test_fatiando_OIB_forward.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "from my_OIB_functions import *\n",
    "import fatiando\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [],
   "source": [
    "def oib_lineplot_cust(data, ptitle='test_lineplot', pname='test_lineplot'):\n",
    "    import matplotlib.pyplot as plt\n",
    "    data.loc[data['HYDROAPPX'] < -1500, 'HYDROAPPX'] = np.nan\n",
    "    fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(12, 8), facecolor='white')\n",
    "    data['FAG070'].where((data['FLTENVIRO'] == 1)).plot(ax=axes[0], legend=True, label='Disturbed', style='r-')\n",
    "    data['FAG070'].where((data['FLTENVIRO'] == 0)).plot(ax=axes[0], legend=True, label='Normal', style='k-')\n",
    "    # data['FAG070'].where((data['FLTENVIRO'] == -1)).plot(ax=axes[0], legend=True, label='Missing', style='b-')\n",
    "#     data['ELEVATION'].plot(ax=axes[1], legend=True, style='y.')\n",
    "    data['ICEBASE'].plot(ax=axes[1], legend=True, marker=\".\", linestyle=\"None\", color=\"brown\")\n",
    "    data['TOPOGRAPHY_radar'].plot(ax=axes[1], legend=True, marker=\".\", linestyle=\"None\", color=\"blue\")\n",
    "#     data['HYDROAPPX'].plot(ax=axes[1], legend=True, color='grey')\n",
    "    data['SURFACE_atm'].where((data['NUMUSED'] > 77)).plot(ax=axes[1], legend=True, \n",
    "                                                           marker=\".\", linestyle=\"None\", color='cyan')\n",
    "    plt.suptitle(ptitle, y=0.98)\n",
    "#     plt.savefig(pname, bbox_inches='tight')   # save the figure to file\n",
    "#     plt.close(fig)\n",
    "    return\n",
    "\n",
    "\n",
    "def haversine(origin, destination):\n",
    "    # Source: http://www.platoscave.net/blog/2009/oct/5/calculate-distance-latitude-longitude-python/\n",
    "    import math\n",
    "    lat1, lon1 = origin\n",
    "    lat2, lon2 = destination\n",
    "    radius = 6371  # km\n",
    "    dlat = math.radians(lat2 - lat1)\n",
    "    dlon = math.radians(lon2 - lon1)\n",
    "    a = math.sin(dlat / 2) * math.sin(dlat / 2) + math.cos(math.radians(lat1)) \\\n",
    "                                                  * math.cos(math.radians(lat2)) * math.sin(dlon / 2) * math.sin(\n",
    "        dlon / 2)\n",
    "    c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))\n",
    "    d = radius * c\n",
    "    return d\n",
    "\n",
    "\n",
    "def make_outline(top, bottom):\n",
    "    outline = np.append(top[0], np.concatenate([np.append(top, top[-1:]), np.append(bottom[-1:], bottom[::-1])], axis=0))\n",
    "    outline = np.append(outline, bottom[0])\n",
    "    outline = np.append(outline, top[0])\n",
    "    return outline\n",
    "\n",
    "def make_outline_dist(x, pad=1e6):\n",
    "    xs = np.append(-pad, np.concatenate([np.append(x, np.max(x)+pad), np.append(np.max(x)+pad, x[::-1])], axis=0))\n",
    "    xs = np.append(xs, -pad)\n",
    "    xs = np.append(xs, -pad)\n",
    "    return xs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in an entire flight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = pd.read_csv('data/integrated/forward/OIB_2016-10-24_forward.csv')\n",
    "df = pd.read_csv('data/integrated/OIB_2014-11-08.csv')\n",
    "# oib_lineplot_cust(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in an entire flight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1415441805\n",
      "1415448658\n",
      "1415449020\n",
      "1415449127\n",
      "1415461548\n",
      "1415461675\n",
      "1415465890\n",
      "1415466045\n",
      "1415468043\n",
      "1415468258\n",
      "1415469275\n",
      "1415469552\n",
      "1415471654\n",
      "1415472100\n",
      "1415474250\n",
      "1415474352\n",
      "1415485286\n",
      "1415485362\n",
      "1415487956\n",
      "1415488064\n",
      "1415488326\n"
     ]
    }
   ],
   "source": [
    "gb = df.groupby(['LINE'])\n",
    "\n",
    "# gb.describe()\n",
    "\n",
    "# gb['FAG070'].describe()\n",
    "\n",
    "# Group the dataframe by regiment, and for each regiment,\n",
    "for name, group in gb:\n",
    "    print(name)\n",
    "#     print(group['FAG070'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [],
   "source": [
    "segment = 1415466045\n",
    "# plt.plot(df['LINE'].where((df['LINE'] == segment)))\n",
    "# oib_lineplot_cust(df.where((df['LINE'] == segment)))\n",
    "\n",
    "# gb[[\"TIME\", \"FAG070\"]].get_group(segment)\n",
    "lf = gb.get_group(segment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "oib_lineplot_cust(lf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {
    "scrolled": true
   },
   "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>iunix</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LONG</th>\n",
       "      <th>DATE</th>\n",
       "      <th>DOY</th>\n",
       "      <th>TIME</th>\n",
       "      <th>FLT</th>\n",
       "      <th>PSX</th>\n",
       "      <th>PSY</th>\n",
       "      <th>WGSHGT</th>\n",
       "      <th>...</th>\n",
       "      <th>QUALITY</th>\n",
       "      <th>SURFACE_atm</th>\n",
       "      <th>NUMUSED</th>\n",
       "      <th>ICEBASE</th>\n",
       "      <th>TOPOGRAPHY_radar</th>\n",
       "      <th>D_gravmask</th>\n",
       "      <th>LINE</th>\n",
       "      <th>surface_recalc</th>\n",
       "      <th>icebase_recalc</th>\n",
       "      <th>HYDROAPPX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48480</th>\n",
       "      <td>2014-11-08 17:00:45.000</td>\n",
       "      <td>-79.729370</td>\n",
       "      <td>-19.116955</td>\n",
       "      <td>2014-11-08</td>\n",
       "      <td>312</td>\n",
       "      <td>61245.0</td>\n",
       "      <td>514</td>\n",
       "      <td>-366406.19</td>\n",
       "      <td>1057106.12</td>\n",
       "      <td>1453.57</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1415466045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48481</th>\n",
       "      <td>2014-11-08 17:00:45.500</td>\n",
       "      <td>-79.729502</td>\n",
       "      <td>-19.120058</td>\n",
       "      <td>2014-11-08</td>\n",
       "      <td>312</td>\n",
       "      <td>61245.5</td>\n",
       "      <td>514</td>\n",
       "      <td>-366458.72</td>\n",
       "      <td>1057072.69</td>\n",
       "      <td>1452.35</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1415466045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48482</th>\n",
       "      <td>2014-11-08 17:00:46.000</td>\n",
       "      <td>-79.729631</td>\n",
       "      <td>-19.123164</td>\n",
       "      <td>2014-11-08</td>\n",
       "      <td>312</td>\n",
       "      <td>61246.0</td>\n",
       "      <td>514</td>\n",
       "      <td>-366511.37</td>\n",
       "      <td>1057039.41</td>\n",
       "      <td>1451.11</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1415466045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48483</th>\n",
       "      <td>2014-11-08 17:00:46.500</td>\n",
       "      <td>-79.729759</td>\n",
       "      <td>-19.126274</td>\n",
       "      <td>2014-11-08</td>\n",
       "      <td>312</td>\n",
       "      <td>61246.5</td>\n",
       "      <td>514</td>\n",
       "      <td>-366564.18</td>\n",
       "      <td>1057006.34</td>\n",
       "      <td>1449.87</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1026.9586</td>\n",
       "      <td>378.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1415466045</td>\n",
       "      <td>1026.9586</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48484</th>\n",
       "      <td>2014-11-08 17:00:47.000</td>\n",
       "      <td>-79.729883</td>\n",
       "      <td>-19.129389</td>\n",
       "      <td>2014-11-08</td>\n",
       "      <td>312</td>\n",
       "      <td>61247.0</td>\n",
       "      <td>514</td>\n",
       "      <td>-366617.16</td>\n",
       "      <td>1056973.50</td>\n",
       "      <td>1448.69</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1026.9586</td>\n",
       "      <td>378.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1415466045</td>\n",
       "      <td>1026.9586</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         iunix        LAT       LONG        DATE  DOY  \\\n",
       "48480  2014-11-08 17:00:45.000 -79.729370 -19.116955  2014-11-08  312   \n",
       "48481  2014-11-08 17:00:45.500 -79.729502 -19.120058  2014-11-08  312   \n",
       "48482  2014-11-08 17:00:46.000 -79.729631 -19.123164  2014-11-08  312   \n",
       "48483  2014-11-08 17:00:46.500 -79.729759 -19.126274  2014-11-08  312   \n",
       "48484  2014-11-08 17:00:47.000 -79.729883 -19.129389  2014-11-08  312   \n",
       "\n",
       "          TIME  FLT        PSX         PSY   WGSHGT    ...      QUALITY  \\\n",
       "48480  61245.0  514 -366406.19  1057106.12  1453.57    ...          NaN   \n",
       "48481  61245.5  514 -366458.72  1057072.69  1452.35    ...          NaN   \n",
       "48482  61246.0  514 -366511.37  1057039.41  1451.11    ...          NaN   \n",
       "48483  61246.5  514 -366564.18  1057006.34  1449.87    ...          NaN   \n",
       "48484  61247.0  514 -366617.16  1056973.50  1448.69    ...          NaN   \n",
       "\n",
       "       SURFACE_atm  NUMUSED  ICEBASE  TOPOGRAPHY_radar  D_gravmask  \\\n",
       "48480          NaN      NaN      NaN               NaN           1   \n",
       "48481          NaN      NaN      NaN               NaN           1   \n",
       "48482          NaN      NaN      NaN               NaN           1   \n",
       "48483    1026.9586    378.0      NaN               NaN           1   \n",
       "48484    1026.9586    378.0      NaN               NaN           1   \n",
       "\n",
       "             LINE  surface_recalc  icebase_recalc  HYDROAPPX  \n",
       "48480  1415466045             NaN             NaN        NaN  \n",
       "48481  1415466045             NaN             NaN        NaN  \n",
       "48482  1415466045             NaN             NaN        NaN  \n",
       "48483  1415466045       1026.9586             NaN        NaN  \n",
       "48484  1415466045       1026.9586             NaN        NaN  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fatiando testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fatiando.gravmag import talwani\n",
    "from fatiando.mesher import Polygon\n",
    "from fatiando.vis import mpl\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "rho_a = 0\n",
    "rho_i = 915\n",
    "rho_w = 1005\n",
    "rho_r = 2670"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crossing Horizons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.ix[selection criteria, columns I want] = value\n",
    "lf.loc[lf['SURFACE_atm']-lf['ICEBASE'] <= 0, 'ICEBASE'] = (lf['SURFACE_atm'] - 0.1)\n",
    "# lf['ICEBASE_adj'] = lf['ICEBASE'].where(lf['SURFACE_atm']-lf['ICEBASE'] <= 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61245.5"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lf['TIME'].values[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in an entire flight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x128b14650>]"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Compute distance along transect\n",
