Sandwich_summarize_all_DEM_minus_transect.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram of all transect points using .txt files produced by Sandwich_compare_DEM_minus_transect.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['D:\\\\crs\\\\proj\\\\2015_Sandwich\\\\2017_Karen_Sandwich_maps\\\\trans_diff\\\\2016-01-22_SandwichTNB.txt', 'D:\\\\crs\\\\proj\\\\2015_Sandwich\\\\2017_Karen_Sandwich_maps\\\\trans_diff\\\\2016-02-11_SandwichTNB.txt', 'D:\\\\crs\\\\proj\\\\2015_Sandwich\\\\2017_Karen_Sandwich_maps\\\\trans_diff\\\\2016-03-30_SandwichTNB.txt', 'D:\\\\crs\\\\proj\\\\2015_Sandwich\\\\2017_Karen_Sandwich_maps\\\\trans_diff\\\\2016-09-21_SandwichTNB.txt', 'D:\\\\crs\\\\proj\\\\2015_Sandwich\\\\2017_Karen_Sandwich_maps\\\\trans_diff\\\\2017-01-09_SandwichTNB.txt', 'D:\\\\crs\\\\proj\\\\2015_Sandwich\\\\2017_Karen_Sandwich_maps\\\\trans_diff\\\\2017-01-25_SandwichTNB.txt', 'D:\\\\crs\\\\proj\\\\2015_Sandwich\\\\2017_Karen_Sandwich_maps\\\\trans_diff\\\\2017-02-14_SandwichTNB.txt', 'D:\\\\crs\\\\proj\\\\2015_Sandwich\\\\2017_Karen_Sandwich_maps\\\\trans_diff\\\\2017-03-16_SandwichTNB.txt', 'D:\\\\crs\\\\proj\\\\2015_Sandwich\\\\2017_Karen_Sandwich_maps\\\\trans_diff\\\\2017-05-04_SandwichTNB.txt', 'D:\\\\crs\\\\proj\\\\2015_Sandwich\\\\2017_Karen_Sandwich_maps\\\\trans_diff\\\\2017-09-18_SandwichTNB.txt']\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import glob\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import rasterio\n",
    "import os\n",
    "%matplotlib inline\n",
    "\n",
    "path ='D:\\\\crs\\\\proj\\\\2015_Sandwich\\\\2017_Karen_Sandwich_maps\\\\trans_diff' # use your path\n",
    "allFiles = glob.glob(os.path.join(path,\"*.txt\"))\n",
    "print(allFiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>#X/Easting (m NAD83(2011) Zone 19N)</th>\n",
       "      <th>Y/Northing (m NAD83(2011) Zone 19N)</th>\n",
       "      <th>Z/Elevation (m NAVD88)</th>\n",
       "      <th>DEM (interp, m)</th>\n",
       "      <th>DEM_minus_trans (m)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>376792.8012</td>\n",
       "      <td>4624911.706</td>\n",
       "      <td>6.5760</td>\n",
       "      <td>6.5256</td>\n",
       "      <td>-0.0504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>376795.6191</td>\n",
       "      <td>4624914.309</td>\n",
       "      <td>6.6849</td>\n",
       "      <td>6.5913</td>\n",
       "      <td>-0.0936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>376799.1375</td>\n",
       "      <td>4624918.084</td>\n",
       "      <td>6.5278</td>\n",
       "      <td>6.4692</td>\n",
       "      <td>-0.0586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>376802.4701</td>\n",
       "      <td>4624922.167</td>\n",
       "      <td>6.3927</td>\n",
       "      <td>6.2715</td>\n",
       "      <td>-0.1212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>376804.7718</td>\n",
       "      <td>4624925.299</td>\n",
       "      <td>6.2496</td>\n",
       "      <td>6.1724</td>\n",
       "      <td>-0.0772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>376806.6299</td>\n",
       "      <td>4624927.373</td>\n",
       "      <td>5.7166</td>\n",
       "      <td>5.6756</td>\n",
       "      <td>-0.0410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>376808.9123</td>\n",
       "      <td>4624930.271</td>\n",
       "      <td>5.0025</td>\n",
       "      <td>4.9774</td>\n",
       "      <td>-0.0251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>376811.4519</td>\n",
       "      <td>4624933.617</td>\n",
       "      <td>4.1594</td>\n",
       "      <td>4.0914</td>\n",
       "      <td>-0.0680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>376813.7792</td>\n",
       "      <td>4624936.864</td>\n",
       "      <td>3.3144</td>\n",
       "      <td>3.2464</td>\n",
       "      <td>-0.0680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>376816.3710</td>\n",
       "      <td>4624940.507</td>\n",
       "      <td>2.5023</td>\n",
       "      <td>2.4743</td>\n",
       "      <td>-0.0280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>376818.8087</td>\n",
       "      <td>4624943.970</td>\n",
       "      <td>2.1802</td>\n",
       "      <td>2.1285</td>\n",
       "      <td>-0.0517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>376821.4005</td>\n",
       "      <td>4624947.852</td>\n",
       "      <td>1.7531</td>\n",
       "      <td>1.6637</td>\n",
       "      <td>-0.0894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>376824.1624</td>\n",
       "      <td>4624952.600</td>\n",
