{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "125fae80",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "df = pd.read_csv('exp_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1ba56894",
   "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>Reference Current [A]</th>\n",
       "      <th>Sensor Reading [V]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-10.11</td>\n",
       "      <td>-1.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-6.93</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.15</td>\n",
       "      <td>3.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.00</td>\n",
       "      <td>4.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.95</td>\n",
       "      <td>6.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.06</td>\n",
       "      <td>8.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.44</td>\n",
       "      <td>10.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Reference Current [A]  Sensor Reading [V]\n",
       "0                 -10.11               -1.55\n",
       "1                  -6.93                0.44\n",
       "2                  -2.15                3.42\n",
       "3                   0.00                4.70\n",
       "4                   2.95                6.60\n",
       "5                   6.06                8.53\n",
       "6                   9.44               10.58"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets look at the imported experimental data file\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aab59367",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sensor Reading [V]')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['Reference Current [A]'], df['Sensor Reading [V]'], 'x')\n",
    "plt.xlabel('Reference Current [A]')\n",
    "plt.ylabel('Sensor Reading [V]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1a6b2d9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets fit a first order function to our experimental data\n",
    "\n",
    "x= np.polyfit(df['Reference Current [A]'],df['Sensor Reading [V]'],1 );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b9be90e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gain:  0.6213316322731629\n",
      "Offset:  4.739969343983164\n"
     ]
    }
   ],
   "source": [
    "# Lets print out the gain and the offset\n",
    "\n",
    "print(\"Gain: \", x[0])\n",
    "print(\"Offset: \", x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7d6aa0c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets get an expression using the obtained gain and offset\n",
    "\n",
    "fit = df['Reference Current [A]']*x[0] + x[1];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c8253993",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['Reference Current [A]'], df['Sensor Reading [V]'], 'x')\n",
    "plt.plot(df['Reference Current [A]'], fit)\n",
    "plt.xlabel('Reference Current [A]')\n",
    "plt.ylabel('Sensor Reading [V]')\n",
    "plt.legend(['Raw data', 'Fit'])\n",
    "plt.savefig('Fit.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03acc820",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}