Script1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validating equations 3, 4 and 5 used to predict cellulose, phospholipid and protein content in expanding pericarp cells"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cellulose data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>DPA</th>\n",
       "      <th>CELLULOSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>0.33444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>0.50483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>0.38278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>0.41773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>0.37801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "      <td>0.36530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>42</td>\n",
       "      <td>4.93422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>42</td>\n",
       "      <td>4.60056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>42</td>\n",
       "      <td>4.52337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42</td>\n",
       "      <td>4.55337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>42</td>\n",
       "      <td>3.94333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>42</td>\n",
       "      <td>3.63326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>59</td>\n",
       "      <td>3.87056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>59</td>\n",
       "      <td>3.89164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>59</td>\n",
       "      <td>4.31025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>59</td>\n",
       "      <td>4.30031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>59</td>\n",
       "      <td>3.33823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>59</td>\n",
       "      <td>3.42691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    DPA  CELLULOSE\n",
       "0    15    0.33444\n",
       "1    15    0.50483\n",
       "2    15    0.38278\n",
       "3    15    0.41773\n",
       "4    15    0.37801\n",
       "5    15    0.36530\n",
       "6    42    4.93422\n",
       "7    42    4.60056\n",
       "8    42    4.52337\n",
       "9    42    4.55337\n",
       "10   42    3.94333\n",
       "11   42    3.63326\n",
       "12   59    3.87056\n",
       "13   59    3.89164\n",
       "14   59    4.31025\n",
       "15   59    4.30031\n",
       "16   59    3.33823\n",
       "17   59    3.42691"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "d={\"DPA\":[15,15,15,15,15,15,42,42,42,42,42,42,59,59,59,59,59,59],\"CELLULOSE\":[334.44,504.83,382.78,417.73,378.01,365.3,4934.22,4600.56,4523.37,4553.37,3943.33,3633.26,3870.56,3891.64,4310.25,4300.31,3338.23,3426.91]}\n",
    "\n",
    "n=0\n",
    "for i in d[\"CELLULOSE\"]:         #converting cellulose to mmol/fruit\n",
    "    d[\"CELLULOSE\"][n]=i/1000\n",
    "    n=n+1\n",
    "\n",
    "df_cellulose = pd.DataFrame(data = d)\n",
    "df_cellulose = df_cellulose[[\"DPA\",\"CELLULOSE\"]]\n",
    "df_cellulose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from studyFunctions import *\n",
    "\n",
    "xlist = list()\n",
    "ylist = list()\n",
    "for i in range(8,61):\n",
    "    xlist.append(i)\n",
    "    ylist.append(celluloseLevels(i,Ncells = 25*(10**6)))\n",
    "\n",
    "SSE = 0\n",
    "for i in range(0,len(df_cellulose[\"CELLULOSE\"])):\n",
    "    SSE=SSE+((ylist[df_cellulose[\"DPA\"][i]-8]-df_cellulose[\"CELLULOSE\"][i])**2)\n",
    "MSE = float(SSE)/len(df_cellulose[\"DPA\"])\n",
    "RMSE = (MSE)**0.5\n",
    "RMSE_cellulose = RMSE\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lipid data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>DPA (days)</th>\n",
       "      <th>Lipids(mg/gFW)</th>\n",
       "      <th>Lipids (mmol/gFW)</th>\n",
       "      <th>Lipids (mmol/fruit)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>5.66</td>\n",
       "      <td>0.008203</td>\n",
       "      <td>0.019195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>5.05</td>\n",
       "      <td>0.007319</td>\n",
       "      <td>0.017127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>3.83</td>\n",
       "      <td>0.005551</td>\n",
       "      <td>0.012989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>6.15</td>\n",
       "      <td>0.008913</td>\n",
       "      <td>0.020857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>5.26</td>\n",
       "      <td>0.007623</td>\n",
       "      <td>0.017839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "      <td>4.21</td>\n",
       "      <td>0.006102</td>\n",
       "      <td>0.014278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>42</td>\n",
       "      <td>3.60</td>\n",
       "      <td>0.005218</td>\n",
       "      <td>0.285139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>42</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.004913</td>\n",
       "      <td>0.268506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>42</td>\n",
       "      <td>2.61</td>\n",
       "      <td>0.003783</td>\n",
       "      <td>0.206726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0.005435</td>\n",