    "dist = np.zeros((np.size(lf['LONG'])))\n",
    "for i in range(2, np.size(lf['LONG'])):\n",
    "    dist[i] = dist[i - 1] + haversine([lf['LAT'].values[i - 1],lf['LONG'].values[i - 1]],\n",
    "              [lf['LAT'].values[i],lf['LONG'].values[i]])\n",
    "plt.plot(dist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'iunix', u'LAT', u'LONG', u'DATE', u'DOY', u'TIME', u'FLT', u'PSX',\n",
       "       u'PSY', u'WGSHGT', u'FX', u'FY', u'FZ', u'EOTGRAV', u'FACOR', u'INTCOR',\n",
       "       u'FAG070', u'FAG100', u'FAG140', u'FLTENVIRO', u'UNIX', u'THICK',\n",
       "       u'ELEVATION', u'FRAME', u'SURFACE_radar', u'BOTTOM', u'QUALITY',\n",
       "       u'SURFACE_atm', u'NUMUSED', u'ICEBASE', u'TOPOGRAPHY_radar',\n",
       "       u'D_gravmask', u'LINE', u'surface_recalc', u'icebase_recalc',\n",
       "       u'HYDROAPPX'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lf.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[61245.  61245.5 61246.  ... 63241.5 63242.  63242.5]\n",
      "[61245.  61245.5 61246.  ... 63241.5 63242.  63242.5]\n",
      "48480    61245.0\n",
      "48481    61245.5\n",
      "48482    61246.0\n",
      "Name: TIME, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([61245. , 61245.5, 61246. , ..., 63241.5, 63242. , 63242.5])"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print lf.loc[:, 'TIME'].values\n",
    "print lf.loc[:, 'TIME'].values\n",
    "print lf.iloc[0:3, 5]\n",
    "lf.loc[27689:, 'TIME'].values\n",
    "# int(np.max(icesfc)+1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ADD block extensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TEG5PKcXyvct5Pu95hpiH8OLQFzEZ5O8jcfWSBeSEaAZN05gWO40nkp9gY9FG/rjrj66ukhCtRv7cEeKsKb2ncLDqIMv2LKN3cG9GR412dZWEaHFyJiDEjzwy6BESuiTw1Oan2Fd+/qRIIdobCQEhfsTD4MGiGxcR4BXA/Rvu58jpI+wt30vO/hzeL3ifsjOylIloX6Q7SIif6OTTicXDFzNt7TRueuemeq8Fewfzp2F/Ii4kzkW1E+LKkjMBIS6gd3BvlgxfwpioMfzhZ3/gw1s+5K+j/4qvyZcZ62bwyX8/afQ92vCFd0I4ySWiQjRDeXU5szfM5qtjX/FI0iP8qu+vAP2AX1VbxdEzRzly5gibSzbzzwP/pNZei5+HHx09O+Ln4Yefpx/eRm+MBn3WskkzYTDUn8VsMpjw9/TH3NHML3v+Eg+Dh4tbLa42MllMiBbSyacTb4x8g8c/f5xndzzLx//9mFN1pzh0/BB1jjrndiaDiZt63ETXDl05WXeSU9ZTnKo7xUnrSapqq7ArO3aHPmHN5rDVm8BmdVg5WXcSu7Lz7rfv8uwNz2LuaHZhq0V7JiEgRDP5mHxYdOMiXtr1Eh8c+IBeQb24Pvx6unToQohPCKG+oUQGRBLgFXDJn6GUYt136/j95t9z6z9vZd5187g58uYr2AohdNIdJEQbVnyqmEf/9Shfln1JyjUpRAdFU3SyCKvDCkq/U1uQVxBjosbQO7g3dY46KmsqqbHV4MCBAQPdO3anrLoMh3Lg7+lPB48OF1yRVbQfzTl2SggI0cbZHDb+/OWfef3fr6NQhHUIc65vZHPYOHrmKFaHFU+DZ70uqXO8jd7U2GucPwd7B3Oj+UaSuibh5+HHsZpjzi4pX5Mv4X7hxIXE4WPyabU2iitLxgSEaEdMBhP3D7yfCdET8DH5EOQdVO/1yppKNvx3AweOHyDIK4gg7yB8TD4YNAM1thr2lO8hKiAKH5MPVbVV7KvYx0fffcT7+99v+DM1E72De2MymDhZdxJLkAV/T38qayqpqKngpPUkSinngnwKRXiHcOZdN0/GL64yciYghBuyOqwcPn6YWnstnX0642n0REOj2lbN/qr97D66my/LvgT0u77tObYHu7IT5BVEsE8wHT06YjQY0dAwaAYUis3Fm/EwerB42GL6h/R3cQvdm5wJCCEuysPgQXRQ9HnlQQQR7hfODeYbmv2eB48fZNbHs5i+bjqLhiy6pPcQrU8miwkhroiogCj+NvpvRAVG8eDGB9l5ZKerqySaQM4EhBBXTCefTrx606tMWzuN+z+5nzdHvUlX367sKd9Dta2aGnsNVrsVq8OKzWHD5rA550cAKPTe6XO91ArlfK5pWr3bg5oMJgK8Aujq25Ug7yA8jZ54G73xMnnhZfTC0+ApV0E1gYSAEOKKCvIO4rWU10jPTefWf96KQTM4D/KtTUOjV1AvFvxiAb2CermkDm2dhIAQ4ooL9wtn6YilvPLlK3Tz68bgsMH4e/rjZfTCw+iBh8EDk8GkPzQTmqahof/Vfu75uZ/R9IO5Usp51qBQ1NnrqKyt5PvT33Oi7gS1tlpq7fUfdoed9wreY+rqqSz8xUJuuuami9TaPcnVQUKIdu1Y9TF+8+lv+KrsKyb0muCcN6GUcnY32Rw2ztjOUGuvxeqwYrVbsSkbdofd+T4/DSnAudwHgFEzYjQY6eLbhTkJc1x6qaxcHSSEEGd19unMGyPe4NF/Pco7376Dj8kHX5MvBs2gH8w1/WopH5OPPpZg9MRkMOGleTkvg3WOVaA4+xSFQtM0jJoRALuyY3PY+Lz4c74o/oI7Yu8gIiCCOnsdJ+pOcKLuBCfrTnLGegaTwYSn0RMPg35W5GHUFwm0OfTgsSs7XXy7OBcobEkSAkKIds/H5MNLQ1/i+9Pf08W3C0aDscU+q+RUCfM2zeOVL18577WOHh3xMflgUzbn4Hidvc55NgH65EAPgwf9O/eXEBBCiCvFoBkI9wtv8c8J9wvn9RGv8/3p76m2VeNh9MDf0x8/D78Gw8fusDuvfmptjX5iYWEhQ4cOpU+fPsTGxvLyyy8D8PTTT9OtWzfi4+OJj49nzZo1zn0yMzOxWCzExMSwbt06Z3lubi4xMTFYLBYWLlzYAs0RQgjX0zSNML8wogKj6N6xOwFeARc9+zh3fwlXaPRMwGQysWjRIq699lpOnjxJQkICKSkpAMyZM4eHHnqo3vZ79+5l5cqV7Nmzh5KSEm666Sa+/fZbAO677z4++ugjzGYzSUlJpKam0rdv3xZolhBCiKZoNATCwsIICwsDoGPHjvTp04fi4uIGt8/JySEtLQ0vLy8iIyOxWCxs374dAIvFQlRUFABpaWnk5ORICAghhAs16/zj8OHD7N69m+TkZAAWL15MXFwc06dPp7KyEoDi4mK6d+/u3MdsNlNcXNxguRBCCNdpcgicOnWKCRMm8NJLL+Hv78/MmTM5cOAA+fn5hIWFMXfuXODCN9fWNK3B8p/KysoiMTGRxMREysrKmtMWIYQQzdSkELBarUyYMIHbbruN8ePHA9ClSxeMRiMGg4G7777b2eVjNpspLCx07ltUVER4eHiD5T+VkZFBXl4eeXl5hISEXFbjhBBCXFyjIaCUYsaMGfTp04cHH3zQWV5aWup8/v7779OvXz8AUlNTWblyJbW1tRw6dIiCggIGDRpEUlISBQUFHDp0iLq6OlauXElqamoLNEkIIURTNTowvGnTJpYvX07//v2Jj48HYMGCBaxYsYL8/Hw0TSMiIoLXXnsNgNjYWCZNmkTfvn0xmUwsWbIEo1G/NGrx4sWMHDkSu93O9OnTiY2NbcGmCSGEaIysHSSEEO1Mc46dclMZIYRwYxICQgjhxiQEhBDCjUkICCGEG5MQEEIINyYhIIQQbkxCQAgh3JiEgBBCuDEJASGEcGMSAkII4cYkBIQQwo1JCAghhBuTEBBCCDcmISCEEG5MQkAIIdyYhIAQQrgxCQEhhHBjEgJCCOHGJASEEMKNSQgIIYQbkxAQQgg3JiEghBBuTEJACCHcmISAEEK4MQkBIYRwY42GQGFhIUOHDqVPnz7Exsby8ssvA1BRUUFKSgrR0dGkpKRQWVkJgFKK2bNnY7FYiIuLY9euXc73ys7OJjo6mujoaLKzs1uoSUIIIZqq0RAwmUwsWrSIffv2sXXrVpYsWcLevXtZuHAhw4cPp6CggOHDh7Nw4UIA1q5dS0FBAQUFBWRlZTFz5kxAD4358+ezbds2tm/fzvz5853BIYQQwjUaDYGwsDCuvfZaADp27EifPn0oLi4mJyeH9PR0ANLT01m1ahUAOTk5TJs2DU3TGDx4MFVVVZSWlrJu3TpSUlIIDg4mKCiIlJQUcnNzW7BpQgghGtOsMYHDhw+ze/dukpOTOXLkCGFhYYAeFEePHgWguLiY7t27O/cxm80UFxc3WC6EEMJ1TE3d8NSpU0yYMIGXXnoJf3//BrdTSp1Xpmlag+U/lZWVRVZWFgBlZWVNrZ4QQohL0KQzAavVyoQJE7jtttsYP348AF26dKG0tBSA0tJSQkNDAf0v/MLCQue+RUVFhIeHN1j+UxkZGeTl5ZGXl0dISMilt0wIIUSjGg0BpRQzZsygT58+PPjgg87y1NRU5xU+2dnZjBs3zln+l7/8BaUUW7duJSAggLCwMEaOHMn69euprKyksrKS9evXM3LkyBZqlhBCiKZotDto06ZNLF++nP79+xMfHw/AggULeOyxx5g0aRJLly6lR48evP322wCMHj2aNWvWYLFY8PX1ZdmyZQAEBwfz5JNPkpSUBMC8efMIDg5uqXYJIYRoAk1dqLO+jUhMTCQvL8/V1RBCiKtKc46dMmNYCCHcmISAEEK4MQkBIYRwYxICQgjhxiQEhBDCjUkICCGEG5MQEEIINyYhIIQQbkxCQAgh3JiEgBBCuDEJASGEcGMSAkII4cYkBIQQwo1JCAghhBuTEBBCCDcmISCEEG5MQkAIIdyYhIAQQrgxCQEhhHBjEgJCCOHGJASEEMKNSQgIIYQbkxAQQgg3JiEghBBuTEJACCHcWKMhMH36dEJDQ+nXr5+z7Omnn6Zbt27Ex8cTHx/PmjVrnK9lZmZisViIiYlh3bp1zvLc3FxiYmKwWCwsXLjwCjdDCCHEpWg0BO644w5yc3PPK58zZw75+fnk5+czevRoAPbu3cvKlSvZs2cPubm5zJo1C7vdjt1u57777mPt2rXs3buXFStWsHfv3ivfmqY6fQxeuwHmB8PvO8MbN8H3X7muPkII4SKNhsANN9xAcHBwk94sJyeHtLQ0vLy8iIyMxGKxsH37drZv347FYiEqKgpPT0/S0tLIycm57MpfErsV/pEOR/dB8j0w+F44XgRvjoKSfNfUSQghXOSSxwQWL15MXFwc06dPp7KyEoDi4mK6d+/u3MZsNlNcXNxg+YVkZWWRmJhIYmIiZWVll1q9hq3/HXz3BaT+CUZlwohn4O4N4B0I79+rh4QQQriJSwqBmTNncuDAAfLz8wkLC2Pu3LkAKKXO21bTtAbLLyQjI4O8vDzy8vIICQm5lOo1bNdy2PYqDJ4FA9J+KPcPh5ufhbJ9sOONK/uZQgjRhpkuZacuXbo4n999992MHTsW0P/CLywsdL5WVFREeHg4QIPlLeardyDmZqj8Dkq/hMrD8NmzEDUUUv5w/va9x0DPYbDhf6BPKgR0++G1cyHWQHAJIcTV6pJCoLS0lLCwMADef/9955VDqampTJ06lQcffJCSkhIKCgoYNGgQSikKCgo4dOgQ3bp1Y+XKlbz11ltXrhU/dawA3rsbjF5gq/6hPHoE3JoNxgs0W9NgzP/CK9fB+/dAtwT4z1o4Uw41x8Fggr6p0KUf2OugulIfYD5TDg4rdAzX36O6Cm56CkJiWq59QghxhTQaAlOmTGHjxo0cO3YMs9nM/Pnz2bhxI/n5+WiaRkREBK+99hoAsbGxTJo0ib59+2IymViyZAlGoxHQxxBGjhyJ3W5n+vTpxMbGtlyrOkfDlL/D9iyI/AX0ulk/8AdFXvyv+eBIGPY7WP9bOPw5WFIg4mfgHaAf8L9cCf/+u76tyQf8QsC3kx4Qe1eByQtstbDiG5i1Rf9ZCCHaME1dqMO+jUhMTCQvL691P1QpOPQv8AuF0D71X7NWg8MORo/zD/C2WtCMcOgz+Ot4uOlp+Pmc1qq1EEI4NefYKTOGf0rTIOrG8wMAwMMHvPwu/Be+yUs/27AMh5jR8NnzUFV4/nZCCNGGXNKYgGjEqIXwymB45059kLmmSh9DsFsh8gboNVLvYhJCCBeTEGgJQdfog8wf/BqKdujdRD5BepfR7uXQIUTvLhowFQxyMiaEcB0JgZYSPwX66JfO4umndzPZ6qA4Dz56CnLu0y9HDboGNIMeEL1Gwc9+A8oOtSf18DAY9H89fC98VZMQQlwGOaq0JK+O9X82ecI118P0dfDV2/DtWjh1FND0IPj0GfjiRbCeAX4yXt8hFG57G8LjW6v2Qgg3ICHgCgYDDJisP37sm9X6I/Aa6NAJHA5w2MBWo89kfmsS3P1p/YlsQghxGSQE2pLeY/THhfQaBUtHwIo0mJ4Lnh1at25CiHZJRiWvFl36wsSl+pLX72Xo8xWEEOIySQhcTXqNhJEL4JsPYe0jeneREEJcBukOutpcNwtOlsDmP8HhL/QZzg6bfnWR0UO/Ckkz6N1F3gEQ0lufuezfwgv2CSGuShICV6OUP0BAd9iZDcER4OENJm99YTul9EtM607rC9/t/D/Yswru+FAWtRNCnEdC4Gqkafpd0ZLvaXzbo/sgOxX+bwyk//PCy2EIIdyWhEB7F9oH7lgN2b+Ev4yDm5+DI3v0SWvWar0byV6nPzSDviKqdyCkzAdzoqtrL4RoYTIw7A5CesG0Vfp8g7fT4fMX9PsgGEzgG6zPSwjtq3cXBV4D5fvhrclwqgVu7ymEaFPkTMBdhPaBjM/0s4BrrtcP/g05ug9e/QV88jSMW9JqVRRCtD45E3AnwZH6ekYXCwDQA+O6WbD7r1C4o3XqJoRwCQkBcWE3PAwdw2Dtwxefj1DwMWxZAiePtF7dhBBXjHQHiQvz6ggUcGB9AAASI0lEQVQpv9fv1Zz/N7j29vqvOxyw5U/w0Tz954/mQWAP8O0MHbtCcJS+ImpjZx1CCJeSMwHRsP63Qvdk+PhpqK76odxu09cw+mge9B0HM7foB/zwa/U5C2X/gS2L4bUb9fEFIUSbJWcComGapl9SmjUEPnsORi3Qu30+exYK1sHITEi+V18Vtcu8+vsW74QVU2H5LfrKp/5hLmmCEOLiJATExYXHQ0I6bF0C/90MJbv18uR79cHjhnRLgF+9q698+ufr9O1vfFQPFiFEmyHdQaJxw54Evy5wbL9+IJ/xkX4f5cZ07Qe3vwfdEmFjJmx6ueXrKoRoFjkTEI3r0Bnu36U/9/Jr3r49But3RHtnuj62EBIDMTdf8SoKIS6NnAmIpvHya34AnKNp+qSzsAHw7l1wZO+VrZsQ4pI1GgLTp08nNDSUfv36OcsqKipISUkhOjqalJQUKisrAVBKMXv2bCwWC3Fxcezatcu5T3Z2NtHR0URHR5Odnd0CTRFtmqcvTFkBnn6wYjKcPubqGgkhaEII3HHHHeTm5tYrW7hwIcOHD6egoIDhw4ezcKHeP7x27VoKCgooKCggKyuLmTNnAnpozJ8/n23btrF9+3bmz5/vDA7hRvzDIe0t/Qqjv98OtSehqtDVtRLCrTUaAjfccAPBwfUn/OTk5JCeng5Aeno6q1atcpZPmzYNTdMYPHgwVVVVlJaWsm7dOlJSUggODiYoKIiUlJTzgkW4CXOC3jX0382QaYaX+sG7d8Ppcj0Qvtsis4+FaEWXNDB85MgRwsL0677DwsI4evQoAMXFxXTv3t25ndlspri4uMFy4abibtXHCY7s0c8G8pbC1++AOrs8hdELRj+vX5oqhGhRV/TqIKXUeWWapjVYfiFZWVlkZWUBUFYmSxm3W/0n6g+AxOnw9bv6VUjBPWHrK/DP2frd0a6bpd8tre6U/i8A6ofnRg/9VppCiEtySSHQpUsXSktLCQsLo7S0lNDQUED/C7+w8Ic+3qKiIsLDwzGbzWzcuLFe+ZAhQy743hkZGWRkZACQmCg3NXELXfrqj3N6DoN37oR1j8O+D6DiEJz6vuH9+/4/GPI4hPZu+boK0c5cUgikpqaSnZ3NY489RnZ2NuPGjXOWL168mLS0NLZt20ZAQABhYWGMHDmSJ554wjkYvH79ejIzM69cK0T7YjTBxDfho6fgmw/1+x+Exek3weHsGaSm6c+PF8GubD0sku6CoU+AT5Aray/E1UU1Ii0tTXXt2lWZTCbVrVs39cYbb6hjx46pYcOGKYvFooYNG6bKy8uVUko5HA41a9YsFRUVpfr166d27NjhfJ+lS5eqnj17qp49e6o333yzsY9VSimVkJDQpO2Emzt1TKkPH1Tq6UClFvVR6tAXrq6REC7VnGOnptQFOuzbiMTERPLy8lxdDXG1KN4F786AysP6WkUdOsOxArCe0ccQDEbQjGf/NfzwvMdgiL0FPHxc3QIhrojmHDtl2QjRfnS7Fu75F6x5RB9cBvA36wPHmgYOOyi7fhWSw6E/t57Ru5M++QNMeAMifubaNgjRyiQERPvi1RFu+TPc8JA+NtDYTW2UgoOfwpqH4a/j9clsluGtU1ch2gBZO0i0T516Nu2uZpqmX400fT10ioaVt0Hply1fPyHaCAkBIQA6dILb3wffTnoQyNpGwk1ICAhxjl8IpP0VTpfB23eA3erqGgnR4iQEhPix8IHwy5fh8OeQ+7irayNEi5OBYSF+akAafP8VbFkMtSfgTLl+DwTlgPipMOx3+qWlQrQDEgJCXEjK7/X5Bv/+OwRHQdQQqK6AL/4Xjn0L41/X75EgxFVOQkCICzEYYdJyOFEEAd3PLlMBbP2z3k2UPRam/F0fR7iQE6X6yqi2GvAKAG9/8PLX/zV5/2jimqn+JDaDEQwe4BMok9dEq5AQEKIhBgME9qhfNnimHgrv3gVvDNfvjXDmGJR9CydL9IP/yZKz3Uf2y/t8vy4wZhH0+eXlvY8QFyEhIERz9RkLd3wIb03WzwjO8e0M/mHQMRwsN8HA2yHADDUn9LGF2hP6c1utHhAOmz6L2WE7O4v57HN7HdQch7058PdfQeIMGPk/cmYgWoSEgBCXwpwIGRth/8fQtb/+MHldeFu/kIa7jS7mul/Dht/D5j/Bd5v1lVU79dTvwOaw6rOdjZ76zOgOnS6nNcKNSQgIcakCu0PinS33/iZPGPGMPij9/kz483X62MF53UwaRKeAOUk/gzh9DGzVekhoGoTGQuUh/WzDOxCCIiDmZgiObLm6i6uGhIAQbZ3lJpi5CT5doC+G9+OzDrsNjnwNe96DgvVg8tFXT/Xw1VdKtZ7Ru5U6hOrdSdVVUHtcv2FPQA/w8oNTR/T3UQ79iqfAHvo9HLoP1u/cVnMcQnrrZxxnjumT6WqO6yGjHD88Aq+RBfiuQrKUtBDtgVJgrb7wZavVlfoZwLkrnCoOwX/WQPFOfR+/LvoVS5qm38bz6DdQslvvcmquwbP0sxeZR+FSspS0EO5G0xqet/DTO60FR8J19138/aw1UJoPaPrKrCW79GU0OoToZxreAfrlrZpB/2ylYNtr+hLeVf+VeRRXEQkBIcT5PLz1m+2c8+N7QDdk9HP6xLrcx2DFZLjtnYYHy0WbIWsHCSGunMH3wv/7Mxz6F7yXoV/2ermU+uEhrjg5ExBCXFnxU/TB44+ehBVn9IHm4p1Qd0a/aslWp4832M/OiTg3TwKAcwf7Bg74P749qMFD7+oK6KYvAW7y0gfGPbz1MQ6Tl75dl1jo+/9+GBMR9UgICCGuvJ/NhtqT8K/n9ANzj2Q9DEw++qWvBg/9yiOjxw9jC5w9SGua/tx50D77XDl+CAzl0McozpTD8WJ9nSdrtb5Mh61GH9Ow1ZwNFwWx42HcYv3qKlGPhIAQomUM+y0M/BX4hbputrPDDl+8CJ/+D1R9p6/3pBnOzqNw/HCZq8MGdaf12dz22rNnKGdncDv96EziXECdm+kNZ9eBMumzxrv2b7UmXi4JASFEywm6xrWfbzDq95sO7QPvzIAXLK3zuYnTIeUP+jyMc+xWfX5F3amzZ0Ke+lmR8ewDzi4lYvshfLwDWryqEgJCiPav9xh9vadd2dC519mDq/bDJa4GD/2SVpMXGL3OHphN+hjEuUtgnc4+V+rs2MTZOREOu36g3/O+fqls/lvQsateVl0F1tPNq7M5Ce76+Eq0/qIkBIQQ7sGcqD9aWvck6Juqh4D1jB4q3gH68uDegXrYOOx6l9O5h63ubBidW17cQw+QViAhIIQQV1qPwfXnWbRhlzVPICIigv79+xMfH09iop6wFRUVpKSkEB0dTUpKCpWVlQAopZg9ezYWi4W4uDh27dp1+bUXQghxWS57stinn35Kfn6+c52KhQsXMnz4cAoKChg+fDgLFy4EYO3atRQUFFBQUEBWVhYzZ8683I8WQghxma74jOGcnBzS09MBSE9PZ9WqVc7yadOmoWkagwcPpqqqitLS0iv98UIIIZrhskJA0zRGjBhBQkICWVlZABw5coSwsDAAwsLCOHr0KADFxcV0797dua/ZbKa4uPhyPl4IIcRluqyB4U2bNhEeHs7Ro0dJSUmhd+/eDW57oRWrtQtM487KynIGSllZ2eVUTwghRCMu60wgPDwcgNDQUG655Ra2b99Oly5dnN08paWlhIaGAvpf/oWFhc59i4qKnPv/WEZGBnl5eeTl5REScgm35BNCCNFklxwCp0+f5uTJk87n69evp1+/fqSmppKdnQ1AdnY248aNAyA1NZW//OUvKKXYunUrAQEBzm4jIYQQrnHJ3UFHjhzhlltuAcBmszF16lRGjRpFUlISkyZNYunSpfTo0YO3334bgNGjR7NmzRosFgu+vr4sW7bsyrRACCHEJWvTt5fs3LkzERERl7x/WVmZ23UpuVub3a29IG12F5fT5sOHD3Ps2LEmbdumQ+ByueM9it2tze7WXpA2u4vWarPcWUwIIdyYhIAQQrgx49NPP/20qyvRkhISElxdhVbnbm12t/aCtNldtEab2/WYgBBCiIuT7iAhhHBj7TIEcnNziYmJwWKxOFcxvZoUFhYydOhQ+vTpQ2xsLC+//DJwact0Z2dnEx0dTXR0tHMSH8DOnTvp378/FouF2bNnX3BZj9Zmt9sZOHAgY8eOBeDQoUMkJycTHR3N5MmTqaurA6C2tpbJkydjsVhITk7m8OHDzvfIzMzEYrEQExPDunXrnOVt8XeiqqqKiRMn0rt3b/r06cOWLVva/Xf84osvEhsbS79+/ZgyZQo1NTXt7nuePn06oaGh9OvXz1nWGt9rQ5/RKNXO2Gw2FRUVpQ4cOKBqa2tVXFyc2rNnj6ur1SwlJSVq586dSimlTpw4oaKjo9WePXvUww8/rDIzM5VSSmVmZqpHHnlEKaXU6tWr1ahRo5TD4VBbtmxRgwYNUkopVV5eriIjI1V5ebmqqKhQkZGRqqKiQimlVFJSktq8ebNyOBxq1KhRas2aNS5oaX2LFi1SU6ZMUWPGjFFKKXXrrbeqFStWKKWUuueee9Qrr7yilFJqyZIl6p577lFKKbVixQo1adIkpZRSe/bsUXFxcaqmpkYdPHhQRUVFKZvN1mZ/J6ZNm6Zef/11pZRStbW1qrKysl1/x0VFRSoiIkKdOXNGKaV/v8uWLWt33/Nnn32mdu7cqWJjY51lrfG9NvQZjWl3IbB582Y1YsQI588LFixQCxYscGGNLl9qaqpav3696tWrlyopKVFK6UHRq1cvpZRSGRkZ6q233nJuf267t956S2VkZDjLz21XUlKiYmJinOU/3c4VCgsL1bBhw9Qnn3yixowZoxwOh+rUqZOyWq1Kqfrf64gRI9TmzZuVUkpZrVbVqVMn5XA4zvuuz23XFn8njh8/riIiIpTD4ahX3p6/46KiImU2m1V5ebmyWq1qzJgxKjc3t11+z4cOHaoXAq3xvTb0GY1pd91B7W3J6sOHD7N7926Sk5ObvUz3xcrNZvN55a70wAMP8Nxzz2Ew6L+S5eXlBAYGYjLpK5v8uI4/bpfJZCIgIIDy8vJm/z+40sGDBwkJCeHOO+9k4MCB3HXXXZw+fbpdf8fdunXjoYceokePHoSFhREQEEBCQkK7/p7PaY3vtaHPaEy7CwHVxCWrrwanTp1iwoQJvPTSS/j7+ze4XUNtbm65q3z44YeEhobWuxzuYnW82tsL+npbu3btYubMmezevZsOHTpctA+7PbS5srKSnJwcDh06RElJCadPn2bt2rXnbdeevufGtIU2trsQaOqS1W2d1WplwoQJ3HbbbYwfPx6g2ct0X6y8qKjovHJX2bRpEx988AERERGkpaWxYcMGHnjgAaqqqrDZbOfV8cftstlsHD9+nODg4Gb/P7iS2WzGbDaTnJwMwMSJE9m1a1e7/Y4BPv74YyIjIwkJCcHDw4Px48ezefPmdv09n9Ma32tDn9GopvVwXT2sVquKjIxUBw8edA4Off31166uVrM4HA51++23q9/85jf1yh966KF6Az8PP/ywUkqpDz/8sN7gUlJSklJKH1yKiIhQFRUVqqKiQkVERKjy8nKllFKJiYlqy5YtzsGl1atXt2ILG/bpp586B4YnTpxYb8BwyZIlSimlFi9eXG/A8NZbb1VKKfX111/XGzCMjIxUNputzf5O/PznP1fffPONUkqpp556Sj300EPt+jveunWr6tu3rzp9+rRyOBxq2rRp6o9//GO7/J5/OibQGt9rQ5/RmHYXAkrpI+7R0dEqKipKPfPMM66uTrN9/vnnClD9+/dXAwYMUAMGDFCrV69Wx44dU8OGDVMWi0UNGzbM+UvhcDjUrFmzVFRUlOrXr5/asWOH872WLl2qevbsqXr27KnefPNNZ/mOHTtUbGysioqKUvfdd995A5Su8uMQOHDggEpKSlI9e/ZUEydOVDU1NUoppaqrq9XEiRNVz549VVJSkjpw4IBz/2eeeUZFRUWpXr161bsapi3+TuzevVslJCSo/v37q3HjxqmKiop2/x3PmzdPxcTEqNjYWPWrX/1K1dTUtLvvOS0tTXXt2lWZTCbVrVs39cYbb7TK99rQZzRGZgwLIYQba3djAkIIIZpOQkAIIdyYhIAQQrgxCQEhhHBjEgJCCOHGJASEEMKNSQgIIYQbkxAQQgg39v8BgLAY9xVrZF0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "Extract data from DataFrame\n",
    "'''\n",
    "\n",
    "# lf['SURFACE_atm'].interpolate(method='linear', limit_area='inside', axis=0, inplace=True)  #limit_direction='forward'\n",
    "lf['TOPOGRAPHY_radar'].interpolate(method='linear', limit_area='inside', axis=0, inplace=True)  #limit_direction='forward'\n",
    "lf['ICEBASE'].interpolate(method='linear', limit_area='inside', axis=0, inplace=True)  #limit_direction='forward'\n",
    "\n",
    "\n",
    "fag070 = lf['FAG070'][0:].values\n",
    "elevation = lf['WGSHGT'][0:].values\n",
    "# icesfc = lf['SURFACE_atm'][0:].values\n",
    "icesfc = lf['TOPOGRAPHY_radar'][0:].values\n",
    "icebase = lf['ICEBASE'][0:].values\n",
    "'''\n",
    "Convert OIB data to polygon arrays\n",
    "'''\n",
    "# iceoutline = np.append(np.concatenate([icesfc, icebase[::-1]], axis=0), icesfc[0]) # no extension\n",
    "iceoutline = make_outline(icesfc, icebase)\n",
    "# plt.figure(facecolor='white'); plt.plot(iceoutline[1180:1220])\n",
    "\n",
    "rockbase = -30000*np.ones_like(icebase)\n",
    "# rockoutline = np.append(icebase[0], np.concatenate([icebase, z_r], axis=0), icebase[0], icebase[0])\n",
    "rockoutline = make_outline(icebase, rockbase)\n",
    "\n",
    "x = np.linspace(0, 100000, len(icebase))#[::-1]\n",
    "xs = make_outline_dist(x, 1e6)\n",
    "# plt.figure(facecolor='white'); plt.plot(xs[1180:1220])\n",
    "\n",
    "# depths of calculations points\n",
    "z = elevation + 2000\n",
    "# z = int(np.nanmax(icesfc)+1)*np.ones_like(x)\n",
    "\n",
    "plt.figure(facecolor='white')\n",
    "plt.plot(xs, iceoutline) \n",
    "# plt.plot(x, icesfc) \n",
    "# plt.plot(x, icebase) \n",
    "plt.plot(x, elevation)\n",
    "plt.plot(x, z) \n",
    "'''\n",
    "Build the Polygon\n",
    "'''\n",
    "props = {'density': rho_i}\n",
    "props2 = {'density': rho_r}\n",
    "polygon = Polygon(np.transpose([xs, iceoutline]), props)\n",
    "polygons = [Polygon(np.transpose([xs, iceoutline]), props), \n",
    "            Polygon(np.transpose([xs, rockoutline]), props2)\n",
    "            ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One Block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nan\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "Forward Model\n",
    "'''\n",
    "gz = talwani.gz(x, z, [polygon])\n",
    "gz_adj = (gz - np.mean(gz)) + np.mean(fag070)\n",
    "n = len(gz_adj)\n",
    "rmse = np.linalg.norm(gz_adj - fag070) / np.sqrt(n)\n",
    "print rmse\n",
    "'''\n",
    "Plot\n",
    "'''\n",
    "mpl.figure(facecolor='white')\n",
    "mpl.axis('scaled')\n",
    "ax1 = mpl.subplot(2, 1, 1)\n",
    "mpl.title(r\"Gravity anomaly produced by the model\")\n",
    "mpl.plot(x, gz, '-r', linewidth=2)\n",
    "# mpl.plot(x, gz-fag070, '-b', linewidth=1)\n",
    "mpl.plot(x, fag070, '.-k', linewidth=2)\n",
    "mpl.xlim(min(x), max(x))\n",
    "mpl.ylabel(\"mGal\")\n",
    "# ax1.annotate(str(int(rmse)), xy=(100, 0), xytext=(100, 0))\n",
    "ax2 = mpl.subplot(2, 1, 2)\n",
    "mpl.polygon(polygon, '.-k', linewidth=1, fill='cyan', alpha=0.5)\n",
    "# mpl.polygon(polygons[1], '.-k', linewidth=1, fill='orange', alpha=0.5)\n",
    "mpl.xlim(min(x), max(x))\n",
    "mpl.ylim(-1000, 2000)\n",
    "mpl.xlabel(\"X\")\n",
    "mpl.ylabel(\"Z\")\n",
    "mpl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TWO blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nan\n",
      "nan\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "cannot concatenate 'str' and 'int' objects",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-360-a5bddf472d49>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0mmpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"X\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0mmpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Z\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0mmpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figs/test_OIB_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msegment\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'_forward.png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox_inches\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'tight'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     34\u001b[0m \u001b[0mmpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: cannot concatenate 'str' and 'int' objects"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "Forward Model\n",
    "'''\n",
    "gz = talwani.gz(x, z, polygons)\n",
    "gz_adj = (gz - np.mean(gz)) + np.mean(fag070)\n",
    "n = len(gz_adj)\n",
    "rmse = np.linalg.norm(gz_adj - fag070) / np.sqrt(n)\n",
    "print (np.mean(gz) - np.mean(fag070))\n",
    "print rmse\n",
    "'''\n",
    "Plot\n",
    "'''\n",
    "###\n",
    "mpl.figure(facecolor='white')\n",
    "mpl.axis('scaled')\n",
    "ax1 = mpl.subplot(2, 1, 1)\n",
    "mpl.title(r\"Gravity anomaly produced by the model\")\n",
    "mpl.plot(x, gz_adj, '-r', linewidth=2, label='Modeled')\n",
    "# mpl.plot(x, gz-fag070, '-b', linewidth=1)\n",
    "mpl.plot(x, fag070, color='k', linestyle=\"None\", marker=',', label='FAG070')\n",
    "mpl.xlim(min(x), max(x))\n",
    "mpl.ylabel(\"mGal\")\n",
    "mpl.legend()\n",
    "# ax1.annotate('rmse: '+str(int(rmse))+' mGal', xy=(1000, 0), xytext=(1000, 0))\n",
    "###\n",
    "ax2 = mpl.subplot(2, 1, 2)\n",
    "mpl.polygon(polygons[0], '.-k', linewidth=1, fill='cyan', alpha=0.5)\n",
    "mpl.polygon(polygons[1], '.-k', linewidth=1, fill='orange', alpha=0.5)\n",
    "mpl.xlim(min(x), max(x))\n",
    "mpl.ylim(-1000, 2000)\n",
    "mpl.xlabel(\"X\")\n",
    "mpl.ylabel(\"Z\")\n",
    "mpl.savefig('figs/test_OIB_' + str(segment) + '_forward.png', bbox_inches='tight')\n",
    "mpl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Three Blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Icebase for this flight...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dporter/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/Users/dporter/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "/Users/dporter/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "52476\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dporter/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "u'the label [52476] is not in the [index]'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-362-c8eaa27293f1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0mwaterstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'icebase_recalc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlast_valid_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwaterstart\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m     \u001b[0mlf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ICEBASE2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwaterstart\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'surface_recalc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwaterstart\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0;31m# Now INNER interpolate new horizon\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/dporter/anaconda2/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1476\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1477\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1478\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1479\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1480\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/dporter/anaconda2/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1909\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1910\u001b[0m         \u001b[0;31m# fall thru to straight lookup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1911\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1912\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/dporter/anaconda2/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_validate_key\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1796\u001b[0m                 \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1797\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1798\u001b[0;31m                 \u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1799\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1800\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/dporter/anaconda2/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36merror\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1783\u001b[0m                 raise KeyError(u\"the label [{key}] is not in the [{axis}]\"\n\u001b[1;32m   1784\u001b[0m                                .format(key=key,\n\u001b[0;32m-> 1785\u001b[0;31m                                        axis=self.obj._get_axis_name(axis)))\n\u001b[0m\u001b[1;32m   1786\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1787\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: u'the label [52476] is not in the [index]'"
