Flume calculations

gistfile1.txt

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.optimize import curve_fit\n",
    "import cv2\n",
    "import imutils as im\n",
    "import os.path\n",
    "import glob\n",
    "import seaborn as sns\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Relationship between surface flow velocity and hyporheic exchange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.698275862068966"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# depth = 7 cm\n",
    "# width = 29 cm\n",
    "# lmin = 120 L/min\n",
    "\n",
    "def lmin_to_cm_s(lmin, depth, width):\n",
    "    cm3_min = lmin * 1000\n",
    "    cm_s = cm3_min / 60\n",
    "    area = depth * width\n",
    "    cm_sec = cm_s / area\n",
    "    return cm_sec\n",
    "\n",
    "lmin_to_cm_s(135, 8, 29)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('ec_discharge_hyporheic flux.csv')\n",
    "df = df.dropna()\n",
    "df['Velocity [cm/s]'] = lmin_to_cm_s(df['Discharge [L/min]'], 7.0, 29)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the function, y = A ^ Bx\n",
    "x, y = df['Velocity [cm/s]'].values, df['Hyporheic flux [cm/d]'].values\n",
    "result, _ = curve_fit(lambda t, a ,b: a*np.exp(b*t), x, y, p0=(4, 0.1))\n",
    "A, B = result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_exp = np.linspace(0,17, 150)\n",
    "y_exp = A * np.exp(B*x_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fce15232668>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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y2PAxhAvLf8/LF9VgZraKcAK6y93/nMf5jgQGufvIzU4cpt+R0OquAXChuz9aye9vSjg5\n7ujuX1RmXjVFruOlkvMcRmjgck0+5ic1i4W/Af3K3V/bxDSvEn4UTXL3g/L43VcARe5+RRmmLfP5\nJy9/yrPwf5LLCM2b83Iw1kbuXq7/YpXDGMKF5rLG8RmhaXqFmdmRwOuEqsNbCL8oZ1ZmnjVFvo4X\nM/spoQT9BaEkczTwt7wEKTWKmR1PqFodvanp3L13FYUwk/D3hM0qz/mn0gnMQsuWLwlVB30qOz/J\nP3e/KYGvPZpQPWaEapFTPF/F/QKW5+NlO0LV79aEqp1fe9Xf6kwKTFQLtitwRnTNr9q5e7kbaJRF\n3qoQRUREqlPB3AtRREQkrkbcmLKoqMg7dOiQdBgiIgVl8uTJi9291eanTKcakcA6dOjApEmTNj+h\niIisZ2blugNP2qgKUURECpISmIiIFCQlMBERKUhKYCIiUpCUwEREpCApgYmISEFSAhMRkYKkBCYi\nIgVJCUxEpBCNGAHdu8Osgv4vcqUogYmIFKLnnoNPPoHWrZOOJDFKYCIihWbNGnjxRTjiCGjQIOlo\nEqMEJiJSaN56C5YsgWOPTTqSRCmBiYgUmhEjoFEj6FO7nyGsBCYiUmhuuAHeeQeaNEk6kkQpgYmI\nFJr69aFLl6SjSJwSmIhIIbn7bvjDH8A96UgSVyMeaCkiUmvcd19oOm+WdCSJUwlMRKRQfPIJTJ9e\n61sfllICExEpFMOHh9djjkk2jpRQAhMRKRTDh8NPfwrt2iUdSSroGpiISCFYswZ22AH23z/pSFJD\nCUxEpBDUqwdPPpl0FKmSygRmZjOBFcBaYI27d0s2IhGRhM2aBe3bJx1FqqT5GthB7r6XkpeI1Hpz\n5kCHDjB4cNKRpEqaE5iIiAA880x47dUr2ThSJq0JzIFXzWyymQ3INoGZDTCzSWY2adGiRdUcnohI\nNRo2DPbeGzp3TjqSVElrAjvA3fcGDgP+x8x6Zk7g7g+4ezd379aqVavqj1BEpDp88QVMmAAnn5x0\nJKmTygTm7vOi14XAcGDfZCMSEUnI00+H1xNPTDaOFEpdAjOzJmbWrLQf6A18lGxUIiIJueACeOEF\n6Ngx6UhSJ43N6LcFhlu4UWU9YKi7/zvZkEREEtK8ORx1VNJRpFLqEpi7fw7oQTciIo8/Dl99BQMH\n6u7zWaSuClFERCI33xya0Ct5ZaUEJiKSRtOnw4cfqvXhJiiBiYik0dChUKcOnHBC0pGklhKYiEja\nrFsHQ4bAIYeEpy9LVkpgIiJp89VXodn8WWclHUmqpa4VoohIrVdUBKNHJx1F6qkEJiKSJt9/DwsX\nJh1FQVACExFJkxEjoE0bmDw56UhSTwlMRCRNhgyBVq2gi+7nsDlKYCIiabFkCbz0EvTrB/XURGFz\nlMBERNLi6afhhx/gjDOSjqQgKIGJiKTFkCGw226qPiwjlVFFRNJi6FD4739178MyUgITEUmL9u1D\nJ2WiKkQRkaStXQtnnglvvZV0JAVFCUxEJGmjRoVnf82fn3QkBUUJTEQkaQ89BFtvrScvl5MSmIhI\nkhYtghdeCFWIDRsmHU1BUQITEUnSkCHhv1/nnpt0JAVHCUxEJEktW8Kpp4b/f0m5KIGJiCTprLPC\n/7+k3JTARESSMmECrFqVdBQFSwlMRCQJK1fCIYfApZcmHUnBUgITEUnC00+HJHb66UlHUrCUwERE\nkjB4MOy6K/TokXQkBUsJTESkuk2cGLoLL9SNeytBCUxEpLq98AI0aaLnflVSahOYmdU1s/fMbETS\nsYiI5NX118OUKbDllklHUtBSm8CAi4HpSQchIpJX7qHacIcdko6k4KUygZlZO+Bw4O9JxyIikjfr\n1sE++8BddyUdSY2QygQG3AFcAazLNYGZDTCzSWY2adGiRdUXmYhIRY0aBe+9B61aJR1JjZC6BGZm\nRwAL3X3ypqZz9wfcvZu7d2ulnUFECsG998I228BxxyUdSY2QugQG/Aw4ysxmAk8BB5vZkGRDEhGp\npFmzYMQIOP98PTYlT1KXwNz99+7ezt07AKcAo91df1UXkcJ2//3hdcCAZOOoQeolHYCISK1w2mnQ\nti0UFycdSY2R6gTm7mOAMQmHISJSebvtpmd+5VnqqhBFRGqUdevCHec//DDpSGocJTARkao0ciTc\ncQdMnZp0JDWOEpiISFW67TZo1w6OPz7pSGocJTARkary/vvwxhswcCDUr590NDWOEpiISFW5/fZw\n1/nzz086khpJCUxEpKoUF4cGHC1aJB1JjaQEJiKSJ4PHljCuZPGGAddfz7j+FzN4bElyQdVgSmAi\nInmyZ7vmXDT0Pd75YCaMHMm4GYu4aOh77NmuedKh1UhKYCIiedKjUxGD+nXlnSv+Cocfzl23/ZNB\n/brSo1NR0qHVSKm+E4eISKHp0bYpe056gTfb78W+xxyk5FWFVAITEcmjkpsH0fSrRSy48BKGjJ+9\n8TUxySslMBGRPBn3yQIa3n4rK7rsw4m/O5NB/bpy0dD3lMSqiBKYiEiezJr4Ea0a1aXZ//4ZzNZf\nE5syd1nSodVI5u5Jx1Bp3bp180mTJiUdhogIrF4N9epBnfSXD8xssrt3SzqOikr/GhYRKQSzZsH3\n30ODBgWRvGoCrWURkcpyhxNPhF/+MulIahUlMBGRyho5EiZOhDPOSDqSWkUJTESkMtzh6quhY0fo\n3z/paGoV/ZFZRKQyXnwR3n0XHnlEj0ypZiqBiYhUxvDh0LkznH560pHUOiqBiYhUxiOPwPz5oem8\nVCuVwEREKmLdOliyBMygTZuko6mVlMBERCrimWegQwf48MOkI6m1lMBERMprzRq49lpo3x523TXp\naGotVdqKiJTXww/Dxx/Dc89B3bpJR1NrqQQmIlIe33wD11wDPXrAMcckHU2tphKYiEh5/PvfsGAB\n/POfoQGHJCZ1CczMGgH/ARoS4nvW3a9JNioRkcjxx8Onn8KOOyYdSa2XxirE74GD3b0LsBfQx8z2\nSzgmERFYtCi8KnmlQuoSmAcro7f1o67wH1omIoWtpASKi+Hxx5OORCKpS2AAZlbXzN4HFgKj3H18\nlmkGmNkkM5u0qPRXkYhIVfnjH8Nzvnr1SjoSiaQygbn7WnffC2gH7Gtmu2eZ5gF37+bu3Vq1alX9\nQYpI7TFuHAwbBpdfDq1bJx2NRFKZwEq5+1LgDaBP0rGISC21bh0MHAht28KVVyYdjcSkLoGZWSsz\naxH1bwH8Evg42ahEpNb64AOYOhVuugmaNEk6GolJXTN6oDXwmJnVJSTYp919RMIxiUht1bUrzJih\nG/amUOoSmLtPAbomHYeICNOmwS67hOpDSZ3UVSGKiKTCxx9Dly5wxx1JRyI5KIGJiGRyh8sug8aN\n4bTTko5GckhdFaKISOKefx5efhluvRW22SbpaCQHlcBEROJWrIDf/CZUHw4cmHQ0sglKYCIicZ9+\nGqoQ778f6qmSKs20dURE4vbZBz7/HBo2TDoS2QyVwEREANauDU9a/uEHJa8CoQQmIgJwzz1w7rmh\n8YYUBCUwEZE5c+BPf4JDD4Ujj0w6GikjJTARqd3c4fzzQxXivfeCWdIRSRmpEYeI1G4PPwyvvAJ3\n3w077JB0NFIOKoGJSO22++5w3nlw4YVJRyLlpBKYiNRu3buHTgqOSmAiUjs9+mgoda1alXQkUkFK\nYCJS+8yZA5dcEh5U2aBB0tFIBSmBiUjtsnYtnHEGrFkTGnDU0WmwUOkamIjULrfcAmPHhuTVqVPS\n0Ugl6KeHiNQey5fDjTfCiSdC//5JRyOVpBKYiNQeW24JEyZAy5b6w3INoBKYiNQOb74Z7rrRuXNI\nYFLwlMBEpOZ79lno2ROGDEk6EskjJTARqdk++wzOOQf22w9OPjnpaCSPlMBEpOb67js44QSoXx+G\nDdN/vmoYNeIQkZpr4ECYMgVGjoTi4qSjkTxTAhORmuvYY0OjjcMOSzoSqQJKYCJS83z3HWyxBfTt\nGzqpkXQNTERqlsWLYY894P77k45EqljqEpiZbW9mb5jZNDObamYXJx2TiBSIH36Ak06CuXOha9ek\no5EqlsYqxDXA5e7+rpk1Ayab2Sh3n5Z0YCKScpdfDm+8AY89Bvvum3Q0UsVSVwJz9/nu/m7UvwKY\nDrRNNioRSb2HHoK774bLLoMzz0w6GqkGqUtgcWbWAegKjE82EhFJvRUrQmvDG29MOhKpJubuSceQ\nlZk1BcYCf3X357KMHwAMACguLt5n1qxZ1RyhiKTCunUbnukV75fNMrPJ7t4t6TgqKpVb2szqA/8E\nnsiWvADc/QF37+bu3Vq1alW9AYpIOixaBD/9Kbz2Wniv5FWrpG5rm5kBDwHT3f22pOMRkZT67js4\n6iiYNg2aNk06GklA6hIY8DPgDOBgM3s/6vRPRBHZYO1aOP10GD8enngi3KhXap3UNaN397cAPWlO\nRLJzh0svheeeg9tug+OOSzoiSUgaS2AiIrmtWwfLloUkdsklSUcjCUpdCUxEJKdVq6BRI3jkkfDe\nVFlTm6kEJiKF4bHHwj0O580LrQ3V4rDW0x4gIuk3fDicey60bw9FRUlHIymhBCYi6fbCC3DyydCt\nGzz/PDRsmHREkhJKYCKSXq+9BieeGO4s/8or+r+XbEQJTEQSNXhsCeNKFm80bFzJYgaPLYG994az\nzoJXX4XmzROKUNJKCUxEErVnu+ZcNPS99UlsXMliHrxhCF1aNYSWLeHBB5W8JCs1oxeRRPXoVMSg\nfl25aOh7nN69mEUPPsZDz99MnZb/hZtuSjo8STElMBFJXI9ORZzevZglt97F/xt1H9azJ/zpT0mH\nJSmnBCYiiRtXsph6N9/EX197mP/s1J0G9zzOfltumXRYknK6BiYiiRpXspirHxjNrycNh379qP/C\ncC587uMfNewQyaQSmIgkZ/VqpsxZynUDDqb+ryZBhw7sX6cOg/rVZ8rcZfTopD8tS25KYCKSjK+/\nhmOP5YKDDoJrrgE2JKsenYqUvGSzVIUoItVv1iz42c9g3Djo3DnpaKRAqQQmItVr3LjwDK9Vq8Ld\nNQ46KOmIpEApgYlI9Vm8GHr3hu22g9GjYdddk45ICpgSmIhUPffw7K6iInjyyVB92LJl0lFJgdM1\nMBGpWosXQ58+4ZEoAEceqeQleaEEJiJV5513wp3kx4yB5cuTjkZqGCUwEck/d7jzTvj5z6F+fXj7\n7XBXeZE8UgITkfx7/XW45BLo2xcmTw6PRRHJMzXiEJH8Wbw4NNTo1QteegkOOyw03hCpAiqBiUjl\nrVkDV18NHTvC9OlhWN++Sl5SpVQCE5HK+fTTcH3rnXegf39o1y7piKSWUAlMRCruzjuhSxf4+GN4\n6il45BFo1izpqKSWUAlMRCpu5kz45S/h/vuhdeuko5FaRglMRMruhx/g9tthv/2gZ0+46SaoV0/X\nuiQRqaxCNLOHzWyhmX2UdCwiEnn7bdhnH7jySnj++TCsfn0lL0lMKhMY8CjQJ+kgRITw3K4LLwz3\nL/z663BLqNtuSzoqkXQmMHf/D/BV0nGICDBsWLjGNXAgTJsGxxyTdEQiQAFfAzOzAcAAgOLi4oSj\nEalhXn8dVqwIyeq88+CAA2D33ZOOSmQjqSyBlYW7P+Du3dy9W6tWrZIOR6RmmDEDjj463EnjppvC\nPQ3r1VPyklQq2AQmInn05Zdw8cXhAZOjR8MNN4RXNdCQFCvYKkQRyaP33oN77oGzz4brrtN/uqQg\npDKBmdmTwIFAkZnNBa5x94eSjUqkBlmxAu64I/T/+c9w6KFQUgLt2ycbl0g5pDKBufupSccgUiMt\nWwb33Qe33hruHN+vX7jOZabkJQVH18BEaotnnoHiYvj978MfksePhyee0HUuKVipLIGJSJ6UlECd\nOuExJzvvHKoKr7pKD5iUGkElMJGaxh3efBNOOgl22ik8pwtgjz3g6aeVvKTGUAlMpCZ58km48Ub4\n4ANo0QIuvxwuuSTpqESqhBKYSKGbOTNc26pTByZPhnXr4IEH4LTToHHjpKMTqTKqQhQpQA++OpVP\nbxsMhxwSrm+NGsW4ksU82PucUPo6/3wlL6nxVAITKSRLl8Lvf8/ZTwyl3orlrNq+PY2uu46Jzdpy\n0dD3GNQfTIWRAAAL/klEQVSvq1oVSq2hBCaSdp9/Dl98EUpbTZvCqFHUO/ooPjr0eM4q2YLTundg\nyOuzGdSvKz06FSUdrUi1UQITSaP//je0GHzySZgwAdq1g9mzw411P/4Y6tVjd+C0Vz/hrtEzGHhw\nZyUvqXV0DUwkbW68MSSsSy+FH34Id4V/660NVYP1wu/OcSWLGTJ+NgMP7syQ8bMZV7I4waBFqp9K\nYCJJcYcPP4Tnnw/dI49Aly6w335wzTVwyinwk59k/ei4ksXrr3n16FTEfp223ui9SG2gBCZS3RYt\ngv/9Xxg5MlzbMoP994eVK8P4X/widJswZe6yjZJVj05FDOrXlSlzlymBSa1h7p50DJXWrVs3nzRp\nUtJhiPyYe7hm9fLLsO224b9Z33wT/rfVo0d4eOSRR4ZxItXMzCa7e7ek46golcBEqsLjj4cS1pgx\nsGBBGHbqqSGBNWkSHiBZT4efSGXoCBKpDHeYMSMkqs8+Cw0uAJ56Kjwk8uCD4cADoXfvjR9XouQl\nUmk6ikQq4qWX4MEH4e23YeHCMKx1a7j22nAHjCefhGbN9KdikSqkBCaSy9q18MknMHEivPNO6J59\nFjp1grlzYfp06NMnNMA48MDQYrA0YW25ZaKhi9QGSmAiAKtWwUcfQZs2oRszBvr2he++C+O33BK6\ndw8NMAAGDIBf/SqxcEVECUxqq+XL4d57YerUcPPb6dNhzRq4/fbw+JGddgoJqmvX8PTiXXYJd3sv\npapBkcSpGb3UTO6hUcW0aSFJTZsWuiOOgOuvh2+/Ddeo2rQJD3rs2jV0P/tZuJYlUguoGb1IUlav\nDs/CmjEjdCUlIflcdVUY36MHLFkS+ouLYdddoUOH8L5x43Bn92bNkohcRPJACUzSa82acFPb2bM3\ndGZw5ZVhfI8e4QGOpZo2DX8KhjDdP/4BrVrBzjtnT1RKXiIFTQlMkrNoUbiV0vz5MG9eSFBLl8Lg\nwWH8SSfB8OEbf2bnnTcksCuugO+/D60CO3cOySp+bapv3+pZDhFJhBKY5I87LFsWSjZ164bGEWPH\nhgRV2i1YEB4P0rAh/OUvcNddGz5fv36o6luzJvzR97zzQhIqLg7d9tuHu1iUOumk6l9GEUkNJTDJ\n7fvvQ8L5+utwLWnRIli8OCSObbYJt0q65ZYwrHTcmjXhelSnTvDqq6GUVK8ebLdduD7VoUNoQNGw\nIZx9NvTqFYa3aROmibf0UwlKRDZBCawmW70a5syBFSvCnc6XLw9VdPvvDx07hpvM3nJLGPb11+F1\n6VJ46KHwx9wRI+CEE3483y5dQgJbty58R6dO4T9SRUWhGq958zDd+edD//6w9dYbJ6ZSe+0VOhGR\nClACS5J7KNl8910olXz3Xei23RZ22CH8uXbYsA3jvvkmJKPevUPJZd48OPPMDQlqxYrQ3XxzSB5T\np8Lee//4ex97LCSwZcvCXdJbtICttgoloV122ZCAunULyaxFi5CcSruttw7jjzgidLm0aJH/dSYi\nEkllAjOzPsCdQF3g7+7+t3zOf/DYEvZs1zw8N2n2bFixgg9KvqRk7lcct1ur0Jptn33CxP/6V0gy\nq1eHKrXVq0M12PHHh/FXXw1ffRXGlY7ff3+4+OIwvmfPUKopTU7ffgtnnRX+MLtmTSixZPrd78JN\nYVetCiWYuIYNQwLp1StcM/r+e2jZMtwotlmz0O28c5i2Y0d49NENw5s1C4mqbdswvnv3kARzad8e\nzjmngmtZRKRqpS6BmVld4B7gl8BcYKKZveju0/L1HXu2a77h6bUnHQvvvksXoEvpBD17hsYHAL/9\nLXz66cYzOPzwDQls2LBw7adhw9A1aBAaG5QqLbFssUXoGjcOCQ5CArr7bmjUaMO4LbYIVXIQbl9U\nUrJheOPG4TOlttkmPGo+lxYtQrIUEamBUncnDjPbH7jW3Q+N3v8ewN1vyPWZityJo/SR7H+qM4vx\nU2Zy9kE7sXP7VhtKOF2idPbFF+G1QYMNCapRo/AqIlLAdCeO/GsLzIm9nwt0z5zIzAYAAwCKi4vL\n/SU9OhVxevdiLhu9moHn/pyde/8k+4QdO5Z73iIiUvWyNA0rDO7+gLt3c/durbJdR9qMcSWLGTJ+\nNgMP7syQ8bMZV7K4CqIUEZGqksYENg+IXUSiXTQsb0qrDwf168plvX/CoH5duWjoe0piIiIFJI0J\nbCKwo5l1NLMGwCnAi/n8gilzl4UGHJ2KgFCdOKhfV6bMXZbPrxERkSqUumtg7r7GzC4CXiE0o3/Y\n3afm8zsu+EWnHw3r0alofUITEZH0S10CA3D3kcDIpOMQEZH0SmMVooiIyGYpgYmISEFSAhMRkYKk\nBCYiIgUpdbeSqggzWwTMquDHi4BC+wOYYq56hRYvKObqUpNibu/u5b8TRErUiARWGWY2qdDuBaaY\nq16hxQuKuboo5vRQFaKIiBQkJTARESlISmDwQNIBVIBirnqFFi8o5uqimFOi1l8DExGRwqQSmIiI\nFCQlMBERKUi1JoGZWR8z+8TMZpjZVVnGNzSzYdH48WbWofqj3Cie7c3sDTObZmZTzeziLNMcaGbL\nzOz9qLs6iVgzYpppZh9G8UzKMt7M7K5oPU8xs72TiDOK5Sexdfe+mS03s0sypkl8HZvZw2a20Mw+\nig1raWajzOyz6HWrHJ89K5rmMzM7K+GYbzazj6PtPtzMWuT47Cb3oWqO+Vozmxfb/n1zfHaT55dq\njHdYLNaZZvZ+js8mso7zzt1rfEd4LEsJsAPQAPgA2DVjmguBwVH/KcCwhGNuDewd9TcDPs0S84HA\niKTXb0ZMM4GiTYzvC7wMGLAfMD7pmGP7yALCHztTtY6BnsDewEexYTcBV0X9VwE3ZvlcS+Dz6HWr\nqH+rBGPuDdSL+m/MFnNZ9qFqjvla4Ldl2Hc2eX6prngzxt8KXJ2mdZzvrraUwPYFZrj75+6+GngK\nODpjmqOBx6L+Z4FDzMyqMcaNuPt8d3836l8BTAfaJhVPHh0N/MODd4AWZtY66aCAQ4ASd6/oHV2q\njLv/B/gqY3B8f30MOCbLRw8FRrn7V+7+NTAK6FNlgcZki9ndX3X3NdHbdwhPW0+NHOu5LMpyfsm7\nTcUbnbtOAp6s6jiSVFsSWFtgTuz9XH6cDNZPEx1ky4CtqyW6zYiqM7sC47OM3t/MPjCzl81st2oN\nLDsHXjWzyWY2IMv4smyLJJxC7oM9besYYFt3nx/1LwC2zTJNWtc1wDmEkng2m9uHqttFUbXnwzmq\natO4nn8OfOnun+UYn7Z1XCG1JYEVLDNrCvwTuMTdl2eMfpdQ5dUFuBt4vrrjy+IAd98bOAz4HzPr\nmXRAm2NmDYCjgGeyjE7jOt6Ihzqhgvk/jJn9EVgDPJFjkjTtQ/cBnYC9gPmEarlCcCqbLn2laR1X\nWG1JYPOA7WPv20XDsk5jZvWA5sCSaokuBzOrT0heT7j7c5nj3X25u6+M+kcC9c2sqJrDzIxpXvS6\nEBhOqF6JK8u2qG6HAe+6+5eZI9K4jiNflla9Rq8Ls0yTunVtZv2BI4DTosT7I2XYh6qNu3/p7mvd\nfR3wYI5YUrWeo/PXccCwXNOkaR1XRm1JYBOBHc2sY/Rr+xTgxYxpXgRKW2mdAIzOdYBVh6gO+yFg\nurvflmOa7Uqv05nZvoTtmVjSNbMmZtastJ9w0f6jjMleBM6MWiPuByyLVYUlJeev1bSt45j4/noW\n8EKWaV4BepvZVlHVV+9oWCLMrA9wBXCUu3+bY5qy7EPVJuP67LE5YinL+aU69QI+dve52UambR1X\nStKtSKqrI7R++5TQWuiP0bDrCAcTQCNCFdIMYAKwQ8LxHkCoFpoCvB91fYELgAuiaS4CphJaPb0D\n9Eg45h2iWD6I4ipdz/GYDbgn2g4fAt0SjrkJISE1jw1L1TomJNf5wA+E6yvnEq7Pvg58BrwGtIym\n7Qb8PfbZc6J9egZwdsIxzyBcKyrdn0tb/bYBRm5qH0ow5sej/XQKISm1zow5ev+j80sS8UbDHy3d\nf2PTpmId57vTraRERKQg1ZYqRBERqWGUwEREpCApgYmISEFSAhMRkYKkBCYiIgVJCUxERAqSEpiI\niBSk/w/sNJnuPhTjcQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fce172bc748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(x, y, 'x')\n",
    "ax.plot(x_exp, y_exp, 'r--')\n",
    "ax.set_title('Hyporheic flux [cm/d] against overlying surface water velocity [cm/s]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Converting percentage oxygen saturation to mass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Determine equilibrium oxygen at nonstandard pressure\n",
    "\n",
    "$$ \\ln P = 5.25 \\times \\ln \\: (1 - \\frac{h}{44.3}) $$\n",
    "\n",
    "Where P = pressure [atm] at altitude h [km] relative to standard partial pressure (Pst) at 101.325 kpa at sea level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95349664354226371"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def p(h):\n",
    "    \"\"\"Determine the atmospheric pressure at flume. h is in km, p is in pst\"\"\"\n",