       "      <td>1.3721</td>\n",
       "      <td>1.3210</td>\n",
       "      <td>-0.0511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>376827.1213</td>\n",
       "      <td>4624957.440</td>\n",
       "      <td>1.1280</td>\n",
       "      <td>0.9660</td>\n",
       "      <td>-0.1620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>376829.8553</td>\n",
       "      <td>4624962.011</td>\n",
       "      <td>0.8979</td>\n",
       "      <td>0.8632</td>\n",
       "      <td>-0.0347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>376833.0966</td>\n",
       "      <td>4624967.236</td>\n",
       "      <td>0.6358</td>\n",
       "      <td>0.6135</td>\n",
       "      <td>-0.0223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>376835.9925</td>\n",
       "      <td>4624971.803</td>\n",
       "      <td>0.3477</td>\n",
       "      <td>0.3447</td>\n",
       "      <td>-0.0030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>376838.9838</td>\n",
       "      <td>4624976.430</td>\n",
       "      <td>0.0586</td>\n",
       "      <td>-0.0824</td>\n",
       "      <td>-0.1410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>376841.9085</td>\n",
       "      <td>4624981.215</td>\n",
       "      <td>-0.2685</td>\n",
       "      <td>-0.1889</td>\n",
       "      <td>0.0796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>376699.8708</td>\n",
       "      <td>4624976.593</td>\n",
       "      <td>6.3834</td>\n",
       "      <td>6.3951</td>\n",
       "      <td>0.0117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>376703.0372</td>\n",
       "      <td>4624978.915</td>\n",
       "      <td>6.2923</td>\n",
       "      <td>6.2477</td>\n",
       "      <td>-0.0446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>376706.4593</td>\n",
       "      <td>4624981.923</td>\n",
       "      <td>6.3272</td>\n",
       "      <td>6.3066</td>\n",
       "      <td>-0.0206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>376710.1750</td>\n",
       "      <td>4624985.642</td>\n",
       "      <td>6.3401</td>\n",
       "      <td>6.3279</td>\n",
       "      <td>-0.0122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>376712.4472</td>\n",
       "      <td>4624988.585</td>\n",
       "      <td>6.2450</td>\n",
       "      <td>6.2330</td>\n",
       "      <td>-0.0120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>376714.4062</td>\n",
       "      <td>4624990.969</td>\n",
       "      <td>5.7389</td>\n",
       "      <td>5.7380</td>\n",
       "      <td>-0.0009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>376716.9672</td>\n",
       "      <td>4624994.184</td>\n",
       "      <td>4.9069</td>\n",
       "      <td>4.8952</td>\n",
       "      <td>-0.0117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>376719.6590</td>\n",
       "      <td>4624997.359</td>\n",
       "      <td>4.0438</td>\n",
       "      <td>4.0303</td>\n",
       "      <td>-0.0135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>376722.0382</td>\n",
       "      <td>4625001.281</td>\n",
       "      <td>3.1077</td>\n",
       "      <td>3.1101</td>\n",
       "      <td>0.0024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>376724.1421</td>\n",
       "      <td>4625004.195</td>\n",
       "      <td>2.6427</td>\n",
       "      <td>2.6448</td>\n",
       "      <td>0.0021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>376726.7519</td>\n",
       "      <td>4625008.313</td>\n",
       "      <td>2.1456</td>\n",
       "      <td>2.1638</td>\n",
       "      <td>0.0182</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>228</td>\n",
       "      <td>376580.8976</td>\n",
       "      <td>4625053.428</td>\n",
       "      <td>3.5786</td>\n",
       "      <td>3.5619</td>\n",
       "      <td>-0.0167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>229</td>\n",
       "      <td>376574.0958</td>\n",
       "      <td>4625049.593</td>\n",
       "      <td>3.0688</td>\n",
       "      <td>3.0537</td>\n",
       "      <td>-0.0151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>230</td>\n",
       "      <td>376568.2945</td>\n",
       "      <td>4625046.903</td>\n",
       "      <td>2.7039</td>\n",
       "      <td>2.6959</td>\n",
       "      <td>-0.0080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>231</td>\n",
       "      <td>376563.0254</td>\n",
       "      <td>4625043.875</td>\n",
       "      <td>2.6901</td>\n",
       "      <td>2.6853</td>\n",
       "      <td>-0.0048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>232</td>\n",
