_correlations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8df54ef0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9e49ade7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\flammmes\\AppData\\Local\\Temp/ipykernel_6480/2702052333.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df.Rsob[df.Rsob<0] = 0\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('D:\\\\Users\\\\flammmes\\\\Downloads\\\\Book1.csv')\n",
    "df['d']=df['d'].str.replace(',','')\n",
    "df['Rsob']=df['Rsob'].str.replace(',','')\n",
    "dfn = pd.read_csv('D:\\\\Users\\\\flammmes\\\\Downloads\\\\Fluids data.csv')\n",
    "\n",
    "\n",
    "dd = df.d.astype('float')\n",
    "df.d = df.d.astype('float')\n",
    "df['Rsob']= df['Rsob'].astype('float')\n",
    "df.Rsob[df.Rsob<0] = 0\n",
    "#df['Rsob']= np.abs(df['Rsob'])\n",
    "x = df.iloc[:,1]/df.iloc[:,8]\n",
    "mu = 1000*(df.iloc[:,3]*x*x+df.iloc[:,4]*x+df.iloc[:,5])/(df.iloc[:,6]*x+1)\n",
    "df.insert(9,'μob',mu)\n",
    "df.insert(12,'Tr',dfn.Tres)\n",
    "\n",
    "df = df[df['μob']<=50] #50,338"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a7eeec49",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_func(a):\n",
    "    \n",
    "    dflist = []\n",
    "    for i in range(len(a)):\n",
    "        \n",
    "        correlations_select = pd.DataFrame({'P':P_i,'μ':mu_i,'Rsob':instance[10],'API':instance[11],'Tr':instance[12]})\n",
    "        pressure_bubble_point = correlations_select.iloc[0,0]*np.linspace(1,1,20)\n",
    "        visc_bubble_point = correlations_select.iloc[0,1]*np.linspace(1,1,20)\n",
    "        correlations_select.insert(0,\"Pb\",pressure_bubble_point)\n",
    "        correlations_select.insert(2,'μob',visc_bubble_point)\n",
    "        dflist.append(correlations_select)\n",
    "        \n",
    "    \n",
    "    totaldf = pd.concat(dflist)\n",
    "    totaldf.Pb = totaldf.Pb*145.038\n",
    "    totaldf.P = totaldf.P*145.038\n",
    "    totaldf.insert(2,'ΔP', totaldf.P-totaldf.Pb)\n",
    "    \n",
    "    totaldf.API = 141.5*1000/totaldf.API -131.5\n",
    "    totaldf.Rsob = totaldf.Rsob*5.614583333333333\n",
    "    #totaldf.drop('P',axis=1,inplace=True)\n",
    "    return totaldf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b404a704",
   "metadata": {},
   "outputs": [],
   "source": [
    "totaldf =gen_func(df)\n",
    "totaldf1 = totaldf[totaldf.Tr.notnull().values & totaldf.API.notnull().values]\n",
    "totaldf2 = totaldf[totaldf.API.notnull().values]\n",
    "totaldf3 = totaldf[totaldf.Rsob.notnull().values & totaldf.API.notnull().values]\n",
    "totaldf4 = totaldf[totaldf.Rsob.notnull().values]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "57adf35a",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 10.313*np.log10(totaldf.Tr)-36.447\n",
    "μod = 3.141*np.power(10,10.0)*np.power(totaldf.Tr,-3.444)*np.power(np.log10(totaldf.API),a)\n",
    "\n",
    "#a = np.power(10,3.0324-0.02023)*totaldf.API\n",
    "#μod = np.power(10,a*np.power(totaldf.Tr,-1.163))-1\n",
    "#a=np.power(10,0.42+8.33/totaldf.API)\n",
    "#μod = 0.32+18*np.power(10,7.0)/np.power(totaldf.API,4.53)*np.power(360/(totaldf.Tr+200),a)\n",
    "totaldf.insert(8,'μod',μod)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f45644c5",
   "metadata": {
    "scrolled": true
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   "outputs": [
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       "      <th></th>\n",
       "      <th>Pb</th>\n",
       "      <th>P</th>\n",
       "      <th>ΔP</th>\n",
       "      <th>μob</th>\n",
       "      <th>μ</th>\n",
       "      <th>Rsob</th>\n",
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       "      <td>2727.638063</td>\n",
       "      <td>0.578429</td>\n",
       "      <td>0.628827</td>\n",
       "      <td>1363.922656</td>\n",
       "      <td>34.424015</td>\n",
       "      <td>388.706</td>\n",
       "      <td>0.576164</td>\n",
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       "    <tr>\n",
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       "      <td>0.578429</td>\n",
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       "    <tr>\n",
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       "             Pb            P           ΔP       μob         μ         Rsob  \\\n",
       "0   1225.002551  1225.002551     0.000000  2.373815  2.373815          NaN   \n",
       "1   1225.002551  1424.476671   199.474120  2.373815  2.403167          NaN   \n",
       "2   1225.002551  1623.950791   398.948240  2.373815  2.448781          NaN   \n",
       "3   1225.002551  1823.424912   598.422361  2.373815  2.497706          NaN   \n",
       "4   1225.002551  2022.899032   797.896481  2.373815  2.547857          NaN   \n",
       "..          ...          ...          ...       ...       ...          ...   \n",
       "15  4560.009224  7287.647287  2727.638063  0.578429  0.628827  1363.922656   \n",
       "16  4560.009224  7469.489824  2909.480601  0.578429  0.633034  1363.922656   \n",
       "17  4560.009224  7651.332362  3091.323138  0.578429  0.637304  1363.922656   \n",
       "18  4560.009224  7833.174899  3273.165676  0.578429  0.641632  1363.922656   \n",
       "19  4560.009224  8015.017437  3455.008213  0.578429  0.646015  1363.922656   \n",
       "\n",
       "          API       Tr       μod  \n",
       "0   32.155010  365.150  0.769950  \n",
       "1   32.155010  365.150  0.769950  \n",
       "2   32.155010  365.150  0.769950  \n",
       "3   32.155010  365.150  0.769950  \n",
       "4   32.155010  365.150  0.769950  \n",
       "..        ...      ...       ...  \n",
       "15  34.424015  388.706  0.576164  \n",
       "16  34.424015  388.706  0.576164  \n",
       "17  34.424015  388.706  0.576164  \n",
       "18  34.424015  388.706  0.576164  \n",
       "19  34.424015  388.706  0.576164  \n",
       "\n",
       "[11420 rows x 9 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totaldf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3ee27b62",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\flammmes\\AppData\\Roaming\\Python\\Python310\\site-packages\\pandas\\core\\arraylike.py:364: RuntimeWarning: divide by zero encountered in log10\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n",
      "C:\\Users\\flammmes\\AppData\\Roaming\\Python\\Python310\\site-packages\\pandas\\core\\arraylike.py:364: RuntimeWarning: divide by zero encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "x=totaldf.copy()\n",
    "x.insert(0,'m',np.nan)\n",
    "        #Labedi Libya(1982)\n",
    "mu1 = x.μob +np.power(x.μod,0.9036)*np.power(x.Pb,0.6151)/np.power(10,2.488+0.01976*x.API)*(x.P/x.Pb-1)\n",
    "        #Labedi Nigeria(1982)\n",
    "mu2 = x.μob+0.0483*np.power(x.μod,0.7374)*(x.P/x.Pb-1)\n",
    "        #Al-Khafaji(1987)\n",