       "      <td>0.297020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>42</td>\n",
       "      <td>3.58</td>\n",
       "      <td>0.005189</td>\n",
       "      <td>0.283555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>42</td>\n",
       "      <td>2.78</td>\n",
       "      <td>0.004029</td>\n",
       "      <td>0.220191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>59</td>\n",
       "      <td>2.94</td>\n",
       "      <td>0.004261</td>\n",
       "      <td>0.244880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>59</td>\n",
       "      <td>3.73</td>\n",
       "      <td>0.005406</td>\n",
       "      <td>0.310681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>59</td>\n",
       "      <td>3.04</td>\n",
       "      <td>0.004406</td>\n",
       "      <td>0.253209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>59</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.001015</td>\n",
       "      <td>0.058305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>59</td>\n",
       "      <td>4.28</td>\n",
       "      <td>0.006203</td>\n",
       "      <td>0.356491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>59</td>\n",
       "      <td>3.45</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.287359</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    DPA (days)  Lipids(mg/gFW)  Lipids (mmol/gFW)  Lipids (mmol/fruit)\n",
       "0           15            5.66           0.008203             0.019195\n",
       "1           15            5.05           0.007319             0.017127\n",
       "2           15            3.83           0.005551             0.012989\n",
       "3           15            6.15           0.008913             0.020857\n",
       "4           15            5.26           0.007623             0.017839\n",
       "5           15            4.21           0.006102             0.014278\n",
       "6           42            3.60           0.005218             0.285139\n",
       "7           42            3.39           0.004913             0.268506\n",
       "8           42            2.61           0.003783             0.206726\n",
       "9           42            3.75           0.005435             0.297020\n",
       "10          42            3.58           0.005189             0.283555\n",
       "11          42            2.78           0.004029             0.220191\n",
       "12          59            2.94           0.004261             0.244880\n",
       "13          59            3.73           0.005406             0.310681\n",
       "14          59            3.04           0.004406             0.253209\n",
       "15          59            0.70           0.001015             0.058305\n",
       "16          59            4.28           0.006203             0.356491\n",
       "17          59            3.45           0.005000             0.287359"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_lipid = pd.DataFrame(data={'DPA (days)':[15,15,15,15,15,15,42,42,42,42,42,42,59,59,59,59,59,59],'Lipids(mg/gFW)':[5.66,5.05,3.83,6.15,5.26,4.21,3.60,3.39,2.61,3.75,3.58,2.78,2.94,3.73,3.04,0.70,4.28,3.45]})\n",
    "FWdict = {8:0.32,15:2.34,22:5.50,28:15.35,34:38.42,42:54.65,50:58.24,52:64.36,59:57.47}\n",
    "MW_lipid=689.979\n",
    "temp = list()\n",
    "for i in range(0,len(df_lipid[\"Lipids(mg/gFW)\"])):\n",
    "    temp.append((df_lipid[\"Lipids(mg/gFW)\"][i])/MW_lipid)\n",
    "df_lipid[\"Lipids (mmol/gFW)\"]=temp\n",
    "\n",
    "temp = list()\n",
    "for i in range(0,len(df_lipid[\"Lipids(mg/gFW)\"])):\n",
    "    temp.append(df_lipid[\"Lipids (mmol/gFW)\"][i]*FWdict[df_lipid[\"DPA (days)\"][i]])\n",
    "df_lipid[\"Lipids (mmol/fruit)\"]=temp\n",
    "df_lipid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from studyFunctions import *\n",
    "\n",
    "xlist2 = list()\n",
    "ylist2 = list()\n",
    "for i in range(8,61):\n",
    "    xlist2.append(i)\n",
    "    ylist2.append(phospholipidLevels(i,Ncells = 25*(10**6),scaling_factor=100.0/8.512820512820287))\n",
    "\n",
    "SSE = 0\n",
    "for i in range(0,len(df_lipid[\"DPA (days)\"])):\n",
    "    SSE=SSE+((ylist2[df_lipid[\"DPA (days)\"][i]-8]-df_lipid[\"Lipids (mmol/fruit)\"][i])**2)\n",
    "MSE = float(SSE)/len(df_lipid[\"DPA (days)\"])\n",
    "RMSE = (MSE)**0.5\n",
    "RMSE_lipid = RMSE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Protein data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DPA (days)</th>\n",
       "      <th>fruit DW (g)</th>\n",
       "      <th>STARCH (umol/gDW)</th>\n",
       "      <th>PROTEIN (ug/mgDW)</th>\n",
       "      <th>PROTEIN (ug/fruit)</th>\n",
       "      <th>PROTEIN (mg/fruit)</th>\n",
       "      <th>PROTEIN (mmol/fruit)</th>\n",
       "      <th>PROTEIN (mmol/m3)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>0.032833</td>\n",
       "      <td>128.024985</td>\n",
       "      <td>98.790388</td>\n",
       "      <td>3243.549841</td>\n",
       "      <td>3.243550</td>\n",
       "      <td>0.007033</td>\n",
       "      <td>7070.750829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>0.032833</td>\n",
       "      <td>142.608028</td>\n",
       "      <td>100.887399</td>\n",
