Histogram_of_dune_grass_heights.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import rasterio\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "# the following names should be changed for each use\n",
    "# specify the location and name of the DEM\n",
    "fdir = \"F:\\\\2017_Karen_Sandwich_maps\\\\\"\n",
    "fname = (\"2017-09-18_SandwichTNB_DEM_10cm_trimmed.tif\")\n",
    "# specify the name of the transect file (assumed to be in same directory)\n",
    "tfname = '2017-09-18_trans.txt'\n",
    "# text for plot titles\n",
    "ttext = \"Sandwich 18-Sep-2017\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      id             n            e    elev  hgt\n",
      "0    100  4.624914e+06  376799.2019  5.8398   50\n",
      "1    101  4.624911e+06  376803.2719  5.8027   35\n",
      "2    102  4.624908e+06  376807.7269  5.7686   25\n",
      "3    103  4.624904e+06  376813.0444  5.6955   45\n",
      "4    104  4.624901e+06  376818.7094  5.4523   50\n",
      "5    105  4.624897e+06  376824.0096  5.0842   45\n",
      "6    106  4.624894e+06  376829.9251  4.6130   55\n",
      "7    107  4.624891e+06  376835.0566  4.3699   55\n",
      "8    108  4.624887e+06  376840.0106  3.9977   55\n",
      "9    109  4.624885e+06  376845.2441  3.7316   55\n",
      "10   110  4.624889e+06  376845.7671  3.6126   40\n",
      "11   111  4.624894e+06  376840.0415  4.0117   45\n",
      "12   112  4.624898e+06  376835.4786  4.5569   50\n",
      "13   113  4.624902e+06  376830.0737  4.9120   50\n",
      "14   114  4.624907e+06  376824.6487  5.0691   60\n",
      "15   115  4.624912e+06  376819.4051  5.2083   50\n",
      "16   116  4.624917e+06  376813.8428  5.4324   50\n",
      "17   117  4.624922e+06  376808.6150  5.3216   55\n",
      "18   118  4.624906e+06  376806.1567  5.5006   40\n",
      "19   119  4.624910e+06  376809.8967  6.0095   50\n",
      "20   120  4.624914e+06  376812.7610  5.8465   60\n",
      "21   121  4.624917e+06  376815.7401  5.1274   45\n",
      "22   122  4.624920e+06  376819.0460  4.1323   25\n",
      "23   123  4.624924e+06  376821.8679  2.8062    0\n",
      "24   124  4.624926e+06  376823.6857  2.2441    0\n",
      "25   125  4.624931e+06  376827.5459  1.6150    0\n",
      "26   126  4.624917e+06  376795.2666  5.9579   55\n",
      "27   127  4.624923e+06  376787.5408  5.7651   40\n",
      "28   128  4.624929e+06  376780.7280  5.8053   50\n",
      "29   129  4.624933e+06  376774.1813  5.8675   30\n",
      "..   ...           ...          ...     ...  ...\n",
      "100  200  4.625062e+06  376623.3042  4.2813    0\n",
      "101  201  4.625067e+06  376601.4465  5.5879   60\n",
      "102  202  4.625077e+06  376592.3794  5.4361   55\n",
      "103  203  4.625088e+06  376581.8213  5.2274   50\n",
      "104  204  4.625098e+06  376572.7473  5.2576   50\n",
      "105  205  4.625108e+06  376563.1739  5.2789   45\n",
      "106  206  4.625119e+06  376552.6144  5.2061   45\n",
      "107  207  4.625131e+06  376540.5526  5.0444   45\n",
      "108  209  4.625135e+06  376535.5835  5.3015   50\n",
      "109  210  4.625142e+06  376528.8890  5.0507   60\n",
      "110  211  4.625145e+06  376530.1015  4.8027   60\n",
      "111  212  4.625138e+06  376538.6650  5.1735   60\n",
      "112  213  4.625130e+06  376547.2617  5.2732   60\n",
      "113  214  4.625121e+06  376555.0931  5.3020   50\n",
      "114  215  4.625113e+06  376562.9539  5.3469   50\n",
      "115  216  4.625102e+06  376574.8132  5.2536   55\n",
      "116  217  4.625093e+06  376584.3610  5.2553   50\n",
      "117  218  4.625083e+06  376594.7201  5.5150   60\n",
      "118  219  4.625071e+06  376606.7604  5.5247   60\n",
      "119  220  4.625076e+06  376585.1108  4.5403   50\n",
      "120  221  4.625078e+06  376587.4441  4.8423   45\n",
      "121  222  4.625080e+06  376589.1119  5.2782   60\n",
      "122  223  4.625085e+06  376592.3108  5.4641   60\n",
      "123  224  4.625087e+06  376594.1214  5.0081   45\n",
      "124  225  4.625097e+06  376566.7200  4.5428   45\n",
      "125  226  4.625100e+06  376569.7681  5.2287   50\n",
      "126  227  4.625105e+06  376573.6997  5.2506   55\n",
      "127  228  4.625122e+06  376545.0655  4.7783   50\n",
      "128  229  4.625124e+06  376547.8920  5.2392   50\n",
      "129  230  4.625127e+06  376550.6131  5.3212   50\n",
      "\n",
      "[130 rows x 5 columns]\n",
      "Read:  130  lines.\n"
     ]
    }
   ],
   "source": [
    "# read in the transect points with dune grass elevations\n",
    "dgfname = \"2017-10-06_Sandwich_Dune_Grass_Elevations.txt\"\n",
    "column_names = ['id','n','e','elev','hgt']\n",
    "import pandas as pd\n",
    "df=pd.read_csv(fdir+dgfname,header = None, names = column_names)\n",
    "print(df)\n",
    "print(\"Read: \",len(df.index),' lines.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rCkwUZtYDeBxYTTBKu76ZDXb32P+LIlJumBmXXHIJ06dPZ8aMGcyfPz9uojjt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\nIiJx/X+Mk3DURFYX5gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1244b8ac4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Histogram of the data\n",
    "x = df['hgt'].as_matrix()\n",
    "x = x/100. # convert to meters\n",
    "xN = len(x)\n",
    "xMAD = np.mean(np.abs(x))\n",
    "xmean = x.mean()\n",
    "xmedian = df['hgt'].median()/100.\n",
    "xstd = x.std()\n",
    "xmin = x.min()\n",
    "xmax = x.max()\n",
    "xRMS = np.sqrt(np.mean(x**2))\n",
    "\n",
    "fig = plt.figure()\n",
    "# next line must be combined with normed=False to get fractional distribution correct\n",
    "weights = np.ones_like(x)/float(len(x))\n",
    "n, bins, patches = plt.hist(x, bins=10, weights=weights, normed=False, facecolor=(.3,.6,.3), alpha=1)\n",
    "xaxmax = 1\n",
    "xaxmin = 0\n",
    "yaxmin = 0.\n",
    "yaxmax = .5\n",
    "\n",
    "plt.xlabel('Dune Grass Heights (m)')\n",
    "plt.ylabel('Fraction of Observations')\n",