    "X = -0.3806-0.1845*x.API+0.004034*np.power(x.API,2.0)-3.716*np.power(10,-5.0)*np.power(x.API,3.0)\n",
    "mu3 = x.μob +np.power(10,X+1.11*np.log10(0.07031*(x.P-x.Pb)))\n",
    "        #Abdul-Majeed(1990)\n",
    "X = 1.9311-0.89941*np.log(x.Rsob)-0.001194*np.power(x.API,2.0)+0.0092545*x.API*np.log(x.Rsob)\n",
    "mu4 = x.μob+1000*np.power(10,X-5.2106+1.11*np.log10(6.894757*(x.P-x.Pb)))\n",
    "        #De Ghetto(1994)\n",
    "x.m[x.API<=10] = x.μob[x.API<=10]-(1-x.P[x.API<=10]/x.Pb[x.API<=10])*np.power(10,-2.19)*np.power(x.μod[x.API<=10],1.055)*np.power(x.Pb[x.API<=10],0.3132)/np.power(10,0.0099*x.API[x.API<=10])\n",
    "x.m[(10<=x.API) & (x.API<=22.3)]  = 0.9886*x.μob[(10<=x.API) & (x.API<=22.3)]+0.002763*(x.P[(10<=x.API) & (x.API<=22.3)]-x.Pb[(10<=x.API) & (x.API<=22.3)])*(-0.01153*np.power(x.μob[(10<=x.API) & (x.API<=22.3)],1.7933)+0.0316*np.power(x.μob[(10<=x.API) & (x.API<=22.3)],1.5939))\n",
    "x.m[(22.3<=x.API) & (x.API<=31.1)] = x.μob[(22.3<=x.API) & (x.API<=31.1)]-(1-x.P[(22.3<=x.API) & (x.API<=31.1)]/x.Pb[(22.3<=x.API) & (x.API<=31.1)])*np.power(10,-3.8055)*np.power(x.Pb[(22.3<=x.API) & (x.API<=31.1)],0.6957)*np.power(x.μod[(22.3<=x.API) & (x.API<=31.1)],1.4131)/np.power(10,-0.00288*x.API[(22.3<=x.API) & (x.API<=31.1)])\n",
    "#x.m[x.API>=31.1] = x.μob[x.API>=31.1]-(1-x.P[x.API>=31.1]/x.Pb[x.API>=31.1])*np.power(10,-1.9)*np.power(x.Pb[x.API>=31.1],0.5026)*np.power(x.μod[x.API>=31.1],0.7423)/np.power(10,-0.00243*x.API[x.API>=31.1])\n",
    "mu5 = x.m\n",
    "        #Almehaideb(1997)\n",
    "mu6 = x.μob*np.power(x.P/x.Pb,0.134819+1.94345*np.power(10,-4.0)*x.Rsob-1.93106*np.power(10,-9.0)*np.power(x.Rsob,2))\n",
    "        #Elsharkawy(1999)\n",
    "mu7 = x.μob+np.power(10,-2.0771)*(x.P-x.Pb)*np.power(x.μod,1.19279)/np.power(x.μob,0.40712)/np.power(x.Pb,0.7941)\n",
    "        #Dindoruk and Christman(2001)\n",
    "X = 0.776644115+0.987658646*np.log10(x.μob)-0.190564677*np.log10(x.Rsob)+0.009147711*x.μob*np.log10(x.Rsob)-0.000019111*(x.P-x.Pb)\n",
    "mu8 = x.μob +0.00006334*x.ΔP*np.power(10,X)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c0d1e024",
   "metadata": {},
   "outputs": [],
   "source": [
    "totaldf = pd.DataFrame({'P':x['P'],'μ':x['μ'],'Pb':x.Pb,'μob':x.μob,'μod':x.μod,'μ1':mu1,\n",
    "                        'μ2':mu2,'μ3':mu3,'μ4':mu4,'μ5':mu5,'μ6':mu6,'μ7':mu7,'μ8':mu8,'Rs':x.Rsob,'API':x.API })\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "57a5843f",
   "metadata": {},
   "outputs": [
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       "      <td>NaN</td>\n",
       "      <td>2.376851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.155010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1623.950791</td>\n",
       "      <td>2.448781</td>\n",
       "      <td>1225.002551</td>\n",
       "      <td>2.373815</td>\n",
       "      <td>0.769950</td>\n",
       "      <td>2.389173</td>\n",
       "      <td>2.386787</td>\n",
       "      <td>2.390778</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.379886</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.155010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1823.424912</td>\n",
       "      <td>2.497706</td>\n",
       "      <td>1225.002551</td>\n",
       "      <td>2.373815</td>\n",
       "      <td>0.769950</td>\n",
       "      <td>2.396851</td>\n",
       "      <td>2.393273</td>\n",
       "      <td>2.400420</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.382921</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.155010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022.899032</td>\n",
       "      <td>2.547857</td>\n",
       "      <td>1225.002551</td>\n",
       "      <td>2.373815</td>\n",
       "      <td>0.769950</td>\n",
       "      <td>2.404530</td>\n",
       "      <td>2.399759</td>\n",
       "      <td>2.410429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.385957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.155010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7287.647287</td>\n",
       "      <td>0.628827</td>\n",
       "      <td>4560.009224</td>\n",
       "      <td>0.578429</td>\n",
       "      <td>0.576164</td>\n",
       "      <td>0.622369</td>\n",
       "      <td>0.597668</td>\n",
       "      <td>0.694991</td>\n",
       "      <td>0.650465</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.696538</td>\n",
       "      <td>0.596806</td>\n",
       "      <td>0.718507</td>\n",
       "      <td>1363.922656</td>\n",
       "      <td>34.424015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7469.489824</td>\n",
       "      <td>0.633034</td>\n",
       "      <td>4560.009224</td>\n",
       "      <td>0.578429</td>\n",
       "      <td>0.576164</td>\n",
       "      <td>0.625298</td>\n",
       "      <td>0.598951</td>\n",
       "      <td>0.703648</td>\n",
       "      <td>0.655815</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.703374</td>\n",
       "      <td>0.598031</td>\n",
       "      <td>0.726655</td>\n",
       "      <td>1363.922656</td>\n",
       "      <td>34.424015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7651.332362</td>\n",
       "      <td>0.637304</td>\n",
       "      <td>4560.009224</td>\n",
       "      <td>0.578429</td>\n",
       "      <td>0.576164</td>\n",
       "      <td>0.628227</td>\n",
       "      <td>0.600233</td>\n",
       "      <td>0.712364</td>\n",
       "      <td>0.661202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.710111</td>\n",
       "      <td>0.599256</td>\n",
       "      <td>0.734664</td>\n",
       "      <td>1363.922656</td>\n",
       "      <td>34.424015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>7833.174899</td>\n",
       "      <td>0.641632</td>\n",
       "      <td>4560.009224</td>\n",
       "      <td>0.578429</td>\n",
       "      <td>0.576164</td>\n",
       "      <td>0.631157</td>\n",
       "      <td>0.601516</td>\n",
       "      <td>0.721137</td>\n",
       "      <td>0.666623</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.716752</td>\n",
       "      <td>0.600481</td>\n",
       "      <td>0.742536</td>\n",
       "      <td>1363.922656</td>\n",
       "      <td>34.424015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8015.017437</td>\n",
       "      <td>0.646015</td>\n",
       "      <td>4560.009224</td>\n",
       "      <td>0.578429</td>\n",
       "      <td>0.576164</td>\n",
       "      <td>0.634086</td>\n",
       "      <td>0.602799</td>\n",
       "      <td>0.729964</td>\n",
       "      <td>0.672078</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.723300</td>\n",
       "      <td>0.601706</td>\n",
       "      <td>0.750272</td>\n",
       "      <td>1363.922656</td>\n",