       "      <td>3312.400278</td>\n",
       "      <td>3.312400</td>\n",
       "      <td>0.007182</td>\n",
       "      <td>7220.840795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>0.032833</td>\n",
       "      <td>156.660539</td>\n",
       "      <td>99.559834</td>\n",
       "      <td>3268.812822</td>\n",
       "      <td>3.268813</td>\n",
       "      <td>0.007088</td>\n",
       "      <td>7125.822664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>0.032833</td>\n",
       "      <td>133.544310</td>\n",
       "      <td>103.930644</td>\n",
       "      <td>3412.318068</td>\n",
       "      <td>3.412318</td>\n",
       "      <td>0.007399</td>\n",
       "      <td>7438.655790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>0.032833</td>\n",
       "      <td>120.220306</td>\n",
       "      <td>108.911064</td>\n",
       "      <td>3575.838420</td>\n",
       "      <td>3.575838</td>\n",
       "      <td>0.007754</td>\n",
       "      <td>7795.120690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8</td>\n",
       "      <td>0.032833</td>\n",
       "      <td>208.173335</td>\n",
       "      <td>100.532013</td>\n",
       "      <td>3300.732024</td>\n",
       "      <td>3.300732</td>\n",
       "      <td>0.007157</td>\n",
       "      <td>7195.404677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15</td>\n",
       "      <td>0.231085</td>\n",
       "      <td>80.307639</td>\n",
       "      <td>50.137586</td>\n",
       "      <td>11586.046744</td>\n",
       "      <td>11.586047</td>\n",
       "      <td>0.025123</td>\n",
       "      <td>44368.296448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15</td>\n",
       "      <td>0.231085</td>\n",
       "      <td>81.617257</td>\n",
       "      <td>51.457765</td>\n",
       "      <td>11891.120524</td>\n",
       "      <td>11.891121</td>\n",
       "      <td>0.025784</td>\n",
       "      <td>45536.564125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15</td>\n",
       "      <td>0.231085</td>\n",
       "      <td>82.275683</td>\n",
       "      <td>49.365831</td>\n",
       "      <td>11407.705820</td>\n",
       "      <td>11.407706</td>\n",
       "      <td>0.024736</td>\n",
       "      <td>43685.347109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15</td>\n",
       "      <td>0.231085</td>\n",
       "      <td>84.364324</td>\n",
       "      <td>47.081455</td>\n",
       "      <td>10879.820624</td>\n",
       "      <td>10.879821</td>\n",
       "      <td>0.023591</td>\n",
       "      <td>41663.832143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>15</td>\n",
       "      <td>0.231085</td>\n",
       "      <td>90.058238</td>\n",
       "      <td>43.771808</td>\n",
       "      <td>10115.010603</td>\n",
       "      <td>10.115011</td>\n",
       "      <td>0.021933</td>\n",
       "      <td>38735.023166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15</td>\n",
       "      <td>0.231085</td>\n",
       "      <td>71.608995</td>\n",
       "      <td>43.538392</td>\n",
       "      <td>10061.071629</td>\n",
       "      <td>10.061072</td>\n",
       "      <td>0.021816</td>\n",
       "      <td>38528.466050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>22</td>\n",
       "      <td>0.545843</td>\n",
       "      <td>63.197619</td>\n",
       "      <td>30.767836</td>\n",
       "      <td>16794.420416</td>\n",
       "      <td>16.794420</td>\n",
       "      <td>0.036416</td>\n",
       "      <td>36181.903970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>22</td>\n",
       "      <td>0.545843</td>\n",
       "      <td>68.049025</td>\n",
       "      <td>33.256145</td>\n",
       "      <td>18152.647182</td>\n",
       "      <td>18.152647</td>\n",
       "      <td>0.039361</td>\n",
       "      <td>39108.068089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>22</td>\n",
       "      <td>0.545843</td>\n",
       "      <td>72.108290</td>\n",
       "      <td>32.427431</td>\n",
       "      <td>17700.299299</td>\n",
       "      <td>17.700299</td>\n",
       "      <td>0.038380</td>\n",
       "      <td>38133.529687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>22</td>\n",
       "      <td>0.545843</td>\n",
       "      <td>62.827293</td>\n",
       "      <td>31.547230</td>\n",
       "      <td>17219.847299</td>\n",
       "      <td>17.219847</td>\n",
       "      <td>0.037339</td>\n",
       "      <td>37098.443764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>22</td>\n",
       "      <td>0.545843</td>\n",
       "      <td>52.485384</td>\n",
       "      <td>27.252360</td>\n",
       "      <td>14875.520774</td>\n",
       "      <td>14.875521</td>\n",
       "      <td>0.032255</td>\n",
       "      <td>32047.826054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>22</td>\n",
       "      <td>0.545843</td>\n",
       "      <td>41.555370</td>\n",
       "      <td>29.874007</td>\n",
       "      <td>16306.529720</td>\n",
       "      <td>16.306530</td>\n",
       "      <td>0.035358</td>\n",
       "      <td>35130.792121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>28</td>\n",
