     18
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.signal import find_peaks\n",
+    "\n",
+    "\n",
+    "def crest_by_deriv_2(z):\n",
+    "    \"\"\"\n",
+    "    Try get crest elevation by taking the difference in 2nd derivative of elevation.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    z = savgol_filter(z, 15, 3, mode='nearest')\n",
+    "    deriv_2_diff = np.diff(np.gradient(z)) * -1\n",
+    "\n",
+    "    # Ensure same size arrays for plotting\n",
+    "    deriv_2_diff = np.insert(deriv_2_diff, 0, deriv_2_diff[0])\n",
+    "    med = np.median(deriv_2_diff)\n",
+    "    std = np.std(deriv_2_diff)\n",
+    "    peaks, peak_props = find_peaks(deriv_2_diff, height=med+std*2, distance=10)\n",
+    "    return peaks, peak_props, deriv_2_diff\n",
+    "\n",
+    "\n",
+    "def crest_by_elevation_peaks(z, min_dune_prominence=1.0):\n",
+    "    # Try find peaks in elevation\n",
+    "    peaks, peak_props = find_peaks(z, distance=10, prominence=min_dune_prominence)\n",
+    "    return peaks, peak_props\n",
+    "\n",
+    "\n",
+    "def get_dune_crest(x, z, xs,zs, x_knots, z_knots, site):\n",
+    "   \n",
+    "    peaks, peak_props = crest_by_elevation_peaks(z)\n",
+    "    \n",
+    "    if peaks.size != 0:\n",
+    "        # Choose crest by most landward peak\n",
+    "        x_crest = x[peaks[-1]]\n",
+    "        z_crest = z[peaks[-1]]\n",
+    "    \n",
+    "    # If no peaks in elevation are found, find by maximum second derivative\n",
+    "    else:\n",
+    "        peaks, peak_props, deriv_2_diff = crest_by_deriv_2(z)\n",
+    "        \n",
+    "        plt.title(\n",
+    "            '{}: Difference of 2nd derivative in elevation'.format(site))\n",
+    "        plt.xlabel('Distance (m)')\n",
+    "        plt.ylabel('Diff(2nd Derivative Elevation)')\n",
+    "        plt.plot(x, deriv_2_diff)\n",
+    "\n",
+    "               \n",
+    "        if peaks.size!= 0:\n",
+    "\n",
+    "            # Choose crest by highest peak in diff of 2nd derivative\n",
+    "            i_best = np.argmax(peak_props['peak_heights'])\n",
+    "            x_crest = x[peaks[i_best]]\n",
+    "            z_crest = z[peaks[i_best]]\n",
+    "            plt.plot(\n",
+    "                x[peaks],\n",
+    "                deriv_2_diff[peaks],\n",
+    "                'o',\n",
+    "                markersize=20,\n",
+    "                fillstyle='none')\n",
+    "            plt.show() \n",
+    "            \n",
+    "        else:\n",
+    "            plt.show() \n",
+    "            # If no peaks in diff of 2nd derivative are found, take landward most point\n",
+    "            if peaks.size == 0:\n",
+    "                x_crest = x_knots[0]\n",
+    "                z_crest = z_knots[0]\n",
+    "\n",
+    "    # Move crest to closest knot\n",
+    "    idx_crest = min(range(len(x_knots)), key=lambda i: abs(x_knots[i]-x_crest))\n",
+    "    x_crest = x_knots[idx_crest]\n",
+    "    z_crest = z_knots[idx_crest]\n",
+    "    \n",
+    "    # Plot for checking\n",
+    "    plt.title('{}: Find dune crest by peak in diff 2nd deriv'.format(site))\n",
+    "    plt.xlabel('Distance (m)')\n",
+    "    plt.ylabel('Elevation (m AHD)')\n",
+    "    plt.plot(x, z, 'k.-', label='Raw profile')\n",
+    "    plt.plot(\n",
+    "        xs,\n",
+    "        zs,\n",
+    "        'r-',\n",
+    "        label='Interpolated profile (s={:.2f})'.format(best_s))\n",
+    "    plt.plot(\n",
+    "        x_knots,\n",
+    "        z_knots,\n",
+    "        'ro',\n",
+    "        markersize=8,\n",
+    "        markeredgewidth=1.5,\n",
+    "        markeredgecolor='orange',\n",
+    "        label='Interpolated knots (n={})'.format(len(x_knots)))\n",
+    "    plt.plot(\n",
+    "        x_crest,\n",
+    "        z_crest,\n",
+    "        'bv',\n",
+    "        markersize=10,\n",
+    "        label='Dune crest (selected) (x={:.1f}m,z={:.1f}m)'.format(x_crest,z_crest))\n",
+    "    plt.plot(\n",
+    "        x[peaks],\n",
+    "        z[peaks],\n",
