{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sth/dev/lonlat_bng/venv/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n",
      "/Users/sth/dev/lonlat_bng/venv/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "import pyproj\n",
    "from convertbng.cutil import convert_bng, convert_etrs89_to_osgb36, convert_etrs89_to_ll, convert_osgb36_to_ll\n",
    "from matplotlib.collections import PatchCollection\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "from shapely.geometry import Point, MultiPoint, MultiPolygon\n",
    "from descartes import PolygonPatch\n",
    "\n",
    "from shapely.geometry import Polygon, MultiPolygon, box\n",
    "from matplotlib.collections import PatchCollection\n",
    "\n",
    "from scipy.spatial import Voronoi, voronoi_plot_2d\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def voronoi_finite_polygons_2d(vor, radius=None):\n",
    "    \"\"\"\n",
    "    Reconstruct infinite voronoi regions in a 2D diagram to finite\n",
    "    regions.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    vor : Voronoi\n",
    "        Input diagram\n",
    "    radius : float, optional\n",
    "        Distance to 'points at infinity'.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    regions : list of tuples\n",
    "        Indices of vertices in each revised Voronoi regions.\n",
    "    vertices : list of tuples\n",
    "        Coordinates for revised Voronoi vertices. Same as coordinates\n",
    "        of input vertices, with 'points at infinity' appended to the\n",
    "        end.\n",
    "    from http://stackoverflow.com/a/20678647/416626\n",
    "    \"\"\"\n",
    "\n",
    "    if vor.points.shape[1] != 2:\n",
    "        raise ValueError(\"Requires 2D input\")\n",
    "\n",
    "    new_regions = []\n",
    "    new_vertices = vor.vertices.tolist()\n",
    "\n",
    "    center = vor.points.mean(axis=0)\n",
    "    if radius is None:\n",
    "        radius = vor.points.ptp().max()\n",
    "\n",
    "    # Construct a map containing all ridges for a given point\n",
    "    all_ridges = {}\n",
    "    for (p1, p2), (v1, v2) in zip(vor.ridge_points, vor.ridge_vertices):\n",
    "        all_ridges.setdefault(p1, []).append((p2, v1, v2))\n",
    "        all_ridges.setdefault(p2, []).append((p1, v1, v2))\n",
    "\n",
    "    # Reconstruct infinite regions\n",
    "    for p1, region in enumerate(vor.point_region):\n",
    "        vertices = vor.regions[region]\n",
    "\n",
    "        if all(v >= 0 for v in vertices):\n",
    "            # finite region\n",
    "            new_regions.append(vertices)\n",
    "            continue\n",
    "\n",
    "        # reconstruct a non-finite region\n",
    "        ridges = all_ridges[p1]\n",
    "        new_region = [v for v in vertices if v >= 0]\n",
    "\n",
    "        for p2, v1, v2 in ridges:\n",
    "            if v2 < 0:\n",
    "                v1, v2 = v2, v1\n",
    "            if v1 >= 0:\n",
    "                # finite ridge: already in the region\n",
    "                continue\n",
    "\n",
    "            # Compute the missing endpoint of an infinite ridge\n",
    "            t = vor.points[p2] - vor.points[p1] # tangent\n",
    "            t /= np.linalg.norm(t)\n",
    "            n = np.array([-t[1], t[0]])  # normal\n",
    "\n",
    "            midpoint = vor.points[[p1, p2]].mean(axis=0)\n",
    "            direction = np.sign(np.dot(midpoint - center, n)) * n\n",
    "            far_point = vor.vertices[v2] + direction * radius\n",
    "\n",
    "            new_region.append(len(new_vertices))\n",
    "            new_vertices.append(far_point.tolist())\n",
    "\n",
    "        # sort region counterclockwise\n",
    "        vs = np.asarray([new_vertices[v] for v in new_region])\n",
    "        c = vs.mean(axis=0)\n",
    "        angles = np.arctan2(vs[:,1] - c[1], vs[:,0] - c[0])\n",
    "        new_region = np.array(new_region)[np.argsort(angles)]\n",
    "\n",
    "        # finish\n",
    "        new_regions.append(new_region.tolist())\n",
    "\n",
    "    return new_regions, np.asarray(new_vertices)\n",
    "\n",
    "\n",
    "def plot_voronoi(lon, lat, ax, **kwargs):\n",
    "    \"\"\" \n",
    "    More flexible Voronoi plotting\n",
    "    kwargs are passed to ax.plot()\n",
    "    returns computed voronoi object\n",
    "    This is a bit of a black box tbh\n",
    "    adapted from http://nbviewer.ipython.org/gist/wiso/6755034\n",
    "    \"\"\"\n",
    "    vor = Voronoi(data[['projected_lon', 'projected_lat']])\n",
    "    xy = np.dstack((lon, lat))[0]\n",
    "    \n",
    "    for simplex in vor.ridge_vertices:\n",
    "        simplex = np.asarray(simplex)\n",
    "        if np.all(simplex >= 0):\n",
    "            ax.plot(vor.vertices[simplex, 0], vor.vertices[simplex, 1], **kwargs)\n",
    "    center = xy.mean(axis=0)\n",
    "    for pointidx, simplex in zip(vor.ridge_points, vor.ridge_vertices):\n",
    "        simplex = np.asarray(simplex)\n",
    "        if np.any(simplex < 0):\n",
    "            i = simplex[simplex >= 0][0]\n",
    "            t = xy[pointidx[1]] - xy[pointidx[0]]\n",
    "            t /= np.linalg.norm(t)\n",
    "            n = np.array([-t[1], t[0]])\n",
    "            midpoint = xy[pointidx].mean(axis=0)\n",
    "            far_point = vor.vertices[i] + \\\n",
    "            np.sign(np.dot(midpoint - center, n)) * n * 10000000\n",
    "            vor.complete_regions, vor.complete_vertices = voronoi_finite_polygons_2d(vor)\n",
    "            #  this plots the far points - maybe consider a dashed line\n",
    "            ax.plot(\n",
    "                [vor.vertices[i, 0], far_point[0]],\n",
    "                [vor.vertices[i, 1], far_point[1]],\n",
    "                **kwargs)\n",
    "    return vor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib import rc\n",
    "rc('font', **{'family':'sans-serif',\n",
    "    'sans-serif':['Helvetica'],\n",
    "    'monospace': ['Inconsolata'],\n",
    "    'serif': ['Helvetica']})\n",
    "rc('text', **{'usetex': True})\n",
    "rc('text', **{'latex.preamble': '\\usepackage{sfmath}'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Ordnance Survey test values, ETRS89 x, y\n",
    "etrs89_e = [331439.16, 362174.408, 151874.984, 339824.598, 241030.731, 599345.196,\n",
    "                 357359.683, 389448.042, 319092.329, 525643.491, 397061.069, 256247.486,\n",
    "                 266961.481, 244687.517, 227685.882, 562079.805, 422143.679, 170277.189,\n",
    "                 530526.413, 247865.448, 247865.718, 167542.805, 292090.289, 424539.719,\n",
    "                 639720.224, 474237.874, 453904.269, 438614.045, 250265.789, 449719.403,\n",
    "                 440623.592, 299624.627, 91400.000, 9500.003, 71622.45, 180766.824, 261500.000,\n",
    "                 395898.578, 421200.000, 330300.000, 337800.000, 334100.000]\n",
    "etrs89_n = [431992.943, 170056.5,\n",
    "                                966535.331,\n",
    "                                556102.504,\n",
    "                                220409.858,\n",
