constitutive_law.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/1563757785.py:1: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_1 = pd.read_csv('5STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] # strain rate 10^-2\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/1563757785.py:2: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_2 = pd.read_csv('10STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/1563757785.py:3: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_3 = pd.read_csv('15STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/1563757785.py:4: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_4 = pd.read_csv('20STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/1563757785.py:5: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_5 = pd.read_csv('25STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/1563757785.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_6= pd.read_csv('30STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/1563757785.py:7: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_7 = pd.read_csv('35STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/1563757785.py:8: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_8 = pd.read_csv('40STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/1563757785.py:9: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_9 = pd.read_csv('45STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/1563757785.py:10: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_10 = pd.read_csv('50STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/1563757785.py:11: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_11 = pd.read_csv('55STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/1563757785.py:12: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_12 = pd.read_csv('60STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/1563757785.py:13: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_13 = pd.read_csv('65STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/1563757785.py:14: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_14 = pd.read_csv('70STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/1563757785.py:15: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_15 = pd.read_csv('75STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/1563757785.py:16: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_16 = pd.read_csv('80STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/1563757785.py:17: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_17 = pd.read_csv('85STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/1563757785.py:18: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_18 = pd.read_csv('90STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/1563757785.py:19: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_19 = pd.read_csv('95STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n"
     ]
    }
   ],
   "source": [
    "df_1 = pd.read_csv('5STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] # strain rate 10^-2\n",
    "df_2 = pd.read_csv('10STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_3 = pd.read_csv('15STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_4 = pd.read_csv('20STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_5 = pd.read_csv('25STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_6= pd.read_csv('30STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_7 = pd.read_csv('35STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_8 = pd.read_csv('40STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_9 = pd.read_csv('45STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_10 = pd.read_csv('50STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_11 = pd.read_csv('55STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_12 = pd.read_csv('60STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_13 = pd.read_csv('65STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_14 = pd.read_csv('70STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_15 = pd.read_csv('75STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_16 = pd.read_csv('80STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_17 = pd.read_csv('85STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_18 = pd.read_csv('90STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_19 = pd.read_csv('95STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/2676745754.py:1: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_20 = pd.read_csv('103-5STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] # strain rate 10^-3\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/2676745754.py:2: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_21 = pd.read_csv('103-10STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/2676745754.py:3: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_22 = pd.read_csv('103-15STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/2676745754.py:4: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_23 = pd.read_csv('103-20STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/2676745754.py:5: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_24 = pd.read_csv('103-25STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/2676745754.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_25 = pd.read_csv('103-30STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/2676745754.py:7: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_26 = pd.read_csv('103-35STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/2676745754.py:8: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_27 = pd.read_csv('103-40STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/2676745754.py:9: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_28 = pd.read_csv('103-45STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/2676745754.py:10: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_29 = pd.read_csv('103-50STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/2676745754.py:11: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_30 = pd.read_csv('103-55STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/2676745754.py:12: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_31 = pd.read_csv('103-60STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/2676745754.py:13: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_32 = pd.read_csv('103-65STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/2676745754.py:14: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_33 = pd.read_csv('103-70STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/2676745754.py:15: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_34 = pd.read_csv('103-75STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/2676745754.py:16: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_35 = pd.read_csv('103-80STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/2676745754.py:17: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_36 = pd.read_csv('103-85STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/2676745754.py:18: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_37 = pd.read_csv('103-90STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/2676745754.py:19: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_38 = pd.read_csv('103-95STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n"
     ]
    }
   ],
   "source": [
    "df_20 = pd.read_csv('103-5STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] # strain rate 10^-3\n",
    "df_21 = pd.read_csv('103-10STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_22 = pd.read_csv('103-15STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_23 = pd.read_csv('103-20STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_24 = pd.read_csv('103-25STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_25 = pd.read_csv('103-30STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_26 = pd.read_csv('103-35STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_27 = pd.read_csv('103-40STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_28 = pd.read_csv('103-45STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_29 = pd.read_csv('103-50STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_30 = pd.read_csv('103-55STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_31 = pd.read_csv('103-60STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_32 = pd.read_csv('103-65STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_33 = pd.read_csv('103-70STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_34 = pd.read_csv('103-75STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_35 = pd.read_csv('103-80STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_36 = pd.read_csv('103-85STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_37 = pd.read_csv('103-90STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_38 = pd.read_csv('103-95STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/497223867.py:1: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_39 = pd.read_csv('104-5STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] # strain rate 10^-4\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/497223867.py:2: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_40 = pd.read_csv('104-10STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/497223867.py:3: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_41 = pd.read_csv('104-15STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/497223867.py:4: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_42 = pd.read_csv('104-20STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/497223867.py:5: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_43 = pd.read_csv('104-25STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/497223867.