    "plt.title('2017-10-06 Dune Grass Heights')\n",
    "plt.axis([xaxmin, xaxmax, yaxmin, yaxmax])\n",
    "plt.grid(True)\n",
    "plt.text(.675*(xaxmax-xaxmin)+xaxmin,.9*(yaxmax-yaxmin)+yaxmin,'N = {}'.format(xN), fontsize=11)\n",
    "plt.text(.675*(xaxmax-xaxmin)+xaxmin,.85*(yaxmax-yaxmin)+yaxmin,'Mean = {:.3f}'.format(xmean), fontsize=11)\n",
    "plt.text(.675*(xaxmax-xaxmin)+xaxmin,.8*(yaxmax-yaxmin)+yaxmin,'Median = {:.3f}'.format(xmedian), fontsize=11)\n",
    "plt.text(.675*(xaxmax-xaxmin)+xaxmin,.75*(yaxmax-yaxmin)+yaxmin,'Std. Dev. = {:.3f}'.format(xstd), fontsize=11)\n",
    "plt.text(.675*(xaxmax-xaxmin)+xaxmin,.7 *(yaxmax-yaxmin)+yaxmin,'Min = {:.3f}'.format(xmin), fontsize=11)\n",
    "plt.text(.675*(xaxmax-xaxmin)+xaxmin,.65*(yaxmax-yaxmin)+yaxmin,'Max = {:.3f}'.format(xmax), fontsize=11)\n",
    "plt.show()\n",
    "\n",
    "fig_name = fdir+\"hist_Dune_Grass_Heights.png\"\n",
    "fig.savefig(fig_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10236, 11762)\n"
     ]
    }
   ],
   "source": [
    "# read DEM channel 1\n",
    "with rasterio.open(fdir+fname) as dem:\n",
    "    z = dem.read(1)\n",
    "    \n",
    "print(np.shape(z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bilinear interpolation\n",
    "def bilint(z,en):\n",
    "    \"\"\"\n",
    "    https://en.wikipedia.org/wiki/Bilinear_interpolation for a unit square\n",
    "    \"\"\"\n",
    "    r = en[1]\n",
    "    c = en[0]\n",
    "    ir = np.int(r)\n",
    "    ic = np.int(c)\n",
    "    rx = r-ir\n",
    "    cy = c-ic\n",
    "    #print(r,ir,rx,'  ',c,ic,cy)\n",
    "    #print(z[ir,ic],z[ir+1,ic])\n",
    "    #print(z[ir+1,ic],z[ir+1,ic+1])\n",
    "    zi = z[ir,ic]*(1.-cy)*(1.-rx)+z[ir+1,ic]*cy*(1.-rx)+z[ir,ic+1]*(1.-cy)*rx+z[ir+1,ic+1]*rx*cy\n",
    "    return zi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# arrays for DEM and differences calcs\n",
    "# using only bilinear interpolation, not corners\n",
    "zdem = np.NaN*np.ones(len(df.index))\n",
    "zdem_minus_meas = np.NaN*np.ones(len(df.index))\n",
    "\n",
    "for index, row in df.iterrows():\n",
    "    # print(row[\"n\"], row[\"e\"])\n",
    "    en = ~dem.transform*(row[\"e\"],row[\"n\"])\n",
    "    # print( index, en )\n",
    "\n",
    "    # this one does bilinear interpolation\n",
    "    try:\n",
    "        zdem[index]=bilint(z,en)\n",
    "    except:\n",
    "        print(\"interpolation problem\")\n",
    "    \n",
    "    # No data values for DEM are -32767\n",
    "    if(zdem[index]<-32766.):\n",
    "        zdem[index]=np.NaN\n",
    "    \n",
    "    zdem_minus_meas[index] = zdem[index]-row[\"elev\"]\n",
    "    #print( zdem[index], zdi[index], row[\"elev\"], zdem_minus_meas[index]  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add columns to the dataframe\n",
    "#df.assign(DEM2=pd.Series(zdem,index=df.index))\n",
    "df['DEM int']=pd.Series(zdem,index=df.index)\n",
    "df['DEM_minus_trans']=pd.Series(zdem_minus_meas,index=df.index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "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>id</th>\n",
       "      <th>n</th>\n",
       "      <th>e</th>\n",
       "      <th>elev</th>\n",
       "      <th>hgt</th>\n",
       "      <th>DEM int</th>\n",
       "      <th>DEM_minus_trans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>4.624914e+06</td>\n",
       "      <td>376799.2019</td>\n",
       "      <td>5.8398</td>\n",
       "      <td>50</td>\n",
       "      <td>6.131773</td>\n",
       "      <td>0.291973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101</td>\n",
       "      <td>4.624911e+06</td>\n",
       "      <td>376803.2719</td>\n",
       "      <td>5.8027</td>\n",
       "      <td>35</td>\n",
       "      <td>5.887519</td>\n",
       "      <td>0.084819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102</td>\n",
       "      <td>4.624908e+06</td>\n",
       "      <td>376807.7269</td>\n",
       "      <td>5.7686</td>\n",
       "      <td>25</td>\n",
       "      <td>5.876554</td>\n",
       "      <td>0.107954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>103</td>\n",
       "      <td>4.624904e+06</td>\n",
       "      <td>376813.0444</td>\n",
       "      <td>5.6955</td>\n",
       "      <td>45</td>\n",
       "      <td>5.704522</td>\n",
       "      <td>0.009022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104</td>\n",
       "      <td>4.624901e+06</td>\n",
       "      <td>376818.7094</td>\n",
       "      <td>5.4523</td>\n",
       "      <td>50</td>\n",
       "      <td>5.599992</td>\n",
       "      <td>0.147692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>105</td>\n",
       "      <td>4.624897e+06</td>\n",
       "      <td>376824.0096</td>\n",
       "      <td>5.0842</td>\n",
       "      <td>45</td>\n",
       "      <td>5.234829</td>\n",
       "      <td>0.150629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>106</td>\n",
       "      <td>4.624894e+06</td>\n",
       "      <td>376829.9251</td>\n",
       "      <td>4.6130</td>\n",
       "      <td>55</td>\n",
       "      <td>4.891301</td>\n",
       "      <td>0.278301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>107</td>\n",
       "      <td>4.624891e+06</td>\n",
       "      <td>376835.0566</td>\n",
       "      <td>4.3699</td>\n",
       "      <td>55</td>\n",
       "      <td>4.660122</td>\n",
       "      <td>0.290222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>108</td>\n",
       "      <td>4.624887e+06</td>\n",
       "      <td>376840.0106</td>\n",
       "      <td>3.9977</td>\n",
       "      <td>55</td>\n",
       "      <td>4.086773</td>\n",
       "      <td>0.089073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>109</td>\n",
       "      <td>4.624885e+06</td>\n",
       "      <td>376845.2441</td>\n",
       "      <td>3.7316</td>\n",
       "      <td>55</td>\n",
       "      <td>3.848282</td>\n",
       "      <td>0.116682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>110</td>\n",
       "      <td>4.624889e+06</td>\n",
       "      <td>376845.7671</td>\n",
       "      <td>3.6126</td>\n",
       "      <td>40</td>\n",
       "      <td>3.612119</td>\n",
       "      <td>-0.000481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>111</td>\n",
       "      <td>4.624894e+06</td>\n",
       "      <td>376840.0415</td>\n",
       "      <td>4.0117</td>\n",
       "      <td>45</td>\n",
       "      <td>4.072951</td>\n",
       "      <td>0.061251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>112</td>\n",
       "      <td>4.624898e+06</td>\n",
       "      <td>376835.4786</td>\n",
       "      <td>4.5569</td>\n",
       "      <td>50</td>\n",
       "      <td>4.613518</td>\n",
       "      <td>0.056618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>113</td>\n",
       "      <td>4.624902e+06</td>\n",
       "      <td>376830.0737</td>\n",
       "      <td>4.9120</td>\n",
       "      <td>50</td>\n",
       "      <td>4.982712</td>\n",
       "      <td>0.070712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>114</td>\n",
       "      <td>4.624907e+06</td>\n",
       "      <td>376824.6487</td>\n",
       "      <td>5.0691</td>\n",
       "      <td>60</td>\n",
       "      <td>5.260945</td>\n",
       "      <td>0.191845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>115</td>\n",
       "      <td>4.624912e+06</td>\n",
       "      <td>376819.4051</td>\n",
       "      <td>5.2083</td>\n",
       "      <td>50</td>\n",
       "      <td>5.373945</td>\n",
       "      <td>0.165645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>116</td>\n",
       "      <td>4.624917e+06</td>\n",
       "      <td>376813.8428</td>\n",
       "      <td>5.4324</td>\n",
       "      <td>50</td>\n",
       "      <td>5.523102</td>\n",
       "      <td>0.090702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>117</td>\n",
       "      <td>4.624922e+06</td>\n",
       "      <td>376808.6150</td>\n",
       "      <td>5.3216</td>\n",
       "      <td>55</td>\n",
       "      <td>5.429180</td>\n",
       "      <td>0.107580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>118</td>\n",
       "      <td>4.624906e+06</td>\n",
       "      <td>376806.1567</td>\n",
       "      <td>5.5006</td>\n",
       "      <td>40</td>\n",
       "      <td>5.842609</td>\n",
       "      <td>0.342009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>119</td>\n",
       "      <td>4.624910e+06</td>\n",
       "      <td>376809.8967</td>\n",
       "      <td>6.0095</td>\n",
       "      <td>50</td>\n",
       "      <td>6.070665</td>\n",
       "      <td>0.061165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>120</td>\n",
       "      <td>4.624914e+06</td>\n",
       "      <td>376812.7610</td>\n",
       "      <td>5.8465</td>\n",
       "      <td>60</td>\n",
       "      <td>6.225037</td>\n",
       "      <td>0.378537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>121</td>\n",
       "      <td>4.624917e+06</td>\n",
       "      <td>376815.7401</td>\n",
       "      <td>5.1274</td>\n",
       "      <td>45</td>\n",
       "      <td>5.159864</td>\n",
       "      <td>0.032464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>122</td>\n",
       "      <td>4.624920e+06</td>\n",
       "      <td>376819.0460</td>\n",
       "      <td>4.1323</td>\n",
       "      <td>25</td>\n",
       "      <td>4.084269</td>\n",
       "      <td>-0.048031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>123</td>\n",
       "      <td>4.624924e+06</td>\n",
       "      <td>376821.8679</td>\n",
       "      <td>2.8062</td>\n",
       "      <td>0</td>\n",
       "      <td>2.815494</td>\n",
       "      <td>0.009294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>124</td>\n",
       "      <td>4.624926e+06</td>\n",
       "      <td>376823.6857</td>\n",
       "      <td>2.2441</td>\n",
       "      <td>0</td>\n",
       "      <td>2.561356</td>\n",
       "      <td>0.317256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>125</td>\n",
       "      <td>4.624931e+06</td>\n",
       "      <td>376827.5459</td>\n",
       "      <td>1.6150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.230047</td>\n",
       "      <td>0.615047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>126</td>\n",
       "      <td>4.624917e+06</td>\n",
       "      <td>376795.2666</td>\n",
       "      <td>5.9579</td>\n",
       "      <td>55</td>\n",
       "      <td>6.245581</td>\n",
       "      <td>0.287681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>127</td>\n",
       "      <td>4.624923e+06</td>\n",
       "      <td>376787.5408</td>\n",
       "      <td>5.7651</td>\n",
       "      <td>40</td>\n",
       "      <td>5.790642</td>\n",
       "      <td>0.025542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>128</td>\n",
       "      <td>4.624929e+06</td>\n",
       "      <td>376780.7280</td>\n",
       "      <td>5.8053</td>\n",
       "      <td>50</td>\n",
       "      <td>5.976126</td>\n",
       "      <td>0.170826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>129</td>\n",
       "      <td>4.624933e+06</td>\n",
       "      <td>376774.1813</td>\n",
       "      <td>5.8675</td>\n",
       "      <td>30</td>\n",
       "      <td>5.857101</td>\n",
       "      <td>-0.010399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>200</td>\n",
       "      <td>4.625062e+06</td>\n",
       "      <td>376623.3042</td>\n",
       "      <td>4.2813</td>\n",
       "      <td>0</td>\n",
       "      <td>4.264677</td>\n",
       "      <td>-0.016623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>201</td>\n",
       "      <td>4.625067e+06</td>\n",
       "      <td>376601.4465</td>\n",