    "                                225801.485,\n",
    "                                383364.152,\n",
    "                                261989.271,\n",
    "                                671010.63,\n",
    "                                470775.61,\n",
    "                                805408.146,\n",
    "                                664760.292,\n",
    "                                846233.646,\n",
    "                                495324.611,\n",
    "                                468918.331,\n",
    "                                319862.042,\n",
    "                                433891.207,\n",
    "                                11652.895,\n",
    "                                178467.043,\n",
    "                                393566.264,\n",
    "                                393568.938,\n",
    "                                797124.153,\n",
    "                                168081.281,\n",
    "                                565080.533,\n",
    "                                169645.824,\n",
    "                                262125.333,\n",
    "                                340910.743,\n",
    "                                114871.192,\n",
    "                                62095.883,\n",
    "                                75415.594,\n",
    "                                1107930.781,\n",
    "                                967256.596,\n",
    "                                11400.000,\n",
    "                                899499.996,\n",
    "                                938567.303,\n",
    "                                1029654.639,\n",
    "                                1025500.000,\n",
    "                                1138780.346,\n",
    "                                1072200.000,\n",
    "                                1017400.000,\n",
    "                                981800.000,\n",
    "                                982100.000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Ordnance Survey test values, ETRS89 lon, lat\n",
    "etrs89_lon = [\n",
    "    -3.0404549,\n",
    "     -2.54407618,\n",
    "     -6.26091467,\n",
    "     -2.93827742,\n",
    "     -4.30852477,\n",
    "     0.89724327,\n",
    "     -2.64049321,\n",
    "     -2.15458614,\n",
    "     -3.2947922,\n",
    "     -0.07773132,\n",
    "     -2.04856032,\n",
    "     -4.29649018,\n",
    "     -4.21926404,\n",
    "     -4.38849118,\n",
    "     -4.63452169,\n",
    "     0.40153548,\n",
    "     -1.66379168,\n",
    "     -5.20304611,\n",
    "     -0.11992556,\n",
    "     -4.28918069,\n",
    "     -4.28917792,\n",
    "     -5.82836702,\n",
    "     -3.55128349,\n",
    "     -1.61657684,\n",
    "     1.44454731,\n",
    "     -0.91248957,\n",
    "     -1.19747657,\n",
    "     -1.45051434,\n",
    "     -4.10864564,\n",
    "     -1.29782277,\n",
    "     -1.2748691,\n",
    "     -3.72631026,\n",
    "     -6.29977753,\n",
    "     -8.57854474,\n",
    "     -7.59255577,\n",
    "     -5.8279935,\n",
    "     -4.41757681,\n",
    "     -2.07382823,\n",
    "     -1.62516965,\n",
    "     -3.21454004,\n",
    "     -3.07392607,\n",
    "     -3.13788291\n",
    "]\n",
    "\n",
    "etrs89_lat = [\n",
    "    53.77911025,\n",
    "     51.42754743,\n",
    "     58.51560462,\n",
    "     54.89542346,\n",
    "     51.85890897,\n",
    "     51.89436637,\n",
    "     53.34480281,\n",
    "     52.25529381,\n",
    "     55.92478316,\n",
    "     54.11685144,\n",
    "     57.13902611,\n",
    "     55.85400002,\n",
    "     57.48625099,\n",
    "     54.32919541,\n",
    "     54.08666318,\n",
    "     52.75136687,\n",
    "     53.8002152,\n",
    "     49.96006138,\n",
    "     51.48936564,\n",
    "     53.41628516,\n",
    "     53.41630925,\n",
    "     57.00606785,\n",
    "     51.4007822,\n",
    "     54.97912283,\n",
    "     51.37447026,\n",
    "     52.25160951,\n",
    "     52.9621911,\n",
    "     50.93127938,\n",
    "     50.43885825,\n",
    "     50.57563665,\n",
    "     59.85409974,\n",
    "     58.58120563,\n",
    "     49.92226394,\n",
    "     57.81351942,\n",
    "     58.2126235,\n",
    "     59.09671707,\n",
    "     59.09335126,\n",
    "     60.13308136,\n",
    "     59.5347087,\n",
    "     59.03743964,\n",
    "     58.71893818,\n",
    "     58.72108386\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# ordnance survey result values, OSGB36 x, y\n",
    "\n",
    "osgb36_test_e = [331534.552, 362269.979, 151968.641, 339921.133, 241124.573, 599445.578,\n",
    "                 357455.831, 389544.178, 319188.423, 525745.658, 397160.479, 256340.914,\n",
    "                 267056.756, 244780.625, 227778.318, 562180.535, 422242.174, 170370.706,\n",
    "                 530624.963, 247958.959, 247959.229, 167634.19, 292184.858, 424639.343,\n",
    "                 639821.823, 474335.957, 454002.822, 438710.908, 250359.798, 449816.359,\n",
    "                 440725.061, 299721.879, 91492.135, 9587.897, 71713.12, 180862.449, 261596.767,\n",
    "                 395999.656, 421300.513, 330398.311, 337898.195, 334198.101]\n",
    "\n",
    "osgb36_test_n = [431920.792,\n",
    "                                169978.688,\n",
    "                                966483.777,\n",
    "                                556034.759,\n",
    "                                220332.638,\n",
    "                                225722.824,\n",
    "                                383290.434,\n",
    "                                261912.151,\n",
    "                                670947.532,\n",
    "                                470703.211,\n",
    "                                805349.734,\n",
    "                                664697.266,\n",
    "                                846176.969,\n",
    "                                495254.884,\n",
    "                                468847.386,\n",
    "                                319784.993,\n",
    "                                433818.699,\n",
    "                                11572.404,\n",
    "                                178388.461,\n",
    "                                393492.906,\n",
    "                                393495.58,\n",
    "                                797067.142,\n",
    "                                168003.462,\n",
    "                                565012.7,\n",
    "                                169565.856,\n",
    "                                262047.752,\n",
    "                                340834.941,\n",
    "                                114792.248,\n",
    "                                62016.567,\n",
    "                                75335.859,\n",
    "                                1107878.445,\n",
    "                                967202.99,\n",
    "                                11318.801,\n",
    "                                899448.993,\n",
    "                                938516.401,\n",
    "                                1029604.111,\n",
    "                                1025447.599,\n",
    "                                1138728.948,\n",