       "      <td>34.424015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11420 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              P         μ           Pb       μob       μod        μ1  \\\n",
       "0   1225.002551  2.373815  1225.002551  2.373815  0.769950  2.373815   \n",
       "1   1424.476671  2.403167  1225.002551  2.373815  0.769950  2.381494   \n",
       "2   1623.950791  2.448781  1225.002551  2.373815  0.769950  2.389173   \n",
       "3   1823.424912  2.497706  1225.002551  2.373815  0.769950  2.396851   \n",
       "4   2022.899032  2.547857  1225.002551  2.373815  0.769950  2.404530   \n",
       "..          ...       ...          ...       ...       ...       ...   \n",
       "15  7287.647287  0.628827  4560.009224  0.578429  0.576164  0.622369   \n",
       "16  7469.489824  0.633034  4560.009224  0.578429  0.576164  0.625298   \n",
       "17  7651.332362  0.637304  4560.009224  0.578429  0.576164  0.628227   \n",
       "18  7833.174899  0.641632  4560.009224  0.578429  0.576164  0.631157   \n",
       "19  8015.017437  0.646015  4560.009224  0.578429  0.576164  0.634086   \n",
       "\n",
       "          μ2        μ3        μ4  μ5        μ6        μ7        μ8  \\\n",
       "0   2.373815  2.373815       NaN NaN  2.373815  2.373815       NaN   \n",
       "1   2.380301  2.381674       NaN NaN       NaN  2.376851       NaN   \n",
       "2   2.386787  2.390778       NaN NaN       NaN  2.379886       NaN   \n",
       "3   2.393273  2.400420       NaN NaN       NaN  2.382921       NaN   \n",
       "4   2.399759  2.410429       NaN NaN       NaN  2.385957       NaN   \n",
       "..       ...       ...       ...  ..       ...       ...       ...   \n",
       "15  0.597668  0.694991  0.650465 NaN  0.696538  0.596806  0.718507   \n",
       "16  0.598951  0.703648  0.655815 NaN  0.703374  0.598031  0.726655   \n",
       "17  0.600233  0.712364  0.661202 NaN  0.710111  0.599256  0.734664   \n",
       "18  0.601516  0.721137  0.666623 NaN  0.716752  0.600481  0.742536   \n",
       "19  0.602799  0.729964  0.672078 NaN  0.723300  0.601706  0.750272   \n",
       "\n",
       "             Rs        API  \n",
       "0           NaN  32.155010  \n",
       "1           NaN  32.155010  \n",
       "2           NaN  32.155010  \n",
       "3           NaN  32.155010  \n",
       "4           NaN  32.155010  \n",
       "..          ...        ...  \n",
       "15  1363.922656  34.424015  \n",
       "16  1363.922656  34.424015  \n",
       "17  1363.922656  34.424015  \n",
       "18  1363.922656  34.424015  \n",
       "19  1363.922656  34.424015  \n",
       "\n",
       "[11420 rows x 15 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totaldf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "64c0e47e",
   "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>% AE</th>\n",
       "      <th>% Std Dev AE</th>\n",
       "      <th>%AAE</th>\n",
       "      <th>% Std Dev AAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Labedi Libya(1982)</th>\n",
       "      <td>-7.505937</td>\n",
       "      <td>8.274091</td>\n",
       "      <td>7.833435</td>\n",
       "      <td>7.964732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Labedi Nigeria(1982)</th>\n",
       "      <td>-8.106470</td>\n",
       "      <td>9.188428</td>\n",
       "      <td>8.847940</td>\n",
       "      <td>8.476793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Al-Khafaji(1987)</th>\n",
       "      <td>-4.325842</td>\n",
       "      <td>12.090407</td>\n",
       "      <td>7.297692</td>\n",
       "      <td>10.565725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abdul-Majeed(1990)</th>\n",
       "      <td>18.332503</td>\n",
       "      <td>159.168463</td>\n",
       "      <td>24.048028</td>\n",
       "      <td>158.405722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>De Ghetto(1994)</th>\n",
       "      <td>-5.474282</td>\n",
       "      <td>9.778127</td>\n",
       "      <td>7.998285</td>\n",
       "      <td>7.849011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Almehaideb(1997)</th>\n",
       "      <td>2.865199</td>\n",
       "      <td>11.486479</td>\n",
       "      <td>6.642839</td>\n",
       "      <td>9.799044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elsharkawy(1999)</th>\n",
       "      <td>-9.271448</td>\n",
       "      <td>8.779672</td>\n",
       "      <td>9.307459</td>\n",
       "      <td>8.741487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dindoruk and Christman(2001)</th>\n",
       "      <td>5.500524</td>\n",
       "      <td>12.958600</td>\n",
       "      <td>7.129909</td>\n",
       "      <td>12.138595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   % AE  % Std Dev AE       %AAE  \\\n",
       "Labedi Libya(1982)            -7.505937      8.274091   7.833435   \n",
       "Labedi Nigeria(1982)          -8.106470      9.188428   8.847940   \n",
       "Al-Khafaji(1987)              -4.325842     12.090407   7.297692   \n",
       "Abdul-Majeed(1990)            18.332503    159.168463  24.048028   \n",
       "De Ghetto(1994)               -5.474282      9.778127   7.998285   \n",
       "Almehaideb(1997)               2.865199     11.486479   6.642839   \n",
       "Elsharkawy(1999)              -9.271448      8.779672   9.307459   \n",
       "Dindoruk and Christman(2001)   5.500524     12.958600   7.129909   \n",
       "\n",
       "                              % Std Dev AAE  \n",
       "Labedi Libya(1982)                 7.964732  \n",
       "Labedi Nigeria(1982)               8.476793  \n",
       "Al-Khafaji(1987)                  10.565725  \n",
       "Abdul-Majeed(1990)               158.405722  \n",
       "De Ghetto(1994)                    7.849011  \n",
       "Almehaideb(1997)                   9.799044  \n",
       "Elsharkawy(1999)                   8.741487  \n",
       "Dindoruk and Christman(2001)      12.138595  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def Stats_matrix(a):\n",
    "    average_error = []\n",
    "    standard_deviation = []\n",
    "    absolute_average_error = []\n",
    "    absolute_standard_deviation =[]\n",
    "    for i in range(8):\n",
    "        average_error.append(np.average((a.iloc[:,i+5][a.iloc[:,i+5].notnull().values]-a.μ[a.iloc[:,i+5].notnull().values])/a.μ[a.iloc[:,i+5].notnull().values]*100))\n",
    "        standard_deviation.append(np.std((a.iloc[:,i+5][a.iloc[:,i+5].notnull().values]-a.μ[a.iloc[:,i+5].notnull().values])/a.μ[a.iloc[:,i+5].notnull().values]*100))\n",
    "        absolute_average_error.append(np.average(np.absolute((a.iloc[:,i+5][a.iloc[:,i+5].notnull().values]-a.μ[a.iloc[:,i+5].notnull().values])/a.μ[a.iloc[:,i+5].notnull().values]*100)))\n",
    "        absolute_standard_deviation.append(np.std(np.absolute((a.iloc[:,i+5][a.iloc[:,i+5].notnull().values]-a.μ[a.iloc[:,i+5].notnull().values])/a.μ[a.iloc[:,i+5].notnull().values]*100)))\n",