    "    ln_p = 5.25 * np.log(1 - (h/44.3))\n",
    "    return np.exp(ln_p)\n",
    "\n",
    "# Flume at 400 metres above sea level, 0.4 km\n",
    "p(0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Cp(t, P):\n",
    "    \"\"\"Determine the equilibrium constant at flume altitude.\n",
    "    Cp = equilibrium oxygen concentration at nonstandard pressure, mg/L\n",
    "    c_star = equilibrium oxygen concentration at standard pressure of 1 atm, mg/L\n",
    "    pwv = partial pressure of water vapour, atm\n",
    "    t = temperature, Celcius\n",
    "    T = temperature, Kelvin\n",
    "    \"\"\"\n",
    "    T = t + 273.15 # Convert temperature from Celcius to Kelvin\n",
    "    c_star = np.exp(7.7117 - 1.31403 * np.log(t + 45.93))\n",
    "    ln_pwv = 11.8571 - (3840.70 / T) - (216961/T**2)\n",
    "    pwv = np.exp(ln_pwv)\n",
    "    theta = 0.000975 - (1.426 * 10**-5 * t) + (6.436 * 10**-8 * t**2)\n",
    "    Cp = c_star * p ( ( (1- (pwv/P)) * (1 - theta*P) ) / ( (1 - pwv) * (1 - theta) )   )\n",
    "    return Cp\n",
    "\n",
    "equilibrium_conc = Cp(25.5, p(0.4)) # Water temperature and flume altitude in km"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ \\text{DO mg/L} = \\frac{C_p \\times \\text{%Sat}}{100} $$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.2630614628824341"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def convert_sat_to_mgl(Cp, percentage_sat):\n",
    "    return (Cp * percentage_sat) / 100\n",
    "convert_sat_to_mgl(equilibrium_conc, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_image(fp):\n",
    "    im_bgra = cv2.imread(fp, -1)\n",
    "    im_gray = cv2.imread(fp, 0)\n",
    "    return im_bgra, im_gray\n",
    "\n",
    "def convert_to_percent(val):\n",
    "    percent = val/255 * 100\n",
    "    return percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "fp = '/home/jason/Desktop/2nd Experiment/Stitched images/18_Neutral Unsteady 300 mins/unsteady neutral 300min t02.tif'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def showimage(image, name=\"No name given\"):\n",
    "    cv2.imshow(name, image)\n",
    "    cv2.waitKey(0)\n",
    "    cv2.destroyAllWindows()\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "im = cv2.imread(fp,0) # import image as greyscale\n",
    "df = pd.DataFrame(im) # convert image to df\n",
    "df_percent = convert_to_percent(df) # convert greyscale to percent\n",
    "df_mgL = convert_sat_to_mgl(equilibrium_conc, df_percent)\n",
    "df_mgL.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path to stitched images\n",
    "root_folder = os.path.join(os.sep, 'home', 'jason', 'Desktop', '2nd Experiment', 'Stitched images', '*/')\n",
    "\n",
    "save_folder = os.path.join(os.sep, 'home', 'jason', 'Desktop', 'O2 in Mass')\n",
    "\n",
    "# Returns a list of all subdirectories, each subdirectory contains stitched image as .tif\n",
    "list_of_subdirs = glob.glob(root_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/home/jason/Desktop/2nd Experiment/Stitched images/08_Gaining Steady 6 cmd/gaining steady 6 cmd.tif']\n",
      "['/home/jason/Desktop/2nd Experiment/Stitched images/14_Losing Steady 3 cmd/losing_03cmd_steady_120.tif']\n",