       "      <td>5.5879</td>\n",
       "      <td>60</td>\n",
       "      <td>5.767541</td>\n",
       "      <td>0.179641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>202</td>\n",
       "      <td>4.625077e+06</td>\n",
       "      <td>376592.3794</td>\n",
       "      <td>5.4361</td>\n",
       "      <td>55</td>\n",
       "      <td>5.639515</td>\n",
       "      <td>0.203415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>203</td>\n",
       "      <td>4.625088e+06</td>\n",
       "      <td>376581.8213</td>\n",
       "      <td>5.2274</td>\n",
       "      <td>50</td>\n",
       "      <td>5.403657</td>\n",
       "      <td>0.176257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>204</td>\n",
       "      <td>4.625098e+06</td>\n",
       "      <td>376572.7473</td>\n",
       "      <td>5.2576</td>\n",
       "      <td>50</td>\n",
       "      <td>5.558399</td>\n",
       "      <td>0.300799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>205</td>\n",
       "      <td>4.625108e+06</td>\n",
       "      <td>376563.1739</td>\n",
       "      <td>5.2789</td>\n",
       "      <td>45</td>\n",
       "      <td>5.617642</td>\n",
       "      <td>0.338742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>206</td>\n",
       "      <td>4.625119e+06</td>\n",
       "      <td>376552.6144</td>\n",
       "      <td>5.2061</td>\n",
       "      <td>45</td>\n",
       "      <td>5.423899</td>\n",
       "      <td>0.217799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>207</td>\n",
       "      <td>4.625131e+06</td>\n",
       "      <td>376540.5526</td>\n",
       "      <td>5.0444</td>\n",
       "      <td>45</td>\n",
       "      <td>5.435882</td>\n",
       "      <td>0.391482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>209</td>\n",
       "      <td>4.625135e+06</td>\n",
       "      <td>376535.5835</td>\n",
       "      <td>5.3015</td>\n",
       "      <td>50</td>\n",
       "      <td>5.757260</td>\n",
       "      <td>0.455760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>210</td>\n",
       "      <td>4.625142e+06</td>\n",
       "      <td>376528.8890</td>\n",
       "      <td>5.0507</td>\n",
       "      <td>60</td>\n",
       "      <td>5.506798</td>\n",
       "      <td>0.456098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>211</td>\n",
       "      <td>4.625145e+06</td>\n",
       "      <td>376530.1015</td>\n",
       "      <td>4.8027</td>\n",
       "      <td>60</td>\n",
       "      <td>5.271061</td>\n",
       "      <td>0.468361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>212</td>\n",
       "      <td>4.625138e+06</td>\n",
       "      <td>376538.6650</td>\n",
       "      <td>5.1735</td>\n",
       "      <td>60</td>\n",
       "      <td>5.630687</td>\n",
       "      <td>0.457187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>213</td>\n",
       "      <td>4.625130e+06</td>\n",
       "      <td>376547.2617</td>\n",
       "      <td>5.2732</td>\n",
       "      <td>60</td>\n",
       "      <td>5.737839</td>\n",
       "      <td>0.464639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>214</td>\n",
       "      <td>4.625121e+06</td>\n",
       "      <td>376555.0931</td>\n",
       "      <td>5.3020</td>\n",
       "      <td>50</td>\n",
       "      <td>5.668141</td>\n",
       "      <td>0.366141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>215</td>\n",
       "      <td>4.625113e+06</td>\n",
       "      <td>376562.9539</td>\n",
       "      <td>5.3469</td>\n",
       "      <td>50</td>\n",
       "      <td>5.718056</td>\n",
       "      <td>0.371156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>216</td>\n",
       "      <td>4.625102e+06</td>\n",
       "      <td>376574.8132</td>\n",
       "      <td>5.2536</td>\n",
       "      <td>55</td>\n",
       "      <td>5.510660</td>\n",
       "      <td>0.257060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>217</td>\n",
       "      <td>4.625093e+06</td>\n",
       "      <td>376584.3610</td>\n",
       "      <td>5.2553</td>\n",
       "      <td>50</td>\n",
       "      <td>5.504519</td>\n",
       "      <td>0.249219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>218</td>\n",
       "      <td>4.625083e+06</td>\n",
       "      <td>376594.7201</td>\n",
       "      <td>5.5150</td>\n",
       "      <td>60</td>\n",
       "      <td>5.858881</td>\n",
       "      <td>0.343881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>219</td>\n",
       "      <td>4.625071e+06</td>\n",
       "      <td>376606.7604</td>\n",
       "      <td>5.5247</td>\n",
       "      <td>60</td>\n",
       "      <td>5.861886</td>\n",
       "      <td>0.337186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>220</td>\n",
       "      <td>4.625076e+06</td>\n",
       "      <td>376585.1108</td>\n",
       "      <td>4.5403</td>\n",
       "      <td>50</td>\n",
       "      <td>4.605154</td>\n",
       "      <td>0.064854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>221</td>\n",
       "      <td>4.625078e+06</td>\n",
       "      <td>376587.4441</td>\n",
       "      <td>4.8423</td>\n",
       "      <td>45</td>\n",
       "      <td>4.923696</td>\n",
       "      <td>0.081396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>222</td>\n",
       "      <td>4.625080e+06</td>\n",
       "      <td>376589.1119</td>\n",
       "      <td>5.2782</td>\n",
       "      <td>60</td>\n",
       "      <td>5.511769</td>\n",
       "      <td>0.233569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>223</td>\n",
       "      <td>4.625085e+06</td>\n",
       "      <td>376592.3108</td>\n",
       "      <td>5.4641</td>\n",
       "      <td>60</td>\n",
       "      <td>5.755544</td>\n",
       "      <td>0.291444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>224</td>\n",
       "      <td>4.625087e+06</td>\n",