       "      <td>376556.5450</td>\n",
       "      <td>4625040.414</td>\n",
       "      <td>2.5993</td>\n",
       "      <td>2.6016</td>\n",
       "      <td>0.0023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>233</td>\n",
       "      <td>376558.7600</td>\n",
       "      <td>4625059.085</td>\n",
       "      <td>2.6672</td>\n",
       "      <td>3.3713</td>\n",
       "      <td>0.7041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>234</td>\n",
       "      <td>376550.0739</td>\n",
       "      <td>4625061.611</td>\n",
       "      <td>2.5804</td>\n",
       "      <td>2.5788</td>\n",
       "      <td>-0.0016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>235</td>\n",
       "      <td>376539.3017</td>\n",
       "      <td>4625063.901</td>\n",
       "      <td>2.4687</td>\n",
       "      <td>2.4943</td>\n",
       "      <td>0.0256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>236</td>\n",
       "      <td>376533.0399</td>\n",
       "      <td>4625072.978</td>\n",
       "      <td>2.4248</td>\n",
       "      <td>2.4192</td>\n",
       "      <td>-0.0056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>237</td>\n",
       "      <td>376534.5637</td>\n",
       "      <td>4625080.506</td>\n",
       "      <td>2.4648</td>\n",
       "      <td>2.4717</td>\n",
       "      <td>0.0069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>238</td>\n",
       "      <td>376534.5765</td>\n",
       "      <td>4625090.024</td>\n",
       "      <td>2.5658</td>\n",
       "      <td>2.5490</td>\n",
       "      <td>-0.0168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>239</td>\n",
       "      <td>376527.7450</td>\n",
       "      <td>4625096.738</td>\n",
       "      <td>2.5289</td>\n",
       "      <td>2.5237</td>\n",
       "      <td>-0.0052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>240</td>\n",
       "      <td>376520.1155</td>\n",
       "      <td>4625102.816</td>\n",
       "      <td>2.4651</td>\n",
       "      <td>2.4835</td>\n",
       "      <td>0.0184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>241</td>\n",
       "      <td>376508.6972</td>\n",
       "      <td>4625106.205</td>\n",
       "      <td>2.3394</td>\n",
       "      <td>2.3579</td>\n",
       "      <td>0.0185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>242</td>\n",
       "      <td>376511.5242</td>\n",
       "      <td>4625117.509</td>\n",
       "      <td>2.6853</td>\n",
       "      <td>2.7082</td>\n",
       "      <td>0.0229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>243</td>\n",
       "      <td>376514.5689</td>\n",
       "      <td>4625124.264</td>\n",
       "      <td>3.0902</td>\n",
       "      <td>3.0900</td>\n",
       "      <td>-0.0002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>244</td>\n",
       "      <td>376500.4579</td>\n",
       "      <td>4625110.271</td>\n",
       "      <td>2.3477</td>\n",
       "      <td>2.3808</td>\n",
       "      <td>0.0331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>245</td>\n",
       "      <td>376481.4347</td>\n",
       "      <td>4625102.674</td>\n",
       "      <td>2.2952</td>\n",
       "      <td>2.3466</td>\n",
       "      <td>0.0514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>246</td>\n",
       "      <td>376490.0967</td>\n",
       "      <td>4625102.003</td>\n",
       "      <td>2.1879</td>\n",
       "      <td>2.2231</td>\n",
       "      <td>0.0352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>247</td>\n",
       "      <td>376506.2679</td>\n",
       "      <td>4625097.316</td>\n",
       "      <td>2.2285</td>\n",
       "      <td>2.2390</td>\n",
       "      <td>0.0105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>248</td>\n",
       "      <td>376518.0150</td>\n",
       "      <td>4625089.034</td>\n",
       "      <td>2.2792</td>\n",
       "      <td>2.3080</td>\n",
       "      <td>0.0288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>249</td>\n",
       "      <td>376548.6132</td>\n",
       "      <td>4625040.261</td>\n",
       "      <td>2.8335</td>\n",
       "      <td>2.8613</td>\n",
       "      <td>0.0278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>250</td>\n",
       "      <td>376544.6067</td>\n",
       "      <td>4625034.559</td>\n",
       "      <td>2.2236</td>\n",
       "      <td>2.2577</td>\n",
       "      <td>0.0341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>251</td>\n",
       "      <td>376544.8725</td>\n",
       "      <td>4625027.225</td>\n",