+    "        'o',\n",
+    "        markersize=10,\n",
+    "        fillstyle='none',\n",
+    "        label='Dune crest (potential)')\n",
+    "\n",
+    "    plt.legend(loc='best')\n",
+    "    plt.show()\n",
+    "        \n",
+    "    return x_crest, z_crest\n",
+    "        \n",
+    "x_crest, z_crest = get_dune_crest(x, z, xs,zs, x_knots, z_knots, site)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get toe by second derivative\n",
+    "def toe_by_deriv_2(z):\n",
+    "    \"\"\"\n",
+    "    Try get crest elevation by taking the difference in 2nd derivative of elevation.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    z = savgol_filter(z,11, 3, mode='nearest')\n",
+    "    deriv_2_diff = np.diff(np.gradient(z))\n",
+    "\n",
+    "    # Ensure same size arrays for plotting\n",
+    "    deriv_2_diff = np.insert(deriv_2_diff, 0, deriv_2_diff[0])\n",
+    "    med = np.median(deriv_2_diff)\n",
+    "    std = np.std(deriv_2_diff)\n",
+    "    peaks, peak_props = find_peaks(deriv_2_diff, height=max(0.01,med+std*3), distance=10)\n",
+    "    return peaks, peak_props, deriv_2_diff\n",
+    "\n",
+    "def get_dune_toe(x, z, xs,zs, x_crest,z_crest,x_knots, z_knots, site):\n",
+    "    peaks, peak_props, deriv_2_diff = toe_by_deriv_2(z)\n",
+    "\n",
+    "    plt.title('{}: Difference of 2nd derivative in elevation'.format(site))\n",
+    "    plt.xlabel('Distance (m)')\n",
+    "    plt.ylabel('Diff(2nd Derivative Elevation)')\n",
+    "    plt.plot(x, deriv_2_diff)\n",
+    "    \n",
+    "    if peaks.size!= 0:\n",
+    "        \n",
+    "        # Remove peaks which are behind the crest\n",
+    "        # TODO What happens if all peaks are removed?\n",
+    "        mask = ma.masked_where(x[peaks] <= x_crest, x[peaks])\n",
+    "        peaks = peaks[~mask.mask]\n",
+    "\n",
+    "    if peaks.size!= 0:\n",
+    "        # Choose toe by highest peak in diff of 2nd derivative\n",
+    "        i_best = np.argmax(peak_props['peak_heights'])\n",
+    "        x_toe = x[peaks[i_best]][0]\n",
+    "        z_toe = z[peaks[i_best]][0]\n",
+    "#         # Move crest to closest knot\n",
+    "#         idx_toe = min(range(len(x_knots)), key=lambda i: abs(x_knots[i]-x_toe))\n",
+    "#         x_toe = x_knots[idx_toe]\n",
+    "#         z_toe = z_knots[idx_toe]\n",
+    "\n",
+    "        plt.plot(\n",
+    "            x[peaks],\n",
+    "            deriv_2_diff[peaks],\n",
+    "            'o',\n",
+    "            markersize=20,\n",
+    "            fillstyle='none')\n",
+    "        plt.show()\n",
+    "    else:\n",
+    "        x_toe = np.nan\n",
+    "        z_toe = np.nan\n",
+    "        plt.show()\n",
+    "\n",
+    "    # Plot for checking\n",
+    "    plt.title('{}: Find dune crest by peak in diff 2nd deriv'.format(site))\n",
+    "    plt.xlabel('Distance (m)')\n",
+    "    plt.ylabel('Elevation (m AHD)')\n",
+    "    plt.plot(x, z, 'k.-', label='Raw profile')\n",
+    "    plt.plot(\n",
+    "        xs,\n",
+    "        zs,\n",
+    "        'r-',\n",
+    "        label='Interpolated profile (s={:.2f})'.format(best_s))\n",
+    "    plt.plot(\n",
+    "        x_knots,\n",
+    "        z_knots,\n",
+    "        'ro',\n",
+    "        markersize=8,\n",
+    "        markeredgewidth=1.5,\n",
+    "        markeredgecolor='orange',\n",
+    "        label='Interpolated knots (n={})'.format(len(x_knots)))\n",
+    "    plt.plot(\n",
+    "        x_crest,\n",
+    "        z_crest,\n",
+    "        'bv',\n",
+    "        markersize=10,\n",
+    "        label='Dune crest(x={:.1f} m,z={:.1f} m)'.format(x_crest,z_crest))\n",
+    "    plt.plot(\n",
+    "        x_toe,\n",
+    "        z_toe,\n",
+    "        'b^',\n",
+    "        markersize=10,\n",
+    "        label='Dune toe (x={:.1f} m,z={:.1f} m)'.format(x_toe,z_toe))\n",
+    "\n",
+    "    plt.legend(loc='best')\n",
+    "    plt.show()\n",
+    "    return x_toe, z_toe\n",