       "      <td>376594.1214</td>\n",
       "      <td>5.0081</td>\n",
       "      <td>45</td>\n",
       "      <td>5.043681</td>\n",
       "      <td>0.035581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>225</td>\n",
       "      <td>4.625097e+06</td>\n",
       "      <td>376566.7200</td>\n",
       "      <td>4.5428</td>\n",
       "      <td>45</td>\n",
       "      <td>4.602509</td>\n",
       "      <td>0.059709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>226</td>\n",
       "      <td>4.625100e+06</td>\n",
       "      <td>376569.7681</td>\n",
       "      <td>5.2287</td>\n",
       "      <td>50</td>\n",
       "      <td>5.447398</td>\n",
       "      <td>0.218698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>227</td>\n",
       "      <td>4.625105e+06</td>\n",
       "      <td>376573.6997</td>\n",
       "      <td>5.2506</td>\n",
       "      <td>55</td>\n",
       "      <td>5.721802</td>\n",
       "      <td>0.471202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>228</td>\n",
       "      <td>4.625122e+06</td>\n",
       "      <td>376545.0655</td>\n",
       "      <td>4.7783</td>\n",
       "      <td>50</td>\n",
       "      <td>4.877265</td>\n",
       "      <td>0.098965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>229</td>\n",
       "      <td>4.625124e+06</td>\n",
       "      <td>376547.8920</td>\n",
       "      <td>5.2392</td>\n",
       "      <td>50</td>\n",
       "      <td>5.503187</td>\n",
       "      <td>0.263987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>230</td>\n",
       "      <td>4.625127e+06</td>\n",
       "      <td>376550.6131</td>\n",
       "      <td>5.3212</td>\n",
       "      <td>50</td>\n",
       "      <td>5.787613</td>\n",
       "      <td>0.466413</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>130 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id             n            e    elev  hgt   DEM int  DEM_minus_trans\n",
       "0    100  4.624914e+06  376799.2019  5.8398   50  6.131773         0.291973\n",
       "1    101  4.624911e+06  376803.2719  5.8027   35  5.887519         0.084819\n",
       "2    102  4.624908e+06  376807.7269  5.7686   25  5.876554         0.107954\n",
       "3    103  4.624904e+06  376813.0444  5.6955   45  5.704522         0.009022\n",
       "4    104  4.624901e+06  376818.7094  5.4523   50  5.599992         0.147692\n",
       "5    105  4.624897e+06  376824.0096  5.0842   45  5.234829         0.150629\n",
       "6    106  4.624894e+06  376829.9251  4.6130   55  4.891301         0.278301\n",
       "7    107  4.624891e+06  376835.0566  4.3699   55  4.660122         0.290222\n",
       "8    108  4.624887e+06  376840.0106  3.9977   55  4.086773         0.089073\n",
       "9    109  4.624885e+06  376845.2441  3.7316   55  3.848282         0.116682\n",
       "10   110  4.624889e+06  376845.7671  3.6126   40  3.612119        -0.000481\n",
       "11   111  4.624894e+06  376840.0415  4.0117   45  4.072951         0.061251\n",
       "12   112  4.624898e+06  376835.4786  4.5569   50  4.613518         0.056618\n",
       "13   113  4.624902e+06  376830.0737  4.9120   50  4.982712         0.070712\n",
       "14   114  4.624907e+06  376824.6487  5.0691   60  5.260945         0.191845\n",
       "15   115  4.624912e+06  376819.4051  5.2083   50  5.373945         0.165645\n",
       "16   116  4.624917e+06  376813.8428  5.4324   50  5.523102         0.090702\n",
       "17   117  4.624922e+06  376808.6150  5.3216   55  5.429180         0.107580\n",
       "18   118  4.624906e+06  376806.1567  5.5006   40  5.842609         0.342009\n",
       "19   119  4.624910e+06  376809.8967  6.0095   50  6.070665         0.061165\n",
       "20   120  4.624914e+06  376812.7610  5.8465   60  6.225037         0.378537\n",
       "21   121  4.624917e+06  376815.7401  5.1274   45  5.159864         0.032464\n",
       "22   122  4.624920e+06  376819.0460  4.1323   25  4.084269        -0.048031\n",
       "23   123  4.624924e+06  376821.8679  2.8062    0  2.815494         0.009294\n",
       "24   124  4.624926e+06  376823.6857  2.2441    0  2.561356         0.317256\n",
       "25   125  4.624931e+06  376827.5459  1.6150    0  2.230047         0.615047\n",
       "26   126  4.624917e+06  376795.2666  5.9579   55  6.245581         0.287681\n",
       "27   127  4.624923e+06  376787.5408  5.7651   40  5.790642         0.025542\n",
       "28   128  4.624929e+06  376780.7280  5.8053   50  5.976126         0.170826\n",
       "29   129  4.624933e+06  376774.1813  5.8675   30  5.857101        -0.010399\n",
       "..   ...           ...          ...     ...  ...       ...              ...\n",
       "100  200  4.625062e+06  376623.3042  4.2813    0  4.264677        -0.016623\n",
       "101  201  4.625067e+06  376601.4465  5.5879   60  5.767541         0.179641\n",
       "102  202  4.625077e+06  376592.3794  5.4361   55  5.639515         0.203415\n",
       "103  203  4.625088e+06  376581.8213  5.2274   50  5.403657         0.176257\n",
       "104  204  4.625098e+06  376572.7473  5.2576   50  5.558399         0.300799\n",
       "105  205  4.625108e+06  376563.1739  5.2789   45  5.617642         0.338742\n",
       "106  206  4.625119e+06  376552.6144  5.2061   45  5.423899         0.217799\n",
       "107  207  4.625131e+06  376540.5526  5.0444   45  5.435882         0.391482\n",
       "108  209  4.625135e+06  376535.5835  5.3015   50  5.757260         0.455760\n",
       "109  210  4.625142e+06  376528.8890  5.0507   60  5.506798         0.456098\n",
       "110  211  4.625145e+06  376530.1015  4.8027   60  5.271061         0.468361\n",
       "111  212  4.625138e+06  376538.6650  5.1735   60  5.630687         0.457187\n",
       "112  213  4.625130e+06  376547.2617  5.2732   60  5.737839         0.464639\n",
       "113  214  4.625121e+06  376555.0931  5.3020   50  5.668141         0.366141\n",