       "      <td>2.0226</td>\n",
       "      <td>2.0363</td>\n",
       "      <td>0.0137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>252</td>\n",
       "      <td>376543.7802</td>\n",
       "      <td>4625018.503</td>\n",
       "      <td>1.8546</td>\n",
       "      <td>1.8756</td>\n",
       "      <td>0.0210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>253</td>\n",
       "      <td>376543.7212</td>\n",
       "      <td>4625009.494</td>\n",
       "      <td>1.5667</td>\n",
       "      <td>1.6000</td>\n",
       "      <td>0.0333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>254</td>\n",
       "      <td>376542.0095</td>\n",
       "      <td>4625000.805</td>\n",
       "      <td>1.4537</td>\n",
       "      <td>1.4924</td>\n",
       "      <td>0.0387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>255</td>\n",
       "      <td>376538.9885</td>\n",
       "      <td>4624993.833</td>\n",
       "      <td>1.4388</td>\n",
       "      <td>1.4630</td>\n",
       "      <td>0.0242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>256</td>\n",
       "      <td>376534.7546</td>\n",
       "      <td>4624988.875</td>\n",
       "      <td>1.4369</td>\n",
       "      <td>1.4856</td>\n",
       "      <td>0.0487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>257</td>\n",
       "      <td>376535.9948</td>\n",
       "      <td>4624974.573</td>\n",
       "      <td>1.9129</td>\n",
       "      <td>1.9848</td>\n",
       "      <td>0.0719</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2423 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  #X/Easting (m NAD83(2011) Zone 19N)  \\\n",
       "0             0                          376792.8012   \n",
       "1             1                          376795.6191   \n",
       "2             2                          376799.1375   \n",
       "3             3                          376802.4701   \n",
       "4             4                          376804.7718   \n",
       "5             5                          376806.6299   \n",
       "6             6                          376808.9123   \n",
       "7             7                          376811.4519   \n",
       "8             8                          376813.7792   \n",
       "9             9                          376816.3710   \n",
       "10           10                          376818.8087   \n",
       "11           11                          376821.4005   \n",
       "12           12                          376824.1624   \n",
       "13           13                          376827.1213   \n",
       "14           14                          376829.8553   \n",
       "15           15                          376833.0966   \n",
       "16           16                          376835.9925   \n",
       "17           17                          376838.9838   \n",
       "18           18                          376841.9085   \n",
       "19           19                          376699.8708   \n",
       "20           20                          376703.0372   \n",
       "21           21                          376706.4593   \n",
       "22           22                          376710.1750   \n",
       "23           23                          376712.4472   \n",
       "24           24                          376714.4062   \n",
       "25           25                          376716.9672   \n",
       "26           26                          376719.6590   \n",
       "27           27                          376722.0382   \n",
       "28           28                          376724.1421   \n",
       "29           29                          376726.7519   \n",
       "..          ...                                  ...   \n",
       "228         228                          376580.8976   \n",
       "229         229                          376574.0958   \n",
       "230         230                          376568.2945   \n",
       "231         231                          376563.0254   \n",
       "232         232                          376556.5450   \n",
       "233         233                          376558.7600   \n",
       "234         234                          376550.0739   \n",
       "235         235                          376539.3017   \n",
       "236         236                          376533.0399   \n",
       "237         237                          376534.5637   \n",
       "238         238                          376534.5765   \n",
       "239         239                          376527.7450   \n",