       "      <td>1.135443</td>\n",
       "      <td>40.857134</td>\n",
       "      <td>21.741812</td>\n",
       "      <td>24686.593134</td>\n",
       "      <td>24.686593</td>\n",
       "      <td>0.053529</td>\n",
       "      <td>15012.255633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>28</td>\n",
       "      <td>1.135443</td>\n",
       "      <td>35.142747</td>\n",
       "      <td>22.041710</td>\n",
       "      <td>25027.109947</td>\n",
       "      <td>25.027110</td>\n",
       "      <td>0.054267</td>\n",
       "      <td>15219.328574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>28</td>\n",
       "      <td>1.135443</td>\n",
       "      <td>28.904277</td>\n",
       "      <td>20.792543</td>\n",
       "      <td>23608.751366</td>\n",
       "      <td>23.608751</td>\n",
       "      <td>0.051192</td>\n",
       "      <td>14356.805281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>28</td>\n",
       "      <td>1.135443</td>\n",
       "      <td>29.596965</td>\n",
       "      <td>22.278169</td>\n",
       "      <td>25295.596268</td>\n",
       "      <td>25.295596</td>\n",
       "      <td>0.054850</td>\n",
       "      <td>15382.598785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>28</td>\n",
       "      <td>1.135443</td>\n",
       "      <td>30.697025</td>\n",
       "      <td>22.966680</td>\n",
       "      <td>26077.361444</td>\n",
       "      <td>26.077361</td>\n",
       "      <td>0.056545</td>\n",
       "      <td>15858.000904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>28</td>\n",
       "      <td>1.135443</td>\n",
       "      <td>24.305218</td>\n",
       "      <td>22.393432</td>\n",
       "      <td>25426.470552</td>\n",
       "      <td>25.426471</td>\n",
       "      <td>0.055133</td>\n",
       "      <td>15462.185231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>34</td>\n",
       "      <td>2.741957</td>\n",
       "      <td>8.869759</td>\n",
       "      <td>16.641612</td>\n",
       "      <td>45630.578906</td>\n",
       "      <td>45.630579</td>\n",
       "      <td>0.098943</td>\n",
       "      <td>17010.003178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>34</td>\n",
       "      <td>2.741957</td>\n",
       "      <td>7.743761</td>\n",
       "      <td>16.421591</td>\n",
       "      <td>45027.291837</td>\n",
       "      <td>45.027292</td>\n",
       "      <td>0.097635</td>\n",
       "      <td>16785.111993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>34</td>\n",
       "      <td>2.741957</td>\n",
       "      <td>6.229308</td>\n",
       "      <td>14.768288</td>\n",
       "      <td>40494.006605</td>\n",
       "      <td>40.494007</td>\n",
       "      <td>0.087805</td>\n",
       "      <td>15095.210220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>34</td>\n",
       "      <td>2.741957</td>\n",
       "      <td>7.686513</td>\n",
       "      <td>19.946206</td>\n",
       "      <td>54691.633219</td>\n",
       "      <td>54.691633</td>\n",
       "      <td>0.118590</td>\n",
       "      <td>20387.750434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>34</td>\n",
       "      <td>2.741957</td>\n",
       "      <td>8.246497</td>\n",
       "      <td>22.640067</td>\n",
       "      <td>62078.082209</td>\n",
       "      <td>62.078082</td>\n",
       "      <td>0.134607</td>\n",
       "      <td>23141.244336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>34</td>\n",
       "      <td>2.741957</td>\n",
       "      <td>8.464649</td>\n",
       "      <td>21.624041</td>\n",
       "      <td>59292.183974</td>\n",
       "      <td>59.292184</td>\n",
       "      <td>0.128566</td>\n",
       "      <td>22102.727207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>42</td>\n",
       "      <td>3.989672</td>\n",
       "      <td>4.566802</td>\n",
       "      <td>14.514626</td>\n",
       "      <td>57908.604058</td>\n",
       "      <td>57.908604</td>\n",
       "      <td>0.125566</td>\n",
       "      <td>19816.990919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>42</td>\n",
       "      <td>3.989672</td>\n",
       "      <td>4.982041</td>\n",
       "      <td>15.524771</td>\n",
       "      <td>61938.751397</td>\n",
       "      <td>61.938751</td>\n",
       "      <td>0.134305</td>\n",
       "      <td>21196.153732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>42</td>\n",
       "      <td>3.989672</td>\n",
       "      <td>4.937143</td>\n",
       "      <td>15.114733</td>\n",
       "      <td>60302.833702</td>\n",
       "      <td>60.302834</td>\n",
       "      <td>0.130757</td>\n",
       "      <td>20636.323864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>42</td>\n",
       "      <td>3.989672</td>\n",
       "      <td>5.036361</td>\n",
       "      <td>19.487515</td>\n",
       "      <td>77748.798995</td>\n",
       "      <td>77.748799</td>\n",
       "      <td>0.168586</td>\n",
       "      <td>26606.534016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>42</td>\n",
       "      <td>3.989672</td>\n",
       "      <td>4.614683</td>\n",