+    "x_toe, z_toe = get_dune_toe(x, z, xs,zs, x_crest,z_crest,x_knots, z_knots, site)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def find_dune_crest_and_toe(site, profile_type, df_profiles):\n",
+    "    x, z = get_profile_data(site, profile_type, df_profiles)\n",
+    "    x_knots, z_knots = simplify_profile(x, z, site)\n",
+    "    x_knots, z_knots = simplify_segments_with_same_slopes(x_knots, x, z, site)\n",
+    "    xs, zs = get_x_z_from_knots(x_knots, z_knots)\n",
+    "    x_crest, z_crest = get_dune_crest(x, z, xs,zs, x_knots, z_knots, site)\n",
+    "    x_toe, z_toe = get_dune_toe(x, z, xs,zs, x_crest,z_crest,x_knots, z_knots, site)\n",
+    "    \n",
+    "find_dune_crest_and_toe('ENTRA0004', 'prestorm', df_profiles)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "hide_input": false,
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/05_twl_exceedence.ipynb b/notebooks/05_twl_exceedence.ipynb
new file mode 100644
index 0000000..848a527
--- /dev/null
+++ b/notebooks/05_twl_exceedence.ipynb
@@ -0,0 +1,362 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TWL Exceedance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.core.debugger import set_trace\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import decimal\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",
+    "from plotly import tools\n",
+    "import plotly.io as pio\n",
+    "from scipy import stats\n",
+    "import math\n",
+    "import matplotlib\n",
+    "from matplotlib import cm\n",
+    "import colorlover as cl\n",
+    "\n",
+    "from ipywidgets import widgets, Output\n",
+    "from IPython.display import display, clear_output, Image, HTML\n",
+    "\n",
+    "from sklearn.metrics import confusion_matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "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_crest_toes = df_from_csv('profile_features_crest_toes.csv', index_col=[0,1])\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": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calculate vertical distribution of wave count\n",
+    "For each site, calculate how many waves reached a certain elevation (store as a binned histogram)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Helper functions\n",
+    "def find_nearest(array, value):\n",
+    "    array = np.asarray(array)\n",
+    "    idx = np.nanargmin((np.abs(array - value)))\n",
+    "    return array[idx], idx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_profile_features_crest_toes.loc[(site_id,'prestorm'),'dune_toe_z']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = []\n",
+    "for site_id, df_site_twl in twls['forecasted']['mean_slope_sto06'].groupby('site_id'):\n",
+    "    \n",
+    "    twl_eles_per_wave = []\n",
+    "    \n",
+    "    # Iterate through each timestamp and calculate the number of waves at each interavl.\n",
+    "    # THIS LOOP IS SLOW\n",
+    "    for row in df_site_twl.itertuples():\n",
+    "        \n",
+    "        distribution = stats.norm(loc=row.tide+row.setup, scale=row.S_total/4) # CHECK\n",
+    "\n",
+    "        # Total number of waves we expect in this period\n",
+    "        n_waves = int(3600 / row.Tp)  # Check that we have 1 hour\n",
+    "        \n",
+    "        # Get z elevation of each wave twl in this hour and append to list\n",
+    "        twl_eles_per_wave.extend([distribution.ppf(1-x/n_waves) for x in range(1,n_waves+1)])\n",
+    "    \n",
+    "    # Remove nans and infs # CHECK WHY INF\n",
+    "    twl_eles_per_wave = list(np.asarray(twl_eles_per_wave)[np.isfinite(twl_eles_per_wave)])\n",
+    "    \n",
+    "    # Sort wave twl z elevations in descending list\n",
+    "    twl_eles_per_wave.sort(reverse=True)    \n",
+    "    \n",
+    "    # Get index of closest value of dune toe. This is the number of waves that exceeded the the dune toe\n",
+    "    try:\n",
+    "        _, idx =  find_nearest(twl_eles_per_wave, dune_toe_z)\n",
+    "    except:\n",
+    "        continue\n",
+    "    \n",
+    "    # Get forecasted and observed impacts\n",
+    "    forecasted_regime = impacts['forecasted']['mean_slope_sto06'].loc[site_id,'storm_regime']\n",