       "240         240                          376520.1155   \n",
       "241         241                          376508.6972   \n",
       "242         242                          376511.5242   \n",
       "243         243                          376514.5689   \n",
       "244         244                          376500.4579   \n",
       "245         245                          376481.4347   \n",
       "246         246                          376490.0967   \n",
       "247         247                          376506.2679   \n",
       "248         248                          376518.0150   \n",
       "249         249                          376548.6132   \n",
       "250         250                          376544.6067   \n",
       "251         251                          376544.8725   \n",
       "252         252                          376543.7802   \n",
       "253         253                          376543.7212   \n",
       "254         254                          376542.0095   \n",
       "255         255                          376538.9885   \n",
       "256         256                          376534.7546   \n",
       "257         257                          376535.9948   \n",
       "\n",
       "     Y/Northing (m NAD83(2011) Zone 19N)  Z/Elevation (m NAVD88)  \\\n",
       "0                            4624911.706                  6.5760   \n",
       "1                            4624914.309                  6.6849   \n",
       "2                            4624918.084                  6.5278   \n",
       "3                            4624922.167                  6.3927   \n",
       "4                            4624925.299                  6.2496   \n",
       "5                            4624927.373                  5.7166   \n",
       "6                            4624930.271                  5.0025   \n",
       "7                            4624933.617                  4.1594   \n",
       "8                            4624936.864                  3.3144   \n",
       "9                            4624940.507                  2.5023   \n",
       "10                           4624943.970                  2.1802   \n",
       "11                           4624947.852                  1.7531   \n",
       "12                           4624952.600                  1.3721   \n",
       "13                           4624957.440                  1.1280   \n",
       "14                           4624962.011                  0.8979   \n",
       "15                           4624967.236                  0.6358   \n",
       "16                           4624971.803                  0.3477   \n",
       "17                           4624976.430                  0.0586   \n",
       "18                           4624981.215                 -0.2685   \n",
       "19                           4624976.593                  6.3834   \n",
       "20                           4624978.915                  6.2923   \n",
       "21                           4624981.923                  6.3272   \n",
       "22                           4624985.642                  6.3401   \n",
       "23                           4624988.585                  6.2450   \n",
       "24                           4624990.969                  5.7389   \n",
       "25                           4624994.184                  4.9069   \n",
       "26                           4624997.359                  4.0438   \n",
       "27                           4625001.281                  3.1077   \n",
       "28                           4625004.195                  2.6427   \n",
       "29                           4625008.313                  2.1456   \n",
       "..                                   ...                     ...   \n",
       "228                          4625053.428                  3.5786   \n",
       "229                          4625049.593                  3.0688   \n",
       "230                          4625046.903                  2.7039   \n",
       "231                          4625043.875                  2.6901   \n",
       "232                          4625040.414                  2.5993   \n",
       "233                          4625059.085                  2.6672   \n",
       "234                          4625061.611                  2.5804   \n",
       "235                          4625063.901                  2.4687   \n",
       "236                          4625072.978                  2.4248   \n",
       "237                          4625080.506                  2.4648   \n",