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_44 = pd.read_csv('104-30STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/497223867.py:7: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_45 = pd.read_csv('104-35STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/497223867.py:8: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_46 = pd.read_csv('104-40STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/497223867.py:9: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_47 = pd.read_csv('104-45STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/497223867.py:10: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_48 = pd.read_csv('104-50STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/497223867.py:11: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_49 = pd.read_csv('104-55STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/497223867.py:12: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_50 = pd.read_csv('104-60STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/497223867.py:13: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_51 = pd.read_csv('104-65STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/497223867.py:14: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_52 = pd.read_csv('104-70STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/497223867.py:15: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_53 = pd.read_csv('104-75STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/497223867.py:16: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_54 = pd.read_csv('104-80STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/497223867.py:17: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_55 = pd.read_csv('104-85STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/497223867.py:18: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_56 = pd.read_csv('104-90STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/497223867.py:19: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_57 = pd.read_csv('104-95STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n"
     ]
    }
   ],
   "source": [
    "df_39 = pd.read_csv('104-5STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] # strain rate 10^-4\n",
    "df_40 = pd.read_csv('104-10STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_41 = pd.read_csv('104-15STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_42 = pd.read_csv('104-20STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_43 = pd.read_csv('104-25STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_44 = pd.read_csv('104-30STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_45 = pd.read_csv('104-35STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_46 = pd.read_csv('104-40STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_47 = pd.read_csv('104-45STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_48 = pd.read_csv('104-50STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_49 = pd.read_csv('104-55STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_50 = pd.read_csv('104-60STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_51 = pd.read_csv('104-65STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_52 = pd.read_csv('104-70STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_53 = pd.read_csv('104-75STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_54 = pd.read_csv('104-80STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_55 = pd.read_csv('104-85STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_56 = pd.read_csv('104-90STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_57 = pd.read_csv('104-95STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/131091397.py:1: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_58 = pd.read_csv('105-5STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] # strain rate 10^-5\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/131091397.py:2: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_59 = pd.read_csv('105-10STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/131091397.py:3: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_60 = pd.read_csv('105-15STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/131091397.py:4: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_61 = pd.read_csv('105-20STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/131091397.py:5: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_62 = pd.read_csv('105-25STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/131091397.py:6: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_63 = pd.read_csv('105-30STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/131091397.py:7: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_64 = pd.read_csv('105-35STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/131091397.py:8: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_65 = pd.read_csv('105-40STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/131091397.py:9: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_66 = pd.read_csv('105-45STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/131091397.py:10: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_67 = pd.read_csv('105-50STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/131091397.py:11: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_68 = pd.read_csv('105-55STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/131091397.py:12: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_69 = pd.read_csv('105-60STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/131091397.py:13: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_70 = pd.read_csv('105-65STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/131091397.py:14: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_71 = pd.read_csv('105-70STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/131091397.py:15: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_72 = pd.read_csv('105-75STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/131091397.py:16: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_73 = pd.read_csv('105-80STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/131091397.py:17: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_74 = pd.read_csv('105-85STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/131091397.py:18: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_75 = pd.read_csv('105-90STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n",
      "/var/folders/y3/y01h3w0s4sldyhwtlxfpx31c0000gn/T/ipykernel_84088/131091397.py:19: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
      "  df_76 = pd.read_csv('105-95STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']]\n"
     ]
    }
   ],
   "source": [
    "df_58 = pd.read_csv('105-5STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] # strain rate 10^-5\n",
    "df_59 = pd.read_csv('105-10STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_60 = pd.read_csv('105-15STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_61 = pd.read_csv('105-20STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_62 = pd.read_csv('105-25STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_63 = pd.read_csv('105-30STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_64 = pd.read_csv('105-35STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_65 = pd.read_csv('105-40STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_66 = pd.read_csv('105-45STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_67 = pd.read_csv('105-50STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_68 = pd.read_csv('105-55STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_69 = pd.read_csv('105-60STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_70 = pd.read_csv('105-65STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_71 = pd.read_csv('105-70STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_72 = pd.read_csv('105-75STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_73 = pd.read_csv('105-80STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_74 = pd.read_csv('105-85STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_75 = pd.read_csv('105-90STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n",
    "df_76 = pd.read_csv('105-95STR_STR.OUT',delim_whitespace=True)[['E11','SCAU11']] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>E11</th>\n",
       "      <th>SCAU11</th>\n",
       "      <th>strain_rate</th>\n",
       "      <th>grain_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>103.590000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000667</td>\n",
       "      <td>104.063333</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001429</td>\n",
       "      <td>104.581429</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.002267</td>\n",
       "      <td>105.183333</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.003161</td>\n",
       "      <td>105.831935</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.095000</td>\n",
       "      <td>158.131954</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.096000</td>\n",
       "      <td>158.570977</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.097000</td>\n",
       "      <td>159.005489</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.098000</td>\n",
       "      <td>159.437744</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.099000</td>\n",
       "      <td>159.868872</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          E11      SCAU11  strain_rate  grain_size\n",