+    "    observed_regime = impacts['observed'].loc[site_id,'storm_regime']\n",
+    "    \n",
+    "    counts, bin_edges = np.histogram(twl_eles_per_wave, bins=100) \n",
+    "    \n",
+    "    data.append({\n",
+    "        'site_id': site_id,\n",
+    "        'forecasted_regime': forecasted_regime,\n",
+    "        'observed_regime': observed_regime,\n",
+    "        'n_waves_exceeding_dune_toe': idx,\n",
+    "        'n_waves': [x for x in range(0,500,1)],\n",
+    "        'truncated_twl_levels': [twl_eles_per_wave[x] for x in range(0,500,1)],\n",
+    "        'truncated_dune_toe_z': df_profile_features_crest_toes.loc[(site_id,'prestorm'),'dune_toe_z'],\n",
+    "        'full_counts': counts,\n",
+    "        'full_bin_edges': bin_edges,\n",
+    "    })\n",
+    "    \n",
+    "    print('Done {}'.format(site_id))\n",
+    "\n",
+    "data_twl = data\n",
+    "# df = pd.DataFrame(data)\n",
+    "# df = df.set_index('site_id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "counts, bin_edges = np.histogram (data_twl[0]['twl_levels'], bins=50) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list(np.asarray(twl_eles_per_wave)[~np.isfinite(twl_eles_per_wave)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = tools.make_subplots(\n",
+    "    rows=2,\n",
+    "    cols=2,\n",
+    "    specs=[[{}, {}], [{}, {}]],\n",
+    "    subplot_titles=('Swash/Swash',  'Swash/Collision', \n",
+    "                    'Collision/Swash', 'Collision/Collision'),\n",
+    " shared_xaxes=True, shared_yaxes=True,)\n",
+    "\n",
+    "data = []\n",
+    "for site in data_twl:\n",
+    "    if site['forecasted_regime'] == 'swash' and site[\n",
+    "            'observed_regime'] == 'swash':\n",
+    "        x_col = 1\n",
+    "        y_col = 1\n",
+    "    elif site['forecasted_regime'] == 'collision' and site[\n",
+    "            'observed_regime'] == 'collision':\n",
+    "        x_col = 2\n",
+    "        y_col = 2\n",
+    "    elif site['forecasted_regime'] == 'swash' and site[\n",
+    "            'observed_regime'] == 'collision':\n",
+    "        x_col = 2\n",
+    "        y_col = 1\n",
+    "    elif site['forecasted_regime'] == 'collision' and site[\n",
+    "            'observed_regime'] == 'swash':\n",
+    "        x_col = 1\n",
+    "        y_col = 2\n",
+    "    else:\n",
+    "        continue\n",
+    "\n",
+    "    fig.append_trace(\n",
+    "        go.Scattergl(\n",
+    "            x=[x - site['dune_toe_z'] for x in site['twl_levels']],\n",
+    "            y=site['n_waves'],\n",
+    "            name=site['site_id'],\n",
+    "            line = dict(\n",
+    "        color = ('rgba(22, 22, 22, 0.2)'),\n",
+    "        width = 0.5,)),\n",
+    "            x_col,\n",
+    "            y_col)\n",
+    "\n",
+    "# layout = go.Layout(\n",
+    "#     xaxis=dict(domain=[0, 0.45]),\n",
+    "#     yaxis=dict(\n",
+    "#         domain=[0, 0.45],\n",
+    "#         type='log',\n",
+    "#     ),\n",
+    "#     xaxis2=dict(domain=[0.55, 1]),\n",
+    "#     xaxis4=dict(domain=[0.55, 1], anchor='y4'),\n",
+    "#     yaxis3=dict(\n",
+    "#         domain=[0.55, 1],\n",
+    "#         type='log',\n",
+    "#     ),\n",
+    "#     yaxis4=dict(\n",
+    "#         domain=[0.55, 1],\n",
+    "#         anchor='x4',\n",
+    "#         type='log',\n",
+    "#     ))\n",
+    "\n",
+    "fig['layout'].update(showlegend=False, title='Specs with Subplot Title',height=800,)\n",
+    "\n",
+    "for ax in ['yaxis','yaxis2']:\n",
+    "#     fig['layout'][ax]['type']='log'\n",
+    "    fig['layout'][ax]['range']= [0,100]\n",
+    "\n",
+    "for ax in ['xaxis', 'xaxis2']:\n",
+    "    fig['layout'][ax]['range']= [-1,1]\n",
+    "\n",
+    "go.FigureWidget(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig['layout']['yaxis']"
+   ]
+  }
+ ],
+ "metadata": {
+  "hide_input": false,
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
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diff --git a/notebooks/06_change_in_slope.ipynb b/notebooks/06_change_in_slope.ipynb