     ]
    }
   ],
   "source": [
    "if lf['icebase_recalc'].isnull().all():\n",
    "    print('No Icebase for this flight...')\n",
    "    lf['icebase_recalc'] = lf['surface_recalc'] - 1\n",
    "\n",
    "lf['ICESFC2'] = lf['surface_recalc']\n",
    "lf['ICESFC2'].interpolate(method='pad', inplace=True)\n",
    "lf['ICEBASE2'] = lf['icebase_recalc']\n",
    "lf['ICEBASE2'].interpolate(method='linear', limit_area='inside', axis=0, inplace=True)\n",
    "            \n",
    "\n",
    "if [segment==1477341085]:\n",
    "    # make ice 1 meter thick at end of line \n",
    "    # TODO not just 'end'\n",
    "    lf['ICEBASE2'].iloc[-1] = lf['surface_recalc'].iloc[-1] - 1\n",
    "    \n",
    "    #Find ice front position\n",
    "    waterstart = lf['icebase_recalc'].last_valid_index() + 1\n",
    "    print(waterstart)\n",
    "    lf['ICEBASE2'].loc[waterstart] = lf['surface_recalc'].loc[waterstart] - 1\n",
    "    \n",
    "    # Now INNER interpolate new horizon\n",
    "    lf['ICEBASE2'].interpolate(method='linear', limit_area='inside', axis=0, inplace=True)  #limit_direction='forward'\n",
    "    \n",
    "    # Check for crossing horizons AGAIN?\n",
    "    lf.loc[lf['ICESFC2']-lf['ICEBASE2'] <= 0, 'ICEBASE2'] = (lf['ICESFC2'] - 0.1)\n",
    "\n",
    "    # Create water base, set it to single depth below ice\n",
    "    lf['WATERBASE2'] = lf['ICEBASE2'] - 1\n",
    "    lf['WATERBASE2'].loc[waterstart:lf['WATERBASE2'].index[-1]] = lf['WATERBASE2'].loc[waterstart - 1]\n",
    "else:\n",
    "    # INNER interpolate new horizon\n",
    "    lf['ICEBASE2'].interpolate(method='linear', limit_area='inside', axis=0, inplace=True)  #limit_direction='forward'\n",
    "    \n",
    "    # Create water base, set it to single depth below ice\n",
    "    lf['WATERBASE2'] = lf['ICEBASE2'] - 1\n",
    "\n",
    "lf['ICESFC2'].interpolate(method='linear', limit_area='inside', axis=0, inplace=True)  #limit_direction='forward'\n",
    "\n",
    "'''\n",
    "Convert OIB data to polygon arrays\n",
    "'''\n",
    "icebase = lf['ICEBASE2'][0:].values\n",
    "waterbase = lf['WATERBASE2'][0:].values\n",
    "# iceoutline = np.append(np.concatenate([icesfc, icebase[::-1]], axis=0), icesfc[0]) # no extension\n",
    "iceoutline = make_outline(icesfc, icebase)\n",
    "# plt.figure(facecolor='white'); plt.plot(iceoutline[1180:1220])\n",
    "\n",
    "watertop = icebase\n",
    "# waterbase = icebase - 1\n",
    "wateroutline = make_outline(watertop, waterbase)\n",
    "\n",
    "rocktop = waterbase\n",
    "rockbase = -30000 * np.ones_like(waterbase)\n",
    "# rockoutline = np.append(icebase[0], np.concatenate([icebase, z_r], axis=0), icebase[0], icebase[0])\n",
    "rockoutline = make_outline(rocktop, rockbase)\n",
    "\n",
    "### Distances\n",
    "x = dist * 1000\n",
    "xs = make_outline_dist(x, 1e6)\n",
    "\n",
    "### Heights\n",
    "# z = int(np.max(elevation))\n",
    "# z = int(np.max(icesfc) + 1) * np.ones_like(x)\n",
    "# z = elevation + int(np.max(icesfc)+1)\n",
    "z = np.max(elevation) * np.ones_like(x)\n",
    "\n",
    "'''\n",
    "Build the Polygon\n",
    "'''\n",
    "props_i = {'density': rho_i}\n",
    "props_r = {'density': rho_r}\n",
    "props_w = {'density': rho_w}\n",
    "# polygon = Polygon(np.transpose([xs, iceoutline]), props_i)\n",
    "# polygons = [Polygon(np.transpose([xs, iceoutline]), props_i),\n",
    "#             Polygon(np.transpose([xs, rockoutline]), props_r)\n",
    "#             ]\n",
    "polygons = [Polygon(np.transpose([xs, iceoutline]), props_i),\n",
    "            Polygon(np.transpose([xs, wateroutline]), props_w),\n",
    "            Polygon(np.transpose([xs, rockoutline]), props_r)\n",
    "            ]\n",
    "\n",
    "'''\n",
    "Forward Model\n",
    "'''\n",
    "gz = talwani.gz(x, z, polygons)\n",
    "gz_adj = (gz - np.mean(gz)) + np.mean(fag070)\n",
    "n = len(gz_adj)\n",
    "rmse = np.linalg.norm(gz_adj - fag070) / np.sqrt(n)\n",
    "print (np.mean(gz) - np.mean(fag070))\n",
    "print rmse\n",
    "'''\n",
    "Plot\n",
    "'''\n",
    "###\n",
    "mpl.figure(figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')\n",
    "mpl.axis('scaled')\n",
    "ax1 = mpl.subplot(2, 1, 1)\n",
    "mpl.title(r\"Gravity anomaly produced by the model\")\n",
    "mpl.plot(x, gz_adj, '-r', linewidth=2, label='Modeled')\n",
    "# mpl.plot(x, gz-fag070, '-b', linewidth=1)\n",
    "mpl.plot(x, fag070, color='k', linestyle=\"None\", marker=',', label='FAG070')\n",
    "mpl.xlim(min(x), max(x))\n",
    "mpl.ylabel(\"mGal\")\n",
    "mpl.legend()\n",
    "# ax1.annotate('rmse: '+str(int(rmse))+' mGal', xy=(1000, 0), xytext=(1000, 0))\n",
    "###\n",
    "ax2 = mpl.subplot(2, 1, 2)\n",
    "mpl.polygon(polygons[0], '.-k', linewidth=1, fill='cyan', alpha=0.5)\n",
    "mpl.polygon(polygons[1], '.-k', linewidth=1, fill='blue', alpha=0.5)\n",
    "mpl.polygon(polygons[2], '.-k', linewidth=1, fill='orange', alpha=0.5)\n",
    "mpl.xlim(min(x), max(x))\n",
    "mpl.ylim(-1500, 2000)\n",
    "mpl.xlabel(\"X\")\n",
    "mpl.ylabel(\"Z\")\n",
    "mpl.savefig('figs/test_OIB_' + str(segment) + '_forward.png', bbox_inches='tight')\n",
    "mpl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1173b0650>]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(12, 8))\n",
    "axes[0].plot(xs, iceoutline, color='cyan', marker='o')\n",
    "axes[1].plot(xs, wateroutline, color='blue')\n",
    "axes[2].plot(xs, rockoutline, color='orange')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Debugging blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dporter/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing data at start of line.\n",
      "NOT floating.\n",
      "Missing data at end of line.\n",
      "LIKELY floating.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dporter/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/Users/dporter/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:35: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/dporter/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:36: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/dporter/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:49: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding water block to END.\n",
      "93.32680000000073\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dporter/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "lf['ICESFC2'] = lf['surface_recalc']\n",
    "lf['ICESFC2'].interpolate(method='pad', inplace=True)\n",
    "lf['ICEBASE2'] = lf['icebase_recalc']\n",
    "lf['ICEBASE2'].interpolate(method='linear', limit_area='inside', axis=0, inplace=True)\n",
    "\n",
    "if [segment==1477341085]: \n",
    "    #Find ice front position\n",
    "    firstmean = np.mean(lf['icebase_recalc'].iloc[:5])\n",
    "    if np.isnan(firstmean):\n",
    "        print('Missing data at start of line.')\n",
    "        firsticepoint = lf['icebase_recalc'].first_valid_index() - 1\n",
    "        if lf['ICESFC2'].loc[firsticepoint] >= 120:\n",
    "            print('NOT floating.')\n",
    "            lf['ICEBASE2'].interpolate(method='linear', limit_direction='backward', axis=0, inplace=True)\n",
    "        else:\n",
    "            # make ice constant thick at end of line\n",
    "            print('LIKELY floating.')\n",
    "            lf['ICEBASE2'].loc[firsticepoint] = lf['ICESFC2'].loc[firsticepoint] - 1\n",
    "            lf['ICEBASE2'].iloc[0] = lf['ICESFC2'].iloc[0] - 1\n",
    "            # TODO or make ice 1 meter thick at end of line?\n",
    "    else:\n",
    "        print('NO missing data at start of line.')\n",
    "    \n",
    "    #Find ice front position\n",
    "    lastmean = np.mean(lf['icebase_recalc'].iloc[-5:])\n",
    "    if np.isnan(lastmean):\n",
    "        print('Missing data at end of line.')\n",
    "        lasticepoint = lf['icebase_recalc'].last_valid_index() + 1\n",
    "        if lf['ICESFC2'].loc[lasticepoint] >= 120:\n",
    "            print('NOT floating.')\n",
    "            lf['ICEBASE2'].interpolate(method='linear', limit_direction='forward', axis=0, inplace=True)\n",
    "        else:\n",
    "            # make ice constant thick at end of line \n",
    "            print('LIKELY floating.')\n",
    "            lf['ICEBASE2'].loc[lasticepoint] = lf['ICESFC2'].loc[lasticepoint] - 1\n",
    "            lf['ICEBASE2'].iloc[-1] = lf['ICESFC2'].iloc[-1] - 1\n",
    "            # TODO or make ice 1 meter thick at end of line?\n",
    "    else:\n",
    "        print('NO missing data at end of line.')\n",
    "                    \n",
    "    ### Final INNER interpolation\n",
    "    lf['ICEBASE2'].interpolate(method='linear', limit_area='inside', axis=0, inplace=True)        \n",
    "\n",
    "    \n",
    "    # Check for crossing horizons AGAIN?\n",
    "#     lf.loc[lf['ICESFC2']-lf['ICEBASE2'] <= 0, 'ICEBASE2'] = (lf['ICESFC2'] - 0.1)\n",
    "\n",
    "    # Create water base, set it to single depth below ice\n",
    "    lf['WATERBASE2'] = lf['ICEBASE2'] - 1\n",
    "    if np.mean(lf['ICESFC2'].iloc[:5]) < 120:\n",
    "        print('Adding water block to START.')\n",
    "        lf['WATERBASE2'].loc[lf['WATERBASE2'].index[0]:firsticepoint] = lf['WATERBASE2'].loc[firsticepoint+1]\n",
    "    if np.mean(lf['ICESFC2'].iloc[-5:]) < 120:\n",
    "        print('Adding water block to END.')\n",
    "        lf['WATERBASE2'].loc[lasticepoint:lf['WATERBASE2'].index[-1]] = lf['WATERBASE2'].loc[lasticepoint-1]\n",
    "else:\n",
    "    # INNER interpolate new horizon\n",
    "    lf['ICEBASE2'].interpolate(method='linear', limit_area='inside', axis=0, inplace=True)  #limit_direction='forward'\n",
    "    \n",
    "    # Create water base, set it to single depth below ice\n",
    "    lf['WATERBASE2'] = lf['ICEBASE2'] - 1\n",
    "\n",
    "\n",
    "\n",
    "'''\n",
    "Convert OIB data to polygon arrays\n",
    "'''\n",
    "icesfc = lf['ICESFC2'][0:].values\n",
    "icebase = lf['ICEBASE2'][0:].values\n",
    "waterbase = lf['WATERBASE2'][0:].values\n",
    "# iceoutline = np.append(np.concatenate([icesfc, icebase[::-1]], axis=0), icesfc[0]) # no extension\n",
    "iceoutline = make_outline(icesfc, icebase)\n",
    "# plt.figure(facecolor='white'); plt.plot(iceoutline[1180:1220])\n",
    "\n",
    "watertop = icebase\n",
    "# waterbase = icebase - 1\n",
    "wateroutline = make_outline(watertop, waterbase)\n",
    "\n",
    "rocktop = waterbase\n",
    "rockbase = -30000 * np.ones_like(waterbase)\n",
    "# rockoutline = np.append(icebase[0], np.concatenate([icebase, z_r], axis=0), icebase[0], icebase[0])\n",
    "rockoutline = make_outline(rocktop, rockbase)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "icesfc (2017,):\n",