       "      <td>20.231838</td>\n",
       "      <td>80718.403532</td>\n",
       "      <td>80.718404</td>\n",
       "      <td>0.175025</td>\n",
       "      <td>27622.766873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>42</td>\n",
       "      <td>3.989672</td>\n",
       "      <td>6.858670</td>\n",
       "      <td>22.564280</td>\n",
       "      <td>90024.083792</td>\n",
       "      <td>90.024084</td>\n",
       "      <td>0.195203</td>\n",
       "      <td>30807.277779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>50</td>\n",
       "      <td>3.464529</td>\n",
       "      <td>2.356891</td>\n",
       "      <td>17.092448</td>\n",
       "      <td>59217.279820</td>\n",
       "      <td>59.217280</td>\n",
       "      <td>0.128404</td>\n",
       "      <td>20275.659232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>50</td>\n",
       "      <td>3.464529</td>\n",
       "      <td>2.392601</td>\n",
       "      <td>17.417848</td>\n",
       "      <td>60344.637625</td>\n",
       "      <td>60.344638</td>\n",
       "      <td>0.130848</td>\n",
       "      <td>20661.660122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>50</td>\n",
       "      <td>3.464529</td>\n",
       "      <td>2.898018</td>\n",
       "      <td>15.856315</td>\n",
       "      <td>54934.661076</td>\n",
       "      <td>54.934661</td>\n",
       "      <td>0.119117</td>\n",
       "      <td>18809.314974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>50</td>\n",
       "      <td>3.464529</td>\n",
       "      <td>2.435506</td>\n",
       "      <td>20.592018</td>\n",
       "      <td>71341.639092</td>\n",
       "      <td>71.341639</td>\n",
       "      <td>0.154693</td>\n",
       "      <td>24426.970771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>50</td>\n",
       "      <td>3.464529</td>\n",
       "      <td>2.592857</td>\n",
       "      <td>23.923623</td>\n",
       "      <td>82884.082487</td>\n",
       "      <td>82.884082</td>\n",
       "      <td>0.179721</td>\n",
       "      <td>28379.037628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>50</td>\n",
       "      <td>3.464529</td>\n",
       "      <td>1.894958</td>\n",
       "      <td>21.947817</td>\n",
       "      <td>76038.846301</td>\n",
       "      <td>76.038846</td>\n",
       "      <td>0.164878</td>\n",
       "      <td>26035.267758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>52</td>\n",
       "      <td>3.897394</td>\n",
       "      <td>4.249138</td>\n",
       "      <td>18.221248</td>\n",
       "      <td>71015.376394</td>\n",
       "      <td>71.015376</td>\n",
       "      <td>0.153986</td>\n",
       "      <td>24339.335877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>52</td>\n",
       "      <td>3.897394</td>\n",
       "      <td>4.451853</td>\n",
       "      <td>18.066183</td>\n",
       "      <td>70411.028732</td>\n",
       "      <td>70.411029</td>\n",
       "      <td>0.152675</td>\n",
       "      <td>24132.205795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>52</td>\n",
       "      <td>3.897394</td>\n",
       "      <td>4.992085</td>\n",
       "      <td>16.106233</td>\n",
       "      <td>62772.331347</td>\n",
       "      <td>62.772331</td>\n",
       "      <td>0.136112</td>\n",
       "      <td>21514.169663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>52</td>\n",
       "      <td>3.897394</td>\n",
       "      <td>5.405575</td>\n",
       "      <td>15.691223</td>\n",
       "      <td>61154.874887</td>\n",
       "      <td>61.154875</td>\n",
       "      <td>0.132605</td>\n",
       "      <td>20959.813437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>52</td>\n",
       "      <td>3.897394</td>\n",
       "      <td>5.978356</td>\n",
       "      <td>11.997713</td>\n",
       "      <td>46759.809903</td>\n",
       "      <td>46.759810</td>\n",
       "      <td>0.101391</td>\n",
       "      <td>16026.144992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>52</td>\n",
       "      <td>3.897394</td>\n",
       "      <td>4.517289</td>\n",
       "      <td>12.931573</td>\n",
       "      <td>50399.430917</td>\n",
       "      <td>50.399431</td>\n",
       "      <td>0.109283</td>\n",
       "      <td>17273.564394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>59</td>\n",
       "      <td>3.288099</td>\n",
       "      <td>1.688344</td>\n",
       "      <td>16.836805</td>\n",
       "      <td>55361.083468</td>\n",
       "      <td>55.361083</td>\n",
       "      <td>0.120042</td>\n",
       "      <td>19022.494612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>59</td>\n",
       "      <td>3.288099</td>\n",
       "      <td>1.463520</td>\n",
       "      <td>16.895076</td>\n",
       "      <td>55552.684982</td>\n",
       "      <td>55.552685</td>\n",
       "      <td>0.120457</td>\n",
       "      <td>19088.330367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>59</td>\n",
       "      <td>3.288099</td>\n",
       "      <td>1.279046</td>\n",
       "      <td>15.212472</td>\n",
       "      <td>50020.116346</td>\n",
       "      <td>50.020116</td>\n",