    "                                1072147.236,\n",
    "                                1017347.013,\n",
    "                                981746.359,\n",
    "                                982046.419]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# define some CRS\n",
    "etrs89 = pyproj.Proj(init='epsg:4258')\n",
    "wgs84 = pyproj.Proj(init='epsg:4326')\n",
    "osgb36 = pyproj.Proj(init='epsg:27700')\n",
    "# include grid shifts\n",
    "osgb36_ostn02 = pyproj.Proj(\"+init=EPSG:27700 +nadgrids=OSTN02_NTv2.gsb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# convert to BNG (OSGB36) using pyproj and convertbng\n",
    "\n",
    "# from etrs89 x, y to osgb36 e, n\n",
    "convertbng_res_e, convertbng_res_n = convert_etrs89_to_osgb36(etrs89_e, etrs89_n)\n",
    "\n",
    "# from osgb36 e, n to WGS84 lon, lat \n",
    "convertbng_res_lon, convertbng_res_lat = convert_osgb36_to_ll(convertbng_res_e, convertbng_res_n)\n",
    "\n",
    "pyproj_res_e, pyproj_res_n = pyproj.transform(etrs89, osgb36, etrs89_lon, etrs89_lat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_plot = pd.DataFrame({\n",
    "        'lons': convertbng_res_lon,\n",
    "        'lats': convertbng_res_lat\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lats</th>\n",
       "      <th>lons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>53.779110</td>\n",
       "      <td>-3.040455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.427547</td>\n",
       "      <td>-2.544076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58.515605</td>\n",
       "      <td>-6.260915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54.895423</td>\n",
       "      <td>-2.938277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.858909</td>\n",
       "      <td>-4.308525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>51.894366</td>\n",
       "      <td>0.897243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>53.344803</td>\n",
       "      <td>-2.640493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52.255294</td>\n",
       "      <td>-2.154586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>55.924783</td>\n",
       "      <td>-3.294792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>54.116851</td>\n",
       "      <td>-0.077731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>57.139026</td>\n",
       "      <td>-2.048560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>55.854000</td>\n",
       "      <td>-4.296490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>57.486251</td>\n",
       "      <td>-4.219264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>54.329195</td>\n",
       "      <td>-4.388491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>54.086663</td>\n",
       "      <td>-4.634522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>52.751367</td>\n",
       "      <td>0.401535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>53.800215</td>\n",
       "      <td>-1.663792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>49.960061</td>\n",
       "      <td>-5.203046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>51.489366</td>\n",
       "      <td>-0.119926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>53.416285</td>\n",
       "      <td>-4.289181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>53.416309</td>\n",
       "      <td>-4.289178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>57.006068</td>\n",
       "      <td>-5.828367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>51.400782</td>\n",
       "      <td>-3.551283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>54.979123</td>\n",
       "      <td>-1.616577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>51.374470</td>\n",
       "      <td>1.444547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>52.251610</td>\n",
       "      <td>-0.912490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>52.962191</td>\n",
       "      <td>-1.197477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>50.931279</td>\n",
       "      <td>-1.450514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>50.438858</td>\n",
       "      <td>-4.108646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>50.575637</td>\n",
       "      <td>-1.297823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>59.854100</td>\n",
       "      <td>-1.274869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>58.581206</td>\n",
       "      <td>-3.726310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>49.922264</td>\n",
       "      <td>-6.299778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>57.813519</td>\n",
       "      <td>-8.578545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>58.212623</td>\n",
       "      <td>-7.592556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>59.096717</td>\n",
       "      <td>-5.827993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>59.093351</td>\n",
       "      <td>-4.417577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>60.133081</td>\n",
       "      <td>-2.073828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>59.534709</td>\n",
       "      <td>-1.625170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>59.037440</td>\n",
       "      <td>-3.214540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>58.718938</td>\n",
       "      <td>-3.073926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>58.721084</td>\n",
       "      <td>-3.137883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lats      lons\n",
       "0   53.779110 -3.040455\n",
       "1   51.427547 -2.544076\n",
       "2   58.515605 -6.260915\n",
       "3   54.895423 -2.938277\n",
       "4   51.858909 -4.308525\n",
       "5   51.894366  0.897243\n",
       "6   53.344803 -2.640493\n",
       "7   52.255294 -2.154586\n",
       "8   55.924783 -3.294792\n",
       "9   54.116851 -0.077731\n",
       "10  57.139026 -2.048560\n",
       "11  55.854000 -4.296490\n",
       "12  57.486251 -4.219264\n",
       "13  54.329195 -4.388491\n",
       "14  54.086663 -4.634522\n",
       "15  52.751367  0.401535\n",
       "16  53.800215 -1.663792\n",
       "17  49.960061 -5.203046\n",
       "18  51.489366 -0.119926\n",
       "19  53.416285 -4.289181\n",
       "20  53.416309 -4.289178\n",
       "21  57.006068 -5.828367\n",
       "22  51.400782 -3.551283\n",
       "23  54.979123 -1.616577\n",
       "24  51.374470  1.444547\n",
       "25  52.251610 -0.912490\n",
       "26  52.962191 -1.197477\n",
       "27  50.931279 -1.450514\n",
       "28  50.438858 -4.108646\n",
       "29  50.575637 -1.297823\n",
       "30  59.854100 -1.274869\n",
       "31  58.581206 -3.726310\n",
       "32  49.922264 -6.299778\n",
       "33  57.813519 -8.578545\n",
       "34  58.212623 -7.592556\n",
       "35  59.096717 -5.827993\n",
       "36  59.093351 -4.417577\n",
       "37  60.133081 -2.073828\n",
       "38  59.534709 -1.625170\n",
       "39  59.037440 -3.214540\n",
       "40  58.718938 -3.073926\n",
       "41  58.721084 -3.137883"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# instantiate a map\n",