       "0    0.000000  103.590000         0.01    0.000095\n",
       "1    0.000667  104.063333         0.01    0.000095\n",
       "2    0.001429  104.581429         0.01    0.000095\n",
       "3    0.002267  105.183333         0.01    0.000095\n",
       "4    0.003161  105.831935         0.01    0.000095\n",
       "..        ...         ...          ...         ...\n",
       "96   0.095000  158.131954         0.01    0.000095\n",
       "97   0.096000  158.570977         0.01    0.000095\n",
       "98   0.097000  159.005489         0.01    0.000095\n",
       "99   0.098000  159.437744         0.01    0.000095\n",
       "100  0.099000  159.868872         0.01    0.000095\n",
       "\n",
       "[101 rows x 4 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1['strain_rate'] = math.pow(10,-2)\n",
    "df_2['strain_rate'] = math.pow(10,-2)\n",
    "df_3['strain_rate'] = math.pow(10,-2)\n",
    "df_4['strain_rate'] = math.pow(10,-2)\n",
    "df_5['strain_rate'] = math.pow(10,-2)\n",
    "df_6['strain_rate'] = math.pow(10,-2)\n",
    "df_7['strain_rate'] = math.pow(10,-2)\n",
    "df_8['strain_rate'] = math.pow(10,-2)\n",
    "df_9['strain_rate'] = math.pow(10,-2)\n",
    "df_10['strain_rate'] = math.pow(10,-2)\n",
    "df_11['strain_rate'] = math.pow(10,-2)\n",
    "df_12['strain_rate'] = math.pow(10,-2)\n",
    "df_13['strain_rate'] = math.pow(10,-2)\n",
    "df_14['strain_rate'] = math.pow(10,-2)\n",
    "df_15['strain_rate'] = math.pow(10,-2)\n",
    "df_16['strain_rate'] = math.pow(10,-2)\n",
    "df_17['strain_rate'] = math.pow(10,-2)\n",
    "df_18['strain_rate'] = math.pow(10,-2)\n",
    "df_19['strain_rate'] = math.pow(10,-2)\n",
    "\n",
    "df_20['strain_rate'] = math.pow(10,-3)\n",
    "df_21['strain_rate'] = math.pow(10,-3)\n",
    "df_22['strain_rate'] = math.pow(10,-3)\n",
    "df_23['strain_rate'] = math.pow(10,-3)\n",
    "df_24['strain_rate'] = math.pow(10,-3)\n",
    "df_25['strain_rate'] = math.pow(10,-3)\n",
    "df_26['strain_rate'] = math.pow(10,-3)\n",
    "df_27['strain_rate'] = math.pow(10,-3)\n",
    "df_28['strain_rate'] = math.pow(10,-3)\n",
    "df_29['strain_rate'] = math.pow(10,-3)\n",
    "df_30['strain_rate'] = math.pow(10,-3)\n",
    "df_31['strain_rate'] = math.pow(10,-3)\n",
    "df_32['strain_rate'] = math.pow(10,-3)\n",
    "df_33['strain_rate'] = math.pow(10,-3)\n",
    "df_34['strain_rate'] = math.pow(10,-3)\n",
    "df_35['strain_rate'] = math.pow(10,-3)\n",
    "df_36['strain_rate'] = math.pow(10,-3)\n",
    "df_37['strain_rate'] = math.pow(10,-3)\n",
    "df_38['strain_rate'] = math.pow(10,-3)\n",
    "\n",
    "\n",
    "\n",
    "df_39['strain_rate'] = math.pow(10,-4)\n",
    "df_40['strain_rate'] = math.pow(10,-4)\n",
    "df_41['strain_rate'] = math.pow(10,-4)\n",
    "df_42['strain_rate'] = math.pow(10,-4)\n",
    "df_43['strain_rate'] = math.pow(10,-4)\n",
    "df_44['strain_rate'] = math.pow(10,-4)\n",
    "df_45['strain_rate'] = math.pow(10,-4)\n",
    "df_46['strain_rate'] = math.pow(10,-4)\n",
    "df_47['strain_rate'] = math.pow(10,-4)\n",
    "df_48['strain_rate'] = math.pow(10,-4)\n",
    "df_49['strain_rate'] = math.pow(10,-4)\n",
    "df_50['strain_rate'] = math.pow(10,-4)\n",
    "df_51['strain_rate'] = math.pow(10,-4)\n",
    "df_52['strain_rate'] = math.pow(10,-4)\n",
    "df_53['strain_rate'] = math.pow(10,-4)\n",
    "df_54['strain_rate'] = math.pow(10,-4)\n",
    "df_55['strain_rate'] = math.pow(10,-4)\n",
    "df_56['strain_rate'] = math.pow(10,-4)\n",
    "df_57['strain_rate'] = math.pow(10,-4)\n",
    "\n",
    "\n",
    "\n",
    "df_58['strain_rate'] = math.pow(10,-5)\n",
    "df_59['strain_rate'] = math.pow(10,-5)\n",
    "df_60['strain_rate'] = math.pow(10,-5)\n",
    "df_61['strain_rate'] = math.pow(10,-5)\n",
    "df_62['strain_rate'] = math.pow(10,-5)\n",
    "df_63['strain_rate'] = math.pow(10,-5)\n",
    "df_64['strain_rate'] = math.pow(10,-5)\n",
    "df_65['strain_rate'] = math.pow(10,-5)\n",
    "df_66['strain_rate'] = math.pow(10,-5)\n",
    "df_67['strain_rate'] = math.pow(10,-5)\n",
    "df_68['strain_rate'] = math.pow(10,-5)\n",
    "df_69['strain_rate'] = math.pow(10,-5)\n",
    "df_70['strain_rate'] = math.pow(10,-5)\n",
    "df_71['strain_rate'] = math.pow(10,-5)\n",
    "df_72['strain_rate'] = math.pow(10,-5)\n",
    "df_73['strain_rate'] = math.pow(10,-5)\n",
    "df_74['strain_rate'] = math.pow(10,-5)\n",
    "df_75['strain_rate'] = math.pow(10,-5)\n",
    "df_76['strain_rate'] = math.pow(10,-5)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "df_19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "# Define the grain size values\n",
    "grain_sizes = [i * (5 * math.pow(10, -6)) for i in range(1, 20)]\n",
    "\n",
    "# Apply grain sizes for df_1 to df_76\n",
    "for i in range(1, 77):\n",
    "    # Select the appropriate grain size based on the index within each group\n",
    "    grain_size_index = (i - 1) % 19\n",
    "    globals()[f'df_{i}']['grain_size'] = grain_sizes[grain_size_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>E11</th>\n",
       "      <th>SCAU11</th>\n",
       "      <th>strain_rate</th>\n",
       "      <th>grain_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000</td>\n",
       "      <td>75.946</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.000065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001</td>\n",
       "      <td>76.448</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.000065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.002</td>\n",
       "      <td>76.921</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.000065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.003</td>\n",
       "      <td>77.443</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.000065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.004</td>\n",
       "      <td>77.965</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.000065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.096</td>\n",
       "      <td>114.880</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.000065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.097</td>\n",
       "      <td>115.190</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.000065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.098</td>\n",
       "      <td>115.500</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.000065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.099</td>\n",
       "      <td>115.800</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.000065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.100</td>\n",
       "      <td>116.110</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.000065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       E11   SCAU11  strain_rate  grain_size\n",
       "0    0.000   75.946      0.00001    0.000065\n",
       "1    0.001   76.448      0.00001    0.000065\n",
       "2    0.002   76.921      0.00001    0.000065\n",
       "3    0.003   77.443      0.00001    0.000065\n",
       "4    0.004   77.965      0.00001    0.000065\n",
       "..     ...      ...          ...         ...\n",
       "96   0.096  114.880      0.00001    0.000065\n",
       "97   0.097  115.190      0.00001    0.000065\n",
       "98   0.098  115.500      0.00001    0.000065\n",
       "99   0.099  115.800      0.00001    0.000065\n",
       "100  0.100  116.110      0.00001    0.000065\n",
       "\n",
       "[101 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_70"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loop through each DataFrame from df_1 to df_76 and drop missing values in place\n",
    "for i in range(1, 77):\n",
    "    # Access each DataFrame by its variable name using locals() and drop rows with NaN values\n",
    "    locals()[f'df_{i}'].dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the smoothing function\n",
    "def smoothing(df):\n",
    "  l = df.columns\n",
    "  for i in l:\n",
    "    alpha = 0.5\n",
    "    df[i] = df[i].ewm(alpha=alpha).mean()\n",
    "  return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply the smoothing function to each DataFrame from df_1 to df_76\n",
    "for i in range(1, 77):\n",
    "    # Use locals() to access and update each DataFrame variable by name\n",
    "    locals()[f'df_{i}'] = smoothing(locals()[f'df_{i}'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate all DataFrames from df_1 to df_76 along rows (axis=0)\n",
    "df_merged = pd.concat([locals()[f'df_{i}'] for i in range(1, 77)], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>E11</th>\n",
       "      <th>SCAU11</th>\n",
       "      <th>strain_rate</th>\n",
       "      <th>grain_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>162.830000</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000667</td>\n",
       "      <td>163.296667</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001429</td>\n",
       "      <td>163.801429</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.002267</td>\n",
       "      <td>164.366000</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.003161</td>\n",
       "      <td>165.033871</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.095000</td>\n",
       "      <td>111.948127</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.096000</td>\n",
       "      <td>112.259063</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.097000</td>\n",
       "      <td>112.569532</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.098000</td>\n",
       "      <td>112.874766</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.000095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.099000</td>\n",