new file mode 100644
index 0000000..2caa57d
--- /dev/null
+++ b/notebooks/06_change_in_slope.ipynb
@@ -0,0 +1,356 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Check change in mean slope\n",
+    "- Check the effect of changes in prestorm and poststorm mean slope.\n",
+    "- If there is a large berm, the prestorm mean slope (between dune toe and MHW) could be too small, and underpredict wave runup and TWL.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "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": null,
+   "metadata": {},
+   "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 tools\n",
+    "import plotly.figure_factory as ff\n",
+    "import plotly.io as pio\n",
+    "\n",
+    "import itertools\n",
+    "\n",
+    "import matplotlib\n",
+    "from matplotlib import cm\n",
+    "import colorlover as cl\n",
+    "\n",
+    "from ipywidgets import widgets, Output\n",
+    "from IPython.display import display, clear_output, Image, HTML\n",
+    "\n",
+    "from sklearn.metrics import confusion_matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import data\n",
+    "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": null,
+   "metadata": {},
+   "outputs": [],
+   "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_crest_toes = df_from_csv('profile_features_crest_toes.csv', index_col=[0,1])\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": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plot prestorm vs poststorm mean slopes\n",
+    "Prestorm slopes have already been calculated as part of the TWL forecasting, however we'll need to extract the poststorm mean slopes from our profiles at each site."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Prestorm slopes are easy as we have already calculated this as part of the \n",
+    "df_slopes_prestorm = twls['forecasted']['mean_slope_sto06'].groupby('site_id').head(1).reset_index().set_index(['site_id']).beta.to_frame()\n",
+    "\n",
+    "# Get x and z at mhw (z=0.7m) for each site\n",
+    "z_mhw = 0.7\n",
+    "mhw_poststorm = []\n",
+    "for site, df in df_profiles.xs('poststorm', level='profile_type').groupby('site_id'):\n",
+    "    df = df.dropna(subset=['z'])\n",
+    "    df = df.iloc[(df['z']-z_mhw).abs().argsort().head(1)].reset_index()\n",
+    "    df = df.iloc[0]\n",
+    "    mhw_poststorm.append({\n",
+    "        'site_id': df.site_id,\n",
+    "        'x_mhw': df.x,\n",
+    "        'z_mhw': df.z\n",
+    "    })\n",
+    "#     break\n",
+    "df_mhw_poststorm = pd.DataFrame(mhw_poststorm)\n",
+    "df_mhw_poststorm = df_mhw_poststorm.set_index('site_id')\n",
+    "\n",
+    "# Get x and z at poststorm dune toe for each site\n",
+    "df_dune_toe_poststorm = df_profile_features_crest_toes.xs('poststorm', level='profile_type')[['dune_toe_x','dune_toe_z']]\n",
+    "\n",
+    "# Join df for mhw and dune toe\n",
+    "df = df_mhw_poststorm.join(df_dune_toe_poststorm)\n",
+    "df['beta'] = -(df['dune_toe_z'] - df['z_mhw']) / (df['dune_toe_x'] -df['x_mhw'])\n",
+    "df_slopes_poststorm = df['beta'].to_frame()\n",
+    "\n",
+    "# Count how many nans\n",
+    "print('Number of nans: {}'.format(df_slopes_poststorm.beta.isna().sum()))\n",
+    "\n",
+    "# Display dataframe\n",
+    "print('df_slopes_poststorm:')\n",
+    "df_slopes_poststorm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's join our post storm slopes, prestorm slopes, observed and forecasted impacts into one data frame to make it easier to plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dfs = [df_slopes_poststorm.rename(columns={'beta':'poststorm_beta'}),\n",
+    "    df_slopes_prestorm.rename(columns={'beta':'prestorm_beta'}),\n",
+    "    impacts['observed']['storm_regime'].to_frame().rename(columns={'storm_regime': 'observed_regime'}),\n",
+    "    impacts['forecasted']['mean_slope_sto06']['storm_regime'].to_frame().rename(columns={'storm_regime': 'forecasted_regime'})\n",
+    "      ]\n",
+    "\n",