    "from itertools import chain\n",
    "\n",
    "bds = [-6.108398, 49.61071, 1.669922, 58.972667]\n",
    "extra = 0.01\n",
    "ll = (bds[0], bds[1])\n",
    "ur = (bds[2], bds[3])\n",
    "coords = list(chain(ll, ur))\n",
    "w, h = coords[2] - coords[0], coords[3] - coords[1]\n",
    "\n",
    "minx, miny, maxx, maxy = bds\n",
    "w, h = maxx - minx, maxy - miny"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# add a basemap and a small additional extent\n",
    "m = Basemap(\n",
    "    projection='merc',\n",
    "    ellps = 'WGS84',\n",
    "    llcrnrlon=minx - 0.2 * w,\n",
    "    llcrnrlat=miny - 0.2 * h,\n",
    "    urcrnrlon=maxx + 0.2 * w,\n",
    "    urcrnrlat=maxy + 0.2 * h,\n",
    "    lat_ts=0,\n",
    "    resolution='h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_plot['projected_lon'], df_plot['projected_lat'] = m(*(df_plot[\"lons\"].values, df_plot[\"lats\"].values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JD5JhGAYwxkbI5/P5Jny2mVRCdF0f06E6efIkXrp0ach11zRNcHY5c4bXURRlwuWd1Nwm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a507350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a new matplotlib figure and axes instance\n",
    "fig = plt.figure(figsize=(1, 2))\n",
    "ax = fig.add_subplot(111, axisbg='none')\n",
    "\n",
    "# m.drawcoastlines(linewidth=0.3)\n",
    "m.drawmapboundary()\n",
    "\n",
    "\n",
    "\n",
    "# generate voronoi decomposition\n",
    "vor = Voronoi(df_plot[['projected_lon', 'projected_lat']])\n",
    "# get finite cells\n",
    "vor.complete_regions, vor.complete_vertices = voronoi_finite_polygons_2d(vor)\n",
    "# create square polygon of map extent\n",
    "clip = box(*(chain(m(bds[0], bds[1]), m(bds[2], bds[3]))))\n",
    "# clip Voronoi cells to map extent by intersecting with box\n",
    "clipped = [clip.intersection(poly) for poly in\n",
    "           (Polygon(pol) for pol in (vor.complete_vertices[region] for region in vor.complete_regions))]\n",
    "\n",
    "\n",
    "patches = [PolygonPatch(pol) for pol in clipped if type(pol) == Polygon]\n",
    "# we can specify a normalize instance (area?) and a colour map here\n",
    "pc = PatchCollection(patches, match_original=False, alpha=0.75, color='#000000', lw=.2, zorder=5)\n",
    "pc.set_facecolor('#ffffff')\n",
    "# uncomment to add the filled voronoi tesselation\n",
    "ax.add_collection(pc)\n",
    "\n",
    "\n",
    "# set axes limits to basemap's coordinate reference system \n",
    "min_x, min_y = m(minx, miny)\n",
    "max_x, max_y = m(maxx, maxy)\n",
    "corr_w, corr_h = max_x - min_x, max_y - min_y\n",
    "ax.set_xlim(min_x - 0.2 * corr_w, max_x + 0.2 * corr_w)\n",
    "ax.set_ylim(min_y - 0.2 * corr_h, max_y + 0.2 * corr_h)\n",
    "# square up axes and basemap\n",
    "ax.set_aspect(1)\n",
    "# buffer units are translated to metres by Basemap\n",
    "patches = [PolygonPatch(Point(m(lon, lat)).buffer(5000),\n",
    "                        fc='#cc00cc', ec='#555555', alpha=0.75, zorder=4)\n",
    "           for lon, lat in zip(df_plot.lons, df_plot.lats)]\n",
    "\n",
    "ax.add_collection(PatchCollection(patches, match_original=True))\n",
    "ax.axis('off')\n",
    "plt.savefig('points.png', dpi=150, alpha=True, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "shapely.geometry.polygon.Polygon"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(clipped[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OS_e</th>\n",
       "      <th>OS_n</th>\n",
       "      <th>convertbng_e</th>\n",
       "      <th>convertbng_n</th>\n",
       "      <th>pyproj_e</th>\n",
       "      <th>pyproj_n</th>\n",
       "      <th>convertbng_e_diff</th>\n",
       "      <th>convertbng_n_diff</th>\n",
       "      <th>pyproj_e_diff</th>\n",
       "      <th>pyproj_n_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>331534.552</td>\n",
       "      <td>431920.792</td>\n",
       "      <td>331534.552</td>\n",
       "      <td>431920.791</td>\n",
       "      <td>331534.550305</td>\n",
       "      <td>431920.790149</td>\n",
       "      <td>5.820766e-11</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001695</td>\n",
       "      <td>0.001851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>362269.979</td>\n",
       "      <td>169978.688</td>\n",
       "      <td>362269.979</td>\n",
       "      <td>169978.688</td>\n",
       "      <td>362269.980162</td>\n",
       "      <td>169978.683970</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.001162</td>\n",
       "      <td>0.004030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>151968.641</td>\n",
       "      <td>966483.777</td>\n",
       "      <td>151968.641</td>\n",
       "      <td>966483.889</td>\n",
       "      <td>151968.642709</td>\n",
       "      <td>966483.888993</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.112</td>\n",
       "      <td>-0.001709</td>\n",
       "      <td>-0.111993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>339921.133</td>\n",
       "      <td>556034.759</td>\n",
       "      <td>339921.132</td>\n",
       "      <td>556034.765</td>\n",
       "      <td>339921.132563</td>\n",
       "      <td>556034.764894</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>0.000437</td>\n",
       "      <td>-0.005894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>241124.573</td>\n",
       "      <td>220332.638</td>\n",
       "      <td>241124.573</td>\n",
       "      <td>220332.638</td>\n",
       "      <td>241124.572998</td>\n",
       "      <td>220332.636264</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.001736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         OS_e        OS_n  convertbng_e  convertbng_n       pyproj_e  \\\n",
       "0  331534.552  431920.792    331534.552    431920.791  331534.550305   \n",
       "1  362269.979  169978.688    362269.979    169978.688  362269.980162   \n",
       "2  151968.641  966483.777    151968.641    966483.889  151968.642709   \n",
       "3  339921.133  556034.759    339921.132    556034.765  339921.132563   \n",
       "4  241124.573  220332.638    241124.573    220332.638  241124.572998   \n",
       "\n",
       "        pyproj_n  convertbng_e_diff  convertbng_n_diff  pyproj_e_diff  \\\n",
       "0  431920.790149       5.820766e-11              0.001       0.001695   \n",
       "1  169978.683970       0.000000e+00              0.000      -0.001162   \n",
       "2  966483.888993       0.000000e+00             -0.112      -0.001709   \n",
       "3  556034.764894       1.000000e-03             -0.006       0.000437   \n",
       "4  220332.636264       0.000000e+00              0.000       0.000002   \n",
       "\n",
       "   pyproj_n_diff  \n",
       "0       0.001851  \n",
       "1       0.004030  \n",
       "2      -0.111993  \n",
       "3      -0.005894  \n",
       "4       0.001736  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"OS_e\": osgb36_test_e,\n",
    "        \"OS_n\": osgb36_test_n,\n",
    "        \"convertbng_e\": convertbng_res_e,\n",
    "        \"convertbng_n\": convertbng_res_n,\n",