       "      <td>113.177383</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.000095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7676 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          E11      SCAU11  strain_rate  grain_size\n",
       "0    0.000000  162.830000      0.01000    0.000005\n",
       "1    0.000667  163.296667      0.01000    0.000005\n",
       "2    0.001429  163.801429      0.01000    0.000005\n",
       "3    0.002267  164.366000      0.01000    0.000005\n",
       "4    0.003161  165.033871      0.01000    0.000005\n",
       "..        ...         ...          ...         ...\n",
       "96   0.095000  111.948127      0.00001    0.000095\n",
       "97   0.096000  112.259063      0.00001    0.000095\n",
       "98   0.097000  112.569532      0.00001    0.000095\n",
       "99   0.098000  112.874766      0.00001    0.000095\n",
       "100  0.099000  113.177383      0.00001    0.000095\n",
       "\n",
       "[7676 rows x 4 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate the 'SCAU11' column as the target variable\n",
    "target = df_merged['SCAU11']\n",
    "\n",
    "# Drop the 'SCAU11' column from df_merged to avoid duplication\n",
    "df_merged.drop('SCAU11', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
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       "    }\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>E11</th>\n",
       "      <th>strain_rate</th>\n",
       "      <th>grain_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000667</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001429</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.002267</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.003161</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        E11  strain_rate  grain_size\n",
       "0  0.000000         0.01    0.000005\n",
       "1  0.000667         0.01    0.000005\n",
       "2  0.001429         0.01    0.000005\n",
       "3  0.002267         0.01    0.000005\n",
       "4  0.003161         0.01    0.000005"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the polynomial degree to 3 for generating polynomial features\n",
    "degree = 2\n",
    "\n",
    "# Initialize the PolynomialFeatures transformer with the specified degree\n",
    "poly_features = PolynomialFeatures(degree=degree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "E11            0\n",
       "strain_rate    0\n",
       "grain_size     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate polynomial features from the df_merged data\n",
    "strain_poly_train = poly_features.fit_transform(df_merged)\n",
    "\n",
    "# strain_poly_train now contains the transformed data with polynomial features up to the specified degree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(strain_poly_train,target)#target is stress and strain_poly_train is strain and strain_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "coefficients = list(model.coef_)\n",
    "intercept = model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0, 581.170941, 24694.1286, -1168663.06, -1368.07412, 12543.1314, -29095.1308, -2102507.11, -9820971.89, 8222685370.0]\n",
      "115.87\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.0,\n",
       " 581.170941,\n",
       " 24694.1286,\n",
       " -1168663.06,\n",
       " -1368.07412,\n",
       " 12543.1314,\n",
       " -29095.1308,\n",
       " -2102507.11,\n",
       " -9820971.89,\n",
       " 8222685370.0]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(coefficients)\n",
    "print(intercept)\n",
    "coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(strain_poly_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the polynomial degree to 3 for generating polynomial features\n",
    "degree = 2\n",
    "\n",
    "# Initialize the PolynomialFeatures transformer with the specified degree\n",
    "poly_features = PolynomialFeatures(degree=degree)\n",
    "\n",
    "# Generate polynomial features from the df_merged data\n",
    "strain_poly_train = poly_features.fit_transform(df_merged)\n",
    "\n",
    "# strain_poly_train now contains the transformed data with polynomial features up to the specified degree\n",
    "model = LinearRegression()\n",
    "model.fit(strain_poly_train,target)#target is stress and strain_poly_train is strain and strain_rate\n",
    "y_pred = model.predict(strain_poly_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0, 579.7916389711852, 24625.750988934502, -1188053.620649229, -1343.9262079151868, 12552.864010048917, -28006.890242109475, -2094754.7771362613, -9880329.999242757, 8380259972.525759]\n",
      "116.35897794530095\n"
     ]
    }
   ],
   "source": [
    "coefficients = list(model.coef_)\n",
    "intercept = model.intercept_\n",
    "print(coefficients)\n",
    "print(intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = list(df_merged['E11'])\n",
    "b = list(target)\n",
    "d = list(y_pred)\n",
    "c = list(df_merged['strain_rate'])\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(c,a,b, color='b', marker='o', label=\"Data Points\") # Original\n",
    "ax.scatter(c,a,d, color='r', marker='x', label=\"Data Points\") # Predicted\n",
    "ax.set_xlabel('strain_rate')\n",
    "ax.set_ylabel('strain')\n",
    "ax.set_zlabel('stress')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting 2D graph of strain (E11) vs stress (target)\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Plot original data points\n",
    "plt.scatter(df_merged['E11'], target, color='b', marker='o', label=\"Actual Stress\")\n",
    "\n",
    "# Plot predicted data points\n",
    "plt.scatter(df_merged['E11'], y_pred, color='r', marker='x', label=\"Predicted Stress\")\n",
    "\n",
    "# Setting labels and title\n",
    "plt.xlabel('Strain (E11)')\n",
    "plt.ylabel('Stress')\n",
    "plt.legend()\n",
    "plt.title('Strain vs Stress')\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9485657844102036\n"
     ]
    }
   ],
   "source": [
    "r_score = model.score(strain_poly_train,target)\n",
    "print(r_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "162.83...146.91623201712468\n",
      "163.29666666666665...147.38575487966816\n",
      "163.80142857142854...147.92088966003573\n",
      "164.36599999999999...148.50773577739173\n",
      "165.0338709677419...149.13208062883766\n",
      "165.69730158730155...149.78157406599277\n",
      "166.41425196850392...150.4466584579038\n",
      "167.10482352941176...151.12054817172285\n",
      "167.83884540117415...151.79872955363624\n",
      "168.53510263929616...152.4783456321502\n",
      "169.26290669272106...153.1576556027368\n",
      "169.9566227106227...153.8356295283465\n",
      "170.67839946282504...154.51167190544743\n",
      "171.36424159189406...155.18544462277015\n",
      "172.0821427045503...155.85675848781744\n",
      "172.76108171206226...156.52550888361665\n",
      "173.46554623066888...157.19163852275207\n",
      "174.13277566061274...157.85511628991478\n",
      "174.79638909604856...158.5159254011809\n",
      "175.49819521731877...159.17405685638482\n",
      "176.16409792618654...159.82950585385262\n",
      "176.81704911876898...160.4822698432193\n",
      "177.47352463764247...161.13234747517544\n",
      "178.13176235805525...161.77973803832236\n",
      "178.78588119852188...162.42444115874852\n",
      "179.43794060897739...163.06645664047227\n",
      "180.08897030933923...163.70578438102976\n",
      "180.73448515707432...164.34242432697263\n",
      "181.3772425797344...164.97637645047845\n",
      "182.01862129046452...165.6076407370919\n",
      "182.65431064552826...166.23621717931587\n",
      "183.28715532291145...166.86210577326815\n",
      "183.91357766152865...167.48530651694156\n",
      "184.5367888308006...168.10581940929976\n",
      "185.15839441541837...168.72364444980832\n",
      "185.77419720771812...169.33878163819202\n",
      "186.3870986038635...169.9512309743093\n",
      "186.998549301934...170.5609924580874\n",
      "187.6042746509681...171.16806608948892\n",
      "188.2071373254846...171.7724518684947\n",
      "188.80856866274257...172.37414979509498\n",
      "189.40428433137143...172.97315986928464\n",
      "189.99714216568577...173.5694820910612\n",
      "190.58357108284292...174.16311646042325\n",
      "191.17178554142146...174.75406297737018\n",
      "191.75589277071074...175.3423216419017\n",
      "192.33294638535537...175.9278924540175\n",
      "192.90647319267768...176.5107754137176\n",
      "193.45323659633883...177.0909705210019\n",
      "194.02161829816941...177.6684777758704\n",
      "194.5908091490847...178.24329717832305\n",
      "195.15540457454236...178.81542872835993\n",
      "195.71270228727118...179.38487242598094\n",
      "196.2713511436356...179.95162827118614\n",
      "196.8656755718178...180.5156962639755\n",
      "197.44283778590892...181.07707640434904\n",
      "198.00141889295446...181.63576869230675\n",
      "198.55070944647724...182.19177312784865\n",
      "199.09535472323861...182.74508971097467\n",
      "199.6326773616193...183.2957184416849\n",
      "200.17133868080964...183.84365931997928\n",
      "200.70566934040482...184.38891234585785\n",
      "201.2328346702024...184.93147751932057\n",
      "201.7614173351012...185.47135484036747\n",
      "202.2857086675506...186.00854430899852\n",
      "202.8078543337753...186.5430459252138\n",
      "203.32392716688764...187.07485968901318\n",
      "203.83696358344383...187.60398560039675\n",
      "204.34848179172192...188.13042365936448\n",