    "    statistics  = pd.DataFrame(list(zip(average_error,standard_deviation,absolute_average_error,\n",
    "                                        absolute_standard_deviation)),columns = ['% AE','% Std Dev AE','%AAE','% Std Dev AAE'],index = ['Labedi Libya(1982)','Labedi Nigeria(1982)','Al-Khafaji(1987)','Abdul-Majeed(1990)','De Ghetto(1994)','Almehaideb(1997)','Elsharkawy(1999)','Dindoruk and Christman(2001)'])\n",
    "    return statistics\n",
    "\n",
    "Stats_matrix(totaldf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0f881485",
   "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>% AE</th>\n",
       "      <th>% Std Dev AE</th>\n",
       "      <th>%AAE</th>\n",
       "      <th>% Std Dev AAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Labedi Libya(1982)</th>\n",
       "      <td>-5.663002</td>\n",
       "      <td>5.898867</td>\n",
       "      <td>5.894963</td>\n",
       "      <td>5.667065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Labedi Nigeria(1982)</th>\n",
       "      <td>-6.112048</td>\n",
       "      <td>6.589210</td>\n",
       "      <td>6.626231</td>\n",
       "      <td>6.071893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Al-Khafaji(1987)</th>\n",
       "      <td>-0.225992</td>\n",
       "      <td>11.019233</td>\n",
       "      <td>4.823055</td>\n",
       "      <td>9.910232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abdul-Majeed(1990)</th>\n",
       "      <td>1.975652</td>\n",
       "      <td>14.917406</td>\n",
       "      <td>6.672426</td>\n",
       "      <td>13.487436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>De Ghetto(1994)</th>\n",
       "      <td>-5.557400</td>\n",
       "      <td>5.576057</td>\n",
       "      <td>5.562749</td>\n",
       "      <td>5.570721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Almehaideb(1997)</th>\n",
       "      <td>4.469823</td>\n",
       "      <td>11.731666</td>\n",
       "      <td>6.414703</td>\n",
       "      <td>10.791797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elsharkawy(1999)</th>\n",
       "      <td>-6.660938</td>\n",
       "      <td>5.861526</td>\n",
       "      <td>6.712626</td>\n",
       "      <td>5.802260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dindoruk and Christman(2001)</th>\n",
       "      <td>2.142794</td>\n",
       "      <td>5.783243</td>\n",
       "      <td>4.313825</td>\n",
       "      <td>4.407764</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  % AE  % Std Dev AE      %AAE  % Std Dev AAE\n",
       "Labedi Libya(1982)           -5.663002      5.898867  5.894963       5.667065\n",
       "Labedi Nigeria(1982)         -6.112048      6.589210  6.626231       6.071893\n",
       "Al-Khafaji(1987)             -0.225992     11.019233  4.823055       9.910232\n",
       "Abdul-Majeed(1990)            1.975652     14.917406  6.672426      13.487436\n",
       "De Ghetto(1994)              -5.557400      5.576057  5.562749       5.570721\n",
       "Almehaideb(1997)              4.469823     11.731666  6.414703      10.791797\n",
       "Elsharkawy(1999)             -6.660938      5.861526  6.712626       5.802260\n",
       "Dindoruk and Christman(2001)  2.142794      5.783243  4.313825       4.407764"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visc_0_1 = totaldf.loc[(totaldf.μob<1) & (totaldf.μob>0)]\n",
    "Stats_matrix(visc_0_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "11b4feda",
   "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>% AE</th>\n",
       "      <th>% Std Dev AE</th>\n",
       "      <th>%AAE</th>\n",
       "      <th>% Std Dev AAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Labedi Libya(1982)</th>\n",
       "      <td>-7.217910</td>\n",
       "      <td>7.357865</td>\n",
       "      <td>7.647162</td>\n",
       "      <td>6.910667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Labedi Nigeria(1982)</th>\n",
       "      <td>-7.413742</td>\n",
       "      <td>9.444263</td>\n",
       "      <td>9.170772</td>\n",
       "      <td>7.749491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Al-Khafaji(1987)</th>\n",
       "      <td>-7.060542</td>\n",
       "      <td>7.409175</td>\n",
       "      <td>7.510909</td>\n",
       "      <td>6.952221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abdul-Majeed(1990)</th>\n",
       "      <td>47.526667</td>\n",
       "      <td>240.787545</td>\n",
       "      <td>50.671606</td>\n",
       "      <td>240.145402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>De Ghetto(1994)</th>\n",
       "      <td>-7.459898</td>\n",
       "      <td>8.691054</td>\n",
       "      <td>8.571196</td>\n",
       "      <td>7.597308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Almehaideb(1997)</th>\n",
       "      <td>2.389494</td>\n",
       "      <td>9.891124</td>\n",
       "      <td>6.523979</td>\n",
       "      <td>7.809079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elsharkawy(1999)</th>\n",
       "      <td>-9.981070</td>\n",
       "      <td>7.887591</td>\n",
       "      <td>9.981070</td>\n",
       "      <td>7.887591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dindoruk and Christman(2001)</th>\n",
       "      <td>5.394042</td>\n",
       "      <td>7.038372</td>\n",
       "      <td>6.400244</td>\n",
       "      <td>6.137691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   % AE  % Std Dev AE       %AAE  \\\n",
       "Labedi Libya(1982)            -7.217910      7.357865   7.647162   \n",
       "Labedi Nigeria(1982)          -7.413742      9.444263   9.170772   \n",
       "Al-Khafaji(1987)              -7.060542      7.409175   7.510909   \n",
       "Abdul-Majeed(1990)            47.526667    240.787545  50.671606   \n",
       "De Ghetto(1994)               -7.459898      8.691054   8.571196   \n",
       "Almehaideb(1997)               2.389494      9.891124   6.523979   \n",
       "Elsharkawy(1999)              -9.981070      7.887591   9.981070   \n",
       "Dindoruk and Christman(2001)   5.394042      7.038372   6.400244   \n",
       "\n",
       "                              % Std Dev AAE  \n",
       "Labedi Libya(1982)                 6.910667  \n",
       "Labedi Nigeria(1982)               7.749491  \n",
       "Al-Khafaji(1987)                   6.952221  \n",
       "Abdul-Majeed(1990)               240.145402  \n",
       "De Ghetto(1994)                    7.597308  \n",
       "Almehaideb(1997)                   7.809079  \n",
       "Elsharkawy(1999)                   7.887591  \n",
       "Dindoruk and Christman(2001)       6.137691  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visc_1_5 = totaldf.loc[(totaldf.μob>1) & (totaldf.μob<5)]\n",
    "Stats_matrix(visc_1_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "df1d8896",