    "        \"pyproj_e\": pyproj_res_e,\n",
    "        \"pyproj_n\": pyproj_res_n\n",
    "    })\n",
    "\n",
    "\n",
    "# calculate differences\n",
    "df['convertbng_e_diff'] = df.OS_e - df.convertbng_e\n",
    "df['convertbng_n_diff'] = df.OS_n - df.convertbng_n\n",
    "\n",
    "df['pyproj_e_diff'] = df.OS_e - df.pyproj_e\n",
    "df['pyproj_n_diff'] = df.OS_n - df.pyproj_n\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109149e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9OERERCQcFjgEQK4xX2YVl0x5mVVcsuW1CwscIiIiEg7n4ATAOThERESxiz04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\nSJKk4FjgSJKk4Px/aqR7Ky3KagwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10869b690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot results\n",
    "\n",
    "plt.clf()\n",
    "fig = plt.figure(figsize=(8, 10))\n",
    "ax = fig.add_subplot(111, axisbg='none')\n",
    "orig = plt.scatter(\n",
    "    df['OS_e'],\n",
    "    df['OS_n'],\n",
    "    color='#33CCFF',\n",
    "    edgecolor='#333333',\n",
    "    marker='o',\n",
    "    lw=1,\n",
    "    s=80,\n",
    "    alpha=1.0)\n",
    "\n",
    "convertbng = plt.scatter(\n",
    "    df['convertbng_e'],\n",
    "    df['convertbng_n'],\n",
    "    marker='o',\n",
    "    color='#006600',\n",
    "    edgecolor='#333333',\n",
    "    lw=1,\n",
    "    s=25,\n",
    "    alpha=0.75)\n",
    "\n",
    "pyproj = plt.scatter(\n",
    "    df['pyproj_e'],\n",
    "    df['pyproj_n'],\n",
    "    marker='o',\n",
    "    color='#CC00CC',\n",
    "    edgecolor='#333333',\n",
    "    lw=1,\n",
    "    s=25,\n",
    "    alpha=0.75)\n",
    "\n",
    "ax.set_xlabel('Eastings')\n",
    "ax.set_ylabel('Northings')\n",
    "plt.title(\"Accuracy of Transformations\")\n",
    "leg = plt.legend(\n",
    "    (orig, convertbng, pyproj),\n",
    "    ('Ordnance Survey Test Data',\n",
    "    \"Convertbng\",\n",
    "   'Pyproj'),\n",
    "    loc='upper left',\n",
    "    scatterpoints=1,\n",
    "    fontsize=9)\n",
    "leg.get_frame().set_alpha(0.5)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "plt.tight_layout()\n",
    "ax.grid(b=None)\n",
    "plt.savefig('test_data.png', format=\"png\", bbox_inches='tight', alpha=True, transparent=True, dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/sth/dev/lonlat_bng/venv/lib/python2.7/site-packages/pyproj/data\n"
     ]
    }
   ],
   "source": [
    "# put ostn02 grid shift file in here\n",
    "pyproj\n",
    "print pyproj.pyproj_datadir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x107588690>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1074dcdd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['convertbng_e_diff'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10756cdd0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3s56kwt3Xtf3h4LeSrgoAkMyiV/QPJb0vSe4+kvQ02YoyE30OSr58Rc6GahZ6CwR3fyxJ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\ncBQ9AAT3/3SQRB28xF7DAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1092b0f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['pyproj_e_diff'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1093a0690>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x109482710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['convertbng_n_diff'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10969ff50>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1096c5510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['pyproj_n_diff'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OS_e</th>\n",
       "      <th>OS_n</th>\n",
       "      <th>convertbng_e</th>\n",
       "      <th>convertbng_n</th>\n",
       "      <th>pyproj_e</th>\n",
       "      <th>pyproj_n</th>\n",
       "      <th>convertbng_e_diff</th>\n",
       "      <th>convertbng_n_diff</th>\n",
       "      <th>pyproj_e_diff</th>\n",
       "      <th>pyproj_n_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>331534.552</td>\n",
       "      <td>431920.792</td>\n",
       "      <td>331534.552</td>\n",
       "      <td>431920.791</td>\n",
       "      <td>331534.774663</td>\n",
       "      <td>4.319224e+05</td>\n",
       "      <td>5.820766e-11</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>-0.222663</td>\n",
       "      <td>-1.633227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>362269.979</td>\n",
       "      <td>169978.688</td>\n",
       "      <td>362269.979</td>\n",
       "      <td>169978.688</td>\n",
       "      <td>362269.948057</td>\n",
       "      <td>1.699785e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.030943</td>\n",
       "      <td>0.221962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>151968.641</td>\n",
       "      <td>966483.777</td>\n",
       "      <td>151968.641</td>\n",
       "      <td>966483.889</td>\n",
       "      <td>151967.507879</td>\n",
       "      <td>9.664809e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.120000e-01</td>\n",
       "      <td>1.133121</td>\n",
       "      <td>2.915748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>339921.133</td>\n",
       "      <td>556034.759</td>\n",
       "      <td>339921.132</td>\n",
       "      <td>556034.765</td>\n",
       "      <td>339920.907833</td>\n",
       "      <td>5.560355e+05</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>-6.000000e-03</td>\n",
       "      <td>0.225167</td>\n",
       "      <td>-0.734340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>241124.573</td>\n",
       "      <td>220332.638</td>\n",
       "      <td>241124.573</td>\n",
       "      <td>220332.638</td>\n",
       "      <td>241123.036187</td>\n",
       "      <td>2.203337e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.536813</td>\n",
       "      <td>-1.027037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>599445.578</td>\n",
       "      <td>225722.824</td>\n",
       "      <td>599445.578</td>\n",
       "      <td>225722.824</td>\n",
       "      <td>599447.608443</td>\n",
       "      <td>2.257241e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-2.030443</td>\n",
       "      <td>-1.306420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>357455.831</td>\n",
       "      <td>383290.434</td>\n",
       "      <td>357455.831</td>\n",
       "      <td>383290.434</td>\n",
       "      <td>357455.855556</td>\n",
       "      <td>3.832922e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.024556</td>\n",
       "      <td>-1.725604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>389544.178</td>\n",
       "      <td>261912.151</td>\n",
       "      <td>389544.178</td>\n",
       "      <td>261912.151</td>\n",
       "      <td>389544.680553</td>\n",
       "      <td>2.619137e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.502553</td>\n",
       "      <td>-1.576546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>319188.423</td>\n",
       "      <td>670947.532</td>\n",
       "      <td>319188.424</td>\n",
       "      <td>670947.588</td>\n",
       "      <td>319188.476915</td>\n",
       "      <td>6.709470e+05</td>\n",
       "      <td>-1.000000e-03</td>\n",