       "      <td>0.108461</td>\n",
       "      <td>17187.297178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>59</td>\n",
       "      <td>3.288099</td>\n",
       "      <td>1.453314</td>\n",
       "      <td>17.999947</td>\n",
       "      <td>59185.609333</td>\n",
       "      <td>59.185609</td>\n",
       "      <td>0.128335</td>\n",
       "      <td>20336.631152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>59</td>\n",
       "      <td>3.288099</td>\n",
       "      <td>1.512404</td>\n",
       "      <td>18.504074</td>\n",
       "      <td>60843.231264</td>\n",
       "      <td>60.843231</td>\n",
       "      <td>0.131929</td>\n",
       "      <td>20906.202812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>59</td>\n",
       "      <td>3.288099</td>\n",
       "      <td>1.502690</td>\n",
       "      <td>18.956771</td>\n",
       "      <td>62331.741996</td>\n",
       "      <td>62.331742</td>\n",
       "      <td>0.135157</td>\n",
       "      <td>21417.666563</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    DPA (days)  fruit DW (g)  STARCH (umol/gDW)  PROTEIN (ug/mgDW)  \\\n",
       "0            8      0.032833         128.024985          98.790388   \n",
       "1            8      0.032833         142.608028         100.887399   \n",
       "2            8      0.032833         156.660539          99.559834   \n",
       "3            8      0.032833         133.544310         103.930644   \n",
       "4            8      0.032833         120.220306         108.911064   \n",
       "5            8      0.032833         208.173335         100.532013   \n",
       "6           15      0.231085          80.307639          50.137586   \n",
       "7           15      0.231085          81.617257          51.457765   \n",
       "8           15      0.231085          82.275683          49.365831   \n",
       "9           15      0.231085          84.364324          47.081455   \n",
       "10          15      0.231085          90.058238          43.771808   \n",
       "11          15      0.231085          71.608995          43.538392   \n",
       "12          22      0.545843          63.197619          30.767836   \n",
       "13          22      0.545843          68.049025          33.256145   \n",
       "14          22      0.545843          72.108290          32.427431   \n",
       "15          22      0.545843          62.827293          31.547230   \n",
       "16          22      0.545843          52.485384          27.252360   \n",
       "17          22      0.545843          41.555370          29.874007   \n",
       "18          28      1.135443          40.857134          21.741812   \n",
       "19          28      1.135443          35.142747          22.041710   \n",
       "20          28      1.135443          28.904277          20.792543   \n",
       "21          28      1.135443          29.596965          22.278169   \n",
       "22          28      1.135443          30.697025          22.966680   \n",
       "23          28      1.135443          24.305218          22.393432   \n",
       "24          34      2.741957           8.869759          16.641612   \n",
       "25          34      2.741957           7.743761          16.421591   \n",
       "26          34      2.741957           6.229308          14.768288   \n",
       "27          34      2.741957           7.686513          19.946206   \n",
       "28          34      2.741957           8.246497          22.640067   \n",
       "29          34      2.741957           8.464649          21.624041   \n",
       "30          42      3.989672           4.566802          14.514626   \n",
       "31          42      3.989672           4.982041          15.524771   \n",
       "32          42      3.989672           4.937143          15.114733   \n",
       "33          42      3.989672           5.036361          19.487515   \n",
       "34          42      3.989672           4.614683          20.231838   \n",
       "35          42      3.989672           6.858670          22.564280   \n",
       "36          50      3.464529           2.356891          17.092448   \n",
       "37          50      3.464529           2.392601          17.417848   \n",
       "38          50      3.464529           2.898018          15.856315   \n",
       "39          50      3.464529           2.435506          20.592018   \n",
       "40          50      3.464529           2.592857          23.923623   \n",
       "41          50      3.464529           1.894958          21.947817   \n",
       "42          52      3.897394           4.249138          18.221248   \n",
       "43          52      3.897394           4.451853          18.066183   \n",
       "44          52      3.897394           4.992085          16.106233   \n",
       "45          52      3.897394           5.405575          15.691223   \n",
       "46          52      3.897394           5.978356          11.997713   \n",
       "47          52      3.897394           4.517289          12.931573   \n",
       "48          59      3.288099           1.688344          16.836805   \n",
       "49          59      3.288099           1.463520          16.895076   \n",