+    "df = pd.concat(dfs, axis='columns')\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_data.index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot our data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = tools.make_subplots(\n",
+    "    rows=2,\n",
+    "    cols=2,\n",
+    "    specs=[[{}, {}], [{}, {}]],\n",
+    "    subplot_titles=('Swash/Swash',  'Swash/Collision', \n",
+    "                    'Collision/Swash', 'Collision/Collision'),\n",
+    " shared_xaxes=True, shared_yaxes=True,)\n",
+    "\n",
+    "\n",
+    "# Loop through combinations of observed/forecasted swash/collision\n",
+    "data = []\n",
+    "for forecasted_regime, observed_regime in itertools.product(['swash','collision'],repeat=2):\n",
+    "    \n",
+    "    # Get data for this combination \n",
+    "    query = 'forecasted_regime==\"{}\" & observed_regime==\"{}\"'.format(forecasted_regime, observed_regime)\n",
+    "    df_data = df.query(query)\n",
+    "    print(query)\n",
+    "                  \n",
+    "    \n",
+    "    # Determine which subplot to plot results in\n",
+    "    if forecasted_regime == 'swash' and observed_regime == 'swash':\n",
+    "        x_col = 1\n",
+    "        y_col = 1\n",
+    "    elif forecasted_regime == 'collision' and observed_regime == 'collision':\n",
+    "        x_col = 2\n",
+    "        y_col = 2\n",
+    "    elif forecasted_regime == 'swash' and observed_regime == 'collision':\n",
+    "        x_col = 2\n",
+    "        y_col = 1\n",
+    "    elif forecasted_regime == 'collision' and observed_regime == 'swash':\n",
+    "        x_col = 1\n",
+    "        y_col = 2\n",
+    "    else:\n",
+    "        print('something went wrong')\n",
+    "        continue\n",
+    "\n",
+    "    fig.append_trace(\n",
+    "        go.Scatter(\n",
+    "            x=df_data.prestorm_beta,\n",
+    "            y=df_data.poststorm_beta,\n",
+    "            text = df_data.index.tolist(),\n",
+    "            hoverinfo = 'text',\n",
+    "            mode = 'markers',\n",
+    "            line = dict(\n",
+    "        color = ('rgba(22, 22, 22, 0.2)'),\n",
+    "        width = 0.5,)),\n",
+    "            x_col,\n",
+    "            y_col)\n",
+    "\n",
+    "# layout = go.Layout(\n",
+    "#     xaxis=dict(domain=[0, 0.45]),\n",
+    "#     yaxis=dict(\n",
+    "#         domain=[0, 0.45],\n",
+    "#         type='log',\n",
+    "#     ),\n",
+    "#     xaxis2=dict(domain=[0.55, 1]),\n",
+    "#     xaxis4=dict(domain=[0.55, 1], anchor='y4'),\n",
+    "#     yaxis3=dict(\n",
+    "#         domain=[0.55, 1],\n",
+    "#         type='log',\n",
+    "#     ),\n",
+    "#     yaxis4=dict(\n",
+    "#         domain=[0.55, 1],\n",
+    "#         anchor='x4',\n",
+    "#         type='log',\n",
+    "#     ))\n",
+    "\n",
+    "fig['layout'].update(showlegend=False, title='Specs with Subplot Title',height=800,)\n",
+    "\n",
+    "for ax in ['yaxis','yaxis2']:\n",
+    "#     fig['layout'][ax]['type']='log'\n",
+    "    fig['layout'][ax]['range']= [0,0.2]\n",
+    "\n",
+    "for ax in ['xaxis', 'xaxis2']:\n",
+    "    fig['layout'][ax]['range']= [0,0.2]\n",
+    "\n",
+    "go.FigureWidget(fig)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looking at the above plot:\n",
+    "- In general, we can see that the prestorm mean slope is flatter than the poststorm mean slope. This can be explained by the presence of prestorm berms, which increase the prestorm mean slope. During the storm, these berms get eroded and decrease the slope.\n",
+    "- **Collision/Collision**: Where we observe and predict collision, we see steeper prestorm slopes. This is to be expected since larger slopes will generate more runup and higher TWLs.\n",
+    "- **Swash/Collision**: Where we predict collision but observe swash, we can see that the prestorm mean slopes >0.1 generate high TWLs. \n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "hide_input": false,
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
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