      "204.85924089586098...188.6541738659164\n",
      "205.3696204479305...189.17523622005248\n",
      "205.87481022396526...189.69361072177273\n",
      "206.37740511198263...190.20929737107716\n",
      "206.87370255599131...190.72229616796574\n",
      "207.37185127799566...191.23260711243853\n",
      "207.86592563899785...191.74023020449545\n",
      "208.35796281949894...192.24516544413652\n",
      "208.84898140974946...192.74741283136183\n",
      "209.33449070487472...193.24697236617126\n",
      "209.81724535243737...193.74384404856488\n",
      "210.29862267621868...194.23802787854265\n",
      "210.77931133810932...194.7295238561046\n",
      "211.25465566905467...195.2183319812507\n",
      "211.72732783452733...195.704452253981\n",
      "212.19866391726367...196.18788467429547\n",
      "212.66933195863183...196.6686292421941\n",
      "213.1346659793159...197.14668595767688\n",
      "213.60233298965795...197.62205482074384\n",
      "214.06616649482896...198.09473583139498\n",
      "214.5230832474145...198.56472898963028\n",
      "214.98154162370724...199.03203429544976\n",
      "215.43577081185362...199.49665174885342\n",
      "215.8878854059268...199.9585813498412\n",
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      "98.89633500225295...107.89959695024731\n",
      "99.27016750112648...108.32230194937537\n",
      "99.64008375056324...108.74231909608758\n",
      "100.01504187528161...109.15964839038395\n",
      "100.3825209376408...109.57428983226448\n",
      "100.7562604688204...109.9862434217292\n",
      "101.1231302344102...110.3955091587781\n",
      "101.4915651172051...110.80208704341116\n",
      "101.85078255860255...111.20597707562838\n",
      "102.22039127930128...111.60717925542978\n",
      "102.57519563965064...112.00569358281534\n",
      "102.93759781982533...112.40152005778509\n",
      "103.29379890991267...112.794658680339\n",
      "103.64689945495633...113.18510945047706\n",
      "103.99844972747816...113.5728723681993\n",
      "104.34922486373908...113.95794743350571\n",
      "104.69461243186954...114.34033464639631\n",
      "105.04230621593477...114.72003400687106\n",
      "105.38615310796739...115.09704551492999\n",
      "105.7280765539837...115.47136917057307\n",
      "106.06903827699185...115.84300497380035\n",
      "106.40951913849592...116.21195292461178\n",
      "106.74975956924797...116.57821302300736\n",
      "107.08487978462398...116.94178526898713\n",
      "107.42243989231198...117.30266966255107\n",
      "107.756219946156...117.6608662036992\n",
      "108.08810997307799...118.01637489243147\n",
      "108.41405498653899...118.36919572874793\n",
      "108.7320274932695...118.71932871264853\n",
      "109.05101374663475...119.06677384413332\n",
      "109.37050687331737...119.41153112320228\n",
      "109.69025343665868...119.7536005498554\n",
      "110.01012671832933...120.0929821240927\n",
      "110.35006335916466...120.42967584591418\n",
      "110.68003167958233...120.7636817153198\n",
      "111.00501583979116...121.09499973230962\n",
      "111.32250791989557...121.42362989688358\n",
      "111.6362539599478...121.74957220904174\n",
      "111.94812697997389...122.07282666878405\n",
      "112.25906348998694...122.39339327611054\n",
      "112.56953174499347...122.71127203102118\n",
      "112.87476587249674...123.02646293351602\n",
      "113.17738293624836...123.33896598359502\n"
     ]
    }
   ],
   "source": [
    "target = list(target)\n",
    "y_pred = list(y_pred)\n",
    "\n",
    "for i in range(len(target)):\n",
    "  print(target[i],y_pred[i],sep='...')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00000000e+00,  5.79791639e+02,  2.46257510e+04, -1.18805362e+06,\n",
       "       -1.34392621e+03,  1.25528640e+04, -2.80068902e+04, -2.09475478e+06,\n",
       "       -9.88033000e+06,  8.38025997e+09])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31.44034706052648\n",
      "Mean Squared Error: 31.44034706052648\n",
      "R-squared: 0.9485657844102036\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "mse = mean_squared_error(target,y_pred)\n",
    "print(mse)\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "# Assuming 'target' is the true values and 'y_pred' is the predicted values\n",
    "mse = mean_squared_error(target, y_pred)\n",
    "r2 = r2_score(target, y_pred)\n",
    "\n",
    "print(\"Mean Squared Error:\", mse)\n",
    "print(\"R-squared:\", r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(df_merged,target,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "strain_poly_train = poly_features.fit_transform(x_train)\n",
    "strain_poly_test = poly_features.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_new = LinearRegression()\n",
    "model_new.fit(strain_poly_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_new = model_new.predict(strain_poly_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "111.42932547476609...108.82661729435867\n",
      "152.52925400908688...150.42500764628355\n",
      "76.52647619047619...77.59496616517197\n",
      "162.0304438290751...161.9210490940019\n",
      "157.4055844838643...155.118772061166\n",
      "98.61116562328303...100.26499071400264\n",
      "148.1180526524915...145.8951886170189\n",
      "138.069069471689...135.60442190173316\n",
      "142.16297571982471...142.33481133743803\n",
      "112.56953174499347...122.54545233567336\n",
      "153.2753321715328...158.93437709160787\n",
      "104.99675658942526...105.99449293610519\n",
      "87.54314481409003...81.3813680299549\n",
      "103.32034095512753...106.81492631726518\n",
      "172.75589570864486...177.62550832047808\n",
      "135.39132885870464...133.78230065074266\n",
      "139.08052545748518...140.47909870555424\n",
      "105.25205502392174...110.28237283007275\n",
      "193.45323659633883...176.61905068771833\n",
      "91.12803137254902...99.4891975174313\n",
      "166.45233870935624...167.96633720473753\n",
      "146.0706744868035...146.28961915251932\n",
      "108.37974477921759...100.18687653750509\n",
      "152.39962743643076...154.06057607522013\n",
      "97.84273713162821...99.69945983928027\n",
      "131.7126114213081...129.1498433717471\n",
      "126.3466305773173...129.54835328033437\n",
      "91.59737997557998...83.41883456222824\n",
      "160.03965636920935...162.45772670449895\n",
      "139.8961680925288...140.05595926865445\n",
      "136.17806974829128...146.130233779441\n",
      "135.48362927894874...136.83747602233908\n",
      "112.84074428122658...111.43574081851466\n",
      "106.43928608611652...103.80457647632716\n",
      "167.72307394917578...169.7176237469607\n",
      "141.4292741957271...146.6012153263598\n",
      "117.94743274267002...110.7473193431996\n",
      "105.61732169051588...101.6512383006147\n",
      "110.29971293045224...114.05849508358835\n",
      "135.9284302775719...140.01805577231974\n",
      "169.12465070653752...173.93687148592028\n",
      "127.8332346294427...119.26004204797239\n",
      "156.61120587565335...160.9917540591646\n",
      "125.85155198398435...120.59394930128317\n",
      "94.93050793650794...103.11839040139606\n",
      "139.3815424753281...138.4330380750931\n",
      "103.10797322182415...110.88883148759844\n",
      "123.77470202654817...119.8730024900432\n",
      "193.7093419545257...194.1212559153167\n",
      "142.26241698416248...141.15384760879343\n",
      "97.67683719465441...100.18132551425522\n",
      "96.87746591168236...88.6260738744502\n",
      "127.0386949811639...126.29279324350912\n",
      "129.67178432085572...135.25927251987346\n",
      "117.17977918582763...115.41913779559978\n",
      "84.66780459208599...87.6418204675995\n",
      "115.26721494628885...118.79363185706512\n",
      "174.3048790909704...156.91881231458814\n",
      "130.21089216045925...135.76621601968768\n",
      "108.10430105181348...102.87615311553395\n",
      "101.03598161706446...107.24715067726511\n",
      "106.63440841433666...101.3991432223126\n",
      "100.51984247808323...103.3687536140192\n",
      "104.88932574716608...110.29732326814606\n",
      "88.6730879335613...82.49089063201765\n",
      "143.9387401574803...144.25552224873812\n",
      "162.8985273144109...161.93772370649936\n",
      "89.41714285714285...83.16854669316938\n",
      "112.16856866067383...111.58877648694529\n",
      "129.35259169119206...133.56273822183232\n",
      "85.999...77.8647200331722\n",
      "102.81722854153638...105.31217103903501\n",
      "109.67259634896924...102.44545625205046\n",
      "86.69009443989536...87.82885158443162\n",
      "126.87045118782301...120.14024819509008\n",
      "158.7068014991073...158.39100896726552\n",
      "120.05027397260274...123.18283796895771\n",
      "106.75698038467701...107.09846478138516\n",
      "123.8805652329236...118.75475540991854\n",
      "103.44431231282893...95.19484236961426\n",
      "119.19372287839005...117.38008507432578\n",
      "164.84058087360773...169.55711763786573\n",
      "109.78189058921888...101.89455967949101\n",
      "123.80076209086468...128.2050985555146\n",
      "110.84027397260273...108.67957522490256\n",
      "84.09912763311488...83.9841147101023\n",
      "141.56861914848693...142.87534667210141\n",
      "132.26742644365768...138.11983272228696\n",
      "114.87946738952895...117.90870065449143\n",
      "118.57235182805846...112.28306459836327\n",
      "88.21735564240353...83.42507020425354\n",
      "115.73526865871362...114.77130119998134\n",
      "124.52806523261538...136.54496665322023\n",
      "106.13881682855795...100.90133429602452\n",
      "111.59460871688438...116.87000638361029\n",