   "metadata": {
    "scrolled": true
   },
   "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>% AE</th>\n",
       "      <th>% Std Dev AE</th>\n",
       "      <th>%AAE</th>\n",
       "      <th>% Std Dev AAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Labedi Libya(1982)</th>\n",
       "      <td>-12.714231</td>\n",
       "      <td>10.688650</td>\n",
       "      <td>13.445359</td>\n",
       "      <td>9.753012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Labedi Nigeria(1982)</th>\n",
       "      <td>-14.788108</td>\n",
       "      <td>9.977928</td>\n",
       "      <td>14.894080</td>\n",
       "      <td>9.819041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Al-Khafaji(1987)</th>\n",
       "      <td>-14.195323</td>\n",
       "      <td>10.516693</td>\n",
       "      <td>14.502656</td>\n",
       "      <td>10.088658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abdul-Majeed(1990)</th>\n",
       "      <td>37.598732</td>\n",
       "      <td>270.203994</td>\n",
       "      <td>46.882197</td>\n",
       "      <td>268.748810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>De Ghetto(1994)</th>\n",
       "      <td>-4.010634</td>\n",
       "      <td>11.491255</td>\n",
       "      <td>8.338659</td>\n",
       "      <td>8.865714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Almehaideb(1997)</th>\n",
       "      <td>-1.709765</td>\n",
       "      <td>8.926027</td>\n",
       "      <td>6.174905</td>\n",
       "      <td>6.668418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elsharkawy(1999)</th>\n",
       "      <td>-16.307971</td>\n",
       "      <td>10.776755</td>\n",
       "      <td>16.345786</td>\n",
       "      <td>10.719313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dindoruk and Christman(2001)</th>\n",
       "      <td>10.893419</td>\n",
       "      <td>9.412985</td>\n",
       "      <td>11.369976</td>\n",
       "      <td>8.831451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   % AE  % Std Dev AE       %AAE  \\\n",
       "Labedi Libya(1982)           -12.714231     10.688650  13.445359   \n",
       "Labedi Nigeria(1982)         -14.788108      9.977928  14.894080   \n",
       "Al-Khafaji(1987)             -14.195323     10.516693  14.502656   \n",
       "Abdul-Majeed(1990)            37.598732    270.203994  46.882197   \n",
       "De Ghetto(1994)               -4.010634     11.491255   8.338659   \n",
       "Almehaideb(1997)              -1.709765      8.926027   6.174905   \n",
       "Elsharkawy(1999)             -16.307971     10.776755  16.345786   \n",
       "Dindoruk and Christman(2001)  10.893419      9.412985  11.369976   \n",
       "\n",
       "                              % Std Dev AAE  \n",
       "Labedi Libya(1982)                 9.753012  \n",
       "Labedi Nigeria(1982)               9.819041  \n",
       "Al-Khafaji(1987)                  10.088658  \n",
       "Abdul-Majeed(1990)               268.748810  \n",
       "De Ghetto(1994)                    8.865714  \n",
       "Almehaideb(1997)                   6.668418  \n",
       "Elsharkawy(1999)                  10.719313  \n",
       "Dindoruk and Christman(2001)       8.831451  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visc_5_20 = totaldf.loc[(totaldf.μob>5) & (totaldf.μob<20)]\n",
    "Stats_matrix(visc_5_20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "baa2603b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>% AE</th>\n",
       "      <th>% Std Dev AE</th>\n",
       "      <th>%AAE</th>\n",
       "      <th>% Std Dev AAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Labedi Libya(1982)</th>\n",
       "      <td>-23.826748</td>\n",
       "      <td>13.085925</td>\n",
       "      <td>23.826748</td>\n",
       "      <td>13.085925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Labedi Nigeria(1982)</th>\n",
       "      <td>-24.933956</td>\n",
       "      <td>14.121232</td>\n",
       "      <td>24.933956</td>\n",
       "      <td>14.121232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Al-Khafaji(1987)</th>\n",
       "      <td>-25.295624</td>\n",
       "      <td>13.685474</td>\n",
       "      <td>25.295624</td>\n",
       "      <td>13.685474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abdul-Majeed(1990)</th>\n",
       "      <td>54.851062</td>\n",
       "      <td>304.343433</td>\n",
       "      <td>85.313835</td>\n",
       "      <td>297.245881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>De Ghetto(1994)</th>\n",
       "      <td>-2.799874</td>\n",
       "      <td>7.406505</td>\n",
       "      <td>5.930974</td>\n",
       "      <td>5.245870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Almehaideb(1997)</th>\n",
       "      <td>-7.585367</td>\n",
       "      <td>14.597580</td>\n",
       "      <td>13.013666</td>\n",
       "      <td>10.063380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elsharkawy(1999)</th>\n",
       "      <td>-26.255389</td>\n",
       "      <td>14.275540</td>\n",
       "      <td>26.255389</td>\n",
       "      <td>14.275540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dindoruk and Christman(2001)</th>\n",
       "      <td>50.875550</td>\n",
       "      <td>38.218021</td>\n",
       "      <td>50.875550</td>\n",
       "      <td>38.218021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   % AE  % Std Dev AE       %AAE  \\\n",
       "Labedi Libya(1982)           -23.826748     13.085925  23.826748   \n",
       "Labedi Nigeria(1982)         -24.933956     14.121232  24.933956   \n",
       "Al-Khafaji(1987)             -25.295624     13.685474  25.295624   \n",
       "Abdul-Majeed(1990)            54.851062    304.343433  85.313835   \n",
       "De Ghetto(1994)               -2.799874      7.406505   5.930974   \n",
       "Almehaideb(1997)              -7.585367     14.597580  13.013666   \n",
       "Elsharkawy(1999)             -26.255389     14.275540  26.255389   \n",
       "Dindoruk and Christman(2001)  50.875550     38.218021  50.875550   \n",
       "\n",
       "                              % Std Dev AAE  \n",
       "Labedi Libya(1982)                13.085925  \n",
       "Labedi Nigeria(1982)              14.121232  \n",
       "Al-Khafaji(1987)                  13.685474  \n",
       "Abdul-Majeed(1990)               297.245881  \n",
       "De Ghetto(1994)                    5.245870  \n",
       "Almehaideb(1997)                  10.063380  \n",
       "Elsharkawy(1999)                  14.275540  \n",
       "Dindoruk and Christman(2001)      38.218021  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visc_20_plus = totaldf.loc[(totaldf.μob>20) ]\n",
    "Stats_matrix(visc_20_plus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cc42a28b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def UndersatErrorPlot(a):\n",