       "50          59      3.288099           1.279046          15.212472   \n",
       "51          59      3.288099           1.453314          17.999947   \n",
       "52          59      3.288099           1.512404          18.504074   \n",
       "53          59      3.288099           1.502690          18.956771   \n",
       "\n",
       "    PROTEIN (ug/fruit)  PROTEIN (mg/fruit)  PROTEIN (mmol/fruit)  \\\n",
       "0          3243.549841            3.243550              0.007033   \n",
       "1          3312.400278            3.312400              0.007182   \n",
       "2          3268.812822            3.268813              0.007088   \n",
       "3          3412.318068            3.412318              0.007399   \n",
       "4          3575.838420            3.575838              0.007754   \n",
       "5          3300.732024            3.300732              0.007157   \n",
       "6         11586.046744           11.586047              0.025123   \n",
       "7         11891.120524           11.891121              0.025784   \n",
       "8         11407.705820           11.407706              0.024736   \n",
       "9         10879.820624           10.879821              0.023591   \n",
       "10        10115.010603           10.115011              0.021933   \n",
       "11        10061.071629           10.061072              0.021816   \n",
       "12        16794.420416           16.794420              0.036416   \n",
       "13        18152.647182           18.152647              0.039361   \n",
       "14        17700.299299           17.700299              0.038380   \n",
       "15        17219.847299           17.219847              0.037339   \n",
       "16        14875.520774           14.875521              0.032255   \n",
       "17        16306.529720           16.306530              0.035358   \n",
       "18        24686.593134           24.686593              0.053529   \n",
       "19        25027.109947           25.027110              0.054267   \n",
       "20        23608.751366           23.608751              0.051192   \n",
       "21        25295.596268           25.295596              0.054850   \n",
       "22        26077.361444           26.077361              0.056545   \n",
       "23        25426.470552           25.426471              0.055133   \n",
       "24        45630.578906           45.630579              0.098943   \n",
       "25        45027.291837           45.027292              0.097635   \n",
       "26        40494.006605           40.494007              0.087805   \n",
       "27        54691.633219           54.691633              0.118590   \n",
       "28        62078.082209           62.078082              0.134607   \n",
       "29        59292.183974           59.292184              0.128566   \n",
       "30        57908.604058           57.908604              0.125566   \n",
       "31        61938.751397           61.938751              0.134305   \n",
       "32        60302.833702           60.302834              0.130757   \n",
       "33        77748.798995           77.748799              0.168586   \n",
       "34        80718.403532           80.718404              0.175025   \n",
       "35        90024.083792           90.024084              0.195203   \n",
       "36        59217.279820           59.217280              0.128404   \n",
       "37        60344.637625           60.344638              0.130848   \n",
       "38        54934.661076           54.934661              0.119117   \n",
       "39        71341.639092           71.341639              0.154693   \n",
       "40        82884.082487           82.884082              0.179721   \n",
       "41        76038.846301           76.038846              0.164878   \n",
       "42        71015.376394           71.015376              0.153986   \n",
       "43        70411.028732           70.411029              0.152675   \n",
       "44        62772.331347           62.772331              0.136112   \n",
       "45        61154.874887           61.154875              0.132605   \n",
       "46        46759.809903           46.759810              0.101391   \n",
       "47        50399.430917           50.399431              0.109283   \n",
       "48        55361.083468           55.361083              0.120042   \n",
       "49        55552.684982           55.552685              0.120457   \n",
       "50        50020.116346           50.020116              0.108461   \n",
       "51        59185.609333           59.185609              0.128335   \n",
       "52        60843.231264           60.843231              0.131929   \n",
       "53        62331.741996           62.331742              0.135157   \n",
       "\n",
       "    PROTEIN (mmol/m3)  \n",
       "0         7070.750829  \n",
       "1         7220.840795  \n",
       "2         7125.822664  \n",
       "3         7438.655790  \n",
       "4         7795.120690  \n",
       "5         7195.404677  \n",
       "6        44368.296448  \n",
       "7        45536.564125  \n",
       "8        43685.347109  \n",
       "9        41663.832143  \n",