       "238                          4625090.024                  2.5658   \n",
       "239                          4625096.738                  2.5289   \n",
       "240                          4625102.816                  2.4651   \n",
       "241                          4625106.205                  2.3394   \n",
       "242                          4625117.509                  2.6853   \n",
       "243                          4625124.264                  3.0902   \n",
       "244                          4625110.271                  2.3477   \n",
       "245                          4625102.674                  2.2952   \n",
       "246                          4625102.003                  2.1879   \n",
       "247                          4625097.316                  2.2285   \n",
       "248                          4625089.034                  2.2792   \n",
       "249                          4625040.261                  2.8335   \n",
       "250                          4625034.559                  2.2236   \n",
       "251                          4625027.225                  2.0226   \n",
       "252                          4625018.503                  1.8546   \n",
       "253                          4625009.494                  1.5667   \n",
       "254                          4625000.805                  1.4537   \n",
       "255                          4624993.833                  1.4388   \n",
       "256                          4624988.875                  1.4369   \n",
       "257                          4624974.573                  1.9129   \n",
       "\n",
       "     DEM (interp, m)  DEM_minus_trans (m)  \n",
       "0             6.5256              -0.0504  \n",
       "1             6.5913              -0.0936  \n",
       "2             6.4692              -0.0586  \n",
       "3             6.2715              -0.1212  \n",
       "4             6.1724              -0.0772  \n",
       "5             5.6756              -0.0410  \n",
       "6             4.9774              -0.0251  \n",
       "7             4.0914              -0.0680  \n",
       "8             3.2464              -0.0680  \n",
       "9             2.4743              -0.0280  \n",
       "10            2.1285              -0.0517  \n",
       "11            1.6637              -0.0894  \n",
       "12            1.3210              -0.0511  \n",
       "13            0.9660              -0.1620  \n",
       "14            0.8632              -0.0347  \n",
       "15            0.6135              -0.0223  \n",
       "16            0.3447              -0.0030  \n",
       "17           -0.0824              -0.1410  \n",
       "18           -0.1889               0.0796  \n",
       "19            6.3951               0.0117  \n",
       "20            6.2477              -0.0446  \n",
       "21            6.3066              -0.0206  \n",
       "22            6.3279              -0.0122  \n",
       "23            6.2330              -0.0120  \n",
       "24            5.7380              -0.0009  \n",
       "25            4.8952              -0.0117  \n",
       "26            4.0303              -0.0135  \n",
       "27            3.1101               0.0024  \n",
       "28            2.6448               0.0021  \n",
       "29            2.1638               0.0182  \n",
       "..               ...                  ...  \n",
       "228           3.5619              -0.0167  \n",
       "229           3.0537              -0.0151  \n",
       "230           2.6959              -0.0080  \n",
       "231           2.6853              -0.0048  \n",
       "232           2.6016               0.0023  \n",
       "233           3.3713               0.7041  \n",
       "234           2.5788              -0.0016  \n",
       "235           2.4943               0.0256  \n",
       "236           2.4192              -0.0056  \n",
       "237           2.4717               0.0069  \n",
       "238           2.5490              -0.0168  \n",
       "239           2.5237              -0.0052  \n",
       "240           2.4835               0.0184  \n",
       "241           2.3579               0.0185  \n",
       "242           2.7082               0.0229  \n",
       "243           3.0900              -0.0002  \n",
       "244           2.3808               0.0331  \n",
       "245           2.3466               0.0514  \n",
       "246           2.2231               0.0352  \n",
       "247           2.2390               0.0105  \n",
       "248           2.3080               0.0288  \n",
       "249           2.8613               0.0278  \n",