      "['/home/jason/Desktop/2nd Experiment/Stitched images/06_Gaining Steady 6 cmd/gaining_06cmd_steady_t10.tif']\n",
      "['/home/jason/Desktop/2nd Experiment/Stitched images/10_Gaining Unsteady 3 cmd/gaining_03cmd_unsteady_t9.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/10_Gaining Unsteady 3 cmd/gaining_03cmd_unsteady_t4.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/10_Gaining Unsteady 3 cmd/gaining_03cmd_unsteady_t7.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/10_Gaining Unsteady 3 cmd/gaining_03cmd_unsteady_t10.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/10_Gaining Unsteady 3 cmd/gaining_03cmd_unsteady_t10_1.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/10_Gaining Unsteady 3 cmd/gaining_03cmd_unsteady_t0.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/10_Gaining Unsteady 3 cmd/gaining_03cmd_unsteady_t2.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/10_Gaining Unsteady 3 cmd/gaining_03cmd_unsteady_t1.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/10_Gaining Unsteady 3 cmd/gaining_03cmd_unsteady_t5.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/10_Gaining Unsteady 3 cmd/gaining_03cmd_unsteady_t3.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/10_Gaining Unsteady 3 cmd/gaining_03cmd_unsteady_t8.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/10_Gaining Unsteady 3 cmd/gaining_03cmd_unsteady_t6.tif']\n",
      "['/home/jason/Desktop/2nd Experiment/Stitched images/04_Losing Unsteady 6 cmd/losing_07cmd_t7.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/04_Losing Unsteady 6 cmd/losing_07cmd_t10.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/04_Losing Unsteady 6 cmd/losing_06cmd_t3.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/04_Losing Unsteady 6 cmd/losing_06cmd_t5.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/04_Losing Unsteady 6 cmd/losing_06cmd_t2.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/04_Losing Unsteady 6 cmd/losing_06cmd_t1.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/04_Losing Unsteady 6 cmd/losing_06cmd_t0.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/04_Losing Unsteady 6 cmd/losing_07cmd_t9.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/04_Losing Unsteady 6 cmd/losing_07cmd_t8.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/04_Losing Unsteady 6 cmd/losing_06cmd_t6.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/04_Losing Unsteady 6 cmd/losing_06cmd_t4.tif']\n",
      "['/home/jason/Desktop/2nd Experiment/Stitched images/05_Losing Steady 6 cmd/losing steady 6 cmd.tif']\n",
      "['/home/jason/Desktop/2nd Experiment/Stitched images/17_Anchor 4/neutral_steady_120Lmin_t13.tif']\n",
      "['/home/jason/Desktop/2nd Experiment/Stitched images/09_Gaining Steady 3 cmd/steady 120 lmin gaining 3 cmd.tif']\n",
      "['/home/jason/Desktop/2nd Experiment/Stitched images/01_Neutral Unsteady/t3_neutral_unsteady_120lmin.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/01_Neutral Unsteady/t9_neutral_unsteady_120lmin.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/01_Neutral Unsteady/t4_neutral_unsteady_120lmin.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/01_Neutral Unsteady/t5_neutral_unsteady_120lmin.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/01_Neutral Unsteady/t2_neutral_unsteady_120lmin.