      "[768.8235 769.1995 769.5859 768.5173 768.3144]...[88.8835 87.9427 87.1213 86.5133 86.0824]\n",
      "\n",
      "icebase (2017,):\n",
      "[-1070.9699 -1070.9699 -1070.9699 -1070.9699 -1070.9699]...[85.14362119 85.12831589 85.1130106  85.0977053  85.0824    ]\n",
      "\n",
      "watertop (2017,):\n",
      "[-1070.9699 -1070.9699 -1070.9699 -1070.9699 -1070.9699]...[85.14362119 85.12831589 85.1130106  85.0977053  85.0824    ]\n",
      "\n",
      "waterbase (2017,):\n",
      "[-1071.9699 -1071.9699 -1071.9699 -1071.9699 -1071.9699]...[-723.0456 -723.0456 -723.0456 -723.0456 -723.0456]\n",
      "\n",
      "rocktop (2017,):\n",
      "[-1071.9699 -1071.9699 -1071.9699 -1071.9699 -1071.9699]...[-723.0456 -723.0456 -723.0456 -723.0456 -723.0456]\n",
      "\n",
      "rockbase (2017,):\n",
      "[-30000. -30000. -30000. -30000. -30000.]...[-30000. -30000. -30000. -30000. -30000.]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"icesfc {}:\\n{}...{}\\n\".format(icesfc.shape, icesfc[:5],icesfc[-5:]))\n",
    "print(\"icebase {}:\\n{}...{}\\n\".format(icebase.shape, icebase[:5],icebase[-5:]))\n",
    "print(\"watertop {}:\\n{}...{}\\n\".format(watertop.shape, watertop[:5],watertop[-5:]))\n",
    "print(\"waterbase {}:\\n{}...{}\\n\".format(waterbase.shape, waterbase[:5],waterbase[-5:]))\n",
    "print(\"rocktop {}:\\n{}...{}\\n\".format(rocktop.shape, rocktop[:5],rocktop[-5:]))\n",
    "print(\"rockbase {}:\\n{}...{}\\n\".format(rockbase.shape, rockbase[:5],rockbase[-5:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x (2017,):\n",
      "[  0.           0.          85.64481127 171.38822003 257.24057938]\n",
      "[217730.09178968 217851.43065794 217972.78434843 218094.13974611\n",
      " 218215.49096255]\n",
      "\n",
      "dist (2017,):\n",
      "[0.         0.         0.08564481 0.17138822 0.25724058]\n",
      "[217.73009179 217.85143066 217.97278435 218.09413975 218.21549096]\n",
      "\n",
      "Iceoutline (4039,):\n",
      "[768.8235 768.8235 769.1995 769.5859 768.5173]\n",
      "[-1070.9699 -1070.9699 -1070.9699 -1070.9699   768.8235]\n",
      "\n",
      "Wateroutline (4039,):\n",
      "[-1070.9699 -1070.9699 -1070.9699 -1070.9699 -1070.9699]\n",
      "[-1071.9699 -1071.9699 -1071.9699 -1071.9699 -1070.9699]\n",
      "\n",
      "Rockoutline (4039,):\n",
      "[-1071.9699 -1071.9699 -1071.9699 -1071.9699 -1071.9699]\n",
      "[-30000.     -30000.     -30000.     -30000.      -1071.9699]\n",
      "\n",
      "xs (4039,):\n",
      "[-1.00000000e+06  0.00000000e+00  0.00000000e+00  8.56448113e+01\n",
      "  1.71388220e+02]\n",
      "[ 8.56448113e+01  0.00000000e+00  0.00000000e+00 -1.00000000e+06\n",
      " -1.00000000e+06]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"x {}:\\n{:}\\n{:}\\n\".format(x.shape, x[:5], x[-5:]))\n",
    "print(\"dist {}:\\n{:}\\n{:}\\n\".format(dist.shape, dist[:5], dist[-5:]))\n",
    "print(\"Iceoutline {}:\\n{}\\n{}\\n\".format(iceoutline.shape, iceoutline[:5],iceoutline[-5:]))\n",
    "print(\"Wateroutline {}:\\n{}\\n{}\\n\".format(wateroutline.shape, wateroutline[:5],wateroutline[-5:]))\n",
    "print(\"Rockoutline {}:\\n{}\\n{}\\n\".format(rockoutline.shape, rockoutline[:5],rockoutline[-5:]))\n",
    "print(\"xs {}:\\n{:}\\n{:}\\n\".format(xs.shape, xs[:5], xs[-5:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x121c5a810>]"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(12, 8))\n",
    "axes[0].plot(xs, iceoutline, color='cyan', marker='o')\n",
    "axes[1].plot(xs, wateroutline, color='blue')\n",
    "axes[2].plot(xs, rockoutline, color='orange')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3295.4567045524605\n",
      "10.831178738357055\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Distances\n",
    "x = dist * 1000\n",
    "xs = make_outline_dist(x, 1e6)\n",
    "\n",
    "### Heights\n",
    "# z = int(np.max(elevation))\n",
    "# z = int(np.max(icesfc) + 1) * np.ones_like(x)\n",
    "# z = elevation + int(np.max(icesfc)+1)\n",
    "z = np.max(elevation) * np.ones_like(x)\n",
    "\n",
    "'''\n",
    "Build the Polygon\n",
    "'''\n",
    "props_i = {'density': rho_i}\n",
    "props_r = {'density': rho_r}\n",
    "props_w = {'density': rho_w}\n",
    "# polygon = Polygon(np.transpose([xs, iceoutline]), props_i)\n",
    "# polygons = [Polygon(np.transpose([xs, iceoutline]), props_i),\n",
    "#             Polygon(np.transpose([xs, rockoutline]), props_r)\n",
    "#             ]\n",
    "polygons = [Polygon(np.transpose([xs, iceoutline]), props_i),\n",
    "            Polygon(np.transpose([xs, wateroutline]), props_w),\n",
    "            Polygon(np.transpose([xs, rockoutline]), props_r)\n",
    "            ]\n",
    "\n",
    "'''\n",
    "Forward Model\n",
    "'''\n",
    "gz = talwani.gz(x, z, polygons)\n",
    "gz_adj = (gz - np.mean(gz)) + np.mean(fag070)\n",
    "n = len(gz_adj)\n",
    "rmse = np.linalg.norm(gz_adj - fag070) / np.sqrt(n)\n",
    "print (np.mean(gz) - np.mean(fag070))\n",
    "print rmse\n",
    "'''\n",
    "Plot\n",
    "'''\n",
    "###\n",
    "mpl.figure(figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')\n",
    "mpl.axis('scaled')\n",
    "ax1 = mpl.subplot(2, 1, 1)\n",
    "mpl.title(r\"Gravity anomaly produced by the model\")\n",
    "mpl.plot(x, gz_adj, '-r', linewidth=2, label='Modeled')\n",
    "# mpl.plot(x, gz-fag070, '-b', linewidth=1)\n",
    "mpl.plot(x, fag070, color='k', linestyle=\"None\", marker=',', label='FAG070')\n",
    "mpl.xlim(min(x), max(x))\n",
    "mpl.ylabel(\"mGal\")\n",
    "mpl.legend()\n",
    "ax1.annotate('rmse: '+str(int(rmse))+' mGal', xy=(1000, 0), xytext=(1000, 0))\n",
    "###\n",
    "ax2 = mpl.subplot(2, 1, 2)\n",
    "mpl.polygon(polygons[0], '.-k', linewidth=1, fill='cyan', alpha=0.5)\n",
    "mpl.polygon(polygons[1], '.-k', linewidth=1, fill='blue', alpha=0.5)\n",
    "mpl.polygon(polygons[2], '.-k', linewidth=1, fill='orange', alpha=0.5)\n",
    "mpl.xlim(min(x), max(x))\n",
    "mpl.ylim(-1500, 2000)\n",
    "mpl.xlabel(\"X\")\n",
    "mpl.ylabel(\"Z\")\n",
    "mpl.savefig('figs/test_OIB_forward.png', bbox_inches='tight')\n",
    "mpl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NO block extension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [],
   "source": [
    "# x = np.linspace(0, 100000, len(icebase))#[::-1]\n",
    "# xs = make_outline_dist(x, 1e1)\n",
    "\n",
    "# plt.figure(facecolor='white'); plt.plot(xs, iceoutline); plt.plot(x, elevation)\n",
    "# '''\n",
    "# Build the Polygon\n",
    "# '''\n",
    "# props = {'density': rho_r - rho_i}\n",
    "# props2 = {'density': rho_r}\n",
    "# polygon = Polygon(np.transpose([xs, iceoutline]), props)\n",
    "# polygons = [Polygon(np.transpose([xs, iceoutline]), props), \n",
    "#             Polygon(np.transpose([xs, rockoutline]), props2)\n",
    "#             ]\n",
    "# '''\n",
    "# Forward Model\n",
    "# '''\n",
    "# gz = talwani.gz(x, z, polygons)\n",
    "# gz_adj = (gz - np.mean(gz)) + np.mean(fag070)\n",
    "# n = len(gz_adj)\n",
    "# rmse = np.linalg.norm(gz_adj - fag070) / np.sqrt(n)\n",
    "# print rmse\n",
    "# '''\n",
    "# Plot\n",
    "# '''\n",
    "# ###\n",
    "# mpl.figure(facecolor='white')\n",
    "# mpl.axis('scaled')\n",
    "# ax1 = mpl.subplot(2, 1, 1)\n",
    "# mpl.title(r\"Gravity anomaly produced by the model\")\n",
    "# mpl.plot(x, gz_adj, '-r', linewidth=2, label='Modeled')\n",
    "# # mpl.plot(x, gz-fag070, '-b', linewidth=1)\n",
    "# mpl.plot(x, fag070, '.-k', linewidth=2, label='FAG070')\n",
    "# mpl.xlim(min(x), max(x))\n",
    "# mpl.ylabel(\"mGal\")\n",
    "# mpl.legend()\n",
    "# # ax1.annotate('rmse: '+str(int(rmse))+' mGal', xy=(1000, 0), xytext=(1000, 0))\n",
    "# ###\n",
    "# ax2 = mpl.subplot(2, 1, 2)\n",
    "# mpl.polygon(polygons[0], '.-k', linewidth=1, fill='cyan', alpha=0.5)\n",
    "# mpl.polygon(polygons[1], '.-k', linewidth=1, fill='orange', alpha=0.5)\n",
    "# mpl.xlim(min(x), max(x))\n",
    "# mpl.ylim(-1000, 2000)\n",
    "# mpl.xlabel(\"X\")\n",
    "# mpl.ylabel(\"Z\")\n",
    "# mpl.savefig('figs/test_OIB_forward.png', bbox_inches='tight')\n",
    "# mpl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Real Distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [],
   "source": [
    "# x = dist * 1000\n",
    "# xs = make_outline_dist(x, 1e6)\n",
    "# # plt.figure(facecolor='white'); plt.plot(xs[1180:1220])\n",
    "\n",
    "# plt.figure(facecolor='white')\n",
    "# plt.plot(xs, iceoutline) \n",
    "# # plt.plot(x, elevation)\n",
    "# plt.plot(x, z) \n",
    "# '''\n",
    "# Build the Polygon\n",
    "# '''\n",
    "# props = {'density': rho_i}\n",
    "# props2 = {'density': rho_r}\n",
    "# polygon = Polygon(np.transpose([xs, iceoutline]), props)\n",
    "# polygons = [Polygon(np.transpose([xs, iceoutline]), props), \n",
    "#             Polygon(np.transpose([xs, rockoutline]), props2)\n",
    "#             ]\n",
    "# '''\n",
    "# Forward Model\n",
    "# '''\n",
    "# gz = talwani.gz(x, z, polygons)\n",
    "# gz_adj = (gz - np.mean(gz)) + np.mean(fag070)\n",
    "# n = len(gz_adj)\n",
    "# rmse = np.linalg.norm(gz_adj - fag070) / np.sqrt(n)\n",
    "# print rmse\n",
    "# '''\n",
    "# Plot\n",
    "# '''\n",
    "# ###\n",
    "# mpl.figure(facecolor='white')\n",
    "# mpl.axis('scaled')\n",
    "# ax1 = mpl.subplot(2, 1, 1)\n",
    "# mpl.title(r\"Gravity anomaly produced by the model\")\n",
    "# mpl.plot(x, gz_adj, '-r', linewidth=2, label='Modeled')\n",
    "# # mpl.plot(x, gz-fag070, '-b', linewidth=1)\n",
    "# mpl.plot(x, fag070, color='k', linestyle=\"None\", marker=',', label='FAG070')\n",
    "# mpl.xlim(min(x), max(x))\n",
    "# ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x/1e3))\n",
    "# ax1.xaxis.set_major_formatter(ticks_x)\n",
    "# mpl.ylabel(\"mGal\")\n",
    "# mpl.legend()\n",
    "# # ax1.annotate('rmse: '+str(int(rmse))+' mGal', xy=(10, 0), xytext=(10, 0))\n",
    "# ###\n",
    "# ax2 = mpl.subplot(2, 1, 2)\n",
    "# mpl.polygon(polygons[0], '.-k', linewidth=1, fill='cyan', alpha=0.5)\n",
    "# mpl.polygon(polygons[1], '.-k', linewidth=1, fill='orange', alpha=0.5)\n",
    "# mpl.xlim(min(x), max(x))\n",
    "# mpl.ylim(-1000, 2000)\n",
    "# mpl.xlabel(\"X\")\n",
    "# mpl.ylabel(\"Z\")\n",
    "# ax2.xaxis.set_major_formatter(ticks_x)\n",
    "# mpl.savefig('figs/test_OIB_'+str(segment)+'_forward.png', bbox_inches='tight')\n",
    "# mpl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test elevation (\"z\") of forward model calculations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.60097937186865\n",
      "10.831178738357055\n",
      "15.331633016452026\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "Forward Model\n",
    "'''\n",
    "# depths of calculations points\n",
    "z = int(np.max(icesfc)+1)*np.ones_like(x)\n",
    "gz = talwani.gz(x, z, polygons)\n",
    "gz_adj = (gz - np.mean(gz)) + np.mean(fag070)\n",
    "n = len(gz_adj)\n",
    "rmse = np.linalg.norm(gz_adj - fag070) / np.sqrt(n)\n",
    "print rmse\n",
    "'''\n",
    "Forward Model #2 (modify Z)\n",
    "'''\n",
    "z2 = np.nanmax(elevation)*np.ones_like(x) #elevation + int(np.max(icesfc)+1)\n",