       "      <td>-5.600000e-02</td>\n",
       "      <td>-0.053915</td>\n",
       "      <td>0.509785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>525745.658</td>\n",
       "      <td>470703.211</td>\n",
       "      <td>525745.658</td>\n",
       "      <td>470703.211</td>\n",
       "      <td>525744.761695</td>\n",
       "      <td>4.707055e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.896305</td>\n",
       "      <td>-2.259462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>397160.479</td>\n",
       "      <td>805349.734</td>\n",
       "      <td>397160.479</td>\n",
       "      <td>805349.836</td>\n",
       "      <td>397159.946751</td>\n",
       "      <td>8.053481e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.020000e-01</td>\n",
       "      <td>0.532249</td>\n",
       "      <td>1.615594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>256340.914</td>\n",
       "      <td>664697.266</td>\n",
       "      <td>256340.914</td>\n",
       "      <td>664697.321</td>\n",
       "      <td>256341.818432</td>\n",
       "      <td>6.646967e+05</td>\n",
       "      <td>-2.910383e-11</td>\n",
       "      <td>-5.500000e-02</td>\n",
       "      <td>-0.904432</td>\n",
       "      <td>0.526310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>267056.756</td>\n",
       "      <td>846176.969</td>\n",
       "      <td>267056.756</td>\n",
       "      <td>846177.079</td>\n",
       "      <td>267056.810841</td>\n",
       "      <td>8.461753e+05</td>\n",
       "      <td>-5.820766e-11</td>\n",
       "      <td>-1.100000e-01</td>\n",
       "      <td>-0.054841</td>\n",
       "      <td>1.680885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>244780.625</td>\n",
       "      <td>495254.884</td>\n",
       "      <td>244780.625</td>\n",
       "      <td>495254.884</td>\n",
       "      <td>244780.894318</td>\n",
       "      <td>4.952562e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.820766e-11</td>\n",
       "      <td>-0.269318</td>\n",
       "      <td>-1.327238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>227778.318</td>\n",
       "      <td>468847.386</td>\n",
       "      <td>227778.318</td>\n",
       "      <td>468847.386</td>\n",
       "      <td>227778.679594</td>\n",
       "      <td>4.688492e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.361594</td>\n",
       "      <td>-1.853549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>562180.535</td>\n",
       "      <td>319784.993</td>\n",
       "      <td>562180.535</td>\n",
       "      <td>319784.992</td>\n",
       "      <td>562181.531714</td>\n",
       "      <td>3.197875e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>-0.996714</td>\n",
       "      <td>-2.493075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>422242.174</td>\n",
       "      <td>433818.699</td>\n",
       "      <td>422242.174</td>\n",
       "      <td>433818.700</td>\n",
       "      <td>422241.874585</td>\n",
       "      <td>4.338204e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.000000e-03</td>\n",
       "      <td>0.299415</td>\n",
       "      <td>-1.710247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>170370.706</td>\n",
       "      <td>11572.404</td>\n",
       "      <td>170370.706</td>\n",
       "      <td>11572.403</td>\n",
       "      <td>170366.823437</td>\n",
       "      <td>1.157107e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>3.882563</td>\n",
       "      <td>1.338870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>530624.963</td>\n",
       "      <td>178388.461</td>\n",
       "      <td>530624.963</td>\n",
       "      <td>178388.461</td>\n",
       "      <td>530626.708705</td>\n",
       "      <td>1.783886e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.745705</td>\n",
       "      <td>-0.162629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>247958.959</td>\n",
       "      <td>393492.906</td>\n",
       "      <td>247958.960</td>\n",
       "      <td>393492.905</td>\n",
       "      <td>247958.550442</td>\n",
       "      <td>3.934950e+05</td>\n",
       "      <td>-1.000000e-03</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>0.408558</td>\n",
       "      <td>-2.049764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>247959.229</td>\n",
       "      <td>393495.580</td>\n",
       "      <td>247959.230</td>\n",
       "      <td>393495.580</td>\n",
       "      <td>247958.820527</td>\n",
       "      <td>3.934976e+05</td>\n",
       "      <td>-1.000000e-03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.408473</td>\n",
       "      <td>-2.049318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>167634.190</td>\n",
       "      <td>797067.142</td>\n",
       "      <td>167634.190</td>\n",
       "      <td>797067.241</td>\n",
       "      <td>167635.106740</td>\n",
       "      <td>7.970648e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.900000e-02</td>\n",
       "      <td>-0.916740</td>\n",
       "      <td>2.375681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>292184.858</td>\n",
       "      <td>168003.462</td>\n",
       "      <td>292184.858</td>\n",
       "      <td>168003.462</td>\n",
       "      <td>292183.852940</td>\n",
       "      <td>1.680034e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.005060</td>\n",
       "      <td>0.024524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>424639.343</td>\n",
       "      <td>565012.700</td>\n",
       "      <td>424639.344</td>\n",
       "      <td>565012.710</td>\n",
       "      <td>424638.472167</td>\n",
       "      <td>5.650135e+05</td>\n",
       "      <td>-1.000000e-03</td>\n",
       "      <td>-1.000000e-02</td>\n",
       "      <td>0.870833</td>\n",
       "      <td>-0.765455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>639821.823</td>\n",
       "      <td>169565.856</td>\n",
       "      <td>639821.823</td>\n",
       "      <td>169565.856</td>\n",
       "      <td>639823.549751</td>\n",
       "      <td>1.695667e+05</td>\n",
       "      <td>-1.164153e-10</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.726751</td>\n",
       "      <td>-0.874993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>474335.957</td>\n",
       "      <td>262047.752</td>\n",
       "      <td>474335.957</td>\n",
       "      <td>262047.752</td>\n",
       "      <td>474336.902652</td>\n",
       "      <td>2.620495e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.945652</td>\n",
       "      <td>-1.728697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>454002.822</td>\n",
       "      <td>340834.941</td>\n",
       "      <td>454002.822</td>\n",
       "      <td>340834.941</td>\n",
       "      <td>454003.017797</td>\n",
       "      <td>3.408372e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.195797</td>\n",
       "      <td>-2.251670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>438710.908</td>\n",
       "      <td>114792.248</td>\n",
       "      <td>438710.908</td>\n",
       "      <td>114792.249</td>\n",
       "      <td>438711.527215</td>\n",