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/01_Neutral Unsteady/t0_neutral_unsteady_120lmin.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/01_Neutral Unsteady/t10_neutral_unsteady_120lmin.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/01_Neutral Unsteady/t7_neutral_unsteady_120lmin.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/01_Neutral Unsteady/t1_neutral_unsteady_120lmin.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/01_Neutral Unsteady/t8_neutral_unsteady_120lmin.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/01_Neutral Unsteady/t6_neutral_unsteady_120lmin.tif']\n",
      "['/home/jason/Desktop/2nd Experiment/Stitched images/16_Neutral Unsteady 900 mins/neutral_900min_unsteady_t1.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/16_Neutral Unsteady 900 mins/neutral_900min_unsteady_t0.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/16_Neutral Unsteady 900 mins/neutral_900min_unsteady_t6.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/16_Neutral Unsteady 900 mins/neutral_900min_unsteady_t4.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/16_Neutral Unsteady 900 mins/neutral_900min_unsteady_t9.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/16_Neutral Unsteady 900 mins/neutral_900min_unsteady_t10.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/16_Neutral Unsteady 900 mins/neutral_900min_unsteady_t8.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/16_Neutral Unsteady 900 mins/neutral_900min_unsteady_t3.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/16_Neutral Unsteady 900 mins/neutral_900min_unsteady_t5.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/16_Neutral Unsteady 900 mins/neutral_900min_unsteady_t2.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/16_Neutral Unsteady 900 mins/neutral_900min_unsteady_t7.tif']\n",
      "['/home/jason/Desktop/2nd Experiment/Stitched images/15_Anchor 3/neutral_steady_120.tif']\n",
      "['/home/jason/Desktop/2nd Experiment/Stitched images/00_Anchor 1/Anchor_120Lmin.tif']\n",
      "['/home/jason/Desktop/2nd Experiment/Stitched images/11_Gaining Steady 3 cmd/gaining_03cmd_steady_120.tif']\n",
      "['/home/jason/Desktop/2nd Experiment/Stitched images/13_Losing Unsteady 3 cmd/Losing unsteady 3 cmd_t8.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/13_Losing Unsteady 3 cmd/Losing unsteady 3 cmd_t3.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/13_Losing Unsteady 3 cmd/Losing unsteady 3 cmd_t6.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/13_Losing Unsteady 3 cmd/Losing unsteady 3 cmd_t1.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/13_Losing Unsteady 3 cmd/Losing unsteady 3 cmd_t0.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/13_Losing Unsteady 3 cmd/Losing unsteady 3 cmd_t7.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/13_Losing Unsteady 3 cmd/Losing unsteady 3 cmd_t9.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/13_Losing Unsteady 3 cmd/Losing unsteady 3 cmd_t5.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/13_Losing Unsteady 3 cmd/Losing unsteady 3 cmd_t2.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/13_Losing Unsteady 3 cmd/Losing unsteady 3 cmd_t4.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/13_Losing Unsteady 3 cmd/Losing unsteady 3 cmd_t10.tif']\n",