       "250           2.2577               0.0341  \n",
       "251           2.0363               0.0137  \n",
       "252           1.8756               0.0210  \n",
       "253           1.6000               0.0333  \n",
       "254           1.4924               0.0387  \n",
       "255           1.4630               0.0242  \n",
       "256           1.4856               0.0487  \n",
       "257           1.9848               0.0719  \n",
       "\n",
       "[2423 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat((pd.read_csv(f) for f in allFiles))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Statistics for interpolated values:\n",
      "count    2383.000000\n",
      "mean        0.010344\n",
      "std         0.083405\n",
      "min        -1.099300\n",
      "25%        -0.019600\n",
      "50%         0.006500\n",
      "75%         0.033350\n",
      "max         1.248900\n",
      "Name: DEM_minus_trans (m), dtype: float64\n",
      "count    2383.000000\n",
      "mean        0.010344\n",
      "std         0.083405\n",
      "min        -1.099300\n",
      "25%        -0.019600\n",
      "50%         0.006500\n",
      "75%         0.033350\n",
      "max         1.248900\n",
      "Name: DEM_minus_trans (m), dtype: float64\n",
      "count    2269.000000\n",
      "mean        0.005592\n",
      "std         0.079011\n",
      "min        -1.099300\n",
      "25%        -0.020400\n",
      "50%         0.004300\n",
      "75%         0.029700\n",
      "max         1.248900\n",
      "Name: DEM_minus_trans (m), dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nStatistics for interpolated values:\")\n",
    "print(df['DEM_minus_trans (m)'].describe())\n",
    "df=df[np.abs(df[\"DEM_minus_trans (m)\"])<3.] \n",
    "print(df['DEM_minus_trans (m)'].describe())\n",
    "e0 = 376488.; n0 = 4625200.\n",
    "df=df[df[\"#X/Easting (m NAD83(2011) Zone 19N)\"]>e0]\n",
    "df=df[df[\"Y/Northing (m NAD83(2011) Zone 19N)\"]<n0] \n",
    "print(df['DEM_minus_trans (m)'].describe())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90 percent lie between 0.09061999999999998 and -0.07836.\n"
     ]
    }
   ],
   "source": [
    "q95 = df.quantile(q=0.95, axis=0, numeric_only=True, interpolation='linear')['DEM_minus_trans (m)']\n",
    "q5 = df.quantile(q=0.05, axis=0, numeric_only=True, interpolation='linear')['DEM_minus_trans (m)']\n",
    "print(\"90 percent lie between {} and {}.\".format(q95,q5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\csherwood\\AppData\\Local\\Continuum\\miniconda3\\envs\\IOOS3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6571: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Users\\csherwood\\AppData\\Local\\Continuum\\miniconda3\\envs\\IOOS3\\lib\\site-packages\\matplotlib\\font_manager.py:1331: UserWarning: findfont: Font family ['normal'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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VVebMmUP//v0BZ7XYlJQUHnjgAcqXL0+LFi3o3r07M2bMCGt7TnaWcBhjjImI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\nABZv0uaJpGBioey1GimICeDLWZ5js+/H6rnzFgTuB+4s1CNKvlcPAgv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      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Histogram of the data\n",
    "dz = df['DEM_minus_trans (m)'].as_matrix()\n",
    "dz = dz[~np.isnan(dz)]\n",
    "dz = dz[np.abs(dz)<5.]\n",
    "\n",
    "dzN = len(dz)\n",
    "dzMAD = np.mean(np.abs(dz))\n",
    "dzmean = dz.mean()\n",
    "dzmin = dz.min()\n",
    "dzmax = dz.max()\n",
    "dzRMS = np.sqrt(np.mean(dz**2))\n",
    "\n",
    "font = {'family' : 'normal',\n",
    "        'weight' : 'normal',\n",
    "        'size'   : 16}\n",
    "\n",
    "plt.rc('font', **font)\n",
    "\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "# next line must be combined with normed=False to get fractional distribution correct\n",
    "weights = np.ones_like(dz)/float(len(dz))\n",
    "n, bins, patches = plt.hist(dz, bins=40, weights=weights, normed=False, facecolor=(.2,.2,.2), alpha=1)\n",
    "xaxmax = 1\n",
    "xaxmin = -1\n",
    "yaxmin = 0.\n",
    "yaxmax = .45\n",
    "\n",
    "plt.xlabel('DEM minus Survey Point (m)')\n",
    "plt.ylabel('Fraction of Observations')\n",
    "plt.title('All Comparison Points')\n",
    "plt.axis([xaxmin, xaxmax, yaxmin, yaxmax])\n",
    "plt.grid(True)\n",
    "plt.text(.75*(xaxmax-xaxmin)+xaxmin,.9*(yaxmax-yaxmin)+yaxmin,'N = {}'.format(dzN), fontsize=12)\n",
    "plt.text(.75*(xaxmax-xaxmin)+xaxmin,.85*(yaxmax-yaxmin)+yaxmin,'Mean = {:.3f}'.format(dzmean), fontsize=12)\n",