       "      <td>1.147913e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.000000e-03</td>\n",
       "      <td>-0.619215</td>\n",
       "      <td>0.913517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>250359.798</td>\n",
       "      <td>62016.567</td>\n",
       "      <td>250359.798</td>\n",
       "      <td>62016.566</td>\n",
       "      <td>250357.821188</td>\n",
       "      <td>6.201521e+04</td>\n",
       "      <td>2.910383e-11</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>1.976812</td>\n",
       "      <td>1.360159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>449816.359</td>\n",
       "      <td>75335.859</td>\n",
       "      <td>449816.359</td>\n",
       "      <td>75335.859</td>\n",
       "      <td>449817.050544</td>\n",
       "      <td>7.533459e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.691544</td>\n",
       "      <td>1.266028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>440725.061</td>\n",
       "      <td>1107878.445</td>\n",
       "      <td>440725.060</td>\n",
       "      <td>1107878.512</td>\n",
       "      <td>440724.861338</td>\n",
       "      <td>1.107879e+06</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>-6.700000e-02</td>\n",
       "      <td>0.199662</td>\n",
       "      <td>-0.690691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>299721.879</td>\n",
       "      <td>967202.990</td>\n",
       "      <td>299721.880</td>\n",
       "      <td>967203.103</td>\n",
       "      <td>299721.357844</td>\n",
       "      <td>9.672016e+05</td>\n",
       "      <td>-1.000000e-03</td>\n",
       "      <td>-1.130000e-01</td>\n",
       "      <td>0.521156</td>\n",
       "      <td>1.430696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>91492.135</td>\n",
       "      <td>11318.801</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91487.427488</td>\n",
       "      <td>1.131840e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.707512</td>\n",
       "      <td>0.399160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>9587.897</td>\n",
       "      <td>899448.993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9588.182533</td>\n",
       "      <td>8.994442e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.285533</td>\n",
       "      <td>4.811184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>71713.120</td>\n",
       "      <td>938516.401</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71712.568322</td>\n",
       "      <td>9.385124e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.551678</td>\n",
       "      <td>4.040666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>180862.449</td>\n",
       "      <td>1029604.111</td>\n",
       "      <td>180862.449</td>\n",
       "      <td>1029604.211</td>\n",
       "      <td>180860.426831</td>\n",
       "      <td>1.029602e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.000000e-01</td>\n",
       "      <td>2.022169</td>\n",
       "      <td>2.272725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>261596.767</td>\n",
       "      <td>1025447.599</td>\n",
       "      <td>261596.767</td>\n",
       "      <td>1025447.700</td>\n",
       "      <td>261595.878513</td>\n",
       "      <td>1.025447e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.010000e-01</td>\n",
       "      <td>0.888487</td>\n",
       "      <td>0.841813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>395999.656</td>\n",
       "      <td>1138728.948</td>\n",
       "      <td>395999.656</td>\n",
       "      <td>1138728.997</td>\n",
       "      <td>395998.704721</td>\n",
       "      <td>1.138730e+06</td>\n",
       "      <td>5.820766e-11</td>\n",
       "      <td>-4.900000e-02</td>\n",
       "      <td>0.951279</td>\n",
       "      <td>-0.777785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>421300.513</td>\n",
       "      <td>1072147.236</td>\n",
       "      <td>421300.513</td>\n",
       "      <td>1072147.320</td>\n",
       "      <td>421300.583564</td>\n",
       "      <td>1.072147e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-8.400000e-02</td>\n",
       "      <td>-0.070564</td>\n",
       "      <td>-0.201103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>330398.311</td>\n",
       "      <td>1017347.013</td>\n",
       "      <td>330398.311</td>\n",
       "      <td>1017347.117</td>\n",
       "      <td>330397.798101</td>\n",
       "      <td>1.017346e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.040000e-01</td>\n",
       "      <td>0.512899</td>\n",
       "      <td>0.755363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>337898.195</td>\n",
       "      <td>981746.359</td>\n",
       "      <td>337898.195</td>\n",
       "      <td>981746.470</td>\n",
       "      <td>337897.871262</td>\n",
       "      <td>9.817452e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.110000e-01</td>\n",
       "      <td>0.323738</td>\n",
       "      <td>1.133432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>334198.101</td>\n",
       "      <td>982046.419</td>\n",
       "      <td>334198.101</td>\n",
       "      <td>982046.531</td>\n",
       "      <td>334197.767473</td>\n",
       "      <td>9.820452e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.120000e-01</td>\n",
       "      <td>0.333527</td>\n",
       "      <td>1.170664</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          OS_e         OS_n  convertbng_e  convertbng_n       pyproj_e  \\\n",
       "0   331534.552   431920.792    331534.552    431920.791  331534.774663   \n",
       "1   362269.979   169978.688    362269.979    169978.688  362269.948057   \n",
       "2   151968.641   966483.777    151968.641    966483.889  151967.507879   \n",
       "3   339921.133   556034.759    339921.132    556034.765  339920.907833   \n",
       "4   241124.573   220332.638    241124.573    220332.638  241123.036187   \n",
       "5   599445.578   225722.824    599445.578    225722.824  599447.608443   \n",
       "6   357455.831   383290.434    357455.831    383290.434  357455.855556   \n",
       "7   389544.178   261912.151    389544.178    261912.151  389544.680553   \n",
       "8   319188.423   670947.532    319188.424    670947.588  319188.476915   \n",
       "9   525745.658   470703.211    525745.658    470703.211  525744.761695   \n",
       "10  397160.479   805349.734    397160.479    805349.836  397159.946751   \n",
       "11  256340.914   664697.266    256340.914    664697.321  256341.818432   \n",
       "12  267056.756   846176.969    267056.756    846177.079  267056.810841   \n",
       "13  244780.625   495254.884    244780.625    495254.884  244780.894318   \n",
       "14  227778.318   468847.386    227778.318    468847.386  227778.679594   \n",
       "15  562180.535   319784.993    562180.535    319784.992  562181.531714   \n",
       "16  422242.174   433818.699    422242.174    433818.700  422241.874585   \n",
       "17  170370.706    11572.404    170370.706     11572.403  170366.823437   \n",
       "18  530624.963   178388.461    530624.963    178388.461  530626.708705   \n",