      "['/home/jason/Desktop/2nd Experiment/Stitched images/03_Losing Steady 6 cmd/Losing steady 10 cmd part 1.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/03_Losing Steady 6 cmd/Losing steady 10 cmd part 2.tif']\n",
      "['/home/jason/Desktop/2nd Experiment/Stitched images/19_Anchor 5/neutral_120_t10.tif']\n",
      "['/home/jason/Desktop/2nd Experiment/Stitched images/07_Gaining Unsteady 6 cmd/gaining_06cmd_t0.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/07_Gaining Unsteady 6 cmd/gaining_06cmd_t4.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/07_Gaining Unsteady 6 cmd/gaining_06cmd_t10.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/07_Gaining Unsteady 6 cmd/gaining_06cmd_t5.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/07_Gaining Unsteady 6 cmd/gaining_06cmd_t1.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/07_Gaining Unsteady 6 cmd/gaining_06cmd_t7.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/07_Gaining Unsteady 6 cmd/gaining_06cmd_t8.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/07_Gaining Unsteady 6 cmd/gaining_06cmd_t6.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/07_Gaining Unsteady 6 cmd/gaining_06cmd_t3.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/07_Gaining Unsteady 6 cmd/gaining_06cmd_t2.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/07_Gaining Unsteady 6 cmd/gaining_06cmd_t9.tif']\n",
      "['/home/jason/Desktop/2nd Experiment/Stitched images/02_Anchor 2/anchor_t10_neutral_steady_120lmin.tif']\n",
      "['/home/jason/Desktop/2nd Experiment/Stitched images/12_Losing Steady 3 cmd/losing_03cmd_steady_120.tif']\n",
      "['/home/jason/Desktop/2nd Experiment/Stitched images/18_Neutral Unsteady 300 mins/unsteady neutral 300min t04.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/18_Neutral Unsteady 300 mins/unsteady neutral 300min t00.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/18_Neutral Unsteady 300 mins/unsteady neutral 300min t02.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/18_Neutral Unsteady 300 mins/unsteady neutral 300min t10.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/18_Neutral Unsteady 300 mins/unsteady neutral 300min t03.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/18_Neutral Unsteady 300 mins/unsteady neutral 300min t06.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/18_Neutral Unsteady 300 mins/unsteady neutral 300min t08.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/18_Neutral Unsteady 300 mins/unsteady neutral 300min t09.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/18_Neutral Unsteady 300 mins/unsteady neutral 300min t07.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/18_Neutral Unsteady 300 mins/unsteady neutral 300min t05.tif', '/home/jason/Desktop/2nd Experiment/Stitched images/18_Neutral Unsteady 300 mins/unsteady neutral 300min t01.tif']\n"
     ]
    }
   ],
   "source": [
    "for subdir in list_of_subdirs:\n",
    "    images = glob.glob(os.path.join(subdir, '*.tif'))\n",
    "    print(images)\n",
    "    #for im in images:\n",
    "        "
   ]
  }
 ],
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