      "123.7527634684793...121.8599078333498\n",
      "154.71201351739867...153.34775763113112\n",
      "108.02480304702995...111.73693101768541\n",
      "148.85904046473013...149.75487521042112\n",
      "120.50884469167165...124.2282312052078\n",
      "95.5088180926564...94.54157135575059\n",
      "92.7121630348532...84.5188451188508\n",
      "132.31407571497616...135.08324871960448\n",
      "146.67966043764386...143.0873355346584\n",
      "110.67874228346292...118.02609978264502\n",
      "86.93906850243552...94.42636048750424\n",
      "160.5040123798997...146.76481667867856\n",
      "117.33083088954056...117.7695845603907\n",
      "106.43051382622205...111.34964357236785\n",
      "116.69954577663667...127.81842862004538\n",
      "117.57...123.97807850234781\n",
      "92.63383170254403...86.35184635011701\n",
      "115.40569553088125...108.21367594169885\n",
      "124.16201573594405...133.07414033653177\n",
      "106.19333333333333...105.88016719208795\n",
      "132.8734222502718...142.6185440847809\n",
      "131.97796553289734...129.70786431520443\n",
      "144.46934901626187...147.2796233486951\n",
      "108.20231183315695...100.97573144783456\n",
      "98.93504653209725...108.15003047202387\n",
      "107.24800310543316...116.75802607181812\n",
      "149.9390000597765...148.33287750076894\n",
      "101.78865209524263...94.51167579813722\n",
      "97.51792063492063...98.66489288181289\n",
      "125.68613795683464...125.97452289184446\n",
      "92.06129961206169...98.02544147752147\n",
      "165.11618748632827...148.27873840573943\n",
      "111.55940364008394...105.26168435306215\n",
      "124.50823939724432...123.98133186832315\n",
      "121.0448521204978...120.64942797351094\n",
      "107.42243989231198...117.18521006107974\n",
      "123.36940405309635...119.47081327461599\n",
      "109.16918742427227...109.2807959982014\n",
      "125.09493954432618...136.11059312095276\n",
      "93.67623809523809...89.81753215275445\n",
      "170.16866267663437...175.00444846555484\n",
      "110.65708605656283...117.89668023703283\n",
      "153.5273582081725...151.11201055509824\n",
      "96.22753671557754...88.34607810548361\n",
      "135.07452170586652...130.66637002223405\n",
      "87.49601837352444...89.64371766145277\n",
      "146.80870347228145...150.96037238251466\n",
      "141.32777091203064...139.58892201981098\n",
      "96.58707086971614...89.3440606393734\n",
      "124.93304461853842...128.25526076276446\n",
      "134.86725855786165...136.22096239013086\n",
      "132.67210428653425...135.25718338868353\n",
      "120.60205680335598...129.0886709176097\n",
      "143.94741661524463...147.96562893289766\n",
      "140.93699023749426...144.51463768529703\n",
      "112.01470182004198...105.72104173017762\n",
      "176.81704911876898...160.0195671243368\n",
      "78.396...78.62721342339809\n",
      "113.34175656248557...116.79297506213813\n",
      "156.90882398571705...156.5813948402053\n",
      "123.1790084756766...115.8989657390701\n",
      "129.04042209873725...131.86314905114173\n",
      "102.88782613345916...96.52537026949376\n",
      "191.60161068550647...174.77173802335182\n",
      "139.3686691849802...137.44244867031065\n",
      "109.3149310667416...114.07482443537671\n",
      "138.25935054652035...137.44214884422144\n",
      "89.89327174510163...85.07622144600494\n",
      "133.06586819987768...130.17022343635765\n",
      "154.80241714243678...155.79618304870962\n",
      "109.41650793650791...107.03893809705312\n",
      "137.11215098781258...142.10749203900141\n",
      "134.81948277423365...131.89364504926772\n",
      "104.28138395800268...104.87310962953642\n",
      "92.93529114920294...94.16019576681866\n",
      "129.52678181740188...129.6209165554057\n",
      "123.39427943806564...127.57675537340555\n",
      "163.51419974772608...163.07371947876644\n",
      "107.48461216281687...110.58009179232923\n",
      "157.60681008865546...157.52273166335107\n",
      "99.40579140582076...101.15594951651217\n",
      "117.46738644415916...128.67917220546826\n",
      "175.38432564383075...175.42869278331227\n",
      "119.58638152290527...121.88784408397039\n",
      "139.10216730499573...137.90963387452317\n",
      "160.02753015152615...158.05147265882326\n",
      "135.75427420101622...135.82564028441726\n",
      "103.76798583712944...100.6633035874562\n",
      "80.87212566471607...87.6702632594315\n",
      "100.73031851084222...107.74659025758393\n",
      "146.08925349858234...129.417562560615\n",
      "120.15902442205024...129.61690555526437\n",
      "104.35489450578685...106.68040727962862\n",
      "117.9242256105261...121.25569675081877\n",
      "202.2857086675506...185.51454872016794\n",
      "122.55883837018592...128.19868491965246\n",
      "153.58934318952646...152.9485397041357\n",
      "99.98127404336893...106.91757242415372\n",
      "155.63448059168343...155.59669699669752\n",
      "160.2975677089349...159.83273081754683\n",
      "118.6210518648748...117.82652708675191\n",
      "143.56611194523464...126.89606484125804\n",
      "115.96285714285715...111.3117949032419\n",
      "147.8860055336533...151.573612433243\n",
      "90.73128959695285...94.76972847992502\n",
      "95.56123956043956...90.3721877386826\n",
      "92.38176068376069...84.5404714533868\n",
      "100.59197392572759...102.47186660849222\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(y_test)):\n",
    "  print(y_test[i],y_pred_new[i],sep='...')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score: 0.94641\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "33.99528425508121"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn.metrics as sm\n",
    "r2 = round(sm.r2_score(y_test,y_pred_new),5)\n",
    "print(\"R2 score:\",r2)\n",
    "mse = mean_squared_error(y_test, y_pred_new)\n",
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00000000e+00,  5.81170941e+02,  2.46941286e+04, -1.16866306e+06,\n",
       "       -1.36807412e+03,  1.25431314e+04, -2.90951308e+04, -2.10250711e+06,\n",
       "       -9.82097189e+06,  8.22268537e+09])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_new.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "115.87587951464256"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_new.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Stress (SCAU11) = 115.87588 + (581.17094) * E11 + (24694.12860) * strain_rate + (-1168663.06234) * E11^2 + (-1368.07412) * E11 * strain_rate + (12543.13143) * strain_rate^2 + (-29095.13076) * E11^3 + (-2102507.10980) * E11^2 * strain_rate + (-9820971.89091) * E11 * strain_rate^2 + (8222685372.73381) * strain_rate^3'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def generate_stress_equation(model, feature_names):\n",
    "    # Get coefficients and intercept from the model\n",
    "    intercept = model.intercept_\n",
    "    coefficients = model.coef_\n",
    "\n",
    "    # Start building the equation with the intercept term\n",
    "    equation = f\"Stress (SCAU11) = {intercept:.5f}\"\n",
    "\n",
    "    # Add terms for each coefficient\n",
    "    for coef, name in zip(coefficients, feature_names):\n",
    "        # Skip terms with a coefficient very close to zero\n",
    "        if abs(coef) > 1e-6:  # threshold to ignore very small coefficients\n",
    "            equation += f\" + ({coef:.5f}) * {name}\"\n",
    "\n",
    "    return equation\n",
    "\n",
    "# Define the feature names generated by PolynomialFeatures for degree 3\n",
    "feature_names = [\n",
    "    \"1\",                # Constant term\n",
    "    \"E11\",              # x1\n",
    "    \"strain_rate\",      # x2\n",
    "    \"E11^2\",            # x1^2\n",
    "    \"E11 * strain_rate\",# x1 * x2\n",
    "    \"strain_rate^2\",    # x2^2\n",
    "    \"E11^3\",            # x1^3\n",
    "    \"E11^2 * strain_rate\", # x1^2 * x2\n",
    "    \"E11 * strain_rate^2\", # x1 * x2^2\n",
    "    \"strain_rate^3\"     # x2^3a\n",
    "]\n",
    "\n",
    "# Generate the equation\n",
    "equation = generate_stress_equation(model_new, feature_names)\n",
    "# print(equation)\n",
    "equation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stress (SCAU11) = 121.11038 + (553.15628) * E11 + (155295.18600) * strain_rate + (-2358134.26000) * grain_size + (-1545.81791) * E11^2 + (97329.70590) * E11 * strain_rate + (-63519.78560) * E11 * grain_size + (-139774263.00000) * strain_rate^2 + (-99091413.60000) * strain_rate * grain_size + (38334866200.00000) * grain_size^2 + (2845.11901) * E11^3 + (-43152.02110) * E11^2 * strain_rate + (-1457889.82000) * E11^2 * grain_size + (-7876787.21000) * E11 * strain_rate^2 + (3942606.16000) * E11 * strain_rate * grain_size + (1624179930.00000) * E11 * grain_size^2 + (12470979600.00000) * strain_rate^3 + (6405428100.00000) * strain_rate^2 * grain_size + (231043982000.00000) * strain_rate * grain_size^2 + (-204210579000000.00000) * grain_size^3\n"
     ]
    }
   ],
   "source": [
    "def generate_stress_equation(coefficients, intercept, feature_names):\n",
    "    # Start building the equation with the intercept term\n",
    "    equation = f\"Stress (SCAU11) = {intercept:.5f}\"\n",
    "\n",
    "    # Add terms for each coefficient and feature name\n",
    "    for coef, name in zip(coefficients, feature_names):\n",
    "        # Skip terms with a coefficient very close to zero\n",
    "        if abs(coef) > 1e-6:  # threshold to ignore very small coefficients\n",
    "            # Format each term with parentheses around the coefficient for clarity\n",