    "    models =  ['Labedi Libya(1982)','Labedi Nigeria(1982)','Al-Khafaji(1987)','Abdul-Majeed(1990)','De Ghetto(1994)','Almehaideb(1997)','Elsharkawy(1999)','Dindoruk and Christman(2001)']\n",
    "    b = models.index(a)\n",
    "    plt.scatter((totaldf.Pb),(totaldf.iloc[:,b+5]-totaldf.μ)/totaldf.μ*100,facecolors='none', edgecolors='b')\n",
    "    plt.title(f'Undersaturated Oil Viscosity \\n {a}')\n",
    "    plt.xlabel('Bubble point pressure(Psia)')\n",
    "    plt.ylabel('% Error')\n",
    "    plt.figure(figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ad792a7f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "UndersatErrorPlot('Dindoruk and Christman(2001)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8edc9418",
   "metadata": {},
   "outputs": [],
   "source": [
    "def UndersatViscPlot(a):\n",
    "    models =  ['Labedi Libya(1982)','Labedi Nigeria(1982)','Al-Khafaji(1987)','Abdul-Majeed(1990)','De Ghetto(1994)','Almehaideb(1997)','Elsharkawy(1999)','Dindoruk and Christman(2001)']\n",
    "    b = models.index(a)\n",
    "    plt.scatter(totaldf.μ,totaldf.iloc[:,b+5],facecolors ='none', edgecolors='b')\n",
    "    plt.title(f'Undersaturated Oil Viscosity \\n {a}')\n",
    "    plt.xlabel('Measured Viscosity(cp)')\n",
    "    plt.ylabel('Calculated Viscosity(cp)')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "517775fa",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "UndersatViscPlot('Abdul-Majeed(1990)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "372fafaf",
   "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>P</th>\n",
       "      <th>μ</th>\n",
       "      <th>Pb</th>\n",
       "      <th>μob</th>\n",
       "      <th>μod</th>\n",
       "      <th>μ1</th>\n",
       "      <th>μ2</th>\n",
       "      <th>μ3</th>\n",
       "      <th>μ4</th>\n",
       "      <th>μ5</th>\n",
       "      <th>μ6</th>\n",
       "      <th>μ7</th>\n",
       "      <th>μ8</th>\n",
       "      <th>Rs</th>\n",
       "      <th>API</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1225.002551</td>\n",
       "      <td>2.373815</td>\n",
       "      <td>1225.002551</td>\n",
       "      <td>2.373815</td>\n",
       "      <td>0.769950</td>\n",
       "      <td>2.373815</td>\n",
       "      <td>2.373815</td>\n",
       "      <td>2.373815</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.373815</td>\n",
       "      <td>2.373815</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.155010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1424.476671</td>\n",
       "      <td>2.403167</td>\n",
       "      <td>1225.002551</td>\n",
       "      <td>2.373815</td>\n",
       "      <td>0.769950</td>\n",
       "      <td>2.381494</td>\n",
       "      <td>2.380301</td>\n",
       "      <td>2.381674</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.376851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.155010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1623.950791</td>\n",
       "      <td>2.448781</td>\n",
       "      <td>1225.002551</td>\n",
       "      <td>2.373815</td>\n",
       "      <td>0.769950</td>\n",
       "      <td>2.389173</td>\n",
       "      <td>2.386787</td>\n",
       "      <td>2.390778</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.379886</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.155010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1823.424912</td>\n",
       "      <td>2.497706</td>\n",
       "      <td>1225.002551</td>\n",
       "      <td>2.373815</td>\n",
       "      <td>0.769950</td>\n",
       "      <td>2.396851</td>\n",
       "      <td>2.393273</td>\n",
       "      <td>2.400420</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.382921</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.155010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022.899032</td>\n",
       "      <td>2.547857</td>\n",
       "      <td>1225.002551</td>\n",
       "      <td>2.373815</td>\n",
       "      <td>0.769950</td>\n",
       "      <td>2.404530</td>\n",
       "      <td>2.399759</td>\n",
       "      <td>2.410429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.385957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.155010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7287.647287</td>\n",
       "      <td>0.628827</td>\n",
       "      <td>4560.009224</td>\n",
       "      <td>0.578429</td>\n",
       "      <td>0.576164</td>\n",
       "      <td>0.622369</td>\n",
       "      <td>0.597668</td>\n",
       "      <td>0.694991</td>\n",
       "      <td>0.650465</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.696538</td>\n",
       "      <td>0.596806</td>\n",
       "      <td>0.718507</td>\n",
       "      <td>1363.922656</td>\n",
       "      <td>34.424015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7469.489824</td>\n",
       "      <td>0.633034</td>\n",
       "      <td>4560.009224</td>\n",
       "      <td>0.578429</td>\n",
       "      <td>0.576164</td>\n",
       "      <td>0.625298</td>\n",
       "      <td>0.598951</td>\n",
       "      <td>0.703648</td>\n",
       "      <td>0.655815</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.703374</td>\n",
       "      <td>0.598031</td>\n",
       "      <td>0.726655</td>\n",
       "      <td>1363.922656</td>\n",
       "      <td>34.424015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7651.332362</td>\n",
       "      <td>0.637304</td>\n",
       "      <td>4560.009224</td>\n",
       "      <td>0.578429</td>\n",
       "      <td>0.576164</td>\n",
       "      <td>0.628227</td>\n",
       "      <td>0.600233</td>\n",
       "      <td>0.712364</td>\n",
       "      <td>0.661202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.710111</td>\n",
       "      <td>0.599256</td>\n",
       "      <td>0.734664</td>\n",
       "      <td>1363.922656</td>\n",
       "      <td>34.424015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>7833.174899</td>\n",
       "      <td>0.641632</td>\n",
       "      <td>4560.009224</td>\n",
       "      <td>0.578429</td>\n",
       "      <td>0.576164</td>\n",
       "      <td>0.631157</td>\n",
       "      <td>0.601516</td>\n",
       "      <td>0.721137</td>\n",
       "      <td>0.666623</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.716752</td>\n",
       "      <td>0.600481</td>\n",
       "      <td>0.742536</td>\n",
       "      <td>1363.922656</td>\n",
       "      <td>34.424015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8015.017437</td>\n",
       "      <td>0.646015</td>\n",
       "      <td>4560.009224</td>\n",
       "      <td>0.578429</td>\n",
       "      <td>0.576164</td>\n",
       "      <td>0.634086</td>\n",
       "      <td>0.602799</td>\n",