    "gz = talwani.gz(x, z2, polygons)\n",
    "gz_adj2 = (gz - np.mean(gz)) + np.mean(fag070)\n",
    "rmse2 = np.linalg.norm(gz_adj2 - fag070) / np.sqrt(n)\n",
    "print rmse2\n",
    "'''\n",
    "Forward Model #2 (modify Z)\n",
    "'''\n",
    "z3 = elevation\n",
    "gz = talwani.gz(x, z3, polygons)\n",
    "gz_adj3 = (gz - np.mean(gz)) + np.mean(fag070)\n",
    "rmse3 = np.linalg.norm(gz_adj3 - fag070) / np.sqrt(n)\n",
    "print rmse3\n",
    "'''\n",
    "Plot\n",
    "'''\n",
    "### Horizons\n",
    "plt.figure(num=None, figsize=(8, 5), dpi=80, facecolor='w', edgecolor='k')\n",
    "plt.plot(xs, iceoutline) \n",
    "# plt.plot(x, icebase, linewidth=1, color='orange', alpha=0.5)\n",
    "plt.plot(x, z, '-r', linewidth=2, ls='dashed', label='Modeled (constant)')\n",
    "plt.plot(x, z2, '-k', linewidth=2, ls='dashed', label='Modeled')\n",
    "plt.plot(x, z3, '-g', linewidth=2, ls='dashed', label='Modeled (draped)')\n",
    "plt.xlim(min(x), max(x))\n",
    "# plt.ylim(-1000, max(z2))\n",
    "# plt.legend()\n",
    "\n",
    "### Model results\n",
    "mpl.figure(num=None, figsize=(8, 5), dpi=80, facecolor='w', edgecolor='k')\n",
    "mpl.axis('scaled')\n",
    "ax1 = mpl.subplot(2, 1, 1)\n",
    "mpl.title(r\"Gravity anomaly produced by the model\")\n",
    "mpl.plot(x, gz_adj, '-r', linewidth=2, label='Modeled (ice max)')\n",
    "mpl.plot(x, gz_adj2, '-b', linewidth=2, label='Modeled (flight max)')\n",
    "mpl.plot(x, gz_adj3, '-g', linewidth=2, label='Modeled (draped)')\n",
    "# mpl.plot(x, gz-fag070, '-b', linewidth=1)\n",
    "mpl.plot(x, fag070, color='k', linestyle=\"None\", marker=',', label='FAG070')\n",
    "mpl.xlim(min(x), max(x))\n",
    "ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x/1e3))\n",
    "ax1.xaxis.set_major_formatter(ticks_x)\n",
    "mpl.ylabel(\"mGal\")\n",
    "mpl.legend()\n",
    "# ax1.annotate('rmse: '+str(int(rmse))+' mGal', \n",
    "#              xy=(min(x)*1.1, np.mean(gz)*1.1), xytext=(min(x)*1.1, np.mean(gz)*1.1))\n",
    "# ax1.annotate('rmse: '+str(int(rmse))+' mGal', xy=(10,0), xytext=(10,0))\n",
    "###\n",
    "ax2 = mpl.subplot(2, 1, 2)\n",
    "mpl.polygon(polygons[0], '.-k', linewidth=1, fill='cyan', alpha=0.5)\n",
    "mpl.polygon(polygons[1], '.-k', linewidth=1, fill='blue', alpha=0.5)\n",
    "mpl.polygon(polygons[2], '.-k', linewidth=1, fill='orange', alpha=0.5)\n",
    "mpl.plot(x, z3, '-g', linewidth=2, label='Modeled (draped)')\n",
    "mpl.xlim(min(x), max(x))\n",
    "mpl.ylim(-1500, 2000)\n",
    "mpl.xlabel(\"Distance (km)\")\n",
    "mpl.ylabel(\"Z (masl)\")\n",
    "ax2.xaxis.set_major_formatter(ticks_x)\n",
    "mpl.savefig('figs/test_OIB_'+str(segment)+'_forward_elevationtest.png', bbox_inches='tight')\n",
    "mpl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Some other segments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) All grounded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dporter/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:5: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \"\"\"\n",
      "/Users/dporter/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/dporter/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/dporter/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:49: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.3707936895992825\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "segment = 1477334733\n",
    "# gb[[\"TIME\", \"FAG070\"]].get_group(segment)\n",
    "lf = gb.get_group(segment)\n",
    "oib_lineplot_cust(lf)\n",
    "lf.ix[lf['SURFACE_atm']-lf['ICEBASE'] <= 0, 'ICEBASE'] = (lf['SURFACE_atm'] - 0.1)\n",
    "\n",
    "# Compute distance along transect\n",
    "dist = np.zeros((np.size(lf['LONG'])))\n",
    "for i in range(2, np.size(lf['LONG'])):\n",
    "    dist[i] = dist[i - 1] + haversine([lf['LAT'].values[i - 1],lf['LONG'].values[i - 1]],\n",
    "              [lf['LAT'].values[i],lf['LONG'].values[i]])\n",
    "'''\n",
    "Extract data from DataFrame\n",
    "'''\n",
    "fag070 = lf['FAG070'][0:].values\n",
    "elevation = lf['WGSHGT'][0:].values\n",
    "icesfc = lf['SURFACE_atm'][0:].values\n",
    "icebase = lf['ICEBASE'][0:].values\n",
    "\n",
    "lf['ICEBASE2'] = lf['icebase_recalc']\n",
    "lf['ICESFC2'] = lf['surface_recalc']\n",
    "\n",
    "# Check for crossing horizons (AGAIN?)\n",
    "lf.loc[lf['ICESFC2']-lf['ICEBASE2'] <= 0, 'ICEBASE2'] = (lf['ICESFC2'] - 0.1)\n",
    "if segment==1477341085:\n",
    "    # make ice 1 meter thick at end of line \n",
    "    # TODO not just 'end'\n",
    "    lf['ICEBASE2'].iloc[-1] = lf['surface_recalc'].iloc[-1] - 1\n",
    "    \n",
    "    #Find ice front position\n",
    "    waterstart = lf['icebase_recalc'].last_valid_index() + 1\n",
    "    print(waterstart)\n",
    "    lf['ICEBASE2'].loc[waterstart] = lf['surface_recalc'].loc[waterstart] - 1\n",
    "    \n",
    "    # Now INNER interpolate new horizon\n",
    "    lf['ICEBASE2'].interpolate(method='linear', limit_area='inside', axis=0, inplace=True)  #limit_direction='forward'\n",
    "    \n",
    "    # Check for crossing horizons AGAIN?\n",
    "    lf.loc[lf['ICESFC2']-lf['ICEBASE2'] <= 0, 'ICEBASE2'] = (lf['ICESFC2'] - 0.1)\n",
    "\n",
    "    # Create water base, set it to single depth below ice\n",
    "    lf['WATERBASE2'] = lf['ICEBASE2'] - 1\n",
    "    lf['WATERBASE2'].loc[waterstart:lf['WATERBASE2'].index[-1]] = lf['WATERBASE2'].loc[waterstart - 1]\n",
    "else:\n",
    "    # INNER interpolate new horizon\n",
    "    lf['ICEBASE2'].interpolate(method='linear', limit_area='inside', axis=0, inplace=True)  #limit_direction='forward'\n",
    "    \n",
    "    # Create water base, set it to single depth below ice\n",
    "    lf['WATERBASE2'] = lf['ICEBASE2'] - 1\n",
    "\n",
    "lf['ICESFC2'].interpolate(method='linear', limit_area='inside', axis=0, inplace=True)  #limit_direction='forward'\n",
    "\n",
    "'''\n",
    "Convert OIB data to polygon arrays\n",
    "'''\n",
    "icesfc = lf['ICESFC2'][0:].values\n",
    "icebase = lf['ICEBASE2'][0:].values\n",
    "waterbase = lf['WATERBASE2'][0:].values\n",
    "# iceoutline = np.append(np.concatenate([icesfc, icebase[::-1]], axis=0), icesfc[0]) # no extension\n",
    "iceoutline = make_outline(icesfc, icebase)\n",
    "# plt.figure(facecolor='white'); plt.plot(iceoutline[1180:1220])\n",
    "\n",
    "watertop = icebase\n",
    "# waterbase = icebase - 1\n",
    "wateroutline = make_outline(watertop, waterbase)\n",
    "\n",
    "rocktop = waterbase\n",
    "rockbase = -30000 * np.ones_like(waterbase)\n",
    "# rockoutline = np.append(icebase[0], np.concatenate([icebase, z_r], axis=0), icebase[0], icebase[0])\n",
    "rockoutline = make_outline(rocktop, rockbase)\n",
    "\n",
    "### Distances\n",
    "x = dist * 1000\n",
    "xs = make_outline_dist(x, 1e6)\n",
    "\n",
    "### Heights\n",
    "# z = int(np.max(elevation))\n",
    "# z = int(np.max(icesfc) + 1) * np.ones_like(x)\n",
    "# z = elevation + int(np.max(icesfc)+1)\n",
    "z = np.max(elevation) * np.ones_like(x)\n",
    "\n",
    "'''\n",
    "Build the Polygon\n",
    "'''\n",
    "props_i = {'density': rho_i}\n",
    "props_r = {'density': rho_r}\n",
    "props_w = {'density': rho_w}\n",
    "# polygon = Polygon(np.transpose([xs, iceoutline]), props_i)\n",
    "# polygons = [Polygon(np.transpose([xs, iceoutline]), props_i),\n",
    "#             Polygon(np.transpose([xs, rockoutline]), props_r)\n",
    "#             ]\n",
    "polygons = [Polygon(np.transpose([xs, iceoutline]), props_i),\n",
    "            Polygon(np.transpose([xs, wateroutline]), props_w),\n",
    "            Polygon(np.transpose([xs, rockoutline]), props_r)\n",
    "            ]\n",
    "\n",
    "\n",
    "'''\n",
    "Forward Model\n",
    "'''\n",
    "gz = talwani.gz(x, z, polygons)\n",
    "gz_adj = (gz - np.mean(gz)) + np.mean(fag070)\n",
    "n = len(gz_adj)\n",
    "rmse = np.linalg.norm(gz_adj - fag070) / np.sqrt(n)\n",
    "print rmse\n",
    "'''\n",
    "Plot\n",
    "'''\n",
    "###\n",
    "mpl.figure(facecolor='white')\n",
    "mpl.axis('scaled')\n",
    "ax1 = mpl.subplot(2, 1, 1)\n",
    "mpl.title(r\"Gravity anomaly produced by the model\")\n",
    "mpl.plot(x, gz_adj, '-r', linewidth=2, label='Modeled')\n",
    "# mpl.plot(x, gz-fag070, '-b', linewidth=1)\n",
    "mpl.plot(x, fag070, color='k', linestyle=\"None\", marker=',', label='FAG070')\n",
    "mpl.xlim(min(x), max(x))\n",
    "ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x/1e3))\n",
    "ax1.xaxis.set_major_formatter(ticks_x)\n",
    "mpl.ylabel(\"mGal\")\n",
    "mpl.legend()\n",
    "# ax1.annotate('rmse: '+str(int(rmse))+' mGal', xy=(10, 0), xytext=(10, 0))\n",
    "###\n",
    "ax2 = mpl.subplot(2, 1, 2)\n",
    "mpl.polygon(polygons[0], '.-k', linewidth=1, fill='cyan', alpha=0.5)\n",
    "mpl.polygon(polygons[1], '.-k', linewidth=1, fill='orange', alpha=0.5)\n",
    "mpl.xlim(min(x), max(x))\n",
    "mpl.ylim(-1000, 2000)\n",
    "mpl.xlabel(\"X\")\n",
    "mpl.ylabel(\"Z\")\n",
    "ax2.xaxis.set_major_formatter(ticks_x)\n",
    "mpl.savefig('figs/test_OIB_'+str(segment)+'_forward.png', bbox_inches='tight')\n",
    "mpl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11a053b50>]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(12, 8))\n",
    "axes[0].plot(xs, iceoutline, color='cyan', marker='o')\n",
    "axes[1].plot(xs, wateroutline, color='blue')\n",
    "axes[2].plot(xs, rockoutline, color='orange')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) No ice base?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.6323105069\n"
     ]
    },
    {
     "data": {
      "image/png": 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Mnz8fHx8fo2OJiIikbBhGRERE0vuNGzdSsWJFAN5880327dvH48ePuX//PocO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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1093415d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1059a8290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fBwwZIvWZLVxatGgBAGjbti3Onj2L27dvI7WO1R3UAL78Uuo++6x0PxY12dgA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\nRIpjuBARkeIYLkREpDiGCxERKe7/A24AN5C1+noJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109038bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "segment = 1274276308\n",
    "# gb[[\"TIME\", \"FAG070\"]].get_group(segment)\n",
    "lf = gb.get_group(segment)\n",
    "oib_lineplot_cust(lf)\n",
    "lf.ix[lf['SURFACE_atm']-lf['ICEBASE'] <= 0, 'ICEBASE'] = (lf['SURFACE_atm'] - 0.1)\n",
    "\n",
    "# Compute distance along transect\n",
    "dist = np.zeros((np.size(lf['LONG'])))\n",
    "for i in range(2, np.size(lf['LONG'])):\n",
    "    dist[i] = dist[i - 1] + haversine([lf['LAT'].values[i - 1],lf['LONG'].values[i - 1]],\n",
    "              [lf['LAT'].values[i],lf['LONG'].values[i]])\n",
    "'''\n",
    "Extract data from DataFrame\n",
    "'''\n",
    "fag070 = lf['FAG070'][0:].values\n",
    "elevation = lf['WGSHGT'][0:].values\n",
    "icesfc = lf['SURFACE_atm'][0:].values\n",
    "icebase = lf['ICEBASE'][0:].values\n",
    "'''\n",
    "Convert OIB data to polygon arrays\n",
    "'''\n",