    "plt.text(.75*(xaxmax-xaxmin)+xaxmin,.8*(yaxmax-yaxmin)+yaxmin,'RMS = {:.3f}'.format(dzRMS), fontsize=12)\n",
    "plt.text(.75*(xaxmax-xaxmin)+xaxmin,.75*(yaxmax-yaxmin)+yaxmin,'MAD = {:.3f}'.format(dzMAD), fontsize=12)\n",
    "plt.text(.75*(xaxmax-xaxmin)+xaxmin,.7 *(yaxmax-yaxmin)+yaxmin,'Min = {:.3f}'.format(dzmin), fontsize=12)\n",
    "plt.text(.75*(xaxmax-xaxmin)+xaxmin,.65*(yaxmax-yaxmin)+yaxmin,'Max = {:.3f}'.format(dzmax), fontsize=12)\n",
    "plt.show()\n",
    "\n",
    "fig_name = os.path.join(path,\"All_DEM_minus_transect_HIST.png\")\n",
    "fig.savefig(fig_name,dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\csherwood\\AppData\\Local\\Continuum\\miniconda3\\envs\\IOOS3\\lib\\site-packages\\ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in greater\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\csherwood\\AppData\\Local\\Continuum\\miniconda3\\envs\\IOOS3\\lib\\site-packages\\ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in less\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "fn = \"F:\\\\2018_Sandwich_Synthesis\\\\zmean.tif\"\n",
    "with rasterio.open(fn) as src:\n",
    "    zmean = src.read(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6800, 8000)\n",
      "(6800, 8000)\n",
      " Upper left:  376488.0 4625200.0 \n",
      " Lower right:  377288.0 4624520.0\n"
     ]
    }
   ],
   "source": [
    "xsize, ysize = 8000, 6800\n",
    "dx = 0.1\n",
    "dy = 0.1\n",
    "e0 = 376488.; n0 = 4625200.\n",
    "X,Y = np.meshgrid(np.linspace(e0,e0+xsize*dx,xsize),\\\n",
    "                  np.linspace(n0,n0-ysize*dy,ysize))\n",
    "print(np.shape(X))\n",
    "print(np.shape(zmean))\n",
    "print(\" Upper left: \",X[0][0],Y[0][0],'\\n',\"Lower right: \",X[-1][-1],Y[-1][-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(680, 800)\n",
      "[[ 1.31534851  1.27639949  1.24518979 ...,         nan         nan\n",
      "          nan]\n",
      " [ 1.37580538  1.34381652  1.30576622 ...,         nan         nan\n",
      "          nan]\n",
      " [ 1.44193316  1.40568399  1.37313414 ...,         nan         nan\n",
      "          nan]\n",
      " ..., \n",
      " [        nan         nan         nan ...,         nan         nan\n",
      "          nan]\n",
      " [        nan         nan         nan ...,         nan         nan\n",
      "          nan]\n",
      " [        nan         nan         nan ...,         nan         nan\n",
      "          nan]]\n"
     ]
    }
   ],
   "source": [
    "xt=X[0:-1:10,0:-1:10]\n",
    "yt=Y[0:-1:10,0:-1:10]\n",
    "zt=zmean[0:-1:10,0:-1:10]\n",
    "Zm = np.ma.masked_where(np.isnan(zt),zt)\n",
    "print(np.shape(zt))\n",
    "print(zt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\csherwood\\AppData\\Local\\Continuum\\miniconda3\\envs\\IOOS3\\lib\\site-packages\\ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in less\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\csherwood\\AppData\\Local\\Continuum\\miniconda3\\envs\\IOOS3\\lib\\site-packages\\matplotlib\\font_manager.py:1331: UserWarning: findfont: Font family ['normal'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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RZGS82JE00jrjaTnmApXTWvjMn/sPjPLJLzzK2lW9ROT5xO8sxnENCPSV4fLL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\n6Cil2Lazh3Ub2giCQ6X7IfnAxvUHyOXKMQlWFvU4jkOhUCCXy8Va8mi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HUuqu4XYUkYuB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\nYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "zt[zt<-10.]=np.nan\n",
    "# a plot to check those coordinates\n",
    "cmap=plt.cm.gray\n",
    "fig = plt.figure()\n",
    "plt.plot(figsize=(12,6))\n",
    "#cmap.set_bad('grey')\n",
    "plt.pcolormesh(xt,yt,zt,shading='flat',edgecolor=None,cmap=cmap,vmin=-2,vmax=8)\n",
    "plt.axis('equal')\n",
    "# make map of differences\n",
    "dz = df['DEM_minus_trans (m)'].as_matrix()\n",
    "x = df['#X/Easting (m NAD83(2011) Zone 19N)'].as_matrix()\n",
    "y = df['Y/Northing (m NAD83(2011) Zone 19N)'].as_matrix()\n",
    "\n",
    "#plt.imshow(z[::],cmap=plt.cm.gray,vmin=-2,vmax=13)\n",
    "cmap=plt.cm.Spectral_r\n",
    "plt.scatter(x,y,6,c=dz,vmin=-.2,vmax=.2,edgecolors='face',cmap=cmap)\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "#plt.xmin = e0\n",
    "#plt.xmax = e0+680\n",
    "#plt.ymin = n0-800\n",
    "#plt.ymax = n0\n",
    "plt.colorbar()\n",
    "\n",
    "fig_name = os.path.join(path,\"All_DEM_minus_transect_MAP.png\")\n",
    "fig.savefig(fig_name,dpi=200)"
   ]
  },
  {
   "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
}