    "            equation += f\" + ({coef:.5f}) * {name}\"\n",
    "\n",
    "    return equation\n",
    "\n",
    "# Define the feature names generated by PolynomialFeatures for degree 3, including grain size\n",
    "feature_names = [\n",
    "    \"1\",                          # Constant term\n",
    "    \"E11\",                        # x1\n",
    "    \"strain_rate\",                # x2\n",
    "    \"grain_size\",                 # x3\n",
    "    \"E11^2\",                      # x1^2\n",
    "    \"E11 * strain_rate\",          # x1 * x2\n",
    "    \"E11 * grain_size\",           # x1 * x3\n",
    "    \"strain_rate^2\",              # x2^2\n",
    "    \"strain_rate * grain_size\",   # x2 * x3\n",
    "    \"grain_size^2\",               # x3^2\n",
    "    \"E11^3\",                      # x1^3\n",
    "    \"E11^2 * strain_rate\",        # x1^2 * x2\n",
    "    \"E11^2 * grain_size\",         # x1^2 * x3\n",
    "    \"E11 * strain_rate^2\",        # x1 * x2^2\n",
    "    \"E11 * strain_rate * grain_size\", # x1 * x2 * x3\n",
    "    \"E11 * grain_size^2\",         # x1 * x3^2\n",
    "    \"strain_rate^3\",              # x2^3\n",
    "    \"strain_rate^2 * grain_size\", # x2^2 * x3\n",
    "    \"strain_rate * grain_size^2\", # x2 * x3^2\n",
    "    \"grain_size^3\"                # x3^3\n",
    "]\n",
    "\n",
    "# Use the coefficients and intercept obtained from the trained model\n",
    "coefficients = [\n",
    "    0.00000000e+00,  5.53156279e+02,  1.55295186e+05, -2.35813426e+06,\n",
    "    -1.54581791e+03,  9.73297059e+04, -6.35197856e+04, -1.39774263e+08,\n",
    "    -9.90914136e+07,  3.83348662e+10,  2.84511901e+03, -4.31520211e+04,\n",
    "    -1.45788982e+06, -7.87678721e+06,  3.94260616e+06,  1.62417993e+09,\n",
    "    1.24709796e+10,  6.40542810e+09,  2.31043982e+11, -2.04210579e+14\n",
    "]\n",
    "intercept = 121.11038148981153\n",
    "\n",
    "# Generate the equation\n",
    "equation = generate_stress_equation(coefficients, intercept, feature_names)\n",
    "print(equation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stress (SCAU11) = 115.87000 + (581.17094) * E11 + (24694.12860) * strain_rate + (-1168663.06000) * grain_size + (-1368.07412) * E11^2 + (12543.13140) * E11 * strain_rate + (-29095.13080) * E11 * grain_size + (-2102507.11000) * strain_rate^2 + (-9820971.89000) * strain_rate * grain_size + (8222685370.00000) * grain_size^2\n"
     ]
    }
   ],
   "source": [
    "def generate_stress_equation(coefficients, intercept, feature_names):\n",
    "    # Start building the equation with the intercept term\n",
    "    equation = f\"Stress (SCAU11) = {intercept:.5f}\"\n",
    "\n",
    "    # Add terms for each coefficient and feature name\n",
    "    for coef, name in zip(coefficients, feature_names):\n",
    "        # Skip terms with a coefficient very close to zero\n",
    "        if abs(coef) > 1e-6:  # threshold to ignore very small coefficients\n",
    "            # Format each term with parentheses around the coefficient for clarity\n",
    "            equation += f\" + ({coef:.5f}) * {name}\"\n",
    "\n",
    "    return equation\n",
    "\n",
    "# Define the feature names for a 2nd-degree polynomial\n",
    "feature_names = [\n",
    "    \"1\",                          # Constant term\n",
    "    \"E11\",                        # x1\n",
    "    \"strain_rate\",                # x2\n",
    "    \"grain_size\",                 # x3\n",
    "    \"E11^2\",                      # x1^2\n",
    "    \"E11 * strain_rate\",          # x1 * x2\n",
    "    \"E11 * grain_size\",           # x1 * x3\n",
    "    \"strain_rate^2\",              # x2^2\n",
    "    \"strain_rate * grain_size\",   # x2 * x3\n",
    "    \"grain_size^2\"                # x3^2\n",
    "]\n",
    "\n",
    "# Use the coefficients and intercept obtained from the trained model for 2nd-degree polynomial\n",
    "coefficients = [\n",
    "    0.00000000e+00,  5.81170941e+02,  2.46941286e+04, -1.16866306e+06,\n",
    "    -1.36807412e+03,  1.25431314e+04, -2.90951308e+04, -2.10250711e+06,\n",
    "    -9.82097189e+06,  8.22268537e+09\n",
    "]\n",
    "intercept = 115.87\n",
    "\n",
    "# Generate the equation\n",
    "equation = generate_stress_equation(coefficients, intercept, feature_names)\n",
    "print(equation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_stress(E11, strain_rate):\n",
    "    # Coefficients from the given equation\n",
    "    intercept = 79.48234\n",
    "    coef_E11 = 541.25902\n",
    "    coef_strain_rate = 149295.46603\n",
    "    coef_E11_squared = -1226.78951\n",
    "    coef_E11_strain_rate = 102286.06000\n",
    "    coef_strain_rate_squared = -137990665.76318\n",
    "    coef_E11_cubed = -144.25389\n",
    "    coef_E11_squared_strain_rate = -45714.88456\n",
    "    coef_E11_strain_rate_squared = -8310358.30173\n",
    "    coef_strain_rate_cubed = 12342145698.93503\n",
    "    \n",
    "    # Calculate stress using the polynomial terms\n",
    "    stress = (\n",
    "        intercept\n",
    "        + coef_E11 * E11\n",
    "        + coef_strain_rate * strain_rate\n",
    "        + coef_E11_squared * E11**2\n",
    "        + coef_E11_strain_rate * E11 * strain_rate\n",
    "        + coef_strain_rate_squared * strain_rate**2\n",
    "        + coef_E11_cubed * E11**3\n",
    "        + coef_E11_squared_strain_rate * E11**2 * strain_rate\n",
    "        + coef_E11_strain_rate_squared * E11 * strain_rate**2\n",
    "        + coef_strain_rate_cubed * strain_rate**3\n",
    "    )\n",
    "    \n",
    "    return stress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculated Stress (SCAU11): 132.48065118303123\n"
     ]
    }
   ],
   "source": [
    "# Sample test case\n",
    "strain = 0.02   # Example strain value\n",
    "strain_rate = 5e-4  # Example strain rate value\n",
    "\n",
    "# Calculate stress\n",
    "stress = calculate_stress(strain, strain_rate)\n",
    "print(\"Calculated Stress (SCAU11):\", stress)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df=pd.read_csv('ujjwal.csv')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uVRPICDsAAJ+w2mWouvTM0FvjHYQdAIBHWW3QsMQA4UBD2AEAeMyqXfmVPhfKH0MMM5+si7ADAHCbi3tx9h3+WTM/+UaX3rnWH4OOxMwnKyPsAADqJBAeq1DX3hrG0lgLYQcAUK1AniFFbw0kwg4A4CKBPEOK3hpUhbADAA1QoMyQYtAw3IGwAwAWF8i9NVyGgjsQdgDAIqzeW8NlKNQVYQcAAhC9NUDNEXYAIMBUdqO+yvhD0CnvxXmiT1u1jg2ntwY+QdgBAD9WWQ/Oo4u3VrhRn78q78Xpn5rg66qgASPsAICfqMmlqUY2+TzoMEMKgYawAwBeVp+BxOd9nXTEmBsEHsIOAHhQoAwkZoYUrIywAwAeEkgDiemtgZURdgDADfxxIDG9NcAFhB0AqIWajrfxh4HE9NYAFxB2AKAK9Rlv4+mBxI1srsegtwaoGmEHACrhr+NtyvtjZv3mRqZ4AzVE2AHQ4PnjeJuqcJM+oPYIOwAaFH+8cR836QM8i7ADwJIC6cZ9DCQGPIuwA8ByKhtv46sb9zGQGPA9wg6AgFbT8TYMJAYaLsIOgIARSONtGEgM+A/CDgC/w3gbAO5E2AHgVxhvA8DdCDsAfIbxNgC8gbADwCv8cbxNVRhvA1gLYQeAx9X00QueHG/DjfuAhouwA8Ct/OXRC5eOt2EgMdBwEXYA1ElNZ0z5Yiq4VP14GwYSAw0LYQdArdVmxpSvpoIz3gZAOcIOgGr5w4wpxtsAqA/CDgAnf5kxxXgbAO5E2AEgyX9mTEmMtwHgXoQdoAHylxlTl2K8DQBPIOwADUxlPTi+uDTFeBsA3kLYASyspj04vr40BQCeRNgBLMAf7nlT1YwpLk0B8DXCDhDgfHXPG2ZMAQgUhB0ggPjLPW8kZkwBCByEHSBA+Gpg8aW4LAUg0BB2AD91cS/OvsM/a+Yn33h8YDEzpgBYEWEH8LGaDi72JGZMAbAywg7gQ7UZXOxOVQ0u5tIUACsi7ABeEiiDiwHAagg7gBd4e3Ax97wBgP9o5MuDT506VTfddJMiIiIUFxenO++8U19//bVLGWOMJk2apMTERDVu3Fg9e/bUV1995VKmpKREY8aMUWxsrMLDw3X77bfr4MGD3mwK4HTuvNEX3x3Rh9v/T198d0Qf78jXw4u2Vhh/4+573lzMERWmN+7uqC1/7Kt3Huym/2/EDXrnwW5a99RtBB0ADY7NGOOzZ//1799fI0aM0E033aSzZ8/qmWee0c6dO5WXl6fw8HBJ0rRp0/T8888rOztbV199tSZPnqzPPvtMX3/9tSIiIiRJDz/8sFauXKns7GzFxMRo3LhxOnr0qLZs2aKgoKDL1qO4uFhRUVEqKipSZGSkR9sM66jNXYs99TiG8ozz2kguSwFoeGr6++3TsHOpn376SXFxccrNzdWtt94qY4wSExOVmZmpp556StKFXpz4+HhNmzZNo0ePVlFRkVq0aKGFCxdq+PDhkqRDhw4pKSlJH3/8sfr163fZ4xJ2UFu+Glh8qQQuSwFowGr6++1XY3aKiookSdHR0ZKkvXv3qqCgQBkZGc4ydrtd6enpWr9+vUaPHq0tW7aorKzMpUxiYqJSU1O1fv36GoUdoDr+MLBY+s84nCf6tFXr2HB6cACghvwm7BhjNHbsWN18881KTU2VJBUUFEiS4uPjXcrGx8frhx9+cJYJDQ1V8+bNK5Qpf/+lSkpKVFJS4lwuLi52WztgLb64azGDiwHAvfwm7Dz22GPasWOH1q1bV2Gbzeb6L1djTIV1l6quzNSpU/Xss8/WvbKwpJr24Hj6rsU8UBMA3Msvws6YMWO0YsUKffbZZ2rZsqVzvcPhkHSh9yYh4T//mi0sLHT29jgcDpWWlurYsWMuvTuFhYVKS0ur9HgTJ07U2LFjncvFxcVKSkpya5sQWHzVgyPxQE0A8DSfhh1jjMaMGaNly5Zp7dq1SklJcdmekpIih8OhnJwc3XjjjZKk0tJS5ebmatq0aZKkTp06KSQkRDk5ORo2bJgkKT8/X7t27dL06dMrPa7dbpfdbvdgy+DP/K0Hh8tSAOBZPg07jz76qBYvXqwPP/xQERERzjE2UVFRaty4sWw2mzIzMzVlyhS1bdtWbdu21ZQpU9SkSRONHDnSWfb+++/XuHHjFBMTo+joaI0fP14dOnRQnz59fNk8+CF/7cEBAHiOT8PO7NmzJUk9e/Z0WT9v3jzde++9kqQnn3xSp0+f1iOPPKJjx46pa9euWr16tfMeO5I0Y8YMBQcHa9iwYTp9+rR69+6t7OzsGt1jB9bl7R4cBhYDgH/yq/vs+Ar32bGeqnpw3H3X4ov3l8DAYgDwqoC8zw5QF77owZEYWAwAgYKwg4DmjTE4DCwGgMBG2EFAubgXZ9/hnzXzk2982oMDAPB/hB0EjMp6cdyJHhwAsCbCDvxSTcfhuAM9OABgbYQd+B1Pj8OhBwcAGhbCDnzKmzOp6MEBgIaJsAOfoQcHAOANhB14BT04AABfIezA47z9PCp6cAAAFyPswK189TyqJ/q0VevYcHpwAAAVEHbgNtzNGADgjwg7qLVLe2+6pEQrJ69ADy/y7fOoAACoDGEHtVJZ740j0q4zZ8/TgwMA8EuEHVSppuNvCopL3HZMenAAAO5G2EGlvDWDih4cAICnEXbg9RlUEj04AADvIew0cPTgAACsjrDTgNCDAwBoiAg7DYSne3BskqKahCgsOEgFxRfN1KIHBwDgY4SdBmDVrnyv3APnhaEd1Le9o8I9eOjBAQD4EmHHgi6+XBUbbtekFXlevQdO9ytj3Hg0AADqh7BjMZVdrnIXxt8AAAIRYSeA1XTAcV0xgwoAYAWEnQDlyQHH9OAAAKyEsBMAPD1lnB4cAICVEXb8HD04AADUD2HHj3l6yjg9OACAhoCw40c8OWXcJik+0q6Xht2gwydL6MEBADQYhB0/4Y0p45Nuv1Y9rop1+/4BAPBnhB0fYMo4AADeQ9jxMgYcAwDgXYQdL3L3gGN6cAAAuDzCjpecO2/07Er3DDimBwcAgJoj7HjJl3uPum3wMT04AADUHGHHSwpP1C3oMGUcAID6Iex4SVxEWK3fw5RxAADqr5GvK9BQdEmJVkJUmKrrj7m0s8YRFabZd3fkchUAAPVAz46XBDWyKWtwez28aKtscp1qzoBjAAA8h7DjRf1TEzT77o4V7rPDgGMAADyHsONl/VMT1Le9w+UOyvTgAADgOYQdHwhqZFP3K2N8XQ0AABoEBigDAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABL82nY+eyzzzR48GAlJibKZrNp+fLlLtvvvfde2Ww2l1e3bt1cypSUlGjMmDGKjY1VeHi4br/9dh08eNCLrQAAAP7Mp2Hn1KlTuv766zVr1qwqy/Tv31/5+fnO18cff+yyPTMzU8uWLdOSJUu0bt06nTx5UoMGDdK5c+c8XX0AABAAgn158AEDBmjAgAHVlrHb7XI4HJVuKyoq0ty5c7Vw4UL16dNHkrRo0SIlJSXpk08+Ub9+/dxeZwAAEFj8fszO2rVrFRcXp6uvvloPPvigCgsLndu2bNmisrIyZWRkONclJiYqNTVV69evr3KfJSUlKi4udnkBAABr8uuwM2DAAP33f/+3/vnPf+qll17Spk2bdNttt6mkpESSVFBQoNDQUDVv3tzlffHx8SooKKhyv1OnTlVUVJTzlZSU5NF2AAAA3/HpZazLGT58uPPv1NRUde7cWcnJyfroo480dOjQKt9njJHNZqty+8SJEzV27FjncnFxMYEHAACL8uuenUslJCQoOTlZe/bskSQ5HA6Vlpbq2LFjLuUKCwsVHx9f5X7sdrsiIyNdXgAAwJoCKuwcOXJEBw4cUEJCgiSpU6dOCgkJUU5OjrNMfn6+du3apbS0NF9VEwAA+BGfXsY6efKkvv32W+fy3r17tX37dkVHRys6OlqTJk3SXXfdpYSEBO3bt09PP/20YmNjNWTIEElSVFSU7r//fo0bN04xMTGKjo7W+PHj1aFDB+fsLAAA0LD5NOxs3rxZvXr1ci6Xj6MZNWqUZs+erZ07d2rBggU6fvy4EhIS1KtXLy1dulQRERHO98yYMUPBwcEaNmyYTp8+rd69eys7O1tBQUFebw8AAPA/NmOM8XUlfK24uFhRUVEqKipi/A4AAAGipr/fATVmBwAAoLYIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNLqFHZWrVqldevWOZdfe+013XDDDRo5cmSFuxkDAAD4Up3Czn/91385nxS+c+dOjRs3Tr/85S/1/fffuzxzCgAAwNfqdFPBvXv3qn379pKkDz74QIMGDdKUKVO0detW/fKXv3RrBQEAAOqjTj07oaGh+vnnnyVJn3zyiTIyMiRJ0dHRzh4fAAAAf1Cnnp2bb75ZY8eOVY8ePfTll19q6dKlkqRvvvlGLVu2dGsFAQAA6qNOPTuzZs1ScHCw3n//fc2ePVtXXHGFJOnvf/+7+vfv79YKAgAA1AfPxhLPxgIAIBB59NlYW7du1c6dO53LH374oe688049/fTTKi0trcsuAQAAPKJOYWf06NH65ptvJEnff/+9RowYoSZNmui9997Tk08+6dYKAgAA1Eedws4333yjG264QZL03nvv6dZbb9XixYuVnZ2tDz74wJ31AwAAqJc6hR1jjM6fPy/pwtTz8nvrJCUl6fDhw+6rHQAAQD3VKex07txZkydP1sKFC5Wbm6uBAwdKunCzwfj4eLdWEAAAoD7qFHZmzpyprVu36rHHHtMzzzyjq666SpL0/vvvKy0tza0VBAAAqA+3Tj0/c+aMgoKCFBIS4q5degVTzwEACDwenXouScePH9dbb72liRMn6ujRo5KkvLw8FRYW1nWXAAAAblenx0Xs2LFDvXv3VrNmzbRv3z49+OCDio6O1rJly/TDDz9owYIF7q4nAABAndSpZ2fs2LG67777tGfPHoWFhTnXDxgwQJ999pnbKgcAAFBfdQo7mzZt0ujRoyusv+KKK1RQUFDvSgEAALhLncJOWFiYiouLK6z/+uuv1aJFi3pXCgAAwF3qFHbuuOMOPffccyorK5Mk2Ww27d+/XxMmTNBdd93l1goCAADUR53Czl//+lf99NNPiouL0+nTp5Wenq6rrrpKERERev75591dRwAAgDqr02ysyMhIrVu3Tv/85z+1detWnT9/Xh07dlSfPn3cXT8AAIB6qXXYOXv2rMLCwrR9+3bddtttuu222zxRLwAAALeo9WWs4OBgJScn69y5c56oDwAAgFvVaczOH//4R5c7JwMAAPirOo3ZeeWVV/Ttt98qMTFRycnJCg8Pd9m+detWt1QOAACgvuoUdu644w7ZbDZ31wUAAMDt3PrU80DFU88BAAg8Hn3qeZs2bXTkyJEK648fP642bdrUZZcAAAAeUaews2/fvkpnY5WUlOjgwYP1rhQAAIC71GrMzooVK5x//+Mf/1BUVJRz+dy5c1qzZo1SUlLcVzsAAIB6qlXYufPOOyVdeBbWqFGjXLaFhISodevWeumll9xWOQAAgPqqVdg5f/68JCklJUWbNm1SbGysRyoFAADgLrUas7Nx40b9/e9/1969e51BZ8GCBUpJSVFcXJx+//vfq6SkxCMVBQAAqItahZ2srCzt2LHDubxz507df//96tOnjyZMmKCVK1dq6tSpbq8kAABAXdUq7PzrX/9S7969nctLlixR165dNWfOHI0dO1avvPKK3n33XbdXEgAAoK5qFXaOHTum+Ph453Jubq769+/vXL7pppt04MAB99UOAACgnmoVduLj47V3715JUmlpqbZu3aru3bs7t584cUIhISHurSEAAEA91Crs9O/fXxMmTNDnn3+uiRMnqkmTJrrllluc23fs2KErr7zS7ZUEAACoq1pNPZ88ebKGDh2q9PR0NW3aVPPnz1doaKhz+9tvv62MjAy3VxIAAKCu6vQg0KKiIjVt2lRBQUEu648ePaqmTZu6BKBAwINAAQAIPDX9/a5Vz065ix8TcbHo6Oi67A4AAMBj6vQgUAAAgEBB2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJbm07Dz2WefafDgwUpMTJTNZtPy5ctdthtjNGnSJCUmJqpx48bq2bOnvvrqK5cyJSUlGjNmjGJjYxUeHq7bb79dBw8e9GIrAACAP/Np2Dl16pSuv/56zZo1q9Lt06dP18svv6xZs2Zp06ZNcjgc6tu3r06cOOEsk5mZqWXLlmnJkiVat26dTp48qUGDBuncuXPeagYAAPBjdXo2lifYbDYtW7ZMd955p6QLvTqJiYnKzMzUU089JelCL058fLymTZum0aNHq6ioSC1atNDChQs1fPhwSdKhQ4eUlJSkjz/+WP369avRsXk2FgAAgaemv99+O2Zn7969KigocHmKut1uV3p6utavXy9J2rJli8rKylzKJCYmKjU11VmmMiUlJSouLnZ5AQAAa/LbsFNQUCBJio+Pd1kfHx/v3FZQUKDQ0FA1b968yjKVmTp1qqKiopyvpKQkN9ceAAD4C78NO+VsNpvLsjGmwrpLXa7MxIkTVVRU5HwdOHDALXUFAAD+x2/DjsPhkKQKPTSFhYXO3h6Hw6HS0lIdO3asyjKVsdvtioyMdHkBAABr8tuwk5KSIofDoZycHOe60tJS5ebmKi0tTZLUqVMnhYSEuJTJz8/Xrl27nGUAAEDDFuzLg588eVLffvutc3nv3r3avn27oqOj1apVK2VmZmrKlClq27at2rZtqylTpqhJkyYaOXKkJCkqKkr333+/xo0bp5iYGEVHR2v8+PHq0KGD+vTp46tmAQAAP+LTsLN582b16tXLuTx27FhJ0qhRo5Sdna0nn3xSp0+f1iOPPKJjx46pa9euWr16tSIiIpzvmTFjhoKDgzVs2DCdPn1avXv3VnZ2toKCgrzeHgAA4H/85j47vsR9dgAACDwBf58dAAAAdyDsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAAS/PrsDNp0iTZbDaXl8PhcG43xmjSpElKTExU48aN1bNnT3311Vc+rDEAAPA3fh12JOnaa69Vfn6+87Vz507ntunTp+vll1/WrFmztGnTJjkcDvXt21cnTpzwYY0BAIA/8fuwExwcLIfD4Xy1aNFC0oVenZkzZ+qZZ57R0KFDlZqaqvnz5+vnn3/W4sWLfVxrAADgL/w+7OzZs0eJiYlKSUnRiBEj9P3330uS9u7dq4KCAmVkZDjL2u12paena/369b6qLgAA8DPBvq5Adbp27aoFCxbo6quv1o8//qjJkycrLS1NX331lQoKCiRJ8fHxLu+Jj4/XDz/8UO1+S0pKVFJS4lwuLi52f+UBAIBf8OuwM2DAAOffHTp0UPfu3XXllVdq/vz56tatmyTJZrO5vMcYU2HdpaZOnapnn33W/RUGAAB+x+8vY10sPDxcHTp00J49e5yzssp7eMoVFhZW6O251MSJE1VUVOR8HThwwGN1BgAAvhVQYaekpES7d+9WQkKCUlJS5HA4lJOT49xeWlqq3NxcpaWlVbsfu92uyMhIlxcAALAmv76MNX78eA0ePFitWrVSYWGhJk+erOLiYo0aNUo2m02ZmZmaMmWK2rZtq7Zt22rKlClq0qSJRo4c6euqAwAAP+HXYefgwYP6zW9+o8OHD6tFixbq1q2bNmzYoOTkZEnSk08+qdOnT+uRRx7RsWPH1LVrV61evVoRERE+rjkAAPAXNmOM8XUlfK24uFhRUVEqKirikhYAAAGipr/fATVmBwAAoLYIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNKCfV0Bqzp33ujLvUdVeOKM4iLC1CUlWkGNbL6uFgAADY5lws7rr7+uF198Ufn5+br22ms1c+ZM3XLLLT6py6pd+Xp2ZZ7yi8441yVEhSlrcHv1T03wSZ0AAGioLHEZa+nSpcrMzNQzzzyjbdu26ZZbbtGAAQO0f/9+r9dl1a58Pbxoq0vQkaSCojN6eNFWrdqV7/U6AQDQkFki7Lz88su6//779cADD+gXv/iFZs6cqaSkJM2ePdur9Th33ujZlXkylWwrX/fsyjydO19ZCQAA4AkBH3ZKS0u1ZcsWZWRkuKzPyMjQ+vXrK31PSUmJiouLXV7u8OXeoxV6dC5mJOUXndGXe4+65XgAAODyAj7sHD58WOfOnVN8fLzL+vj4eBUUFFT6nqlTpyoqKsr5SkpKcktdCk9UHXTqUg4AANRfwIedcjab60wnY0yFdeUmTpyooqIi5+vAgQNuqUNcRJhbywEAgPoL+NlYsbGxCgoKqtCLU1hYWKG3p5zdbpfdbnd7XbqkRCshKkwFRWcqHbdjk+SIujANHQAAeEfA9+yEhoaqU6dOysnJcVmfk5OjtLQ0r9YlqJFNWYPbS7oQbC5Wvpw1uD332wEAwIsCPuxI0tixY/XWW2/p7bff1u7du/XEE09o//79euihh7xel/6pCZp9d0c5olwvVTmiwjT77o7cZwcAAC8L+MtYkjR8+HAdOXJEzz33nPLz85WamqqPP/5YycnJPqlP/9QE9W3v4A7KAAD4AZsxpsHf9KW4uFhRUVEqKipSZGSkr6sDAABqoKa/35a4jAUAAFAVwg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0Szwuor7KbyJdXFzs45oAAICaKv/dvtzDIAg7kk6cOCFJSkpK8nFNAABAbZ04cUJRUVFVbufZWJLOnz+vQ4cOKSIiQjab+x7WWVxcrKSkJB04cKBBPXOLdtPuhoB2N5x2N8Q2S4HRbmOMTpw4ocTERDVqVPXIHHp2JDVq1EgtW7b02P4jIyP99j8UT6LdDQvtblgaYrsbYpsl/293dT065RigDAAALI2wAwAALI2w40F2u11ZWVmy2+2+ropX0W7a3RDQ7obT7obYZsla7WaAMgAAsDR6dgAAgKURdgAAgKURdgAAgKURdgAAgKURdqrx+uuvKyUlRWFhYerUqZM+//zzasvn5uaqU6dOCgsLU5s2bfTGG29UKPPBBx+offv2stvtat++vZYtW1bv47qbu9s9Z84c3XLLLWrevLmaN2+uPn366Msvv3QpM2nSJNlsNpeXw+Fwe9uq4+52Z2dnV2iTzWbTmTNn6nVcd3N3u3v27FlpuwcOHOgsE2jfd35+vkaOHKlrrrlGjRo1UmZmZqXlrHZ+16TdgXB+u7vNVjy3a9LuQDm3K2VQqSVLlpiQkBAzZ84ck5eXZx5//HETHh5ufvjhh0rLf//996ZJkybm8ccfN3l5eWbOnDkmJCTEvP/++84y69evN0FBQWbKlClm9+7dZsqUKSY4ONhs2LChzsd1N0+0e+TIkea1114z27ZtM7t37zb33XefiYqKMgcPHnSWycrKMtdee63Jz893vgoLCz3e3nKeaPe8efNMZGSkS5vy8/PrdVx380S7jxw54tLeXbt2maCgIDNv3jxnmUD7vvfu3Wv+8Ic/mPnz55sbbrjBPP744xXKWPH8rkm7/f389kSbrXhu16TdgXBuV4WwU4UuXbqYhx56yGVdu3btzIQJEyot/+STT5p27dq5rBs9erTp1q2bc3nYsGGmf//+LmX69etnRowYUefjupsn2n2ps2fPmoiICDN//nznuqysLHP99dfXveL15Il2z5s3z0RFRbn1uO7mje97xowZJiIiwpw8edK5LtC+74ulp6dX+kNgxfP7YlW1+1L+dn57os1WPLcvVtPv2h/P7apwGasSpaWl2rJlizIyMlzWZ2RkaP369ZW+54svvqhQvl+/ftq8ebPKysqqLVO+z7oc15081e5L/fzzzyorK1N0dLTL+j179igxMVEpKSkaMWKEvv/++3q0puY82e6TJ08qOTlZLVu21KBBg7Rt27Z6HdedvPV9z507VyNGjFB4eLjL+kD6vmvCiud3XfjT+e3JNlvt3K4Lfzu3q0PYqcThw4d17tw5xcfHu6yPj49XQUFBpe8pKCiotPzZs2d1+PDhasuU77Mux3UnT7X7UhMmTNAVV1yhPn36ONd17dpVCxYs0D/+8Q/NmTNHBQUFSktL05EjR+rZqsvzVLvbtWun7OxsrVixQu+8847CwsLUo0cP7dmzp87HdSdvfN9ffvmldu3apQceeMBlfaB93zVhxfO7Lvzp/PZUm614bteWP57b1eGp59Ww2Wwuy8aYCusuV/7S9TXZZ22P626eaHe56dOn65133tHatWsVFhbmXD9gwADn3x06dFD37t115ZVXav78+Ro7dmyd2lFb7m53t27d1K1bN+f2Hj16qGPHjnr11Vf1yiuv1Pm47ubJ73vu3LlKTU1Vly5dXNYH4vftrn0G2vddG/56fru7zVY9t2vDn8/tytCzU4nY2FgFBQVVSMCFhYUVknI5h8NRafng4GDFxMRUW6Z8n3U5rjt5qt3l/vrXv2rKlClavXq1rrvuumrrEh4erg4dOjj/peRJnm53uUaNGummm25ytsnq3/fPP/+sJUuWVPiXX2X8/fuuCSue37Xhj+e3tz5zK5zbteGv53Z1CDuVCA0NVadOnZSTk+OyPicnR2lpaZW+p3v37hXKr169Wp07d1ZISEi1Zcr3WZfjupOn2i1JL774ov7yl79o1apV6ty582XrUlJSot27dyshIaEOLakdT7b7YsYYbd++3dkmK3/fkvTuu++qpKREd99992Xr4u/fd01Y8fyuKX89v731mVvh3K4Nfz23q+Xd8dCBo3za3ty5c01eXp7JzMw04eHhZt++fcYYYyZMmGDuueceZ/nyKblPPPGEycvLM3Pnzq0wJfd///d/TVBQkHnhhRfM7t27zQsvvFDl1NSqjhuI7Z42bZoJDQ0177//vst0xBMnTjjLjBs3zqxdu9Z8//33ZsOGDWbQoEEmIiIioNs9adIks2rVKvPdd9+Zbdu2mfvuu88EBwebjRs31vi4gdjucjfffLMZPnx4pccNtO/bGGO2bdtmtm3bZjp16mRGjhxptm3bZr766ivndiue3zVpt7+f355osxXP7Zq0u5w/n9tVIexU47XXXjPJyckmNDTUdOzY0eTm5jq3jRo1yqSnp7uUX7t2rbnxxhtNaGioad26tZk9e3aFfb733nvmmmuuMSEhIaZdu3bmgw8+qNVxvcHd7U5OTjaSKryysrKcZYYPH24SEhJMSEiISUxMNEOHDq30JPMkd7c7MzPTtGrVyoSGhpoWLVqYjIwMs379+lod1xs88d/5119/bSSZ1atXV3rMQPy+K/tvODk52aWMFc/vy7U7EM5vd7fZqud2Tf4bD4RzuzI2Y/7f6EIAAAALYswOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOgAZv3759stls2r59u6+rAsADCDsA/FJhYaFGjx6tVq1ayW63y+FwqF+/fvriiy8kXXii8/Lly91yrKSkJOXn5ys1NdUt+wPgX4J9XQEAqMxdd92lsrIyzZ8/X23atNGPP/6oNWvW6OjRozXeR1lZWZUPZr1YUFCQHA5HfaoLwI/RswPA7xw/flzr1q3TtGnT1KtXLyUnJ6tLly6aOHGiBg4cqNatW0uShgwZIpvN5lyeNGmSbrjhBr399ttq06aN7Ha7jDFatWqVbr75ZjVr1kwxMTEaNGiQvvvuO+fxLr2MtXbtWtlsNq1Zs0adO3dWkyZNlJaWpq+//trLnwQAdyDsAPA7TZs2VdOmTbV8+XKVlJRU2L5p0yZJ0rx585Sfn+9clqRvv/1W7777rj744ANneDl16pTGjh2rTZs2ac2aNWrUqJGGDBmi8+fPV1uPZ555Ri+99JI2b96s4OBg/e53v3NfIwF4DZexAPid4OBgZWdn68EHH9Qbb7yhjh07Kj09XSNGjNB1112nFi1aSJKaNWtW4fJTaWmpFi5c6CwjXbgkdrG5c+cqLi5OeXl51Y7Tef7555Weni5JmjBhggYOHKgzZ84oLCzMXU0F4AX07ADwS3fddZcOHTqkFStWqF+/flq7dq06duyo7Ozsat+XnJzsEnQk6bvvvtPIkSPVpk0bRUZGKiUlRZK0f//+avd13XXXOf9OSEiQdGHgNIDAQtgB4LfCwsLUt29f/fnPf9b69et17733Kisrq9r3hIeHV1g3ePBgHTlyRHPmzNHGjRu1ceNGSRd6gapz8eBmm80mSZe99AXA/xB2AASM9u3b69SpU5IuBJFz585d9j1HjhzR7t279cc//lG9e/fWL37xCx07dszTVQXgRxizA8DvHDlyRL/+9a/1u9/9Ttddd50iIiK0efNmTZ8+XXfccYckqXXr1lqzZo169Oghu92u5s2bV7qv5s2bKyYmRm+++aYSEhK0f/9+TZgwwZvNAeBj9OwA8DtNmzZV165dNWPGDN16661KTU3Vn/70Jz344IOaNWuWJOmll15STk6OkpKSdOONN1a5r0aNGmnJkiXasmWLUlNT9cQTT+jFF1/0VlMA+AGbMcb4uhIAAACeQs8OAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwNMIOAACwtP8fcOukQqRsVikAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the data from CSV\n",
    "df = pd.read_csv('ujjwal.csv')\n",
    "\n",
    "# Create a scatter plot\n",
    "plt.scatter(df['Strain'], df['Stress'])\n",
    "\n",
    "# Add labels for clarity\n",
    "plt.xlabel('Strain')\n",
    "plt.ylabel('Stress')\n",
    "plt.title('Strain vs. Stress')\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}