    "# iceoutline = np.append(np.concatenate([icesfc, icebase[::-1]], axis=0), icesfc[0]) # no extension\n",
    "iceoutline = make_outline(icesfc, icebase)\n",
    "# plt.figure(facecolor='white'); plt.plot(iceoutline[1180:1220])\n",
    "\n",
    "rockbase = -30000*np.ones_like(icebase)\n",
    "# rockoutline = np.append(icebase[0], np.concatenate([icebase, z_r], axis=0), icebase[0], icebase[0])\n",
    "rockoutline = make_outline(icebase, rockbase)\n",
    "\n",
    "x = dist * 1000\n",
    "xs = make_outline_dist(x, 1e6)\n",
    "# plt.figure(facecolor='white'); plt.plot(xs[1180:1220])\n",
    "\n",
    "# depths of calculations points\n",
    "# z = elevation + 2000\n",
    "z = int(np.max(icesfc)+1)*np.ones_like(x)\n",
    "\n",
    "plt.figure(facecolor='white')\n",
    "plt.plot(xs, iceoutline) \n",
    "plt.plot(x, elevation)\n",
    "plt.plot(x, z) \n",
    "'''\n",
    "Build the Polygon\n",
    "'''\n",
    "props = {'density': rho_r - rho_i}\n",
    "props2 = {'density': rho_r}\n",
    "polygon = Polygon(np.transpose([xs, iceoutline]), props)\n",
    "polygons = [Polygon(np.transpose([xs, iceoutline]), props), \n",
    "            Polygon(np.transpose([xs, rockoutline]), props2)\n",
    "            ]\n",
    "'''\n",
    "Forward Model\n",
    "'''\n",
    "gz = talwani.gz(x, z, polygons)\n",
    "gz_adj = (gz - np.mean(gz)) + np.mean(fag070)\n",
    "n = len(gz_adj)\n",
    "rmse = np.linalg.norm(gz_adj - fag070) / np.sqrt(n)\n",
    "print rmse\n",
    "'''\n",
    "Plot\n",
    "'''\n",
    "###\n",
    "mpl.figure(facecolor='white')\n",
    "mpl.axis('scaled')\n",
    "ax1 = mpl.subplot(2, 1, 1)\n",
    "mpl.title(r\"Gravity anomaly produced by the model\")\n",
    "mpl.plot(x, gz_adj, '-r', linewidth=2, label='Modeled')\n",
    "# mpl.plot(x, gz-fag070, '-b', linewidth=1)\n",
    "mpl.plot(x, fag070, color='k', linestyle=\"None\", marker=',', label='FAG070')\n",
    "mpl.xlim(min(x), max(x))\n",
    "ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x/1e3))\n",
    "ax1.xaxis.set_major_formatter(ticks_x)\n",
    "mpl.ylabel(\"mGal\")\n",
    "mpl.legend()\n",
    "ax1.annotate('rmse: '+str(int(rmse))+' mGal', xy=(10, 0), xytext=(10, 0))\n",
    "###\n",
    "ax2 = mpl.subplot(2, 1, 2)\n",
    "mpl.polygon(polygons[0], '.-k', linewidth=1, fill='cyan', alpha=0.5)\n",
    "mpl.polygon(polygons[1], '.-k', linewidth=1, fill='orange', alpha=0.5)\n",
    "mpl.xlim(min(x), max(x))\n",
    "mpl.ylim(-1000, 2000)\n",
    "mpl.xlabel(\"X\")\n",
    "mpl.ylabel(\"Z\")\n",
    "ax2.xaxis.set_major_formatter(ticks_x)\n",
    "mpl.savefig('figs/test_OIB_'+str(segment)+'_forward.png', bbox_inches='tight')\n",
    "mpl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For FUN, let's try inverting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/fatiando/inversion/optimization.py:302: RuntimeWarning: Exited because maximum iterations reached. Might not have achieved convergence. Try inscreasing the maximum number of iterations allowed.\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<fatiando.inversion.base.MultiObjective at 0x105bf2fd0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from fatiando.inversion import Smoothness1D\n",
    "from fatiando.gravmag.basin2d import PolygonalBasinGravity\n",
    "from fatiando import utils\n",
    "\n",
    "# Make the solver using smoothness regularization and run the inversion\n",
    "misfit = PolygonalBasinGravity(x, z, fag070, 100, props, top=0)\n",
    "regul = Smoothness1D(misfit.nparams)\n",
    "solver = misfit + 1e-4*regul\n",
    "# print dir(misfit)\n",
    "\n",
    "# This is a non-linear problem so we need to pick an initial estimate\n",
    "initial = -100*np.ones(misfit.nparams)\n",
    "solver.config('levmarq', initial=initial).fit()\n",
    "# solver.estimate_.vertices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8HpYvX67X/gO6tzIhposCgplQdWXOGEN6erpe82EMzVaEkIGhgGAmjOXKnCanEWK6KCCY\nCWO5Mqd7KxNiuiggmAljujKPj49HUlISRo8ejTt37mDjxo00yogQE0ABwUwY05U5DT0lxDTR0hVE\n62jVU0KMAy1dMUQZ0+xgY+ngJoRohgKCGTC2Jhpj6eAmhGiGAoIZMLbZwao6uHk8HubOnWuQ/BBC\n+ocCghkwtiaa+Ph4LF++XGkZDcYYUlJSqGOZECNGAcEMGGMTTXp6eo/7K9OaRoQYNwoIZiApKQmW\nlpZKaZaWlgadHWxstRZCSN8oIJiJ7lfjhh5N7OTkpFE6IcTwKCCYgXXr1vW4U5pUKjXK5hmJRGLo\nLBBC1KCAYOJSU1NRXV2t8jlDNs+ou0tbc3MzVq1apefcEEL6w6wDwsyZM8Hj8dQ+7OzsTHrUS2pq\nKpYtW6b2eUN2Kvf23rt27aKgQIgRMtuAMHPmTJw+fbrXbZqamrB06dIegcLFxcXoA8XMmTOxdOlS\nyOVytdsYslO5r/fetWsXZs6cqafcEEL6hZmAqKgojV8DQGsPPp/PEhMTdVCy/jt48CCztbXtd56d\nnZ0Nml/GGHN2dtb58U1MTGQ8Hm9An+uIESPYwYMHtVxq06fJd83Z2dnsj6Gmvz1jOi6anjspIAzg\noesP++DBg/0+map7GMOX8eDBgwM+WRvTw1h+3IOhje+UuR9LQx0jXR4TCgj3GfqLb+gfnbFITEw0\n+PHQx0OftQ1jObnr8/usrWNrqsduoMeAAsJ9M2bMMPiHaIiHpaWl0V2BDZWgMJgftameqOihv8dA\nfttGFxC2bdvGALDKykrGGGNyuZytWbOGBQQEsPDwcJaTk9PnPgYSEBgbekHB2tra6IKBwlD7LOhB\nD108fHx8NPrdaXru1Okoo+LiYnz//fdKQxAzMjIgFoshFouRnJyMxMREnb3/qVOnwDqDntLj4MGD\ncHZ21tn7GsKMGTPQ2tpqtPcuPnXqFGbMmGHobBBi0nQ9t0inAeHll1/GP//5T6VVL9PS0rBs2TLw\neDxMnjwZdXV1KC8v12U2eoiPj0dVVZVZBAlnZ2ccPHgQp06dMnRW+nTq1CmTPc6EGANdzy3SWUD4\n5ptv4OXlhcjISKX00tJSeHt7c38LhUKUlpbqKhv9ZipBQhEAFHmsqqoy2lqBKl2PszaPb/fj0tvD\nGD9XY9TbMU1MTFS60BsK+vMd0+Vx0cuClRo1MHUzY8YMFhoa2uNx9OhRNnHiRFZXV8cYY8zHx4fr\nQ5g7dy47e/Yst4/p06ez7OzsHvv+6KOPWFRUFIuKimKjR48eTDa1whCdfjRO3nDMtZPXUHNqBjKW\n31APfQ2N1WQOjUmPMvr555+Zq6sr8/HxYT4+PszCwoJ5e3uz8vJylpCQwD777DNu2+DgYFZWVtbr\n/gbaqUyIrhk6cBjzuP7B0mUQMYbJpvpgFAGhu641hGPHjrE5c+YwuVzOLly4wGJiYvp8PQUEYso0\nuRIcKicqoh+anjt5jOl+4XxfX19kZ2fDxcUFjDG89NJLOHHiBGxsbLBv3z5ER0f3+noXFxf4+voO\n+P0rKyvh6uo64NeboqFYZoDKPdQMxXJrUubCwkJUVVX1e996CQiGFh0djezsbENnQ6+GYpkBKvdQ\nMxTLrcsym+1qp4QQQjRDAYEQQggAwOKNN954w9CZ0IeoqChDZ0HvhmKZASr3UDMUy62rMg+JPgRC\nCCF9oyYjQgghACggEEIIuc+sA8KJEycwZswYBAYGYuvWrYbOjlb4+voiPDwcIpGIm79RU1OD2NhY\nBAUFITY2FrW1tQAAxhjWrl2LwMBARERE4MqVK9x+UlJSEBQUhKCgIKSkpBikLL1ZsWIF3NzcEBYW\nxqVps5w5OTkIDw9HYGAg1q5dC2NoOVVV5jfeeANeXl4QiUQQiURIT0/nntuyZQsCAwMxZswYnDx5\nkktX972/ffs2Jk2ahKCgICxZsgTt7e36KVgfiouLMW3aNISEhCA0NBQffPABAPP+vNWV2eCftxYn\nxRkVmUzG/P39WUFBAWtra2MREREsPz/f0NkatK6zvhXWr1/PtmzZwhhjbMuWLezPf/4zY4yx48eP\nK80KnzhxImOMserqaubn58eqq6tZTU0N8/PzYzU1NfotSB9+/PFHlpOTw0JDQ7k0bZYzJiaGnT9/\nnsnlcjZnzhyWnp6u5xL2pKrMr7/+Otu2bVuPbfPz81lERASTSCTs1q1bzN/fn8lksl6/94sXL2aH\nDh1ijDH24osvsp07d+qnYH0oKyvj7ovS0NDAgoKCWH5+vll/3urKbOjP22xrCFlZWQgMDIS/vz8s\nLS0RFxeHtLQ0Q2dLJ9LS0rB8+XIAwPLly3H06FEuXdVS4ydPnkRsbCycnJzg6OiI2NhYnDhxwpBF\n6GHq1KlwcnJSStNWOcvLy9HQ0IApU6aAx+Nh2bJl3L4MSVWZ1UlLS0NcXBysrKzg5+eHwMBAZGVl\nqf3eM8aQmZmJRYsWAVA+fobm4eGBCRMmAADs7OwQEhKC0tJSs/681ZVZHX193mYbEIx1me3B4vF4\nmDVrFqKiopCcnAwAqKiogIeHB4DOL9q9e/cAqD8GpnpstFXO0tJSCIXCHunGavv27YiIiMCKFSu4\nZhNNy1xdXY2RI0dCIBAopRubwsJCXL16FZMmTRoyn3fXMgOG/bzNNiAwFW2E5rB++7lz53DlyhVk\nZGRgx44d+Pe//612W3XHwNyOjablNKXyJyYmoqCgALm5ufDw8MCrr74KwDzL3NTUhIULF+L999+H\nvb292u3Mqezdy2zoz9tsA4JQKERxcTH3d0lJCTw9PQ2YI+1QlMHNzQ1PPPEEsrKy4O7uzt11rry8\nHG5ubgDUHwNTPTbaKqdQKERJSUmPdGPk7u4OCwsL8Pl8vPDCC8jKygKgeZldXFxQV1cHmUymlG4s\npFIpFi5ciPj4eCxYsACA+X/e6sps0M974N0ixk0qlTI/Pz9269YtrrMlLy/P0NkalKamJtbQ0MD9\nf8qUKSwjI4O99tprSp1v69evZ4ypX2q8urqa+fr6spqaGlZTU8N8fX1ZdXW1YQrVi9u3byt1sGqz\nnNHR0ezChQtcJ+Px48f1XDrVupe5671C3n33XbZkyRLGGGN5eXlKnYx+fn5MJpP1+r1ftGiRUifj\njh079Fgy9eRyOXvmmWfYunXrlNLN+fNWV2ZDf95mGxAY6xyNEBQUxPz9/dnmzZsNnZ1BKygoYBER\nESwiIoKNGzeOK1NVVRWbPn06CwwMZNOnT+d+BHK5nK1atYr5+/uzsLAwdvnyZW5fe/bsYQEBASwg\nIIDt3bvXIOXpTVxcHBs1ahQTCATMy8uLffLJJ1ot5+XLl1loaCjz9/dnq1evZnK5XO9l7E5VmZcu\nXcrCwsJYeHg4e+yxx5ROGJs3b2b+/v4sODhYadSMuu99QUEBi4mJYQEBAWzRokVMIpHotXzqnD17\nlgFg4eHhLDIykkVGRrLjx4+b9eetrsyG/rwHvXRFcXExli1bhrt374LP5yMhIQHr1q1DTU0NlixZ\ngsLCQvj6+uJf//oXHB0dwRjDunXrkJ6eDhsbG+zfv5/rbSeEEGI4g+5DEAgEeOedd3D9+nVcvHgR\nO3bswLVr17B161bMmDEDYrEYM2bM4CZMZGRkQCwWQywWIzk5GYmJiYMuBCGEkMEbdEDQ1hhiQggh\nhqXVUUaDGUNMCCHEsATa2tFgxxB3l5yczE28unHjBsaOHautrBJCyJCg6T2VtRIQehtD7OHh0a8x\nxN0lJCQgISEBwNC8byohhAyWYgHM/hp0kxFjDH/84x8REhKCV155hUufN28et9pgSkoK5s+fz6Uf\nOHAAjDFcvHgRDg4OXNMSIYQQwxl0DeHcuXP49NNPuSWZAeCtt97Chg0b8OSTT2LPnj0YPXo0vvji\nCwDA3LlzkZ6ejsDAQNjY2GDfvn2DzQIhhBAtMIlbaFKTESGEaE7Tc6fWOpUJIURbpFIpSkpKIJFI\nDJ0Vk2BtbQ2hUIhhw4YNaj8UEAghRqekpAR2dnbw9fU1mpVJjRVjDNXV1SgpKYGfn9+g9mW2q50S\nQkyXRCKBs7MzBYN+4PF4cHZ21kptigICIcQoUTDoP20dKwoIhBCiQkVFBZ5++mn4+/sjKioKU6ZM\nwddff23obOkUBQRCCOmGMYbHH38cU6dOxa1bt5CTk4PDhw8r3WjHHFFAIISQbjIzM2FpaYmVK1dy\naT4+PlizZg3279+Pl156iUt/9NFHcebMGQDAd999hylTpmDChAlYvHgxmpqaAAAbNmzAuHHjEBER\ngddeew0A8MUXXyAsLAyRkZGYOnWq/grXCxplRAgh3eTn52t8n5aqqips3rwZp06dgq2tLd5++228\n++67eOmll/D111/jxo0b4PF4qKurAwBs2rQJJ0+ehJeXF5dmaFRDIIQYPR6Pp/WHJlavXo3IyEjE\nxMSo3ebixYu4du0aHnzwQYhEIqSkpKCoqAj29vawtrbG888/j6+++go2NjYAgAcffBDPPvssPv74\nY3R0dAzq+GgL1RAIIUZP3wsqhIaG4ssvv+T+3rFjB6qqqhAdHQ2BQAC5XM49pxjuyRhDbGwsDh06\n1GN/WVlZOH36NA4fPozt27cjMzMTu3fvxqVLl3D8+HGIRCLk5ubC2dlZ94XrBdUQSL9cv34db775\nJqRSqaGzQojOTZ8+HRKJBLt27eLSWlpaAAC+vr7Izc2FXC5HcXExsrKyAACTJ0/GuXPncPPmTW77\n3377DU1NTaivr8fcuXPx/vvvIzc3FwBQUFCASZMmYdOmTXBxcVFaBdpQqIZA+iSTybB48WLcu3cP\nZWVl+OijjwydJUJ0isfj4ejRo3j55Zfxz3/+E66urly/wIMPPgg/Pz+Eh4cjLCyM62twdXXF/v37\n8dRTT6GtrQ0AsHnzZtjZ2WH+/PmQSCRgjOG9994DAKxfvx5isRiMMcyYMQORkZEGK68CBQTSp3Pn\nzqGurg7r1q1TqkYTYs48PDxw+PBhlc+lpqaqTJ8+fTouX77cI11Ri+jqq6++GlwGdYCajEifcnJy\nEBQUBH9/f9y+fdvQ2SGE6AgFBNKnn376CT4+PnB3d0dbWxt3f2xCiHmhgED6lJ+fj4CAAPD5fAiF\nQq5TjBBiXiggkF7J5XLcvHkTUqkUs2bNglgsxi+//GLobBFCdIACAulVS0sLmpub8c0330AmkwEA\n8vLyDJwrQoguUEAgvaqpqYGtrS0KCwvh6ekJANRkRIiZooBAelVdXQ1bW1s0NjYiLCwMAAUEMjTQ\n8teEdFNbWwuZTAa5XI4JEyZw67A0NDQYOGeE6A4tf02ICrW1taipqYGFhQUsLS0xcuRIAJ1DUQkx\nV7T8NSEqKGoIw4cPB9A5e7OsrAxXrlzB7373OwPnjhDdoOWvCVFB8UW1tbUFAAQFBQEAzp8/b7A8\nkaGHlr/WD60EhBUrVsDNzY3rdAQ6R6fExsYiKCgIsbGxqK2tBdDZNrd27VoEBgYiIiICV65c0UYW\niIbee+89PPXUU31uV19fDwDcsrwBAQEAwFWRCdEHxpjWH70JDQ1VOjft2LEDp0+fRmVlZZ/LX+fm\n5iI3NxfXrl3Dnj17IBAIkJWVhYULF+Lo0aOYM2cOAGD37t3YvHkziouLIRKJUF1drYMjpxmtBIRn\nn30WJ06cUErbunUrZsyYAbFYjBkzZmDr1q0AgIyMDIjFYojFYiQnJyMxMVEbWSAa6OjowDvvvIMv\nv/yyzzkFikAuFAoBAAKBAHw+H/fu3aOOZWK2aPnrQZg6dSoKCwuV0tLS0riryOXLl+Phhx/G22+/\njbS0NCxbtgw8Hg+TJ09GXV0dysvL4eHhoY2skH44ceIELCwsIJVKER4eDolEAisrK5Xb1tTUAACC\ng4O5NGtra7S0tCA7OxvTp0/XS54J0Sda/lrLKioquJO8h4cHtyBaaWkpvL29ue2EQiFKS0spIOhR\nZmYmxo8fj5qaGjQ1NWHnzp14+eWXVW57/fp1AICbmxuX5urqiqKiIojFYgoIxGzR8td6oKrtTlUH\nT3JyMqKjoxEdHY3Kykp9ZG3IuHjxIsaNG8ddxWRmZqrd9tdff+2RNnbsWADArVu3dJNBQohB6Cwg\nuLu7o7x74fSQAAAauklEQVS8HABQXl7OXWEKhUKltrKSkhJuSYSuEhISkJ2djezsbLi6uuoqm0OO\nXC7HL7/8goiICG5tovT0dLXbt7a2gs9X/pooqsjnzp3TXUYJIXqns4Awb948pKSkAABSUlIwf/58\nLv3AgQNgjOHixYtwcHCg5qJ+OnjwILy9vTV6REZGKnX45+fnw8rKCi4uLmCMcSMmFDWB5ubmHu+r\nGCanYGlpCYACAiHmRit9CE899RTOnDmDqqoqCIVCvPnmm9iwYQOefPJJ7NmzB6NHj8YXX3wBAJg7\ndy7S09MRGBgIGxsb7Nu3TxtZGBJ++uknhISEIC4urt+vycjIwEcffcQNdbt8+TL8/f0hFosBdM4v\nqK+vx88//4ycnBy89tprKC4uhoWFBbcPJyenHvt1d3dHRUXFIEtECDEmWgkIhw4dUpl++vTpHmk8\nHg87duzQxtsOOc3NzXB1dYW/v3+/XyMSiZQ6r3Jzc+Hj44P8/Hzw+Xw4OTmhvr4eBQUF+Pbbb3Hv\n3j2cPn0as2bN4l7j4+PTY79z5sxBSkoKKisrqUmPEDNBM5VNSEtLi9rhoep4eXmhrKyM+/uXX37B\niBEjcOzYMQgEAnh5eQEAsrOzkZubi/nz5+Ozzz4D8N8JN4rZyV35+voCAD799NOBFIUQYoQoIJiQ\nlpYWWFtb92vbwsJC/O1vf4OHhweqqqq4DuScnBzs27cPxcXFcHZ25k723333Hfz9/REdHY2ff/4Z\nwH9HEbm7u6t9n5MnTw6mSIQYpREjRuj8Pfbv3690sdYfhYWFSitCaBstbmdCWltb+x0Q0tLScOHC\nBVhZWcHe3h63b9+GTCZDY2MjbG1t8ac//QkCgYALFI2NjQgICICnpyf3JVUMR1V0IqtCt9MkRHMd\nHR3Yv38/wsLCVI6yNBSqIZgQTZqMFFf3RUVFcHNzg1gsxrx58wAAzz33HASCzmsBxVIUQGfTkIeH\nB6qrqyGVSlVOpulOMbSYEIO7cAHYsqXzXy05c+YMHn74YSxatAhjx45FfHw8GGPIyMjAk08+qbTd\nY489BkD9Eti+vr7YtGkTHnroIRw6dAjZ2dmIj4+HSCRCa2srcnJy8Pvf/x5RUVGYPXs299vKyclB\nZGQkpkyZovP+VwoIJqS1tbXfAUExAuj8+fNwc3PDzZs3cfPmTbi7u3ML1SmMGjUKADBu3DhYWlrC\nwcEBt2/fVjkpravuw1EJMZgLF4AZM4C//73zXy0GhatXr+L999/HtWvXcOvWLZw7dw6xsbG4ePEi\nN0z7888/x5IlS5SWwL5y5Qqio6Px7rvvcvuytrbGf/7zHyxduhTR0dFITU1Fbm4uBAIB1qxZgyNH\njiAnJwcrVqzAxo0bAXRewH344Ye4oMUyqUMBwYRIJJJ+NxkpFp7Lz8+Hv78/9u7dCwB45plnemz7\n+9//ntv/+vXr0dbWhps3b+LOnTu9vkfX5SwI0Sker/fHAw8Ara1AR0fnvw880Pdr+mnixIkQCoXg\n8/kQiUQoLCyEQCDAnDlz8O2330Imk+H48eOYP3++2iWwFZYsWaLyPX799Vfk5eUhNjYWIpEImzdv\nRklJCerr61FXV8f9RlX9frWJ+hBMiCYBoa2tDZaWligpKUFcXBz2798P4L/3NegqODgY1tbWWLVq\nFfh8PuRyOcRiMbewnTr+/v49FjUkRCf6WK6aqyG0twOWlsDp08CUKVp56661cgsLC67fbcmSJdix\nYwecnJwQExMDOzs7bglsdUPxVf3+gM4lfUJDQ3vUAurq6jS+d8NgUA3BhPQ3ICjugezh4YGKigpu\n7aHexMfHw87ODmvXrgUAfPjhh2hvb++xbEVXoaGhAID29vZ+loAQHZkypTMI/OMfWg0GvXn44Ydx\n5coVfPzxx9yVv7olsFWxs7NDY2MjAGDMmDGorKzkAoJUKkV+fj5GjhwJBwcH/Oc//wGgflE9baGA\nYEL6GxC+/fZb8Hg8PPbYY5BIJDh69CgA9BoYhEIhXnnlFTg4OMDKyorrlFbcOlMVxQxmGnpKjMKU\nKcBf/6qXYAB01hYeffRRZGRk4NFHHwWgvAR2REQEJk+ejBs3bqh8/bPPPouVK1dCJBKho6MDR44c\nwV/+8hdERkZCJBJxdyXct28fVq9ejSlTpvT6e9QGHuvr1kFGIDo6GtnZ2YbOhsE5OTlh586dXCew\nOvHx8WhtbcXatWuxZ88eVFRUQCqV4rXXXlNbZe3qyJEjyM/PB9C5BHBCQoLabd98800kJiZi586d\nmhWGkF5cv34dISEhhs6GSVF1zDQ9d1INwYS0t7f3WUOQyWQoLy/HAw88AABYsGABpFIprK2t+xUM\ngP92MgPAlH5cbVGwJsQ8UKeyCWlra+uzypiZmQnGGEQiEQDA0dERPj4+Gk1+6bo2UXh4eJ/b030R\nCDEPFBBMRFtbG+RyOYYNG9brdsePH4etrS038QzobKsciK4rnvbGGG4OTggZPAoIJqK5uRmWlpa9\njvoBgJs3b0IoFA76/f70pz/1qwPb2tqaWwSPEG1ijOl1yKUp01ZXMPUhmIjm5uY+ZynX1dWhpaUF\nDz744KDfTzHaqC+Ojo6Dfi9CurO2tkZ1dbXWTnTmjDGG6urqfs9R6g3VEExEU1NTr4vMAZ33oRYI\nBBg9erSectV5rwRaz4hom1AoRElJCd1PvZ+sra210jJAAcFENDU19XnFfv78+T6HpGrb5MmTcfHi\nRdy7d4+WsiBaM2zYMPj5+Rk6G0MONRmZiL5qCA0NDaivr8e0adP0mKvOpiUAeO+99/T6voQQ7aOA\nYCL6Wvr6k08+gYWFhUa319SmtLQ0g7wvIUR7TKLJqKGhAefPn4eFhQUEAgGcnJwwbNiwHqMQFP/n\n8XhcZ5TiXx6Px6V33a7rqB0+n6/0XFeMMcjl8h7PKfbbnao0qVSKO3fuwNbWFtbW1tz7KfKluDeB\nIl+Kf/l8PqqqqnoNCIplrg3lxo0baG1thUQiwZkzZxAaGgo3NzdYWFio/IwAcMe+r+cVuncwdj/G\n6kakqPt8VI1iUfd5EjIQDQ0NGDZsmM6XnNAWkwgIYrG43yNn7Ozs+r1fdSMYVKVrOtpB1fY8Hg+O\njo6QyWRob28HY0wpcHV/AOCCEGMMDz/8sNr3q6mpwaxZszTKo7a4u7ujoqICNjY2GDZsGHx9fVFV\nVQWJRIKOjg5uu67HpK9jPJjRJX29VpcjV/oKJgMJNpoEOn3R9Xv3d//9vRjT9v7681rGGKRSKeRy\nOSwsLLh5RKouOvu6uOktD71tq2mLgUkEhK7r6TDG0NjYiKtXr6K0tBS1tbVobW1Fe3s7pFIp2tvb\nERISgvj4eERHRxs457pXUlKC999/H4wxg5V35cqV+PLLL3Hz5k1kZGQYJA/aMphgootA1N+LFkVt\nR1/DNDs6OlBfX4+qqio0NTVBKpVixIgREAqFsLOz63O+jCrda/WqnlOc7LpeNPV3v/1N62v+g1wu\nV3ny7/r68+fP49SpU7hz5w4aGhrQ1tYGoLOVgMfjQSAQwNraGo6Ojhg1ahR8fHwQHByM4ODgHkvM\naHKh1H3bbdu29bp9dyYRELri8Xiwt7dXWm9Hob6+HidPnsRvv/2G9evXg8/nw9XVFRMmTMDjjz+O\n4OBgA+RYtz788EPk5OTAxsZGaXayvoWGhiIvL09v71dSUoKLFy/ixo0bKCsrQ0NDA5qbm9HW1oaO\njg6lHyiPx4OVlRVsbGzg6OgILy8viEQiTJ06lesUV9DFFb4pKSsrw6VLl5Cfn487d+6guroaLS0t\n3JLqihprXywsLGBjYwNXV1eEhIRg6tSpGDduHGxtbc3uGLa1tSEzMxOZmZn47bffuJtTWVtbw9XV\nFeHh4Rg3bhzc3NxQVVWFO3fu4O7du6isrERDQwPu3buHnJwcpdo0n8+HpaUlhg8fDnt7ezg7O8Pb\n2xsPPvggoqOjdXYMTWK1U09Pz15X3FSlvb0dFy5cQH5+Purq6iCVSsHn8+Hp6Yn/+Z//wZgxY3SU\nW/2aP38+HB0d8fTTT/c5T0GXZDIZkpKS8OGHH/Zr/aPeNDc3o6CgALdv30ZpaSlKS0tRXl6udHIC\nOn80w4YNg5WVFYYPHw4bGxuMGDGCC46K/pe2tjbU1dWhvr4eLS0tkEgkXJMdj8eDpaUlRowYgZEj\nR8LV1RVCoRABAQEICwuDl5eXWZ3AJBIJbt68iby8PNy4cQPFxcWoqalBc3MzpFIpgP8eV8XJyNHR\nEXZ2drCzs+PW53dwcFA5EUomk6GsrAxFRUUoLi5WqkF0pegjs7CwwLBhw7iTn62tLRwcHDBy5Eh4\neHggICAAkZGRGDlypF6OT19aWlrw448/4sKFC7h58yaqq6vR3t4OHo+H4cOHw93dHePHj0dISMiA\nLtAkEgnKysq4gFFXV4fGxkbuO6s4jlZWVnB0dISbmxs8PDwAgKuxdf2ei0QijRafNFhAOHHiBNat\nW4eOjg48//zz2LBhg9ptBxIQupNIJMjOzkZWVhYaGxsREBCApKQkuLu7D2q/mpDJZDh79iyKiopw\n79493Lt3D3fv3kVtbS0kEgnkcjkEAgHs7e0hFAoRERGBmTNnwsfHR+3+Zs2ahfnz5yMyMlJv5VDn\nrbfegoODA8aOHYvAwEBERUVh7NixEAgE3G05xWIxCgsLcffuXdTU1HBf3vb2du4qVKHrCWP48OFK\nJwmhUNjnuk59Udy85M6dO6isrOR+RFKpFDKZTKmGwefzIRAIYGVlBVtbW7i7u2Ps2LF46KGHMGbM\nGIPVzjo6OnDt2jWcPXsW169fR1lZGZqbm5VqSaqaV7pegY4cORKjRo2Cn58ffH19B31c1eVTIpGg\npaUF9fX1aGhoQFNTE/dQHPu2tjbu+Mtksh5XzYrvgp2dHVxcXODl5YVx48Zh0qRJ3P05tEEqleLS\npUs4f/48bt68iYqKCjQ1NXHNRVZWVrC3t4enpyfGjx+vt8mgHR0duHnzJq5du4aKigq0tLSgra2N\n+60oasL29vZwd3dHY2Oj8QeEjo4OBAcH4/vvv4dQKERMTAwOHTqEcePGqdxeGwGhq59//hkZGRmQ\nSCRwcXHB4sWLsWjRIq7ds6KiAleuXEFhYSF3ZVpfX4/W1lalTl5FnwWfz0dISAgSExNV1jxyc3Ox\ne/duiMViroNJcaKzsbGBi4sLRo8eDQ8PD4jFYu6qrbW1lftBWFpaYtKkSdi0aRMAYOfOnWhtbcWx\nY8ewceNGgzYXKZw9exZXr15Fa2srpFKp0o9Zgc/nK10VKq7qHR0d4eTkhFGjRsHd3b1fy2boWkdH\nBxobG7nvQG1tLRoaGtDY2Ijm5malz0cgEMDGxgbOzs7w9fVFSEgIJk2aBG9vb63UMFpbW3H9+nVc\nvXoVeXl5KC4uRkNDA3fF2LVN2tXVFba2tlztydLSEtbW1rCzs4Ozs7NWljjQJ6lUivLycpSVlaGy\nshK1tbVobGzk+g67B2/F76v7QyAQYPjw4QgMDISTkxPXFDN8+HDcu3cPv/zyi9JVPwDuO+rs7Awf\nHx+EhYWZ1HItx44dM/6AcOHCBbzxxhvcnba2bNkCAPjrX/+qcvtge3v8efFilHh7azUfv/32G06d\nOoWqqirui6T4gSn+FggE3A9q+PDhXNBQXGE5OTmhsbERYrEYra2tsLKyQnh4OKZPn47MzEzk5eVB\nIpHA1tYWIpEIDz/8sEYn7/b2dhQUFCAnJwe3b9/G6dOnkZeXhzVr1gDoXJ56wYIFWj0u2sIYw927\nd9Ha2gpPT0+TOxH1V2lpKX777TdukIOi+UVx8aA4Wdvb23NNUsHBwfD09ERFRQWKi4u5oFNXV4fm\n5mZIJBLuCrnrT9TCwoJryhEKhQgJCYG3t3e/V6Y1V4qmqpqaGtTW1qKpqYmrbSguTmQyGSQSiVIN\nqutQcktLS9jb22P06NEQiUTw8vIycKkGZ0JODjb9+9/Irq/v92sMEhCOHDmCEydO4JNPPgEAfPrp\np7h06RK2b9+ucvtoHg8XBAKkLFum9aAAdH6ZsrKy0NDQgICAAPj5+Q3oiruurg7ff/89bt26BYlE\nguHDh8Pb2xt/+MMfBt0G2t7eji1btmDnzp149913UV5ejtjYWKNoKiKqyWQyFBYWck2EiuaxtrY2\nrnlM1YWHra0t7Ozs4ODgACcnJ7i4uMDFxcVsAyrRvgk5OXjs2DHEAMjW4BRvkHYGdWP0u0pOTkZy\ncjL39zCZDM/v3avzvOHSJe3tq7UV+O23zocWvAUAq1YhUZFw9Gjng5guxgCZrPMhkQD3R6gQYggG\nCQhCoRDFxcXc3yUlJT3u6JWQkMD1G0TzeJDqsIZgKj755BOUl5fjiSeeQFhYmKGzQwgxUooagqYM\n0mQkk8kQHByM06dPw8vLCzExMfjss88QGhqqcntd9SGYGkV750Am/RBChpaB9CEYpIYgEAiwfft2\nzJ49Gx0dHVixYoXaYAAATSNGDPlgAFAgIIT035WoKEDDe5UYbKzi3LlzMXfuXEO9PSGEkG7okpMQ\nQggACgiEEELuo4BACCEEAAUEQggh91FAIIQQAoACAiGEkPsoIBBCCAFAAYEQQsh9FBAIIYQAoIBA\nCCHkPgoIhBBCAFBAIIQQch8FBEIIIQAoIBBCCLmPAgIhhBAAFBAIIYTcRwGBEEIIAAoIhBBC7qOA\nQAghBAAFBEIIIfdRQCCEEAKAAgIhhJD7KCAQQggBQAGBEELIfYMKCF988QVCQ0PB5/ORnZ2t9NyW\nLVsQGBiIMWPG4OTJk1z6iRMnMGbMGAQGBmLr1q2DeXtCCCFaNKiAEBYWhq+++gpTp05VSr927RoO\nHz6M/Px8nDhxAqtWrUJHRwc6OjqwevVqZGRk4Nq1azh06BCuXbs2qAIQQgjRDsFgXhwSEqIyPS0t\nDXFxcbCysoKfnx8CAwORlZUFAAgMDIS/vz8AIC4uDmlpaRg3btxgskEIIUQLdNKHUFpaCm9vb+5v\noVCI0tJStemEEEIMr88awsyZM3H37t0e6UlJSZg/f77K1zDGeqTxeDzI5XKV6aokJycjOTkZAFBT\nU4Njx471ldUho7KyEq6urobOhtGg46GMjoeyoXw8CgsLNdq+z4Bw6tQpjTMhFApRXFzM/V1SUgJP\nT08AUJveXUJCAhISEgAA0dHRPTqthzI6HsroeCij46GMjkf/6aTJaN68eTh8+DDa2tpw+/ZtiMVi\nTJw4ETExMRCLxbh9+zba29tx+PBhzJs3TxdZIIQQoqFBdSp//fXXWLNmDSorK/HII49AJBLh5MmT\nCA0NxZNPPolx48ZBIBBgx44dsLCwAABs374ds2fPRkdHB1asWIHQ0FCtFIQQQsjg8JiqBn8jk5yc\nzDUfEToe3dHxUEbHQxkdj/4ziYBACCFE92jpCkIIIQBMICAMpaUufH19ER4eDpFIhOjoaACdQ25j\nY2MRFBSE2NhY1NbWAugc2rt27VoEBgYiIiICV65c4faTkpKCoKAgBAUFISUlxSBlGYgVK1bAzc0N\nYWFhXJo2y5+Tk4Pw8HAEBgZi7dq1KodHGxNVx+ONN96Al5cXRCIRRCIR0tPTuec0XS7m9u3bmDRp\nEoKCgrBkyRK0t7frp2ADUFxcjGnTpiEkJAShoaH44IMPAAzt74dOMCMmk8mYv78/KygoYG1tbSwi\nIoLl5+cbOls64+PjwyorK5XS1q9fz7Zs2cIYY2zLli3sz3/+M2OMsePHj7M5c+YwuVzOLly4wCZO\nnMgYY6y6upr5+fmx6upqVlNTw/z8/FhNTY1+CzJAP/74I8vJyWGhoaFcmjbLHxMTw86fP8/kcjmb\nM2cOS09P13MJNaPqeLz++uts27ZtPbbNz89nERERTCKRsFu3bjF/f38mk8l6/Q0tXryYHTp0iDHG\n2Isvvsh27typn4INQFlZGcvJyWGMMdbQ0MCCgoJYfn7+kP5+6IJR1xCysrK4pS4sLS25pS6GkrS0\nNCxfvhwAsHz5chw9epRLX7ZsGXg8HiZPnoy6ujqUl5fj5MmTiI2NhZOTExwdHREbG4sTJ04Ysgj9\nNnXqVDg5OSmlaav85eXlaGhowJQpU8Dj8bBs2TJuX8ZK1fFQR91yMep+Q4wxZGZmYtGiRQCUj60x\n8vDwwIQJEwAAdnZ2CAkJQWlp6ZD+fuiCUQeEobbUBY/Hw6xZsxAVFcXN0q6oqICHhweAzh/FvXv3\nAAyd5UG0Vf7S0lIIhcIe6aZo+/btiIiIwIoVK7gmEk2PR3V1NUaOHAmBQKCUbgoKCwtx9epVTJo0\nib4fWmbUAYGpWQLDXJ07dw5XrlxBRkYGduzYgX//+99qt1V3bIbKMdO0/OZyXBITE1FQUIDc3Fx4\neHjg1VdfBTB0jkdTUxMWLlyI999/H/b29mq3GyrHQ9uMOiD0tgSGOVKUzc3NDU888QSysrLg7u6O\n8vJyAEB5eTnc3NwAqD825nbMtFV+oVCIkpKSHummxt3dHRYWFuDz+XjhhRe4VYQ1PR4uLi6oq6uD\nTCZTSjdmUqkUCxcuRHx8PBYsWACAvh/aZtQBYSgtddHc3IzGxkbu/9999x3CwsIwb948biRESkoK\nt6DgvHnzcODAATDGcPHiRTg4OMDDwwOzZ8/Gd999h9raWtTW1uK7777D7NmzDVauwdJW+T08PGBn\nZ4eLFy+CMYYDBw6oXZzRmClOfkDnSgGKEUiaLhfD4/Ewbdo0HDlyBIDysTVGjDH88Y9/REhICF55\n5RUunb4fWqbnTmyNHT9+nAUFBTF/f3+2efNmQ2dHZwoKClhERASLiIhg48aN48paVVXFpk+fzgID\nA9n06dNZdXU1Y4wxuVzOVq1axfz9/VlYWBi7fPkyt689e/awgIAAFhAQwPbu3WuQ8gxEXFwcGzVq\nFBMIBMzLy4t98sknWi3/5cuXWWhoKPP392erV69mcrlc72XUhKrjsXTpUhYWFsbCw8PZY489xsrK\nyrjtN2/ezPz9/VlwcLDSCBl1v6GCggIWExPDAgIC2KJFi5hEItFr+TRx9uxZBoCFh4ezyMhIFhkZ\nyY4fPz6kvx+6QDOVCSGEADDyJiNCCCH6QwGBEEIIAAoIhBBC7qOAQAghBAAFBEIIIfdRQCCEEAKA\nAgIhhJD7KCAQQggBAPw/rL4+7wAc5pYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109418e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.figure(facecolor='white')\n",
    "mpl.subplot(2, 1, 1)\n",
    "mpl.plot(x, gz_adj, 'ok', label='Gravity Data')\n",
    "# mpl.plot(x, solver[0].predicted(), '-r', linewidth=2, label='predicted Gravity')\n",
    "mpl.legend()\n",
    "ax = mpl.subplot(2, 1, 2)\n",
    "mpl.polygon(polygons[0], fill='gray', alpha=0.5, label='Guess')\n",
    "mpl.polygon(polygons[1], fill='black', alpha=0.5, label='Guess')\n",
    "# The estimate_ property of our solver gives us the estimate basin as a polygon\n",
    "# So we can directly pass it to plotting and forward modeling functions\n",
    "# mpl.plot(x[:70], depths[:29:-1], color='black', linewidth=10, label='Constraints')\n",
    "mpl.polygon(solver.estimate_, style='.-r', label='Inverted')\n",
    "# ax.invert_yaxis()\n",
    "mpl.xlim(min(x), max(x))\n",
    "mpl.ylim(-100, 200)\n",
    "mpl.legend()\n",
    "mpl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sample regional gravity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pickle' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-0d7674c85c1a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'antgg2015_bouger.p'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Compute bathymetry\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mlon\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'LONG'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mlat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'LAT'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'pickle' is not defined"
     ]
    }
   ],
   "source": [
    "T = pickle.load(open('antgg2015_bouger.p','rb'))\n",
    "\n",
    "# Compute bathymetry\n",
    "lon = lf['LONG'][0:].values\n",
    "lat = lf['LAT'][0:].values\n",
    "    \n",
    "fag = np.zeros((np.size(lon)))\n",
    "for i in range(np.size(lon)):\n",
    "    cost_func = ((T['lons']-lon[i])**2) + ((T['lats']-lat[i])**2)\n",
    "    xmin, ymin = np.unravel_index(cost_func.argmin(), cost_func.shape)\n",
    "    fag[i] = -T['ell'][xmin, ymin]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x110f41d10>]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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D3A0EuBy4r9U3AY+0541teWNb9yXg5W2bu4GrJ+jvrwH/Dfhse30HcG1b/jDwxrb8JuDD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b12db10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, fag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DEBUGGING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 262.0974  275.5374  288.9532 ...,   17.8396   17.2373   16.6203]\n"
     ]
    }
   ],
   "source": [
    "bottom = np.array(lf['ICEBASE'])\n",
    "# bottom = lf['BED_flat']\n",
    "print bottom\n",
    "# print lf['BED_flat'][28767]\n",
    "# lf['BED_flat'][28767:b]\n",
    "# lf['BED_flat'][0:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}