       "10       38735.023166  \n",
       "11       38528.466050  \n",
       "12       36181.903970  \n",
       "13       39108.068089  \n",
       "14       38133.529687  \n",
       "15       37098.443764  \n",
       "16       32047.826054  \n",
       "17       35130.792121  \n",
       "18       15012.255633  \n",
       "19       15219.328574  \n",
       "20       14356.805281  \n",
       "21       15382.598785  \n",
       "22       15858.000904  \n",
       "23       15462.185231  \n",
       "24       17010.003178  \n",
       "25       16785.111993  \n",
       "26       15095.210220  \n",
       "27       20387.750434  \n",
       "28       23141.244336  \n",
       "29       22102.727207  \n",
       "30       19816.990919  \n",
       "31       21196.153732  \n",
       "32       20636.323864  \n",
       "33       26606.534016  \n",
       "34       27622.766873  \n",
       "35       30807.277779  \n",
       "36       20275.659232  \n",
       "37       20661.660122  \n",
       "38       18809.314974  \n",
       "39       24426.970771  \n",
       "40       28379.037628  \n",
       "41       26035.267758  \n",
       "42       24339.335877  \n",
       "43       24132.205795  \n",
       "44       21514.169663  \n",
       "45       20959.813437  \n",
       "46       16026.144992  \n",
       "47       17273.564394  \n",
       "48       19022.494612  \n",
       "49       19088.330367  \n",
       "50       17187.297178  \n",
       "51       20336.631152  \n",
       "52       20906.202812  \n",
       "53       21417.666563  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df_protein = pd.read_csv(\"Data/Data_Starch_Protein_oirginal.csv\",sep=\"\\t\")\n",
    "MW = 461.181100469\n",
    "temp = list()\n",
    "for i in range(0,len(df_protein[\"DPA (days)\"])):\n",
    "    temp.append(df_protein[\"PROTEIN (ug/mgDW)\"][i]*df_protein[\"fruit DW (g)\"][i]*1000)\n",
    "df_protein[\"PROTEIN (ug/fruit)\"]=temp\n",
    "\n",
    "temp = list()\n",
    "for i in range(0,len(df_protein[\"DPA (days)\"])):\n",
    "    temp.append(df_protein[\"PROTEIN (ug/fruit)\"][i]/1000)\n",
    "df_protein[\"PROTEIN (mg/fruit)\"]=temp\n",
    "\n",
    "temp = list()\n",
    "for i in range(0,len(df_protein[\"DPA (days)\"])):\n",
    "    temp.append(df_protein[\"PROTEIN (mg/fruit)\"][i]/MW)\n",
    "df_protein[\"PROTEIN (mmol/fruit)\"]=temp\n",
    "\n",
    "temp = list()\n",
    "for i in range(0,len(df_protein[\"DPA (days)\"])):\n",
    "    temp.append(df_protein[\"PROTEIN (mmol/fruit)\"][i]/float(estimateVcyt(df_protein[\"DPA (days)\"][i])))\n",
    "df_protein[\"PROTEIN (mmol/m3)\"]=temp\n",
    "df_protein"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "xlist3 = list()\n",
    "ylist3 = list()\n",
    "for i in range(8,60):\n",
    "    xlist3.append(i)\n",
    "    ylist3.append(estimateProteinContent(i,ProtConc=19659.7704473,Ncell = 25*(10**6)))\n",
    "    \n",
    "SSE = 0\n",
    "for i in range(0,len(df_protein[\"DPA (days)\"])):\n",
    "    SSE=SSE+((ylist3[df_protein[\"DPA (days)\"][i]-8]-df_protein[\"PROTEIN (mmol/fruit)\"][i])**2)\n",
    "MSE = float(SSE)/len(df_protein[\"DPA (days)\"])\n",
    "RMSE = (MSE)**0.5\n",
    "RMSE_protein = RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x1080 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams.update({'font.size': 20}) #sets a global fontsize\n",
    "plt.rcParams['xtick.major.size'] = 5 # adjusts tick line length and width\n",
    "plt.rcParams['xtick.major.width'] = 1\n",
    "plt.rcParams['ytick.major.size'] = 5\n",
    "plt.rcParams['ytick.major.width'] = 1\n",
    "plt.rcParams['axes.linewidth']=3 # makes axes line thicker\n",
    "plt.figure(figsize=(6,15))\n",
    "\n",
    "ax1 = plt.subplot(3,1,1)\n",
    "ax1.scatter(df_cellulose[\"DPA\"],df_cellulose[\"CELLULOSE\"],label=\"Measured\",color=\"black\")\n",
    "ax1.plot(xlist,ylist,\"k--\",label=\"Predicted\")\n",
    "#ax1.set_xlabel('DPA', fontsize=16)\n",
    "ax1.set_ylabel('cellulose content (mmol/fruit)', fontsize=20)\n",
    "ax1.legend(loc=\"upper left\",fontsize=16)\n",
    "ax1.text(8,3.8,\"RMSD=\"+str(round(RMSE_cellulose,3)),fontsize=14)\n",
    "\n",
    "ax2 = plt.subplot(3,1,2)\n",
    "ax2.scatter(df_lipid[\"DPA (days)\"],df_lipid[\"Lipids (mmol/fruit)\"],label=\"Measured\",color=\"black\")\n",
    "ax2.plot(xlist2,ylist2,\"k--\",label=\"Predicted\")\n",
    "#ax2.set_xlabel('DPA', fontsize=16)\n",
    "ax2.set_ylabel('lipid content (mmol/fruit)', fontsize=20)\n",
    "ax2.legend(loc=\"upper left\",fontsize=16)\n",
    "ax2.text(8,0.28,\"RMSD=\"+str(round(RMSE_lipid,3)),fontsize=14)\n",
    "\n",
    "ax3 = plt.subplot(3,1,3)\n",
    "ax3.scatter(df_protein[\"DPA (days)\"],df_protein[\"PROTEIN (mmol/fruit)\"],color=\"black\",label=\"Measured\")\n",
    "ax3.plot(xlist3,ylist3,\"k--\",label=\"Predicted\")\n",
    "ax3.set_xlabel('DPA', fontsize=20)\n",
    "ax3.set_ylabel(\"protein content (mmol/fruit)\", fontsize=20)\n",
    "ax3.legend(loc=\"upper left\",fontsize=16)\n",
    "ax3.text(8,0.155,\"RMSD=\"+str(round(RMSE_protein,3)),fontsize=14)\n",
    "\n",
    "plt.legend(loc=\"upper left\",fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}