       "19  247958.959   393492.906    247958.960    393492.905  247958.550442   \n",
       "20  247959.229   393495.580    247959.230    393495.580  247958.820527   \n",
       "21  167634.190   797067.142    167634.190    797067.241  167635.106740   \n",
       "22  292184.858   168003.462    292184.858    168003.462  292183.852940   \n",
       "23  424639.343   565012.700    424639.344    565012.710  424638.472167   \n",
       "24  639821.823   169565.856    639821.823    169565.856  639823.549751   \n",
       "25  474335.957   262047.752    474335.957    262047.752  474336.902652   \n",
       "26  454002.822   340834.941    454002.822    340834.941  454003.017797   \n",
       "27  438710.908   114792.248    438710.908    114792.249  438711.527215   \n",
       "28  250359.798    62016.567    250359.798     62016.566  250357.821188   \n",
       "29  449816.359    75335.859    449816.359     75335.859  449817.050544   \n",
       "30  440725.061  1107878.445    440725.060   1107878.512  440724.861338   \n",
       "31  299721.879   967202.990    299721.880    967203.103  299721.357844   \n",
       "32   91492.135    11318.801           NaN           NaN   91487.427488   \n",
       "33    9587.897   899448.993           NaN           NaN    9588.182533   \n",
       "34   71713.120   938516.401           NaN           NaN   71712.568322   \n",
       "35  180862.449  1029604.111    180862.449   1029604.211  180860.426831   \n",
       "36  261596.767  1025447.599    261596.767   1025447.700  261595.878513   \n",
       "37  395999.656  1138728.948    395999.656   1138728.997  395998.704721   \n",
       "38  421300.513  1072147.236    421300.513   1072147.320  421300.583564   \n",
       "39  330398.311  1017347.013    330398.311   1017347.117  330397.798101   \n",
       "40  337898.195   981746.359    337898.195    981746.470  337897.871262   \n",
       "41  334198.101   982046.419    334198.101    982046.531  334197.767473   \n",
       "\n",
       "        pyproj_n  convertbng_e_diff  convertbng_n_diff  pyproj_e_diff  \\\n",
       "0   4.319224e+05       5.820766e-11       1.000000e-03      -0.222663   \n",
       "1   1.699785e+05       0.000000e+00       0.000000e+00       0.030943   \n",
       "2   9.664809e+05       0.000000e+00      -1.120000e-01       1.133121   \n",
       "3   5.560355e+05       1.000000e-03      -6.000000e-03       0.225167   \n",
       "4   2.203337e+05       0.000000e+00       0.000000e+00       1.536813   \n",
       "5   2.257241e+05       0.000000e+00       0.000000e+00      -2.030443   \n",
       "6   3.832922e+05       0.000000e+00       0.000000e+00      -0.024556   \n",
       "7   2.619137e+05       0.000000e+00       0.000000e+00      -0.502553   \n",
       "8   6.709470e+05      -1.000000e-03      -5.600000e-02      -0.053915   \n",
       "9   4.707055e+05       0.000000e+00       0.000000e+00       0.896305   \n",
       "10  8.053481e+05       0.000000e+00      -1.020000e-01       0.532249   \n",
       "11  6.646967e+05      -2.910383e-11      -5.500000e-02      -0.904432   \n",
       "12  8.461753e+05      -5.820766e-11      -1.100000e-01      -0.054841   \n",
       "13  4.952562e+05       0.000000e+00       5.820766e-11      -0.269318   \n",
       "14  4.688492e+05       0.000000e+00       0.000000e+00      -0.361594   \n",
       "15  3.197875e+05       0.000000e+00       1.000000e-03      -0.996714   \n",
       "16  4.338204e+05       0.000000e+00      -1.000000e-03       0.299415   \n",
       "17  1.157107e+04       0.000000e+00       1.000000e-03       3.882563   \n",
       "18  1.783886e+05       0.000000e+00       0.000000e+00      -1.745705   \n",
       "19  3.934950e+05      -1.000000e-03       1.000000e-03       0.408558   \n",
       "20  3.934976e+05      -1.000000e-03       0.000000e+00       0.408473   \n",
       "21  7.970648e+05       0.000000e+00      -9.900000e-02      -0.916740   \n",
       "22  1.680034e+05       0.000000e+00       0.000000e+00       1.005060   \n",
       "23  5.650135e+05      -1.000000e-03      -1.000000e-02       0.870833   \n",
       "24  1.695667e+05      -1.164153e-10       0.000000e+00      -1.726751   \n",
       "25  2.620495e+05       0.000000e+00       0.000000e+00      -0.945652   \n",
       "26  3.408372e+05       0.000000e+00       0.000000e+00      -0.195797   \n",
       "27  1.147913e+05       0.000000e+00      -1.000000e-03      -0.619215   \n",
       "28  6.201521e+04       2.910383e-11       1.000000e-03       1.976812   \n",
       "29  7.533459e+04       0.000000e+00       0.000000e+00      -0.691544   \n",
       "30  1.107879e+06       1.000000e-03      -6.700000e-02       0.199662   \n",
       "31  9.672016e+05      -1.000000e-03      -1.130000e-01       0.521156   \n",
       "32  1.131840e+04                NaN                NaN       4.707512   \n",
       "33  8.994442e+05                NaN                NaN      -0.285533   \n",
       "34  9.385124e+05                NaN                NaN       0.551678   \n",
       "35  1.029602e+06       0.000000e+00      -1.000000e-01       2.022169   \n",
       "36  1.025447e+06       0.000000e+00      -1.010000e-01       0.888487   \n",
       "37  1.138730e+06       5.820766e-11      -4.900000e-02       0.951279   \n",
       "38  1.072147e+06       0.000000e+00      -8.400000e-02      -0.070564   \n",
       "39  1.017346e+06       0.000000e+00      -1.040000e-01       0.512899   \n",
       "40  9.817452e+05       0.000000e+00      -1.110000e-01       0.323738   \n",
       "41  9.820452e+05       0.000000e+00      -1.120000e-01       0.333527   \n",
       "\n",
       "    pyproj_n_diff  \n",
       "0       -1.633227  \n",
       "1        0.221962  \n",
       "2        2.915748  \n",
       "3       -0.734340  \n",
       "4       -1.027037  \n",
       "5       -1.306420  \n",
       "6       -1.725604  \n",
       "7       -1.576546  \n",
       "8        0.509785  \n",
       "9       -2.259462  \n",
       "10       1.615594  \n",
       "11       0.526310  \n",
       "12       1.680885  \n",
       "13      -1.327238  \n",
       "14      -1.853549  \n",
       "15      -2.493075  \n",
       "16      -1.710247  \n",
       "17       1.338870  \n",
       "18      -0.162629  \n",
       "19      -2.049764  \n",
       "20      -2.049318  \n",
       "21       2.375681  \n",
       "22       0.024524  \n",
       "23      -0.765455  \n",
       "24      -0.874993  \n",
       "25      -1.728697  \n",
       "26      -2.251670  \n",
       "27       0.913517  \n",
       "28       1.360159  \n",
       "29       1.266028  \n",
       "30      -0.690691  \n",
       "31       1.430696  \n",
       "32       0.399160  \n",
       "33       4.811184  \n",
       "34       4.040666  \n",
       "35       2.272725  \n",
       "36       0.841813  \n",
       "37      -0.777785  \n",
       "38      -0.201103  \n",
       "39       0.755363  \n",
       "40       1.133432  \n",
       "41       1.170664  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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