       "      <td>0.729964</td>\n",
       "      <td>0.672078</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.723300</td>\n",
       "      <td>0.601706</td>\n",
       "      <td>0.750272</td>\n",
       "      <td>1363.922656</td>\n",
       "      <td>34.424015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11420 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              P         μ           Pb       μob       μod        μ1  \\\n",
       "0   1225.002551  2.373815  1225.002551  2.373815  0.769950  2.373815   \n",
       "1   1424.476671  2.403167  1225.002551  2.373815  0.769950  2.381494   \n",
       "2   1623.950791  2.448781  1225.002551  2.373815  0.769950  2.389173   \n",
       "3   1823.424912  2.497706  1225.002551  2.373815  0.769950  2.396851   \n",
       "4   2022.899032  2.547857  1225.002551  2.373815  0.769950  2.404530   \n",
       "..          ...       ...          ...       ...       ...       ...   \n",
       "15  7287.647287  0.628827  4560.009224  0.578429  0.576164  0.622369   \n",
       "16  7469.489824  0.633034  4560.009224  0.578429  0.576164  0.625298   \n",
       "17  7651.332362  0.637304  4560.009224  0.578429  0.576164  0.628227   \n",
       "18  7833.174899  0.641632  4560.009224  0.578429  0.576164  0.631157   \n",
       "19  8015.017437  0.646015  4560.009224  0.578429  0.576164  0.634086   \n",
       "\n",
       "          μ2        μ3        μ4  μ5        μ6        μ7        μ8  \\\n",
       "0   2.373815  2.373815       NaN NaN  2.373815  2.373815       NaN   \n",
       "1   2.380301  2.381674       NaN NaN       NaN  2.376851       NaN   \n",
       "2   2.386787  2.390778       NaN NaN       NaN  2.379886       NaN   \n",
       "3   2.393273  2.400420       NaN NaN       NaN  2.382921       NaN   \n",
       "4   2.399759  2.410429       NaN NaN       NaN  2.385957       NaN   \n",
       "..       ...       ...       ...  ..       ...       ...       ...   \n",
       "15  0.597668  0.694991  0.650465 NaN  0.696538  0.596806  0.718507   \n",
       "16  0.598951  0.703648  0.655815 NaN  0.703374  0.598031  0.726655   \n",
       "17  0.600233  0.712364  0.661202 NaN  0.710111  0.599256  0.734664   \n",
       "18  0.601516  0.721137  0.666623 NaN  0.716752  0.600481  0.742536   \n",
       "19  0.602799  0.729964  0.672078 NaN  0.723300  0.601706  0.750272   \n",
       "\n",
       "             Rs        API  \n",
       "0           NaN  32.155010  \n",
       "1           NaN  32.155010  \n",
       "2           NaN  32.155010  \n",
       "3           NaN  32.155010  \n",
       "4           NaN  32.155010  \n",
       "..          ...        ...  \n",
       "15  1363.922656  34.424015  \n",
       "16  1363.922656  34.424015  \n",
       "17  1363.922656  34.424015  \n",
       "18  1363.922656  34.424015  \n",
       "19  1363.922656  34.424015  \n",
       "\n",
       "[11420 rows x 15 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totaldf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fab85a62",
   "metadata": {},
   "outputs": [],
   "source": [
    "totaldf.insert(15,'ΔP',totaldf.P-totaldf.Pb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ffe5ef68",
   "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>P</th>\n",
       "      <th>μ</th>\n",
       "      <th>Pb</th>\n",
       "      <th>μob</th>\n",
       "      <th>μod</th>\n",
       "      <th>μ1</th>\n",
       "      <th>μ2</th>\n",
       "      <th>μ3</th>\n",
       "      <th>μ4</th>\n",
       "      <th>μ5</th>\n",
       "      <th>μ6</th>\n",
       "      <th>μ7</th>\n",
       "      <th>μ8</th>\n",
       "      <th>Rs</th>\n",
       "      <th>API</th>\n",
       "      <th>ΔP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1225.002551</td>\n",
       "      <td>2.373815</td>\n",
       "      <td>1225.002551</td>\n",
       "      <td>2.373815</td>\n",
       "      <td>0.769950</td>\n",
       "      <td>2.373815</td>\n",
       "      <td>2.373815</td>\n",
       "      <td>2.373815</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.373815</td>\n",
       "      <td>2.373815</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.155010</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1424.476671</td>\n",
       "      <td>2.403167</td>\n",
       "      <td>1225.002551</td>\n",
       "      <td>2.373815</td>\n",
       "      <td>0.769950</td>\n",
       "      <td>2.381494</td>\n",
       "      <td>2.380301</td>\n",
       "      <td>2.381674</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.376851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.155010</td>\n",
       "      <td>199.474120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1623.950791</td>\n",
       "      <td>2.448781</td>\n",
       "      <td>1225.002551</td>\n",
       "      <td>2.373815</td>\n",
       "      <td>0.769950</td>\n",
       "      <td>2.389173</td>\n",
       "      <td>2.386787</td>\n",
       "      <td>2.390778</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.379886</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.155010</td>\n",
       "      <td>398.948240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1823.424912</td>\n",
       "      <td>2.497706</td>\n",
       "      <td>1225.002551</td>\n",
       "      <td>2.373815</td>\n",
       "      <td>0.769950</td>\n",
       "      <td>2.396851</td>\n",
       "      <td>2.393273</td>\n",
       "      <td>2.400420</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.382921</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.155010</td>\n",
       "      <td>598.422361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022.899032</td>\n",
       "      <td>2.547857</td>\n",
       "      <td>1225.002551</td>\n",
       "      <td>2.373815</td>\n",
       "      <td>0.769950</td>\n",
       "      <td>2.404530</td>\n",
       "      <td>2.399759</td>\n",
       "      <td>2.410429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.385957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.155010</td>\n",
       "      <td>797.896481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7287.647287</td>\n",
       "      <td>0.628827</td>\n",
       "      <td>4560.009224</td>\n",
       "      <td>0.578429</td>\n",
       "      <td>0.576164</td>\n",
       "      <td>0.622369</td>\n",
       "      <td>0.597668</td>\n",
       "      <td>0.694991</td>\n",
       "      <td>0.650465</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.696538</td>\n",
       "      <td>0.596806</td>\n",
       "      <td>0.718507</td>\n",
       "      <td>1363.922656</td>\n",
       "      <td>34.424015</td>\n",
       "      <td>2727.638063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7469.489824</td>\n",
       "      <td>0.633034</td>\n",
       "      <td>4560.009224</td>\n",