       "114  215  4.625113e+06  376562.9539  5.3469   50  5.718056         0.371156\n",
       "115  216  4.625102e+06  376574.8132  5.2536   55  5.510660         0.257060\n",
       "116  217  4.625093e+06  376584.3610  5.2553   50  5.504519         0.249219\n",
       "117  218  4.625083e+06  376594.7201  5.5150   60  5.858881         0.343881\n",
       "118  219  4.625071e+06  376606.7604  5.5247   60  5.861886         0.337186\n",
       "119  220  4.625076e+06  376585.1108  4.5403   50  4.605154         0.064854\n",
       "120  221  4.625078e+06  376587.4441  4.8423   45  4.923696         0.081396\n",
       "121  222  4.625080e+06  376589.1119  5.2782   60  5.511769         0.233569\n",
       "122  223  4.625085e+06  376592.3108  5.4641   60  5.755544         0.291444\n",
       "123  224  4.625087e+06  376594.1214  5.0081   45  5.043681         0.035581\n",
       "124  225  4.625097e+06  376566.7200  4.5428   45  4.602509         0.059709\n",
       "125  226  4.625100e+06  376569.7681  5.2287   50  5.447398         0.218698\n",
       "126  227  4.625105e+06  376573.6997  5.2506   55  5.721802         0.471202\n",
       "127  228  4.625122e+06  376545.0655  4.7783   50  4.877265         0.098965\n",
       "128  229  4.625124e+06  376547.8920  5.2392   50  5.503187         0.263987\n",
       "129  230  4.625127e+06  376550.6131  5.3212   50  5.787613         0.466413\n",
       "\n",
       "[130 rows x 7 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_csv('2017-09-18_Sandwich_Dune_Grass.csv',na_rep='-32767',sep=',',float_format='% 12.4f')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    130.000000\n",
       "mean       0.208508\n",
       "std        0.160148\n",
       "min       -0.093684\n",
       "25%        0.065316\n",
       "50%        0.213548\n",
       "75%        0.319084\n",
       "max        0.615047\n",
       "Name: DEM_minus_trans, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['DEM_minus_trans'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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www+PDFeuXJn8/PyY7caMGRPzee0Ac+fO5eSTTy407fbbb+fJJ4NHL02ePJmG\nDRvyr3/9KzJ/69atSIrZGbFhw4Z89tln1K1bFwiOVHr37g0EHTKfffbZSNtWrVrRsmVLVq1axT//\n+c9IWZitW7fy3HPP8e2333LzzTeXuA8yJZlkUqUgkQCY2ceS/O4u51yFUFIP+ENV2iOTm266iZtu\nuikyfvDgQfbv389bb71F9+7dS+z5PmjQIO6//346d+7M+vXrWbJkCTNmzACC2mJ169ZFEtOmTaNa\ntWqcddZZSCp0d9jw4cPp3Lkzo0ePPoR3mx7JJJOlkqYCBQ8CuAK/AO+cq0Cin/1REVSqVInf/va3\n/PznP2fv3r00atSIxx57LDK/Q4cOzJ8/n/r16zNu3DiGDx9O06ZNqVy5Mg888AA1a9YEYN68edx5\n551I4uSTT+bpp58u1zLzJVFJFTIBJFUDbgC6E1wzeQO4r6J1YmzRooWtW7cuccNylpOTQ69evco7\njIQ8zvTyONMrG+LMhhgBJC0zs86prifhkUmYNO4OX84551wxcZOJpNlmdqmkjwhqcRViZu0yGplz\nzrmsUVI/k/8X/r0AuDDGyznnyk0y5VTKy8cff8wNN9xA8+bN6datG+vXr4/bdvbs2bRt25Y2bdrQ\ntm3byF1gL7/8Mp07d6ZatWqRjpcFJkyYwLHHHhvp+3LDDTdk9P0kI24yMbN/hoP/aWafRb+A/0xm\n5ZLOk7ROUq6kYrUEJPUInymfL2lgkXnDJK0PX8NK86accz8OicqplJdRo0YxYMCASFK5/vrrY7Zb\nunQpEyZM4JVXXmHVqlW89dZbHHXUUUBwl9qDDz7IuHHjYi47dOhQli9fzvLly7n33nsz9l6SlUw5\nlXNiTEtYCEZSZeDesG0rYIikVkWabQKGA08UWbY2cAvQFegC3CKpVhKxOud+RBKVU3nttdfo1q0b\nHTt2pG3btsycOROAPXv20K5du0gfjoULF3LKKaewc+fOlGPasmULH3zwQeQ5KkOGDOGDDz7g66+/\nLtZ28uTJjB07luOPPx6Ao446KtIXpmnTpnTs2JHDDkvmptvyFzeZSPp5eL2khaSVUa9PgZVJrLsL\nkGtmG8xsPzATKFTU38w2mtlKij8G+FzgFTP71sy2Aa8A55XifTnnfgQSlVM59dRTeeutt/jwww95\n9dVXGTt2LNu2baN69erMnj2bX/ziF7z//vuMGDGCGTNmRG7JjTZp0qS4pVXefPPNYu0///xzTjjh\nBCpXrgwEnSPr168fKZESbc2aNWzYsIEePXpw6qmnctttt5X4DPpoM2fOpF27dvTt25fFixcntUwm\nlZTyngAWAHcA0aeodppZ8QKuEgDyAAAfLElEQVQzxZ0ARO+9zQRHGsmItewJRRtJGgmMBKhbty45\nOTlJrr785OXleZxp5HGmVzbFuXfvXpYuXUqXLl2YOHEic+fO5Z577uH5559n/fr15OTk8Pnnn/PA\nAw+wefNmKleuzNatW5kxYwatWgUnSS6//HK6devGDTfcwI4dO2K+99NPPz3ukxcPHDhQbJl169ax\na9euQvty9+7dLF26lO+++65Q2x07drBo0SJuueUW8vPz+fWvf82uXbs499xzI202btzInj17Cm2n\nXbt2TJs2jcMOO4ylS5fSr18/pk2bFjlFVh7iJhMz2wHsAIYASDoWOByoIamGmW1KsO5YPWuSS7lJ\nLmtmDwAPQNDPJBvu6c6We889zvTyONMrJyeHww8/nNNOO42ePXtGyqn079+fp59+mmbNmtGrVy/O\nOussBg4cyJgxY5BE8+bNadWqVeQ9PvXUUxx77LFUr1497vueNGlS5PRYUffccw9nnnlmoWmtWrXi\nN7/5TWSdBw4cYPv27fzsZz+LlEyJbnvppZfSt29fAFatWsWmTZsKxZKTk0NeXl7c+Hr16sWMGTM4\n+uij6dmzZ+KdlyEJr5lIulDSeuBT4HWCgo8LSlwosBk4MWq8AVD8GZrpX9Y59yNSUE7l97//fbF5\n27dvp1GjRkjilVdeITc3NzLv6aef5o033mD16tW88MILzJ8/P+b6x48fH7nQXfRVNJEAkbusFi5c\nCMCMGTPo2LFjsUQCwZHRyy+/jJnx/fff89prrxV7ln0sX3zxRWR4+fLlbNy4kRYtWiRcLpOSuQB/\nG3A68LGZNQbOAt5OYrklQDNJjSVVBQYTPBslGS8BfSXVCi+89w2nOedcMSNHjoz5JTxp0iTGjh1L\nt27dmDNnDu3aBd3jNm7cyJgxY5g9eza1a9dm5syZjBo1is2bN6clnilTpvDUU0/RvHlz7rnnHqZM\nmRKZ169fv0hl48GDB3PsscfSqlUrOnToQOvWrRkxYgQAb731Fg0aNODuu+/m/vvvp0GDBpE71373\nu9/Rpk0b2rdvz3XXXcejjz4auYhfbsysxBewNPy7AqgUDr+faLmwXT/gY+AT4KZw2kTgonD4NIKj\nkF3AN8DqqGWvAXLD19WJttW8eXPLBosWLSrvEJLicaaXx5le2RBnNsRoZlbwHZ/qK5l7zrZLqkFQ\nk+txSVuA2HWbiyeq+cD8ItNujhpeQnAKK9ayDwEPJbMd55xz5SuZ01z9CZ4D/0vgRYKjDO8B75xz\nLiKZI5ORwJNmtpngiYvOOedcIckcmRwJvCTpTUk3SDou4RLOOed+VBImEzO71cxaEzzTpD7wuqRX\nMx6Zc865rJHMkUmBLcBXBHddHZuZcJxzzmWjZDot/lxSDvAaUAe4zvxZJs4556IkcwG+IfBfZrY8\n08E455zLTiUemUiqBFzoicQ551xJSkwmZnYQWCGpYRnF45xzLgslc5qrHrBa0vsEZU8AMLOLMhaV\nc865rJJMMrk141E455zLagmTiZm9LukkoJmZvSrpCKBy5kNzzjmXLZK5Nfg6YA5wfzjpBOCZTAbl\nnHMuuyTTafEG4AzgOwAzW493WnTOORclmWSyz8z2F4xIOozkH7/rnHPuRyCZZPK6pN8B1SWdAzwJ\nPJfZsJxzzmWTZJLJeOBr4CPgeoKHXf13JoNyzjmXXZK5m+sg8CDwoKTaQIPwUY/OOecckNzdXDmS\njgwTyXLgYUl3Zz4055xz2SKZ01xHmdl3wMXAw2bWCTg7s2E555zLJskkk8Mk1QMuBZ4vzcolnSdp\nnaRcSeNjzK8maVY4/z1JjcLpVSRNl/SRpLWSflua7TrnnCtbySSTicBLwCdmtkRSE2B9ooUkVQbu\nBc4HWgFDJLUq0mwEsM3MmgKTgTvD6YOAambWFugEXF+QaJxzzlU8yTy290kza2dmPw/HN5jZJUms\nuwuQG7bfD8wE+hdp0x+YHg7PAc6SJIJ+LD8J+7RUB/YTdpp0zjlX8SjRjVnhkcj/AacTfMkvJnhY\n1qcJlhsInGdm14bjVwFdzWx0VJtVYZvN4fgnQFdgB/AocBZwBPBLM3sgxjZGAiMB6tat22n27NnJ\nvOdylZeXR40aNco7jIQ8zvTyONMrG+LMhhgBevfuvczMOqe6nmSqBj9BcLrqZ+H4YIKjjK4JllOM\naUUzV7w2XYADQH2gFvCmpFfNbEOhhkGCeQCgRYsW1qtXrwQhlb+cnBw8zvTxONPL40yfbIgxnZK5\nZiIze9TM8sPXYyRXTmUzcGLUeAPgy3htwlNaRwHfApcDL5rZ92a2BXgbSDlzOuecy4y4yURS7bBv\nySJJ4yU1knSSpF8DLySx7iVAM0mNJVUlOKKZV6TNPGBYODwQWBh2iNwE9FHgJwSn2P5RurfmnHOu\nrJR0mmsZwRFIwamo66PmGfA/Ja3YzPIljSa4E6wy8JCZrZY0EVhqZvOAqcCjknIJjkgGh4vfCzwM\nrAq3/7CZrSzVO3POOVdm4iYTM2uc6srNbD5BLa/oaTdHDe8luA246HJ5saY755yrmEq8AC/pWILn\nmbQmOBpZA9wbXsdwzjnngJKvmZxBcN0D4BHgsXD4/XCec845B5R8ZPInYICZfRg17VlJTxM8wjfR\nrcHOOed+JEq6NfjIIokEADNbDtTMXEjOOeeyTUnJRJJqxZhYO8FyzjnnfmRKSgqTgZcl9ZRUM3z1\nAhaE85xzzjmg5FuDH5D0JUF/kui7uW4zM38GvHPOuYgSbw02s+cp5TNMnHPO/fj4tQ/nnHMpS6Zq\nsHPOlanOnf9d13Xp0qXlGIlLVkmdFv9f+Nc7KDrnnCtRSae5rg7/3lMWgTjnnMteJZ3mWitpI1BX\nUnTFXgFmZu0yGplzzrmsUdKtwUMkHU9QQv6isgvJOedctkl0a/BXQPvw4VbNw8nrzOz7jEfmnHMu\nayS8m0tST4KqwRsJTnGdKGmYmb2R4dicc85liWRuDb4b6Gtm6wAkNQdmAJ0yGZhzzrnskUynxSoF\niQTAzD4GqmQuJOecc9kmmSOTpZKmAo+G41cQPB/eOeecA5JLJj8neHTvGIJrJm8A92UyKOecc9kl\n4WkuM9tnZneb2cVm9jMzm2xm+5JZuaTzJK2TlCtpfIz51STNCue/J6lR1Lx2khZLWi3pI0mHl+aN\nOeecKzsZq80lqTJwL3AOsBlYImmema2JajYC2GZmTSUNBu4ELpN0GMEz568ysxWSjgH8dmT3g1VR\na1FV1LhcxZPJqsFdgFwz22Bm+4GZQP8ibfoD08PhOcBZkgT0BVaa2QoAM/vGzA5kMFbnnHMpkJll\nZsXSQOA8M7s2HL8K6Gpmo6ParArbbA7HPwG6AlcS3Hp8LFAXmGlmf4ixjZHASIC6det2mj17dkbe\nSzrl5eVRo0aN8g4jIY8zvRLFuXbt2shwy5YtyyKkmIrGWV5xJdpuNnzu2RAjQO/evZeZWefELUuW\nTKfF5sA44KTo9mbWJ9GiMaYVzVzx2hwGdAdOA3YDr0laZmavFWpo9gDwAECLFi2sV69eCUIqfzk5\nOXic6fNDiXPs2LGR4fI8nVQ0zvKKK9F2s+Fzz4YY0ymZayZPAlOAB4HSnGraDJwYNd4A+DJOm83h\ndZKjgG/D6a+b2VYASfOBU4HXcM45V+Ekc80k38z+Zmbvm9myglcSyy0BmklqHNb2GgzMK9JmHjAs\nHB4ILLTgvNtLQDtJR4RJpifB8+edc85VQMkcmTwn6T+Bp4HILcFm9m1JC5lZvqTRBImhMvCQma2W\nNBFYambzgKnAo5JyCY5IBofLbpN0N0FCMmC+mb1Q+rfnnHOuLCSTTAqOHMZFTTOgSaIFzWw+ML/I\ntJujhvcCg+Is+xjB7cHOOecquITJxMwal0Ugzjnnslcyd3NVISip0iOclAPc7880cc45VyCZ01x/\nI6gSXFCP66pw2rWZCso551x2SeZurtPMbJiZLQxfVxP0/3DOJaFz586sXbu2UGkS535okkkmBySd\nXDAiqQml62/inHPuBy6Z01zjgEWSNhD0WD8JuDqjUTnnnMsqydzN9ZqkZkALgmTyj2RL0DvnnPtx\niJtMJPUxs4WSLi4y62RJmNlTGY7NOedclijpyKQnsBC4MMY8AzyZOOecA0pIJmZ2Szg40cw+jZ4n\nyTsyOueci0jmbq65MabNSXcgzjnnsldJ10xOAVoDRxW5bnIk4M9jd845F1HSNZMWwAXA0RS+brIT\nuC6TQTnnnMsuJV0zeRZ4VlI3M1tchjE555zLMsl0Whwlaa2ZbQeQVAv4k5ldk9nQnMteFaF0SnQM\nyTxyt3PnzowYMaLQI3MzsR33w5TMBfh2BYkEggdXAR0zF5Jzzrlsk0wyqRQejQAgqTbJHdE455z7\nkUgmKfwJeEdSwe3Ag4DbMxeSc865bJNMba5HJC0DehPU5rrYzNZkPDLnnHNZI6nTVWa2WtLXhP1L\nJDU0s00Zjcw551zWSHjNRNJFktYDnwKvAxuBBcmsXNJ5ktZJypU0Psb8apJmhfPfk9SoyPyGkvIk\nle72Euecc2UqmQvw/wOcDnxsZo2Bs4C3Ey0kqTJwL3A+0AoYIqlVkWYjgG1m1hSYDNxZZP5kkkxc\nzjnnyk8yyeR7M/uG4K6uSma2COiQxHJdgFwz22Bm+4GZQP8ibfoD08PhOcBZkgQgaQCwAVidxLac\nc86VI5lZyQ2kV4EBwB1AHWALwXPhf5pguYHAeWZ2bTh+FdDVzEZHtVkVttkcjn8CdAX2AK8C5wBj\ngTwzuyvGNkYCIwHq1q3bafbs2cm853KVl5dHjRo1yjuMhDzO1Kxdu7bQeJ06ddi6dSstW7ZM2D5e\nm1RiSGada9eujcQZS0WKvaJ+7tGyIUaA3r17LzOzlHvZJpNMfkLw5V4JuAI4Cng8PFopablBwLlF\nkkkXM/tFVJvVYZvoZNIF+C3wvpnNljSBOMkkWosWLWzdunUlvpeKICcnh169epV3GAl5nKkp2gN+\nxIgRTJ06NW4P8Uz0Ij/UHvBTp06NOb8ixV5RP/do2RAjgKS0JJMS7+YKr3s8a2ZnAwf59ympZGwG\nTowabwB8GafNZkmHESSqbwmOTgZK+gNBocmDkvaa2V9LsX3nslIypVjKo2yJl01xJSkxmZjZAUm7\nJR1lZjtKue4lQLPwQVpfAIOBy4u0mQcMAxYDA4GFFhwqnVnQIOrIxBOJc85VUMn0M9kLfCTpFWBX\nwUQzG1PSQmaWL2k08BJQGXgo7K8yEVhqZvOAqcCjknIJjkgGH+L7cM45V46SSSYvhK9SM7P5wPwi\n026OGt5LUJ6lpHVMOJRtO+ecKzslPWmxoZltMrPSXCdxzjn3I1RSP5NnCgYkxXoOvHPOOQeUnEwU\nNdwk04E455zLXiUlE4sz7JxzzhVS0gX49pK+IzhCqR4OE46bmR2Z8eicc85lhbjJxMwql2Ugzjnn\nslcyhR6dc865Evmz3J1Lk2TKoKTSPl3rzGRZFC+58uPlRybOOedS5snEOedcyjyZOOecS5knE+ec\ncynzZOKccy5lnkycc86lzJOJc865lHkycc45lzJPJs4551LmycQ551zKvJyKcz9imSjpEmvdyZRW\nyWQsLvP8yMQ551zKMppMJJ0naZ2kXEnjY8yvJmlWOP89SY3C6edIWibpo/Bvn0zG6ZxzLjUZSyaS\nKgP3AucDrYAhkloVaTYC2GZmTYHJwJ3h9K3AhWbWFhgGPJqpOJ1zzqUuk0cmXYBcM9tgZvuBmUD/\nIm36A9PD4TnAWZJkZh+a2Zfh9NXA4ZKqZTBW55xzKZBZZh7vLmkgcJ6ZXRuOXwV0NbPRUW1WhW02\nh+OfhG22FlnPKDM7O8Y2RgIjAerWrdtp9uzZGXkv6ZSXl0eNGjXKO4yEPM7SW7t2bdx5derUYevW\nrbRs2TKp9olEryeZGJLdbkGcpdl+Mu8jXrzRkom9QEX63OPJhhgBevfuvczMUr77IZPJZBBwbpFk\n0sXMfhHVZnXYJjqZdDGzb8Lx1sA8oK+ZfVLS9lq0aGHr1q3LyHtJp5ycHHr16lXeYSTkcZZeSXcj\njRgxgqlTpxa6qymVu5fi3R0Vb53JbrcgztJsP5n3kcrdXLGWrUifezzZECOApLQkk0ye5toMnBg1\n3gD4Ml4bSYcBRwHfhuMNgKeBoYkSiXPOufKVyWSyBGgmqbGkqsBggqOMaPMILrADDAQWmplJOhp4\nAfitmb2dwRidc86lQcaSiZnlA6OBl4C1wGwzWy1poqSLwmZTgWMk5QK/AgpuHx4NNAV+L2l5+Do2\nU7E655xLTUZ7wJvZfGB+kWk3Rw3vBQbFWO424LZMxuaccy59vJyKc2UoXSVDSruedJcqSWX7yVyM\nd9nHy6k455xLmScT55xzKfNk4pxzLmWeTJxzzqXMk4lzzrmUeTJxzjmXMk8mzjnnUubJxDnnXMo8\nmTjnnEuZJxPnnHMp82TinHMuZV6by7kkxKstle6aVz8GmahPlu6Hb7nS8yMT55xzKfNk4pxzLmWe\nTJxzzqXMk4lzzrmUeTJxzjmXMk8mzjnnUubJxDnnXMoymkwknSdpnaRcSeNjzK8maVY4/z1JjaLm\n/Tacvk7SuZmM0znnXGoylkwkVQbuBc4HWgFDJLUq0mwEsM3MmgKTgTvDZVsBg4HWwHnAfeH6nHPO\nVUCZPDLpAuSa2QYz2w/MBPoXadMfmB4OzwHOkqRw+kwz22dmnwK54fqcc85VQJksp3IC8HnU+Gag\na7w2ZpYvaQdwTDj93SLLnlB0A5JGAiPD0X2SVqUn9IyqA2wt7yCS4HHGEfzeKZ1ly5Zlxf6siHHG\n2d+F4jyUzyQdyyZQ4fZlHC3SsZJMJpNYn5Al2SaZZTGzB4AHACQtNbMKXyjJ40wvjzO9PM70yYYY\nIYgzHevJ5GmuzcCJUeMNgC/jtZF0GHAU8G2SyzrnnKsgMplMlgDNJDWWVJXggvq8Im3mAcPC4YHA\nQjOzcPrg8G6vxkAz4P0Mxuqccy4FGTvNFV4DGQ28BFQGHjKz1ZImAkvNbB4wFXhUUi7BEcngcNnV\nkmYDa4B84AYzO5Bgkw9k6r2kmceZXh5nenmc6ZMNMUKa4lRwIOCcc84dOu8B75xzLmWeTJxzzqUs\nq5KJpEGSVks6KCnuLXfxyriENwO8J2l9WMalaobirC3plXA7r0iqFaNNb0nLo157JQ0I502T9GnU\nvA7lFWfY7kBULPOiplek/dlB0uLw38dKSZdFzcvY/syWkkFJxPkrSWvCffeapJOi5sX8/MspzuGS\nvo6K59qoecPCfyPrJQ0rumwZxzk5KsaPJW2Pmlcm+1PSQ5K2KE7/OwX+Er6HlZJOjZpX+n1pZlnz\nAloSdLDJATrHaVMZ+ARoAlQFVgCtwnmzgcHh8BTg5xmK8w/A+HB4PHBngva1CW5AOCIcnwYMLIP9\nmVScQF6c6RVmfwLNgWbhcH3gn8DRmdyfJf1bi2rzn8CUcHgwMCscbhW2rwY0DtdTOUP7L5k4e0f9\n+/t5QZwlff7lFOdw4K8xlq0NbAj/1gqHa5VXnEXa/4LgBqSy3p89gFOBVXHm9wMWEPTrOx14L5V9\nmVVHJma21szWJWgWs4yLJAF9CMq2QFDGZUCGQo0uE5PMdgYCC8xsd4biiae0cUZUtP1pZh+b2fpw\n+EtgC1A3Q/EUyJaSQQnjNLNFUf/+3iXo21XWktmf8ZwLvGJm35rZNuAVgrp+FSHOIcCMDMUSl5m9\nQfAjNZ7+wCMWeBc4WlI9DnFfZlUySVKsMi4nEJRp2W5m+UWmZ8JxZvZPgPDvsQnaD6b4P7bbw0PP\nyZKqZSJIko/zcElLJb1bcCqOCrw/JXUh+MX4SdTkTOzPeP/WYrYJ91V0yaBEy6ZLabc1guAXa4FY\nn38mJBvnJeFnOUdSQefmCrk/w9OFjYGFUZPLan8mEu99HNK+zGQ5lUMi6VXg+BizbjKzZ5NZRYxp\nSZdoSVZJcZZyPfWAtgT9cQr8FviK4AvxAeA3wMRyjLOhmX0pqQmwUNJHwHcx2lWU/fkoMMzMDoaT\n07Y/i24uxrS0lgxKk6S3JelKoDPQM2pysc/fzD6JtXwZxPkcMMPM9kkaRXDU1yfJZdOlNNsaDMyx\nwv3kymp/JpLWf5sVLpmY2dkpriJeKZatBIdxh4W/EFMq0VJSnJL+Jamemf0z/HLbUsKqLgWeNrPv\no9b9z3Bwn6SHgbHlGWd42ggz2yApB+gIzKWC7U9JRwIvAP8dHrYXrDtt+7OI0pQM2qzyKxmU1LYk\nnU2QvHua2b6C6XE+/0x8+SWM08y+iRp9kPCxFeGyvYosm5P2CP+9rWQ/u8HADdETynB/JhLvfRzS\nvvwhnuaKWcbFgitLiwiuT0BQxiWZI51DEV0mJtF2ip1PDb8wC65LDAAyVQ05YZySahWcFpJUBzgD\nWFPR9mf4WT9NcA74ySLzMrU/s6VkUMI4JXUE7gcuMrMtUdNjfv7lGGe9qNGLgLXh8EtA3zDeWkBf\nCh/tl2mcYawtCC5gL46aVpb7M5F5wNDwrq7TgR3hD69D25dlcVdBul7Azwiy5j7gX8BL4fT6wPyo\ndv2Ajwmy/U1R05sQ/IfNBZ4EqmUozmOA14D14d/a4fTOwN+j2jUCvgAqFVl+IfARwZfeY0CN8ooT\n+GkYy4rw74iKuD+BK4HvgeVRrw6Z3p+x/q0RnEK7KBw+PNw3ueG+ahK17E3hcuuA8zP8fydRnK+G\n/6cK9t28RJ9/OcV5B7A6jGcRcErUsteE+zkXuLo84wzHJwCTiixXZvuT4EfqP8P/F5sJroWNAkaF\n80XwAMNPwlg6Ry1b6n3p5VScc86l7Id4mss551wZ82TinHMuZZ5MnHPOpcyTiXPOuZR5MnHOOZcy\nTyYubaKqoa6WtEJBJdpK4bxeknaocKXks8N5JunRqPUcpqAy7PMpxDKxYP2ZIOl0BVWAl0taK2lC\nprZVGpI2Svoo3P8vS4pVVSC6/XxJRydoM1xS/RLm/1lSj1LEWFfSi8m2d9mhwvWAd1ltj5l1AJB0\nLPAEQY/vW8L5b5rZBTGW2wW0kVTdzPYA5xD0vzlkZnZzKssnYTpwqZmtkFSZoJp1UsLOk7J/l3tJ\nt95mtlXS/wK/A8bEa2hm/ZJY33CCPjqxes3XBk43s/9KNjgz+1rSPyWdYWZvJ7ucq9j8yMRlhAW9\nqEcCo8Mvz0QWAP8RDsetshr+Sn5G0nMKnlEyOjwC+lBB4bzaYbtpkgaGwxsl3Srpg/BX+ynh9AmS\nxkate5WkRpJ+IumF8Nf9KkU9GyXKsQQdwjCzA2a2JsE6G4VHMPcBHwC/l/SHIu/rnnD4Sknvh0c9\n90uqLGmEpMlR7a+TdHeCffoG0DRsPyR876skFZQgKdg3daLiezA8snxZUvVwH3YGHg/jqV5kGwOB\nF4us738VPFtmqaRTJb0k6RMFtbQKPANckSB+l0U8mbiMMbMNBP/GCqr8nlnkNNfJUc1nEpQXORxo\nB7xXwqrbAJcTlAK/HdhtZh0JylYMjbPMVjM7FfgbiWtznQd8aWbtzawNUV+WUSYD6yQ9Len6MO5E\nWhCUe+kI3AdcHDXvMmCWpJbh8BnhUd4Bgi/dmcBFkqqE7a8GHk6wvQuAj8JTVHcSFETsAJym2NVq\nmwH3mllrYDtwiZnNAZYCV5hZh/DIMdoZwLIi0z43s27Am4TPkiF4XkZ0cc2lwJkJ4ndZxJOJy7To\no5I3wy+kglekuJ2ZrSQoLzMEmJ9gnYvMbKeZfU1Q0v25cPpH4TpieSr8u6yENgU+As6WdKekM81s\nR9EGZjaR4Bf7ywSJLZlrAJ9ZWIAyjH1DeO3lGIJE8zZwFtAJWCJpeTjexMx2EZSFuSA8sqpiZh/F\n2c6icNkjCcqPnAbkmNnXFhTlfJzgwUlFfWpmy8PhZPYTQD3g6yLTCupUfUTwwKWCz2pv1PWZLQRl\nkNwPhF8zcRmjoMT2AYIvjpZJLDIPuIugYukxJbTbFzV8MGr8IPH/TRe0ORDVJp/CP6gOh+BBW5I6\nEdRfukPSy2HyKCRMhn+T9CDwdZgUYq4ztKvIKmYRVI3+B0HlaAtPCU43s9/GeA9/J7gG8g9KPirp\nbWZbC0aSPM0IhffrAaDoKa1Y9lD4PUavJ/qzKRgv2PeHh8u6Hwg/MnEZIakuwaN8/2rJF4B7CJhY\nwi/udNtI8FhTFDz/unE4XJ/g1NljBMnt1KILSvqPqC/pZgRfvtvjrTOOpwiqGA8hSCwQFLIcGN7A\ngKTaCp/HbmbvEZQMv5zSPbnvPaBneG2kcri910ux/E6gZpx5awmvy5RSczJXDduVAz8ycelUPTy9\nUoXgF/qjQPRF4jPD+QVuC8/JA2Bmm4H/K5NIA3MJSnAvJygr/nE4vS3wR0kHCSqu/jzGslcBkyXt\nJnivV5jZAUnx1lmMmW2TtIbg+eHvh9PWSPpv4GUFt1V/T/A8jM/CxWYTVEPeluybtOA5ML8lqLIr\nggrbpXlcwDRgiqQ9QLci101eAK4nOGoqjd7hsu4HwqsGO5dFFPS9mWxmr5V3LAUkvQVcYGbbS7HM\nG0D/0iRFV7H5aS7nsoCkoyV9TNCXp8IkktCNQMNkG4enQO/2RPLD4kcmzjnnUuZHJs4551LmycQ5\n51zKPJk455xLmScT55xzKfNk4pxzLmX/HxF/QRR7NLRhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1247fca82e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Histogram of the data\n",
    "x = df['DEM_minus_trans'].as_matrix()\n",
    "x = x[~np.isnan(x)]\n",
    "x = x[np.abs(x)<5.]\n",
    "\n",
    "xN = len(x)\n",
    "xMAD = np.mean(np.abs(x))\n",
    "xmean = x.mean()\n",
    "xmin = x.min()\n",
    "xmax = x.max()\n",
    "xRMS = np.sqrt(np.mean(x**2))\n",
    "\n",
    "fig = plt.figure()\n",
    "# next line must be combined with normed=False to get fractional distribution correct\n",
    "weights = np.ones_like(x)/float(len(x))\n",
    "n, bins, patches = plt.hist(x, bins=40, weights=weights, normed=False, facecolor=(.2,.2,.2), alpha=1)\n",
    "xaxmax = 1\n",
    "xaxmin = -1\n",
    "yaxmin = 0.\n",
    "yaxmax = .15\n",
    "\n",
    "plt.xlabel('DEM minus Survey Point (m)')\n",
    "plt.ylabel('Fraction of Observations')\n",
    "plt.title(ttext)\n",
    "plt.axis([xaxmin, xaxmax, yaxmin, yaxmax])\n",
    "plt.grid(True)\n",
    "plt.text(.75*(xaxmax-xaxmin)+xaxmin,.9*(yaxmax-yaxmin)+yaxmin,'N = {}'.format(xN), fontsize=11)\n",
    "plt.text(.75*(xaxmax-xaxmin)+xaxmin,.85*(yaxmax-yaxmin)+yaxmin,'Mean = {:.3f}'.format(xmean), fontsize=11)\n",
    "plt.text(.75*(xaxmax-xaxmin)+xaxmin,.8*(yaxmax-yaxmin)+yaxmin,'RMS = {:.3f}'.format(xRMS), fontsize=11)\n",
    "plt.text(.75*(xaxmax-xaxmin)+xaxmin,.75*(yaxmax-yaxmin)+yaxmin,'MAD = {:.3f}'.format(xMAD), fontsize=11)\n",
    "plt.text(.75*(xaxmax-xaxmin)+xaxmin,.7 *(yaxmax-yaxmin)+yaxmin,'Min = {:.3f}'.format(xmin), fontsize=11)\n",
    "plt.text(.75*(xaxmax-xaxmin)+xaxmin,.65*(yaxmax-yaxmin)+yaxmin,'Max = {:.3f}'.format(xmax), fontsize=11)\n",
    "plt.show()\n",
    "\n",
    "fig_name = fdir+\"hist_DEM_minus_transect_at_dune_grass_transects.png\"\n",
    "fig.savefig(fig_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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O22wb85bOa3hUU0inIbwdurfmdUyrPEiSFHrNbE4D6z4KWGNmawEkXUdoi+hL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\nfTwpOOec6+NJwTnnXB9PCs455/r8f28zkXxmDmGpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1244c554cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Histogram of the data\n",
    "diff = df['DEM_minus_trans'].as_matrix()\n",
    "dgh = df['hgt'].as_matrix()/100.\n",
    "\n",
    "xaxmax = .85\n",
    "xaxmin = 0.\n",
    "yaxmin = 0.\n",
    "yaxmax = .85\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.plot(dgh,diff,color=(.3,.6,.3),marker='o',linestyle='none')\n",
    "plt.xlabel('Dune Grass Height (m)')\n",
    "plt.ylabel('DEM minus Survey Point (m)')\n",
    "plt.title('DEM from 18-Sep; Grass Heights from 9-Oct')\n",
    "plt.axis([xaxmin, xaxmax, yaxmin, yaxmax])\n",
    "fig_name = fdir+\"scatter_DEM_minus_transect_at_dune_grass_transects.png\"\n",
    "fig.savefig(fig_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1247fd7cdd8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Dhw5heHgYiUQCADztsiWhUflvSz4YI7PJTe4PdHr/9/f3m/M4ODj4Y01EppQKo0Ni/11r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\nkQVBkD3/APFYgQRID8sTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1247fd7c940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(figsize=(12,6));plt.imshow(z[::],cmap=plt.cm.gray,vmin=-2,vmax=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}