       "      <td>0.578429</td>\n",
       "      <td>0.576164</td>\n",
       "      <td>0.625298</td>\n",
       "      <td>0.598951</td>\n",
       "      <td>0.703648</td>\n",
       "      <td>0.655815</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.703374</td>\n",
       "      <td>0.598031</td>\n",
       "      <td>0.726655</td>\n",
       "      <td>1363.922656</td>\n",
       "      <td>34.424015</td>\n",
       "      <td>2909.480601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7651.332362</td>\n",
       "      <td>0.637304</td>\n",
       "      <td>4560.009224</td>\n",
       "      <td>0.578429</td>\n",
       "      <td>0.576164</td>\n",
       "      <td>0.628227</td>\n",
       "      <td>0.600233</td>\n",
       "      <td>0.712364</td>\n",
       "      <td>0.661202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.710111</td>\n",
       "      <td>0.599256</td>\n",
       "      <td>0.734664</td>\n",
       "      <td>1363.922656</td>\n",
       "      <td>34.424015</td>\n",
       "      <td>3091.323138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>7833.174899</td>\n",
       "      <td>0.641632</td>\n",
       "      <td>4560.009224</td>\n",
       "      <td>0.578429</td>\n",
       "      <td>0.576164</td>\n",
       "      <td>0.631157</td>\n",
       "      <td>0.601516</td>\n",
       "      <td>0.721137</td>\n",
       "      <td>0.666623</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.716752</td>\n",
       "      <td>0.600481</td>\n",
       "      <td>0.742536</td>\n",
       "      <td>1363.922656</td>\n",
       "      <td>34.424015</td>\n",
       "      <td>3273.165676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8015.017437</td>\n",
       "      <td>0.646015</td>\n",
       "      <td>4560.009224</td>\n",
       "      <td>0.578429</td>\n",
       "      <td>0.576164</td>\n",
       "      <td>0.634086</td>\n",
       "      <td>0.602799</td>\n",
       "      <td>0.729964</td>\n",
       "      <td>0.672078</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.723300</td>\n",
       "      <td>0.601706</td>\n",
       "      <td>0.750272</td>\n",
       "      <td>1363.922656</td>\n",
       "      <td>34.424015</td>\n",
       "      <td>3455.008213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11420 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              P         μ           Pb       μob       μod        μ1  \\\n",
       "0   1225.002551  2.373815  1225.002551  2.373815  0.769950  2.373815   \n",
       "1   1424.476671  2.403167  1225.002551  2.373815  0.769950  2.381494   \n",
       "2   1623.950791  2.448781  1225.002551  2.373815  0.769950  2.389173   \n",
       "3   1823.424912  2.497706  1225.002551  2.373815  0.769950  2.396851   \n",
       "4   2022.899032  2.547857  1225.002551  2.373815  0.769950  2.404530   \n",
       "..          ...       ...          ...       ...       ...       ...   \n",
       "15  7287.647287  0.628827  4560.009224  0.578429  0.576164  0.622369   \n",
       "16  7469.489824  0.633034  4560.009224  0.578429  0.576164  0.625298   \n",
       "17  7651.332362  0.637304  4560.009224  0.578429  0.576164  0.628227   \n",
       "18  7833.174899  0.641632  4560.009224  0.578429  0.576164  0.631157   \n",
       "19  8015.017437  0.646015  4560.009224  0.578429  0.576164  0.634086   \n",
       "\n",
       "          μ2        μ3        μ4  μ5        μ6        μ7        μ8  \\\n",
       "0   2.373815  2.373815       NaN NaN  2.373815  2.373815       NaN   \n",
       "1   2.380301  2.381674       NaN NaN       NaN  2.376851       NaN   \n",
       "2   2.386787  2.390778       NaN NaN       NaN  2.379886       NaN   \n",
       "3   2.393273  2.400420       NaN NaN       NaN  2.382921       NaN   \n",
       "4   2.399759  2.410429       NaN NaN       NaN  2.385957       NaN   \n",
       "..       ...       ...       ...  ..       ...       ...       ...   \n",
       "15  0.597668  0.694991  0.650465 NaN  0.696538  0.596806  0.718507   \n",
       "16  0.598951  0.703648  0.655815 NaN  0.703374  0.598031  0.726655   \n",
       "17  0.600233  0.712364  0.661202 NaN  0.710111  0.599256  0.734664   \n",
       "18  0.601516  0.721137  0.666623 NaN  0.716752  0.600481  0.742536   \n",
       "19  0.602799  0.729964  0.672078 NaN  0.723300  0.601706  0.750272   \n",
       "\n",
       "             Rs        API           ΔP  \n",
       "0           NaN  32.155010     0.000000  \n",
       "1           NaN  32.155010   199.474120  \n",
       "2           NaN  32.155010   398.948240  \n",
       "3           NaN  32.155010   598.422361  \n",
       "4           NaN  32.155010   797.896481  \n",
       "..          ...        ...          ...  \n",
       "15  1363.922656  34.424015  2727.638063  \n",
       "16  1363.922656  34.424015  2909.480601  \n",
       "17  1363.922656  34.424015  3091.323138  \n",
       "18  1363.922656  34.424015  3273.165676  \n",
       "19  1363.922656  34.424015  3455.008213  \n",
       "\n",
       "[11420 rows x 16 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totaldf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "31c3f7d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '% Average Absolute Error')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "press_0_25 = totaldf.loc[(totaldf.ΔP<2500)]\n",
    "press_25_50 = totaldf.loc[(totaldf.ΔP<5000)&(totaldf.ΔP>2500)]\n",
    "press_50_100 = totaldf.loc[(totaldf.ΔP<10000)&(totaldf.ΔP>5000)]\n",
    "visc_0_1 = totaldf.loc[(totaldf.μob>0) & (totaldf.μob<1)]\n",
    "visc_1_5 = totaldf.loc[(totaldf.μob>1) & (totaldf.μob<5)]\n",
    "visc_5_20 = totaldf.loc[(totaldf.μob>5) & (totaldf.μob<20)]\n",
    "visc_20_plus = totaldf.loc[(totaldf.μob>20) ]\n",
    "\n",
    "full_data = {\n",
    "    'Pdif 0-2500' : Stats_matrix(press_0_25).iloc[:,2],\n",
    "    'Pdif 2500-5000' : Stats_matrix(press_25_50).iloc[:,2],\n",
    "    'Pdif 5000-10000' : Stats_matrix(press_50_100).iloc[:,2],\n",
    "    'BP V 0-1' : Stats_matrix(visc_0_1).iloc[:,2],\n",
    "    'BP V 1-5' : Stats_matrix(visc_1_5).iloc[:,2],\n",
    "    'BP V 5-20' : Stats_matrix(visc_5_20).iloc[:,2],\n",
    "    'BP V 20+' : Stats_matrix(visc_20_plus).iloc[:,2],\n",
    "}    \n",
    "models =  ['Labedi Libya(1982)','Labedi Nigeria(1982)','Al-Khafaji(1987)','Abdul-Majeed(1990)','De Ghetto(1994)','Almehaideb(1997)','Elsharkawy(1999)','Dindoruk(2001)']\n",
    "\n",
    "\n",
    "\n",
    "%run my_bar_plot.ipynb\n",
    "fig, ax = plt.subplots()\n",
    "plt.rcParams['figure.figsize'] = (17, 8)\n",
    "bar_plot(ax, full_data, total_width=.8, single_width=.9,)\n",
    "plt.xticks(range(8), models)\n",
    "plt.title('Undersaturated Oil Viscosity ')\n",
    "plt.ylabel(\"% Average Absolute Error\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}